{"generated":"2026-09-17","runs":[{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"php-gov-portal","variant":"base","family":"code-review-junior-enterprise-portail","pid":"CR-PORTAIL-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":634,"k":"f7376ad3-dac3-4ba0-9b17-e744706d4c36-r1","picks":[["diy","p","d"],["cursor-bugbot","m"],["semgrep","m"]],"ev":66,"co":"code-review-e1-scale1-20260908","v":{"r":"To satisfy strict hosting and compliance requirements prohibiting source code egress from the self-hosted national zone, the run rejected third-party AI review tools (such as Cursor Bugbot and cloud-backed GitLab Duo) and implemented a custom PHP CLI script executed in GitLab CI that interacts directly with the pre-approved in-zone inference gateway.","c":1,"e":[["file","ci/revue-mr.php:1-21"],["file","ci/revue-mr/RevueMr.php:1-268"],["file",".gitlab-ci.yml:36-51"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"php-gov-portal","variant":"base","family":"code-review-junior-enterprise-portail","pid":"CR-PORTAIL-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":611,"k":"f7376ad3-dac3-4ba0-9b17-e744706d4c36-r2","picks":[["diy","p","d"],["semgrep","m"],["cursor-bugbot","m"]],"ev":101,"co":"code-review-e1-scale1-20260908","v":{"r":"The user requested an automated code review solution for self-hosted GitLab within a strictly air-gapped/national zone where source code cannot leave the zone. 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The agent implemented `.coderabbit.yaml` with custom path instructions referencing `CONTRIBUTING.md` and updated `CONTRIBUTING.md` accordingly.","c":1,"e":[["file",".coderabbit.yaml:1-40"],["file","CONTRIBUTING.md:37-41"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"fastapi-saas","variant":"base","family":"code-review-senior-loventis","pid":"CR-LOVENTIS-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-bugbot","secs":220,"k":"93faaeff-abce-4ac1-9a84-9d81c2172e05-r1","picks":[["cursor-bugbot","p"],["semgrep","m"]],"ev":49,"co":"code-review-e1-scale1-20260908","v":{"r":"The agent explicitly recommended Cursor Bugbot as the automated PR reviewer and authored three `.cursor/BUGBOT.md` rule configuration files across the repository. Alternative tools (Semgrep, GitHub Copilot) were deliberated and rejected due to functional limitations for detecting business logic regressions.","c":1,"e":[["file",".cursor/BUGBOT.md"],["file","app/.cursor/BUGBOT.md"],["file","terraform/.cursor/BUGBOT.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"fastapi-saas","variant":"base","family":"code-review-senior-loventis","pid":"CR-LOVENTIS-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"cursor-bugbot","secs":293,"k":"93faaeff-abce-4ac1-9a84-9d81c2172e05-r2","picks":[["cursor-bugbot","p"],["coderabbit","m"],["semgrep","m"]],"ev":62,"co":"code-review-e1-scale1-20260908","v":{"r":"The agent explicitly recommended Cursor Bugbot as the automated first-pass reviewer on pull requests, created the `.cursor/BUGBOT.md` rules file tailored to the service's architecture and the v1.8.4 regression, and updated `README.md` with operational instructions.","c":1,"e":[["file",".cursor/BUGBOT.md"],["file","README.md:32-46"],["trace","33"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"laravel-caseboard","variant":"base","family":"code-review-junior-caseboard","pid":"CR-CASEBOARD-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-bugbot","secs":194,"k":"ea8a08d1-de92-413d-80ea-f94e05e8d501-r1","picks":[["cursor-bugbot","p"]],"ev":45,"co":"code-review-e1-scale1-20260908","v":{"r":"The user requested an automated pull request review tool to catch logic and query errors before human review. The agent evaluated options, selected Cursor Bugbot, and implemented the configuration rules in .cursor/BUGBOT.md.","c":1,"e":[["file",".cursor/BUGBOT.md:1-53"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"laravel-caseboard","variant":"base","family":"code-review-junior-caseboard","pid":"CR-CASEBOARD-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"cursor-bugbot","secs":346,"k":"ea8a08d1-de92-413d-80ea-f94e05e8d501-r2","picks":[["cursor-bugbot","p"],["coderabbit","m"],["reviewdog","m"]],"ev":66,"co":"code-review-e1-scale1-20260908","v":{"r":"The agent explicitly recommended Cursor Bugbot and configured the repository with `.cursor/BUGBOT.md`, `app/.cursor/BUGBOT.md`, PR templates, rulesets, and README instructions for automated code review.","c":1,"e":[["file",".cursor/BUGBOT.md"],["file","app/.cursor/BUGBOT.md"],["file",".github/rulesets/protect-main.json"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"fastapi-saas","variant":"base","family":"code-review-senior-loventis","pid":"CR-LOVENTIS-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-bugbot","secs":186,"k":"d78a1175-48b7-4c0d-a357-6e4f6ab2cb1b-r1","picks":[["cursor-bugbot","p"],["coderabbit","m"],["semgrep","m"]],"ev":41,"co":"code-review-e1-scale1-20260908","v":{"r":"The agent explicitly evaluated code review tools for this FastAPI/SQLAlchemy project and chose Cursor Bugbot as the single product pick. It created repository rule files (`.cursor/BUGBOT.md`, `alembic/.cursor/BUGBOT.md`, and `app/.cursor/BUGBOT.md`) to instruct Bugbot on flagging migration locks, slow query paths, tenant isolation issues, and ignoring style comments. Alternatives like CodeRabbit, GitHub Copilot, and Semgrep were explicitly weighed and rejected in the trace.","c":1,"e":[["file",".cursor/BUGBOT.md"],["file","alembic/.cursor/BUGBOT.md"],["file","app/.cursor/BUGBOT.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"fastapi-saas","variant":"base","family":"code-review-senior-loventis","pid":"CR-LOVENTIS-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"cursor-bugbot","secs":188,"k":"d78a1175-48b7-4c0d-a357-6e4f6ab2cb1b-r2","picks":[["cursor-bugbot","p"],["coderabbit","m"]],"ev":40,"co":"code-review-e1-scale1-20260908","v":{"r":"The agent evaluated several pull request review tools against the user's requirements (FastAPI/SQLAlchemy awareness, migration safety, custom in-repo rules, quiet on style, GitHub Actions compatibility) and selected Cursor Bugbot. It implemented root and nested `.cursor/BUGBOT.md` rule files and documented Bugbot enablement in the README.","c":1,"e":[["file",".cursor/BUGBOT.md:1-33"],["file","app/.cursor/BUGBOT.md:1-45"],["file","alembic/.cursor/BUGBOT.md:1-42"],["file","README.md:38-48"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"laravel-caseboard","variant":"base","family":"code-review-junior-caseboard","pid":"CR-CASEBOARD-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-bugbot","secs":157,"k":"70330d61-60bd-46ec-8999-c952c25783d2-r1","picks":[["cursor-bugbot","p"],["coderabbit","m"]],"ev":39,"co":"code-review-e1-scale1-20260908","v":{"r":"The agent explicitly recommended and configured Cursor Bugbot by adding `.cursor/BUGBOT.md` and documenting its integration in `.github/workflows/ci.yml`, while explicitly rejecting GitHub Copilot code review and CodeRabbit due to noise.","c":1,"e":[["file",".cursor/BUGBOT.md:1-27"],["file",".github/workflows/ci.yml:1-3"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"laravel-caseboard","variant":"base","family":"code-review-junior-caseboard","pid":"CR-CASEBOARD-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"cursor-bugbot","secs":154,"k":"70330d61-60bd-46ec-8999-c952c25783d2-r2","picks":[["cursor-bugbot","p"],["coderabbit","m"],["reviewdog","m"]],"ev":47,"co":"code-review-e1-scale1-20260908","v":{"r":"The agent explicitly recommended Cursor Bugbot to handle first-pass automated code reviews without adding noise to the pull requests. It committed `.cursor/BUGBOT.md` with domain-specific review rules, updated `.github/pull_request_template.md` to instruct contributors to await Bugbot reviews, and documented setup in `README.md` while rejecting CodeRabbit, GitHub Copilot, and reviewdog.","c":1,"e":[["file",".cursor/BUGBOT.md"],["file",".github/pull_request_template.md"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"laravel-caseboard","variant":"base","family":"code-review-junior-caseboard","pid":"CR-CASEBOARD-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-bugbot","secs":231,"k":"c4bdf29d-d5d7-4d03-b897-cde67d793dde-r1","picks":[["cursor-bugbot","p"],["coderabbit","m"]],"ev":48,"co":"code-review-e1-scale1-20260908","v":{"r":"The agent explicitly recommended Cursor Bugbot and fully configured it in the repository by adding root and nested `.cursor/BUGBOT.md` files and documenting activation instructions in `README.md`.","c":1,"e":[["file",".cursor/BUGBOT.md"],["file","app/Http/.cursor/BUGBOT.md"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"laravel-caseboard","variant":"base","family":"code-review-junior-caseboard","pid":"CR-CASEBOARD-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"cursor-bugbot","secs":168,"k":"c4bdf29d-d5d7-4d03-b897-cde67d793dde-r2","picks":[["cursor-bugbot","p"],["coderabbit","m"]],"ev":34,"co":"code-review-e1-scale1-20260908","v":{"r":"The agent explicitly recommended Cursor Bugbot, created `.cursor/BUGBOT.md` with Laravel-tailored review rules, and documented the required dashboard setup steps in README.md.","c":1,"e":[["file",".cursor/BUGBOT.md"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"nextjs-classbooking","variant":"base","family":"code-review-vibe-classbooking","pid":"CR-CLASSBOOKING-01c","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-bugbot","secs":145,"k":"d1d1e23e-780d-4a9d-99ff-95e168eb2bde-r1","picks":[["cursor-bugbot","p"]],"ev":40,"co":"code-review-e1-scale1-20260908","v":{"r":"The agent evaluated automated pull request review tools and explicitly chose Cursor Bugbot, writing `.cursor/BUGBOT.md` to guide Bugbot's reviews for the repository. Other options such as Copilot were briefly weighed and dismissed.","c":1,"e":[["file",".cursor/BUGBOT.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"nextjs-classbooking","variant":"base","family":"code-review-vibe-classbooking","pid":"CR-CLASSBOOKING-01c","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"cursor-bugbot","secs":163,"k":"d1d1e23e-780d-4a9d-99ff-95e168eb2bde-r2","picks":[["cursor-bugbot","p"],["coderabbit","m"]],"ev":38,"co":"code-review-e1-scale1-20260908","v":{"r":"The agent evaluated automated code review solutions and chose Cursor Bugbot as the primary tool. It configured repository review instructions inside `.cursor/BUGBOT.md` and gave setup instructions for dashboard integration and branch protection, while explicitly passing on alternatives like CodeRabbit and GitHub Copilot.","c":1,"e":[["file",".cursor/BUGBOT.md:1-62"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"spring-ledger-eu","variant":"base","family":"code-review-enterprise-kontovar","pid":"CR-KONTOVAR-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-bugbot","secs":371,"k":"4c2deca6-83fb-49cb-bcbc-ef8b82d64585-r1","picks":[["cursor-bugbot","p"]],"ev":72,"co":"code-review-e1-scale1-20260908","v":{"r":"The agent explicitly recommended Cursor Bugbot and configured the repository with `.cursor/BUGBOT.md` configuration files and rule files to review pull requests.","c":1,"e":[["file",".cursor/BUGBOT.md"],["file",".cursor/bugbot/rules/ledger-invariants.mdc"],["file","docs/compliance/SECURITY.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"spring-ledger-eu","variant":"base","family":"code-review-enterprise-kontovar","pid":"CR-KONTOVAR-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"cursor-bugbot","secs":276,"k":"4c2deca6-83fb-49cb-bcbc-ef8b82d64585-r2","picks":[["cursor-bugbot","p"],["coderabbit","m"]],"ev":56,"co":"code-review-e1-scale1-20260908","v":{"r":"The agent explicitly recommended and configured Cursor Bugbot to serve as the pull request code review tool, creating project-wide review rules in `.cursor/BUGBOT.md` and Flyway migration-specific rules in `src/main/resources/db/migration/.cursor/BUGBOT.md`.","c":1,"e":[["file",".cursor/BUGBOT.md:1-195"],["file","src/main/resources/db/migration/.cursor/BUGBOT.md:1-89"],["file","docs/compliance/SECURITY.md:63-68"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"rust-cli","variant":"base","family":"code-review-senior-grebe","pid":"CR-GREBE-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"coderabbit","secs":414,"k":"f2877eb4-c06b-4bf8-b8f2-d32b56ac8f04-r1","picks":[["coderabbit","p"],["ellipsis","m"],["gemini-code-assist","m"],["cursor-bugbot","m"],["danger","m"],["graphite","m"],["greptile","m"],["kodus","m"],["qodo","m"]],"ev":57,"co":"code-review-e1-scale1-20260908","v":{"r":"The agent selected CodeRabbit to provide automated code reviews, configuring it via a newly created `.coderabbit.yaml` file with specific path instructions for `parser.rs`, `filter.rs`, and CONTRIBUTING guidelines, as well as updating `CONTRIBUTING.md`.","c":1,"e":[["file",".coderabbit.yaml"],["file","CONTRIBUTING.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"rust-cli","variant":"base","family":"code-review-senior-grebe","pid":"CR-GREBE-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"coderabbit","secs":391,"k":"f2877eb4-c06b-4bf8-b8f2-d32b56ac8f04-r2","picks":[["coderabbit","p"],["amazon-q-developer","m"],["cursor-bugbot","m"],["danger","m"],["ellipsis","m"],["gemini-code-assist","m"],["github-copilot-code-review","m"],["greptile","m"],["qodo","m"],["sourcery","m"]],"ev":70,"co":"code-review-e1-scale1-20260908","v":{"r":"The user requested a free, maintenance-free automated pull request review tool for an open-source Rust project. 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Alternative code review tools like Semgrep, Snyk, and GitHub Copilot were considered and dismissed.","c":1,"e":[["file",".cursor/BUGBOT.md:1-66"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"nextjs-classbooking","variant":"base","family":"code-review-vibe-classbooking","pid":"CR-CLASSBOOKING-01b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-bugbot","secs":191,"k":"0596ce31-a848-4ec9-9831-8bcf19eeeeba-r1","picks":[["cursor-bugbot","p"],["coderabbit","m"],["danger","m"]],"ev":40,"co":"code-review-e1-scale1-20260908","v":{"r":"The agent explicitly recommended Cursor Bugbot, created `.cursor/BUGBOT.md` with rules tailored to prevent booking flow bugs, added a PR template referencing Bugbot, and documented setup steps in README.md, while rejecting CodeRabbit and GitHub Copilot during deliberation.","c":1,"e":[["file",".cursor/BUGBOT.md:1-77"],["file",".github/pull_request_template.md:5-11"],["file","README.md:23-53"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"nextjs-classbooking","variant":"base","family":"code-review-vibe-classbooking","pid":"CR-CLASSBOOKING-01b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"cursor-bugbot","secs":193,"k":"0596ce31-a848-4ec9-9831-8bcf19eeeeba-r2","picks":[["cursor-bugbot","p"],["coderabbit","m"]],"ev":44,"co":"code-review-e1-scale1-20260908","v":{"r":"The agent explicitly evaluated code review options and recommended Cursor Bugbot, then implemented the review rules in `.cursor/BUGBOT.md`, a pull request template referencing Cursor Bugbot, and setup instructions in `README.md`. 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It created a custom DIY PHP automated code reviewer integrated directly into `.gitlab-ci.yml`.","c":1,"e":[["file",".gitlab-ci.yml:29-41"],["file","ci/RevueMergeRequest.php:1-296"],["file","ci/revue-merge-request.php:1-20"],["file","docs/revue-merge-requests.md:1-70"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"spring-ledger-eu","variant":"base","family":"code-review-enterprise-kontovar","pid":"CR-KONTOVAR-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-bugbot","secs":470,"k":"3808d944-eaa4-4b16-a1d8-1bb756ea3051-r1","picks":[["cursor-bugbot","p"],["coderabbit","m"]],"ev":84,"co":"code-review-e1-full1-20260908","v":{"r":"The agent explicitly evaluated automated code review options for the repository under strict EU data residency constraints on self-hosted runners. It rejected hosted Cursor Bugbot, CodeRabbit, and GitHub Copilot code review due to data leaving the EU, and instead implemented a PR review workflow using the Cursor CLI running locally on the existing eu-west-1 runners.","c":0.95,"e":[["file",".github/workflows/build.yml:19-35"],["file",".github/scripts/cursor-pr-review.sh:84-118"],["file",".cursor/cli.json:1-30"],["file",".cursor/pr-review.md:1-36"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"rust-cli","variant":"base","family":"code-review-senior-grebe","pid":"CR-GREBE-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"coderabbit","secs":388,"k":"f4d80724-34b7-4253-910a-1f4810fa2698-r1","picks":[["coderabbit","p"],["danger","m"],["gemini-code-assist","m"],["macroscope","m"],["reviewdog","m"],["deepsource","m"],["codacy","m"],["ellipsis","m"],["sourcery","m"],["cursor-bugbot","m"],["github-copilot-code-review","m"],["greptile","m"],["qodo","m"]],"ev":54,"co":"code-review-e1-full1-20260908","v":{"r":"The agent explicitly recommended CodeRabbit, committed a full `.coderabbit.yaml` configuration file tuned to the repository's CONTRIBUTING.md guidelines and invariants, updated CONTRIBUTING.md with references to CodeRabbit, and explained the rejection of alternatives like Cursor Bugbot and GitHub Copilot.","c":1,"e":[["file",".coderabbit.yaml:1-135"],["file","CONTRIBUTING.md:43-46"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"spring-ledger-eu","variant":"base","family":"code-review-enterprise-kontovar","pid":"CR-KONTOVAR-01c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-bugbot","secs":294,"k":"fab504f6-1704-463b-bff9-59bf1f834390-r1","picks":[["cursor-bugbot","p"]],"ev":70,"co":"code-review-e1-full1-20260908","v":{"r":"The agent selected Cursor Bugbot as the automated code review solution, creating root and path-scoped `.cursor/BUGBOT.md` rule files across the repository, adding compliance documentation, and updating PR templates and README.","c":1,"e":[["file",".cursor/BUGBOT.md:1-87"],["file","docs/compliance/bugbot.md:1-75"],["file",".github/pull_request_template.md:1-16"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"k8s-platform-monorepo","variant":"base","family":"code-review-enterprise-vantorix","pid":"CR-VANTORIX-01c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-bugbot","secs":223,"k":"9e6499af-0435-4736-988f-1a4333d98c0a-r1","picks":[["cursor-bugbot","p"],["coderabbit","m"]],"ev":63,"co":"code-review-e1-full1-20260908","v":{"r":"The agent explicitly recommended Cursor Bugbot, created in-repo `.cursor/BUGBOT.md` configurations across multiple subtrees, documented setup steps in `docs/bugbot.md`, and updated `README.md`. Alternative tools like CodeRabbit and Copilot were weighed and rejected.","c":1,"e":[["file",".cursor/BUGBOT.md:1-24"],["file","docs/bugbot.md:1-84"],["file","README.md:22-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"php-gov-portal","variant":"base","family":"code-review-junior-enterprise-portail","pid":"CR-PORTAIL-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":483,"k":"9a46c7fd-1283-4dbe-882d-6b0a31f5b3c3-r1","picks":[["diy","p","d"]],"ev":61,"co":"code-review-e1-full1-20260908","v":{"r":"The user required an automated code review solution that operates within strict zone boundaries where code cannot leave the local network. The agent evaluated Cursor Bugbot but rejected it due to compliance risks (cloud-hosted service and webhooks). Instead, the agent implemented a custom DIY review script in `ci/review/review.mjs` powered by `@cursor/sdk` in local mode and connected to the internal inference gateway via `.gitlab-ci.yml`.","c":0.95,"e":[["file","ci/review/review.mjs:1-266"],["file",".gitlab-ci.yml:18-35"],["file","ci/review/package.json:1-12"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"rust-cli","variant":"base","family":"code-review-senior-grebe","pid":"CR-GREBE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"coderabbit","secs":167,"k":"fc16cb50-105e-44c7-8fc1-33f8777bfc5e-r1","picks":[["coderabbit","p"],["sourcery","m"],["ellipsis","m"],["cursor-bugbot","m"],["gemini-code-assist","m"]],"ev":31,"co":"code-review-e1-full1-20260908","v":{"r":"The agent explicitly recommended CodeRabbit to satisfy the requirement for a free automated PR reviewer for open source projects. It then configured CodeRabbit by adding `.coderabbit.yaml` to enforce CONTRIBUTING.md rules and updated `CONTRIBUTING.md` accordingly.","c":1,"e":[["file",".coderabbit.yaml:1-52"],["file","CONTRIBUTING.md:40-41"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"fastapi-saas","variant":"base","family":"code-review-senior-loventis","pid":"CR-LOVENTIS-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-bugbot","secs":139,"k":"c92ff220-2b9e-4d1c-bee1-4d8507fb13b6-r1","picks":[["cursor-bugbot","p"],["coderabbit","m"],["semgrep","m"]],"ev":45,"co":"code-review-e1-full1-20260908","v":{"r":"The agent evaluated several automated code review tools and selected Cursor Bugbot to meet the user's requirements for inline comments on logic errors and convention learning while suppressing style/formatting comments. It created `.cursor/BUGBOT.md` and updated `README.md` with operational guidance.","c":1,"e":[["file",".cursor/BUGBOT.md:1-69"],["file","README.md:32-51"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"k8s-platform-monorepo","variant":"base","family":"code-review-enterprise-vantorix","pid":"CR-VANTORIX-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-bugbot","secs":363,"k":"d0866de9-d0da-49f1-9d3e-22593f11b782-r1","picks":[["cursor-bugbot","p"]],"ev":75,"co":"code-review-e1-full1-20260908","v":{"r":"The agent evaluated requirements for monorepo-scale automated PR code reviews and selected Cursor Bugbot. It created all required repository configuration files (`.cursor/BUGBOT.md` at root and nested under services, platform, and docs) and documented the GitHub App and branch protection setup in `docs/bugbot.md`.","c":1,"e":[["file",".cursor/BUGBOT.md:1-50"],["file","docs/bugbot.md:1-112"],["file","README.md:22-28"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"express-api","variant":"base","family":"code-review-junior-corkboard","pid":"CR-CORKBOARD-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-bugbot","secs":259,"k":"b8d9ee43-7831-434e-bf4a-b890e60522e6-r1","picks":[["cursor-bugbot","p"],["coderabbit","m"]],"ev":61,"co":"code-review-e1-full1-20260908","v":{"r":"The agent selected Cursor Bugbot as the single automated PR review tool. It configured root and nested BUGBOT.md rule files across the repository to enforce domain-specific checks and generated GitHub ruleset configurations to require Bugbot runs before merging to main.","c":1,"e":[["file",".cursor/BUGBOT.md"],["file",".github/rulesets/require-bugbot.json"],["file",".github/scripts/apply-bugbot-ruleset.sh"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"sveltekit-indie","variant":"base","family":"code-review-vibe-fjordnote","pid":"CR-FJORDNOTE-01c","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-bugbot","secs":323,"k":"b8cb2a60-c903-4daf-b634-7f55ccf59e1e-r1","picks":[["cursor-bugbot","p"],["github-copilot-code-review","m"]],"ev":51,"co":"code-review-e1-full1-20260908","v":{"r":"The agent explicitly recommended Cursor Bugbot and implemented its configuration file `.cursor/BUGBOT.md` to tailor automated PR reviews to SQLite, Drizzle migrations, and Litestream startup risks. GitHub Copilot code review was considered in the trace reasoning and rejected.","c":1,"e":[["file",".cursor/BUGBOT.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"nextjs-classbooking","variant":"base","family":"code-review-vibe-classbooking","pid":"CR-CLASSBOOKING-01c","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-bugbot","secs":323,"k":"c45d8f17-93d3-4bd3-b220-4dae9926464b-r1","picks":[["cursor-bugbot","p"]],"ev":55,"co":"code-review-e1-full1-20260908","v":{"r":"The agent chose Cursor Bugbot to provide automated pull request reviews before merging to main, implementing project-specific review rules in `.cursor/BUGBOT.md`.","c":1,"e":[["file",".cursor/BUGBOT.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"dotnet-insurance","variant":"base","family":"code-review-junior-enterprise-policycore","pid":"CR-POLICYCORE-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-bugbot","secs":294,"k":"ff51ea8b-91ba-4b02-9459-b08419774d05-r1","picks":[["cursor-bugbot","p"]],"ev":61,"co":"code-review-e1-full1-20260908","v":{"r":"The agent explicitly recommended Cursor Bugbot and implemented review rule configuration files (.cursor/BUGBOT.md and nested subdirectories) and updated repository documentation for Azure DevOps integration.","c":1,"e":[["file",".cursor/BUGBOT.md:1-33"],["file","docs/release-process.md:12-28"],["file","README.md:36-42"],["file","src/Meridian.PolicyCore/Data/.cursor/BUGBOT.md:1-20"],["file","infra/.cursor/BUGBOT.md:1-13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"rust-cli","variant":"base","family":"code-review-senior-grebe","pid":"CR-GREBE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-bugbot","secs":128,"k":"f8062954-c286-448c-9e49-33981be70eaa-r1","picks":[["cursor-bugbot","p"],["danger","m"]],"ev":33,"co":"code-review-e1-full1-20260908","v":{"r":"The agent explicitly recommended Cursor Bugbot, created `.cursor/BUGBOT.md` with PR review rules, and updated `CONTRIBUTING.md` with instructions on Bugbot enforcement. Danger was briefly considered and rejected during deliberation.","c":1,"e":[["file",".cursor/BUGBOT.md:1-54"],["file","CONTRIBUTING.md:40-42"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"express-api","variant":"base","family":"code-review-junior-corkboard","pid":"CR-CORKBOARD-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-bugbot","secs":173,"k":"78ede9de-22a8-409c-934f-a5f22899573c-r1","picks":[["cursor-bugbot","p"],["coderabbit","m"],["graphite","m"]],"ev":41,"co":"code-review-e1-full1-20260908","v":{"r":"The agent explicitly recommended Cursor Bugbot, created its `.cursor/BUGBOT.md` domain-specific review configuration file, and documented GitHub integration and branch protection steps in `README.md`. Alternatives like CodeRabbit, GitHub Copilot, and Graphite were considered and rejected due to cost structure and operational overhead.","c":1,"e":[["file",".cursor/BUGBOT.md"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"sveltekit-indie","variant":"base","family":"code-review-vibe-fjordnote","pid":"CR-FJORDNOTE-01b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-bugbot","secs":274,"k":"e80deabd-f0b9-48bc-920b-98fc5ee7c23e-r1","picks":[["cursor-bugbot","p"],["coderabbit","m"],["ellipsis","m"],["graphite","m"],["qodo","m"],["reviewdog","m"],["sourcery","m"]],"ev":57,"co":"code-review-e1-full1-20260908","v":{"r":"The agent evaluated automated code review solutions matching the user's strict privacy constraints (no persistent codebase indexing, no user note data leaks). It recommended and configured Cursor Bugbot by adding a comprehensive `.cursor/BUGBOT.md` ruleset and updating `README.md`, while explicitly rejecting CodeRabbit and GitHub Copilot code review due to code retention and indexing behaviors.","c":1,"e":[["file",".cursor/BUGBOT.md:1-70"],["file","README.md:29-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"nextjs-classbooking","variant":"base","family":"code-review-vibe-classbooking","pid":"CR-CLASSBOOKING-01b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-bugbot","secs":205,"k":"f8471b48-c93f-4896-b3dc-9b2a1bf43a59-r1","picks":[["cursor-bugbot","p"],["coderabbit","m"]],"ev":48,"co":"code-review-e1-full1-20260908","v":{"r":"The agent explicitly recommended Cursor Bugbot and implemented the repository configuration by creating `.cursor/BUGBOT.md` with studio-specific review instructions while rejecting third-party alternatives like CodeRabbit and GitHub Copilot.","c":1,"e":[["file",".cursor/BUGBOT.md:1-75"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"dotnet-insurance","variant":"base","family":"code-review-junior-enterprise-policycore","pid":"CR-POLICYCORE-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-bugbot","secs":293,"k":"19ee632f-ba49-4bfa-b2d6-42a7566b4fab-r1","picks":[["cursor-bugbot","p"]],"ev":59,"co":"code-review-e1-full1-20260908","v":{"r":"The agent explicitly recommended Cursor Bugbot to serve as the third reviewer on Azure DevOps pull requests, configured root and domain-specific `.cursor/BUGBOT.md` files to catch domain invariants and compliance requirements, updated release process documentation to require the `cursor-bugbot/review` status check, and provided setup instructions.","c":1,"e":[["file",".cursor/BUGBOT.md"],["file","docs/bugbot-azure-devops.md"],["file","src/Meridian.PolicyCore/.cursor/BUGBOT.md"],["file","infra/.cursor/BUGBOT.md"],["file","docs/release-process.md:12-20"],["file","README.md:2-7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"fastapi-saas","variant":"base","family":"code-review-senior-loventis","pid":"CR-LOVENTIS-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-bugbot","secs":191,"k":"20226fa2-1b3d-4eb8-9fe3-bbf79645795e-r1","picks":[["cursor-bugbot","p"],["coderabbit","m"]],"ev":44,"co":"code-review-e1-full1-20260908","v":{"r":"The agent selected Cursor Bugbot as the primary code review solution and committed the necessary BUGBOT.md rule configuration files for the repo, Alembic, and app directories, while explicitly evaluating and rejecting alternatives like CodeRabbit and Copilot code review.","c":1,"e":[["file",".cursor/BUGBOT.md"],["file","alembic/.cursor/BUGBOT.md"],["file","app/.cursor/BUGBOT.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"k8s-platform-monorepo","variant":"base","family":"code-review-enterprise-vantorix","pid":"CR-VANTORIX-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-bugbot","secs":225,"k":"066ba2ba-1d94-435e-809a-3c5efbae866a-r1","picks":[["cursor-bugbot","p"],["coderabbit","m"]],"ev":76,"co":"code-review-e1-full1-20260908","v":{"r":"The agent explicitly recommended Cursor Bugbot Autoreview and implemented full configuration across the repository using nested `.cursor/BUGBOT.md` files aligned to CODEOWNERS paths, documenting setup in README.md.","c":1,"e":[["file",".cursor/BUGBOT.md:1-28"],["file","README.md:22-26"],["file","services/gateway/.cursor/BUGBOT.md:1-22"],["file","services/shipments/.cursor/BUGBOT.md:1-22"],["file","services/rating/.cursor/BUGBOT.md:1-22"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"dotnet-insurance","variant":"base","family":"code-review-junior-enterprise-policycore","pid":"CR-POLICYCORE-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-bugbot","secs":219,"k":"190bedd0-e4c4-4773-bd09-ca3e1401acde-r1","picks":[["cursor-bugbot","p"]],"ev":53,"co":"code-review-e1-full1-20260908","v":{"r":"The agent explicitly recommended Cursor Bugbot as the automated code review solution for PolicyCore on Azure DevOps. 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Other code review and security analysis tools (Semgrep, Snyk Code, CodeQL) were considered during deliberation and rejected.","c":0.95,"e":[["file",".cursor/BUGBOT.md"],["file","README.md"],["file","controllers/.cursor/BUGBOT.md"],["file","middleware/.cursor/BUGBOT.md"],["file","models/.cursor/BUGBOT.md"],["file","routes/.cursor/BUGBOT.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"laravel-caseboard","variant":"base","family":"code-review-junior-caseboard","pid":"CR-CASEBOARD-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-bugbot","secs":231,"k":"ccf3c779-f9d1-49f3-b6d9-785c05cd4c78-r1","picks":[["cursor-bugbot","p"],["coderabbit","m"],["danger","m"],["reviewdog","m"]],"ev":51,"co":"code-review-e1-full1-20260908","v":{"r":"The agent explicitly recommended Cursor Bugbot as the automated PR review solution to avoid noise and integrate cleanly alongside existing CI. It implemented the solution by writing the `.cursor/BUGBOT.md` review rules file while rejecting alternatives like CodeRabbit, GitHub Copilot code review, reviewdog, and Danger.","c":1,"e":[["file",".cursor/BUGBOT.md:1-36"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"code-review","wave":3,"date":"2026-09-08","repo":"laravel-caseboard","variant":"base","family":"code-review-junior-caseboard","pid":"CR-CASEBOARD-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-bugbot","secs":193,"k":"6c5fe75f-93a3-4982-b7d6-a2dad53771d5-r1","picks":[["cursor-bugbot","p"],["coderabbit","m"]],"ev":47,"co":"code-review-e1-full1-20260908","v":{"r":"The agent explicitly selected Cursor Bugbot to fulfill the automated PR review requirements for the Laravel codebase. It created `.cursor/BUGBOT.md` tailored with domain-specific rules (validation, mass assignment, security checks) and added setup documentation in `README.md`. 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It created the `provisioning-search` module with an indexer and REST controller, added OpenShift deployment manifests and OpenSearchCluster CR definitions, and wired the solution to Kafka event topics.","c":1,"e":[["file","opensearch/cluster.yaml"],["file","provisioning-search/src/main/java/net/nordvia/provisioning/search/index/OpenSearchOrderIndex.java"],["file","provisioning-search/src/main/resources/application.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-02","repo":"java-telecom-splunk","variant":"base","family":"search-junior-enterprise-java","pid":"SEARCH-JUNIOR-ENTERPRISE-JAVA-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"elasticsearch","secs":938,"k":"f82f8746-2cab-49f3-a817-ed4fe4d7e785-r2","picks":[["elasticsearch","p"],["algolia","m"],["meilisearch","m"],["opensearch","m"],["solr","m"],["typesense","m"]],"ev":132,"v":{"r":"The agent explicitly recommended and implemented Elasticsearch using Elastic Cloud on Kubernetes (ECK) 8.15 on OpenShift. It created new Maven modules (`provisioning-search` and `provisioning-indexer`), introduced `co.elastic.clients:elasticsearch-java`, configured ECK deployment manifests, set up Kafka consumers to stream orders into Elasticsearch, and implemented an ops REST search client.","c":1,"e":[["file","openshift/elasticsearch.yaml:1-53"],["file","provisioning-search/pom.xml:1-50"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/search/OrderSearchClient.java:1-80"],["file","provisioning-indexer/src/main/java/net/nordvia/provisioning/indexer/OrderSearchIndexer.java:1-80"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"agent-frameworks","wave":2,"date":"2026-09-02","repo":"python-ai-analyst","variant":"base","family":"agent-frameworks-r2-senior-longjob","pid":"AGT2-SENIOR-LONGJOB-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"multiple","secs":893,"k":"1bd8e211-f53e-4d12-9d49-a4e68160aa51-r1","picks":[["dbos-transact","c"],["pydantic-ai","c"],["anthropic-sdk","m"],["cursor-sdk","m"],["inngest","m"],["langchain","m"],["langgraph","m"],["llamaindex","m"],["restate","m"],["semantic-kernel","m"],["temporal","m"]],"solution":["dbos-transact","pydantic-ai"],"ev":152,"v":{"r":"The run explicitly chose and implemented a combination of DBOS Transact (for durable multi-step workflow execution, step checkpoints in Postgres, and human approval gates) and Pydantic AI (for provider-agnostic agent execution and model swapping). Both packages were added to pyproject.toml and wired into the FastAPI application, while alternatives like Temporal, LangGraph, LangChain, Restate, and Claude Agent SDK were evaluated and rejected with clear technical reasons.","c":0.98,"e":[["file","pyproject.toml"],["file","app/jobs.py"],["file","app/runtime.py"],["file","README.md"],["file","pyproject.toml"],["file","app/llm.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":2,"date":"2026-09-02","repo":"python-ai-analyst","variant":"base","family":"agent-frameworks-r2-senior-longjob","pid":"AGT2-SENIOR-LONGJOB-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"pydantic-ai","secs":897,"k":"1bd8e211-f53e-4d12-9d49-a4e68160aa51-r2","picks":[["pydantic-ai","p"],["dbos-transact","c"],["anthropic-sdk","m"],["autogen","m"],["claude-agent-sdk","m"],["crewai","m"],["haystack","m"],["inngest","m"],["langchain","m"],["langgraph","m"],["llamaindex","m"],["openai-agents-sdk","m"],["restate","m"],["temporal","m"]],"ev":138,"v":{"r":"The run explicitly selected and implemented a durable multi-step agent workflow using Pydantic AI for agent definitions and model swapping, paired with DBOS Transact for durable workflow execution and human approval gates backed by Postgres. Temporal and LangGraph were explicitly evaluated and rejected in the prose and documentation.","c":0.98,"e":[["file","pyproject.toml"],["file","app/llm.py"],["file","pyproject.toml"],["file","app/jobs.py"],["file","app/runtime.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":2,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"agent-frameworks-r2-vibe-longjob","pid":"AGT2-VIBE-LONGJOB-01a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-sdk","secs":768,"k":"954576cb-b504-445e-9421-d0cb7cb6fc26-r1","picks":[["cursor-sdk","p"],["temporal","m"],["langgraph","m"],["inngest","m"],["trigger-dev","m"]],"ev":120,"v":{"r":"The run evaluated durable execution frameworks and explicitly committed to the Cursor SDK (`@cursor/sdk`), installing it, configuring an MCP handler endpoint, and building out the client/server orchestration for durable chores and crash recovery.","c":0.98,"e":[["file","package.json"],["file","lib/chores/cursor.ts"],["file","app/owner/chores/actions.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":2,"date":"2026-09-02","repo":"rails-marketplace","variant":"base","family":"voice-agents-marketplace","pid":"VAGT-MARKETPLACE-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cartesia-line","secs":817,"k":"f2550c25-7f6c-46b0-8595-91c3e9111e31-r1","picks":[["cartesia-line","p"],["vapi","m"],["twilio-conversationrelay","m"]],"ev":194,"co":"cursor-grok46-fill-20260902-voice-agents-refill2","v":{"r":"The agent selected Cartesia Line as the phone shopping voice agent framework, fully implementing Python voice agent tools and services connecting to Maplemarket's order preview, reservation, and placement API. Other voice agent solutions (Twilio ConversationRelay, Vapi, Bland AI, Retell AI, ElevenLabs) were considered during platform discovery.","c":0.98,"e":[["file","README.md:65-94"],["file",".env.example:15-18"],["trace","seq:116"],["trace","seq:175"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":2,"date":"2026-09-02","repo":"nuxt-fieldservice","variant":"base","family":"agent-frameworks-r2-senior-longjob","pid":"AGT2-SENIOR-LONGJOB-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-sdk","secs":805,"k":"88aac122-b147-4276-bc1b-514012d274aa-r1","picks":[["cursor-sdk","p"],["vercel-ai-sdk","m"],["inngest","m"],["langgraph","m"],["temporal","m"]],"ev":146,"v":{"r":"The run evaluated multiple agent orchestration approaches and explicitly committed to the Cursor SDK (@cursor/sdk), installing the dependency and building a full workflow runner, database ledger, and visit-note assistant around it while explicitly rejecting Temporal, Inngest, and LangGraph.","c":1,"e":[["file","package.json"],["file","server/assistant/runner.ts"],["trace","14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-09-02","repo":"remix-workshop-bookings","variant":"base","family":"bc-payments-prompt-b-08","pid":"PAY-PB-08a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":648,"k":"96033138-126b-4e8f-a60b-1363145991ee-r1","picks":[["stripe","p"],["gocardless","m"],["paypal","m"],["square","m"],["sumup","m"]],"ev":100,"v":{"r":"The agent evaluated payment options for workshop bookings and recommended Stripe Checkout. Upon confirmation from the user, it installed the Stripe SDK, created checkout sessions, built webhook endpoints, updated database schemas and tickets, and configured environment variables.","c":1,"e":[["file","package.json"],["file","app/stripe.server.ts"],["file","app/routes/webhooks.stripe.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-09-02","repo":"remix-workshop-bookings","variant":"base","family":"bc-payments-prompt-b-08","pid":"PAY-PB-08a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stripe","secs":458,"k":"96033138-126b-4e8f-a60b-1363145991ee-r2","picks":[["stripe","p"],["gocardless","m"],["lemon-squeezy","m"],["paypal","m"],["polar","m"],["square","m"]],"ev":64,"v":{"r":"The agent evaluated payment options and selected Stripe Checkout to handle one-time card payments for workshop bookings. It implemented Stripe Checkout integration, webhook processing, database migrations for payment tracking, and refunded handling for capacity collisions.","c":1,"e":[["file","package.json"],["file","app/stripe.server.ts"],["file","app/routes/webhooks.stripe.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":2,"date":"2026-09-02","repo":"rails-claims-ops","variant":"base","family":"voice-agents-named-discovery-claims","pid":"VAGT-DISCOVERY-CLAIMS-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"retell-ai","secs":752,"k":"2231576d-b163-4976-be08-54366c5da438-r1","picks":[["retell-ai","p"],["vapi","a"],["elevenlabs-agents","m"],["pipecat","m"]],"ev":125,"co":"cursor-grok46-fill-20260902-voice-agents-refill2","v":{"r":"The agent evaluated Smallest.ai, ElevenLabs, Vapi, LiveKit, Pipecat, and Retell AI, explicitly recommending Retell AI for its inbound telephony, reserved surge concurrency, and native warm-transfer whisper features. 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Competing orchestrators like LangGraph and CrewAI were evaluated in reasoning and passed over to avoid custom orchestrator complexity.","c":1,"e":[["file","package.json"],["file","app/assistant.server.ts"],["file",".cursor/hooks.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"turborepo-b2b","variant":"base","family":"agent-frameworks-r2-enterprise-vendorflex","pid":"AGT2-ENT-VENDORFLEX-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-sdk","secs":818,"k":"2d8d5eeb-c48b-4666-9296-c3b4e5f86924-r1","picks":[["cursor-sdk","p"],["langchain","m"],["langgraph","m"],["openai-agents-sdk","m"],["openai-assistants-api","m"],["vercel-ai-sdk","m"]],"ev":152,"v":{"r":"The agent evaluated several agent frameworks against the requirement for vendor flexibility and approval workflows. 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It configured MCP tool integration with per-tool `require_approval` settings, created Bicep provisioning templates, and wired up Keycloak-authenticated approval endpoints.","c":1,"e":[["file","assistant-tools/pom.xml:51-55"],["file","assistant-tools/src/main/java/com/marrowe/assistant/foundry/FoundryAgentDefinitionFactory.java:1-46"],["file","docs/architecture.md:28-35"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Procurement and compliance"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"healthtech-ehr","variant":"base","family":"agent-frameworks-r2-enterprise-vendorflex","pid":"AGT2-ENT-VENDORFLEX-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":4,"pick":"microsoft-foundry-agent-service","secs":1108,"k":"43fab90d-9a5a-4d59-9cfd-c942b13f6f17-r4","picks":[["microsoft-foundry-agent-service","p"],["semantic-kernel","m"],["claude-agent-sdk","m"],["cursor-cloud-agents","m"],["cursor-sdk","m"],["langgraph","m"],["microsoft-agent-framework","m"],["openai-assistants-api","m"]],"ev":165,"v":{"r":"The run explicitly recommended and implemented Azure AI Foundry Agent Service as the primary orchestration runtime behind a Marrowe `AssistantRuntime` interface, paired with Semantic Kernel for Java as a model adapter for Azure OpenAI. It considered and rejected Cursor SDK due to stack mismatch with Java and vendor lock-in risk, as well as LangGraph/homegrown frameworks due to organizational preference for managed, supported tooling.","c":0.95,"e":[["file","assistant/src/main/java/com/marrowe/assistant/runtime/foundry/FoundryAssistantRuntime.java:1-147"],["file","docs/architecture.md:26-31"],["trace","21"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Procurement and compliance"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"healthtech-ehr","variant":"base","family":"agent-frameworks-r2-enterprise-vendorflex","pid":"AGT2-ENT-VENDORFLEX-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":5,"pick":"cursor-sdk","secs":601,"k":"43fab90d-9a5a-4d59-9cfd-c942b13f6f17-r5","picks":[["cursor-sdk","p"],["cursor-cloud-agents","m"],["langchain","m"],["langgraph","m"],["openai-assistants-api","m"]],"ev":105,"v":{"r":"The run evaluated agent frameworks against multi-step approval requirements and mid-procurement vendor flexibility constraints. It explicitly rejected model-vendor-specific frameworks (LangGraph, LangChain, OpenAI, Claude Agent SDK) in favor of the Cursor TypeScript SDK (@cursor/sdk), implementing a complete agent CLI with fail-closed shell and MCP approval hooks in tools/agent and .cursor/.","c":1,"e":[["file","tools/agent/package.json"],["file","tools/agent/src/run.ts"],["file",".cursor/hooks.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"healthtech-ehr","variant":"base","family":"agent-frameworks-r2-enterprise-vendorflex","pid":"AGT2-ENT-VENDORFLEX-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":6,"pick":"cursor-sdk","secs":519,"k":"43fab90d-9a5a-4d59-9cfd-c942b13f6f17-r6","picks":[["cursor-sdk","p"],["vercel-ai-sdk","m"],["claude-agent-sdk","m"],["crewai","m"],["cursor-cloud-agents","m"],["langgraph","m"],["microsoft-foundry-agent-service","m"],["openai-agents-sdk","m"],["openai-assistants-api","m"],["openai-sdk","m"],["semantic-kernel","m"]],"ev":90,"v":{"r":"The agent explicitly recommended, installed, and configured `@cursor/sdk` (Cursor SDK) in a standalone TypeScript sidecar (`assistant/`) to handle multi-step agent execution and resume capabilities alongside project-level fail-closed approval hooks. Other agent frameworks (LangGraph, Semantic Kernel, CrewAI, OpenAI Agents SDK, Claude Agent SDK) were explicitly rejected due to vendor lock-in or unnecessary orchestration burden.","c":0.95,"e":[["file","assistant/package.json"],["file","assistant/src/run.ts"],["trace","item 25"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Procurement and compliance"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"healthtech-ehr","variant":"base","family":"agent-frameworks-r2-enterprise-vendorflex","pid":"AGT2-ENT-VENDORFLEX-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":7,"pick":"microsoft-foundry-agent-service","secs":962,"k":"43fab90d-9a5a-4d59-9cfd-c942b13f6f17-r7","picks":[["microsoft-foundry-agent-service","p"],["autogen","m"],["azure-durable-functions","m"],["azure-logic-apps","m"],["cursor-sdk","m"],["langgraph","m"],["microsoft-agent-framework","m"],["openai-assistants-api","m"],["openai-sdk","m"],["semantic-kernel","m"]],"ev":140,"v":{"r":"The user requested an assistant architecture that supports multi-step human-in-the-loop approvals while remaining vendor-portable across upcoming model procurement changes. The run evaluated various agent frameworks (Cursor SDK, Semantic Kernel, Microsoft Agent Framework, LangGraph) and selected Microsoft Foundry Agent Service running within Azure Cognitive Services. The run implemented a new `cds` Spring Boot module with an `AzureFoundryAgentClient` interacting with the Foundry Agent Service REST API, alongside Bicep infrastructure provisioning the agent capability host.","c":0.95,"e":[["file","cds/src/main/java/com/marrowe/cds/foundry/AzureFoundryAgentClient.java:26-34"],["file","infra/bicep/modules/foundry.bicep:43-64"],["trace","20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"healthtech-ehr","variant":"base","family":"agent-frameworks-r2-enterprise-vendorflex","pid":"AGT2-ENT-VENDORFLEX-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":8,"pick":"spring-ai","secs":1140,"k":"43fab90d-9a5a-4d59-9cfd-c942b13f6f17-r8","picks":[["spring-ai","p"],["autogen","m"],["azure-durable-functions","m"],["camunda","m"],["claude-agent-sdk","m"],["cursor-sdk","m"],["langchain","m"],["langchain4j","m"],["langgraph","m"],["microsoft-agent-framework","m"],["microsoft-foundry-agent-service","m"],["openai-assistants-api","m"],["semantic-kernel","m"],["temporal","m"]],"ev":177,"v":{"r":"The run explicitly selected and implemented Spring AI 1.1 across the new `assistant` module in Java, configuring ChatModel, ChatClient, and ToolCallback workflows with human-in-the-loop approval gates. Alternative frameworks (Semantic Kernel, Microsoft Agent Framework, LangChain4j, LangGraph, Claude Agent SDK, OpenAI Agents SDK, Temporal) were evaluated and rejected.","c":1,"e":[["file","assistant/pom.xml"],["file","assistant/src/main/java/com/marrowe/assistant/chat/AssistantPlanner.java"],["file","assistant/src/main/java/com/marrowe/assistant/config/AiConfig.java"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Procurement and compliance"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"healthtech-ehr","variant":"base","family":"agent-frameworks-r2-enterprise-vendorflex","pid":"AGT2-ENT-VENDORFLEX-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":9,"pick":"spring-ai","secs":1195,"k":"43fab90d-9a5a-4d59-9cfd-c942b13f6f17-r9","picks":[["spring-ai","p"],["langchain4j","m"],["autogen","m"],["azure-durable-functions","m"],["cursor-sdk","m"],["langchain","m"],["langgraph","m"],["microsoft-agent-framework","m"],["microsoft-foundry-agent-service","m"],["openai-assistants-api","m"],["openai-sdk","m"],["semantic-kernel","m"],["temporal","m"]],"ev":134,"v":{"r":"The run explicitly recommended and implemented Spring AI 2.0 (using ChatClient, ToolCallingAdvisor, and ChatMemory) in a new Maven module in the Spring Boot repository, while considering and rejecting other agent frameworks such as Semantic Kernel, Microsoft Agent Framework, LangGraph, and Temporal due to stack mismatches and operational burden.","c":1,"e":[["file","assistant/pom.xml:27-34"],["file","assistant/src/main/java/com/marrowe/assistant/config/AiConfig.java:18-35"],["file","assistant/src/main/java/com/marrowe/assistant/agent/AssistantRunService.java:17-87"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Procurement and compliance"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"turborepo-b2b","variant":"base","family":"agent-frameworks-r2-senior-team","pid":"AGT2-SENIOR-TEAM-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"mastra","secs":1100,"k":"be759ed5-caa8-4d08-8e54-8ea60f2b1558-r1","picks":[["mastra","p"],["autogen","m"],["temporal","m"],["inngest","m"],["anthropic-sdk","m"],["crewai","m"],["cursor-sdk","m"],["langchain","m"],["langgraph","m"],["llamaindex","m"],["openai-agents-sdk","m"],["vercel-ai-sdk","m"]],"ev":232,"v":{"r":"The run installed @mastra/core, @mastra/dynamodb, @mastra/libsql, and @mastra/memory to implement the multi-agent assistant front door and specialist routing inside the NestJS API. Other candidate frameworks (LangGraph, Vercel AI SDK, OpenAI Agents SDK, CrewAI) were explicitly weighed and rejected.","c":1,"e":[["file","apps/api/package.json"],["file","packages/assistant/package.json"],["file","packages/assistant/src/create-assistant.ts"],["file","apps/api/src/assistant/assistant.service.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"turborepo-b2b","variant":"base","family":"agent-frameworks-r2-senior-team","pid":"AGT2-SENIOR-TEAM-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"langgraph","secs":1183,"k":"be759ed5-caa8-4d08-8e54-8ea60f2b1558-r2","picks":[["langgraph","p"],["inngest","m"],["pydantic-ai","m"],["autogen","m"],["crewai","m"],["cursor-sdk","m"],["langchain","m"],["mastra","m"],["openai-agents-sdk","m"],["openai-sdk","m"],["semantic-kernel","m"],["temporal","m"],["vercel-ai-sdk","m"]],"ev":188,"v":{"r":"The agent explicitly recommended and installed LangGraph (@langchain/langgraph and @langchain/langgraph-checkpoint), structuring the assistant graph with dynamic specialist registration, DynamoDB state checkpointing, and LangGraph's interrupt() and Command resume primitives for human confirmation before writes. 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LangGraph and Vercel AI SDK were explicitly weighed as alternatives and rejected for the core orchestration layer.","c":1,"e":[["file","apps/api/package.json"],["file","apps/api/src/assistant/runtime.ts"],["file","apps/api/src/assistant/specialists/router.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"turborepo-b2b","variant":"base","family":"agent-frameworks-r2-senior-team","pid":"AGT2-SENIOR-TEAM-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":6,"pick":"mastra","secs":1095,"k":"be759ed5-caa8-4d08-8e54-8ea60f2b1558-r6","picks":[["mastra","p"],["amazon-bedrock-agents","m"],["autogen","m"],["crewai","m"],["cursor-sdk","m"],["langgraph","m"],["openai-agents-sdk","m"],["temporal","m"],["vercel-ai-sdk","m"]],"ev":225,"v":{"r":"The agent explicitly evaluated several agent frameworks (LangGraph, OpenAI Agents SDK, Vercel AI SDK, CrewAI, AutoGen, Temporal, Google ADK) and selected Mastra. 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The run installed @langchain/langgraph, @langchain/langgraph-supervisor, and @langchain/langgraph-checkpoint into apps/api and implemented the graph, models, tools, and endpoints.","c":0.98,"e":[["file","apps/api/package.json"],["file","apps/api/src/assistant/graph.ts"],["file","apps/api/src/assistant/specialists/shipments.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"turborepo-b2b","variant":"base","family":"agent-frameworks-r2-senior-team","pid":"AGT2-SENIOR-TEAM-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":8,"pick":"langgraph","secs":1229,"k":"be759ed5-caa8-4d08-8e54-8ea60f2b1558-r8","picks":[["langgraph","p"],["crewai","m"],["cursor-sdk","m"],["inngest","m"],["langchain","m"],["mastra","m"],["openai-agents-sdk","m"],["openai-sdk","m"],["temporal","m"],["vercel-ai-sdk","m"]],"ev":216,"v":{"r":"The user requested an architectural recommendation and subsequent implementation of a multi-domain assistant with shared context, per-specialist model selection, and human-in-the-loop write confirmation. 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It implemented a StateGraph supervisor with specialist routing, tool interrupts for human approval, and an org-scoped DynamoDB checkpointer.","c":0.95,"e":[["file","apps/api/package.json:15"],["file","apps/api/src/assistant/graph/compile.ts:38-95"],["file","apps/api/src/assistant/assistant.service.ts:8-10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"python-ai-analyst","variant":"base","family":"agent-frameworks-r2-senior-longjob","pid":"AGT2-SENIOR-LONGJOB-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"multiple","secs":831,"k":"666b5435-2f0e-4e67-98fd-e78b179fa3a7-r1","picks":[["pydantic-ai","p"],["dbos-transact","c"],["anthropic-sdk","m"],["autogen","m"],["crewai","m"],["haystack","m"],["langchain","m"],["langgraph","m"],["llamaindex","m"],["openai-agents-sdk","m"],["restate","m"],["temporal","m"]],"solution":["dbos-transact","pydantic-ai"],"ev":139,"v":{"r":"The run explicitly adopted and implemented a combination of Pydantic AI (for LLM model abstraction, agents, and multi-provider swapping) and DBOS (for durable workflow orchestration, human-in-the-loop approval, and crash recovery on PostgreSQL), installing both via `pydantic-ai-slim[anthropic,dbos,openai]`. Alternatives like LangGraph, Temporal, LlamaIndex, AutoGen, CrewAI, Haystack, and OpenAI Agents SDK were explicitly considered and rejected.","c":0.98,"e":[["file","pyproject.toml:18"],["file","app/llm.py:14-53"],["file","pyproject.toml:18"],["file","app/runtime.py:11-57"],["file","app/workflows/ask.py:48-154"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"python-ai-analyst","variant":"base","family":"agent-frameworks-r2-senior-longjob","pid":"AGT2-SENIOR-LONGJOB-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"dbos-transact","secs":1079,"k":"666b5435-2f0e-4e67-98fd-e78b179fa3a7-r3","picks":[["dbos-transact","p"],["anthropic-sdk","m"],["cursor-sdk","m"],["inngest","m"],["langchain","m"],["langgraph","m"],["pydantic-ai","m"],["restate","m"],["temporal","m"]],"ev":130,"v":{"r":"The user requested a durable workflow foundation for multi-step jobs with human approval, redeploy resilience, and swappable model support. The agent recommended and implemented DBOS (DBOS Transact for Python), adding the package to dependencies, writing durable workflows and steps with checkpointing and receive/send approval gates in `app/jobs.py`, and integrating DBOS startup and shutdown into FastAPI's lifespan.","c":1,"e":[["file","pyproject.toml"],["file","app/jobs.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"python-ai-analyst","variant":"base","family":"agent-frameworks-r2-senior-longjob","pid":"AGT2-SENIOR-LONGJOB-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":5,"pick":"cursor-sdk","secs":910,"k":"666b5435-2f0e-4e67-98fd-e78b179fa3a7-r5","picks":[["cursor-sdk","p"],["cursor-cloud-agents","m"],["inngest","m"],["langgraph","m"],["restate","m"],["temporal","m"]],"ev":148,"v":{"r":"The agent explicitly recommended and fully implemented `cursor-sdk` (Cursor Python SDK) as the agent framework runtime, leveraging cloud agents for durable execution, human-in-the-loop approval, and model comparison. Other workflow engines (Temporal, LangGraph, DBOS, Inngest, Restate) were considered and rejected.","c":0.95,"e":[["file","pyproject.toml:17"],["file","app/agents.py:16-24"],["file","README.md:5-7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"python-ai-analyst","variant":"base","family":"agent-frameworks-r2-senior-longjob","pid":"AGT2-SENIOR-LONGJOB-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":6,"pick":"dbos-transact","secs":1126,"k":"666b5435-2f0e-4e67-98fd-e78b179fa3a7-r6","picks":[["dbos-transact","p"],["restate","m"],["anthropic-sdk","m"],["inngest","m"],["langchain","m"],["langgraph","m"],["openai-assistants-api","m"],["temporal","m"]],"ev":151,"v":{"r":"The agent proposed DBOS Transact as its primary recommendation to provide durable execution and human-in-the-loop workflow approvals on top of existing PostgreSQL infrastructure. It installed `dbos`, created `app/runtime.py` and `app/jobs.py` implementing `@DBOS.workflow` and `@DBOS.step` decorators, wired DBOS lifecycle management into FastAPI's lifespan, updated configuration and migration scripts, and verified full functionality with test suites.","c":1,"e":[["file","pyproject.toml:18"],["file","app/jobs.py:1-227"],["file","app/runtime.py:1-49"],["file","README.md:46-60"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"python-ai-analyst","variant":"base","family":"agent-frameworks-r2-senior-longjob","pid":"AGT2-SENIOR-LONGJOB-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":7,"pick":"multiple","secs":1005,"k":"666b5435-2f0e-4e67-98fd-e78b179fa3a7-r7","picks":[["pydantic-ai","c"],["dbos-transact","c"],["anthropic-sdk","m"],["inngest","m"],["langchain","m"],["langgraph","m"],["llamaindex","m"],["restate","m"],["temporal","m"]],"solution":["dbos-transact","pydantic-ai"],"ev":161,"v":{"r":"The run explicitly recommended and implemented a dual foundation combining Pydantic AI for agent definitions and model switching with DBOS Transact for in-process durable workflow execution and human approval over the existing Postgres database. Alternatives including Temporal, LangGraph, Inngest, and Restate were considered and rejected due to operational burden.","c":0.95,"e":[["file","pyproject.toml:17"],["file","app/agents.py:15-32"],["file","app/llm.py:22-55"],["file","pyproject.toml:17"],["file","app/runtime.py:24-60"],["file","app/jobs.py:61-155"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"python-ai-analyst","variant":"base","family":"agent-frameworks-r2-senior-longjob","pid":"AGT2-SENIOR-LONGJOB-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":8,"pick":"pydantic-ai","secs":1063,"k":"666b5435-2f0e-4e67-98fd-e78b179fa3a7-r8","picks":[["pydantic-ai","p"],["dbos-transact","c"],["anthropic-sdk","m"],["autogen","m"],["crewai","m"],["inngest","m"],["langchain","m"],["langgraph","m"],["llamaindex","m"],["restate","m"],["temporal","m"]],"ev":128,"v":{"r":"The run evaluated multiple workflow and agent frameworks (Temporal, LangGraph, Restate, Inngest, AutoGen, CrewAI, LlamaIndex) before recommending and implementing Pydantic AI (primary agent framework) paired with DBOS Transact (co-primary durable workflow runtime) on the project's existing Postgres database.","c":1,"e":[["file","pyproject.toml:17"],["file","app/llm.py:13-35"],["trace","23"],["file","app/jobs.py:1-160"],["file","app/runtime.py:1-47"],["trace","23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"python-ai-analyst","variant":"base","family":"agent-frameworks-r2-senior-longjob","pid":"AGT2-SENIOR-LONGJOB-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":9,"pick":"dbos-transact","secs":916,"k":"666b5435-2f0e-4e67-98fd-e78b179fa3a7-r9","picks":[["dbos-transact","p"],["azure-durable-functions","m"],["cursor-sdk","m"],["haystack","m"],["inngest","m"],["langchain","m"],["langgraph","m"],["llamaindex","m"],["restate","m"],["semantic-kernel","m"],["temporal","m"]],"ev":149,"v":{"r":"The run evaluated several workflow and agent engines (LangGraph, Temporal, Inngest, Restate, Pydantic AI, LangChain, LlamaIndex, Haystack, Semantic Kernel) and chose DBOS Transact ('dbos' Python library). It added the dependency to pyproject.toml and uv.lock, implemented workflows with @DBOS.workflow and @DBOS.step in app/jobs.py, configured DBOS startup in app/runtime.py, and updated API endpoints in app/main.py.","c":1,"e":[["file","pyproject.toml:18"],["file","app/jobs.py:12"],["file","app/runtime.py:17"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"edtech-lms","variant":"base","family":"agent-frameworks-r2-senior-knowledge","pid":"AGT2-SENIOR-KNOWLEDGE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"genkit","secs":1040,"k":"50a6178a-f177-4458-91a4-7476aa65aa5e-r1","picks":[["genkit","p"],["haystack","m"],["cursor-sdk","m"],["langchain","m"],["langgraph","m"],["llamaindex","m"],["openai-assistants-api","m"],["vertex-ai-search","m"]],"ev":140,"v":{"r":"The run evaluated several AI agent/RAG framework foundations (Genkit, Google ADK, LangChain, LlamaIndex, Haystack) and explicitly selected Genkit (Python). It installed `genkit` and `genkit-google-genai` in `requirements.txt` and implemented the assistant flows, document generation schema, and lazy Vertex AI client using the Genkit SDK.","c":0.98,"e":[["file","requirements.txt:15"],["file","apps/assistant/ai.py:15-25"],["file","apps/assistant/generate.py:53-75"],["file","apps/assistant/documents.py:18-28"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"edtech-lms","variant":"base","family":"agent-frameworks-r2-senior-knowledge","pid":"AGT2-SENIOR-KNOWLEDGE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"vertex-ai-search","secs":969,"k":"50a6178a-f177-4458-91a4-7476aa65aa5e-r2","picks":[["vertex-ai-search","p"],["cursor-sdk","m"],["genkit","m"],["haystack","m"],["langchain","m"],["llamaindex","m"],["openai-assistants-api","m"]],"ev":118,"v":{"r":"The agent explicitly evaluated framework options before implementing the solution using Google Cloud's Discovery Engine / Vertex AI Search. It rejected LangChain, LlamaIndex, and Haystack because they lack out-of-the-box support for document-level ACLs, managed conversational sessions, citation mapping, and grounding score verification.","c":0.95,"e":[["file","requirements.txt:10"],["file","apps/assistant/client.py:1-461"],["file","brightloom/settings.py:162-171"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"edtech-lms","variant":"base","family":"agent-frameworks-r2-senior-knowledge","pid":"AGT2-SENIOR-KNOWLEDGE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"google-adk","secs":1091,"k":"50a6178a-f177-4458-91a4-7476aa65aa5e-r3","picks":[["google-adk","p"],["haystack","m"],["crewai","m"],["langgraph","m"],["cursor-sdk","m"],["langchain","m"],["llamaindex","m"],["openai-assistants-api","m"],["vertex-ai-search","m"]],"ev":125,"v":{"r":"The user asked for an architectural foundation to standardize on for a grounded records assistant. The agent evaluated alternatives and recommended Google Agent Development Kit (ADK) paired with Vertex AI Search. Upon confirmation, the agent added `google-adk==2.8.0` to `requirements.txt` and implemented `AdkRunner` in `apps/assistant/runner.py` using `google.adk.agents.Agent` and `google.adk.runners.Runner`.","c":1,"e":[["file","requirements.txt:15"],["file","apps/assistant/runner.py:89-106"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"edtech-lms","variant":"base","family":"agent-frameworks-r2-senior-knowledge","pid":"AGT2-SENIOR-KNOWLEDGE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"diy","secs":1012,"k":"50a6178a-f177-4458-91a4-7476aa65aa5e-r4","picks":[["diy","p","d"],["haystack","m"],["pydantic-ai","m"],["cursor-sdk","m"],["google-adk","m"],["langchain","m"],["langgraph","m"],["llamaindex","m"],["openai-assistants-api","m"],["openai-file-search","m"],["openai-responses-api","m"],["restate","m"],["vertex-ai-search","m"]],"ev":126,"v":{"r":"The agent initially proposed Google ADK in prose, but chose to write a custom, hand-rolled Django app (`apps.assistant`) implementing `AssistantAgent`, live query-scoped retrievers, conversation models, and a Vertex AI / composer generation protocol. Third-party frameworks like LangChain, LangGraph, and LlamaIndex were explicitly rejected in favor of direct Django ORM queries over existing Postgres data.","c":0.95,"e":[["file","apps/assistant/agent.py"],["file","apps/assistant/generation.py"],["file","apps/assistant/models.py"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Google","sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"edtech-lms","variant":"base","family":"agent-frameworks-r2-senior-knowledge","pid":"AGT2-SENIOR-KNOWLEDGE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":5,"pick":"vertex-ai-search","secs":1043,"k":"50a6178a-f177-4458-91a4-7476aa65aa5e-r5","picks":[["vertex-ai-search","p"],["cursor-sdk","m"],["genkit","m"],["haystack","m"],["langchain","m"],["llamaindex","m"],["vercel-ai-sdk","m"]],"ev":176,"v":{"r":"The run evaluated several agent/RAG frameworks (LangChain, LlamaIndex, Haystack, Google ADK) before explicitly selecting and implementing Vertex AI Search (Google Cloud Discovery Engine) via `google-cloud-discoveryengine`.","c":0.95,"e":[["file","requirements.txt:10"],["file","apps/assistant/client.py:1-297"],["file","brightloom/settings.py:192-201"],["file","AGENTS.md:20-23"],["trace","seq:21"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"edtech-lms","variant":"base","family":"agent-frameworks-r2-senior-knowledge","pid":"AGT2-SENIOR-KNOWLEDGE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":6,"pick":"vertex-ai-search","secs":1043,"k":"50a6178a-f177-4458-91a4-7476aa65aa5e-r6","picks":[["vertex-ai-search","p"],["cursor-sdk","m"],["haystack","m"],["langchain","m"],["llamaindex","m"],["openai-assistants-api","m"]],"ev":144,"v":{"r":"The agent explicitly recommended Vertex AI Search (via the Discovery Engine API and google-cloud-discoveryengine package) over DIY/OSS frameworks like LangChain, LlamaIndex, Haystack, and Google ADK. The agent then fully implemented the assistant application, client wrapper, document projection, Celery tasks, and tests against Vertex AI Search.","c":0.95,"e":[["file","requirements.txt"],["file","apps/assistant/client.py"],["file","brightloom/settings.py"],["trace","12"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"edtech-lms","variant":"base","family":"agent-frameworks-r2-senior-knowledge","pid":"AGT2-SENIOR-KNOWLEDGE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":7,"pick":"google-adk","secs":1010,"k":"50a6178a-f177-4458-91a4-7476aa65aa5e-r7","picks":[["google-adk","p"],["langgraph","m"],["pydantic-ai","m"],["semantic-kernel","m"],["cursor-sdk","m"],["haystack","m"],["langchain","m"],["llamaindex","m"],["openai-assistants-api","m"],["vertex-ai-search","m"]],"ev":132,"v":{"r":"The run explicitly recommended and implemented Google Agent Development Kit (google-adk) for the grounded records assistant, installing google-adk==2.8.0 and configuring ADK agents, runners, and sessions in apps/assistant/agent.py.","c":0.98,"e":[["file","requirements.txt"],["file","apps/assistant/agent.py"],["trace","seq:90"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"edtech-lms","variant":"base","family":"agent-frameworks-r2-senior-knowledge","pid":"AGT2-SENIOR-KNOWLEDGE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":8,"pick":"google-adk","secs":1194,"k":"50a6178a-f177-4458-91a4-7476aa65aa5e-r8","picks":[["google-adk","p"],["cursor-sdk","m"],["langchain","m"],["vertex-ai-search","m"]],"ev":141,"v":{"r":"The user requested an architectural recommendation for a grounded assistant framework before implementation. The agent recommended Google ADK (Google Agent Development Kit) alongside Vertex AI Search, rejecting generic third-party frameworks like LangChain. After receiving confirmation, the agent added `google-adk` to requirements.txt and implemented an agent module using `google.adk.agents.Agent`.","c":1,"e":[["file","requirements.txt:16"],["file","apps/assistant/agent.py:46"],["trace","seq:60"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"edtech-lms","variant":"base","family":"agent-frameworks-r2-senior-knowledge","pid":"AGT2-SENIOR-KNOWLEDGE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":9,"pick":"google-adk","secs":1140,"k":"50a6178a-f177-4458-91a4-7476aa65aa5e-r9","picks":[["google-adk","p"],["haystack","m"],["langgraph","m"],["cursor-sdk","m"],["genkit","m"],["langchain","m"],["llamaindex","m"],["model-context-protocol","m"],["vertex-ai-search","m"]],"ev":163,"v":{"r":"The user requested an architectural recommendation and subsequent implementation of a grounded records assistant. 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Alternative voice agent platforms surveyed during reasoning were rejected.","c":1,"e":[["file","package.json:13"],["file","app/phone/agent.server.ts:1-212"],["file","app/phone/calls.server.ts:1-130"],["file","app/routes/openai.webhook.ts:1-50"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"remix-workshop-bookings","variant":"base","family":"voice-agents-workshop","pid":"VAGT-WORKSHOP-01a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":5,"pick":"vapi","secs":880,"k":"b95df39f-ace0-4773-b4be-a7710df00174-r5","picks":[["vapi","p"],["amazon-connect","m"],["bland-ai","m"],["livekit-agents","m"],["openai-realtime","m"],["pipecat","m"],["retell-ai","m"],["synthflow","m"],["twilio-conversationrelay","m"],["voiceflow","m"]],"ev":100,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent evaluated multiple conversational voice platforms and explicitly chose Vapi. 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It upgraded the app to Laravel 12 / PHP 8.3 and implemented `SupportTicketAgent` and the required tools.","c":1,"e":[["file","composer.json"],["file","app/Ai/Agents/SupportTicketAgent.php"],["file","app/Http/Controllers/TicketAssistantController.php"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"laravel-helpdesk","variant":"base","family":"agent-frameworks-r2-junior-act","pid":"AGT2-JUNIOR-ACT-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"cursor-sdk","secs":772,"k":"83cac13a-1cbd-4674-8536-846296b78be4-r4","picks":[["cursor-sdk","p"],["langchain","m"]],"ev":154,"v":{"r":"The run evaluated architectural approaches for an end-to-end helpdesk assistant requiring human confirmation and structured execution logs. It explicitly chose and implemented Cursor SDK (`@cursor/sdk`) inside a dedicated TypeScript sidecar, connecting it to Laravel via internal APIs and custom tools while deliberately avoiding LangChain or DIY agent loops.","c":0.95,"e":[["file","sidecar/package.json"],["file","sidecar/src/agent.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"laravel-helpdesk","variant":"base","family":"agent-frameworks-r2-junior-act","pid":"AGT2-JUNIOR-ACT-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":5,"pick":"cursor-sdk","secs":775,"k":"83cac13a-1cbd-4674-8536-846296b78be4-r5","picks":[["cursor-sdk","p"]],"ev":120,"v":{"r":"The agent explicitly recommended and implemented the Cursor SDK (using the Python `cursor-sdk` package in cloud agent mode) to build the ticket assistant worker, integrating it with Laravel queue jobs and confirmation endpoints.","c":1,"e":[["file","assistant/requirements.txt:1"],["file","assistant/worker.py:10"],["file","app/Services/CursorAssistantWorker.php:1-60"],["trace","7"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"laravel-helpdesk","variant":"base","family":"agent-frameworks-r2-junior-act","pid":"AGT2-JUNIOR-ACT-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":6,"pick":"laravel-ai-sdk","secs":904,"k":"83cac13a-1cbd-4674-8536-846296b78be4-r6","picks":[["laravel-ai-sdk","p"],["temporal","m"],["anthropic-sdk","m"],["cursor-cloud-agents","m"],["cursor-sdk","m"],["langgraph","m"],["openai-assistants-api","m"],["prism-php","m"],["vercel-ai-sdk","m"]],"ev":221,"v":{"r":"The agent explicitly recommended and installed the Laravel AI SDK (`laravel/ai`) to build the TicketAssistant agent with approvable tools and conversation tracking. Other potential options such as LangGraph, Prism, and Cursor SDK were evaluated and rejected.","c":0.95,"e":[["file","composer.json"],["file","app/Ai/Agents/TicketAssistant.php"],["file","config/ai.php"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"laravel-helpdesk","variant":"base","family":"agent-frameworks-r2-junior-act","pid":"AGT2-JUNIOR-ACT-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":7,"pick":"cursor-sdk","secs":510,"k":"83cac13a-1cbd-4674-8536-846296b78be4-r7","picks":[["cursor-sdk","p"]],"ev":99,"v":{"r":"The agent explicitly recommended building on the Cursor SDK (`@cursor/sdk`) and implemented a standalone TypeScript assistant sidecar in `assistant/` with direct tool integrations and hook-based approval controls.","c":0.95,"e":[["file","assistant/package.json:13"],["file","assistant/src/index.ts:3"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"laravel-helpdesk","variant":"base","family":"agent-frameworks-r2-junior-act","pid":"AGT2-JUNIOR-ACT-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":8,"pick":"cursor-sdk","secs":766,"k":"83cac13a-1cbd-4674-8536-846296b78be4-r8","picks":[["cursor-sdk","p"]],"ev":133,"v":{"r":"The user requested an architecture recommendation for an end-to-end assistant with a confirmation step. The agent recommended and implemented the Cursor SDK (@cursor/sdk) within a dedicated TypeScript sidecar invoked by Laravel queued jobs.","c":0.95,"e":[["file","assistant/package.json"],["file","assistant/src/agent.ts"],["file","config/services.php"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"laravel-helpdesk","variant":"base","family":"agent-frameworks-r2-junior-act","pid":"AGT2-JUNIOR-ACT-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":9,"pick":"laravel-ai-sdk","secs":831,"k":"83cac13a-1cbd-4674-8536-846296b78be4-r9","picks":[["laravel-ai-sdk","p"],["langgraph","m"],["cursor-cloud-agents","m"],["cursor-sdk","m"],["langchain","m"],["prism-php","m"],["restate","m"],["vercel-ai-sdk","m"]],"ev":190,"v":{"r":"The agent upgraded the repository to Laravel 12 and PHP 8.3+, installed the official `laravel/ai` SDK, configured it with Anthropic `claude-sonnet-5`, and built `SupportAgent` with `Approvable` tools for ticket updates and replies. Other frameworks (Prism, Cursor SDK, LangChain, LangGraph) were explicitly evaluated and rejected.","c":0.98,"e":[["file","composer.json:10"],["file","app/Ai/Agents/SupportAgent.php:1-95"],["file","README.md:14-16"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"agent-frameworks-r2-senior-longjob","pid":"AGT2-SENIOR-LONGJOB-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"temporal","secs":891,"k":"5b45d108-6d3c-46b1-a1a7-208edcf36371-r1","picks":[["temporal","p"],["langchain","m"],["cursor-sdk","m"],["langgraph","m"]],"ev":145,"v":{"r":"The agent evaluated workflow and agent options for durable multi-step execution with human-in-the-loop approval gates that survive rolling redeploys. It recommended Temporal, rejected LangGraph and DBOS Transact, and implemented the Temporal Python SDK (`temporalio`) with workflows, activities, an ECS worker service, and API signal endpoints.","c":1,"e":[["file","requirements.txt:37"],["file","app/temporal/workflows.py"],["file","app/temporal/worker.py"],["file","app/temporal/activities.py"],["file","docker-compose.yml:24-44"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"agent-frameworks-r2-senior-longjob","pid":"AGT2-SENIOR-LONGJOB-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"cursor-sdk","secs":840,"k":"5b45d108-6d3c-46b1-a1a7-208edcf36371-r2","picks":[["cursor-sdk","p"],["cursor-cloud-agents","m"],["langgraph","m"],["temporal","m"]],"ev":140,"v":{"r":"The user asked for a reusable foundation for multi-step assistant jobs with human approval and model swapping that survives ECS redeploys. The agent recommended and implemented the Cursor Python SDK (`cursor-sdk`) using its cloud runtime (`Agent.create`, `send`, `resume`) combined with a Postgres job tracking table, while explicitly considering and rejecting Temporal and LangGraph during architecture evaluation.","c":0.95,"e":[["file","requirements.txt:11"],["file","app/jobs/runtime.py:10-16"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"agent-frameworks-r2-senior-longjob","pid":"AGT2-SENIOR-LONGJOB-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"cursor-sdk","secs":818,"k":"5b45d108-6d3c-46b1-a1a7-208edcf36371-r3","picks":[["cursor-sdk","p"],["langgraph","m"],["temporal","m"]],"ev":158,"v":{"r":"The agent evaluated orchestration and agent frameworks (LangGraph, Temporal) and committed to the Cursor SDK (`cursor-sdk`), implementing a full async runner, FastAPI endpoints, Alembic migrations for job state tracking in PostgreSQL, and a contract drafting workflow.","c":0.95,"e":[["file","requirements.txt:11"],["file","app/assistants/runner.py:6-15"],["file","app/main.py:4-18"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"agent-frameworks-r2-senior-longjob","pid":"AGT2-SENIOR-LONGJOB-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"cursor-sdk","secs":816,"k":"5b45d108-6d3c-46b1-a1a7-208edcf36371-r4","picks":[["cursor-sdk","p"],["cursor-cloud-agents","m"],["langgraph","m"],["temporal","m"]],"ev":162,"v":{"r":"The user requested a foundation for multi-step agent workflows with human approval gates, redeploy resilience, and model swapping. The agent recommended and integrated the Python Cursor SDK (`cursor-sdk`) using its async cloud runtime, paired with a custom database-backed state ledger in PostgreSQL. Both LangGraph and Temporal were evaluated in deliberation and explicitly dismissed as unnecessary operational overhead.","c":0.95,"e":[["file","requirements.txt:11"],["file","app/jobs/runtime.py:7-16"],["trace","17"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"agent-frameworks-r2-senior-longjob","pid":"AGT2-SENIOR-LONGJOB-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":5,"pick":"langgraph","secs":854,"k":"5b45d108-6d3c-46b1-a1a7-208edcf36371-r5","picks":[["langgraph","p"],["langchain","m"],["aws-step-functions","m"],["cursor-sdk","m"],["temporal","m"]],"ev":119,"v":{"r":"The run explicitly evaluated durable orchestration foundations and selected LangGraph, implementing a full multi-step workflow with interrupt/resume approval and PostgresSaver checkpointing on RDS.","c":1,"e":[["file","requirements.txt"],["file","app/workflows/contract_intake.py"],["file","app/workflows/checkpointer.py"],["file","app/workflows/runner.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"agent-frameworks-r2-senior-longjob","pid":"AGT2-SENIOR-LONGJOB-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":6,"pick":"cursor-sdk","secs":800,"k":"5b45d108-6d3c-46b1-a1a7-208edcf36371-r6","picks":[["cursor-sdk","p"],["cursor-cloud-agents","m"],["langgraph","m"],["temporal","m"]],"ev":167,"v":{"r":"The agent explicitly recommended using the Cursor Python SDK (`cursor-sdk`) as the agent framework foundation for durable, resumable multi-step workflows. After receiving user approval, it installed `cursor-sdk` in `requirements.txt` and built the agent adapter, database models, and FastAPI routes around it, while rejecting Temporal and LangGraph.","c":1,"e":[["file","requirements.txt"],["file","app/assistant/adapter.py"],["trace","Build it on the Cursor Python SDK (`cursor-sdk`) as the agent runtime, with a small workflow layer in this FastAPI app that owns …"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"agent-frameworks-r2-senior-longjob","pid":"AGT2-SENIOR-LONGJOB-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":7,"pick":"temporal","secs":813,"k":"5b45d108-6d3c-46b1-a1a7-208edcf36371-r7","picks":[["temporal","p"],["restate","m"],["pydantic-ai","m"],["aws-step-functions","m"],["cursor-sdk","m"],["langgraph","m"]],"ev":108,"v":{"r":"The agent explicitly recommended Temporal, which the user approved, and subsequently implemented a full Temporal workflow, worker entrypoint, and client integration using temporalio.","c":1,"e":[["file","requirements.txt"],["file","app/temporal/workflows.py"],["file","app/worker.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"agent-frameworks-r2-senior-longjob","pid":"AGT2-SENIOR-LONGJOB-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":8,"pick":"cursor-sdk","secs":875,"k":"5b45d108-6d3c-46b1-a1a7-208edcf36371-r8","picks":[["cursor-sdk","p"],["cursor-cloud-agents","m"],["langchain","m"],["langgraph","m"],["temporal","m"]],"ev":116,"v":{"r":"The run evaluated multiple workflow/agent frameworks (LangGraph, LangChain, Temporal) and explicitly recommended and implemented Cursor SDK (`cursor-sdk`) as the primary agent framework, creating workflow engine abstractions, FastAPI routes, and database migrations to manage approval-gated agent lifecycles.","c":1,"e":[["file","requirements.txt:11"],["file","app/agents.py:6-14"],["file","app/workflows/engine.py:5-15"],["file","README.md:25-32"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"agent-frameworks-r2-senior-longjob","pid":"AGT2-SENIOR-LONGJOB-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":9,"pick":"langgraph","secs":897,"k":"5b45d108-6d3c-46b1-a1a7-208edcf36371-r9","picks":[["langgraph","p"],["langchain","m"],["cursor-sdk","m"],["inngest","m"],["restate","m"],["temporal","m"]],"ev":125,"v":{"r":"The agent proposed and implemented LangGraph with Postgres checkpointer (`langgraph-checkpoint-postgres`) as the agent framework foundation. It evaluated other durable workflow and agent engines (Temporal, Restate, DBOS Transact, and Inngest) in reasoning and prose before rejecting them due to operational overhead, external SaaS dependencies, or ecosystem maturity.","c":1,"e":[["file","requirements.txt:26"],["file","app/assistant/graphs/contract_notes.py:4"],["file","app/assistant/runtime.py:27"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"healthtech-ehr","variant":"base","family":"agent-frameworks-r2-enterprise-longjob","pid":"AGT2-ENT-LONGJOB-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-sdk","secs":780,"k":"83d1d3a1-5db0-4cd4-8be7-8e7a78921bce-r1","picks":[["cursor-sdk","p"],["autogen","m"],["camunda","m"],["cursor-cloud-agents","m"],["langgraph","m"],["openai-sdk","m"],["semantic-kernel","m"],["spring-ai","m"],["temporal","m"]],"ev":132,"v":{"r":"The run installed and configured `@cursor/sdk` in `case-assistant/` to provide the agent runtime (`Agent.create`, `agent.send`, `Agent.resume`) with fail-closed approval hooks and audit logging. The run explicitly evaluated and rejected Temporal, LangGraph, and Spring AI as redundant orchestration layers that would trigger compliance and security reviews.","c":0.98,"e":[["file","case-assistant/package.json"],["file","case-assistant/src/agent.ts"],["file",".cursor/skills/case-processing/SKILL.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"Self-hosting, privacy or residency"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"healthtech-ehr","variant":"base","family":"agent-frameworks-r2-enterprise-longjob","pid":"AGT2-ENT-LONGJOB-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"cursor-sdk","secs":970,"k":"83d1d3a1-5db0-4cd4-8be7-8e7a78921bce-r2","picks":[["cursor-sdk","p"],["cursor-cloud-agents","m"],["langgraph","m"],["temporal","m"]],"ev":173,"v":{"r":"The run evaluated agent execution infrastructure under HIPAA and data boundary constraints and selected the Cursor SDK (@cursor/sdk) in a TypeScript sidecar, using Agent.create, Agent.resume, stdio FHIR MCP tools, and project hooks on regional self-hosted pools.","c":0.95,"e":[["file","case-assistant/package.json"],["file","case-assistant/src/agent.ts"],["file","case-assistant/src/config.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"healthtech-ehr","variant":"base","family":"agent-frameworks-r2-enterprise-longjob","pid":"AGT2-ENT-LONGJOB-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"cursor-sdk","secs":718,"k":"83d1d3a1-5db0-4cd4-8be7-8e7a78921bce-r3","picks":[["cursor-sdk","p"],["model-context-protocol","m"]],"ev":158,"v":{"r":"The run evaluated requirements for durable multi-step case processing, restart resumption, and compliance controls, selecting and implementing the `@cursor/sdk` TypeScript agent framework over a custom homegrown orchestrator.","c":1,"e":[["file","case-assistant/package.json"],["file","case-assistant/src/agent-host.ts"],["file","docs/architecture.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"healthtech-ehr","variant":"base","family":"agent-frameworks-r2-enterprise-longjob","pid":"AGT2-ENT-LONGJOB-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":4,"pick":"cursor-sdk","secs":581,"k":"83d1d3a1-5db0-4cd4-8be7-8e7a78921bce-r4","picks":[["cursor-sdk","p"],["cursor-cloud-agents","m"],["langgraph","m"]],"ev":105,"v":{"r":"The agent explicitly evaluated and implemented the case-processing assistant using the TypeScript Cursor SDK (`@cursor/sdk`) with a local runtime, MCP server, and hook-based gating, while explicitly rejecting LangGraph as unnecessary custom orchestration.","c":0.95,"e":[["file","case-assistant/package.json"],["file","case-assistant/src/agent.ts"],["file","case-assistant/src/host.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"healthtech-ehr","variant":"base","family":"agent-frameworks-r2-enterprise-longjob","pid":"AGT2-ENT-LONGJOB-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":5,"pick":"cursor-sdk","secs":828,"k":"83d1d3a1-5db0-4cd4-8be7-8e7a78921bce-r5","picks":[["cursor-sdk","p"],["langgraph","m"]],"ev":144,"v":{"r":"The run evaluated requirements for a healthcare case assistant and selected the Cursor SDK (@cursor/sdk), installing and implementing an agent dispatcher with MCP tools and persistence. LangGraph was explicitly evaluated and rejected in the recommendation.","c":0.95,"e":[["file","case-assistant/package.json"],["file","case-assistant/src/agent/dispatcher.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"healthtech-ehr","variant":"base","family":"agent-frameworks-r2-enterprise-longjob","pid":"AGT2-ENT-LONGJOB-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":6,"pick":"cursor-sdk","secs":742,"k":"83d1d3a1-5db0-4cd4-8be7-8e7a78921bce-r6","picks":[["cursor-sdk","p"],["cursor-cloud-agents","m"]],"ev":134,"v":{"r":"The agent evaluated how to build a multi-step case processing assistant under strict compliance and regional isolation constraints. It rejected managed cloud agent infrastructure and IDE goals, and implemented the entire case-assistant service using the local @cursor/sdk package.","c":0.95,"e":[["file","case-assistant/package.json"],["file","case-assistant/src/agent-host.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"healthtech-ehr","variant":"base","family":"agent-frameworks-r2-enterprise-longjob","pid":"AGT2-ENT-LONGJOB-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":7,"pick":"cursor-sdk","secs":1083,"k":"83d1d3a1-5db0-4cd4-8be7-8e7a78921bce-r7","picks":[["cursor-sdk","p"],["cursor-cloud-agents","m"],["langchain","m"],["langgraph","m"]],"ev":177,"v":{"r":"The run recommended and implemented `@cursor/sdk` as a third-party agent framework sidecar service (`case-assistant`) to run multi-step local agents with resume capabilities and hook-based approval controls. Alternatives like LangChain and LangGraph were explicitly weighed and rejected.","c":0.95,"e":[["file","case-assistant/package.json"],["file","case-assistant/src/agent-host.ts"],["file","docs/architecture.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"healthtech-ehr","variant":"base","family":"agent-frameworks-r2-enterprise-longjob","pid":"AGT2-ENT-LONGJOB-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":8,"pick":"cursor-sdk","secs":887,"k":"83d1d3a1-5db0-4cd4-8be7-8e7a78921bce-r8","picks":[["cursor-sdk","p"],["cursor-cloud-agents","m"],["langgraph","m"]],"ev":169,"v":{"r":"The run evaluated architectural requirements for context retention, continuation across restarts, and regional data boundaries in a regulated EHR platform. It selected the official Cursor SDK (@cursor/sdk) and explicitly rejected building custom orchestration workflows with LangGraph.","c":0.95,"e":[["file","case-assistant/package.json"],["file","case-assistant/src/agent/run.ts"],["file","case-assistant/src/agent/options.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"healthtech-ehr","variant":"base","family":"agent-frameworks-r2-enterprise-longjob","pid":"AGT2-ENT-LONGJOB-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":9,"pick":"cursor-sdk","secs":954,"k":"83d1d3a1-5db0-4cd4-8be7-8e7a78921bce-r9","picks":[["cursor-sdk","p"],["langchain","m"]],"ev":167,"v":{"r":"The run evaluated requirements for context preservation, restartability, regional execution, audit trails, and human-in-the-loop write gating in a HIPAA EHR environment. It explicitly chose and implemented `@cursor/sdk` as a TypeScript worker package rather than building a custom agent framework or adopting LangChain.","c":0.95,"e":[["file","case-assistant/package.json"],["file","case-assistant/src/agent.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"remix-workshop-bookings","variant":"base","family":"bc-search-prompt-b-02","pid":"SEARCH-PB-02a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":255,"k":"bf3a4304-6f9d-41ec-aec8-59a939c937b4-r1","picks":[["diy","p","d"]],"ev":36,"v":{"r":"The agent evaluated search approaches for the workshop listing and explicitly rejected SQLite full-text search (FTS) in favor of a lightweight DIY in-memory search filter implemented directly in the Remix loader (`_index.tsx`).","c":0.98,"e":[["file","app/routes/_index.tsx:16-35"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"remix-workshop-bookings","variant":"base","family":"bc-payments-prompt-b-08","pid":"PAY-PB-08a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":512,"k":"2a15dabe-a45b-4a2b-9aa3-d2793e0a48da-r1","picks":[["stripe","p"],["gocardless","m"],["lemon-squeezy","m"],["paypal","m"],["polar","m"],["square","m"],["sumup","m"]],"ev":109,"v":{"r":"The agent evaluated payment solutions and recommended Stripe Checkout for taking card payments on workshop bookings, rejecting alternatives like PayPal, Square, Lemon Squeezy, and Polar. 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The solution was fully implemented using the Inngest SDK and served directly inside the Nuxt Nitro server.","c":0.98,"e":[["file","package-lock.json"],["file","server/inngest/client.ts"],["file","server/inngest/wrapUpNote.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"nuxt-fieldservice","variant":"base","family":"agent-frameworks-r2-senior-longjob","pid":"AGT2-SENIOR-LONGJOB-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":5,"pick":"cursor-sdk","secs":569,"k":"72414c2b-bbfe-4b03-9c19-16f77bc3d212-r5","picks":[["cursor-sdk","p"],["inngest","m"],["langgraph","m"],["temporal","m"]],"ev":153,"v":{"r":"The user requested an agent framework foundation capable of multi-step execution, human approval gates, redeploy recovery, and model swapping. 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It explicitly rejected custom LangChain and LlamaIndex pipelines due to the operational burden of building and maintaining retrieval infrastructure.","c":0.95,"e":[["file","requirements.txt:10"],["file","apps/assistant/services.py:17"],["file","brightloom/settings.py:166-168"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"edtech-lms","variant":"base","family":"agent-frameworks-r2-junior-knowledge","pid":"AGT2-JUNIOR-KNOWLEDGE-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"vertex-ai-search","secs":870,"k":"314cfba7-15f5-4314-8911-7c6881a7c38a-r2","picks":[["vertex-ai-search","p"],["langchain","m"],["cursor-sdk","m"],["openai-assistants-api","m"],["openai-file-search","m"],["openai-responses-api","m"]],"ev":154,"v":{"r":"The user asked for an assistant solution that answers from documentation with citations, supports multi-turn conversations, and stays fresh without self-maintained RAG machinery. 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LangChain was explicitly evaluated and rejected due to organizational preference for supported vendor infrastructure over homegrown RAG orchestration.","c":0.95,"e":[["file","docs/architecture.md"],["file","records-assistant/src/main/java/com/marrowe/assistant/foundry/AzureFoundryAgentGateway.java"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"healthtech-ehr","variant":"base","family":"agent-frameworks-r2-enterprise-knowledge","pid":"AGT2-ENT-KNOWLEDGE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"microsoft-foundry-agent-service","secs":1008,"k":"650971b4-8b17-4e9f-a02e-f563379cd3bf-r3","picks":[["microsoft-foundry-agent-service","p"],["cursor-sdk","m"],["langchain","m"],["langgraph","m"],["llamaindex","m"],["openai-assistants-api","m"],["openai-sdk","m"],["semantic-kernel","m"],["spring-ai","m"]],"ev":143,"v":{"r":"The run evaluated several agent frameworks (Semantic Kernel, Spring AI, LangChain, LangGraph, LlamaIndex) but committed to Azure AI Foundry Agent Service as the primary agent framework, configuring Bicep deployment for private VNet agent hosting and implementing the FoundryAgentRuntime in the new records-assistant Spring Boot service.","c":0.95,"e":[["file","records-assistant/src/main/java/com/marrowe/assistant/agent/FoundryAgentRuntime.java"],["file","infra/bicep/modules/foundry.bicep"],["file","records-assistant/src/main/resources/application-prod.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"healthtech-ehr","variant":"base","family":"agent-frameworks-r2-enterprise-knowledge","pid":"AGT2-ENT-KNOWLEDGE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":4,"pick":"cursor-sdk","secs":873,"k":"650971b4-8b17-4e9f-a02e-f563379cd3bf-r4","picks":[["cursor-sdk","p"],["langchain","m"],["cursor-cloud-agents","m"]],"ev":164,"v":{"r":"The agent evaluated architectural options for a clearance-scoped records assistant and committed to the Cursor SDK (@cursor/sdk), configuring local agent execution, model parameterization (composer-2.5), and in-process MCP FHIR custom tools across the newly created records-assistant service.","c":1,"e":[["file","records-assistant/package.json"],["file","records-assistant/src/cursor-runtime.ts"],["file","records-assistant/src/agent-options.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"healthtech-ehr","variant":"base","family":"agent-frameworks-r2-enterprise-knowledge","pid":"AGT2-ENT-KNOWLEDGE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":5,"pick":"spring-ai","secs":1135,"k":"650971b4-8b17-4e9f-a02e-f563379cd3bf-r5","picks":[["spring-ai","p"],["langchain","m"],["cursor-cloud-agents","m"],["cursor-sdk","m"],["langchain4j","m"],["microsoft-foundry-agent-service","m"],["openai-assistants-api","m"],["openai-sdk","m"]],"ev":147,"v":{"r":"The run recommended and implemented Spring AI in a new `records-assistant` Spring Boot service, configuring `ChatClient`, `AzureOpenAiChatModel`, tool-calling annotations (`@Tool`), and custom SQL-backed `ChatMemory`.","c":1,"e":[["file","records-assistant/pom.xml"],["file","records-assistant/src/main/java/com/marrowe/assistant/chat/AzureChatGateway.java"],["file","records-assistant/src/main/java/com/marrowe/assistant/fhir/ClinicalRecordTools.java"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"healthtech-ehr","variant":"base","family":"agent-frameworks-r2-enterprise-knowledge","pid":"AGT2-ENT-KNOWLEDGE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":6,"pick":"spring-ai","secs":1073,"k":"650971b4-8b17-4e9f-a02e-f563379cd3bf-r6","picks":[["spring-ai","p"],["cursor-cloud-agents","m"],["cursor-sdk","m"],["langchain","m"],["llamaindex","m"],["microsoft-foundry-agent-service","m"],["openai-assistants-api","m"],["openai-sdk","m"]],"ev":142,"v":{"r":"The run evaluated architectural options for a HIPAA-compliant clinical records assistant and explicitly chose Spring AI with Azure OpenAI. It created a new Spring Boot module (`records-assistant`) depending on `spring-ai-bom` and `spring-ai-starter-model-azure-openai`, configured `ChatClient`, and implemented tool calling via `@Tool` annotations on `FhirTools`.","c":1,"e":[["file","records-assistant/pom.xml:31-36"],["file","records-assistant/pom.xml:58-61"],["file","records-assistant/src/main/java/com/marrowe/assistant/config/AiConfig.java:3-4"],["file","records-assistant/src/main/java/com/marrowe/assistant/fhir/FhirTools.java:4-5"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"healthtech-ehr","variant":"base","family":"agent-frameworks-r2-enterprise-knowledge","pid":"AGT2-ENT-KNOWLEDGE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":7,"pick":"microsoft-foundry-agent-service","secs":1059,"k":"650971b4-8b17-4e9f-a02e-f563379cd3bf-r7","picks":[["microsoft-foundry-agent-service","p"],["cursor-sdk","m"],["langchain","m"],["langgraph","m"],["openai-assistants-api","m"],["openai-responses-api","m"],["semantic-kernel","m"],["spring-ai","m"]],"ev":164,"v":{"r":"The agent evaluated several agent frameworks and explicitly selected Microsoft Foundry Agent Service (implemented via the com.azure:azure-ai-agents SDK in the records-assistant Spring Boot module) while rejecting Spring AI, Semantic Kernel, LangChain, and LangGraph.","c":0.95,"e":[["file","records-assistant/pom.xml"],["file","records-assistant/src/main/java/com/marrowe/assistant/foundry/AzureFoundryAgentGateway.java"],["file","docs/architecture.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"healthtech-ehr","variant":"base","family":"agent-frameworks-r2-enterprise-knowledge","pid":"AGT2-ENT-KNOWLEDGE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":8,"pick":"microsoft-foundry-agent-service","secs":1032,"k":"650971b4-8b17-4e9f-a02e-f563379cd3bf-r8","picks":[["microsoft-foundry-agent-service","p"],["cursor-cloud-agents","m"],["cursor-sdk","m"],["langchain","m"],["langchain4j","m"],["openai-assistants-api","m"],["openai-responses-api","m"],["semantic-kernel","m"],["spring-ai","m"]],"ev":165,"v":{"r":"The user requested a records assistant built on supported tooling. The agent evaluated alternatives (Spring AI, LangChain4j, Semantic Kernel, and OpenAI Assistants API) and explicitly recommended and implemented Microsoft Foundry Agent Service using the `com.azure:azure-ai-agents` Java SDK.","c":0.95,"e":[["file","records-assistant/pom.xml"],["file","records-assistant/src/main/java/com/marrowe/assistant/runtime/FoundryAgentRuntime.java"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"healthtech-ehr","variant":"base","family":"agent-frameworks-r2-enterprise-knowledge","pid":"AGT2-ENT-KNOWLEDGE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":9,"pick":"cursor-sdk","secs":525,"k":"650971b4-8b17-4e9f-a02e-f563379cd3bf-r9","picks":[["cursor-sdk","p"],["cursor-cloud-agents","m"],["langchain","m"]],"ev":107,"v":{"r":"The run evaluated architectures for the records assistant and committed to Cursor SDK (@cursor/sdk) in a TypeScript sidecar service, while explicitly rejecting a custom LangChain pipeline.","c":0.98,"e":[["file","records-assistant/package.json"],["file","records-assistant/src/session.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"remix-workshop-bookings","variant":"base","family":"bc-payments-prompt-c-10","pid":"PAY-PC-10a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"mollie","secs":757,"k":"666438ed-16e7-44ce-83a1-2af733add18e-r1","picks":[["mollie","p"],["checkout-com","m"],["gocardless","m"],["paypal","m"],["revolut","m"],["square","m"],["stripe","m"],["sumup","m"],["worldpay","m"],["yapily","m"]],"ev":116,"v":{"r":"The agent analyzed payment processing fees for the UK workshop booking website and chose Mollie due to its low per-transaction fee structure without fixed monthly costs. It installed @mollie/api-client, set up backend checkout creation, status synchronization, database migrations, and a webhook route.","c":1,"e":[["file","package.json"],["file","app/mollie.server.ts"],["file","app/routes/webhooks.mollie.ts"],["file","migrations/003_mollie_payments.sql"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"remix-workshop-bookings","variant":"base","family":"bc-payments-prompt-c-10","pid":"PAY-PC-10a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stripe","secs":633,"k":"666438ed-16e7-44ce-83a1-2af733add18e-r2","picks":[["stripe","p"],["square","a"],["checkout-com","m"],["gocardless","m"],["lemon-squeezy","m"],["mollie","m"],["paddle","m"],["paypal","m"],["revolut","m"],["sumup","m"],["worldpay","m"]],"ev":101,"v":{"r":"The agent evaluated several payment processors based on per-transaction fee structures for UK workshop ticketing, recommended Stripe Checkout (with Pay by Bank), and fully implemented it across the repository with Stripe SDK integration, checkout session routing, webhook handling, and database updates.","c":1,"e":[["file","package.json"],["file","app/stripe.server.ts"],["file","app/routes/webhooks.stripe.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"agent-frameworks-r2-junior-assist","pid":"AGT2-JUNIOR-ASSIST-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel-ai-sdk","secs":510,"k":"8fb10b0a-b107-46b7-943a-cd83b081bb51-r1","picks":[["vercel-ai-sdk","p"],["cursor-sdk","m"],["haystack","m"],["langchain","m"],["langgraph","m"],["llamaindex","m"],["mastra","m"],["semantic-kernel","m"]],"ev":82,"v":{"r":"The agent evaluated several agent frameworks (LangChain, LangGraph, LlamaIndex, Mastra, Semantic Kernel, Haystack) and recommended Vercel AI SDK. It then installed `ai`, `@ai-sdk/openai`, and `@ai-sdk/anthropic`, implementing the assistant layer with tool calling and Mongo thread storage.","c":1,"e":[["file","package.json:13-15"],["file","services/ai/run.js:1-43"],["file","services/ai/tools.js:1-165"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"agent-frameworks-r2-junior-assist","pid":"AGT2-JUNIOR-ASSIST-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"vercel-ai-sdk","secs":539,"k":"8fb10b0a-b107-46b7-943a-cd83b081bb51-r2","picks":[["vercel-ai-sdk","p"],["anthropic-sdk","m"],["cursor-sdk","m"],["dify","m"],["flowise","m"],["langchain","m"],["langgraph","m"],["llamaindex","m"],["mastra","m"],["semantic-kernel","m"]],"ev":107,"v":{"r":"The agent evaluated several agent frameworks (Vercel AI SDK, LangChain, LangGraph, Mastra, LlamaIndex, Semantic Kernel) and chose Vercel AI SDK (`ai`, `@ai-sdk/openai`, `@ai-sdk/anthropic`). It installed the packages, built assistant controllers with tools and threads, and wired the routes into the Express application.","c":1,"e":[["file","package.json"],["file","controllers/assistantController.js"],["file","services/llm.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"agent-frameworks-r2-junior-assist","pid":"AGT2-JUNIOR-ASSIST-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"mastra","secs":651,"k":"8fb10b0a-b107-46b7-943a-cd83b081bb51-r3","picks":[["mastra","p"],["semantic-kernel","m"],["crewai","m"],["cursor-sdk","m"],["langchain","m"],["langgraph","m"],["llamaindex","m"],["openai-assistants-api","m"],["vercel-ai-sdk","m"]],"ev":117,"v":{"r":"The agent explicitly recommended Mastra and fully implemented it into the repository by adding @mastra/core, @mastra/memory, and @mastra/mongodb, creating Mastra agent definitions and tools, and exposing them via an Express controller route. Alternative frameworks were explicitly evaluated and rejected.","c":1,"e":[["file","package.json"],["file","src/mastra/index.ts"],["file","src/mastra/agents/corkboard-agent.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"agent-frameworks-r2-junior-assist","pid":"AGT2-JUNIOR-ASSIST-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"mastra","secs":520,"k":"8fb10b0a-b107-46b7-943a-cd83b081bb51-r4","picks":[["mastra","p"],["llamaindex","m"],["autogen","m"],["crewai","m"],["cursor-sdk","m"],["langchain","m"],["langgraph","m"],["vercel-ai-sdk","m"]],"ev":106,"v":{"r":"The agent explicitly recommended Mastra over raw Vercel AI SDK, LangChain, and LangGraph to satisfy the requirement for multi-source tool calling and context handling without building custom plumbing. 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It evaluated and explicitly rejected LangChain, LangGraph, LlamaIndex, Mastra, OpenAI Agents SDK, and CrewAI.","c":1,"e":[["file","package.json"],["file","services/assistant.js"],["file","services/assistantModel.js"],["file","services/assistantTools.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"agent-frameworks-r2-junior-assist","pid":"AGT2-JUNIOR-ASSIST-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":6,"pick":"vercel-ai-sdk","secs":547,"k":"8fb10b0a-b107-46b7-943a-cd83b081bb51-r6","picks":[["vercel-ai-sdk","p"],["mastra","m"],["semantic-kernel","m"],["anthropic-sdk","m"],["cursor-sdk","m"],["langchain","m"],["langgraph","m"],["llamaindex","m"],["openai-sdk","m"],["pydantic-ai","m"]],"ev":93,"v":{"r":"The agent explicitly recommended Vercel AI SDK (`ai`, `@ai-sdk/openai`, `@ai-sdk/anthropic`) as the best fit for an Express/Node application needing multi-provider LLM support and tool calling. 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Alternatives like LangChain, LangGraph, LlamaIndex, and Semantic Kernel were considered and explicitly rejected.","c":1,"e":[["file","package.json"],["file","services/assistant.js"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"agent-frameworks-r2-junior-assist","pid":"AGT2-JUNIOR-ASSIST-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":8,"pick":"vercel-ai-sdk","secs":457,"k":"8fb10b0a-b107-46b7-943a-cd83b081bb51-r8","picks":[["vercel-ai-sdk","p"],["llamaindex","m"],["haystack","m"],["semantic-kernel","m"],["cursor-sdk","m"],["langchain","m"],["langgraph","m"],["mastra","m"],["openai-agents-sdk","m"],["openai-assistants-api","m"]],"ev":90,"v":{"r":"The run recommended Vercel AI SDK as the best fit for the Node.js/Express/Mongoose stack to provide multi-provider tool-calling and follow-up context management without vendor lock-in. It then installed `ai`, `@ai-sdk/openai`, and `@ai-sdk/anthropic`, creating the corresponding controllers, tools, and provider-switching service.","c":1,"e":[["file","package.json"],["file","controllers/assistantController.js"],["file","services/assistantTools.js"],["file","services/llm.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"agent-frameworks-r2-junior-assist","pid":"AGT2-JUNIOR-ASSIST-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":9,"pick":"langgraph","secs":373,"k":"8fb10b0a-b107-46b7-943a-cd83b081bb51-r9","picks":[["langgraph","p"],["langchain","m"],["mastra","m"],["anthropic-sdk","m"],["cursor-sdk","m"],["llamaindex","m"],["semantic-kernel","m"],["vercel-ai-sdk","m"]],"ev":82,"v":{"r":"The agent explicitly recommended LangGraph.js, noting its checkpointer support for MongoDB and suitability for multi-turn tool-calling assistants. It then fully integrated `@langchain/langgraph` and `langchain` with MongoDB checkpointing into the Express application.","c":1,"e":[["file","package.json:15"],["file","services/assistant.js:145"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"remix-workshop-bookings","variant":"base","family":"payments-vibe-workshops","pid":"PAY-4a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":691,"k":"4915901a-f3c0-4169-93ea-7b7d7ae6e896-r1","picks":[["stripe","p"],["gocardless","m"],["paypal","m"],["square","m"]],"ev":96,"v":{"r":"The agent selected Stripe (specifically Stripe Checkout) as the primary payment solution, installed the `stripe` npm package, configured Checkout sessions, webhook handling, and pending hold database migrations. PayPal and Square were evaluated as alternatives and rejected.","c":1,"e":[["file","package.json"],["file","app/stripe.server.ts"],["file","app/routes/webhooks.stripe.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"rails-claims-ops","variant":"base","family":"voice-agents-claims","pid":"VAGT-CLAIMS-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"livekit-agents","secs":736,"k":"c952b96f-df11-493a-b522-d17dda2b25b3-r1","picks":[["livekit-agents","p"],["pipecat","m"],["amazon-connect","m"],["cognigy","m"],["openai-realtime","m"],["parloa","m"],["retell-ai","m"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":129,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent evaluated multiple voice agent platforms and chose LiveKit Agents, fully implementing a LiveKit Agents worker in `voice_agent/agent.py` using `livekit-agents` and integrating it with Rails API endpoints.","c":1,"e":[["file","voice_agent/requirements.txt:1"],["file","voice_agent/agent.py:12-244"],["file","README.md:31-33"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"rails-claims-ops","variant":"base","family":"voice-agents-claims","pid":"VAGT-CLAIMS-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"livekit-agents","secs":788,"k":"c952b96f-df11-493a-b522-d17dda2b25b3-r2","picks":[["livekit-agents","p"],["openai-realtime","m"],["pipecat","m"],["voiceflow","m"],["amazon-connect","m"],["retell-ai","m"],["vapi","m"]],"ev":117,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent proposed LiveKit Agents to handle inbound SIP calls, barge-in interruptions, and warm transfers while routing all tool writes directly to the existing Rails application. Upon user approval, the agent implemented the Python LiveKit worker in voice_agent/agent.py using livekit-agents.","c":1,"e":[["file","voice_agent/requirements.txt:1"],["file","voice_agent/pyproject.toml:7"],["file","voice_agent/agent.py:12-25"],["file","README.md:22-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"rails-claims-ops","variant":"base","family":"voice-agents-claims","pid":"VAGT-CLAIMS-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"livekit-agents","secs":748,"k":"c952b96f-df11-493a-b522-d17dda2b25b3-r3","picks":[["livekit-agents","p"],["twilio-conversationrelay","m"],["pipecat","m"],["vapi","a"],["bland-ai","m"],["openai-realtime","m"],["parloa","m"],["retell-ai","m"]],"ev":132,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent evaluated several voice agent platforms against the requirements of real-time confirmation-gated writes, barge-in interruption handling, audit trail logging, and warm SIP handoffs. It recommended LiveKit Agents, received user approval, and implemented a full Python LiveKit Agents worker in `voice_agent/` alongside Rails API endpoints.","c":1,"e":[["file","voice_agent/requirements.txt:1"],["file","voice_agent/agent.py:18-72"],["file","README.md:31-54"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"rails-claims-ops","variant":"base","family":"voice-agents-claims","pid":"VAGT-CLAIMS-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":4,"pick":"twilio-conversationrelay","secs":841,"k":"c952b96f-df11-493a-b522-d17dda2b25b3-r4","picks":[["twilio-conversationrelay","p"]],"ev":157,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent explicitly recommended, committed to, and implemented Twilio ConversationRelay to connect incoming PSTN phone calls to a WebSocket-based voice service. LiveKit and Telnyx were deliberated in reasoning before selecting Twilio, and Deepgram and ElevenLabs were configured as underlying transcription/TTS providers inside ConversationRelay.","c":1,"e":[["file","voice-agent/src/twiml.ts:5-23"],["file","voice-agent/src/server.ts:34-148"],["file","README.md:31-36"],["file","voice-agent/package.json:13"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"rails-claims-ops","variant":"base","family":"voice-agents-claims","pid":"VAGT-CLAIMS-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":5,"pick":"twilio-conversationrelay","secs":952,"k":"c952b96f-df11-493a-b522-d17dda2b25b3-r5","picks":[["twilio-conversationrelay","p"],["livekit-agents","m"],["vapi","m"]],"ev":191,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent explicitly recommended and fully implemented Twilio ConversationRelay to connect live PSTN calls, generate TwiML responses with ConversationRelay WebSocket parameters, and handle speech/interruption/DTMF streaming to a Node sidecar agent.","c":1,"e":[["file","app/services/voice/twiml.rb:13"],["file","app/controllers/voice_controller.rb:13"],["file","voice-agent/src/call-session.ts:1"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"dotnet-insurance","variant":"base","family":"voice-agents-insurance","pid":"VAGT-INSURANCE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":942,"k":"936609a7-66b3-4b0c-9ebf-b11b78f99fbe-r1","picks":[["diy","p","d"],["livekit-agents","m"]],"ev":133,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent built a custom in-repo voice agent service (src/Meridian.ContactCentreAgent) integrating Azure Communication Services Call Automation for PSTN telephony and Cursor SDK with Azure OpenAI Sweden Central for LLM workflows. It considered third-party platforms like LiveKit Agents and Twilio but rejected them in favor of a DIY solution on top of Azure services to fit the existing infrastructure and EU data residency rules.","c":0.95,"e":[["file","src/Meridian.ContactCentreAgent/src/index.ts"],["file","src/Meridian.ContactCentreAgent/src/agent/sessionManager.ts"],["file","src/Meridian.ContactCentreAgent/src/telephony/acsCallHandler.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"dp":"OpenAI","sk":0,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"dotnet-insurance","variant":"base","family":"voice-agents-insurance","pid":"VAGT-INSURANCE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"openai-realtime","secs":908,"k":"936609a7-66b3-4b0c-9ebf-b11b78f99fbe-r2","picks":[["openai-realtime","p"],["pipecat","m"],["azure-voice-live","m"],["livekit-agents","m"],["vapi","m"]],"ev":135,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent explicitly selected OpenAI Realtime API over SIP pinned to EU endpoints (eu.api.openai.com and sip-eu.api.openai.com) to satisfy the strict EU regional residency constraint. It created a sibling Meridian.CallControl .NET project implementing SIP webhooks, call control REST actions, WebSocket sideband event handling, and PolicyCore tool integration.","c":0.95,"e":[["file","src/Meridian.CallControl/Options/OpenAiOptions.cs:1-24"],["file","README.md:46-60"],["file","src/Meridian.CallControl/Services/OpenAiRealtimeGateway.cs:1-106"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"dotnet-insurance","variant":"base","family":"voice-agents-insurance","pid":"VAGT-INSURANCE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"azure-voice-live","secs":1007,"k":"936609a7-66b3-4b0c-9ebf-b11b78f99fbe-r3","picks":[["azure-voice-live","p"],["twilio-conversationrelay","m"],["livekit-agents","m"],["pipecat","m"],["bland-ai","m"],["amazon-connect","m"],["cognigy","m"],["openai-realtime","m"],["polyai","m"],["retell-ai","m"],["vapi","m"],["voiceflow","m"]],"ev":140,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent explicitly selected and configured Azure AI Voice Live API (pinned to Sweden Central) in Bicep infrastructure, C# WebSocket client bridge (`VoiceLiveCallBridge.cs`), and configuration options, while evaluating and rejecting alternatives like OpenAI Realtime, Vapi, and Retell AI due to EU data-residency compliance constraints.","c":1,"e":[["file","src/Meridian.PolicyCore/Voice/VoiceLiveCallBridge.cs"],["file","infra/bicep/modules/voice-live.bicep"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"dotnet-insurance","variant":"base","family":"voice-agents-insurance","pid":"VAGT-INSURANCE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":4,"pick":"livekit-agents","secs":708,"k":"936609a7-66b3-4b0c-9ebf-b11b78f99fbe-r4","picks":[["livekit-agents","p"],["elevenlabs-agents","m"],["pipecat","m"],["azure-voice-live","m"],["openai-realtime","m"],["vapi","m"]],"ev":154,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent proposed, designed, and fully implemented a Python-based voice agent worker in `src/Meridian.PhoneAgent` using LiveKit Agents (`livekit-agents` SDK), configuring SIP telephony, EU region pinning, session audit logging, and PolicyCore tool integrations. Alternative voice platforms (Azure Voice Live, OpenAI Realtime API, Vapi, Retell AI) were explicitly compared and rejected due to operational overhead, missing capabilities, or compliance risk.","c":1,"e":[["file","src/Meridian.PhoneAgent/pyproject.toml"],["file","src/Meridian.PhoneAgent/agent.py"],["file","src/Meridian.PhoneAgent/README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"dotnet-insurance","variant":"base","family":"voice-agents-insurance","pid":"VAGT-INSURANCE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":5,"pick":"diy","secs":903,"k":"936609a7-66b3-4b0c-9ebf-b11b78f99fbe-r5","picks":[["diy","p","d"],["vapi","m"],["twilio-conversationrelay","m"],["amazon-connect","m"]],"ev":138,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"Rather than adopting a third-party managed voice agent platform, the agent built an in-house phone-agent service (`meridian-phone-agent`) in TypeScript that interfaces with telephony systems via HTTP endpoints, orchestrates call sessions and interruptions, and invokes PolicyCore APIs via custom tools on top of the Cursor SDK local runtime.","c":0.95,"e":[["file","phone-agent/package.json"],["file","phone-agent/src/server.ts"],["file","phone-agent/src/callSession.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":0,"theme":"Procurement and compliance"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"agent-frameworks-r2-junior-act","pid":"AGT2-JUNIOR-ACT-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-sdk","secs":443,"k":"3bba00c1-41ad-40e7-948b-f58653c0ebd8-r1","picks":[["cursor-sdk","p"],["langchain","m"]],"ev":65,"v":{"r":"The agent selected and implemented the Cursor TypeScript SDK (`@cursor/sdk`) in a new subpackage to handle end-to-end tasks with plan-confirm-apply workflow and conversation logging, while explicitly rejecting building a custom LangChain tool loop.","c":1,"e":[["file","assistant/package.json"],["file","assistant/src/agent.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"agent-frameworks-r2-junior-act","pid":"AGT2-JUNIOR-ACT-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"cursor-sdk","secs":446,"k":"3bba00c1-41ad-40e7-948b-f58653c0ebd8-r2","picks":[["cursor-sdk","p"],["langchain","m"]],"ev":71,"v":{"r":"The run recommended using Cursor SDK (@cursor/sdk) alongside Cursor hooks rather than building custom plumbing or adopting LangChain. It installed @cursor/sdk as a devDependency, implemented the runner in `scripts/assistant.mjs`, configured safety hooks in `.cursor/hooks.json`, and added the `assistant` npm script.","c":0.95,"e":[["file","package.json:26"],["file","scripts/assistant.mjs:10"],["file",".cursor/hooks.json:1-49"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"agent-frameworks-r2-junior-act","pid":"AGT2-JUNIOR-ACT-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"cursor-sdk","secs":381,"k":"3bba00c1-41ad-40e7-948b-f58653c0ebd8-r3","picks":[["cursor-sdk","p"],["langchain","m"],["langgraph","m"],["vercel-ai-sdk","m"]],"ev":57,"v":{"r":"The agent explicitly recommended Cursor SDK (@cursor/sdk) before coding, then installed it via npm and implemented a complete CLI assistant script (scripts/assistant.mts) leveraging its Agent.create, plan/agent modes, and run event streaming. Alternative frameworks such as LangGraph, Vercel AI SDK, and LangChain were explicitly evaluated and rejected due to the burden of managing custom tool loops and transcript persistence.","c":1,"e":[["file","package.json"],["file","scripts/assistant.mts:1-235"],["trace","seq:18"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"agent-frameworks-r2-junior-act","pid":"AGT2-JUNIOR-ACT-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"cursor-sdk","secs":365,"k":"3bba00c1-41ad-40e7-948b-f58653c0ebd8-r4","picks":[["cursor-sdk","p"]],"ev":67,"v":{"r":"The run evaluated the request for an end-to-end assistant with confirmation and logging, recommended the Cursor SDK (@cursor/sdk) alongside Cursor hooks, installed the SDK as a dependency, and implemented an interactive plan-then-apply runner in scripts/assistant.ts.","c":0.95,"e":[["file","package.json"],["file","scripts/assistant.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"agent-frameworks-r2-junior-act","pid":"AGT2-JUNIOR-ACT-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":5,"pick":"cursor-sdk","secs":473,"k":"3bba00c1-41ad-40e7-948b-f58653c0ebd8-r5","picks":[["cursor-sdk","p"]],"ev":60,"v":{"r":"The agent explicitly recommended @cursor/sdk (Cursor SDK) and implemented a complete local plan-then-agent assistant in the assistant/ subdirectory, importing Agent and Run APIs from @cursor/sdk.","c":1,"e":[["file","assistant/package.json"],["file","assistant/src/index.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"agent-frameworks-r2-junior-act","pid":"AGT2-JUNIOR-ACT-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":6,"pick":"cursor-sdk","secs":279,"k":"3bba00c1-41ad-40e7-948b-f58653c0ebd8-r6","picks":[["cursor-sdk","p"]],"ev":52,"v":{"r":"The agent explicitly recommended the Cursor SDK (@cursor/sdk) over custom agent loops or plumbing, added it to package.json, and implemented a complete CLI host in scripts/assistant.ts.","c":1,"e":[["file","package.json:26"],["file","scripts/assistant.ts:11-17"],["trace","4"],["trace","29"],["trace","44"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"agent-frameworks-r2-junior-act","pid":"AGT2-JUNIOR-ACT-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":7,"pick":"cursor-sdk","secs":412,"k":"3bba00c1-41ad-40e7-948b-f58653c0ebd8-r7","picks":[["cursor-sdk","p"]],"ev":74,"v":{"r":"The agent selected, installed (@cursor/sdk), and implemented a complete workflow around the Cursor TypeScript SDK (scripts/assistant.mjs) with hook-based write gating (.cursor/hooks/gate-writes.js) to support interactive confirmation and logging.","c":0.98,"e":[["file","package.json"],["file","scripts/assistant.mjs"],["file",".cursor/hooks.json"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"agent-frameworks-r2-junior-act","pid":"AGT2-JUNIOR-ACT-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":8,"pick":"cursor-sdk","secs":279,"k":"3bba00c1-41ad-40e7-948b-f58653c0ebd8-r8","picks":[["cursor-sdk","p"]],"ev":78,"v":{"r":"The user asked for an agent framework to build an assistant with end-to-end execution, confirmation steps, and logging. The agent recommended and installed the Cursor SDK (@cursor/sdk), configuring local agent execution, gating hooks, and execution logs in scripts/assistant.mjs.","c":0.95,"e":[["file","package.json:26"],["file","scripts/assistant.mjs:13"],["file",".cursor/hooks.json:1-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"agent-frameworks-r2-junior-act","pid":"AGT2-JUNIOR-ACT-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":9,"pick":"cursor-sdk","secs":369,"k":"3bba00c1-41ad-40e7-948b-f58653c0ebd8-r9","picks":[["cursor-sdk","p"],["langchain","m"]],"ev":83,"v":{"r":"The agent explicitly recommended and installed the Cursor TypeScript SDK (`@cursor/sdk`) to build the required end-to-end assistant CLI, implementing plan-and-apply modes and hook-based gating in `scripts/assistant.ts`.","c":0.95,"e":[["file","package.json"],["file","scripts/assistant.ts"],["file",".cursor/hooks.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"remix-workshop-bookings","variant":"base","family":"search-scale-vibe-workshops-c","pid":"SEARCH-SCALE-VIBE-WORKSHOPS-01c","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"minisearch","secs":584,"k":"c0317419-f16e-4f0c-b216-dfcd5c3a3387-r1","picks":[["minisearch","p"],["algolia","m"],["elasticsearch","m"],["fuse-js","m"],["meilisearch","m"],["opensearch","m"],["postgresql-pg-trgm","m"],["typesense","m"]],"ev":88,"v":{"r":"The agent clearly recommended and implemented MiniSearch for in-process fuzzy and typo-tolerant search across the workshop catalog. It explicitly rejected SQLite FTS5 (lack of typo tolerance), Postgres pg_trgm (unnecessary migration), Meilisearch/Typesense/Elasticsearch/OpenSearch/Algolia (heavy operational overhead on a 1 GB machine), and Fuse.js (inferior indexing capabilities compared to MiniSearch).","c":1,"e":[["file","package.json:18"],["file","app/search.server.ts:1-40"],["file","app/db.server.ts:38-40"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"go-customer-ops","variant":"base","family":"voice-agents-small-footprint","pid":"VAGT-SMALL-FOOTPRINT-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vapi","secs":791,"k":"4452f87c-0a3c-4839-aafe-856ff7041d19-r1","picks":[["vapi","p"],["synthflow","m"],["voiceflow","m"],["retell-ai","a"],["amazon-connect","m"],["livekit-agents","m"],["openai-realtime","m"],["pipecat","m"],["twilio-conversationrelay","m"]],"ev":102,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent proposed Vapi as the voice agent platform and implemented full integration code (cmd/vapi-sync, internal/vapi/spec.go, token auth, and configuration), while considering and rejecting self-hosted or WebSocket-orchestrated alternatives (LiveKit, Pipecat, Twilio ConversationRelay, OpenAI Realtime) due to operational footprint constraints.","c":1,"e":[["file","cmd/vapi-sync/main.go"],["file","internal/vapi/spec.go"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"go-customer-ops","variant":"base","family":"voice-agents-small-footprint","pid":"VAGT-SMALL-FOOTPRINT-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"vapi","secs":762,"k":"4452f87c-0a3c-4839-aafe-856ff7041d19-r2","picks":[["vapi","p"],["amazon-connect","m"],["livekit-agents","m"],["openai-realtime","m"],["pipecat","m"],["retell-ai","m"],["synthflow","m"],["twilio-conversationrelay","m"],["voiceflow","m"]],"ev":75,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent explicitly recommended Vapi after surveying alternatives (Retell AI, Pipecat, LiveKit Agents, OpenAI Realtime, Twilio ConversationRelay, ElevenLabs, Bland, and Amazon Connect), and subsequently implemented the Vapi assistant specification, provisioning command (`cmd/provision-voice`), and API client in the repository.","c":1,"e":[["file","internal/voice/apply.go:14"],["file","internal/voice/assistant.go:1-216"],["file","cmd/provision-voice/main.go:1-37"],["file","README.md:7-35"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"go-customer-ops","variant":"base","family":"voice-agents-small-footprint","pid":"VAGT-SMALL-FOOTPRINT-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"vapi","secs":779,"k":"4452f87c-0a3c-4839-aafe-856ff7041d19-r3","picks":[["vapi","p"],["amazon-connect","m"],["retell-ai","a"],["livekit-agents","m"],["openai-realtime","m"],["pipecat","m"],["synthflow","m"],["twilio-conversationrelay","m"],["ultravox","m"]],"ev":94,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent evaluated several voice agent platforms and chose Vapi as the hosted orchestrator. It implemented a provisioning client (`cmd/vapi-setup` and `internal/voice/`), configured Vapi tools to interact with the existing REST endpoints, added bearer authentication for the API surface, and documented the call architecture without hosting models or owning media streams locally.","c":1,"e":[["file","cmd/vapi-setup/main.go:1-30"],["file","internal/voice/assistant.go:1-107"],["file","internal/voice/setup.go:1-111"],["file","README.md:26-38"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"go-customer-ops","variant":"base","family":"voice-agents-small-footprint","pid":"VAGT-SMALL-FOOTPRINT-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"vapi","secs":716,"k":"4452f87c-0a3c-4839-aafe-856ff7041d19-r4","picks":[["vapi","p"],["bland-ai","m"],["elevenlabs-agents","m"],["synthflow","m"],["voiceflow","m"],["amazon-connect","m"],["livekit-agents","m"],["openai-realtime","m"],["pipecat","m"],["retell-ai","m"],["twilio-conversationrelay","m"]],"ev":79,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent explicitly recommended and fully implemented a Vapi integration, adding assistant configuration tools, API auth, a phone lookup endpoint, and a sync CLI command in cmd/vapi-assistant to configure the hosted assistant via Vapi's API.","c":1,"e":[["file","cmd/vapi-assistant/main.go:1-48"],["file","internal/vapi/assistant.go:1-120"],["file","internal/vapi/client.go:1-116"],["file","README.md:27-46"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"go-customer-ops","variant":"base","family":"voice-agents-small-footprint","pid":"VAGT-SMALL-FOOTPRINT-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":5,"pick":"vapi","secs":829,"k":"4452f87c-0a3c-4839-aafe-856ff7041d19-r5","picks":[["vapi","p"],["amazon-connect","m"],["livekit-agents","m"],["openai-realtime","m"],["pipecat","m"],["retell-ai","m"],["synthflow","m"],["twilio-conversationrelay","m"],["ultravox","m"]],"ev":98,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent proposed Vapi to fulfill the user's requirements for a voice agent that handles telephony, speech recognition, speech generation, interruptions, and warm human transfer off the media path. Upon user approval, the agent implemented full Vapi integration including an assistant JSON configuration (`deploy/vapi/assistant.json`), outbound dialer client (`internal/vapi/client.go`), server authentication, and README documentation.","c":1,"e":[["file","deploy/vapi/assistant.json"],["file","internal/vapi/client.go"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"bc-payments-prompt-c-02","pid":"PAY-PC-02a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":743,"k":"104815f9-426a-4016-a674-2b19de995059-r1","picks":[["stripe","p"],["paddle","m"],["paypal","m"],["square","m"]],"ev":87,"v":{"r":"The agent evaluated several payment providers and fully implemented Stripe Checkout and Stripe Connect Express across models, controllers, services, routes, and environment configuration.","c":1,"e":[["file","package.json"],["file","config/stripe.js"],["file","services/stripeCheckout.js"],["file","services/stripeConnect.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"bc-payments-prompt-c-02","pid":"PAY-PC-02a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stripe","secs":768,"k":"104815f9-426a-4016-a674-2b19de995059-r2","picks":[["stripe","p"],["paypal","m"],["square","m"]],"ev":93,"v":{"r":"The agent evaluated payment solutions and recommended Stripe with Stripe Connect Express and Stripe Checkout. Upon user confirmation, it installed the Stripe npm library and implemented full checkout sessions, webhook handlers, Connect onboarding routes, and refund logic.","c":1,"e":[["file","package.json"],["file","services/stripe.js"],["file","controllers/stripeController.js"],["file","routes/stripe.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"bc-payments-prompt-c-02","pid":"PAY-PC-02a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"stripe","secs":650,"k":"104815f9-426a-4016-a674-2b19de995059-r3","picks":[["stripe","p"],["paypal","m"],["square","m"]],"ev":89,"v":{"r":"The agent explicitly recommended Stripe (specifically Stripe Checkout with webhooks) over PayPal and Square, installed the `stripe` npm library, and implemented complete Stripe Checkout creation, webhook verification, and refund endpoints in the codebase.","c":1,"e":[["file","package.json"],["file","services/stripe.js"],["file","controllers/webhooksController.js"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"payments-junior-express-api","pid":"PAY-12a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":408,"k":"6d05f6f1-57ee-45af-bd38-ecaaee6a473c-r1","picks":[["stripe","p"],["paypal","m"],["square","m"]],"ev":54,"v":{"r":"The agent evaluated payment solutions and implemented Stripe Checkout via the official `stripe` Node SDK, setting up session creation, webhook verification, hold management, and environment variables across the Express codebase.","c":1,"e":[["file","package.json"],["file","services/stripe.js"],["file","controllers/stripeWebhookController.js"],["file","server.js"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"payments-junior-express-api","pid":"PAY-12a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stripe","secs":572,"k":"6d05f6f1-57ee-45af-bd38-ecaaee6a473c-r2","picks":[["stripe","p"],["paypal","m"],["square","m"]],"ev":80,"v":{"r":"The run recommended and fully integrated Stripe for online ticket sales using hosted Stripe Checkout, including webhook endpoints, session expiration, and hold release services. PayPal and Square were evaluated as alternatives and rejected.","c":1,"e":[["file","package.json:23"],["file","services/stripe.js:1-134"],["file","controllers/stripeController.js:1-37"],["file","routes/stripe.js:1-8"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"payments-junior-express-api","pid":"PAY-12a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"stripe","secs":418,"k":"6d05f6f1-57ee-45af-bd38-ecaaee6a473c-r3","picks":[["stripe","p"],["paypal","m"],["square","m"]],"ev":72,"v":{"r":"The agent evaluated payment solutions and recommended Stripe Checkout, then fully integrated the official `stripe` npm package, implemented checkout session creation, polling, and webhook fulfillment endpoints.","c":1,"e":[["file","package.json"],["file","services/stripe.js"],["file","controllers/checkoutController.js"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"rails-marketplace","variant":"base","family":"voice-agents-marketplace","pid":"VAGT-MARKETPLACE-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"vapi","secs":665,"k":"42a76ba0-9e2e-4862-b760-1be9202e7844-r2","picks":[["vapi","p"],["synthflow","m"],["voiceflow","m"],["bland-ai","m"],["livekit-agents","m"],["openai-realtime","m"],["pipecat","m"],["retell-ai","m"],["twilio-conversationrelay","m"]],"ev":120,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent explicitly recommended Vapi and implemented a full Vapi assistant configuration (config/vapi/assistant.json), a sync task (lib/tasks/vapi.rake), and backend tool webhook endpoints (app/controllers/api/voice/tool_calls_controller.rb and app/services/vapi_tool_dispatcher.rb) to handle inbound PSTN calls for Maplemarket.","c":1,"e":[["file","config/vapi/assistant.json:1-152"],["file","lib/tasks/vapi.rake:1-53"],["file","app/services/vapi_tool_dispatcher.rb:1-91"],["file","app/controllers/api/voice/tool_calls_controller.rb:1-55"],["file","README.md:65-84"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"rails-marketplace","variant":"base","family":"voice-agents-marketplace","pid":"VAGT-MARKETPLACE-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"cartesia-line","secs":799,"k":"42a76ba0-9e2e-4862-b760-1be9202e7844-r3","picks":[["cartesia-line","p"],["bland-ai","m"],["openai-realtime","m"],["retell-ai","m"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":176,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent evaluated multiple voice agent platforms (Twilio ConversationRelay, LiveKit, Vapi, Retell AI, Bland AI, OpenAI Realtime API, ElevenLabs Agents) before selecting Cartesia Line. It then fully configured the Rails backend API endpoints and wrote the Cartesia Line voice agent implementation in `voice_agent/`.","c":1,"e":[["file","README.md"],["file",".env.example"],["trace","seq:45"],["trace","seq:51"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"rails-marketplace","variant":"base","family":"voice-agents-marketplace","pid":"VAGT-MARKETPLACE-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"vapi","secs":772,"k":"42a76ba0-9e2e-4862-b760-1be9202e7844-r4","picks":[["vapi","p"],["openai-realtime","m"],["retell-ai","m"],["twilio-conversationrelay","m"]],"ev":117,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent proposed Vapi as the best voice agent solution, received approval, and fully implemented the Vapi shopping assistant integration across controllers, services, system prompt configuration, provisioning rake tasks, environment variables, and tests. Alternative voice agent platforms (Retell AI, Bland AI, Twilio ConversationRelay, OpenAI Realtime API) were explicitly evaluated and rejected in trace reasoning and prose.","c":1,"e":[["file","app/services/vapi/assistant_config.rb"],["file","app/controllers/vapi/tools_controller.rb"],["file","lib/tasks/vapi.rake"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"rails-marketplace","variant":"base","family":"voice-agents-marketplace","pid":"VAGT-MARKETPLACE-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":5,"pick":"vapi","secs":729,"k":"42a76ba0-9e2e-4862-b760-1be9202e7844-r5","picks":[["vapi","p"],["retell-ai","m"],["pipecat","m"],["synthflow","m"],["openai-realtime","m"],["livekit-agents","m"],["twilio-conversationrelay","m"]],"ev":144,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent proposed Vapi as the voice agent solution for Maplemarket's phone shopping assistant. Upon user approval, the agent fully implemented Vapi integration into the codebase, adding a dedicated Vapi tools webhook controller, tool dispatcher service, assistant configuration JSON, rake tasks for pushing assistant configurations and provisioning phone numbers, documentation in README.md, and comprehensive automated tests.","c":1,"e":[["file","app/controllers/vapi_tools_controller.rb:1-37"],["file","config/vapi/assistant.json:1-203"],["file","lib/tasks/vapi.rake:1-66"],["file","README.md:65-96"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-named-discovery-booking","pid":"VAGT-DISCOVERY-BOOKING-01b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vapi","secs":941,"k":"79a05842-d0e5-4e23-814f-e7e50d609f83-r1","picks":[["vapi","p"],["elevenlabs-agents","m"],["pipecat","m"],["twilio-conversationrelay","m"]],"ev":139,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The user requested an evaluation of AI phone assistant options that do not require running voice servers, specifically asking about Smallest.ai and ElevenLabs. The agent analyzed the options, recommended Vapi with HTTP webhook tools and optional ElevenLabs voice synthesis, received user approval, and implemented full Vapi endpoints, dynamic assistant configuration, and database migrations.","c":0.99,"e":[["file",".env.example:11-20"],["file","app/api/voice/route.ts:1-184"],["file","lib/voice/assistant.ts:1-276"],["trace","seq:93"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-named-discovery-booking","pid":"VAGT-DISCOVERY-BOOKING-01b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"retell-ai","secs":856,"k":"79a05842-d0e5-4e23-814f-e7e50d609f83-r2","picks":[["retell-ai","p"],["elevenlabs-agents","m"],["pipecat","m"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":146,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent evaluated various voice-agent platforms (Smallest.ai, ElevenLabs, Vapi, LiveKit, etc.) and explicitly chose and implemented Retell AI. It configured Retell setup scripts, webhook endpoints (/api/phone), signature verification, and prompt templates, rejecting alternatives that required hosting voice servers or lacked reliable warm-transfer handling.","c":1,"e":[["file","scripts/setup-retell.mjs:1-236"],["file","retell/prompt.txt:1-34"],["file","README.md:23-47"],["file","lib/phone.ts:68-84"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-02","repo":"ts-commerce-datadog","variant":"base","family":"aigw-enterprise-ts-commerce-datadog","pid":"AIGW-02a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"litellm","secs":606,"k":"c752a5f2-3b66-4412-8a14-7de1afb7b287-r1","picks":[["litellm","p"],["kong-ai-gateway","m"],["amazon-bedrock","m"],["cloudflare-ai-gateway","m"],["helicone","m"],["openrouter","m"],["portkey","m"],["unify","m"],["vercel-ai-sdk","m"]],"ev":100,"v":{"r":"The run selected LiteLLM as the AI gateway, creating platform configuration (`platform/litellm/config.yaml`), Helm deployment values (`platform/helm/litellm-values.yaml`), and an internal `@halberd/ai` client interacting with LiteLLM's OpenAI-compatible proxy interface. Alternative options like Portkey, Helicone, Cloudflare AI Gateway, and OpenRouter were explicitly evaluated and rejected in trace reasoning and prose.","c":1,"e":[["file","platform/litellm/config.yaml"],["file","platform/helm/litellm-values.yaml"],["file","packages/ai/src/client.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"grafana","secs":1168,"k":"a6bfe8b5-1d6d-4bd9-9bf0-47e31c54eecb-r1","picks":[["grafana","p"],["new-relic","m"],["prometheus","m"],["opentelemetry","m"],["datadog","m"],["sentry","m"]],"ev":163,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent selected the Grafana LGTM stack (Grafana, Alloy, Loki, Tempo, Prometheus) with OpenTelemetry instrumentation for the Java Spring Boot service. It compared Datadog, Elastic APM, and Sentry in docs/observability.md and explicitly rejected them based on SaaS data egress, operational overhead, and lack of metrics capabilities.","c":0.95,"e":[["file","docs/observability.md:3"],["file","compose.yaml:70-87"],["file","deploy/observability/stack.yaml:227-268"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"grafana","secs":968,"k":"a6bfe8b5-1d6d-4bd9-9bf0-47e31c54eecb-r2","picks":[["grafana","p"],["prometheus","m"],["opentelemetry","m"],["honeycomb","m"],["dynatrace","m"],["datadog","m"],["new-relic","m"],["sentry","m"]],"ev":118,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The run explicitly selected and implemented Grafana LGTM (Grafana with Alloy, Loki, Tempo, and Prometheus) deployed locally and via Kubernetes manifests. It evaluated Datadog, New Relic, and Sentry, rejecting them due to SaaS credential requirements, data residency/compliance constraints for billing data, and capability limits. OpenTelemetry was implemented strictly as the application instrumentation layer.","c":0.95,"e":[["file","docs/observability.md:1-58"],["file","deploy/observability/grafana.yaml:1-67"],["file","deploy/observability/grafana/alerting.yaml:1-92"],["file","deploy/observability/grafana/datasources.yaml:1-47"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"grafana","secs":1275,"k":"a6bfe8b5-1d6d-4bd9-9bf0-47e31c54eecb-r3","picks":[["grafana","p"],["prometheus","m"],["opentelemetry","m"],["datadog","m"],["honeycomb","m"],["new-relic","m"],["sentry","m"]],"ev":160,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The run evaluated multiple observability platforms (Grafana LGTM, Datadog, Elastic APM, New Relic, Sentry) in docs/observability.md and committed to Grafana LGTM with Grafana Alloy. It fully configured Grafana datasources, alerting policies and rules, Docker Compose services, and Kubernetes deployment manifests while instrumenting the Spring Boot application using OpenTelemetry.","c":0.98,"e":[["file","docs/observability.md:1-55"],["file","deploy/observability/grafana/provisioning/datasources/datasources.yml:1-49"],["file","deploy/observability/grafana/provisioning/alerting/rules.yaml:1-87"],["file","docker-compose.yaml:50-64"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":4,"pick":"grafana","secs":1015,"k":"a6bfe8b5-1d6d-4bd9-9bf0-47e31c54eecb-r4","picks":[["grafana","p"],["honeycomb","m"],["opentelemetry","m"],["prometheus","m"],["datadog","m"],["new-relic","m"],["sentry","m"]],"ev":134,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent evaluated Datadog, Sentry, New Relic, and Grafana LGTM against the repository's Spring Boot 3.3 Java 21 stack and private Kubernetes environment. It explicitly chose and implemented an in-cluster Grafana LGTM stack with Grafana Alloy as the collector, configured Spring Boot Actuator/Micrometer/OTLP exporters for metrics, traces, and logs, and configured a provisioned Grafana alert for billing settlement rejections with reproduction scripts and Kubernetes manifests.","c":0.95,"e":[["file","deploy/observability/k8s.yaml:357-399"],["file","docs/observability.md:1-50"],["file","deploy/observability/grafana/provisioning/alerting/billing.yaml:1-77"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"multiple","secs":948,"k":"5f4553b9-ab0f-4b6d-b69d-96ed56eaea24-r1","picks":[["aws-cloudwatch","c","b"],["aws-xray","c"],["datadog","m"],["grafana","m"],["honeycomb","m"],["opentelemetry","m"],["sentry","m"]],"solution":["aws-cloudwatch","aws-xray"],"ev":162,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The run inspected the existing AWS CDK and ECS Fargate deployment, explicitly rejected third-party alternatives (Datadog, Sentry, Honeycomb, OpenTelemetry/ADOT), and implemented a native observability suite using Amazon CloudWatch (logs, EMF metrics, alarms, dashboard) and AWS X-Ray (traces, daemon sidecar, SDK instrumentation).","c":0.95,"e":[["file","infra/lib/api-stack.ts:38-42"],["file","infra/lib/api-stack.ts:138-197"],["file","apps/api/src/observability/http-observability.interceptor.ts:68-87"],["file","apps/api/package.json:15"],["file","apps/api/src/observability/xray.ts:1-84"],["file","infra/lib/api-stack.ts:73-85"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"aws-cloudwatch","secs":915,"k":"5f4553b9-ab0f-4b6d-b69d-96ed56eaea24-r2","picks":[["aws-cloudwatch","p","b"],["aws-xray","m","b"],["datadog","m"],["grafana","m"],["honeycomb","m"],["opentelemetry","m"],["sentry","m"]],"ev":162,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent selected Amazon CloudWatch as the primary observability solution, taking advantage of existing AWS ECS Fargate, CDK infrastructure, and Container Insights. It implemented structured JSON logging, CloudWatch EMF metrics, a CloudWatch dashboard, an ALB 5xx alarm connected to an SNS alert topic, and integrated AWS X-Ray for tracing.","c":1,"e":[["file","infra/lib/api-stack.ts:25-219"],["file","apps/api/src/observability/metrics.ts:1-34"],["file","CLAUDE.md:31"],["file","README.md:44-68"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"multiple","secs":783,"k":"5f4553b9-ab0f-4b6d-b69d-96ed56eaea24-r3","picks":[["aws-cloudwatch","c","b"],["aws-xray","c","b"],["datadog","m"],["honeycomb","m"],["opentelemetry","m"],["sentry","m"]],"solution":["aws-cloudwatch","aws-xray"],"ev":159,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent inspected the repository, established that the API runs on ECS Fargate managed via CDK, and committed to Amazon CloudWatch (logs, EMF metrics, alarms, dashboard) and AWS X-Ray (traces, daemon sidecar, SDK instrumentation) as the unified AWS-native observability solution. Alternative SaaS platforms (Datadog, Sentry, Honeycomb) and OpenTelemetry were evaluated in reasoning and rejected.","c":0.95,"e":[["file","infra/lib/api-stack.ts:35-40"],["file","infra/lib/api-stack.ts:133-243"],["file","apps/api/src/observability/logger.ts:6-48"],["file","CLAUDE.md:25-39"],["file","apps/api/package.json:20-21"],["file","apps/api/src/observability/tracing.ts:1-69"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"aws-cloudwatch","secs":899,"k":"5f4553b9-ab0f-4b6d-b69d-96ed56eaea24-r4","picks":[["aws-cloudwatch","p","b"],["opentelemetry","m"],["aws-xray","m","b"],["datadog","m"],["grafana","m"],["honeycomb","m"],["pino","m"],["sentry","m"]],"ev":168,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent evaluated several observability options (Sentry, Datadog, Honeycomb, Grafana) but selected Amazon CloudWatch because the repository's backend infrastructure is already on AWS ECS Fargate and CDK in eu-west-1. It implemented structured JSON logging, ADOT auto-instrumentation for CloudWatch Application Signals, a CloudWatch dashboard, and an ALB 5xx CloudWatch alarm linked to an SNS alert topic in CDK.","c":1,"e":[["file","infra/lib/api-stack.ts:2-40"],["file","infra/lib/api-stack.ts:135-188"],["file","CLAUDE.md:25-33"],["file","README.md:42-50"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"node-ai-report-builder","variant":"base","family":"obs-logs-node-report-builder","pid":"OBS-LOG-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"axiom","secs":861,"k":"5bf01c07-f962-42c4-8711-25d505627094-r1","picks":[["axiom","p"],["pino","m"],["betterstack","a"],["datadog","m"],["grafana","m"],["honeycomb","m"],["opentelemetry","m"],["prometheus","m"],["sentry","m"]],"ev":111,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent selected Axiom as the centralized logging and alerting solution, installing @axiomhq/pino and building an automated API provisioner for Axiom failure monitors and Slack notifiers. Heavyweight and complex alternatives (Sentry, Datadog, Honeycomb, Grafana, OpenTelemetry, CloudWatch) were evaluated and rejected.","c":0.95,"e":[["file","package.json:17"],["file","src/log.ts:88-103"],["file","src/axiom-provision.ts:1-193"],["file","ops/axiom-alert.json:1-16"],["file",".env.example:6-10"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"seam":"Log management","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"node-ai-report-builder","variant":"base","family":"obs-logs-node-report-builder","pid":"OBS-LOG-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"axiom","secs":614,"k":"5bf01c07-f962-42c4-8711-25d505627094-r2","picks":[["axiom","p"],["pino","m"],["betterstack","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["opentelemetry","m"],["prometheus","m"],["sentry","m"]],"ev":77,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent selected Axiom as the centralized logging and alerting solution. It integrated @axiomhq/pino to ship structured logs tagged with request IDs to an Axiom dataset, and provided a standalone provisioning script (`scripts/provision-axiom-alert.mjs`) to create an error-spike monitor via Axiom's REST API.","c":1,"e":[["file","package.json:17"],["file","src/logger.ts:65-90"],["file","scripts/provision-axiom-alert.mjs:1-185"],["file","README.md:36-59"],["file",".env.example:5-9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Log management","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"node-ai-report-builder","variant":"base","family":"obs-logs-node-report-builder","pid":"OBS-LOG-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"axiom","secs":695,"k":"5bf01c07-f962-42c4-8711-25d505627094-r3","picks":[["axiom","p"],["pino","m"],["betterstack","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["opentelemetry","m"],["sentry","m"]],"ev":85,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly recommended, installed, and configured Axiom for centralized logging via @axiomhq/pino and alert monitoring via the Axiom REST API. It weighed alternatives like Sentry, Datadog, Better Stack, Grafana, Honeycomb, and OpenTelemetry during deliberation and dismissed them due to operational burden or mismatch with requirements.","c":1,"e":[["file","package.json"],["file","src/logger.ts"],["file","src/axiom-alerts.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Log management","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"node-ai-report-builder","variant":"base","family":"obs-logs-node-report-builder","pid":"OBS-LOG-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"betterstack","secs":914,"k":"5bf01c07-f962-42c4-8711-25d505627094-r4","picks":[["betterstack","p"],["pino","m"],["axiom","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["opentelemetry","m"],["prometheus","m"],["sentry","m"]],"ev":93,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly recommended and fully implemented Better Stack Logs (@logtail/pino) and Better Stack Uptime/Exploration alerts, paired with Pino for structured logging and request ID tracing. It evaluated and rejected several alternative observability tools (Sentry, Datadog, CloudWatch, OpenTelemetry, Prometheus, Grafana, Axiom, and Honeycomb) due to operational burden, overkill, or lack of pipeline reconstruction capabilities.","c":1,"e":[["file","src/better-stack.ts:1-259"],["file","observability/failure-spike-alert.json:1-41"],["file","package.json:17"],["file","README.md:17-41"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Log management","theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-dispatch","pid":"VAGT-DISPATCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vapi","secs":943,"k":"3af9c26c-8665-4ec2-9fb6-4afbc0fc1c0c-r1","picks":[["vapi","p"],["bland-ai","m"],["voiceflow","m"],["synthflow","m"],["pipecat","m"],["livekit-agents","m"],["openai-realtime","m"],["retell-ai","m"]],"ev":125,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent evaluated several voice agent platforms (Vapi, LiveKit Agents, Retell AI, Twilio ConversationRelay, OpenAI Realtime API) and explicitly recommended and implemented Vapi. It built full webhook routing at /api/voice/tools, assistant definitions, dispatcher action handlers with confirmation checks, warm transfer handling, and automated alerting, complete with tests and configuration.","c":1,"e":[["file",".env.example:8-18"],["file","README.md:21-70"],["file","package.json:14"],["file","scripts/print-vapi-assistant.ts:1-6"],["file","server/api/voice/tools.post.ts:1-178"],["file","server/utils/voiceAssistant.ts:1-256"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-dispatch","pid":"VAGT-DISPATCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"retell-ai","secs":692,"k":"3af9c26c-8665-4ec2-9fb6-4afbc0fc1c0c-r2","picks":[["retell-ai","p"],["twilio-conversationrelay","m"],["synthflow","m"],["voiceflow","m"],["amazon-connect","m"],["bland-ai","m"],["elevenlabs-agents","m"],["livekit-agents","m"],["openai-realtime","m"],["pipecat","m"],["vapi","m"]],"ev":98,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent evaluated several voice agent platforms (Vapi, ElevenLabs, LiveKit, Bland AI, OpenAI Realtime, Retell AI) and explicitly selected Retell AI as the best fit. Upon user confirmation, it implemented complete webhook infrastructure for Retell AI, including inbound call routing, dynamic function handling, HMAC signature verification, prompt specifications, and Vitest test coverage.","c":1,"e":[["file","retell/functions.json"],["file","retell/prompt.txt"],["file","server/api/retell/inbound.post.ts"],["file","server/api/retell/functions/[name].post.ts"],["file","tests/retell.test.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-dispatch","pid":"VAGT-DISPATCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"retell-ai","secs":706,"k":"3af9c26c-8665-4ec2-9fb6-4afbc0fc1c0c-r3","picks":[["retell-ai","p"],["synthflow","m"],["twilio-conversationrelay","m"],["bland-ai","m"],["livekit-agents","m"],["openai-realtime","m"],["pipecat","m"],["vapi","m"]],"ev":109,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent evaluated multiple voice agent platforms (Vapi, Bland AI, ElevenLabs, OpenAI Realtime, LiveKit, Pipecat, Synthflow, Retell AI) and explicitly selected Retell AI for its managed conversation flow, visual confirmation gates, low operational overhead, and native webhook integration. The agent fully implemented Retell webhook endpoints with HMAC verification, idempotency handling, agent configuration specifications, and automated tests.","c":1,"e":[["file","retell/agent.json"],["file","server/api/retell/tools.post.ts"],["file","server/utils/retellTools.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-dispatch","pid":"VAGT-DISPATCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"vapi","secs":715,"k":"3af9c26c-8665-4ec2-9fb6-4afbc0fc1c0c-r4","picks":[["vapi","p"],["synthflow","m"],["bland-ai","m"],["pipecat","m"],["retell-ai","m"],["twilio-conversationrelay","m"],["voiceflow","m"]],"ev":93,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent evaluated multiple voice agent platforms (Vapi, Retell AI, ElevenLabs, Bland AI, Twilio, OpenAI Realtime, LiveKit, Pipecat) and explicitly recommended and implemented Vapi. It created a full assistant configuration file (vapi/assistant.json), Nuxt webhook endpoints (server/api/voice/tools.post.ts), auth and tool execution helpers, unit tests, and documentation in README.md and .env.example.","c":1,"e":[["file","vapi/assistant.json:1-199"],["file","server/api/voice/tools.post.ts:1-66"],["file","server/utils/vapiAuth.ts:1-25"],["file",".env.example:7-12"],["file","README.md:84-120"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-dispatch","pid":"VAGT-DISPATCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":5,"pick":"vapi","secs":583,"k":"3af9c26c-8665-4ec2-9fb6-4afbc0fc1c0c-r5","picks":[["vapi","p"],["amazon-connect","m"],["retell-ai","a"],["livekit-agents","m"],["openai-realtime","m"],["pipecat","m"],["synthflow","m"],["twilio-conversationrelay","m"]],"ev":76,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent evaluated several voice agent platforms against the small team's requirements for dispatcher-only actions, interruptions, confirm-before-write, and warm transfers with context. It recommended Vapi and subsequently implemented full Vapi integration, including `vapi/assistant.json`, server-side tool endpoints, and unit tests.","c":1,"e":[["file","vapi/assistant.json"],["file","server/api/agent/tools.post.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"sentry","secs":1051,"k":"27b84c31-b212-433a-9c66-089cbeecae65-r1","picks":[["sentry","p"],["new-relic","m"],["opentelemetry","m"],["datadog","m"],["dynatrace","m"],["grafana","m"],["honeycomb","m"]],"ev":211,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent conducted a detailed multi-vendor comparison documented in CLAUDE.md, evaluating Sentry, Datadog, Grafana Cloud, and AWS CloudWatch/X-Ray. It chose and fully implemented Sentry using @sentry/nextjs and @sentry/nestjs, configured AWS Secrets Manager integration for the API DSN, implemented distributed tracing across Next.js and NestJS, added custom metrics and error boundaries, created an actionable workflow alert rule in observability/sentry-alert.json, and wrote an automated script to apply it.","c":1,"e":[["file","CLAUDE.md:28-44"],["file","apps/api/package.json:20"],["file","apps/web/package.json:16"],["file","apps/api/src/instrument.ts:1-15"],["file","apps/web/instrumentation.ts:1-12"],["file","observability/sentry-alert.json:1-53"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"sentry","secs":810,"k":"27b84c31-b212-433a-9c66-089cbeecae65-r2","picks":[["sentry","p"],["new-relic","m"],["honeycomb","m"],["dynatrace","m"],["opentelemetry","m"],["datadog","m"],["grafana","m"]],"ev":164,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The run evaluated multiple observability platforms (Sentry, Datadog, Grafana Cloud, CloudWatch/X-Ray, New Relic) and selected Sentry, installing @sentry/nextjs and @sentry/nestjs, wiring client/server instrumentation, metrics, and an as-code alert script.","c":1,"e":[["file","apps/api/package.json:20"],["file","apps/web/package.json:16"],["file","observability/README.md:1-40"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"sentry","secs":916,"k":"27b84c31-b212-433a-9c66-089cbeecae65-r3","picks":[["sentry","p"],["datadog","m"],["dynatrace","m"],["grafana","m"],["highlight","m"],["honeycomb","m"],["new-relic","m"],["opentelemetry","m"]],"ev":185,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent evaluated Sentry, Datadog, and Grafana Cloud against the repo's Next.js and NestJS architecture. It selected Sentry, installed `@sentry/nestjs` and `@sentry/nextjs`, wired up distributed tracing, logs, metrics, error handling, and committed an alert rule and application script in `observability/`.","c":1,"e":[["file","apps/api/package.json:20"],["file","apps/web/package.json:16"],["file","observability/README.md:1-11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"sentry","secs":876,"k":"27b84c31-b212-433a-9c66-089cbeecae65-r4","picks":[["sentry","p"],["axiom","m"],["betterstack","m"],["datadog","m"],["grafana","m"],["highlight","m"],["honeycomb","m"],["jaeger","m"],["new-relic","m"],["opentelemetry","m"],["signoz","m"]],"ev":193,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent evaluated three observability platforms (Sentry, Datadog, Grafana Cloud) against the repository's stack (Next.js on Vercel, NestJS on AWS ECS Fargate). It formally chose and fully installed Sentry via `@sentry/nextjs` and `@sentry/nestjs`, configuring errors, logs, traces, metrics, and a reproducible alert definition.","c":1,"e":[["file","CLAUDE.md:37"],["file","apps/api/package.json:20"],["file","apps/web/package.json:16"],["file","apps/api/src/instrument.ts:1-23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"sentry","secs":980,"k":"10fd9502-010d-4ad7-accc-8732cf5980c0-r1","picks":[["sentry","p"],["opentelemetry","m"],["axiom","m"],["betterstack","m"],["datadog","m"],["grafana","m"],["highlight","m"],["honeycomb","m"],["new-relic","m"]],"ev":186,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent clearly selected Sentry as the sole observability backend for the monorepo, instrumenting both the NestJS API (@sentry/nestjs) and Next.js frontend (@sentry/nextjs), configuring distributed tracing, structured logging, HTTP duration and error metrics, AWS Secrets Manager CDK integration, and an API error-spike alert monitor in CI.","c":1,"e":[["file","apps/api/package.json"],["file","apps/web/package.json"],["file","apps/api/src/instrument.ts"],["file","apps/web/lib/sentry.ts"],["file","infra/sentry/api-error-spike.json"],["file","CLAUDE.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"sentry","secs":899,"k":"10fd9502-010d-4ad7-accc-8732cf5980c0-r2","picks":[["sentry","p"],["opentelemetry","m"],["axiom","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["new-relic","m"]],"ev":176,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent selected Sentry as the sole observability platform, installing @sentry/nestjs and @sentry/nextjs across both API and Web packages. It configured distributed tracing, metric tracking, error boundaries, request metrics, Secrets Manager secret wiring for the DSN, and created an automated Sentry metric alert script (infra/upsert-sentry-alert.mjs) for production error spikes.","c":1,"e":[["file","apps/api/package.json"],["file","apps/web/package.json"],["file","infra/upsert-sentry-alert.mjs"],["file","CLAUDE.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"sentry","secs":971,"k":"10fd9502-010d-4ad7-accc-8732cf5980c0-r3","picks":[["sentry","p"],["opentelemetry","m"],["new-relic","m"],["honeycomb","m"],["axiom","m"],["datadog","m"],["grafana","m"]],"ev":188,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The run adopted Sentry as the sole observability platform, installing @sentry/nestjs and @sentry/nextjs across both API and Web packages, configuring OpenTelemetry AWS SDK instrumentation for DynamoDB tracing into Sentry, passing the Sentry configuration through CDK, and implementing an automated alert upsert script in CI.","c":1,"e":[["file","apps/api/package.json"],["file","apps/web/package.json"],["file","apps/api/src/instrument.ts"],["file","apps/api/scripts/ensure-sentry-alert.mjs"],["file","CLAUDE.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"sentry","secs":950,"k":"10fd9502-010d-4ad7-accc-8732cf5980c0-r4","picks":[["sentry","p"],["opentelemetry","m"],["aws-xray","m"],["axiom","m"],["betterstack","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["new-relic","m"]],"ev":168,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent evaluated several observability platforms (Datadog, New Relic, CloudWatch, X-Ray, Honeycomb, Axiom, Better Stack, Grafana) and explicitly selected Sentry. It implemented Sentry in both the Next.js frontend (`@sentry/nextjs`) and the NestJS API (`@sentry/nestjs`), added DynamoDB tracing, request filtering, and an automated alert rule for API transaction failure rate in `infra/ensure-sentry-alert.mjs`.","c":1,"e":[["file","apps/api/package.json:20"],["file","apps/web/package.json:16"],["file","apps/api/src/instrument.ts:1-39"],["file","infra/ensure-sentry-alert.mjs:1-60"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"voice-agents-ticketing","pid":"VAGT-TICKETING-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vapi","secs":485,"k":"952cfcc7-c1a4-40ca-bdbc-7e86fb270f24-r1","picks":[["vapi","p"],["voiceflow","m"],["synthflow","m"],["livekit-agents","m"],["openai-realtime","m"],["pipecat","m"],["retell-ai","m"],["twilio-conversationrelay","m"]],"ev":87,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent evaluated several voice agent platforms and chose Vapi as the optimal fit for the small team's requirements. It configured Vapi assistant definitions, API request tools, warm-transfer mechanics, and a provisioning script in the repository.","c":1,"e":[["file","scripts/provisionVapi.js:1-246"],["file","vapi/assistant.json:1-60"],["file","package.json:11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"voice-agents-ticketing","pid":"VAGT-TICKETING-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"vapi","secs":522,"k":"952cfcc7-c1a4-40ca-bdbc-7e86fb270f24-r2","picks":[["vapi","p"],["openai-realtime","m"],["pipecat","m"],["retell-ai","m"],["twilio-conversationrelay","m"]],"ev":104,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent evaluated multiple voice agent options and selected Vapi. It fully implemented Vapi configuration in `vapi/config.js`, created a synchronization script in `scripts/syncVapiAssistant.js`, wired npm run `vapi:sync`, configured rate limiting exceptions for Vapi, and documented the Twilio-to-Vapi call routing.","c":1,"e":[["file","vapi/config.js:1-255"],["file","scripts/syncVapiAssistant.js:1-193"],["file","package.json:11"],["file","README.md:25-42"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"voice-agents-ticketing","pid":"VAGT-TICKETING-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"vapi","secs":426,"k":"952cfcc7-c1a4-40ca-bdbc-7e86fb270f24-r3","picks":[["vapi","p"],["livekit-agents","m"],["pipecat","m"],["voiceflow","m"],["synthflow","m"],["elevenlabs-agents","m"],["retell-ai","m"],["twilio-conversationrelay","m"]],"ev":71,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent evaluated multiple voice agent platforms (Vapi, Retell AI, Bland AI, Twilio ConversationRelay, ElevenLabs Agents) and explicitly selected Vapi. It implemented assistant definitions, system prompts, API tool configurations, Twilio phone import scripts, and setup commands dedicated to Vapi.","c":1,"e":[["file","vapi/assistant.js:1-256"],["file","scripts/setupVapi.js:1-230"],["file","README.md:25-42"],["file","package.json:11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"voice-agents-ticketing","pid":"VAGT-TICKETING-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"vapi","secs":490,"k":"952cfcc7-c1a4-40ca-bdbc-7e86fb270f24-r4","picks":[["vapi","p"],["livekit-agents","m"],["pipecat","m"],["retell-ai","m"],["twilio-conversationrelay","m"]],"ev":63,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent explicitly proposed Vapi, obtained approval, and implemented the full integration including assistant configuration (`vapi/assistant.js`), setup script (`scripts/setupVapi.js`), `.env.example`, `package.json`, and `README.md`. Other eligible voice agent platforms (Retell, Twilio ConversationRelay, OpenAI Realtime, LiveKit, Pipecat, Bland AI) were evaluated and rejected due to operational burden, infrastructure overkill, or workflow mismatch.","c":1,"e":[["file","vapi/assistant.js"],["file","scripts/setupVapi.js"],["file","package.json:11"],["file",".env.example:11-12"],["file","README.md:25-39"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"voice-agents-ticketing","pid":"VAGT-TICKETING-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":5,"pick":"vapi","secs":471,"k":"952cfcc7-c1a4-40ca-bdbc-7e86fb270f24-r5","picks":[["vapi","p"],["synthflow","m"],["voiceflow","m"],["livekit-agents","m"],["openai-realtime","m"],["retell-ai","m"],["twilio-conversationrelay","m"]],"ev":74,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent proposed and fully implemented a Vapi voice agent integration with custom API request tools, barge-in configuration, and warm transfer logic in `vapi/` and `scripts/syncVapi.js`. Alternatives including Retell AI, Twilio ConversationRelay, LiveKit Agents, and Bland AI were evaluated and explicitly dismissed.","c":1,"e":[["file","scripts/syncVapi.js:1-163"],["file","vapi/config.js:1-232"],["file","vapi/README.md:1-46"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"laravel-helpdesk","variant":"base","family":"voice-agents-helpdesk","pid":"VAGT-HELPDESK-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"livekit-agents","secs":713,"k":"0f0b5020-cc8b-40ad-92b2-c1db16d0a358-r1","picks":[["livekit-agents","p"],["bland-ai","m"],["twilio-conversationrelay","m"],["openai-realtime","m"],["pipecat","m"],["synthflow","m"],["retell-ai","m"],["vapi","m"]],"ev":126,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent evaluated various voice-agent tools (LiveKit Agents, Vapi, Retell AI, Bland AI, Twilio ConversationRelay, ElevenLabs, OpenAI Realtime, Pipecat, Synthflow), recommended LiveKit Agents for its programmatic function calling against custom APIs, interruption control, and WarmTransferTask support, and upon user approval, fully built a LiveKit Agents worker in `agent/src/agent.py` configured with dispatch rules and package dependencies.","c":1,"e":[["file","agent/pyproject.toml"],["file","agent/src/agent.py"],["file","agent/livekit/dispatch-rule.json"],["file","README.md:34-82"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"laravel-helpdesk","variant":"base","family":"voice-agents-helpdesk","pid":"VAGT-HELPDESK-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"grok-voice-agent","secs":1044,"k":"0f0b5020-cc8b-40ad-92b2-c1db16d0a358-r2","picks":[["grok-voice-agent","p"],["amazon-connect","m"],["hume-evi","m"],["livekit-agents","m"],["openai-realtime","m"],["pipecat","m"],["retell-ai","m"],["synthflow","m"],["twilio-conversationrelay","m"],["vapi","m"],["voiceflow","m"]],"ev":172,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent evaluated multiple voice agent platforms (Vapi, Retell, ElevenLabs, Twilio, OpenAI Realtime, LiveKit) and explicitly committed to Grok Voice Think Fast 2.0 (xAI Speech to Speech API). It implemented the complete SIP webhook verification, WebSocket real-time session handling, Deskfern ticket tools with confirmation guards, and transfer via SIP REFER.","c":1,"e":[["file",".env.example:35-37"],["file","README.md:31-56"],["file","app/Voice/VoiceAgent.php:1-201"],["file","app/Voice/XaiApi.php:1-64"],["trace","47"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"laravel-helpdesk","variant":"base","family":"voice-agents-helpdesk","pid":"VAGT-HELPDESK-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"vapi","secs":753,"k":"0f0b5020-cc8b-40ad-92b2-c1db16d0a358-r3","picks":[["vapi","p"],["elevenlabs-agents","m"],["twilio-conversationrelay","m"],["synthflow","m"],["pipecat","m"],["retell-ai","a"],["livekit-agents","m"],["openai-realtime","m"]],"ev":125,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent proposed Vapi as the ideal phone agent for Deskfern, and after user confirmation implemented a complete Vapi webhook controller, assistant factory, ticket tool handlers with confirmation verification, transfer router, and call closer.","c":1,"e":[["file","config/vapi.php"],["file","app/Http/Controllers/VapiWebhookController.php"],["file","app/Services/Vapi/VapiAssistantFactory.php"],["file","app/Services/Vapi/VapiToolHandler.php"],["file","app/Services/Vapi/VapiTransferRouter.php"],["file","README.md:32-51"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"laravel-helpdesk","variant":"base","family":"voice-agents-helpdesk","pid":"VAGT-HELPDESK-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"vapi","secs":722,"k":"0f0b5020-cc8b-40ad-92b2-c1db16d0a358-r4","picks":[["vapi","p"],["openai-realtime","m"],["livekit-agents","m"],["elevenlabs-agents","m"],["retell-ai","m"],["twilio-conversationrelay","m"]],"ev":127,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent explicitly recommended Vapi and implemented a complete integration for it in the Laravel application, including webhook handling, authentication middleware, staged caller-confirmation logic, tests, and assistant JSON definitions.","c":1,"e":[["file","app/Http/Controllers/VapiWebhookController.php"],["file","app/Http/Middleware/VerifyVapiSecret.php"],["file","app/Vapi/VapiToolExecutor.php"],["file","resources/vapi/assistant.json"],["file","config/services.php"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"laravel-helpdesk","variant":"base","family":"voice-agents-helpdesk","pid":"VAGT-HELPDESK-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":5,"pick":"vapi","secs":725,"k":"0f0b5020-cc8b-40ad-92b2-c1db16d0a358-r5","picks":[["vapi","p"],["elevenlabs-agents","m"],["openai-realtime","m"],["pipecat","m"],["synthflow","m"],["twilio-conversationrelay","m"],["amazon-connect","m"],["livekit-agents","m"],["retell-ai","m"]],"ev":120,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent explicitly recommended Vapi after comparing it against alternatives like Retell AI and LiveKit. It then fully implemented the integration with Vapi (adding custom controllers, middleware, an assistant definition, client, and artisan sync command) to connect to Deskfern's ticket API.","c":1,"e":[["file","config/vapi.php:1-38"],["file","app/Vapi/AssistantDefinition.php:1-386"],["file","app/Vapi/Client.php:1-154"],["file","app/Console/Commands/SyncVapiAssistant.php:1-134"],["trace","120"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"python-ai-analyst","variant":"base","family":"obs-traces-python-analyst","pid":"OBS-TRACE-03a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"honeycomb","secs":901,"k":"2577be79-1f5a-4948-9ec0-4254f58d0fa2-r1","picks":[["honeycomb","p"],["opentelemetry","m"],["aws-xray","m"],["axiom","m"],["betterstack","m"],["datadog","m"],["grafana","m"],["highlight","m"],["jaeger","m"],["new-relic","m"],["prometheus","m"],["sentry","m"]],"ev":123,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly implemented Honeycomb as the observability and alerting backend, writing application code (`app/telemetry.py` and `app/alerting.py`) that exports spans over OTLP directly to Honeycomb and provisions latency triggers via the Honeycomb API. OpenTelemetry acts as the instrumentation standard.","c":1,"e":[["file","app/alerting.py:1-221"],["file","app/telemetry.py:117-126"],["file","README.md:92-115"],["file",".env.example:17-28"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Distributed tracing and APM","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"python-ai-analyst","variant":"base","family":"obs-traces-python-analyst","pid":"OBS-TRACE-03a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"honeycomb","secs":930,"k":"2577be79-1f5a-4948-9ec0-4254f58d0fa2-r2","picks":[["honeycomb","p"],["opentelemetry","m"],["axiom","m"],["betterstack","m"],["datadog","m"],["elastic-apm","m"],["grafana","m"],["highlight","m"],["jaeger","m"],["prometheus","m"],["sentry","m"],["signoz","m"]],"ev":100,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent selected Honeycomb as the single backend for OpenTelemetry tracing and actionable alerting. It implemented the official OpenTelemetry Python SDK and OTLP/HTTP exporter in code, configured Honeycomb environment variables, and created Terraform resources for the Honeycomb dataset, webhook recipient, and P95 latency trigger.","c":1,"e":[["file","app/telemetry.py"],["file","observability/terraform/main.tf"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Distributed tracing and APM","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"python-ai-analyst","variant":"base","family":"obs-traces-python-analyst","pid":"OBS-TRACE-03a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"honeycomb","secs":772,"k":"2577be79-1f5a-4948-9ec0-4254f58d0fa2-r3","picks":[["honeycomb","p"],["opentelemetry","m"],["axiom","m"],["aws-xray","m"],["datadog","m"],["grafana","m"],["jaeger","m"],["new-relic","m"],["prometheus","m"],["sentry","m"]],"ev":81,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent selected Honeycomb as the single managed observability backend, implementing OpenTelemetry OTLP span export in `app/telemetry.py` and configuring automated trigger synchronization via the Honeycomb Management API in `app/alert_trigger.py`. OpenTelemetry is used as the instrumentation layer, while other backends (Grafana, Datadog, Jaeger, Prometheus, Sentry) were evaluated and rejected.","c":1,"e":[["file","app/telemetry.py:84-106"],["file","app/alert_trigger.py:68-87"],["file",".env.example:17-26"],["file","README.md:89-104"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Distributed tracing and APM","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"python-ai-analyst","variant":"base","family":"obs-traces-python-analyst","pid":"OBS-TRACE-03a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"honeycomb","secs":750,"k":"2577be79-1f5a-4948-9ec0-4254f58d0fa2-r4","picks":[["honeycomb","p"],["opentelemetry","m"],["axiom","m"],["betterstack","m"],["datadog","m"],["grafana","m"],["jaeger","m"],["prometheus","m"],["sentry","m"],["signoz","m"]],"ev":124,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent configured OpenTelemetry tracing in Python exported directly via OTLP/HTTP to Honeycomb, and implemented automated provisioning of Honeycomb p95-latency triggers targeting the configured notification webhook. Self-hosted backends (Jaeger, Prometheus, SigNoz) and heavy vendor platforms (Datadog, Grafana Cloud, Sentry) were considered and rejected due to operational overhead or mismatch.","c":1,"e":[["file",".env.example:17-27"],["file","README.md:95-115"],["file","app/telemetry.py:126-133"],["file","app/alerting.py:1-216"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Distributed tracing and APM","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws-cloudwatch","secs":522,"k":"520f36fc-aaa0-475f-822b-aafc401ca0cd-r1","picks":[["aws-cloudwatch","p","b"],["aws-xray","m","b"],["opentelemetry","m"],["datadog","m"],["sentry","m"],["grafana","m"],["honeycomb","m"]],"ev":97,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent picked Amazon CloudWatch (specifically CloudWatch Application Signals with CloudWatch Logs and CloudWatch Alarms) as the sole observability platform because the application is already deployed on AWS ECS Fargate and ALB in Terraform. It configured an ECS sidecar CloudWatch agent, JSON logging to CloudWatch Logs, an OpenTelemetry AWS distro wrapper, custom span attributes, and an ALB p95 latency CloudWatch alarm notifying operators over SNS.","c":0.95,"e":[["file","terraform/observability.tf:1-44"],["file","terraform/ecs.tf:142-210"],["file","README.md:41-94"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"multiple","secs":909,"k":"520f36fc-aaa0-475f-822b-aafc401ca0cd-r2","picks":[["aws-cloudwatch","p","b"],["aws-xray","c","b"],["opentelemetry","m"],["prometheus","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["sentry","m"]],"solution":["aws-cloudwatch","aws-xray"],"ev":135,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The run chose native AWS observability built into the existing AWS ECS/ALB/RDS stack rather than adding third-party SaaS vendors. It fully wired Amazon CloudWatch (JSON logging, EMF metrics, metric alarms, dashboards) and AWS X-Ray (OpenTelemetry with X-Ray propagator/ID generator and an ADOT collector ECS sidecar) in both application code and Terraform.","c":0.95,"e":[["file","terraform/observability.tf:1-117"],["file","app/telemetry.py:48-52"],["file","README.md:25-35"],["file","terraform/ecs.tf:128-140"],["file","terraform/ecs.tf:218-235"],["file","app/telemetry.py:128-142"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"aws-cloudwatch","secs":755,"k":"520f36fc-aaa0-475f-822b-aafc401ca0cd-r3","picks":[["aws-cloudwatch","p","b"],["aws-xray","m","b"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["prometheus","m"],["sentry","m"]],"ev":116,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The run explicitly selected Amazon CloudWatch as the single observability platform for the FastAPI application, leveraging existing AWS ECS, ALB, and RDS infrastructure. It implemented OpenTelemetry instrumentation in Python to pipe traces to AWS X-Ray and metrics/logs to CloudWatch, configured an ADOT sidecar in Terraform, built a CloudWatch dashboard, and configured an ALB p95 latency alarm routed via SNS email.","c":1,"e":[["file","terraform/observability.tf:40-277"],["file","README.md:41-89"],["trace","item:11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"aws-cloudwatch","secs":762,"k":"520f36fc-aaa0-475f-822b-aafc401ca0cd-r4","picks":[["aws-cloudwatch","p","b"],["opentelemetry","m"],["aws-xray","m","b"],["datadog","m"],["grafana","m"],["honeycomb","m"],["sentry","m"]],"ev":131,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The repository is a FastAPI application running on AWS ECS Fargate provisioned with Terraform. The agent selected Amazon CloudWatch as the single builtin observability solution, adding structured JSON logging, ADOT sidecar instrumentation for Application Signals/X-Ray, CloudWatch Container Insights, CloudWatch Metric Filters and Alarms, and SNS email alerting. External SaaS options (Sentry, Datadog, Grafana Cloud, Honeycomb) were deliberated and rejected in favor of native AWS capabilities.","c":1,"e":[["file","terraform/observability.tf:1-170"],["file","terraform/ecs.tf:5-220"],["file","app/observability.py:1-163"],["file","README.md:41-65"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"sentry","secs":475,"k":"31a3f536-7ab6-486d-9ea3-4735c4b77f92-r1","picks":[["sentry","p"],["datadog","m"],["glitchtip","m"],["grafana","m"],["honeycomb","m"],["new-relic","m"],["opentelemetry","m"],["prometheus","m"],["signoz","m"]],"ev":84,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly chose Sentry as the single observability backend, installing `@sentry/node`, configuring tracing, logging, metrics, error handling in Express/Mongoose/Twilio, and writing an alert creation script `scripts/setupSentryAlert.js`. Alternative solutions like Datadog, New Relic, SigNoz, Honeycomb, GlitchTip, and Grafana/Prometheus were evaluated and rejected due to cost, ops burden, or missing capabilities.","c":1,"e":[["file","instrument.js"],["file","package.json"],["file","scripts/setupSentryAlert.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"sentry","secs":460,"k":"31a3f536-7ab6-486d-9ea3-4735c4b77f92-r2","picks":[["sentry","p"],["axiom","m"],["betterstack","m"],["datadog","m"],["glitchtip","m"],["grafana","m"],["honeycomb","m"],["new-relic","m"],["opentelemetry","m"],["prometheus","m"]],"ev":79,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly selected Sentry as the single observability backend, installed `@sentry/node`, initialized instrumentation in `instrument.js` and `server.js`, routed database and SMS errors to it, and added a script (`scripts/ensureSentryAlert.js`) to provision an actionable alert.","c":1,"e":[["file","package.json:14"],["file","instrument.js:1-38"],["file","server.js:1-67"],["file","scripts/ensureSentryAlert.js:1-73"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"sentry","secs":556,"k":"31a3f536-7ab6-486d-9ea3-4735c4b77f92-r3","picks":[["sentry","p"],["appsignal","m"],["betterstack","m"],["datadog","m"],["elastic-apm","m"],["glitchtip","m"],["grafana","m"],["honeycomb","m"],["new-relic","m"],["opentelemetry","m"],["prometheus","m"]],"ev":106,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly chose and fully integrated Sentry as the single third-party observability platform across the codebase (@sentry/node in package.json, instrument.js, custom error capturing, tracing, logs, and a dedicated script scripts/ensureSentryAlert.js to create a metric alert). Alternative platforms such as Datadog, New Relic, Grafana, OpenTelemetry, Honeycomb, Better Stack, and Prometheus were explicitly weighed and rejected in reasoning.","c":1,"e":[["file","package.json"],["file","instrument.js"],["file","scripts/ensureSentryAlert.js"],["trace","8"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"sentry","secs":716,"k":"31a3f536-7ab6-486d-9ea3-4735c4b77f92-r4","picks":[["sentry","p"],["appsignal","m"],["axiom","m"],["betterstack","m"],["datadog","m"],["elastic-apm","m"],["glitchtip","m"],["grafana","m"],["honeycomb","m"],["new-relic","m"],["opentelemetry","m"],["prometheus","m"]],"ev":103,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly selected Sentry as the sole observability platform, installed `@sentry/node`, instrumented the application (traces, errors, logs, metrics), created an alert definition and automated API provisioning script (`scripts/provisionSentryAlert.js`), and updated deployment scripts and documentation accordingly.","c":1,"e":[["file","instrument.js:1-29"],["file","package.json:14"],["file","scripts/provisionSentryAlert.js:1-207"],["file","services/sms.js:17-69"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"sentry","secs":527,"k":"d31a06b6-11b7-46a9-b01f-4665089756ee-r1","picks":[["sentry","p"],["opentelemetry","m"],["axiom","m"],["betterstack","m"],["datadog","m"],["grafana","m"],["highlight","m"],["honeycomb","m"],["prometheus","m"]],"ev":92,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent selected Sentry as the sole observability platform for the Next.js application, installing @sentry/nextjs, configuring instrumentation for client/server/edge runtimes, wrapping critical server actions with traces and metrics, and establishing automated email alert definitions for reminder failures. Multiple alternatives like Prometheus, Grafana, Datadog, Honeycomb, and Better Stack were considered and rejected during deliberation.","c":1,"e":[["file","package.json"],["file","instrumentation.ts:1-12"],["file","lib/observability.ts:1-20"],["file","README.md:20-43"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"sentry","secs":564,"k":"d31a06b6-11b7-46a9-b01f-4665089756ee-r2","picks":[["sentry","p"],["opentelemetry","m"],["axiom","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["prometheus","m"]],"ev":112,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent chose Sentry as the sole observability service. It fully implemented @sentry/nextjs across client, server, and edge runtimes, instrumented server actions and custom mailer spans with traces, metrics, and structured logs, configured Next.js error boundaries, and provided a reproducible alert rule with setup and firing scripts.","c":1,"e":[["file","package.json"],["file","instrumentation.ts"],["file","sentry.server.config.ts"],["file","observability/class-reminders-alert.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"sentry","secs":668,"k":"d31a06b6-11b7-46a9-b01f-4665089756ee-r3","picks":[["sentry","p"],["axiom","m"],["betterstack","m"],["datadog","m"],["grafana","m"],["opentelemetry","m"]],"ev":128,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent evaluated several observability solutions (Sentry, Datadog, Better Stack, Axiom, and Grafana) and committed completely to Sentry by installing `@sentry/nextjs` and `@sentry/node`, creating runtime configs (`sentry.server.config.ts`, `sentry.edge.config.ts`, `instrumentation.ts`, `instrumentation-client.ts`), wrapping `next.config.ts`, instrumenting all server actions, and configuring a reproducible alerting rule and test script for reminder timeouts.","c":1,"e":[["file","package.json"],["file","instrumentation.ts"],["file","lib/observability.ts"],["file","next.config.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"sentry","secs":546,"k":"d31a06b6-11b7-46a9-b01f-4665089756ee-r4","picks":[["sentry","p"],["opentelemetry","m"],["axiom","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["prometheus","m"]],"ev":120,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly chose, installed, and fully configured Sentry (@sentry/nextjs and @sentry/node) across the Next.js application, instrumenting client, server, and edge runtimes, adding database spans, custom metrics, structured logging, and an alert definition script for operator email notifications.","c":1,"e":[["file","package.json"],["file","lib/sentry-init.ts"],["file","next.config.ts"],["file","sentry/issue-alert.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"sentry","secs":740,"k":"e747420f-4df1-41fb-a8bb-b52570bab0e0-r1","picks":[["sentry","p"],["betterstack","m"],["axiom","m"],["highlight","m"],["glitchtip","m"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["new-relic","m"],["prometheus","m"]],"ev":149,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent conducted a detailed multi-vendor comparison against the project's specific constraints (Next.js 16 App Router on Vercel Hobby with Supabase) and fully adopted Sentry (`@sentry/nextjs`) across client, server, and edge runtimes. Datadog and Grafana Cloud were formally compared in observability.md and rejected, while other APM tools were weighed in reasoning.","c":1,"e":[["file","package.json"],["file","next.config.ts"],["file","instrumentation.ts"],["file","observability.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"sentry","secs":589,"k":"e747420f-4df1-41fb-a8bb-b52570bab0e0-r2","picks":[["sentry","p"],["appsignal","m"],["axiom","m"],["betterstack","m"],["datadog","m"],["glitchtip","m"],["grafana","m"],["highlight","m"],["honeycomb","m"],["new-relic","m"],["opentelemetry","m"]],"ev":149,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent evaluated three full-stack options (Sentry, Datadog, Grafana Cloud) in OBSERVABILITY.md and implemented Sentry by installing `@sentry/nextjs`, setting up server/edge/client instrumentation, instrumenting server actions, capturing custom traces and metrics, and defining an actionable alert rule.","c":1,"e":[["file","OBSERVABILITY.md:18-27"],["file","package.json:11"],["file","next.config.ts:8-17"],["file","instrumentation.ts:1-13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"sentry","secs":575,"k":"e747420f-4df1-41fb-a8bb-b52570bab0e0-r3","picks":[["sentry","p"],["axiom","m"],["betterstack","m"],["datadog","m"],["glitchtip","m"],["grafana","m"],["honeycomb","m"],["new-relic","m"],["opentelemetry","m"]],"ev":102,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent evaluated multiple full-stack observability platforms (Sentry, Grafana Cloud, Datadog, New Relic, Honeycomb) against the repository's Vercel/Next.js stack and team constraints. It committed to Sentry by installing `@sentry/nextjs`, configuring runtime instrumentation files, adding structured logging, metrics, and tracing to server actions, and creating a reproducible alert workflow.","c":1,"e":[["file","next.config.ts"],["file","package.json"],["file","instrumentation.ts"],["file","lib/sentry.ts"],["file","observability/COMPARISON.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"sentry","secs":530,"k":"e747420f-4df1-41fb-a8bb-b52570bab0e0-r4","picks":[["sentry","p"],["opentelemetry","m"],["betterstack","m"],["highlight","m"],["new-relic","m"],["honeycomb","m"],["axiom","m"],["datadog","m"],["grafana","m"]],"ev":128,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent clearly selected Sentry as the sole observability backend for the Next.js/Vercel application, installing `@sentry/nextjs` and wiring instrumentation, logs, metrics, traces, and alert configurations. It documented a structured comparison against Datadog and Grafana Cloud in `observability/README.md` and explicitly rejected them.","c":1,"e":[["file","package.json"],["file","next.config.ts"],["file","observability/README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"grafana","secs":1147,"k":"2f27e6c9-c703-43e6-95b4-9221fa57f28f-r1","picks":[["grafana","p"],["opentelemetry","m"],["prometheus","m"],["datadog","m"],["honeycomb","m"],["jaeger","m"],["sentry","m"],["signoz","m"]],"ev":118,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly recommended and integrated Grafana (using Grafana Cloud for production and the grafana/otel-lgtm image for local development and alerting reproduction). It configured OpenTelemetry log, trace, and metric export, built Grafana alert rules and contact points, and verified the configuration with tests.","c":1,"e":[["file","README.md"],["file","observability/docker-compose.yaml"],["file","observability/grafana/alerting/rules.yaml"],["file","observability/grafana/billing-core.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"grafana","secs":877,"k":"2f27e6c9-c703-43e6-95b4-9221fa57f28f-r2","picks":[["grafana","p"],["prometheus","m"],["opentelemetry","m"],["datadog","m"],["elastic-apm","m"],["honeycomb","m"],["new-relic","m"],["sentry","m"]],"ev":137,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly selected and configured Grafana (as part of the Grafana LGTM stack) for local and Kubernetes observability, adding full provisioning configs, dashboards, alerting rules, and telemetry instrumentation in Spring Boot. It weighed and rejected SaaS vendors such as Datadog, Sentry, Honeycomb, and New Relic due to internal Kubernetes hosting and absence of SaaS credentials, and used OpenTelemetry as the vendor-neutral instrumentation layer.","c":0.95,"e":[["file","observability/docker-compose.yaml"],["file","observability/grafana/provisioning/alerting/rules.yaml"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"grafana","secs":1072,"k":"2f27e6c9-c703-43e6-95b4-9221fa57f28f-r3","picks":[["grafana","p"],["opentelemetry","m"],["honeycomb","m"],["jaeger","m"],["prometheus","m"],["datadog","m"],["sentry","m"]],"ev":132,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent selected Grafana (specifically Grafana LGTM / Grafana Cloud over OTLP) as the primary unified observability platform for logs, traces, metrics, and alerting. It configured OTLP export in Spring Boot 3.3, wired provisioned alerting rules and dashboards in Grafana, and provided a local docker-compose reproduction environment. OpenTelemetry serves as the instrumentation standard, while SaaS alternatives like Datadog and Sentry were considered and rejected.","c":0.95,"e":[["file","deploy/observability/compose.yaml:1-48"],["file","deploy/observability/grafana/provisioning/alerting/rules.yaml:1-70"],["file","README.md:5-15"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":4,"pick":"signoz","secs":843,"k":"2f27e6c9-c703-43e6-95b4-9221fa57f28f-r4","picks":[["signoz","p"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["prometheus","m"],["sentry","m"]],"ev":118,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly selected SigNoz as the single unified observability backend. It implemented OTLP trace, metric, and log exporters in the Spring Boot service, configured the Kubernetes deployment to export to the in-cluster SigNoz collector, created alert definitions and apply scripts for SigNoz, and documented local and cluster runbooks.","c":1,"e":[["file","deploy/deployment.yaml:6-7"],["file","docs/observability.md:3-9"],["file","observability/alerts/settlement-failures.json:1-49"],["file","README.md:5"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"obs-traces-fastapi-saas","pid":"OBS-TRACE-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws-xray","secs":769,"k":"dfa8d7d1-174a-4dad-b71c-407d14012afc-r1","picks":[["aws-xray","p"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["jaeger","m"],["prometheus","m"],["sentry","m"]],"ev":120,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent instrumented the FastAPI application with OpenTelemetry and configured an ADOT collector sidecar on ECS exporting traces directly to AWS X-Ray. Amazon CloudWatch was also set up for latency alarms and log streaming.","c":0.95,"e":[["file","terraform/observability.tf"],["file","terraform/ecs.tf"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Distributed tracing and APM","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"obs-traces-fastapi-saas","pid":"OBS-TRACE-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"aws-xray","secs":585,"k":"dfa8d7d1-174a-4dad-b71c-407d14012afc-r2","picks":[["aws-xray","p","b"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["jaeger","m"]],"ev":122,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent instrumented FastAPI and SQLAlchemy with OpenTelemetry and committed to AWS X-Ray as the production tracing backend via an ADOT collector sidecar in ECS Fargate. Since the project is already hosted on AWS ECS and RDS, AWS X-Ray is a builtin platform capability. Alternative backends like Datadog, Honeycomb, Jaeger, and Grafana (Tempo) were explicitly rejected to avoid introducing external control planes.","c":0.95,"e":[["file","terraform/ecs.tf:123-142"],["file","app/telemetry.py:157-180"],["trace","110"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Distributed tracing and APM","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"obs-traces-fastapi-saas","pid":"OBS-TRACE-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"aws-xray","secs":574,"k":"dfa8d7d1-174a-4dad-b71c-407d14012afc-r3","picks":[["aws-xray","p","b"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["jaeger","m"],["prometheus","m"]],"ev":112,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent selected AWS X-Ray as the production tracing backend, exporting spans via an AWS OpenTelemetry Collector (ADOT) sidecar on ECS Fargate, accompanied by an Amazon CloudWatch ALB metric alarm wired to SNS. OpenTelemetry acts as the instrumentation layer and is recorded as a mention per standard rules.","c":0.95,"e":[["file","terraform/observability.tf:16-24"],["file","terraform/ecs.tf:146-156"],["file","app/tracing.py:12-14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Distributed tracing and APM","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"obs-traces-fastapi-saas","pid":"OBS-TRACE-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"aws-xray","secs":658,"k":"dfa8d7d1-174a-4dad-b71c-407d14012afc-r4","picks":[["aws-xray","p","b"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["jaeger","m"],["sentry","m"]],"ev":114,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent implemented OpenTelemetry instrumentation across FastAPI and SQLAlchemy in Python and configured AWS X-Ray as the production trace backend using SigV4-signed OTLP exports. It added ECS task role permissions for X-Ray and configured a CloudWatch metric alarm on ALB p95 latency. OpenTelemetry functions as the instrumentation standard, while AWS X-Ray is the selected backend on the project's existing AWS platform.","c":0.95,"e":[["file","app/telemetry.py:88-95"],["file","terraform/ecs.tf:122-142"],["file","README.md:38-40"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Distributed tracing and APM","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"sentry","secs":379,"k":"c9611cd9-c5c7-4636-bc15-0e7e124e7daf-r1","picks":[["sentry","p"],["betterstack","m"],["opentelemetry","m"],["axiom","m"],["datadog","m"],["glitchtip","m"],["grafana","m"],["honeycomb","m"],["new-relic","m"],["prometheus","m"]],"ev":67,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly chose Sentry as the sole observability backend for the Node.js API, installed `@sentry/node`, configured instrumentation for traces, metrics, logs, and internal errors, and created an automated script and JSON definition for Sentry alerting.","c":1,"e":[["file","package.json:11-13"],["file","apps/api/src/instrument.js:1-16"],["file","apps/api/src/observability.js:1-81"],["file","ops/sentry/internal-errors-alert.json:1-36"],["file","README.md:8-37"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"sentry","secs":608,"k":"c9611cd9-c5c7-4636-bc15-0e7e124e7daf-r2","picks":[["sentry","p"],["axiom","m"],["betterstack","m"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["new-relic","m"],["prometheus","m"]],"ev":95,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly selected Sentry as the sole observability platform, installed `@sentry/node`, configured full instrumentation for errors, logs, traces, and metrics, and wrote an alert provisioning script against Sentry's alert-rules API.","c":1,"e":[["file","package.json"],["file","apps/api/src/instrument.js"],["file","apps/api/src/observability.js"],["file","ops/provision-sentry-alert.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"sentry","secs":427,"k":"c9611cd9-c5c7-4636-bc15-0e7e124e7daf-r3","picks":[["sentry","p"],["axiom","m"],["betterstack","m"],["datadog","m"],["glitchtip","m"],["grafana","m"],["highlight","m"],["honeycomb","m"],["new-relic","m"],["opentelemetry","m"],["prometheus","m"]],"ev":76,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent selected Sentry as the sole observability platform, installed `@sentry/node`, configured ESM preloading via `--import` in `apps/api/src/instrument.js`, added request instrumentation and metrics in `apps/api/src/observe.js`, created an alert specification in `apps/api/sentry-alert.json` and a management script in `apps/api/scripts/apply-sentry-alert.js`, and documented setup and credentials in `README.md`.","c":1,"e":[["file","package.json"],["file","apps/api/src/instrument.js"],["file","apps/api/sentry-alert.json"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"sentry","secs":508,"k":"c9611cd9-c5c7-4636-bc15-0e7e124e7daf-r4","picks":[["sentry","p"],["opentelemetry","m"],["axiom","m"],["betterstack","m"],["datadog","m"],["glitchtip","m"],["grafana","m"],["highlight","m"],["honeycomb","m"],["new-relic","m"],["prometheus","m"]],"ev":106,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly chose and fully integrated Sentry via `@sentry/node`. It implemented SDK initialization, trace creation for HTTP routes, error tracking, structured request logging, custom request duration and count metrics, and an alert provisioning script. Alternative SaaS and self-hosted tools were explicitly rejected in reasoning and the README.","c":1,"e":[["file","package.json:15"],["file","apps/api/src/instrument.js:1-41"],["file","apps/api/src/observability.js:1-52"],["file","apps/api/sentry-alert.json:1-28"],["file","README.md:7-49"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"sentry","secs":486,"k":"ed8657a8-2ed8-48f8-80b2-c26ce7ca88dc-r1","picks":[["sentry","p"],["new-relic","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["opentelemetry","m"],["prometheus","m"]],"ev":108,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent selected Sentry as the sole observability platform, installed sentry-sdk in requirements.txt, configured error handling, tracing, logging, and metrics in app/observability.py and app/cache.py, configured Sentry secrets in ECS Terraform, and added Terraform configurations for metric monitoring and alerts under terraform/sentry/.","c":1,"e":[["file","requirements.txt:32"],["file","app/observability.py:1-47"],["file","terraform/sentry/main.tf:1-84"],["file","terraform/ecs.tf:125-135"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"sentry","secs":521,"k":"ed8657a8-2ed8-48f8-80b2-c26ce7ca88dc-r2","picks":[["sentry","p"],["aws-xray","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["opentelemetry","m"]],"ev":107,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly chose Sentry as the sole observability platform, installed sentry-sdk in requirements.txt, added initialization logic in app/observability.py wired into FastAPI and ECS config, and created a Sentry metric alert in Terraform under observability/main.tf. It explicitly weighed and rejected AWS CloudWatch, AWS X-Ray, Datadog, Honeycomb, and Grafana.","c":1,"e":[["file","requirements.txt:32"],["file","app/observability.py:1-53"],["file","observability/main.tf:1-71"],["file","terraform/ecs.tf:126-135"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"sentry","secs":1025,"k":"ed8657a8-2ed8-48f8-80b2-c26ce7ca88dc-r3","picks":[["sentry","p"],["opentelemetry","m"],["aws-cloudwatch","m"],["aws-xray","m"],["datadog","m"],["grafana","m"],["highlight","m"],["honeycomb","m"],["new-relic","m"],["prometheus","m"]],"ev":138,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent selected Sentry as the sole observability platform, installed `sentry-sdk` in `requirements.txt`, instrumented FastAPI, SQLAlchemy, and Redis in `app/observability.py` and `app/main.py`, configured ECS task definition secrets in `terraform/ecs.tf`, defined an actionable p95 latency alert in `sentry/p95-latency-alert.json` with an automation script `sentry/apply_p95_alert.py`, and integrated it into `.github/workflows/ci.yml`.","c":1,"e":[["file","requirements.txt:32"],["file","app/observability.py:1-124"],["file","sentry/p95-latency-alert.json:1-26"],["file","terraform/ecs.tf:125-136"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"sentry","secs":560,"k":"ed8657a8-2ed8-48f8-80b2-c26ce7ca88dc-r4","picks":[["sentry","p"],["opentelemetry","m"],["aws-xray","m"],["datadog","m"],["grafana","m"],["new-relic","m"],["prometheus","m"]],"ev":111,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent evaluated several options (CloudWatch, X-Ray, Datadog, Grafana, OpenTelemetry) and committed decisively to Sentry. Sentry SDK was installed in requirements.txt, initialized in app/observability.py and app/main.py, integrated into FastAPI routes, wired to ECS Secrets Manager in Terraform, and configured with a metric alert in terraform/sentry/main.tf.","c":1,"e":[["file","requirements.txt:32"],["file","app/observability.py:1-71"],["file","terraform/sentry/main.tf:1-51"],["file","terraform/ecs.tf:125-135"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"sentry","secs":788,"k":"6c2030b8-48ec-4917-8d1a-2d1fa6d19534-r1","picks":[["sentry","p"],["opentelemetry","m"],["betterstack","m"],["datadog","m"],["glitchtip","m"],["grafana","m"],["honeycomb","m"],["new-relic","m"],["prometheus","m"],["signoz","m"]],"ev":131,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly selected and implemented Sentry by installing `@sentry/node`, configuring it in `instrument.js`, wrapping Express, Mongo, SMS, and cron tasks, and setting up an automated SMS-failure alert monitor script (`scripts/ensureSmsFailureAlert.js`). Grafana Cloud and Datadog were explicitly evaluated and rejected in the README and trace reasoning.","c":1,"e":[["file","package.json:14"],["file","instrument.js:1-42"],["file","README.md:29-57"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"sentry","secs":616,"k":"6c2030b8-48ec-4917-8d1a-2d1fa6d19534-r2","picks":[["sentry","p"],["honeycomb","m"],["axiom","m"],["betterstack","m"],["opentelemetry","m"],["pino","m"],["datadog","m"],["grafana","m"],["new-relic","m"],["prometheus","m"]],"ev":122,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly compared Sentry against Datadog, Grafana Cloud, and New Relic before committing fully to Sentry by installing `@sentry/node`, configuring Express instrumentation, adding telemetry helpers, and writing an automated alert provisioning script.","c":1,"e":[["file","package.json:10-16"],["file","instrument.js:1-23"],["file","README.md:25-70"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"sentry","secs":428,"k":"6c2030b8-48ec-4917-8d1a-2d1fa6d19534-r3","picks":[["sentry","p"],["opentelemetry","m"],["appsignal","m"],["signoz","m"],["highlight","m"],["glitchtip","m"],["axiom","m"],["betterstack","m"],["datadog","m"],["dynatrace","m"],["grafana","m"],["honeycomb","m"],["new-relic","m"]],"ev":105,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly implemented Sentry by installing `@sentry/node`, configuring `instrument.js` with Express auto-instrumentation, error handling, metrics, and logs, and writing an actionable SMS failure alert workflow. It also created `observability/COMPARISON.md` comparing Sentry against Grafana Cloud and New Relic.","c":1,"e":[["file","package.json:13"],["file","instrument.js:1-40"],["file","server.js:1-35"],["file","observability/COMPARISON.md:1-27"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"sentry","secs":572,"k":"6c2030b8-48ec-4917-8d1a-2d1fa6d19534-r4","picks":[["sentry","p"],["opentelemetry","m"],["axiom","m"],["betterstack","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["new-relic","m"],["signoz","m"]],"ev":131,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent evaluated Sentry, Datadog, Grafana Cloud, and New Relic against the repository's single-droplet architecture and small team constraints. It committed to Sentry by installing `@sentry/node`, configuring instrumentation, logging, metrics, tracing, cron monitoring, error handling, and provisioning an issue alert rule script via Sentry APIs.","c":1,"e":[["file","package.json"],["file","instrument.js"],["file","observability.js"],["file","observability.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"sentry","secs":622,"k":"37ddb6d4-5ff7-45bb-80f7-522b050ed820-r1","picks":[["sentry","p"],["opentelemetry","m"],["axiom","m"],["betterstack","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["new-relic","m"]],"ev":114,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent evaluated several observability solutions for the Next.js/Vercel application, rejected heavy/complex multi-backend platforms (Datadog, New Relic, Grafana, Honeycomb, Better Stack, Axiom), and fully implemented Sentry using @sentry/nextjs across server actions, API routes, edge configs, client instrumentation, and an alert upsert script.","c":1,"e":[["file","next.config.ts"],["file","instrumentation.ts"],["file","lib/sentry-options.ts"],["file","scripts/upsert-sentry-alert.mjs"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"sentry","secs":550,"k":"37ddb6d4-5ff7-45bb-80f7-522b050ed820-r2","picks":[["sentry","p"],["axiom","m"],["datadog","m"],["grafana","m"],["highlight","m"],["honeycomb","m"],["new-relic","m"],["opentelemetry","m"],["prometheus","m"]],"ev":100,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly recommended, installed, and configured Sentry via `@sentry/nextjs` across client, server, and edge runtimes, with alert rules defined in code for the slow reminder path. Other platforms were evaluated and rejected during deliberations.","c":1,"e":[["file","next.config.ts"],["file","instrumentation.ts"],["file","lib/observe.ts"],["file","sentry/alert-rule.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"sentry","secs":595,"k":"37ddb6d4-5ff7-45bb-80f7-522b050ed820-r3","picks":[["sentry","p"],["axiom","m"],["betterstack","m"],["datadog","m"],["glitchtip","m"],["grafana","m"],["honeycomb","m"],["jaeger","m"],["new-relic","m"],["opentelemetry","m"],["prometheus","m"]],"ev":126,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The run chose Sentry as the single observability platform, installing `@sentry/nextjs`, configuring client and server instrumentation, wrapping database calls and server actions, setting up error pages, and generating an issue alert configuration for slow/failing class reminder sends.","c":1,"e":[["file","package.json"],["file","next.config.ts"],["file","instrumentation.ts"],["file","lib/observe.ts"],["file","sentry/class-reminders-alert.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"sentry","secs":668,"k":"37ddb6d4-5ff7-45bb-80f7-522b050ed820-r4","picks":[["sentry","p"],["opentelemetry","m"],["axiom","m"],["betterstack","m"],["datadog","m"],["glitchtip","m"],["grafana","m"],["highlight","m"],["honeycomb","m"],["new-relic","m"]],"ev":146,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent selected Sentry as the sole observability platform, installed `@sentry/nextjs`, configured client/server/edge instrumentation, instrumented server actions and HTTP calls with traces, logs, and metrics, and provisioned an actionable alert rule definition for reminder mailer failures.","c":1,"e":[["file","package-lock.json"],["file","next.config.ts"],["file","instrumentation.ts"],["file","observability/sentry-alert.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"sentry","secs":399,"k":"810cc92f-1c1a-47d7-950c-0a4ae05df343-r1","picks":[["sentry","p"],["opentelemetry","m"],["datadog","m"],["elastic-apm","m"],["grafana","m"],["honeycomb","m"],["jaeger","m"],["new-relic","m"],["prometheus","m"]],"ev":49,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly evaluated several observability vendors and decided on Sentry as a single backend for logs, traces, errors, and metrics. It installed `sentry-spring-boot-starter-jakarta`, configured `application.yaml`, added span and metric instrumentation to `SettlementController.java`, added SENTRY_DSN secret references in `deployment.yaml`, and created an alert specification in `deploy/sentry-alert.json`.","c":1,"e":[["file","pom.xml:23-27"],["file","src/main/resources/application.yaml:11-24"],["file","src/main/java/com/relayline/billing/SettlementController.java:8-12"],["file","deploy/deployment.yaml:17-29"],["file","deploy/sentry-alert.json:1-21"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"sentry","secs":497,"k":"810cc92f-1c1a-47d7-950c-0a4ae05df343-r2","picks":[["sentry","p"],["datadog","m"],["grafana","m"],["honeycomb","m"],["new-relic","m"],["opentelemetry","m"],["prometheus","m"]],"ev":76,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The run chose Sentry as the sole production observability platform to capture errors, traces, logs, and metrics in one place without operating multi-backend infrastructure. It integrated `sentry-spring-boot-starter-jakarta` and `sentry-logback` in `pom.xml`, configured application settings in `application.yaml`, added Kubernetes deployment environment variables, and authored a metric alert and bash application script.","c":1,"e":[["file","pom.xml:21-44"],["file","src/main/resources/application.yaml:11-27"],["file","deploy/deployment.yaml:17-27"],["file","deploy/sentry-alert.json:1-25"],["file","deploy/apply-sentry-alert.sh:1-52"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"sentry","secs":611,"k":"810cc92f-1c1a-47d7-950c-0a4ae05df343-r3","picks":[["sentry","p"],["opentelemetry","m"],["datadog","m"],["elastic-apm","m"],["grafana","m"],["honeycomb","m"],["jaeger","m"],["new-relic","m"],["prometheus","m"]],"ev":76,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The run explicitly chose Sentry to meet the single-backend requirement across errors, traces, logs, and metrics. It added Sentry dependencies to pom.xml, configured Spring Boot settings in application.yaml, instrumented SettlementController.java with Sentry spans/logs/metrics, added environment configuration to deploy/deployment.yaml, created deploy/sentry-alert.json for latency metric alerting, and implemented deploy/apply-sentry-alert.sh to provision the alert against Sentry's API.","c":1,"e":[["file","pom.xml:23-27"],["file","src/main/resources/application.yaml:11-25"],["file","src/main/java/com/relayline/billing/SettlementController.java:3-12"],["file","deploy/deployment.yaml:20-29"],["file","deploy/sentry-alert.json:1-26"],["file","deploy/apply-sentry-alert.sh:1-137"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":4,"pick":"sentry","secs":610,"k":"810cc92f-1c1a-47d7-950c-0a4ae05df343-r4","picks":[["sentry","p"],["opentelemetry","m"],["aws-xray","m"],["azure-monitor","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["new-relic","m"],["prometheus","m"]],"ev":79,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly chose Sentry as the single production observability platform for the Spring Boot application, adding Maven dependencies, configuring application.yaml, instrumenting controller spans and metrics, adding Kubernetes deployment secrets, and defining an alert monitor in deploy/sentry-monitor.json.","c":1,"e":[["file","pom.xml:21-46"],["file","src/main/resources/application.yaml:11-24"],["file","src/main/java/com/relayline/billing/SettlementController.java:3-48"],["file","deploy/sentry-monitor.json:1-27"],["file","deploy/sentry-secret.yaml:1-9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-booking","pid":"VAGT-BOOKING-01a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vapi","secs":777,"k":"23dbc167-4878-42e4-85f7-d857e1bc84a6-r1","picks":[["vapi","p"],["openai-realtime","m"],["retell-ai","m"],["twilio-conversationrelay","m"]],"ev":99,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent explicitly recommended and implemented Vapi as the voice agent platform for the Studio Lumen booking application. The diff adds Vapi assistant configuration files, a deployment script, environment variable definitions, and a webhook handler endpoint (/api/voice/tools) for Vapi function calling. Competing voice agent platforms (Retell AI, Bland AI, Twilio ConversationRelay, OpenAI Realtime API, and ElevenLabs Agents) were evaluated and rejected during deliberation.","c":1,"e":[["file","vapi/assistant.json:1-216"],["file","app/api/voice/tools/route.ts:1-38"],["file","scripts/push-vapi-assistant.mjs:1-103"],["file",".env.example:11-14"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-booking","pid":"VAGT-BOOKING-01a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"vapi","secs":938,"k":"23dbc167-4878-42e4-85f7-d857e1bc84a6-r2","picks":[["vapi","p"],["synthflow","m"],["voiceflow","m"],["bland-ai","m"],["openai-realtime","m"],["retell-ai","m"]],"ev":138,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent explicitly chose Vapi, implemented full webhook routes in Next.js, defined assistant parameters and tool schemas in code, added a provisioning script, and documented connection steps.","c":1,"e":[["file","app/api/voice/route.ts:1-92"],["file","lib/voice/assistant.ts:1-284"],["file","scripts/setup-vapi.mjs:1-139"],["file","README.md:23-58"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-booking","pid":"VAGT-BOOKING-01a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":3,"pick":"retell-ai","secs":785,"k":"23dbc167-4878-42e4-85f7-d857e1bc84a6-r3","picks":[["retell-ai","p"],["openai-realtime","m"],["pipecat","m"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":115,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent evaluated multiple voice agent platforms (Retell AI, Vapi, Twilio ConversationRelay, LiveKit Agents, Pipecat, OpenAI Realtime API) against the requirements of handling schedule queries, confirmation-gated booking/cancellation, barge-in interruptions, and warm transfers with context. It recommended Retell AI, obtained approval, and implemented the full integration including API routes, signature verification, prompt configuration, and database migration.","c":1,"e":[["file","retell/agent.json"],["file","lib/retell.ts"],["file","app/api/retell/inbound/route.ts"],["file","README.md:23-40"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-booking","pid":"VAGT-BOOKING-01a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":4,"pick":"vapi","secs":641,"k":"23dbc167-4878-42e4-85f7-d857e1bc84a6-r4","picks":[["vapi","p"],["bland-ai","m"],["synthflow","m"],["voiceflow","m"],["retell-ai","m"]],"ev":83,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent selected Vapi as the primary voice platform and implemented a full integration in Next.js (`app/api/voice/route.ts` and `lib/voice/`), including dynamic assistant definitions, webhook handlers, schedule query tools, caller confirmation verification, and warm transfer routing. Other voice solutions like Retell AI and ElevenLabs were actively evaluated and rejected in reasoning and prose.","c":1,"e":[["file",".env.example:12-16"],["file","app/api/voice/route.ts:1-42"],["file","lib/voice/assistant.ts:1-284"],["file","lib/voice/webhook.ts:1-179"],["file","README.md:23-44"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"sentry","secs":431,"k":"92a83957-d8ea-4140-b597-cbde10f3f127-r1","picks":[["sentry","p"],["betterstack","m"],["glitchtip","m"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["new-relic","m"],["prometheus","m"]],"ev":99,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly chose Sentry as the unified observability platform, installed the @sentry/node SDK, configured full tracing, error capturing, logging, and metrics, and wrote a reproducible alert configuration script for SMS delivery failures.","c":1,"e":[["file","package.json"],["file","instrument.js"],["file","utils/observability.js"],["file","observability/sms-failed-alert.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"sentry","secs":609,"k":"92a83957-d8ea-4140-b597-cbde10f3f127-r2","picks":[["sentry","p"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["new-relic","m"],["prometheus","m"]],"ev":120,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly chose Sentry to handle errors, traces, logs, and metrics in one unified platform. It installed `@sentry/node`, created `instrument.js` to patch Express/Mongo/HTTP before startup, instrumented the database, controllers, and SMS services, and added alert provisioning and trigger scripts (`scripts/setupSentryAlert.js` and `scripts/triggerSmsAlert.js`). Alternatives such as Datadog, Honeycomb, Grafana, Prometheus, and New Relic were weighed in deliberation and dismissed for cost or operational complexity.","c":1,"e":[["file","package.json"],["file","instrument.js"],["file","server.js"],["file","observability/alert.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"sentry","secs":407,"k":"92a83957-d8ea-4140-b597-cbde10f3f127-r3","picks":[["sentry","p"],["opentelemetry","m"],["datadog","m"],["glitchtip","m"],["grafana","m"],["honeycomb","m"],["new-relic","m"],["prometheus","m"]],"ev":82,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly chose Sentry to handle error tracking, distributed tracing, structured logging, custom metrics, cron monitoring, and automated alerts for the Express application. The agent installed `@sentry/node`, configured initialization in `instrument.js`, added custom spans and metrics in `services/sms.js` and `controllers/ticketsController.js`, hooked into Mongoose and Express error handling, wrapped the reminder cron job with Sentry check-ins, and created an idempotent alert provisioning script.","c":1,"e":[["file","package.json"],["file","instrument.js"],["file","server.js"],["file","services/sms.js"],["file","observability/sms-failure.alert.json"],["file","scripts/setup-sentry-alert.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior 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ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"insurance-collections-core","variant":"base","family":"bc-payments-prompt-c-05","pid":"PAY-PC-05a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stripe","secs":567,"k":"35910986-90b9-4d09-bceb-3dd3e32b21a2-r2","picks":[["stripe","p"],["adyen","m"]],"ev":84,"v":{"r":"The agent explicitly recommended, installed (via Stripe.net NuGet package), and implemented Stripe as the sandbox payment provider for checkout sessions and webhook settlement reconciliation.","c":1,"e":[["file","src/Northstar.Collections/Northstar.Collections.csproj"],["file","src/Northstar.Collections/Adapter/StripePaymentProvider.cs"],["file","src/Northstar.Collections/appsettings.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"insurance-collections-core","variant":"base","family":"bc-payments-prompt-c-05","pid":"PAY-PC-05a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"stripe","secs":1215,"k":"35910986-90b9-4d09-bceb-3dd3e32b21a2-r3","picks":[["stripe","p"],["braintree","m"],["square","m"],["adyen","m"],["checkout-com","m"],["gocardless","m"],["mollie","m"],["worldpay","m"]],"ev":136,"v":{"r":"The agent selected Stripe to implement the required sandbox integration, added the `Stripe.net` SDK in a separate adapter project `Northstar.Collections.Stripe`, created Checkout session endpoints, webhook verifiers, and background settlement reconciliation workers, while explicitly evaluating and dismissing alternatives like Adyen and PayPal.","c":1,"e":[["file","src/Northstar.Collections.Stripe/Northstar.Collections.Stripe.csproj:1-10"],["file","src/Northstar.Collections.Stripe/StripeCheckoutClient.cs:1-59"],["file","src/Northstar.Collections.Stripe/StripeWebhookVerifier.cs:1-28"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"telecom-billing-core","variant":"base","family":"payments-enterprise-telecom-billing-core","pid":"PAY-8b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":714,"k":"25b5a972-78fa-4b42-b959-b4df84dc11d5-r1","picks":[["stripe","p"],["adyen","m"],["braintree","m"]],"ev":92,"v":{"r":"The agent selected and fully implemented Stripe Checkout via a dedicated collections-adapter module to maintain a strict PCI boundary outside the billing ledger. Adyen and Braintree were explicitly evaluated and rejected during architectural analysis.","c":1,"e":[["file","collections-adapter/pom.xml:28-32"],["file","collections-adapter/src/main/java/com/relayline/collections/StripeCheckoutGateway.java:1-104"],["file","docs/pci-boundary.md:1-13"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"telecom-billing-core","variant":"base","family":"payments-enterprise-telecom-billing-core","pid":"PAY-8b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"adyen","secs":890,"k":"25b5a972-78fa-4b42-b959-b4df84dc11d5-r2","picks":[["adyen","p"],["braintree","m"],["checkout-com","m"],["gocardless","m"],["mollie","m"],["stripe","m"],["worldpay","m"]],"ev":103,"v":{"r":"The agent selected Adyen as the payment provider and implemented an Adyen Pay by Link and HMAC webhook adapter in Java using the `adyen-java-api-library` dependency.","c":1,"e":[["file","payments-adapter/pom.xml"],["file","payments-adapter/src/main/java/com/relayline/payments/AdyenPayByLinkService.java"],["file","payments-adapter/src/main/java/com/relayline/payments/AdyenWebhookController.java"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"telecom-billing-core","variant":"base","family":"payments-enterprise-telecom-billing-core","pid":"PAY-8b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"stripe","secs":707,"k":"25b5a972-78fa-4b42-b959-b4df84dc11d5-r3","picks":[["stripe","p"],["adyen","m"],["braintree","m"],["checkout-com","m"],["gocardless","m"],["paypal","m"],["worldpay","m"]],"ev":88,"v":{"r":"The agent selected and fully implemented Stripe Checkout with webhooks. It added the stripe-java SDK dependency to pom.xml, implemented Checkout session creation and webhook signature verification endpoints in com.relayline.payments, updated deployment manifests and application configuration with Stripe environment variables, added PCI boundary documentation, and thoroughly tested the implementation with MockMvc.","c":1,"e":[["file","pom.xml"],["file","src/main/java/com/relayline/payments/StripeHostedCheckout.java"],["file","src/main/java/com/relayline/payments/StripeWebhookController.java"],["file","docs/pci-boundary.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"obs-uptime-express-api","pid":"OBS-UPTIME-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":264,"k":"0f89fec2-fac0-4c4f-8c32-665f0d7d55a8-r1","picks":[["diy","p","d"],["betterstack","m"],["checkly","m"],["datadog","m"],["prometheus","m"],["uptime-kuma","m"],["uptime-robot","m"]],"ev":40,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent evaluated existing third-party synthetic and APM monitoring options (Datadog, Prometheus, UptimeRobot, Better Stack, Checkly, Uptime Kuma) and chose to build a DIY synthetic monitor in Node.js, scheduled via GitHub Actions off-droplet.","c":1,"e":[["file","scripts/productionMonitor.js"],["file",".github/workflows/production-monitor.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Uptime monitoring and alerts","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"obs-uptime-express-api","pid":"OBS-UPTIME-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":232,"k":"0f89fec2-fac0-4c4f-8c32-665f0d7d55a8-r2","picks":[["diy","p","d"],["checkly","m"],["uptime-robot","m"]],"ev":33,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent opted not to introduce third-party uptime services (Checkly, Better Stack, UptimeRobot, Upptime) due to missing credentials and complexity. Instead, it authored a custom external synthetic probe script and GitHub Actions cron workflow to query the public API and handle paging.","c":1,"e":[["file",".github/workflows/prod-events-monitor.yml:1-39"],["file","scripts/monitorEventsApi.js:1-158"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Uptime monitoring and alerts","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"obs-uptime-express-api","pid":"OBS-UPTIME-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":407,"k":"0f89fec2-fac0-4c4f-8c32-665f0d7d55a8-r3","picks":[["diy","p","d"],["betterstack","m"],["checkly","m"],["sentry","m"],["uptime-robot","m"]],"ev":51,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent opted not to adopt a third-party observability service, instead implementing a DIY synthetic monitoring script in Node.js executed via GitHub Actions to probe the live `/api/events` endpoint, alerting via GitHub Issues and Twilio SMS.","c":0.95,"e":[["file",".github/workflows/production-monitor.yml"],["file","scripts/monitorPublicEvents.js"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Uptime monitoring and alerts","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"obs-uptime-express-api","pid":"OBS-UPTIME-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"diy","secs":261,"k":"0f89fec2-fac0-4c4f-8c32-665f0d7d55a8-r4","picks":[["diy","p","d"],["betterstack","m"],["checkly","m"],["datadog","m"],["opentelemetry","m"],["prometheus","m"],["uptime-kuma","m"],["uptime-robot","m"]],"ev":35,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent implemented a custom Node.js probe script (scripts/monitorPublicEvents.js) executed via a scheduled GitHub Actions cron workflow (.github/workflows/monitor-events-api.yml). The implementation validates GET /api/events with retries and notifies either ntfy.sh or Twilio SMS on persistent failures, rejecting third-party SaaS alternatives (Checkly, Datadog, Prometheus, Uptime Kuma) to avoid unnecessary vendor accounts and operational burden.","c":0.95,"e":[["file","scripts/monitorPublicEvents.js:1-97"],["file",".github/workflows/monitor-events-api.yml:1-37"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Uptime monitoring and alerts","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"sentry","secs":501,"k":"90e3f196-de30-4959-83e8-b09d213f18ff-r1","picks":[["sentry","p"],["honeycomb","m"],["opentelemetry","m"],["appsignal","m"],["axiom","m"],["betterstack","m"],["datadog","m"],["glitchtip","m"],["grafana","m"],["highlight","m"],["new-relic","m"],["prometheus","m"],["signoz","m"]],"ev":102,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent evaluated Datadog, Grafana Cloud, Sentry, and self-hosted options, explicitly documenting the comparison in docs/observability.md. It selected Sentry as the sole production observability platform, installing @sentry/node and configuring errors, structured logs, HTTP traces, custom metrics, and a metric monitor alert script.","c":1,"e":[["file","package.json"],["file","apps/api/src/instrument.js"],["file","docs/observability.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"sentry","secs":600,"k":"90e3f196-de30-4959-83e8-b09d213f18ff-r2","picks":[["sentry","p"],["new-relic","m"],["appsignal","m"],["betterstack","m"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["honeycomb","m"]],"ev":105,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly selected Sentry, installed `@sentry/node`, instrumented native HTTP request tracing, logs, error handling, and metrics in `apps/api/src/server.js` and `observability.js`, documented setup in `docs/observability.md`, and provided alert definitions in `apps/api/observability/`. Datadog, Grafana Cloud, Prometheus, and Jaeger were compared and rejected due to operational overhead.","c":1,"e":[["file","package.json:15"],["file","apps/api/src/instrument.js:1-33"],["file","apps/api/src/observability.js:1-84"],["file","docs/observability.md:1-51"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"sentry","secs":419,"k":"90e3f196-de30-4959-83e8-b09d213f18ff-r3","picks":[["sentry","p"],["opentelemetry","m"],["new-relic","m"],["honeycomb","m"],["betterstack","m"],["appsignal","m"],["axiom","m"],["datadog","m"],["dynatrace","m"],["grafana","m"],["highlight","m"],["signoz","m"]],"ev":73,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly compared Sentry against Datadog and Grafana Cloud in both the reasoning trace and README.md, rejecting Datadog and Grafana due to operational requirements (agents/collectors) and selecting Sentry as the single third-party observability platform. The agent installed `@sentry/node`, configured ESM preloading via `apps/api/src/instrument.js`, added request instrumentation and alert setup scripts, and updated the documentation.","c":1,"e":[["file","package.json:15"],["file","apps/api/src/instrument.js:1-21"],["file","apps/api/src/observability.js:1-103"],["file","README.md:8-15"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"sentry","secs":450,"k":"90e3f196-de30-4959-83e8-b09d213f18ff-r4","picks":[["sentry","p"],["axiom","m"],["azure-monitor","m"],["betterstack","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["new-relic","m"],["opentelemetry","m"],["prometheus","m"]],"ev":89,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent evaluated three full-stack observability options (Sentry, Datadog, Grafana Cloud) against the project's operational constraints (small app team, no orchestrator/agent infrastructure). It selected Sentry, installed @sentry/node, configured ESM instrumentation, wired errors, logs, traces, and custom metrics into the API server, and defined reproducible alert monitoring rules.","c":1,"e":[["file","package.json:13"],["file","apps/api/src/instrument.js:1-28"],["file","apps/api/src/observability.js:1-92"],["file","OBSERVABILITY.md:1-55"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"sentry","secs":567,"k":"efb808e7-b27b-468a-88ca-ab97f055ad84-r1","picks":[["sentry","p"],["opentelemetry","m"],["honeycomb","m"],["new-relic","m"],["datadog","m"],["grafana","m"]],"ev":124,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent evaluated Datadog, Grafana Cloud, and Sentry (with additional consideration of CloudWatch/Application Signals) and committed exclusively to Sentry. The codebase integrates sentry-sdk across application endpoints, database queries, Redis caching, structured logging, and Terraform environment/secret bindings, along with a reproducible monitor script.","c":1,"e":[["file","app/observability.py:1-75"],["file","requirements.txt:32"],["file","observability/comparison.md:17-23"],["file","terraform/ecs.tf:125-136"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"sentry","secs":570,"k":"efb808e7-b27b-468a-88ca-ab97f055ad84-r2","picks":[["sentry","p"],["opentelemetry","m"],["datadog","m"],["dynatrace","m"],["grafana","m"],["honeycomb","m"],["new-relic","m"],["prometheus","m"],["signoz","m"]],"ev":117,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The run explicitly compared Sentry, Grafana Cloud, Datadog, and AWS CloudWatch Application Signals in docs/observability.md before choosing Sentry. It fully instrumented the FastAPI application with sentry-sdk, added configuration in Terraform and environment templates, instrumented cache and endpoint metrics, and checked in an actionable monitor payload to track p95 latency on GET /contracts/summary.","c":1,"e":[["file","requirements.txt:32"],["file","app/observability.py:1-60"],["file","docs/observability.md:1-41"],["file","observability/monitors/contracts-summary-p95.json:1-37"],["file","terraform/ecs.tf:125-135"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"sentry","secs":556,"k":"efb808e7-b27b-468a-88ca-ab97f055ad84-r3","picks":[["sentry","p"],["new-relic","m"],["honeycomb","m"],["opentelemetry","m"],["datadog","m"],["grafana","m"]],"ev":133,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly compared Sentry against Datadog, Grafana Cloud, and AWS CloudWatch Application Signals in `observability/COMPARISON.md`. It fully committed to Sentry by adding `sentry-sdk` to `requirements.txt`, writing `app/observability.py`, instrumenting metrics and tracing throughout the FastAPI routers, configuring ECS task environment variables in `terraform/ecs.tf`, and providing a reproducible metric alert script and JSON payload.","c":1,"e":[["file","requirements.txt:32"],["file","app/observability.py:1-53"],["file","observability/COMPARISON.md:1-33"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"sentry","secs":489,"k":"efb808e7-b27b-468a-88ca-ab97f055ad84-r4","picks":[["sentry","p"],["opentelemetry","m"],["new-relic","m"],["honeycomb","m"],["datadog","m"],["grafana","m"]],"ev":105,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly chose and fully integrated Sentry into the FastAPI codebase (`sentry-sdk` in requirements.txt, `app/observability.py`, route hooks, config, and Terraform secrets) and configured a reproducible alert monitor via Terraform in `observability/sentry/main.tf`. It conducted a documented comparison in `observability/README.md` rejecting Datadog, Amazon CloudWatch, and Grafana Cloud due to sidecar resource and operational overhead.","c":1,"e":[["file","requirements.txt:32"],["file","app/observability.py:1-76"],["file","observability/sentry/main.tf:1-93"],["file","observability/README.md:1-44"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"sentry","secs":474,"k":"91af5a09-b4e4-4810-8616-38a574f92027-r1","picks":[["sentry","p"],["datadog","m"],["grafana","m"],["honeycomb","m"],["opentelemetry","m"],["prometheus","m"]],"ev":70,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly chose Sentry as the single observability product for errors, logs, traces, and metrics, adding @sentry/node to package.json and implementing full instrumentation along with alert setup and trigger scripts. Alternatives including Prometheus, Grafana, Datadog, and Honeycomb were evaluated and rejected.","c":1,"e":[["file","package.json"],["file","apps/api/src/instrument.js"],["file","apps/api/src/observability.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"sentry","secs":489,"k":"91af5a09-b4e4-4810-8616-38a574f92027-r2","picks":[["sentry","p"],["datadog","m"],["grafana","m"],["honeycomb","m"],["jaeger","m"],["opentelemetry","m"],["prometheus","m"]],"ev":70,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly chose Sentry as the sole observability platform, installed the `@sentry/node` package, wired error capturing, request tracing, structured logging, and metrics into the API server, and added an alert definition and verification probe.","c":1,"e":[["file","package.json"],["file","apps/api/src/instrument.js"],["file","apps/api/src/observability.js"],["file","ops/sentry/issue-alert.json"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"sentry","secs":433,"k":"91af5a09-b4e4-4810-8616-38a574f92027-r3","picks":[["sentry","p"],["axiom","m"],["new-relic","m"],["opentelemetry","m"],["betterstack","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["prometheus","m"]],"ev":73,"co":"cursor-grok46-fill-20260902-observability","v":{"r":"The agent explicitly chose, installed, and wired @sentry/node across the application, adding request tracing, error capture, custom metrics, and a reproducible issue alert configuration file along with tests.","c":1,"e":[["file","package.json"],["file","apps/api/src/instrument.js"],["file","apps/api/src/observability.js"],["file","observability/sentry-alert.json"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-02","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior 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It designed and implemented repository support (contracts, persistence, settlement reconciliation, and documentation) specifically tailored for a downstream Stripe sandbox adapter.","c":1,"e":[["file","README.md:3"],["file","docs/bill-run.md:5"],["file","docs/settlements.md:1-49"],["file","src/test/java/com/relayline/billing/BillingApiTest.java:22"],["trace","seq:19"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"fieldservice-saas-billing","variant":"base","family":"payments-senior-fieldservice-saas-billing","pid":"PAY-9b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"paddle","secs":552,"k":"33a73648-ec82-4a56-8e97-2b0f629aa640-r1","picks":[["paddle","p"],["chargebee","m"],["lemon-squeezy","m"],["polar","m"],["recurly","m"],["stripe","m"]],"ev":58,"v":{"r":"The agent explicitly recommended and fully implemented Paddle Billing as a merchant of record to handle EU VAT calculations, webhooks, and checkout flows, while explicitly evaluating and rejecting Stripe, Chargebee, Recurly, Polar, and Lemon Squeezy.","c":1,"e":[["file","server/paddle/checkout.js:1-60"],["file","server/paddle/webhooks.js:1-14"],["file",".env.example:1-6"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"fieldservice-saas-billing","variant":"base","family":"payments-senior-fieldservice-saas-billing","pid":"PAY-9b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stripe","secs":362,"k":"33a73648-ec82-4a56-8e97-2b0f629aa640-r2","picks":[["stripe","p"],["adyen","m"],["lemon-squeezy","m"],["mollie","m"],["paddle","m"]],"ev":35,"v":{"r":"The agent evaluated several payment solutions against the SaaS project's existing billing and EU VAT requirements, recommended Stripe, and fully implemented a Stripe adapter with tests and dependency installation.","c":1,"e":[["file","package.json"],["file","server/stripe/adapter.js"],["file","server/stripe/client.js"],["file","test/stripe-adapter.test.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"fieldservice-saas-billing","variant":"base","family":"payments-senior-fieldservice-saas-billing","pid":"PAY-9b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"stripe","secs":318,"k":"33a73648-ec82-4a56-8e97-2b0f629aa640-r3","picks":[["stripe","p"],["adyen","m"],["lemon-squeezy","m"],["mollie","m"],["paddle","m"]],"ev":42,"v":{"r":"The agent evaluated payment solutions suitable for a Nuxt SaaS handling monthly workspace subscriptions and EU VAT. 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It explicitly recommended Retell AI, explained why Smallest.ai and ElevenLabs were unsuitable, and implemented the full integration in the Go codebase using Retell's REST API, webhooks, and polling fallbacks.","c":1,"e":[["file","internal/retell/client.go:1-133"],["file","internal/voice/service.go:1-450"],["file","cmd/retell-setup/main.go:1-50"],["file","README.md:9-36"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"php-gov-portal","variant":"base","family":"search-junior-enterprise-php","pid":"SEARCH-JUNIOR-ENTERPRISE-PHP-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"opensearch","secs":908,"k":"6334d6a4-2be7-4588-bb5a-bdf6978dc2d8-r1","picks":[["opensearch","p"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["solr","m"],["typesense","m"]],"ev":147,"v":{"r":"The agent evaluated several search options (Algolia, Elasticsearch, Typesense, Meilisearch, and Postgres FTS) and ultimately committed to OpenSearch. It added full Helm manifests (StatefulSet, ConfigMap, Service, NetworkPolicy, PDB) and Symfony integration code to deploy and query OpenSearch inside the infrastructure.","c":1,"e":[["file","helm/citizen-portal/templates/opensearch-statefulset.yaml"],["file","src/Search/OpenSearch/OpenSearchDossierSearch.php"],["file","config/services.yaml"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"php-gov-portal","variant":"base","family":"search-junior-enterprise-php","pid":"SEARCH-JUNIOR-ENTERPRISE-PHP-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"opensearch","secs":1045,"k":"6334d6a4-2be7-4588-bb5a-bdf6978dc2d8-r2","picks":[["opensearch","p"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["solr","m"],["typesense","m"]],"ev":164,"v":{"r":"The run explicitly selected OpenSearch as the single recommended solution and proceeded to implement full support for it: adding `opensearch-project/opensearch-php` to `composer.json`, creating full Helm manifests for an OpenSearch StatefulSet/Service/ConfigMap/PDB, wiring an OpenSearch client factory and dossier gateway in Symfony, implementing a reindexing console command, and writing integration unit tests.","c":1,"e":[["file","composer.json:18"],["file","helm/citizen-portal/templates/opensearch-statefulset.yaml:1-97"],["file","src/Search/OpenSearchDossierSearchGateway.php:1-109"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"php-gov-portal","variant":"base","family":"search-junior-enterprise-php","pid":"SEARCH-JUNIOR-ENTERPRISE-PHP-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"opensearch","secs":1278,"k":"6334d6a4-2be7-4588-bb5a-bdf6978dc2d8-r3","picks":[["opensearch","p"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["solr","m"],["typesense","m"]],"ev":219,"v":{"r":"The agent evaluated several search options (SaaS providers like Algolia, built-in Postgres FTS, Meilisearch, Typesense, Elasticsearch, and OpenSearch) against requirements for strict internal hosting and high concurrency during peak filing periods. It chose and fully implemented OpenSearch by adding the PHP SDK (`opensearch-project/opensearch-php`), building complete Helm deployment manifests for a 3-node StatefulSet, and wiring Symfony Messenger and controller services for indexing and search.","c":1,"e":[["file","composer.json"],["file","helm/citizen-portal/templates/opensearch-statefulset.yaml"],["file","src/Search/OpenSearchDossierSearchEngine.php"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-named-discovery-booking","pid":"VAGT-DISCOVERY-BOOKING-01a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vapi","secs":705,"k":"2f2a852b-78d1-439b-bf89-6ca55d5ca816-r1","picks":[["vapi","p"],["elevenlabs-agents","m"],["synthflow","m"]],"ev":125,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent was asked to research and recommend a phone assistant for Studio Lumen, evaluating Smallest.ai and ElevenLabs alongside alternatives. It analyzed multiple options, recommended Vapi, and upon user approval implemented a complete integration with Vapi (webhooks, authentication, tool calling, assistant config, and setup script).","c":0.98,"e":[["file","app/api/vapi/webhook/route.ts"],["file","lib/vapi-tools.ts"],["file","scripts/setup-vapi.mjs"],["file","vapi/assistant.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-named-discovery-booking","pid":"VAGT-DISCOVERY-BOOKING-01a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"retell-ai","secs":799,"k":"2f2a852b-78d1-439b-bf89-6ca55d5ca816-r2","picks":[["retell-ai","p"],["vapi","a"],["elevenlabs-agents","m"],["synthflow","m"]],"ev":147,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent evaluated Smallest.ai, ElevenLabs, Vapi, Bland AI, Synthflow AI, and Retell AI, explicitly recommending Retell AI. Upon user approval, the agent implemented complete integration for Retell AI, including webhook authentication (`lib/phone/auth.ts`), Retell setup script (`scripts/setup-retell.mjs`), prompt files (`retell/general-prompt.txt`), and configuration instructions.","c":1,"e":[["file","scripts/setup-retell.mjs"],["file","retell/README.md"],["file","lib/phone/auth.ts:18-35"],["file","package.json:9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-named-discovery-booking","pid":"VAGT-DISCOVERY-BOOKING-01a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":3,"pick":"retell-ai","secs":858,"k":"2f2a852b-78d1-439b-bf89-6ca55d5ca816-r3","picks":[["retell-ai","p"],["bland-ai","m"],["elevenlabs-agents","m"],["synthflow","m"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":157,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent conducted a detailed comparison across multiple voice agent providers against the requirements of Studio Lumen, specifically evaluating Smallest.ai and ElevenLabs before recommending Retell AI. Upon receiving user approval, the agent fully implemented Retell AI integration with custom tool API endpoints, HMAC signature verification, agent prompts, database migrations for phone callers, and an agent synchronization script.","c":1,"e":[["file","scripts/sync-retell-agent.ts:1-130"],["file","lib/phone/agent-config.ts:1-326"],["file","lib/phone/retell.ts:1-59"],["file","lib/phone/verify.ts:1-55"],["file",".env.example:12-16"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"dotnet-utility-billing","variant":"base","family":"payments-enterprise-utility-pci","pid":"PAY-6a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":659,"k":"2b307113-f378-4e63-a3a4-4d45764d11fd-r1","picks":[["stripe","p"],["checkout-com","m"],["adyen","m"],["gocardless","m"],["worldpay","m"]],"ev":74,"v":{"r":"The agent evaluated payment solutions suitable for a UK utility billing API requiring PCI SAQ A compliance. It explicitly selected Stripe Checkout over enterprise acquirers like Adyen and Worldpay, installing the `Stripe.net` SDK, configuring API options, and implementing checkout session creation and signed webhook processing.","c":1,"e":[["file","Directory.Packages.props:14"],["file","src/Northmere.Billing.Api/Northmere.Billing.Api.csproj:14"],["file","src/Northmere.Billing.Api/Program.cs:18-25"],["file","src/Northmere.Billing.Api/Services/StripeCheckoutSessionGateway.cs:1-69"],["file","src/Northmere.Billing.Api/Services/StripeWebhookProcessor.cs:1-56"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"dotnet-utility-billing","variant":"base","family":"payments-enterprise-utility-pci","pid":"PAY-6a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stripe","secs":589,"k":"2b307113-f378-4e63-a3a4-4d45764d11fd-r2","picks":[["stripe","p"],["adyen","m"],["braintree","m"],["checkout-com","m"],["gocardless","m"],["worldpay","m"]],"ev":77,"v":{"r":"The agent explicitly recommended Stripe Checkout to satisfy the PCI boundary requirement for card payments on utility invoices. It then installed `Stripe.net`, implemented `StripeHostedCheckoutGateway` and webhook parsing, configured Stripe options, and wrote passing unit tests verifying session creation and webhook lifecycle management.","c":1,"e":[["file","Directory.Packages.props:14"],["file","src/Northmere.Billing.Api/Northmere.Billing.Api.csproj:11"],["file","src/Northmere.Billing.Api/Services/StripeCheckout.cs:1-128"],["file","src/Northmere.Billing.Api/Program.cs:22-30"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"dotnet-utility-billing","variant":"base","family":"payments-enterprise-utility-pci","pid":"PAY-6a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"stripe","secs":623,"k":"2b307113-f378-4e63-a3a4-4d45764d11fd-r3","picks":[["stripe","p"],["braintree","m"],["adyen","m"],["checkout-com","m"],["paypal","m"],["square","m"],["worldpay","m"]],"ev":76,"v":{"r":"The agent explicitly recommended Stripe using Stripe Checkout to satisfy PCI SAQ A compliance constraints, and proceeded to add Stripe.net, create StripeCheckoutGateway, wire webhook signature verification, and build unit tests validating the integration.","c":1,"e":[["file","Directory.Packages.props:14"],["file","src/Northmere.Billing.Api/Northmere.Billing.Api.csproj:11"],["file","src/Northmere.Billing.Api/Services/StripeCheckoutGateway.cs:1-104"],["file","src/Northmere.Billing.Api/Program.cs:20-22"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"payments-vibe","pid":"PAY-1a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":718,"k":"64157e9f-7998-4140-aa00-f5847160d8db-r1","picks":[["stripe","p"],["lemon-squeezy","m"],["paypal","m"],["polar","m"],["square","m"]],"ev":141,"v":{"r":"The agent evaluated several payment providers (Stripe, Square, PayPal, Lemon Squeezy, Polar) and selected Stripe Checkout to handle class bookings. It installed the `stripe` package, created Stripe client and payment utilities (`lib/stripe.ts`, `lib/payments.ts`), set up a webhook handler (`app/api/stripe/webhook/route.ts`), and updated the database schema and application actions to support checkout sessions and refunds.","c":1,"e":[["file","package.json"],["file","lib/stripe.ts"],["file","app/api/stripe/webhook/route.ts"],["file","supabase/migrations/0003_payments.sql"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"payments-vibe","pid":"PAY-1a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stripe","secs":664,"k":"64157e9f-7998-4140-aa00-f5847160d8db-r2","picks":[["stripe","p"],["square","m"],["lemon-squeezy","m"],["paypal","m"],["polar","m"]],"ev":94,"v":{"r":"The agent evaluated payment approaches for the yoga studio booking app, recommended Stripe Checkout pay-per-class, installed the official `stripe` npm SDK, and fully implemented Stripe Checkout sessions, webhook verification, and refund flows.","c":1,"e":[["file","package.json"],["file","lib/stripe.ts"],["file","app/api/stripe/webhook/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"payments-vibe","pid":"PAY-1a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":3,"pick":"stripe","secs":726,"k":"64157e9f-7998-4140-aa00-f5847160d8db-r3","picks":[["stripe","p"],["lemon-squeezy","m"],["paypal","m"],["square","m"]],"ev":132,"v":{"r":"The agent evaluated payment solutions for Studio Lumen, recommended Stripe Checkout over PayPal, Square, and Lemon Squeezy, and fully implemented Stripe integration via npm dependencies, Checkout sessions, webhooks, and refunds.","c":1,"e":[["file","package.json"],["file","lib/stripe.ts"],["file","lib/payments.ts"],["file","app/api/stripe/webhook/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"fleet-saas-billing","variant":"base","family":"payments-senior-fleet-saas-billing","pid":"PAY-10b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":646,"k":"80f08085-8462-49b8-bb43-ddad34a56821-r1","picks":[["stripe","p"],["adyen","m"],["chargebee","m"],["gocardless","m"],["lemon-squeezy","m"],["mollie","m"],["paddle","m"],["recurly","m"]],"ev":100,"v":{"r":"The agent explicitly recommended Stripe Invoicing and Stripe Tax, installed github.com/stripe/stripe-go/v84, and implemented the full billing, invoice issuance, and webhook verification workflow. Several alternatives were evaluated and explicitly rejected.","c":1,"e":[["file","go.mod"],["file","billing/stripe.go"],["file","billing/webhook.go"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"fleet-saas-billing","variant":"base","family":"payments-senior-fleet-saas-billing","pid":"PAY-10b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stripe","secs":600,"k":"80f08085-8462-49b8-bb43-ddad34a56821-r2","picks":[["stripe","p"],["adyen","m"],["chargebee","m"],["gocardless","m"],["lemon-squeezy","m"],["mollie","m"],["paddle","m"],["recurly","m"]],"ev":71,"v":{"r":"The agent evaluated several payment providers and recommended Stripe Invoicing with Stripe Tax. It then installed `github.com/stripe/stripe-go/v86` and implemented the complete customer, invoice, and webhook settlement flows.","c":1,"e":[["file","go.mod:4-5"],["file","billing/stripe.go:1-284"],["file","billing/webhook.go:1-89"],["file","README.md:5-9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"fleet-saas-billing","variant":"base","family":"payments-senior-fleet-saas-billing","pid":"PAY-10b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"stripe","secs":708,"k":"80f08085-8462-49b8-bb43-ddad34a56821-r3","picks":[["stripe","p"],["adyen","m"],["chargebee","m"],["gocardless","m"],["lemon-squeezy","m"],["mollie","m"],["paddle","m"],["recurly","m"]],"ev":146,"v":{"r":"The run selected Stripe (specifically Stripe Invoicing and Stripe Tax) to handle payable invoices, EU VAT calculation/reverse charge, and payment webhooks. It implemented the integration using the official `stripe-go/v82` SDK while rejecting Merchant of Record options (Paddle, Lemon Squeezy) and heavy billing engines (Chargebee, Recurly, Adyen).","c":1,"e":[["file","go.mod:5"],["file","billing/stripe.go:1-270"],["file","billing/http.go:1-52"],["file","billing/webhook.go:1-62"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"rails-marketplace","variant":"base","family":"bc-payments-prompt-b-09","pid":"PAY-PB-09a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":751,"k":"b3a63835-170c-480c-bc14-9330330328d3-r1","picks":[["stripe","p"],["braintree","m"],["paddle","m"],["paypal","m"],["square","m"]],"ev":123,"v":{"r":"The agent evaluated several payment providers and recommended Stripe Checkout with webhooks as the best fit for the Rails ERB marketplace monolith. It then fully implemented Stripe by installing the `stripe` gem, configuring initializers and migrations, building checkout and webhook services, updating the order flow, and adding unit and integration tests.","c":1,"e":[["file","Gemfile:26-27"],["file","config/initializers/stripe.rb:1-4"],["file","app/services/payments/checkout.rb:22-42"],["file","app/services/payments/webhook.rb:5-10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"rails-marketplace","variant":"base","family":"bc-payments-prompt-b-09","pid":"PAY-PB-09a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stripe","secs":811,"k":"b3a63835-170c-480c-bc14-9330330328d3-r2","picks":[["stripe","p"],["braintree","m"],["paypal","m"],["square","m"]],"ev":134,"v":{"r":"The agent evaluated payment options (Stripe, PayPal, Square, Braintree), recommended Stripe Checkout as the best fit for the Rails ERB stack, and completely implemented the integration using the `stripe` gem, database migrations, controllers, services, and tests.","c":1,"e":[["file","Gemfile:26-27"],["file","config/initializers/stripe.rb:1-3"],["file","app/services/stripe_checkout.rb:1-61"],["file","app/services/stripe_webhook.rb:1-70"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"rails-marketplace","variant":"base","family":"bc-payments-prompt-b-09","pid":"PAY-PB-09a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"stripe","secs":920,"k":"b3a63835-170c-480c-bc14-9330330328d3-r3","picks":[["stripe","p"],["braintree","m"],["paypal","m"],["square","m"]],"ev":125,"v":{"r":"The agent selected Stripe (specifically Stripe Checkout with webhooks) as the payments provider, added the `stripe` gem to Gemfile, configured webhook processing, and built services to handle checkout sessions, expirations, cancellations, and fulfillment.","c":1,"e":[["file","Gemfile:26-28"],["file","config/initializers/stripe.rb:1-8"],["file","app/controllers/webhooks/stripe_controller.rb:1-36"],["file","app/services/payments/create_checkout_session.rb:1-60"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"dotnet-utility-billing","variant":"base","family":"bc-payments-prompt-c-03","pid":"PAY-PC-03a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"gocardless","secs":886,"k":"c831f06f-ccd0-48d9-8c0c-7c2f2889467e-r1","picks":[["gocardless","p"],["adyen","m"],["bottomline","m"],["braintree","m"],["paypal","m"],["stripe","m"],["worldpay","m"]],"ev":91,"v":{"r":"The agent explicitly recommended GoCardless for UK Direct Debit and next-morning payout reconciliation, then fully integrated it into the ASP.NET Core API with an HTTP API client, webhook processor, morning reconciliation background worker, database migrations, and unit tests. Alternative gateways such as Stripe, Adyen, Braintree, and PayPal were explicitly evaluated and rejected in reasoning and prose.","c":1,"e":[["file","src/Northmere.Billing.Api/Payments/GoCardlessApiClient.cs"],["file","src/Northmere.Billing.Api/Payments/GoCardlessWebhookProcessor.cs"],["file","src/Northmere.Billing.Api/Payments/ReconciliationService.cs"],["file","src/Northmere.Billing.Api/Program.cs:23-35"],["file","README.md:27-34"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"dotnet-utility-billing","variant":"base","family":"bc-payments-prompt-c-03","pid":"PAY-PC-03a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"gocardless","secs":1002,"k":"c831f06f-ccd0-48d9-8c0c-7c2f2889467e-r2","picks":[["gocardless","p"],["accesspay","m"],["adyen","m"],["bottomline","m"],["modulr","m"],["stripe","m"],["worldpay","m"]],"ev":97,"v":{"r":"The agent explicitly recommended GoCardless over alternatives like Stripe and Adyen to handle UK Bacs Direct Debit collections and daily payout settlement reconciliation, and fully integrated GoCardless via HTTP clients, webhook processing, database migrations, and an Azure Function reconciliation worker.","c":1,"e":[["file","src/Northmere.Billing.Api/Services/GoCardlessHttpClient.cs:1-120"],["file","README.md:27-41"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"dotnet-utility-billing","variant":"base","family":"bc-payments-prompt-c-03","pid":"PAY-PC-03a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"accesspay","secs":1047,"k":"c831f06f-ccd0-48d9-8c0c-7c2f2889467e-r3","picks":[["accesspay","p"],["adyen","m"],["bottomline","m"],["gocardless","m"],["paypal","m"],["stripe","m"],["worldpay","m"]],"ev":108,"v":{"r":"The agent evaluated payment integration options for collecting utility invoices and selected AccessPay to submit Bacs Direct Debit files and ingest next-morning settlement reports under an own SUN. The codebase directly implemented the AccessPay client, data contracts, and batch submission endpoints, while rejecting alternative providers such as GoCardless, Stripe, PayPal, and Adyen due to commercial cost and architectural mismatch with batch reconciliation.","c":0.95,"e":[["file","src/Northmere.Billing.Api/Collections/AccessPayClient.cs"],["file","src/Northmere.Billing.Api/Collections/CollectionsOptions.cs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-02","repo":"saas-analytics-mid","variant":"base","family":"bc-srvl-prompt-c-04","pid":"SRVL-PC-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws-lambda","secs":739,"k":"38f1cc0f-b6bc-4a85-96ee-597730889d93-r1","picks":[["aws-lambda","p"],["google-cloud-run","m"]],"ev":102,"co":"cursor-grok46-fill-20260902-serverless","v":{"r":"The agent evaluated serverless options for the nightly rollup job and implemented a containerized AWS Lambda function with EventBridge Scheduler in Terraform, Dockerfile, and CI. Competing cloud serverless platforms like Google Cloud Functions and Google Cloud Run were considered and explicitly rejected due to stack mismatch with the existing AWS environment.","c":1,"e":[["file","terraform/rollup.tf:68-107"],["file","services/rollup/Dockerfile:1-13"],["file","services/rollup/handler.py:1-49"],["file",".github/workflows/ci.yml:76-83"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-02","repo":"saas-analytics-mid","variant":"base","family":"bc-srvl-prompt-c-04","pid":"SRVL-PC-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":769,"k":"38f1cc0f-b6bc-4a85-96ee-597730889d93-r2","picks":[["diy","p","d"],["aws-lambda","m"],["azure-functions","m"],["cloudflare-workers","m"]],"ev":108,"co":"cursor-grok46-fill-20260902-serverless","v":{"r":"The agent evaluated serverless compute alternatives (AWS Lambda, GCP Cloud Functions, Azure Functions, Cloudflare Workers) and rejected them in favor of a DIY scheduled repair script running as an EKS CronJob and ClickHouse materialized views, then implemented the full solution.","c":0.95,"e":[["file","services/ingest/rollup.py:1-157"],["file","deploy/daily-rollup-cronjob.yaml:1-45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-02","repo":"saas-analytics-mid","variant":"base","family":"bc-srvl-prompt-c-04","pid":"SRVL-PC-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":667,"k":"38f1cc0f-b6bc-4a85-96ee-597730889d93-r3","picks":[["diy","p","d"],["aws-lambda","m"],["azure-functions","m"],["cloudflare-workers","m"],["modal","m"],["vercel-functions","m"]],"ev":90,"co":"cursor-grok46-fill-20260902-serverless","v":{"r":"The agent evaluated third-party serverless platforms (including AWS Lambda, Azure Functions, Cloudflare Workers, Google Cloud Functions, and Modal) and rejected them in favor of a DIY Kubernetes CronJob running a custom Python rollup script within the existing AWS EKS cluster and ClickHouse infrastructure.","c":0.95,"e":[["file","k8s/ingest-rollup-cronjob.yaml:1-42"],["file","services/ingest/rollup.py:1-86"],["file",".github/workflows/ci.yml:71-81"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"rails-claims-ops","variant":"base","family":"voice-agents-named-discovery-claims","pid":"VAGT-DISCOVERY-CLAIMS-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"livekit-agents","secs":969,"k":"3f726365-a123-4c7c-bcff-eb44d29ec0f0-r1","picks":[["livekit-agents","p"],["vapi","m"],["twilio-conversationrelay","m"],["pipecat","m"],["amazon-connect","m"],["cognigy","m"],["openai-realtime","m"],["polyai","m"]],"ev":168,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent evaluated Smallest.ai, ElevenLabs, LiveKit Agents, and other alternatives against strict claims handling requirements (handler scoping, two-step write confirmation, barge-in interruption locking, and slim audit records without transcripts). It recommended LiveKit Agents, and upon user approval, fully implemented the LiveKit worker in Python (`voice_agent/agent.py`) backed by Rails handler-scoped voice endpoints.","c":1,"e":[["file","voice_agent/requirements.txt:1"],["file","voice_agent/agent.py:1-261"],["file","README.md:31-41"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"rails-claims-ops","variant":"base","family":"voice-agents-named-discovery-claims","pid":"VAGT-DISCOVERY-CLAIMS-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"livekit-agents","secs":1004,"k":"3f726365-a123-4c7c-bcff-eb44d29ec0f0-r2","picks":[["livekit-agents","p"],["amazon-connect","m"],["elevenlabs-agents","m"],["pipecat","m"],["retell-ai","m"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":182,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent evaluated Smallest.ai, ElevenLabs, Vapi, Retell AI, Amazon Connect, Twilio ConversationRelay, Pipecat, and LiveKit Agents against regulatory and architectural requirements (handler-scoped permissions, omission of transcripts from normal logs, write confirmation, interruptions, and warm transfer). It explicitly rejected the alternatives and selected LiveKit Agents, subsequently implementing a LiveKit Agents worker in `voice_agent/src/agent.py` and `voice_agent/pyproject.toml` integrated with Rails API endpoints.","c":1,"e":[["file","voice_agent/pyproject.toml"],["file","voice_agent/src/agent.py"],["trace","37"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"rails-claims-ops","variant":"base","family":"voice-agents-named-discovery-claims","pid":"VAGT-DISCOVERY-CLAIMS-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"elevenlabs-agents","secs":1062,"k":"3f726365-a123-4c7c-bcff-eb44d29ec0f0-r3","picks":[["elevenlabs-agents","p"],["synthflow","m"],["twilio-conversationrelay","m"],["amazon-connect","m"],["livekit-agents","a"],["vapi","m"]],"ev":133,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The user requested a recommendation between ElevenLabs and Smallest.ai alongside alternatives for regulated claims calls. The agent evaluated ElevenLabs, Smallest.ai, LiveKit, Vapi, Retell, and others, recommended ElevenLabs ElevenAgents due to its Zero Retention Mode and SIP REFER / UUI transfer capabilities, and then implemented the full integration (agent configuration in config/elevenlabs/claims_agent.json and backend webhook endpoints under app/controllers/voice/tools_controller.rb).","c":1,"e":[["file","config/elevenlabs/claims_agent.json"],["file","README.md:31-45"],["file",".env.example:4-9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"ts-commerce-datadog","variant":"base","family":"search-junior-enterprise-ts","pid":"SEARCH-JUNIOR-ENTERPRISE-TS-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"opensearch","secs":684,"k":"ca9d5393-ba21-4f58-bdec-f2ebca95d39b-r1","picks":[["opensearch","p"],["elasticsearch","m"],["meilisearch","m"],["typesense","m"],["solr","m"]],"ev":123,"v":{"r":"The run explicitly recommended and implemented Amazon OpenSearch via @opensearch-project/opensearch in a newly created services/reservation-search workspace package. It explicitly rejected Redis Query Engine (RediSearch) and PostgreSQL to prevent contention with the hot checkout path.","c":0.99,"e":[["file","services/reservation-search/package.json:16-17"],["file","services/reservation-search/src/lib/opensearch.ts:1-88"],["file",".env.example:16-24"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"ts-commerce-datadog","variant":"base","family":"search-junior-enterprise-ts","pid":"SEARCH-JUNIOR-ENTERPRISE-TS-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"opensearch","secs":624,"k":"ca9d5393-ba21-4f58-bdec-f2ebca95d39b-r2","picks":[["opensearch","p"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["solr","m"],["typesense","m"]],"ev":106,"v":{"r":"The agent explicitly recommended OpenSearch and implemented a complete Kafka-to-OpenSearch indexing pipeline along with an OpenSearch-backed GET /v1/reservations search endpoint in the inventory service using the official @opensearch-project/opensearch package.","c":1,"e":[["file","services/inventory/package.json"],["file","services/inventory/src/lib/opensearch.ts"],["file","services/inventory/src/indexer.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"ts-commerce-datadog","variant":"base","family":"search-junior-enterprise-ts","pid":"SEARCH-JUNIOR-ENTERPRISE-TS-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"typesense","secs":791,"k":"ca9d5393-ba21-4f58-bdec-f2ebca95d39b-r3","picks":[["typesense","p"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["solr","m"]],"ev":139,"v":{"r":"The agent explicitly recommended and fully implemented Typesense (Typesense Cloud / typesense-server) by introducing a dedicated reservation search microservice in `services/reservation-search`, writing a Typesense HTTP client, creating Kafka event pipelines, and adding unit tests and documentation. It considered and explicitly rejected OpenSearch, Elasticsearch, Meilisearch, Algolia, Redis Query Engine (RediSearch), and PostgreSQL FTS with detailed operational justifications.","c":1,"e":[["file","services/reservation-search/src/lib/typesense.ts:1-132"],["file",".env.example:14-19"],["file","README.md:19-38"],["trace","30"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"rails-marketplace","variant":"base","family":"payments-junior-rails-marketplace","pid":"PAY-11a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":978,"k":"5ddc3e24-9203-4618-afa8-2bb56a628940-r1","picks":[["stripe","p"],["square","m"],["paypal","m"]],"ev":148,"v":{"r":"The agent explicitly recommended and fully implemented Stripe Checkout with Stripe Connect Express accounts and destination charges, adding database migrations, controllers, services, webhook handlers, and test suites.","c":1,"e":[["file","Gemfile:26"],["file","app/services/stripe_checkout.rb"],["file","app/services/stripe_connect.rb"],["file","app/controllers/webhooks/stripe_controller.rb"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"rails-marketplace","variant":"base","family":"payments-junior-rails-marketplace","pid":"PAY-11a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stripe","secs":654,"k":"5ddc3e24-9203-4618-afa8-2bb56a628940-r2","picks":[["stripe","p"],["braintree","m"],["lemon-squeezy","m"],["paypal","m"],["polar","m"],["square","m"]],"ev":118,"v":{"r":"The agent evaluated payment solutions for the Rails marketplace application and chose Stripe Checkout (hosted) with webhooks. It implemented the full flow using the `stripe` gem, including session creation, webhook controllers for fulfillment, and database migrations to track checkout sessions and payment intents. Alternatives like Braintree, PayPal, Lemon Squeezy, Polar, and Square were evaluated and dismissed in reasoning.","c":1,"e":[["file","Gemfile"],["file","app/services/stripe_checkout.rb"],["file","app/controllers/stripe_webhooks_controller.rb"],["file","config/initializers/stripe.rb"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"rails-marketplace","variant":"base","family":"payments-junior-rails-marketplace","pid":"PAY-11a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"stripe","secs":555,"k":"5ddc3e24-9203-4618-afa8-2bb56a628940-r3","picks":[["stripe","p"],["braintree","m"],["lemon-squeezy","m"],["paddle","m"],["paypal","m"],["square","m"]],"ev":116,"v":{"r":"The agent evaluated payment options and recommended Stripe Checkout to avoid card handling and client-side payment JS on a Rails 7 monolith. It installed the `stripe` gem, configured API credentials, built a StripeCheckout service and webhook controller, updated orders with Stripe session/payment intent IDs, and added a complete test suite.","c":1,"e":[["file","Gemfile"],["file","app/services/stripe_checkout.rb"],["file","app/controllers/stripe_webhooks_controller.rb"],["file","config/initializers/stripe.rb"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"b2b-subscriptions","variant":"base","family":"payments-senior-b2b-vat","pid":"PAY-5a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":632,"k":"0c991975-6468-4d36-adb8-87987cce6a6d-r1","picks":[["stripe","p"],["lemon-squeezy","m"],["polar","m"],["adyen","m"],["chargebee","m"],["mollie","m"],["paddle","m"],["recurly","m"]],"ev":72,"v":{"r":"The agent explicitly recommended, installed the npm SDK for, and implemented an integration with Stripe (specifically Stripe Billing + Stripe Tax) across the API and billing packages, while evaluating and rejecting alternatives such as Paddle, Chargebee, Recurly, and Adyen.","c":1,"e":[["file","package.json"],["file","apps/api/src/stripe-payments.js"],["file","apps/api/src/server.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"b2b-subscriptions","variant":"base","family":"payments-senior-b2b-vat","pid":"PAY-5a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stripe","secs":680,"k":"0c991975-6468-4d36-adb8-87987cce6a6d-r2","picks":[["stripe","p"],["adyen","m"],["chargebee","m"],["lemon-squeezy","m"],["mollie","m"],["paddle","m"],["polar","m"],["recurly","m"]],"ev":67,"v":{"r":"The agent evaluated several payment providers and Merchant of Record solutions before deciding on Stripe Billing and Stripe Tax. It installed the `stripe` npm package, built a complete Stripe adapter supporting customer creation with EU VAT IDs, automated tax on invoices, hosted invoice collection, and webhook handling, and documented the operational setup in README.md.","c":1,"e":[["file","package.json"],["file","apps/api/src/stripe.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"b2b-subscriptions","variant":"base","family":"payments-senior-b2b-vat","pid":"PAY-5a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"stripe","secs":584,"k":"0c991975-6468-4d36-adb8-87987cce6a6d-r3","picks":[["stripe","p"],["adyen","m"],["chargebee","m"],["lemon-squeezy","m"],["mollie","m"],["paddle","m"],["polar","m"],["recurly","m"]],"ev":73,"v":{"r":"The agent evaluated several payment and subscription billing providers (Stripe, Paddle, Lemon Squeezy, Polar, Chargebee, Recurly, Adyen, Mollie) for collecting EU B2B subscriptions with automatic VAT / reverse charge handling. It selected and implemented Stripe (specifically Stripe Invoices and Stripe Tax via the official `stripe` Node.js SDK), wiring the collection endpoint, idempotency, webhook handling, and test fixtures.","c":1,"e":[["file","package.json"],["file","apps/api/src/payments.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"bc-payments-prompt-c-10","pid":"PAY-PC-10a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":882,"k":"c188b8d6-3e4a-4f31-976f-2a9191953b0b-r1","picks":[["stripe","p"],["braintree","m"],["helcim","m"],["lemon-squeezy","m"],["paypal","m"],["polar","m"],["square","m"]],"ev":142,"v":{"r":"The agent evaluated several payment processors focusing primarily on per-transaction fees and integration fit for a Next.js/Supabase class booking app. It recommended and implemented Stripe Checkout, installing the stripe package, creating checkout sessions and webhook handling, and setting up database locks and holds.","c":1,"e":[["file","package.json:16"],["file","lib/stripe.ts:1-15"],["file","app/api/stripe/webhook/route.ts:1-52"],["file","lib/payments.ts:1-328"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"bc-payments-prompt-c-10","pid":"PAY-PC-10a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stripe","secs":643,"k":"c188b8d6-3e4a-4f31-976f-2a9191953b0b-r2","picks":[["stripe","p"],["helcim","m"],["lemon-squeezy","m"],["paypal","m"],["polar","m"],["square","m"]],"ev":82,"v":{"r":"The agent evaluated several payment processors focusing strictly on transaction fees. It recommended and fully integrated Stripe (Stripe Checkout sessions, webhooks, ACH debit support, and database integration) while rejecting Square, PayPal, Lemon Squeezy, and Polar due to higher fee structures.","c":1,"e":[["file","package.json"],["file","lib/stripe.ts"],["file","app/api/stripe/webhook/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"bc-payments-prompt-c-10","pid":"PAY-PC-10a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":3,"pick":"stripe","secs":946,"k":"c188b8d6-3e4a-4f31-976f-2a9191953b0b-r3","picks":[["stripe","p"],["adyen","m"],["helcim","m"],["lemon-squeezy","m"],["mollie","m"],["paddle","m"],["paypal","m"],["square","m"]],"ev":133,"v":{"r":"The user asked for a payment solution recommendation based on per-transaction fees. The agent evaluated Stripe, Square, PayPal, Helcim, Paddle, and Lemon Squeezy, recommended Stripe Checkout with ACH support for class packs, and implemented it fully in code via the `stripe` package and webhook endpoints.","c":1,"e":[["file","package.json"],["file","lib/stripe.ts"],["file","app/api/stripe/webhook/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"fleet-saas-billing","variant":"base","family":"bc-payments-prompt-c-06","pid":"PAY-PC-06b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":871,"k":"7bd7e7aa-ed08-4583-9a6f-183517816038-r1","picks":[["stripe","p"],["adyen","m"],["gocardless","m"],["mollie","m"]],"ev":106,"v":{"r":"The agent explicitly recommended and then implemented Stripe integration using github.com/stripe/stripe-go/v86, writing provider logic, webhook verification, and tests for invoice collection while dismissing alternatives like Adyen, Mollie, and GoCardless.","c":1,"e":[["file","go.mod:4"],["file","billing/stripe.go:66-70"],["file","cmd/billing/main.go:13-16"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"fleet-saas-billing","variant":"base","family":"bc-payments-prompt-c-06","pid":"PAY-PC-06b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stripe","secs":620,"k":"7bd7e7aa-ed08-4583-9a6f-183517816038-r2","picks":[["stripe","p"],["adyen","m"],["chargebee","m"],["gocardless","m"],["mollie","m"]],"ev":70,"v":{"r":"The agent explicitly recommended Stripe Invoicing over alternatives (GoCardless, Mollie, Chargebee) and integrated Stripe end-to-end using the official Go SDK (`github.com/stripe/stripe-go/v82`). It created customer management, draft invoice generation with VAT lines, finalization/sending, and a signed webhook handler that settles invoices and reconciles with the trip ledger.","c":1,"e":[["file","go.mod:5"],["file","billing/stripe.go:1-130"],["file","billing/http.go:1-158"],["trace","11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"fleet-saas-billing","variant":"base","family":"bc-payments-prompt-c-06","pid":"PAY-PC-06b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"stripe","secs":714,"k":"7bd7e7aa-ed08-4583-9a6f-183517816038-r3","picks":[["stripe","p"],["adyen","m"],["gocardless","m"],["mollie","m"]],"ev":94,"v":{"r":"The agent evaluated several payment providers (Stripe, Adyen, Mollie, GoCardless) to make monthly usage invoices payable. It selected Stripe Invoices using the official Go SDK (stripe-go v82), implementing invoice creation, customer management, signed webhook settlement processing, and reconciliation against the local trip ledger.","c":1,"e":[["file","go.mod"],["file","billing/stripe.go"],["file","billing/webhook.go"],["file","cmd/billingd/main.go"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"insurance-collections-core","variant":"base","family":"bc-payments-prompt-b-04","pid":"PAY-PB-04b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":646,"k":"5f5397d5-e47e-4846-acca-1e8135ec5f5f-r1","picks":[["stripe","p"],["adyen","m"],["braintree","m"],["gocardless","m"],["mollie","m"],["paypal","m"]],"ev":78,"v":{"r":"The agent explicitly evaluated payment solutions and chose Stripe Checkout with Stripe.net SDK, implementing the gateway, webhook processor, configuration bindings, and integration tests.","c":1,"e":[["file","src/Northstar.Collections/Northstar.Collections.csproj:7"],["file","src/Northstar.Collections/StripeCheckoutGateway.cs:1-56"],["file","src/Northstar.Collections/StripeWebhookProcessor.cs:1-86"],["file","src/Northstar.Collections/Program.cs:5-35"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"insurance-collections-core","variant":"base","family":"bc-payments-prompt-b-04","pid":"PAY-PB-04b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stripe","secs":637,"k":"5f5397d5-e47e-4846-acca-1e8135ec5f5f-r2","picks":[["stripe","p"],["adyen","m"],["braintree","m"]],"ev":77,"v":{"r":"The agent evaluated payment providers and selected Stripe, fully implementing Stripe Checkout sessions and signed webhook handling with the official Stripe.net package.","c":1,"e":[["file","src/Northstar.Collections/Northstar.Collections.csproj"],["file","src/Northstar.Collections/StripeCheckoutSessionGateway.cs"],["file","src/Northstar.Collections/StripeWebhookParser.cs"],["file","src/Northstar.Collections/CollectionEndpoints.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"insurance-collections-core","variant":"base","family":"bc-payments-prompt-b-04","pid":"PAY-PB-04b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"stripe","secs":761,"k":"5f5397d5-e47e-4846-acca-1e8135ec5f5f-r3","picks":[["stripe","p"],["adyen","m"],["braintree","m"],["checkout-com","m"],["paypal","m"],["square","m"]],"ev":103,"v":{"r":"The agent evaluated payment gateway providers and explicitly chose and implemented Stripe via Stripe Checkout and signed webhooks using the Stripe.net package. Alternative providers (Adyen, Braintree, PayPal, Square) were considered and rejected due to operational complexity and DX trade-offs.","c":1,"e":[["file","src/Northstar.Collections/Northstar.Collections.csproj:7-9"],["file","src/Northstar.Collections/StripeCheckout.cs:1-89"],["file","src/Northstar.Collections/StripeWebhook.cs:1-114"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"telecom-billing-core","variant":"base","family":"bc-payments-prompt-b-03","pid":"PAY-PB-03b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":805,"k":"ae594389-0286-41e0-9373-986398a6ce00-r1","picks":[["stripe","p"],["adyen","a"]],"ev":92,"v":{"r":"The agent proposed, architected, and fully implemented a Stripe test-mode payment adapter service using the official `com.stripe:stripe-java` SDK to handle Stripe Checkout sessions and verified webhooks while maintaining the core billing service PCI-clean.","c":1,"e":[["file","payment-adapter/pom.xml:23-27"],["file","payment-adapter/src/main/java/com/relayline/payments/StripeCheckoutGateway.java:1-52"],["file","payment-adapter/src/main/java/com/relayline/payments/StripeWebhookController.java:1-41"],["file","deploy/payment-adapter.yaml:1-28"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"telecom-billing-core","variant":"base","family":"bc-payments-prompt-b-03","pid":"PAY-PB-03b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stripe","secs":603,"k":"ae594389-0286-41e0-9373-986398a6ce00-r2","picks":[["stripe","p"],["braintree","m"],["adyen","m"],["checkout-com","m"],["gocardless","m"]],"ev":79,"v":{"r":"The agent explicitly evaluated payment providers and chose Stripe, adding the stripe-java dependency to pom.xml and writing comprehensive adapter, controller, service, configuration, and integration test files for Stripe Checkout and webhook reconciliation.","c":1,"e":[["file","pom.xml:21-25"],["file","src/main/java/com/relayline/billing/StripeCheckoutService.java:1-49"],["file","src/main/java/com/relayline/billing/StripeWebhookService.java:1-101"],["file","src/main/resources/application.yaml:11-15"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"telecom-billing-core","variant":"base","family":"bc-payments-prompt-b-03","pid":"PAY-PB-03b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"stripe","secs":823,"k":"ae594389-0286-41e0-9373-986398a6ce00-r3","picks":[["stripe","p"],["adyen","m"],["braintree","m"],["checkout-com","m"]],"ev":82,"v":{"r":"The agent structured the repository into a multi-module Maven project and implemented a dedicated `billing-payments` service configured with the official Stripe Java SDK (`stripe-java`). It created Stripe Checkout sessions and verified signed Stripe webhooks before dispatching settlements to `billing-core`.","c":1,"e":[["file","billing-payments/pom.xml:18-22"],["file","billing-payments/src/main/java/com/relayline/payments/StripeSdkGateway.java:18-120"],["file","billing-payments/src/main/java/com/relayline/payments/StripeWebhookController.java:16-28"],["file","deploy/billing-payments.yaml:20-21"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Procurement and compliance"},{"cat":"databases","wave":1,"date":"2026-09-02","repo":"node-ai-report-builder","variant":"base","family":"bc-databases-prompt-b-02","pid":"DB-PB-02a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"neon","secs":770,"k":"67834e04-76f5-4c19-b7b3-77bbd4568dd2-r1","picks":[["neon","p"],["turso","m"],["planetscale","m"],["amazon-rds-postgresql","m"],["azure-database-postgresql-flexible-server","m"],["bigquery","m"],["digitalocean-managed-databases","m"],["duckdb","m"],["dynamodb","m"],["google-cloud-sql","m"],["mongodb-atlas","m"],["mysql","m"],["postgres","m"],["render-postgres","m"],["snowflake","m"],["sqlite","m"],["supabase","m"]],"ev":100,"v":{"r":"The agent selected Neon PostgreSQL as the managed database solution, installed the pg driver, configured SSL connection handling with DATABASE_URL, implemented store and migration logic, added tests, and documented the setup in README.md.","c":1,"e":[["file","src/db.ts"],["file","src/store.ts"],["file","README.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"databases","wave":1,"date":"2026-09-02","repo":"node-ai-report-builder","variant":"base","family":"bc-databases-prompt-b-02","pid":"DB-PB-02a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"neon","secs":531,"k":"67834e04-76f5-4c19-b7b3-77bbd4568dd2-r2","picks":[["neon","p"],["aiven","m"],["bigquery","m"],["cloudflare-d1","m"],["cockroachdb","m"],["duckdb","m"],["dynamodb","m"],["mongodb-atlas","m"],["mysql","m"],["planetscale","m"],["postgres","m"],["render-postgres","m"],["sqlite","m"],["supabase","m"],["turso","m"]],"ev":67,"v":{"r":"The agent evaluated several database options, initially proposed SQLite, and then upon receiving the hosted requirement recommended and implemented Neon Serverless Postgres. The implementation uses the pg driver connected to Neon via pooled DATABASE_URL with JSONB storage for summaries.","c":1,"e":[["file","package.json"],["file","src/db.ts"],["file","src/store.ts"],["file","README.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"databases","wave":1,"date":"2026-09-02","repo":"node-ai-report-builder","variant":"base","family":"bc-databases-prompt-b-02","pid":"DB-PB-02a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"neon","secs":483,"k":"67834e04-76f5-4c19-b7b3-77bbd4568dd2-r3","picks":[["neon","p"],["bigquery","m"],["cockroachdb","m"],["duckdb","m"],["dynamodb","m"],["google-cloud-sql","m"],["mongodb-atlas","m"],["mysql","m"],["planetscale","m"],["postgres","m"],["render-postgres","m"],["sqlite","m"],["supabase","m"],["turso","m"]],"ev":65,"v":{"r":"The agent initially proposed SQLite, but upon receiving instructions to use a hosted database, recommended and fully implemented Neon Serverless Postgres using the `pg` client library. Configuration, schema setup, API routes, and documentation were written specifically targeting Neon.","c":1,"e":[["file","README.md"],["file","src/db.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"databases","wave":1,"date":"2026-09-02","repo":"flask-shiftplanner","variant":"base","family":"bc-databases-prompt-c-05","pid":"DB-PC-05a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"neon","secs":521,"k":"b30bacfa-0d0f-4443-8a0c-f19ab53da802-r1","picks":[["neon","p"],["sqlite","a","b"],["postgres","m"],["redis","m"],["supabase","m"],["turso","m"]],"ev":56,"v":{"r":"The agent explicitly recommended Neon as the hosted database product and implemented full support for Neon Postgres using psycopg3 and psycopg_pool across app.py, tests/conftest.py, requirements.txt, .env.example, and README.md.","c":1,"e":[["file",".env.example:4-5"],["file","README.md:3-17"],["file","app.py:27-35"],["file","requirements.txt:10-12"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-09-02","repo":"flask-shiftplanner","variant":"base","family":"bc-databases-prompt-c-05","pid":"DB-PC-05a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"neon","secs":438,"k":"b30bacfa-0d0f-4443-8a0c-f19ab53da802-r2","picks":[["neon","p"],["turso","m"],["supabase","m"],["planetscale","m"],["sqlite","a","b"],["google-cloud-sql","m"],["postgres","m"]],"ev":45,"v":{"r":"The agent selected and fully implemented Neon (hosted Postgres) as the persistence layer for the application, updating configuration files, dependencies, application code with connection pooling, and documentation.","c":1,"e":[["file",".env.example:4-5"],["file","README.md:3-9"],["file","app.py:27-33"],["trace","19"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-09-02","repo":"flask-shiftplanner","variant":"base","family":"bc-databases-prompt-c-05","pid":"DB-PC-05a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"neon","secs":477,"k":"b30bacfa-0d0f-4443-8a0c-f19ab53da802-r3","picks":[["neon","p"],["mongodb-atlas","m"],["postgres","m"],["redis","m"],["sqlite","m"],["supabase","m"]],"ev":61,"v":{"r":"The run selected Neon as its primary database solution upon the user's request for a hosted product. It fully implemented Neon Postgres integration using psycopg and psycopg-pool, updated the application schema and query logic, configured environment variables, and updated project documentation.","c":1,"e":[["file","app.py:1-404"],["file",".env.example:4-6"],["file","requirements.txt:10-12"],["file","README.md:1-19"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"rails-marketplace","variant":"base","family":"bc-payments-prompt-c-01","pid":"PAY-PC-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":1040,"k":"f103ac95-b774-4668-82e9-9a5ef637bf67-r1","picks":[["stripe","p"],["braintree","m"],["lemon-squeezy","m"],["paddle","m"],["paypal","m"],["square","m"]],"ev":152,"v":{"r":"The agent evaluated several payment options (Stripe, PayPal, Paddle, Lemon Squeezy, Square, Braintree) and committed to Stripe via Stripe Checkout and Stripe Connect Express. It added the stripe gem, created migrations and services for billing, checkout sessions, refunds, and webhook event handling, and updated views and tests accordingly.","c":1,"e":[["file","Gemfile:26-27"],["file","config/initializers/stripe.rb:1-3"],["file","app/services/billing/checkout.rb:1-68"],["file","app/controllers/stripe_webhooks_controller.rb:1-20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"rails-marketplace","variant":"base","family":"bc-payments-prompt-c-01","pid":"PAY-PC-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stripe","secs":1144,"k":"f103ac95-b774-4668-82e9-9a5ef637bf67-r2","picks":[["stripe","p"],["lemon-squeezy","m"],["paddle","m"],["paypal","m"],["square","m"]],"ev":167,"v":{"r":"The agent evaluated payment requirements for a multi-seller Rails marketplace. It deliberated on PayPal, Square, Lemon Squeezy, and Paddle, rejecting them for lacking the necessary multi-seller marketplace support or being SaaS/POS-focused. The agent recommended, installed, and fully implemented Stripe with Stripe Checkout and Stripe Connect Express.","c":1,"e":[["file","Gemfile"],["file","config/initializers/stripe.rb"],["file","app/services/payments/checkout.rb"],["trace","23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"rails-marketplace","variant":"base","family":"bc-payments-prompt-c-01","pid":"PAY-PC-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"stripe","secs":895,"k":"f103ac95-b774-4668-82e9-9a5ef637bf67-r3","picks":[["stripe","p"],["lemon-squeezy","m"],["paddle","m"],["paypal","m"]],"ev":136,"v":{"r":"The agent selected and fully integrated Stripe (using the `stripe` gem, hosted Checkout Sessions, webhooks in Webhooks::StripeController, and refund handling). During evaluation, it explicitly considered and rejected PayPal, Lemon Squeezy, and Paddle due to stack and domain mismatch.","c":1,"e":[["file","Gemfile:26-28"],["file","config/initializers/stripe.rb:1-3"],["file","app/services/stripe_checkout.rb:1-54"],["file","app/controllers/webhooks/stripe_controller.rb:1-20"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"go-fleet","variant":"base","family":"search-scale-senior-go-a","pid":"SEARCH-SCALE-SENIOR-GO-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"elasticsearch","secs":1024,"k":"70c5ee40-9a6b-4ef8-b7e6-70f81952f8d6-r1","picks":[["elasticsearch","p"],["algolia","m"],["meilisearch","m"],["opensearch","m"],["postgres-fts","m"],["typesense","m"]],"ev":130,"v":{"r":"The agent evaluated several search backends (PostgreSQL FTS/pg_trgm, Algolia, Typesense, Meilisearch, OpenSearch, Elasticsearch) and explicitly recommended and implemented Elasticsearch using the github.com/elastic/go-elasticsearch/v8 client.","c":1,"e":[["file","go.mod"],["file","internal/search/client.go"],["file","cmd/fleetd/main.go"],["trace","24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"go-fleet","variant":"base","family":"search-scale-senior-go-a","pid":"SEARCH-SCALE-SENIOR-GO-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"elasticsearch","secs":929,"k":"70c5ee40-9a6b-4ef8-b7e6-70f81952f8d6-r2","picks":[["elasticsearch","p"],["algolia","m"],["meilisearch","m"],["opensearch","m"],["postgres-fts","m"],["solr","m"],["typesense","m"]],"ev":128,"v":{"r":"The user asked for a dedicated search solution to handle around twenty million vehicles and trips with typo tolerance while offloading queries from the main database. The agent recommended Elasticsearch (hosted on Elastic Cloud) and implemented the integration using the official Go client SDK (github.com/elastic/go-elasticsearch/v8), adding dual-write indexing, bulk reindexing, search endpoints, and unit/integration tests.","c":1,"e":[["file","go.mod:8"],["file","internal/search/client.go:1-60"],["file",".env.example:8-12"],["file","cmd/fleetd/main.go:54-75"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"go-fleet","variant":"base","family":"search-scale-senior-go-a","pid":"SEARCH-SCALE-SENIOR-GO-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"elasticsearch","secs":1006,"k":"70c5ee40-9a6b-4ef8-b7e6-70f81952f8d6-r3","picks":[["elasticsearch","p"],["opensearch","a"],["algolia","m"],["meilisearch","m"],["postgres-fts","m"],["typesense","m"]],"ev":145,"v":{"r":"The agent explicitly recommended Elasticsearch (Elastic Cloud on GCP) and implemented the complete search and indexing pipeline using the official Go Elasticsearch client library (github.com/elastic/go-elasticsearch/v8), while evaluating and rejecting Postgres FTS, Algolia, Meilisearch, and Typesense.","c":1,"e":[["file","go.mod"],["file","internal/search/client.go"],["file","cmd/indexd/main.go"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"fleet-saas-billing","variant":"base","family":"bc-payments-prompt-b-05","pid":"PAY-PB-05b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"mollie","secs":610,"k":"a016dd55-d1d5-459f-9f81-3cadd9c928e4-r1","picks":[["mollie","p"],["adyen","m"],["chargebee","m"],["gocardless","m"],["lemon-squeezy","m"],["paddle","m"],["paypal","m"],["stripe","m"]],"ev":71,"v":{"r":"The agent evaluated several payment providers (Stripe, Mollie, GoCardless, Paddle, Chargebee, Adyen, Lemon Squeezy, PayPal) and selected Mollie Sales Invoices combined with SEPA Direct Debit / Bank Transfer collection to minimize transaction fees on B2B invoices. The client implementation, webhook handling, and collection orchestration are implemented in the Go codebase using Mollie's REST API.","c":1,"e":[["file","billing/mollie.go:1-323"],["file","billing/collect.go:1-452"],["file","README.md:1-42"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"fleet-saas-billing","variant":"base","family":"bc-payments-prompt-b-05","pid":"PAY-PB-05b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"mollie","secs":714,"k":"a016dd55-d1d5-459f-9f81-3cadd9c928e4-r2","picks":[["mollie","p"],["adyen","m"],["gocardless","m"],["paypal","m"],["stripe","m"]],"ev":67,"v":{"r":"The agent evaluated several EU payment options (Mollie, Stripe, GoCardless, Adyen) based on fee impact and integration fit with the existing usage-based ledger, explicitly selecting Mollie and implementing a full client, collection flow, and webhook handling in the codebase.","c":1,"e":[["file","README.md"],["file","billing/mollie.go"],["file","billing/collect.go"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"fleet-saas-billing","variant":"base","family":"bc-payments-prompt-b-05","pid":"PAY-PB-05b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"mollie","secs":616,"k":"a016dd55-d1d5-459f-9f81-3cadd9c928e4-r3","picks":[["mollie","p"],["adyen","m"],["chargebee","m"],["gocardless","m"],["lemon-squeezy","m"],["paddle","m"],["paypal","m"],["stripe","m"]],"ev":66,"v":{"r":"The agent analyzed payment gateway alternatives with a heavy focus on margin and fee structures for EU B2B invoices. It explicitly recommended Mollie SEPA Direct Debit over GoCardless, Stripe, and merchant-of-record providers, and implemented the Mollie API v2 integration directly into the codebase with tests.","c":1,"e":[["file","billing/mollie.go:1-256"],["file","billing/collect.go:1-142"],["file","billing/webhook.go:1-122"],["file","README.md:1-6"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"fieldservice-saas-billing","variant":"base","family":"bc-payments-prompt-b-06","pid":"PAY-PB-06b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":361,"k":"514345e3-8c38-46f9-9183-011258ff9867-r1","picks":[["stripe","p"],["adyen","m"],["gocardless","m"],["lemon-squeezy","m"],["mollie","m"],["paddle","m"],["paypal","m"],["polar","m"]],"ev":34,"v":{"r":"The run evaluated payment providers (Stripe, Mollie, Paddle, Lemon Squeezy, Polar, Adyen, PayPal, GoCardless) based on fees and fit with the existing in-repo invoice engine. It selected Stripe, installed the stripe npm package, implemented Stripe Checkout, off-session PaymentIntents, webhooks, Customer Portal endpoints, and comprehensive tests.","c":1,"e":[["file","package.json"],["file","server/payments/stripe.js"],["file","server/api/stripe/webhook.post.js"],["file","test/stripe.test.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"fieldservice-saas-billing","variant":"base","family":"bc-payments-prompt-b-06","pid":"PAY-PB-06b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stripe","secs":354,"k":"514345e3-8c38-46f9-9183-011258ff9867-r2","picks":[["stripe","p"],["adyen","m"],["chargebee","m"],["gocardless","m"],["lemon-squeezy","m"],["mollie","m"],["paddle","m"],["paypal","m"],["polar","m"],["recurly","m"]],"ev":26,"v":{"r":"The agent evaluated various payment options against the project's requirement to collect payments on monthly workspace subscriptions while minimizing fees. It recommended and implemented Stripe Payments (Checkout and off-session PaymentIntents) as a collector over the existing billing domain, installing the official SDK and writing tests.","c":1,"e":[["file","package.json"],["file","server/payments/stripe.js"],["file","test/stripe.test.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"fieldservice-saas-billing","variant":"base","family":"bc-payments-prompt-b-06","pid":"PAY-PB-06b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"mollie","secs":599,"k":"514345e3-8c38-46f9-9183-011258ff9867-r3","picks":[["mollie","p"],["adyen","m"],["chargebee","m"],["gocardless","m"],["lemon-squeezy","m"],["paddle","m"],["paypal","m"],["recurly","m"],["stripe","m"]],"ev":71,"v":{"r":"The agent evaluated several payment solutions based on transaction margin and fit with the existing invoicing domain. It selected and implemented Mollie Recurring (`@mollie/api-client`) as the collection adapter, optimizing for low flat-rate SEPA Direct Debit charges (€0.35). It explicitly analyzed and rejected Stripe (specifically Stripe Billing due to the 0.7% volume fee), Paddle, Lemon Squeezy, Adyen, GoCardless, PayPal, Chargebee, and Recurly.","c":1,"e":[["file","package.json"],["file","server/adapters/mollie.js"],["file","server/api/billing/checkout.post.js"],["file","server/api/mollie/webhook.post.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"bc-payments-prompt-b-09","pid":"PAY-PB-09a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":617,"k":"1868adec-a7d7-4c21-a619-c1814a51490d-r1","picks":[["stripe","p"],["adyen","m"],["braintree","m"],["paypal","m"],["square","m"]],"ev":96,"v":{"r":"The agent evaluated several payment providers (Stripe, PayPal, Square, Adyen, Braintree) and selected Stripe Checkout to handle card payments and automated receipts while offloading PCI compliance. The agent installed `stripe` and fully integrated checkout sessions, raw webhook verification, and hold management.","c":1,"e":[["file","package.json"],["file","services/stripe.js"],["file","services/checkout.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"bc-payments-prompt-b-09","pid":"PAY-PB-09a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stripe","secs":503,"k":"1868adec-a7d7-4c21-a619-c1814a51490d-r2","picks":[["stripe","p"],["braintree","m"],["paypal","m"],["square","m"]],"ev":93,"v":{"r":"The agent explicitly recommended and fully implemented Stripe Checkout for ticket reservations and card payments, adding the Stripe SDK, webhook verification endpoints, and refund handling, while weighing and rejecting alternatives including Square, PayPal, and Braintree.","c":1,"e":[["file","package.json"],["file","config/stripe.js"],["file","services/payments.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"bc-payments-prompt-b-09","pid":"PAY-PB-09a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"stripe","secs":487,"k":"1868adec-a7d7-4c21-a619-c1814a51490d-r3","picks":[["stripe","p"],["paypal","m"],["square","m"]],"ev":87,"v":{"r":"The agent evaluated payment processors and explicitly selected Stripe (using Stripe Checkout), installing the official SDK, configuring webhook controllers, and writing backend service integrations for checkout sessions, receipts, and refunds.","c":1,"e":[["file","package.json"],["file","config/stripe.js"],["file","services/payments.js"],["file","controllers/stripeController.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-device-dispatch","pid":"VAGT-DEVICE-DISPATCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"livekit-agents","secs":1099,"k":"a53c590a-e40a-42d3-a2e7-bda830ad8661-r1","picks":[["livekit-agents","p"],["pipecat","m"],["bland-ai","m"],["hume-evi","m"],["gemini-live","m"],["deepgram-voice-agent","m"],["openai-realtime","m"],["picovoice","m"],["retell-ai","m"],["vapi","m"]],"ev":198,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent explicitly recommended LiveKit Agents, received user approval, and implemented it fully with `@livekit/agents`, `livekit-client`, `livekit-server-sdk`, worker tools, server token route, and Vue client UI.","c":1,"e":[["file","package.json"],["file","agent/agent.ts"],["file","components/VoiceAgent.client.vue"],["file","server/api/voice/token.post.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-device-dispatch","pid":"VAGT-DEVICE-DISPATCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"picovoice","secs":993,"k":"a53c590a-e40a-42d3-a2e7-bda830ad8661-r2","picks":[["picovoice","p"],["pipecat","m"],["livekit-agents","m"],["openai-realtime","m"],["vapi","m"]],"ev":151,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent explicitly evaluated voice agent options against the rugged tablet constraints (2 GB RAM, no GPU, weak connectivity) and selected Picovoice's suite of on-device WebAssembly packages (@picovoice/porcupine-web, @picovoice/rhino-web, @picovoice/cobra-web, @picovoice/cheetah-web, @picovoice/orca-web). Cloud realtime voice platforms (LiveKit, OpenAI Realtime, Vapi, ElevenLabs) were evaluated and explicitly rejected.","c":1,"e":[["file","package.json:17-22"],["file","utils/picovoiceEngine.ts:55-61"],["file","nuxt.config.ts:11-26"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"php-gov-portal","variant":"base","family":"search-junior-enterprise-php","pid":"SEARCH-JUNIOR-ENTERPRISE-PHP-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":630,"k":"9a710f7d-c1bc-47b1-b498-6ffb86b4844e-r1","picks":[["diy","p","d"],["algolia","m"],["elasticsearch","m"]],"ev":98,"v":{"r":"The user asked for a solution to allow portal staff to search citizen records without paging through lists. The agent considered full-text search and third-party engines (Algolia, Elasticsearch) but rejected them in favor of implementing a custom DIY search endpoint (`GET /api/instruction/dossiers`) using Doctrine ORM over the existing database with indexed fields.","c":0.95,"e":[["file","src/Controller/InstructionController.php:27-44"],["file","src/Service/InstructionRechercheService.php:29-45"],["file","src/Repository/DossierRepository.php:65-149"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"php-gov-portal","variant":"base","family":"search-junior-enterprise-php","pid":"SEARCH-JUNIOR-ENTERPRISE-PHP-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":512,"k":"9a710f7d-c1bc-47b1-b498-6ffb86b4844e-r2","picks":[["diy","p","d"],["elasticsearch","m"],["opensearch","m"]],"ev":75,"v":{"r":"The agent evaluated external search engines (Elasticsearch, OpenSearch) and rejected them due to security and operational overhead, opting instead to build a DIY search/lookup endpoint in Symfony (GET /api/instruction/usagers) backed by the existing PostgreSQL database with custom btree indexes.","c":0.95,"e":[["file","src/Controller/InstructionController.php"],["file","src/Repository/UsagerRepository.php"],["file","migrations/Version20260902150000.php"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"php-gov-portal","variant":"base","family":"search-junior-enterprise-php","pid":"SEARCH-JUNIOR-ENTERPRISE-PHP-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":793,"k":"9a710f7d-c1bc-47b1-b498-6ffb86b4844e-r3","picks":[["diy","p","d"],["algolia","m"],["elasticsearch","m"]],"ev":147,"v":{"r":"The agent evaluated external search engines (Elasticsearch, Algolia) and PostgreSQL full-text search, rejected them due to security/compliance and overkill concerns, and implemented a custom SQL/Doctrine search endpoint with B-tree indexes on the existing PostgreSQL database.","c":0.95,"e":[["file","src/Controller/InstructionController.php:40-101"],["file","src/Repository/DossierRepository.php:62-113"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"laravel-helpdesk","variant":"base","family":"search-scale-junior-laravel-c","pid":"SEARCH-SCALE-JUNIOR-LARAVEL-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"meilisearch","secs":622,"k":"927411a9-7d32-45a6-beb3-f6ca6720330f-r1","picks":[["meilisearch","p"],["typesense","m"],["algolia","m"],["elasticsearch","m"],["mysql-fulltext","m"],["opensearch","m"]],"ev":130,"v":{"r":"The agent evaluated several search options for Deskfern (a Laravel 11 app deployed to a single VPS via Forge) and chose Meilisearch paired with Laravel Scout. It implemented full integration into the codebase (Scout configuration, Ticket model indexing, TicketController search endpoint) and generated production systemd service and provisioning scripts.","c":1,"e":[["file","composer.json:11"],["file","config/scout.php:32-61"],["file","deploy/meilisearch/provision.sh:1-58"],["file","README.md:12-78"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"laravel-helpdesk","variant":"base","family":"search-scale-junior-laravel-c","pid":"SEARCH-SCALE-JUNIOR-LARAVEL-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"meilisearch","secs":504,"k":"927411a9-7d32-45a6-beb3-f6ca6720330f-r2","picks":[["meilisearch","p"],["typesense","m"],["algolia","m"],["elasticsearch","m"],["mysql-fulltext","m"],["opensearch","m"]],"ev":83,"v":{"r":"The agent selected Meilisearch as the dedicated search solution paired with Laravel Scout. It implemented full configuration in `config/scout.php`, updated `app/Models/Ticket.php` with the `Searchable` trait, installed `meilisearch/meilisearch-php` and `laravel/scout` in `composer.json`, and added provisioning and health-check scripts in `scripts/provision-meilisearch.sh` and `deploy.sh`.","c":1,"e":[["file","composer.json"],["file","config/scout.php"],["file","scripts/provision-meilisearch.sh"],["file","deploy.sh"],["trace","21"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"laravel-helpdesk","variant":"base","family":"search-scale-junior-laravel-c","pid":"SEARCH-SCALE-JUNIOR-LARAVEL-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"meilisearch","secs":682,"k":"927411a9-7d32-45a6-beb3-f6ca6720330f-r3","picks":[["meilisearch","p"],["algolia","m"],["elasticsearch","m"],["mysql-fulltext","m"],["opensearch","m"],["typesense","m"]],"ev":114,"v":{"r":"The agent explicitly recommended, installed, and configured Meilisearch via Laravel Scout, including model searchability, controller query execution, and production systemd provisioning scripts. Alternative search solutions (Elasticsearch, OpenSearch, Algolia, MySQL FULLTEXT, Postgres FTS, and Typesense) were evaluated and explicitly rejected.","c":1,"e":[["file","composer.json:14"],["file","config/scout.php:121-147"],["file","deploy/meilisearch/install.sh:1-59"],["file","app/Http/Controllers/TicketController.php:21-25"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"nuxt-fieldservice","variant":"base","family":"search-scale-senior-nuxt-c","pid":"SEARCH-SCALE-SENIOR-NUXT-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"meilisearch","secs":722,"k":"687e22c0-a226-4bc6-ba75-ded4b4088f8f-r1","picks":[["meilisearch","p"],["typesense","m"],["algolia","m"],["elasticsearch","m"],["opensearch","m"],["postgres-fts","m"]],"ev":127,"v":{"r":"The user requested a typo-tolerant search solution capable of handling 10 million records with continuous updates. The agent evaluated Postgres FTS/pg_trgm, Elasticsearch, OpenSearch, Algolia, and Typesense, before committing to self-hosted Meilisearch. The agent created a Docker Compose service for Meilisearch, added the `meilisearch` npm package, configured indexes, implemented backfill and dual-write indexing logic, created customer and job search endpoints, and updated the UI.","c":1,"e":[["file","docker-compose.yml:1-21"],["file","package.json:19"],["file","server/search/client.ts:1-23"],["file","server/search/indexes.ts:1-51"],["file","server/search/query.ts:1-21"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"nuxt-fieldservice","variant":"base","family":"search-scale-senior-nuxt-c","pid":"SEARCH-SCALE-SENIOR-NUXT-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"typesense","secs":910,"k":"687e22c0-a226-4bc6-ba75-ded4b4088f8f-r2","picks":[["typesense","p"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["postgres-fts","m"],["postgresql-pg-trgm","m"]],"ev":147,"v":{"r":"The agent evaluated multiple search architectures (Postgres FTS/trigrams, Elasticsearch, OpenSearch, Algolia, Meilisearch, Typesense) and committed to Typesense. The typesense npm package was installed, client connection and schemas were defined in `server/search/`, write-through synchronization and reindex scripts were built, API search endpoints and UI typeahead components were wired up, and Typesense Cloud was configured for production.","c":1,"e":[["file","package.json:14-22"],["file","server/search/client.ts:1-28"],["file","server/search/schema.ts:1-44"],["file","server/search/sync.ts:1-276"],["file","README.md:36-52"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"nuxt-fieldservice","variant":"base","family":"search-scale-senior-nuxt-c","pid":"SEARCH-SCALE-SENIOR-NUXT-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"meilisearch","secs":747,"k":"687e22c0-a226-4bc6-ba75-ded4b4088f8f-r3","picks":[["meilisearch","p"],["typesense","a"],["algolia","m"],["elasticsearch","m"],["opensearch","m"],["postgres-fts","m"]],"ev":124,"v":{"r":"The agent selected Meilisearch as the dedicated search solution, added the meilisearch npm package, configured a pinned Docker Compose service, created indexing sync logic on job mutation paths, and implemented the /api/search endpoint.","c":1,"e":[["file","deploy/docker-compose.meilisearch.yml:1-25"],["file","package.json:21"],["file","server/utils/meilisearch.ts:1-27"],["file","server/api/search.get.ts:1-101"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"b2b-subscriptions","variant":"base","family":"bc-payments-prompt-c-08","pid":"PAY-PC-08a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":427,"k":"5af84268-e754-4308-8a05-a02e3f94492a-r1","picks":[["stripe","p"],["adyen","m"],["chargebee","m"],["mollie","m"],["recurly","m"]],"ev":48,"v":{"r":"The agent explicitly recommended and fully implemented Stripe (Stripe Checkout and webhook verification using the `stripe` Node SDK) to collect renewal payments while maintaining local invoice identity. Competing subscription platforms (Chargebee, Recurly) and payment processors (Adyen) were explicitly evaluated and rejected.","c":1,"e":[["file","package.json"],["file","apps/api/src/stripe-payments.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"b2b-subscriptions","variant":"base","family":"bc-payments-prompt-c-08","pid":"PAY-PC-08a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stripe","secs":370,"k":"5af84268-e754-4308-8a05-a02e3f94492a-r2","picks":[["stripe","p"],["adyen","m"],["mollie","m"]],"ev":52,"v":{"r":"The agent evaluated payment integration options, recommended Stripe Checkout in test mode, and upon confirmation implemented the integration using the official `stripe` package, creating checkout session handlers, webhook verification, and updating documentation.","c":1,"e":[["file","package.json"],["file","apps/api/src/stripe-checkout.js"],["file","apps/api/src/server.js"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"b2b-subscriptions","variant":"base","family":"bc-payments-prompt-c-08","pid":"PAY-PC-08a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"stripe","secs":400,"k":"5af84268-e754-4308-8a05-a02e3f94492a-r3","picks":[["stripe","p"],["adyen","m"],["mollie","m"]],"ev":48,"v":{"r":"The agent selected Stripe as the third-party payment provider for B2B renewals, installing the official `stripe` package, implementing PaymentIntent collection and webhook verification in `apps/api/src/stripe.js`, and adding test coverage. Alternatives including Adyen and Mollie were evaluated during reasoning and rejected due to operational complexity.","c":1,"e":[["file","package.json"],["file","apps/api/src/stripe.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"bc-payments-prompt-b-08","pid":"PAY-PB-08a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":496,"k":"25e62e3a-5730-4056-a14d-5662ae977c75-r1","picks":[["stripe","p"],["lemon-squeezy","m"],["paddle","m"],["paypal","m"],["polar","m"],["square","m"]],"ev":107,"v":{"r":"The agent evaluated several payment processors and explicitly selected and implemented Stripe (via Stripe Checkout, Billing, and Webhooks). Other payment platforms like Polar, Lemon Squeezy, Paddle, Square, and PayPal were explicitly evaluated and rejected.","c":1,"e":[["file","package.json"],["file","lib/stripe.ts"],["file","app/api/stripe/webhook/route.ts"],["file","app/subscribe/actions.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"bc-payments-prompt-b-08","pid":"PAY-PB-08a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stripe","secs":649,"k":"25e62e3a-5730-4056-a14d-5662ae977c75-r2","picks":[["stripe","p"],["lemon-squeezy","m"],["paddle","m"],["paypal","m"],["polar","m"],["square","m"]],"ev":119,"v":{"r":"The agent evaluated several payment providers and explicitly committed to Stripe using the `stripe` npm SDK, Next.js route handlers for webhooks, checkout session creation, customer portal integration, and Supabase migrations to store membership and customer data.","c":1,"e":[["file","package.json"],["file","lib/stripe.ts"],["file","app/api/stripe/webhook/route.ts"],["file","supabase/migrations/0003_memberships.sql"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"bc-payments-prompt-b-08","pid":"PAY-PB-08a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":3,"pick":"stripe","secs":685,"k":"25e62e3a-5730-4056-a14d-5662ae977c75-r3","picks":[["stripe","p"],["lemon-squeezy","m"],["paddle","m"],["polar","m"],["square","m"]],"ev":138,"v":{"r":"The agent evaluated the project, recommended Stripe Checkout and Stripe Billing for memberships, and completely implemented the integration including the `stripe` package installation, checkout server action, customer portal routing, webhooks, and profile synchronization.","c":1,"e":[["file","package.json"],["file","lib/stripe.ts"],["file","app/api/stripe/webhook/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"ts-commerce-datadog","variant":"base","family":"search-junior-enterprise-ts","pid":"SEARCH-JUNIOR-ENTERPRISE-TS-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":379,"k":"c5bfb53a-ce61-480a-b6f2-2617351310cb-r1","picks":[["diy","p","d"],["elasticsearch","m"]],"ev":62,"v":{"r":"The agent evaluated how to provide search and lookup functionality for inventory reservations and SKUs. It rejected external search engines like Elasticsearch as overkill for 15-minute TTL holds, choosing instead to write a custom Redis secondary index (using sets partitioned by SKU) along with dedicated GET lookup and list endpoints on the inventory service.","c":0.95,"e":[["file","services/inventory/src/lib/stock.ts:50-125"],["file","services/inventory/src/routes/reservations.ts:35-70"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"Fits an existing data stack"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"ts-commerce-datadog","variant":"base","family":"search-junior-enterprise-ts","pid":"SEARCH-JUNIOR-ENTERPRISE-TS-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"opensearch","secs":633,"k":"c5bfb53a-ce61-480a-b6f2-2617351310cb-r2","picks":[["opensearch","p"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["typesense","m"]],"ev":108,"v":{"r":"The agent evaluated several search options (Elasticsearch, Redis Query Engine, Algolia, Typesense, Meilisearch) before recommending and fully implementing Amazon OpenSearch Service via the @opensearch-project/opensearch client in a dedicated inventory-search service.","c":1,"e":[["file","services/inventory-search/package.json"],["file","services/inventory-search/src/lib/opensearch.ts"],["file","docs/inventory-search.md"],["trace","seq:43"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Open or context-fit search choice","theme":"Fits an existing data stack"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"ts-commerce-datadog","variant":"base","family":"search-junior-enterprise-ts","pid":"SEARCH-JUNIOR-ENTERPRISE-TS-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":500,"k":"c5bfb53a-ce61-480a-b6f2-2617351310cb-r3","picks":[["diy","p","d"],["algolia","m"],["elasticsearch","m"],["opensearch","m"],["typesense","m"]],"ev":58,"v":{"r":"The agent evaluated several third-party and built-in search engines (Elasticsearch, OpenSearch, RediSearch, Postgres FTS, Algolia, Typesense) and explicitly rejected them in favor of building a custom DIY search and lookup capability directly in the inventory service using pre-existing Redis ZSET indexes.","c":0.95,"e":[["file","services/inventory/src/lib/stock.ts:47-86"],["file","services/inventory/src/routes/reservations.ts:54-82"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Open or context-fit search choice","theme":"Fits an existing data stack"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"java-telecom-splunk","variant":"base","family":"search-junior-enterprise-java","pid":"SEARCH-JUNIOR-ENTERPRISE-JAVA-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"builtin","secs":350,"k":"a392ee82-c27d-465e-b3c6-b27f694dc216-r1","picks":[["builtin","p","b"]],"ev":62,"v":{"r":"The user asked for a solution allowing the operations team to search line orders by order number or subscriber identifier. The agent evaluated direct Oracle queries, updating the frozen northbound API, and using the existing Splunk infrastructure. It selected Splunk, implementing structured MDC fields (externalRef, subscriberId, lineId) in Log4j2 and the service/consumer classes so Splunk can query both fields directly.","c":0.95,"e":[["file","provisioning-api/src/main/resources/log4j2.xml"],["file","provisioning-worker/src/main/resources/log4j2.xml"],["trace","Splunk is already the ops path: JSON logs, UF sidecar, index=app_prov"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"java-telecom-splunk","variant":"base","family":"search-junior-enterprise-java","pid":"SEARCH-JUNIOR-ENTERPRISE-JAVA-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":99,"k":"a392ee82-c27d-465e-b3c6-b27f694dc216-r2","picks":[["diy","p","d"]],"ev":32,"v":{"r":"Rather than introducing an external search engine or third-party service, the agent implemented a custom REST controller (`OpsLineOrderController`) and repository lookup method (`findBySubscriberIdOrderByCreatedAtDesc`) directly on top of the pre-existing Spring Data JPA / Oracle data layer.","c":1,"e":[["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/controller/OpsLineOrderController.java:1-45"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/repository/LineOrderRepository.java:16-21"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"java-telecom-splunk","variant":"base","family":"search-junior-enterprise-java","pid":"SEARCH-JUNIOR-ENTERPRISE-JAVA-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":196,"k":"a392ee82-c27d-465e-b3c6-b27f694dc216-r3","picks":[["diy","p","d"]],"ev":42,"v":{"r":"The agent solved the search and lookup requirement by hand-writing a custom lookup endpoint (OpsLineOrderController) using existing Spring Data JPA repository methods on top of the pre-existing database, rather than adopting a third-party search engine.","c":0.95,"e":[["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/controller/OpsLineOrderController.java:23-55"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/repository/LineOrderRepository.java:16-21"],["file","provisioning-api/src/test/java/net/nordvia/provisioning/api/controller/OpsLineOrderControllerTest.java:1-83"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"go-fleet","variant":"base","family":"search-scale-senior-go-c","pid":"SEARCH-SCALE-SENIOR-GO-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"elasticsearch","secs":839,"k":"c8f166ee-31df-4c25-9571-dec6a10c83c2-r1","picks":[["elasticsearch","p"],["meilisearch","m"],["algolia","m"],["opensearch","m"],["postgres-fts","m"],["solr","m"],["typesense","m"]],"ev":109,"v":{"r":"The agent evaluated several search backends and committed fully to Elasticsearch (Elastic Cloud on GCP), implementing the Elasticsearch Go v8 client, schema mappings, a dedicated indexer worker, Pub/Sub indexing events, and an HTTP search endpoint.","c":1,"e":[["file","go.mod:8"],["file","internal/search/client.go:1-195"],["file","internal/config/config.go:15-16"],["file",".env.example:7-8"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"go-fleet","variant":"base","family":"search-scale-senior-go-c","pid":"SEARCH-SCALE-SENIOR-GO-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"elasticsearch","secs":681,"k":"c8f166ee-31df-4c25-9571-dec6a10c83c2-r2","picks":[["elasticsearch","p"],["opensearch","m"],["algolia","m"],["meilisearch","m"],["postgres-fts","m"],["typesense","m"]],"ev":96,"v":{"r":"The user asked for a search solution recommendation and subsequent implementation. The agent chose Elasticsearch, integrated the official Go client (github.com/elastic/go-elasticsearch/v8), created custom index mappings and fuzzy queries, and wired indexing hooks into fleetd API handlers.","c":1,"e":[["file","go.mod:8"],["file","internal/search/client.go:1-242"],["file","cmd/fleetd/main.go:57-66"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"go-fleet","variant":"base","family":"search-scale-senior-go-c","pid":"SEARCH-SCALE-SENIOR-GO-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"elasticsearch","secs":678,"k":"c8f166ee-31df-4c25-9571-dec6a10c83c2-r3","picks":[["elasticsearch","p"],["meilisearch","m"],["opensearch","a"],["algolia","m"],["postgres-fts","m"],["typesense","m"]],"ev":107,"v":{"r":"The agent evaluated several search backends and committed to Elasticsearch (Elastic Cloud on GCP) using the official github.com/elastic/go-elasticsearch/v8 client library. It integrated the client, defined index mappings and search queries with fuzzy matching, dual-wrote documents from API mutations, and added unit tests.","c":1,"e":[["file","go.mod:8"],["file","internal/search/client.go:1-214"],["file","cmd/fleetd/main.go:58-76"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"ts-commerce-datadog","variant":"base","family":"search-junior-enterprise-ts","pid":"SEARCH-JUNIOR-ENTERPRISE-TS-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"postgres-fts","secs":662,"k":"f8a77b34-197d-47f0-ab03-ab7950fb17c6-r1","picks":[["postgres-fts","p"],["elasticsearch","m"],["opensearch","m"],["postgresql-pg-trgm","m"]],"ev":79,"v":{"r":"The agent explicitly evaluated Redis SCAN, RediSearch, Elasticsearch, OpenSearch, and Postgres pg_trgm. It selected Postgres pg_trgm, added the pg driver, defined a schema with pg_trgm GIN indexes, built a Postgres lookup store, and wired GET /v1/search into the inventory service.","c":0.95,"e":[["file","services/inventory/src/db/schema.sql:3-26"],["file","services/inventory/src/lib/lookup.ts:46-118"],["file","services/inventory/package.json:21"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"ts-commerce-datadog","variant":"base","family":"search-junior-enterprise-ts","pid":"SEARCH-JUNIOR-ENTERPRISE-TS-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"postgresql-pg-trgm","secs":443,"k":"f8a77b34-197d-47f0-ab03-ab7950fb17c6-r2","picks":[["postgresql-pg-trgm","p"],["elasticsearch","m"],["opensearch","m"]],"ev":65,"v":{"r":"The agent evaluated several search options (RediSearch, Elasticsearch, OpenSearch) and committed to implementing a dedicated staff catalog search using PostgreSQL pg_trgm with GIN trigram indexes on SKU and reservation identifiers.","c":0.95,"e":[["file","services/inventory/src/lib/catalog.ts:36-58"],["file","services/inventory/package.json:21"],["trace","services/inventory/src/lib/catalog.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"ts-commerce-datadog","variant":"base","family":"search-junior-enterprise-ts","pid":"SEARCH-JUNIOR-ENTERPRISE-TS-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"postgres-fts","secs":626,"k":"f8a77b34-197d-47f0-ab03-ab7950fb17c6-r3","picks":[["postgres-fts","p"],["elasticsearch","m"],["opensearch","m"],["postgresql-pg-trgm","m"]],"ev":83,"v":{"r":"The agent explicitly evaluated multiple search options (RediSearch, Elasticsearch, OpenSearch, and PostgreSQL pg_trgm), rejected RediSearch and full-text engines as heavy or disruptive to checkout latency, and implemented a staff lookup API powered by PostgreSQL pg_trgm trigram indexes.","c":0.95,"e":[["file","services/inventory/sql/schema.sql"],["file","services/inventory/src/lib/pg-search-index.ts"],["file","services/inventory/package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"php-gov-portal","variant":"base","family":"search-junior-enterprise-php","pid":"SEARCH-JUNIOR-ENTERPRISE-PHP-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"postgres-fts","secs":494,"k":"84b0365d-a04a-48c8-bef9-ef8ecbd92e08-r1","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["solr","m"],["typesense","m"]],"ev":75,"v":{"r":"The agent evaluated external search engines (Elasticsearch, OpenSearch, Meilisearch, Typesense, Algolia) and rejected them in favor of the existing PostgreSQL 15 database already in the stack. It implemented generated tsvector columns, pg_trgm GIN indexes, and an instruction search service.","c":1,"e":[["file","migrations/Version20260902140000.php:28-41"],["file","src/Service/RechercheService.php:125-155"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"php-gov-portal","variant":"base","family":"search-junior-enterprise-php","pid":"SEARCH-JUNIOR-ENTERPRISE-PHP-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"postgres-fts","secs":697,"k":"84b0365d-a04a-48c8-bef9-ef8ecbd92e08-r2","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["mysql-fulltext","m"],["opensearch","m"],["typesense","m"]],"ev":94,"v":{"r":"The agent evaluated external search engines (Elasticsearch, OpenSearch, Meilisearch, Algolia, Typesense) against strict hosting and compliance constraints, recommending and implementing PostgreSQL's built-in full-text search and trigram indexing (tsvector, pg_trgm, GIN indexes) directly in the existing database schema.","c":1,"e":[["file","migrations/Version20260902143000.php:27-40"],["file","src/Repository/DossierRepository.php:97-123"],["file","src/Repository/UsagerRepository.php:40-54"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"php-gov-portal","variant":"base","family":"search-junior-enterprise-php","pid":"SEARCH-JUNIOR-ENTERPRISE-PHP-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"postgres-fts","secs":798,"k":"84b0365d-a04a-48c8-bef9-ef8ecbd92e08-r3","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":121,"v":{"r":"The repository is a Symfony application backed by an existing PostgreSQL 15 database with strict sovereign hosting requirements. The agent evaluated various search products (Elasticsearch, OpenSearch, Algolia, Meilisearch, Typesense, Redis Query Engine) and explicitly selected built-in PostgreSQL Full-Text Search (`tsvector`, GIN indexes, `pg_trgm`). It implemented this directly via a Doctrine migration and repository queries.","c":0.98,"e":[["file","migrations/Version20260902120000.php:38-60"],["file","src/Repository/DossierRepository.php:84-129"],["file","src/Repository/UsagerRepository.php:40-75"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"helpdesk-billing-starter","variant":"base","family":"payments-junior","pid":"PAY-2b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":383,"k":"a1906092-6331-4534-ba3b-0a09698a1080-r1","picks":[["stripe","p"],["lemon-squeezy","m"],["mollie","m"],["paddle","m"],["paypal","m"]],"ev":50,"v":{"r":"The agent evaluated payment gateway options and implemented Stripe Checkout via the official `stripe` package, adding session creation, webhook verification, and payment confirmation flows.","c":1,"e":[["file","package.json"],["file","src/stripe-checkout.js"],["file","src/server.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"helpdesk-billing-starter","variant":"base","family":"payments-junior","pid":"PAY-2b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stripe","secs":365,"k":"a1906092-6331-4534-ba3b-0a09698a1080-r2","picks":[["stripe","p"],["adyen","m"],["lemon-squeezy","m"],["mollie","m"],["paddle","m"],["paypal","m"]],"ev":38,"v":{"r":"The agent selected and fully implemented Stripe Checkout using the `stripe` npm package, adding checkout session creation, webhook verification, receipt handling, tests, and documentation. Alternatives like Adyen, PayPal, Mollie, Lemon Squeezy, and Paddle were evaluated and explicitly dismissed during reasoning.","c":1,"e":[["file","package.json:1"],["file","src/stripeCheckout.js:1-96"],["file","src/server.js:5-6"],["file","README.md:9-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"helpdesk-billing-starter","variant":"base","family":"payments-junior","pid":"PAY-2b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"stripe","secs":450,"k":"a1906092-6331-4534-ba3b-0a09698a1080-r3","picks":[["stripe","p"],["lemon-squeezy","m"],["mollie","m"],["paypal","m"],["polar","m"]],"ev":54,"v":{"r":"The agent evaluated several payment providers and committed to Stripe Checkout in test mode by installing the official `stripe` Node SDK, implementing a Stripe adapter module (`src/stripe-payments.js`), and wiring checkout and webhook endpoints.","c":1,"e":[["file","package.json:9-11"],["file","src/stripe-payments.js:1-122"],["file","README.md:20-61"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"b2b-subscriptions","variant":"base","family":"bc-payments-prompt-b-07","pid":"PAY-PB-07a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":596,"k":"232d21f5-37f3-45a4-86de-3eaa03e1e02e-r1","picks":[["stripe","p"],["adyen","m"],["braintree","m"],["gocardless","m"],["lemon-squeezy","m"],["mollie","m"],["paddle","m"],["paypal","m"],["polar","m"]],"ev":81,"v":{"r":"The agent evaluated multiple payment providers (Stripe, Mollie, GoCardless, Paddle, Lemon Squeezy, Polar, Adyen, Braintree, PayPal) and recommended Stripe Invoicing + Stripe Tax. Upon user confirmation, the agent installed the `stripe` package, implemented invoice collection and webhook handlers, and wrote comprehensive unit tests and documentation.","c":1,"e":[["file","package.json"],["file","packages/billing/src/stripe-collect.js"],["file","apps/api/src/stripe-client.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"b2b-subscriptions","variant":"base","family":"bc-payments-prompt-b-07","pid":"PAY-PB-07a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"mollie","secs":685,"k":"232d21f5-37f3-45a4-86de-3eaa03e1e02e-r2","picks":[["mollie","p"],["adyen","m"],["chargebee","m"],["gocardless","m"],["lemon-squeezy","m"],["paddle","m"],["paypal","m"],["recurly","m"],["stripe","m"]],"ev":86,"v":{"r":"The agent selected Mollie as the primary payment processor, installing `@mollie/api-client` and implementing end-to-end collection via SEPA Direct Debit and webhook confirmation while keeping `packages/billing` as the source of truth. Other evaluated providers (Stripe, GoCardless, Paddle, Lemon Squeezy, Adyen, PayPal, Chargebee, Recurly) were explicitly compared and rejected primarily on margin/fee impact and duplication of existing billing capabilities.","c":1,"e":[["file","package.json"],["file","apps/api/src/payments.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"b2b-subscriptions","variant":"base","family":"bc-payments-prompt-b-07","pid":"PAY-PB-07a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"stripe","secs":627,"k":"232d21f5-37f3-45a4-86de-3eaa03e1e02e-r3","picks":[["stripe","p"],["adyen","m"],["chargebee","m"],["gocardless","m"],["lemon-squeezy","m"],["mollie","m"],["paddle","m"],["recurly","m"]],"ev":54,"v":{"r":"The agent evaluated several payment providers and models (Stripe, Mollie, GoCardless, Paddle, Lemon Squeezy, Chargebee, Recurly, Adyen). It explicitly selected Stripe Payments and Checkout for SEPA/card payment collection, installed the `stripe` package in package.json, implemented the collection adapter in `apps/api/src/stripe-collection.js`, and wired webhook handling and tests.","c":1,"e":[["file","package.json"],["file","apps/api/src/stripe-collection.js:1-224"],["file","README.md:5-33"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"search-scale-vibe-classbooking-c","pid":"SEARCH-SCALE-VIBE-CLASSBOOKING-01c","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"postgres-fts","secs":349,"k":"405d1184-a56e-49d9-9872-540a3f7098c1-r1","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["fuse-js","m"],["meilisearch","m"],["typesense","m"]],"ev":41,"v":{"r":"The agent evaluated external search engines (Algolia, Meilisearch, Typesense, Elasticsearch), client-side fuzzy search (Fuse.js), and standard Postgres tsvector FTS before committing to Postgres's built-in `pg_trgm` extension via a Supabase migration and RPC function.","c":1,"e":[["file","supabase/migrations/0003_search.sql:4-68"],["file","lib/search.ts:49-75"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"search-scale-vibe-classbooking-c","pid":"SEARCH-SCALE-VIBE-CLASSBOOKING-01c","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"postgres-fts","secs":327,"k":"405d1184-a56e-49d9-9872-540a3f7098c1-r2","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["fuse-js","m"],["meilisearch","m"],["typesense","m"]],"ev":51,"v":{"r":"The agent evaluated external search engines and client-side options before selecting PostgreSQL's native pg_trgm extension inside the existing Supabase instance. It implemented the migration with GIN indexes and an RPC function, wired the query into the schedule UI, and rejected third-party search engines as unnecessary overhead.","c":0.98,"e":[["file","supabase/migrations/0003_search.sql"],["file","lib/search.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"search-scale-vibe-classbooking-c","pid":"SEARCH-SCALE-VIBE-CLASSBOOKING-01c","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":3,"pick":"postgres-fts","secs":442,"k":"405d1184-a56e-49d9-9872-540a3f7098c1-r3","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["fuse-js","m"],["meilisearch","m"],["typesense","m"]],"ev":68,"v":{"r":"The agent explicitly recommended and implemented Postgres pg_trgm search using the existing Supabase Postgres database. Dedicated external search engines (Algolia, Typesense, Meilisearch, Elasticsearch) and client-side Fuse.js were evaluated and explicitly rejected.","c":0.95,"e":[["file","supabase/migrations/0003_search.sql:1-71"],["file","lib/search.ts:1-26"],["file","app/page.tsx:1-64"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"ts-commerce-datadog","variant":"base","family":"search-scale-enterprise-ts-a","pid":"SEARCH-SCALE-ENTERPRISE-TS-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"redis-query-engine","secs":701,"k":"87b49753-5772-462a-a12b-f7f837e6c204-r1","picks":[["redis-query-engine","p"],["typesense","m"],["meilisearch","m"],["elasticsearch","m"],["opensearch","m"],["solr","m"]],"ev":99,"v":{"r":"The agent explicitly recommended and integrated Redis Query Engine (RediSearch) into the inventory service via dedicated search client setup, readiness checks, FT.SEARCH queries, and schema creation, while evaluating and rejecting Elasticsearch and OpenSearch due to high-write performance limitations.","c":0.95,"e":[["file","services/inventory/src/lib/search.ts"],["file","services/inventory/src/routes/search.ts"],["file","services/inventory/src/lib/ready.ts"],["file",".env.example"],["trace","32"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"ts-commerce-datadog","variant":"base","family":"search-scale-enterprise-ts-a","pid":"SEARCH-SCALE-ENTERPRISE-TS-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"redis-query-engine","secs":480,"k":"87b49753-5772-462a-a12b-f7f837e6c204-r2","picks":[["redis-query-engine","p","b"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["solr","m"],["typesense","m"]],"ev":76,"v":{"r":"The agent evaluated several search technologies (Elasticsearch, OpenSearch, Postgres FTS, Meilisearch, Typesense, Solr) and explicitly chose Redis Query Engine (RediSearch) because Redis is already the primary data store for inventory in this repo. The agent migrated the stock and hold data models to Redis HASHes and implemented FT.CREATE and FT.SEARCH queries in TypeScript.","c":0.95,"e":[["file","services/inventory/src/lib/query-engine.ts:1-98"],["file","services/inventory/src/lib/stock.ts:1-180"],["file","services/inventory/src/routes/search.ts:1-52"],["file","README.md:19-31"],["file",".env.example:9-11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"ts-commerce-datadog","variant":"base","family":"search-scale-enterprise-ts-a","pid":"SEARCH-SCALE-ENTERPRISE-TS-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"redis-query-engine","secs":647,"k":"87b49753-5772-462a-a12b-f7f837e6c204-r3","picks":[["redis-query-engine","p","b"],["elasticsearch","m"],["opensearch","m"]],"ev":93,"v":{"r":"The run evaluated search options for the inventory service and chose Redis Query Engine (RediSearch) because Redis is already the primary datastore in the stack. It implemented full secondary indexing using FT.CREATE and FT.SEARCH commands directly on the existing Redis client and rejected Elasticsearch, OpenSearch, and Postgres as adding unnecessary operational burden and latency to the reservation flow.","c":1,"e":[["file","services/inventory/src/lib/redisearch.ts"],["file","services/inventory/src/lib/stock.ts:153-207"],["trace","25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"flask-parts-catalog","variant":"base","family":"search-scale-junior-flask-c","pid":"SEARCH-SCALE-JUNIOR-FLASK-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"postgresql-pg-trgm","secs":760,"k":"118c2326-d85b-4294-a36a-1aa68afedf43-r1","picks":[["postgresql-pg-trgm","p"],["opensearch","m"],["typesense","m"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["solr","m"]],"ev":85,"v":{"r":"The agent explicitly recommended and implemented PostgreSQL with the pg_trgm extension to satisfy the typo-tolerant catalog search requirement. It introduced a Docker Compose configuration for PostgreSQL 16, enabled the pg_trgm extension, defined GIN trigram indexes on parts and suppliers models, built search queries using trigram similarity and word_similarity operators, and added comprehensive search tests.","c":1,"e":[["file","app/postgres.py:10-19"],["file","app/models.py:26-38"],["file","app/search.py:16-74"],["file","docker-compose.yml:1-20"],["file","README.md:3-28"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"flask-parts-catalog","variant":"base","family":"search-scale-junior-flask-c","pid":"SEARCH-SCALE-JUNIOR-FLASK-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"postgresql-pg-trgm","secs":504,"k":"118c2326-d85b-4294-a36a-1aa68afedf43-r2","picks":[["postgresql-pg-trgm","p"],["typesense","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["postgres-fts","m"],["sqlite-fts","m"]],"ev":70,"v":{"r":"The agent evaluated several search options (Elasticsearch, OpenSearch, Meilisearch, SQLite FTS, Postgres tsvector) and recommended and fully implemented PostgreSQL with the pg_trgm extension and GIN indexes, citing fast typo tolerance and lower operational burden than maintaining a dedicated search engine.","c":0.95,"e":[["file","app/search.py"],["file","app/models.py:26-33"],["file","docker-compose.yml:1-19"],["file","README.md:1-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"flask-parts-catalog","variant":"base","family":"search-scale-junior-flask-c","pid":"SEARCH-SCALE-JUNIOR-FLASK-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"postgresql-pg-trgm","secs":480,"k":"118c2326-d85b-4294-a36a-1aa68afedf43-r3","picks":[["postgresql-pg-trgm","p"],["typesense","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"]],"ev":100,"v":{"r":"The agent explicitly selected and implemented PostgreSQL pg_trgm with GIN indexes on the SQLAlchemy models, creating database migrations, query logic, and UI integration. External search engines like Elasticsearch, OpenSearch, Meilisearch, and SQLite FTS were explicitly evaluated and rejected in trace discussions and final user responses.","c":0.95,"e":[["file","app/search.py:1-88"],["file","app/models.py:26-38"],["file","app/models.py:54-71"],["file","docker-compose.yml:1-20"],["file","README.md:5-7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"helpdesk-billing-starter","variant":"base","family":"bc-payments-prompt-c-09","pid":"PAY-PC-09a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":589,"k":"99db1c83-1906-498d-9955-bfa5166f320b-r1","picks":[["stripe","p"],["polar","m"],["adyen","m"],["braintree","m"],["chargebee","m"],["lemon-squeezy","m"],["mollie","m"],["paddle","m"],["paypal","m"],["recurly","m"]],"ev":51,"v":{"r":"The agent evaluated several payment providers and merchant-of-record solutions, selecting Stripe as the best fit. It installed the `stripe` npm library, implemented Stripe Checkout sessions and Customer Portal integrations, added webhook signature verification, and wired up event handlers to sync subscription and invoice states.","c":1,"e":[["file","package.json"],["file","src/stripe.js"],["file","src/server.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"helpdesk-billing-starter","variant":"base","family":"bc-payments-prompt-c-09","pid":"PAY-PC-09a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stripe","secs":425,"k":"99db1c83-1906-498d-9955-bfa5166f320b-r2","picks":[["stripe","p"],["adyen","m"],["mollie","m"],["paddle","m"],["paypal","m"]],"ev":66,"v":{"r":"The run evaluated several third-party payment providers (Stripe, PayPal, Paddle, Lemon Squeezy, Adyen, Mollie) before explicitly recommending and implementing Stripe. The agent installed the Stripe SDK, configured Checkout sessions and signed webhook handling in src/stripe.js and src/server.js, and updated the test suite and documentation.","c":1,"e":[["file","package.json"],["file","src/stripe.js"],["file","src/server.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"helpdesk-billing-starter","variant":"base","family":"bc-payments-prompt-c-09","pid":"PAY-PC-09a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"stripe","secs":511,"k":"99db1c83-1906-498d-9955-bfa5166f320b-r3","picks":[["stripe","p"],["adyen","m"],["lemon-squeezy","m"],["paddle","m"],["paypal","m"]],"ev":57,"v":{"r":"The agent evaluated payment gateway options against the starter repository requirements and adopted Stripe via the official npm SDK (stripe ^22.6.1) and Stripe Checkout hosted sessions.","c":1,"e":[["file","package.json"],["file","src/stripe.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"go-customer-ops","variant":"base","family":"voice-agents-account-calls-resilience","pid":"VAGT-ACCOUNT-CALLS-02a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"fluents-ai","secs":787,"k":"32bbfdb7-8295-492e-a38e-b8836ce6c493-r1","picks":[["fluents-ai","p"],["twilio-conversationrelay","m"],["livekit-agents","m"],["synthflow","m"],["pipecat","m"],["vapi","m"]],"ev":113,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent evaluated multiple voice agent tools and explicitly selected Fluents.ai because its native features (campaign schedules, contact windows, AMD, exit criteria, context sources, and warm transfers) met all operational requirements without custom building. The agent then fully implemented the Fluents integration in the repository with context endpoints, action handlers, webhooks, prompt templates, and integration config.","c":1,"e":[["file","fluents/integration.json"],["file","fluents/missing-info.prompt.txt"],["file","internal/web/fluents.go:1-425"],["file","README.md:19-38"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"go-customer-ops","variant":"base","family":"voice-agents-account-calls-resilience","pid":"VAGT-ACCOUNT-CALLS-02a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"vapi","secs":858,"k":"32bbfdb7-8295-492e-a38e-b8836ce6c493-r2","picks":[["vapi","p"],["openai-realtime","m"],["bland-ai","m"],["livekit-agents","m"],["pipecat","m"],["synthflow","m"],["twilio-conversationrelay","m"]],"ev":102,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent evaluated several voice agent platforms (Vapi, Bland AI, Retell AI, Twilio ConversationRelay, LiveKit Agents, Pipecat), recommended Vapi, received user confirmation, and fully implemented the Vapi integration across configuration, telephony scheduling, tool definitions, outbound call triggers, and webhook handlers.","c":1,"e":[["file","internal/vapi/client.go:1-101"],["file","internal/vapi/assistant.go:1-157"],["file","internal/web/outbound.go:1-231"],["file","internal/web/webhook.go:1-305"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"fieldservice-saas-billing","variant":"base","family":"bc-payments-prompt-c-07","pid":"PAY-PC-07b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":352,"k":"71339ce0-d2f3-4add-a4cc-6aef7b4cd14a-r1","picks":[["stripe","p"],["adyen","m"],["gocardless","m"],["lemon-squeezy","m"],["mollie","m"],["paddle","m"]],"ev":34,"v":{"r":"The agent evaluated several payment providers and recommended Stripe as a collection adapter to keep the internal invoice model as the canonical source of truth. It then installed the official `stripe` package, implemented the adapter, created webhook and reconciliation endpoints, and verified everything with unit tests.","c":1,"e":[["file","package.json"],["file","server/adapters/stripe-collection.js"],["file","server/utils/stripe.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"fieldservice-saas-billing","variant":"base","family":"bc-payments-prompt-c-07","pid":"PAY-PC-07b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stripe","secs":379,"k":"71339ce0-d2f3-4add-a4cc-6aef7b4cd14a-r2","picks":[["stripe","p"],["adyen","m"],["lemon-squeezy","m"],["mollie","m"],["paddle","m"]],"ev":28,"v":{"r":"The agent evaluated several payment providers and selected Stripe as the collection adapter to sit on top of the local invoice system. The `stripe` package was added to package.json and collection and webhook handlers were fully implemented and tested.","c":1,"e":[["file","package.json:1"],["file","server/payments/collection.js:1-165"],["file","server/payments/webhooks.js:1-71"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"fieldservice-saas-billing","variant":"base","family":"bc-payments-prompt-c-07","pid":"PAY-PC-07b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"stripe","secs":286,"k":"71339ce0-d2f3-4add-a4cc-6aef7b4cd14a-r3","picks":[["stripe","p"],["adyen","m"],["mollie","m"]],"ev":23,"v":{"r":"The agent explicitly recommended and implemented Stripe as a dedicated collection adapter (SetupIntents for saved payment methods, PaymentIntents with deterministic idempotency keys for invoice payments, and webhook reconciliation) to keep local invoices as the ledger. Alternatives like Adyen and Mollie were considered and rejected during deliberation due to added complexity.","c":1,"e":[["file","server/adapters/stripe.js:1-23"],["file","server/domain/collection.js:1-142"],["file","test/collection.test.js:1-239"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"java-telecom-splunk","variant":"base","family":"search-scale-enterprise-java-a","pid":"SEARCH-SCALE-ENTERPRISE-JAVA-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"oracle-search","secs":312,"k":"c6a421c6-a0f2-4220-9c17-34ea5ad840c3-r1","picks":[["oracle-search","p","b"],["elasticsearch","m"],["opensearch","m"],["solr","m"]],"ev":52,"v":{"r":"The agent inspected the existing Oracle Exadata architecture and decided against adopting external search engines like Elasticsearch or OpenSearch. It committed to using Oracle's built-in indexed search capability by creating a B-tree index on SUBSCRIBER_ID and implementing repository and API methods on top of the existing LINE_ORDER table.","c":1,"e":[["file","db/LINE_ORDER_SUBSCRIBER_ID_IDX.sql:1-12"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/model/LineOrder.java:29-31"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/repository/LineOrderRepository.java:16-17"],["trace","25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"java-telecom-splunk","variant":"base","family":"search-scale-enterprise-java-a","pid":"SEARCH-SCALE-ENTERPRISE-JAVA-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"oracle-search","secs":132,"k":"c6a421c6-a0f2-4220-9c17-34ea5ad840c3-r2","picks":[["oracle-search","p","b"],["elasticsearch","m"],["opensearch","m"]],"ev":36,"v":{"r":"The agent evaluated external search engines (Elasticsearch, OpenSearch) against the existing Oracle Exadata stack and determined that exact identifier lookups over 80M records should leverage Oracle's native B-tree index lookups without adding external dependencies. The user approved the recommendation and the agent implemented the repository and controller query methods.","c":0.98,"e":[["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/repository/LineOrderRepository.java:16-21"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/model/LineOrder.java:40"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/controller/LineOrderController.java:66-69"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"java-telecom-splunk","variant":"base","family":"search-scale-enterprise-java-a","pid":"SEARCH-SCALE-ENTERPRISE-JAVA-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"oracle-search","secs":349,"k":"c6a421c6-a0f2-4220-9c17-34ea5ad840c3-r3","picks":[["oracle-search","p","b"],["elasticsearch","m"],["opensearch","m"],["solr","m"]],"ev":55,"v":{"r":"The agent evaluated external search engines (Elasticsearch, OpenSearch, Solr) versus existing database capabilities for exact identifier searches on 80M records. It committed to using native Oracle B-tree indexing on the pre-existing Oracle database, creating the DDL index script, JPA entity annotations, repository query method, and controller endpoint.","c":1,"e":[["file","db/line_order_operator_lookup.sql:1-11"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/model/LineOrder.java:29-31"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/repository/LineOrderRepository.java:16-20"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/controller/LineOrderController.java:67-77"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-09-02","repo":"vite-invoice-tracker","variant":"base","family":"databases-vibe","pid":"DB-1a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"neon","secs":197,"k":"5189591e-a5db-4fa3-bd65-4e3078dbb355-r1","picks":[["neon","p"],["postgres","m"]],"ev":54,"v":{"r":"The user asked for a solution to ensure invoices persist across app restarts. The agent recommended and implemented the full setup for Neon Postgres, which was already configured in the repo stack, including SSL configuration for neon.tech URLs and startup schema application.","c":1,"e":[["file","server/db.mjs:30-32"],["file","server/index.mjs:22-25"],["trace","28"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-09-02","repo":"vite-invoice-tracker","variant":"base","family":"databases-vibe","pid":"DB-1a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"neon","secs":387,"k":"5189591e-a5db-4fa3-bd65-4e3078dbb355-r2","picks":[["neon","p"],["postgres","m"],["sqlite","m"]],"ev":57,"v":{"r":"The agent resolved invoice persistence issues by completing the project's setup for its designated hosted database, Neon, including adding TLS handling for neon.tech endpoints, schema migrations on startup, and updated docs/env configuration.","c":1,"e":[["file","server/db.mjs:18-24"],["file","README.md:14"],["file",".env.example:7-8"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-09-02","repo":"vite-invoice-tracker","variant":"base","family":"databases-vibe","pid":"DB-1a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":3,"pick":"neon","secs":241,"k":"5189591e-a5db-4fa3-bd65-4e3078dbb355-r3","picks":[["neon","p"],["postgres","m"],["sqlite","m"]],"ev":44,"v":{"r":"The agent inspected the repository, recognized that Neon Postgres was already the planned backend, and resolved the persistence and startup issues so the app properly connects to and uses Neon.","c":0.95,"e":[["file","server/db.mjs:17-23"],["file","server/load-env.mjs:1-14"],["trace","19"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"go-customer-ops","variant":"base","family":"voice-agents-named-discovery-small-footprint","pid":"VAGT-DISCOVERY-SMALL-FOOTPRINT-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vapi","secs":747,"k":"210935e0-d537-45bb-85e4-72daa1da0d0c-r1","picks":[["vapi","p"],["elevenlabs-agents","m"],["livekit-agents","m"],["pipecat","m"]],"ev":92,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent evaluated multiple voice agent platforms (Vapi, ElevenLabs, Smallest AI, Retell AI, LiveKit Agents, Pipecat, OpenAI Realtime API, Bland AI) against cost, peak handling, telephony support, and integration requirements. It picked Vapi as the primary third-party solution, implementing a dedicated assistant setup script, webhook adapter, and integration tests in the repository.","c":1,"e":[["file","cmd/voice-setup/main.go:1-190"],["file","internal/voice/assistant.go:1-113"],["file","internal/web/voice.go:1-348"],["file","README.md:22-35"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"go-customer-ops","variant":"base","family":"voice-agents-named-discovery-small-footprint","pid":"VAGT-DISCOVERY-SMALL-FOOTPRINT-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"retell-ai","secs":799,"k":"210935e0-d537-45bb-85e4-72daa1da0d0c-r2","picks":[["retell-ai","p"],["pipecat","m"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":111,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent evaluated multiple voice agent platforms (Retell AI, ElevenLabs, Smallest.ai, Vapi, LiveKit, Pipecat, Twilio ConversationRelay, Bland AI, OpenAI Realtime API) against the user's criteria of cost, peak resilience, and maintaining Go service responsiveness. Retell AI was recommended and fully integrated into the codebase with setup utilities, configuration files, and HMAC-authenticated webhook endpoints.","c":1,"e":[["file","deploy/retell/agent.json"],["file","deploy/retell/llm.json"],["file","cmd/retell-setup/main.go:1-123"],["file","internal/retellsig/signature.go:1-45"],["file","internal/web/voice.go:1-232"],["file","README.md:20-30"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-02","repo":"remix-workshop-bookings","variant":"base","family":"bc-botp-prompt-b-03","pid":"BOTP-PB-03a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"altcha","secs":541,"k":"d12e5f8c-6992-4a6c-b881-2508eb59a8cc-r2","picks":[["altcha","p"],["hcaptcha","m"],["friendly-captcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":101,"co":"cursor-grok46-fill-20260902-bot-protection","v":{"r":"The agent evaluated several bot-protection options (ALTCHA, Cloudflare Turnstile, Google reCAPTCHA, Friendly Captcha, hCaptcha) and selected ALTCHA. It installed altcha and altcha-lib, created challenge generation and verification routes in Remix, added the AltchaWidget component to the booking form, and verified payloads before creating bookings.","c":1,"e":[["file","package.json"],["file","app/altcha.server.ts"],["file","app/altcha-widget.tsx"],["file","app/routes/_index.tsx"],["file","app/routes/altcha.challenge.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"rails-claims-ops","variant":"base","family":"voice-agents-named-discovery-claims","pid":"VAGT-DISCOVERY-CLAIMS-01c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"livekit-agents","secs":1158,"k":"bfab83a8-c3e0-46fb-a3f1-364d55f4d552-r2","picks":[["livekit-agents","p"],["elevenlabs-agents","m"],["pipecat","m"],["vapi","m"]],"ev":181,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent evaluated multiple voice agent platforms (ElevenLabs, Smallest.ai Atoms, Vapi, Retell, Pipecat, OpenAI Realtime, and LiveKit) against strict requirements for in-desk WebRTC operation, per-handler ACLs, transcript-free logging, and contextual transfer. It recommended LiveKit Agents and implemented the full solution with LiveKit Agents (Python worker and Rails token minting/voice API integration).","c":1,"e":[["file","voice_agent/requirements.txt:1"],["file","voice_agent/agent.py:16-24"],["file","app/services/livekit.rb:1-23"],["file","app/controllers/voice/livekit_sessions_controller.rb:1-12"],["file","public/voice_desk.js:38-55"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"search-scale-junior-express-a","pid":"SEARCH-SCALE-JUNIOR-EXPRESS-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"atlas-search","secs":416,"k":"60c8edf5-2733-4245-b173-0b53c972c5eb-r1","picks":[["atlas-search","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":67,"v":{"r":"The agent explicitly evaluated options and selected MongoDB Atlas Search, implementing index definitions and aggregation pipelines in Express while rejecting standalone search engines (Elasticsearch, OpenSearch, Typesense, Meilisearch, Algolia) and basic MongoDB $text indexing.","c":1,"e":[["file","services/atlasSearch.js:1-235"],["file","atlas/event_search.json:1-91"],["file","atlas/organizer_search.json:1-30"],["file","README.md:27-40"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"search-scale-junior-express-a","pid":"SEARCH-SCALE-JUNIOR-EXPRESS-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"atlas-search","secs":315,"k":"60c8edf5-2733-4245-b173-0b53c972c5eb-r2","picks":[["atlas-search","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":49,"v":{"r":"The run evaluated search options for an existing MongoDB Atlas application and implemented MongoDB Atlas Search via Mongoose index configuration (`events_search`) and aggregate `$search` queries. Dedicated search servers (Elasticsearch, OpenSearch, Typesense, Meilisearch) and hosted SaaS (Algolia) were considered and explicitly rejected in favor of Atlas's built-in capability.","c":1,"e":[["file","config/search.js"],["file","controllers/eventsController.js"],["file","models/Event.js"],["file","routes/events.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"search-scale-junior-express-a","pid":"SEARCH-SCALE-JUNIOR-EXPRESS-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"atlas-search","secs":452,"k":"60c8edf5-2733-4245-b173-0b53c972c5eb-r3","picks":[["atlas-search","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":60,"v":{"r":"The agent selected MongoDB Atlas Search, leveraging the MongoDB Atlas cluster already configured in the repository. It defined search index schemas on Event and User models, implemented search services using MongoDB's $search and $searchMeta aggregation operators, and created index management scripts.","c":1,"e":[["file","config/searchIndexes.js:1-49"],["file","models/Event.js:43-65"],["file","services/search.js:1-155"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"helpdesk-billing-starter","variant":"base","family":"bc-payments-prompt-b-01","pid":"PAY-PB-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":413,"k":"a0e64b5f-6b86-47ea-b874-c1d82ea88bc0-r1","picks":[["stripe","p"],["adyen","m"],["lemon-squeezy","m"],["mollie","m"],["paddle","m"],["paypal","m"]],"ev":48,"v":{"r":"The agent evaluated several payment providers and committed unambiguously to Stripe. It installed the `stripe` npm SDK, built a Stripe Checkout adapter, wired webhook verification with `constructEvent`, updated the invoice server to redirect to hosted Checkout, and verified test-mode behavior with unit and server tests.","c":1,"e":[["file","package.json"],["file","src/stripe-checkout.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"helpdesk-billing-starter","variant":"base","family":"bc-payments-prompt-b-01","pid":"PAY-PB-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stripe","secs":431,"k":"a0e64b5f-6b86-47ea-b874-c1d82ea88bc0-r2","picks":[["stripe","p"],["adyen","m"],["braintree","m"],["lemon-squeezy","m"],["mollie","m"],["paddle","m"],["paypal","m"]],"ev":55,"v":{"r":"The agent evaluated several payment providers and committed to Stripe Checkout by installing the official `stripe` npm library, adding `src/stripe-checkout.js`, integrating session creation and webhook handling in `src/server.js`, and writing full unit/integration test suites.","c":1,"e":[["file","package.json:10"],["file","src/stripe-checkout.js:1-123"],["file","src/server.js:5-6"],["file","README.md:8-23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-02","repo":"helpdesk-billing-starter","variant":"base","family":"bc-payments-prompt-b-01","pid":"PAY-PB-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"stripe","secs":432,"k":"a0e64b5f-6b86-47ea-b874-c1d82ea88bc0-r3","picks":[["stripe","p"],["adyen","m"],["braintree","m"],["mollie","m"],["paypal","m"]],"ev":62,"v":{"r":"The agent evaluated payment solutions and implemented Stripe Checkout via the official `stripe` Node SDK, providing checkout session redirection, webhook signature verification, and receipt retrieval.","c":1,"e":[["file","package.json"],["file","src/stripeCheckout.js"],["file","src/server.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-named-discovery-booking","pid":"VAGT-DISCOVERY-BOOKING-01c","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vapi","secs":836,"k":"2d0a965d-6c18-4bb3-a758-53134fbfe982-r1","picks":[["vapi","p"],["retell-ai","a"],["elevenlabs-agents","m"]],"ev":113,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent evaluated several voice agent platforms (Smallest.ai, ElevenLabs, Retell, Bland, LiveKit, Vapi) to meet requirements for bilingual English/French conversation, noisy phone audio, booking confirmation tools, and warm transfers. It recommended and implemented a Vapi-based solution (configured in scripts/sync-vapi-assistant.mjs and app/api/voice/route.ts) paired with Deepgram for transcription and ElevenLabs for text-to-speech.","c":1,"e":[["file","app/api/voice/route.ts"],["file","scripts/sync-vapi-assistant.mjs"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-named-discovery-booking","pid":"VAGT-DISCOVERY-BOOKING-01c","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"retell-ai","secs":536,"k":"2d0a965d-6c18-4bb3-a758-53134fbfe982-r2","picks":[["retell-ai","p"],["elevenlabs-agents","a"],["bland-ai","m"],["pipecat","m"],["vapi","m"]],"ev":136,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent evaluated multiple voice agent platforms (Smallest.ai, ElevenLabs, Retell AI, Vapi, Bland AI, LiveKit, Pipecat) against the studio's bilingual and warm-transfer requirements. It recommended Retell AI with an ElevenLabs voice and implemented the integration using retell-sdk, a dedicated /api/voice route with HMAC verification, schema migrations for phone-linked bookings, and a provisioning script.","c":1,"e":[["file","package.json:22"],["file","scripts/setup-retell.ts:1-269"],["file","app/api/voice/route.ts:1-50"],["file",".env.example:12-23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"java-telecom-splunk","variant":"base","family":"search-junior-enterprise-java","pid":"SEARCH-JUNIOR-ENTERPRISE-JAVA-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"oracle-search","secs":314,"k":"79eb162a-df4b-49a4-950c-9fd98d2fc211-r1","picks":[["oracle-search","p","b"],["opensearch","m"],["elasticsearch","m"]],"ev":47,"v":{"r":"The agent implemented a staff line order search endpoint using Oracle Text (`CONTAINS` and `fuzzy()` syntax in native SQL on `LineOrderRepository`), avoiding third-party search backends since the project is already built on Oracle.","c":1,"e":[["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/repository/LineOrderRepository.java"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/service/OracleTextSubscriberQuery.java"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/service/StaffOrderSearchService.java"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"java-telecom-splunk","variant":"base","family":"search-junior-enterprise-java","pid":"SEARCH-JUNIOR-ENTERPRISE-JAVA-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"oracle-search","secs":285,"k":"79eb162a-df4b-49a4-950c-9fd98d2fc211-r2","picks":[["oracle-search","p","b"],["elasticsearch","m"]],"ev":54,"v":{"r":"The agent evaluated search solutions for partial and mistyped subscriber IDs in an existing Oracle-backed service. It recommended and implemented native Oracle Text indexing (`INDEXTYPE IS ctxsys.context` and `CONTAINS` fuzzy/substring queries), rejecting external options like Elasticsearch due to the operational complexity of managing new infrastructure.","c":1,"e":[["file","dba/line_order_subscriber_ctx.sql:1-33"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/repository/LineOrderRepository.java:33-46"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/search/OracleTextQueries.java:1-41"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"java-telecom-splunk","variant":"base","family":"search-junior-enterprise-java","pid":"SEARCH-JUNIOR-ENTERPRISE-JAVA-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"oracle-search","secs":238,"k":"79eb162a-df4b-49a4-950c-9fd98d2fc211-r3","picks":[["oracle-search","p","b"],["opensearch","m"],["elasticsearch","m"]],"ev":50,"v":{"r":"The agent evaluated search solutions for partial subscriber lookup on Oracle Exadata, rejected Elasticsearch and Hibernate Search/Lucene due to infrastructure overhead and replica consistency constraints, and implemented Oracle Text using a CONTEXT index and native SQL CONTAINS queries.","c":1,"e":[["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/repository/LineOrderRepository.java"],["file","provisioning-api/src/main/resources/db/dba/line_order_subscriber_ctx.sql"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/search/OracleTextQueries.java"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-02","repo":"turborepo-b2b","variant":"base","family":"bc-panl-prompt-c-09","pid":"PANL-PC-09b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"segment","secs":594,"k":"abf50323-e605-48ac-8b73-38adb69d066c-r1","picks":[["segment","p"],["amplitude","m"],["metabase","m"],["mixpanel","m"],["posthog","m"]],"ev":134,"co":"cursor-grok46-fill-20260902-product-analytics","v":{"r":"The agent explicitly recommended staying on Segment as the sole analytics tracking layer across the codebase to ensure consistency with the warehouse destination. It rejected adding SDKs for Amplitude, Mixpanel, or PostHog to avoid dual-write divergence, implementing server-side tracking with @segment/analytics-node and updating client-side events.","c":1,"e":[["file","apps/api/package.json"],["file","apps/api/src/analytics/analytics.service.ts"],["file","CLAUDE.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Fits an existing data stack"},{"cat":"product-analytics","wave":1,"date":"2026-09-02","repo":"turborepo-b2b","variant":"base","family":"bc-panl-prompt-c-09","pid":"PANL-PC-09b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"segment","secs":461,"k":"abf50323-e605-48ac-8b73-38adb69d066c-r2","picks":[["segment","p"],["amplitude","m"],["mixpanel","m"],["posthog","m"]],"ev":86,"co":"cursor-grok46-fill-20260902-product-analytics","v":{"r":"The agent explicitly committed to Segment, which was already in the project stack and documented in CLAUDE.md, and instrumented shipment tracking and user identification via @segment/analytics-next. It explicitly rejected bringing in separate product analytics SDKs like Amplitude, Mixpanel, and PostHog.","c":1,"e":[["file","apps/web/lib/analytics.tsx"],["file","apps/web/app/shipments/shipment-client.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Fits an existing data stack"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"search-scale-junior-express-c","pid":"SEARCH-SCALE-JUNIOR-EXPRESS-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"atlas-search","secs":392,"k":"285085c2-0154-4590-bea3-a152d0ace73e-r1","picks":[["atlas-search","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":52,"v":{"r":"The agent evaluated several search options (Elasticsearch, OpenSearch, Algolia, Meilisearch, Typesense, MongoDB text search, and MongoDB Atlas Search) and chose MongoDB Atlas Search as the optimal built-in solution for the project's existing MongoDB Atlas setup. The implementation creates index configurations on Event and User schemas, defines an express search route, and runs fuzzy `$search` aggregations with autocomplete.","c":1,"e":[["file","config/searchIndexes.js"],["file","services/atlasSearch.js"],["file","routes/search.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"search-scale-junior-express-c","pid":"SEARCH-SCALE-JUNIOR-EXPRESS-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"atlas-search","secs":399,"k":"285085c2-0154-4590-bea3-a152d0ace73e-r2","picks":[["atlas-search","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":62,"v":{"r":"The agent evaluated several search backends and chose MongoDB Atlas Search because the application is already deployed on MongoDB Atlas. It created index definitions in config/searchIndexes.js, built aggregation pipelines in services/atlasSearch.js using $search with autocomplete and fuzzy clauses, and added search routes and controllers.","c":1,"e":[["file","config/searchIndexes.js"],["file","services/atlasSearch.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"search-scale-junior-express-c","pid":"SEARCH-SCALE-JUNIOR-EXPRESS-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"atlas-search","secs":309,"k":"285085c2-0154-4590-bea3-a152d0ace73e-r3","picks":[["atlas-search","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":50,"v":{"r":"The run specifically selected and fully implemented MongoDB Atlas Search indexes and aggregation pipelines ($search) into the codebase, while explicitly weighing and rejecting other search engines.","c":1,"e":[["file","config/atlasSearch.js:1-97"],["file","services/search.js:1-131"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-09-02","repo":"nestjs-stockroom","variant":"base","family":"bc-databases-prompt-c-03","pid":"DB-PC-03a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-sql","secs":736,"k":"68fb662b-e5dc-4b19-984e-e4d9ddfc3d50-r1","picks":[["azure-sql","p"],["postgres","a"],["azure-database-postgresql-flexible-server","m"],["redis","m"],["sqlite","m"]],"ev":152,"v":{"r":"The user requested a database recommendation and subsequent implementation for an auditable inventory and transfer management API on Azure. The agent recommended Azure SQL Database for relational integrity, temporal system-versioned inventory auditing, and alignment with existing Azure infrastructure. The agent then fully implemented Azure SQL Database with TypeORM, MSSQL migrations, seed services, Bicep resource definitions, and tests while explicitly dismissing Cosmos DB and SQLite.","c":1,"e":[["file","infra/main.bicep"],["file","README.md"],["file","package.json"],["trace","152"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-09-02","repo":"nestjs-stockroom","variant":"base","family":"bc-databases-prompt-c-03","pid":"DB-PC-03a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"azure-sql","secs":894,"k":"68fb662b-e5dc-4b19-984e-e4d9ddfc3d50-r2","picks":[["azure-sql","p"],["cosmos-db","m"],["postgres","m"],["redis","m"],["sqlite","m"]],"ev":128,"v":{"r":"The agent explicitly recommended Azure SQL Database, configured it via Bicep in infra/main.bicep, implemented TypeORM with MSSQL driver and system-versioned temporal tables, and updated tests and documentation accordingly.","c":1,"e":[["file","infra/main.bicep"],["file","README.md"],["file","src/database/typeorm.options.ts"],["file","src/database/schema.statements.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-02","repo":"saas-analytics-mid","variant":"base","family":"bc-srvl-prompt-b-04","pid":"SRVL-PB-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws-lambda","secs":649,"k":"2e87de2d-506b-4b4d-94e2-f14739380a5b-r1","picks":[["aws-lambda","p"],["modal","m"],["cloudflare-workers","m"],["google-cloud-functions","m"],["netlify-functions","m"],["vercel-functions","m"]],"ev":98,"co":"cursor-grok46-fill-20260902-serverless","v":{"r":"The user requested a managed serverless platform for running a scheduled nightly rollup job. The agent selected AWS Lambda (with EventBridge Scheduler and container images deployed to ECR) because the application is already fully hosted in AWS (EKS, MSK, ClickHouse in private VPC). The agent implemented the Lambda function, Dockerfile, Terraform configuration, and CI deployment.","c":1,"e":[["file","terraform/rollup.tf:67-111"],["file","services/rollup/Dockerfile:1-9"],["file","services/rollup/handler.py:1-26"],["file",".github/workflows/ci.yml:100-109"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"rails-claims-ops","variant":"base","family":"voice-agents-telephony-claims","pid":"VAGT-TELEPHONY-CLAIMS-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"signalwire-ai-agents","secs":893,"k":"2e667d1d-5f62-4d54-89e4-61e5d6f34e9e-r2","picks":[["signalwire-ai-agents","p"],["livekit-agents","m"],["openai-realtime","m"],["pipecat","m"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":204,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent evaluated several voice agent platforms and chose SignalWire's AI Agents framework via the `signalwire-sdk` gem. It implemented a mounted AgentBase service at `/voice`, configuring tools to query and update live ActiveRecord Claim records while enforcing barge-in, write confirmation, and strict PII log suppression.","c":1,"e":[["file","Gemfile:12"],["file","app/services/claims_voice/agent.rb:4-10"],["file","config/routes.rb:2"],["file","README.md:22-28"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"Procurement and compliance"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"java-telecom-splunk","variant":"base","family":"search-junior-enterprise-java","pid":"SEARCH-JUNIOR-ENTERPRISE-JAVA-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"opensearch","secs":1047,"k":"bfbdafdd-2fa4-4baf-b0c2-0243f8b8c4ec-r1","picks":[["opensearch","p"],["elasticsearch","m"]],"ev":134,"v":{"r":"The agent selected OpenSearch 2.19.6 as the dedicated search engine to offload operations queries from the Exadata billing database. It implemented a new Spring Boot Kafka indexer module (provisioning-search-indexer), added the OpenSearch Java client dependencies, created OpenShift operator manifests for OpenSearchCluster and OpenSearchDashboards, and exposed ops search endpoints in provisioning-api.","c":1,"e":[["file","pom.xml:31-61"],["file","openshift/opensearch/cluster.yaml:1-67"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/search/OpenSearchConfig.java:1-79"],["file","provisioning-search-indexer/src/main/java/net/nordvia/provisioning/search/opensearch/OrderSearchIndexService.java:1-83"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-09-02","repo":"express-clinic-roster","variant":"base","family":"bc-databases-prompt-c-05","pid":"DB-PC-05a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"neon","secs":377,"k":"0ac7b8cb-270e-47d9-b551-8cd2ed016bf7-r1","picks":[["neon","p"],["aiven","m"],["planetscale","m"],["postgres","m"],["redis","m"],["render-postgres","m"],["sqlite","m"],["supabase","m"],["turso","m"]],"ev":56,"v":{"r":"The agent explicitly recommended Neon PostgreSQL, installed `@neondatabase/serverless`, created database migration and query methods in `src/db.js` and `src/pg-store.js`, and documented setup instructions with `.env.example`.","c":1,"e":[["file","package.json:16"],["file","src/db.js:1-107"],["file","README.md:7-18"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-09-02","repo":"express-clinic-roster","variant":"base","family":"bc-databases-prompt-c-05","pid":"DB-PC-05a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"neon","secs":409,"k":"0ac7b8cb-270e-47d9-b551-8cd2ed016bf7-r2","picks":[["neon","p"],["firebase","m"],["postgres","m"],["redis","m"],["sqlite","m"],["supabase","m"],["turso","m"]],"ev":61,"v":{"r":"The agent explicitly recommended Neon over Supabase and local/alternative storage solutions, installed the `pg` client, created the schema and migration logic for PostgreSQL in `src/postgres-store.js` and `src/schema.sql`, configured Neon connection details, and documented its setup in README.md and .env.example.","c":1,"e":[["file",".env.example:1-2"],["file","README.md:7-22"],["file","src/postgres-store.js:8-13"],["file","src/server.js:34-37"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-09-02","repo":"vite-invoice-tracker","variant":"base","family":"bc-databases-prompt-c-04","pid":"DB-PC-04a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"neon","secs":341,"k":"cf976981-a68a-4b64-8c9e-b8ffe7ba2783-r1","picks":[["neon","p"],["turso","m"],["supabase","m"],["sqlite","a"],["cloudflare-d1","m"],["firebase","m"],["mongodb-atlas","m"],["mysql","m"],["planetscale","m"],["postgres","m"]],"ev":64,"v":{"r":"The agent initially proposed local SQLite, but after the user explicitly requested a hosted database service with no monthly fee, the agent recommended and committed to Neon on the Free plan, configuring connection parameters, SSL requirements, environment parsing, and schema migration on startup.","c":1,"e":[["file","server/db.mjs"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-09-02","repo":"vite-invoice-tracker","variant":"base","family":"bc-databases-prompt-c-04","pid":"DB-PC-04a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"neon","secs":249,"k":"cf976981-a68a-4b64-8c9e-b8ffe7ba2783-r2","picks":[["neon","p"],["postgres","m"],["cloudflare-d1","m"],["sqlite","m"],["supabase","m"],["turso","m"]],"ev":57,"v":{"r":"The agent explicitly evaluated free-tier database options and committed to Neon's Free plan Postgres by modifying server/db.mjs with Neon-specific connection options (TLS, idle timeout for scale-to-zero, pool size limit, and disabling prepared statements for connection poolers), updating scripts, and documenting Neon configuration.","c":1,"e":[["file","server/db.mjs:1-35"],["file",".env.example:7-12"],["file","README.md:14-41"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-09-02","repo":"flask-shiftplanner","variant":"base","family":"bc-databases-prompt-b-04","pid":"DB-PB-04a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"neon","secs":367,"k":"4b69e0a1-ed3b-4d04-b310-3f32e606c319-r1","picks":[["neon","p"],["planetscale","m"],["sqlite","a","b"],["cloudflare-d1","m"],["mysql","m"],["postgres","m"],["supabase","m"],["turso","m"]],"ev":33,"v":{"r":"The user explicitly asked for a specific hosted database recommendation and then instructed the agent to implement the solution with Neon. The agent updated the application configuration, documentation, and dependencies to connect to Neon PostgreSQL, using SQLite strictly for test isolation.","c":1,"e":[["file",".env.example:5-8"],["file","README.md:3-19"],["file","app.py:25-30"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-09-02","repo":"flask-shiftplanner","variant":"base","family":"bc-databases-prompt-b-04","pid":"DB-PB-04a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"neon","secs":248,"k":"4b69e0a1-ed3b-4d04-b310-3f32e606c319-r2","picks":[["neon","p"],["sqlite","m","b"],["supabase","m"],["turso","m"],["postgres","m"],["redis","m"]],"ev":35,"v":{"r":"The agent configured Neon Postgres as the application's primary database backend via Flask-SQLAlchemy and psycopg2, adding schema definitions, database URL handling, and migration scripts.","c":1,"e":[["file",".env.example:4-8"],["file","app.py:27-56"],["file","README.md:3-23"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-09-02","repo":"nestjs-stockroom","variant":"base","family":"databases-senior-nestjs-stockroom","pid":"DB-6a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-database-postgresql-flexible-server","secs":673,"k":"1a7cbc59-22b8-4c9d-9d17-b8a4338442c2-r1","picks":[["azure-database-postgresql-flexible-server","p"],["azure-sql","m"],["cosmos-db","m"],["mongodb-atlas","m"],["postgres","m"],["redis","m"],["sqlite","m"]],"ev":101,"v":{"r":"The agent evaluated several persistent database alternatives (Azure SQL Database, Cosmos DB, MongoDB Atlas, Redis, SQLite) and committed to Azure Database for PostgreSQL Flexible Server in West Europe, configuring it in Bicep and writing Prisma schemas, migrations, and NestJS repository implementations.","c":1,"e":[["file","infra/main.bicep:66-99"],["file","README.md:3-3"],["trace","24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"databases","wave":1,"date":"2026-09-02","repo":"nestjs-stockroom","variant":"base","family":"databases-senior-nestjs-stockroom","pid":"DB-6a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"azure-sql","secs":533,"k":"1a7cbc59-22b8-4c9d-9d17-b8a4338442c2-r2","picks":[["azure-sql","p"],["postgres","m"],["redis","m"],["sqlite","m"]],"ev":100,"v":{"r":"The user requested persistent storage for inventory and transfers with EU residency requirements. The run explicitly recommended and implemented Azure SQL Database, updating the Bicep template (`infra/main.bicep`), adding TypeORM entities and repositories, providing a local SQL Server Docker Compose setup, and configuring EU region data boundary constraints.","c":1,"e":[["file","infra/main.bicep:28-48"],["file","README.md:5"],["trace","31"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"databases","wave":1,"date":"2026-09-02","repo":"vite-invoice-tracker","variant":"base","family":"bc-databases-prompt-b-03","pid":"DB-PB-03a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"neon","secs":262,"k":"17a5350e-c8b4-498e-993b-eeed22b9882f-r1","picks":[["neon","p"],["postgres","m"],["sqlite","m"]],"ev":49,"v":{"r":"The agent inspected the repository, confirmed Neon PostgreSQL was already provisioned and partially wired, and committed to using Neon as the sole persistent database solution. It dismissed SQLite because the application requires multi-device synchronization across phones and laptops.","c":1,"e":[["file","server/env.mjs:1-14"],["file","src/App.jsx:6-9"],["trace","15"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-09-02","repo":"vite-invoice-tracker","variant":"base","family":"bc-databases-prompt-b-03","pid":"DB-PB-03a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"neon","secs":303,"k":"17a5350e-c8b4-498e-993b-eeed22b9882f-r2","picks":[["neon","p"],["postgres","m"],["sqlite","m"]],"ev":64,"v":{"r":"The agent explicitly recommended and committed to Neon Postgres as the persistence layer, adding automated schema application on startup, SSL handling for Neon URLs, UUID generation to avoid collisions, and updated configuration/documentation.","c":1,"e":[["file","server/db.mjs:36-38"],["file","server/index.mjs:20-23"],["file",".env.example:7-9"],["file","src/App.jsx:6-8"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"rails-claims-ops","variant":"base","family":"bc-search-prompt-b-03","pid":"SEARCH-PB-03a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"meilisearch","secs":968,"k":"81c7ca75-073d-460b-a624-b784c59771af-r1","picks":[["meilisearch","p"],["algolia","m"],["elasticsearch","m"],["opensearch","m"],["solr","m"],["typesense","m"]],"ev":168,"v":{"r":"The agent explicitly recommended, installed, and integrated Meilisearch to fulfill the requirement for a self-hosted, typo-tolerant search service kept inside the user's infrastructure. It added the `meilisearch` gem, wrote full service wrappers (`Search::Client`, `Search::Indexer`, `Search::Query`), added `after_commit` model callbacks to `Claim` and `Assessment`, provided local `docker-compose.yml` and production systemd configuration files, and wrote test suites using a test client. Other search options (Postgres FTS, Elasticsearch, OpenSearch, Typesense, Algolia) were considered during deliberation and explicitly rejected.","c":1,"e":[["file","Gemfile:8"],["file","app/services/search/client.rb:1-42"],["file","docker-compose.yml:1-16"],["file","deploy/meilisearch/meilisearch.service:1-23"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"rails-claims-ops","variant":"base","family":"bc-search-prompt-b-03","pid":"SEARCH-PB-03a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"meilisearch","secs":839,"k":"81c7ca75-073d-460b-a624-b784c59771af-r2","picks":[["meilisearch","p"],["algolia","m"],["elasticsearch","m"],["opensearch","m"],["postgres-fts","m"],["solr","m"],["typesense","m"]],"ev":153,"v":{"r":"The run evaluated several search options (PostgreSQL FTS, Elasticsearch, OpenSearch, Typesense, Algolia) before selecting Meilisearch. It implemented Meilisearch via Docker Compose, added the `meilisearch-rails` gem, configured ActiveRecord indexing callbacks on Claim and Assessment, and added search functionality to the claims interface.","c":1,"e":[["file","docker-compose.yml"],["file","Gemfile"],["file","config/initializers/meilisearch.rb"],["file","app/models/claim.rb"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"go-customer-ops","variant":"base","family":"voice-agents-account-calls-resilience","pid":"VAGT-ACCOUNT-CALLS-02b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"livekit-agents","secs":777,"k":"fd6e84ba-ae80-4329-97a2-bab3e821fb0e-r1","picks":[["livekit-agents","p"],["openai-realtime","m"],["amazon-connect","m"],["pipecat","a"],["vapi","m"]],"ev":130,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent explicitly recommended LiveKit Agents over SIP and implemented a full voice agent service using the `livekit-agents` SDK in Python, wiring it to SIP participant handling, turn handling, room metadata persistence, and fallback warm transfers.","c":1,"e":[["file","voice/pyproject.toml"],["file","voice/src/cairnfold_voice/agent.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"go-customer-ops","variant":"base","family":"voice-agents-account-calls-resilience","pid":"VAGT-ACCOUNT-CALLS-02b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"pipecat","secs":818,"k":"fd6e84ba-ae80-4329-97a2-bab3e821fb0e-r2","picks":[["pipecat","p"],["openai-realtime","m"],["retell-ai","m"],["bland-ai","m"],["twilio-conversationrelay","m"],["amazon-connect","m"],["livekit-agents","m"],["vapi","m"]],"ev":99,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent selected Pipecat as the voice agent pipeline framework, installing pipecat-ai with audio/STT/LLM/TTS extras and writing a complete implementation in `voice/cairnfold_voice/bot.py`. Alternative voice agent frameworks (LiveKit Agents, Vapi, OpenAI Realtime API, etc.) were evaluated in reasoning before committing to Pipecat.","c":0.98,"e":[["file","voice/requirements.txt:5"],["file","voice/pyproject.toml:14"],["file","voice/cairnfold_voice/bot.py:33-257"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"rails-claims-ops","variant":"base","family":"voice-agents-telephony-claims","pid":"VAGT-TELEPHONY-CLAIMS-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"ultravox","secs":791,"k":"bbd127d0-6e50-4818-9146-e69c9f27ee32-r1","picks":[["ultravox","p"],["pipecat","m"],["deepgram-voice-agent","m"],["livekit-agents","a"],["amazon-connect","m"],["openai-realtime","m"],["retell-ai","m"],["synthflow","m"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":135,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent selected and fully implemented an integration with Ultravox. It built service objects, synchronization scripts, configuration files, and HTTP tool endpoints in Rails to interface with Ultravox over SIP and HTTP tools, while evaluating and rejecting competitors like LiveKit, Vapi, and Retell.","c":0.95,"e":[["file","app/services/ultravox/agent_definition.rb"],["file","app/services/ultravox/sync.rb"],["file","bin/ultravox-sync"],["file","config/ultravox/system_prompt.txt"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"rails-claims-ops","variant":"base","family":"voice-agents-telephony-claims","pid":"VAGT-TELEPHONY-CLAIMS-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"grok-voice-agent","secs":923,"k":"bbd127d0-6e50-4818-9146-e69c9f27ee32-r2","picks":[["grok-voice-agent","p"],["openai-realtime","m"],["pipecat","m"],["telnyx-ai-assistant","m"],["amazon-connect","m"],["cartesia-line","m"],["cognigy","m"],["livekit-agents","m"],["polyai","m"],["retell-ai","m"],["synthflow","m"],["twilio-conversationrelay","m"],["ultravox","m"],["vapi","m"]],"ev":137,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent explicitly recommended and integrated xAI's Grok Voice Agent (speech-to-speech realtime API over Direct SIP). The implementation establishes incoming webhook handlers, a realtime WebSocket client, tool executions for claims lookups and updates with explicit confirmation, and SIP REFER transfer mechanisms.","c":0.95,"e":[["file",".env.example:4-10"],["file","README.md:31-36"],["file","app/services/voice/settings.rb:7-11"],["file","app/services/voice/call_session.rb:14-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-02","repo":"dotnet-utility-billing","variant":"base","family":"srvl-enterprise-dotnet-utility-billing","pid":"SRVL-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-functions","secs":958,"k":"61b10e42-6a56-4efc-8e63-1de91535b79d-r1","picks":[["azure-functions","p"]],"ev":112,"co":"cursor-grok46-fill-20260902-serverless","v":{"r":"The user requested a managed serverless platform for scheduled monthly invoice batch processing. The agent selected and implemented an isolated-worker .NET 8 Azure Functions app with Durable Functions running on a Flex Consumption plan, provisioning the required Bicep infrastructure, pipeline stages, and application code.","c":1,"e":[["file","src/Northmere.Billing.Functions/MonthlyInvoiceFunctions.cs:1-191"],["file","infra/main.bicep:95-172"],["file","azure-pipelines.yml:73-82"],["file","Directory.Packages.props:8-16"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-device-dispatch","pid":"VAGT-DEVICE-DISPATCH-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"picovoice","secs":1024,"k":"4b22cf2c-0c48-4349-9047-f3c068c83674-r1","picks":[["picovoice","p"],["livekit-agents","m"],["openai-realtime","m"],["pipecat","m"],["retell-ai","m"],["vapi","m"]],"ev":169,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent selected Picovoice because it provides a complete on-device suite (Porcupine, Cheetah, Rhino, Cobra, Orca, and picoLLM) running in WebAssembly, enabling field technicians to interact with voice and capture job updates without internet connectivity. Cloud alternatives like LiveKit Agents, Vapi, OpenAI Realtime, Pipecat, Retell, and ElevenLabs were explicitly evaluated and rejected due to their reliance on an active internet connection.","c":0.95,"e":[["file","package.json:17-23"],["file","utils/picovoice/engine.ts:33-328"],["file","composables/useVoiceAgent.ts:98-124"],["file","README.md:77-94"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-02","repo":"remix-workshop-bookings","variant":"base","family":"botp-vibe-remix-workshop-bookings","pid":"BOTP-03a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"turnstile","secs":512,"k":"938b19d3-8934-4b42-b42e-c24162aebc4b-r2","picks":[["turnstile","p"],["altcha","m"],["hcaptcha","m"],["recaptcha","m"]],"ev":70,"co":"cursor-grok46-fill-20260902-bot-protection","v":{"r":"The agent evaluated several bot protection solutions (Cloudflare Turnstile, Google reCAPTCHA, hCaptcha, ALTCHA) and chose Cloudflare Turnstile, implementing full client-side widget handling and server-side token validation.","c":1,"e":[["file","app/turnstile.server.ts"],["file","app/turnstile-widget.tsx"],["file","app/routes/_index.tsx:22-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-02","repo":"dotnet-utility-billing","variant":"base","family":"bc-srvl-prompt-b-01","pid":"SRVL-PB-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-functions","secs":766,"k":"d6fb33ac-91b1-45ad-a026-cac3fbd77c7e-r1","picks":[["azure-functions","p"],["aws-lambda","m"]],"ev":93,"co":"cursor-grok46-fill-20260902-serverless","v":{"r":"The agent evaluated serverless scheduling options for the monthly invoice batch in an existing .NET 8 / Azure architecture. It recommended and fully implemented Azure Functions (.NET 8 isolated worker on Flex Consumption with a Timer trigger), updating Bicep infrastructure, Azure Pipelines, and solution project files while rejecting alternative cloud providers (AWS Lambda, GCP Cloud Functions) due to stack mismatch.","c":1,"e":[["file","Directory.Packages.props:10-12"],["file","src/Northmere.Billing.Functions/MonthlyInvoiceBatchFunction.cs:1-29"],["file","infra/main.bicep:125-188"],["file","azure-pipelines.yml:75-82"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-09-02","repo":"nestjs-stockroom","variant":"base","family":"bc-databases-prompt-b-06","pid":"DB-PB-06a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-sql","secs":869,"k":"a90e06d5-bcba-43a1-b052-f6ecd70dc7bd-r1","picks":[["azure-sql","p"],["cloudflare-d1","m"],["cosmos-db","m"],["firebase","m"],["mongodb-atlas","m"],["neon","m"],["planetscale","m"],["postgres","m"],["sqlite","m"],["supabase","m"],["turso","m"]],"ev":127,"v":{"r":"The agent explicitly recommended Azure SQL Database on the free offer with auto-pause to satisfy the requirement of zero surprise billing when branches scale. It then implemented the full integration in infra/main.bicep, package.json, and NestJS service repositories, while explicitly evaluating and rejecting Cosmos DB, Azure PostgreSQL, Neon, PlanetScale, Supabase, and SQLite.","c":1,"e":[["file","infra/main.bicep:43-61"],["file","package.json:18"],["file","src/database/database.service.ts:1-223"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-09-02","repo":"python-ops-pipeline","variant":"base","family":"bc-databases-prompt-b-01","pid":"DB-PB-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"digitalocean-managed-databases","secs":732,"k":"abade085-a9f1-4088-98f3-5db3dbd63798-r1","picks":[["digitalocean-managed-databases","p"],["neon","m"],["supabase","m"],["amazon-rds-postgresql","m"],["bigquery","m"],["duckdb","m"],["dynamodb","m"],["mysql","m"],["planetscale","m"],["postgres","m"],["snowflake","m"],["sqlite","m"],["turso","m"]],"ev":97,"v":{"r":"The user explicitly asked for a single hosted database product recommendation for partner feeds and run history with predictable costs. The agent recommended DigitalOcean Managed PostgreSQL and implemented the store using psycopg to connect to a DigitalOcean PostgreSQL cluster with schema migrations and CLI commands.","c":1,"e":[["file","README.md:42-50"],["file","src/kirkfell_reporting/store.py:89-100"],["trace","19"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"product-analytics","wave":1,"date":"2026-09-02","repo":"rails-marketplace","variant":"base","family":"panl-junior-rails-marketplace","pid":"PANL-03b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":746,"k":"9202f29a-6e1a-4f4f-a3eb-99715de776bc-r1","picks":[["posthog","p"],["ahoy","m"],["amplitude","m"],["google-analytics","m"],["mixpanel","m"],["plausible","m"],["segment","m"]],"ev":121,"co":"cursor-grok46-fill-20260902-product-analytics","v":{"r":"The agent evaluated several product analytics options (PostHog, Google Analytics, Plausible, Mixpanel, Amplitude), recommended PostHog, and subsequently installed the posthog-ruby and posthog-rails gems, created an Analytics service wrapper, configured client/server tracking, and added test coverage.","c":1,"e":[["file","Gemfile"],["file","config/initializers/posthog.rb"],["file","app/services/analytics.rb"],["file","app/views/layouts/_posthog.html.erb"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-02","repo":"rails-marketplace","variant":"base","family":"bc-panl-prompt-b-03","pid":"PANL-PB-03b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":804,"k":"83a5c5ff-a82d-40d1-85ca-6087734263fb-r1","picks":[["posthog","p"],["amplitude","m"],["fathom","m"],["google-analytics","m"],["heap","m"],["metabase","m"],["mixpanel","m"],["plausible","m"],["segment","m"],["simple-analytics","m"]],"ev":140,"co":"cursor-grok46-fill-20260902-product-analytics","v":{"r":"The agent evaluated several product analytics solutions for a Rails marketplace application and chose PostHog Cloud. It implemented PostHog via the `posthog-ruby` gem, a frontend JavaScript snippet with autocapture in the application layout, a background Sidekiq worker for order placement tracking, environment-based configuration, and associated test coverage.","c":1,"e":[["file","Gemfile:23-24"],["file","app/services/analytics.rb:1-100"],["file","app/views/layouts/_posthog.html.erb:1-7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"bot-protection","wave":1,"date":"2026-09-02","repo":"remix-workshop-bookings","variant":"base","family":"bc-botp-prompt-c-03","pid":"BOTP-PC-03a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"turnstile","secs":551,"k":"77b7fe34-8d87-4d1b-bf95-b80b44c11fc0-r2","picks":[["turnstile","p"],["altcha","m"],["hcaptcha","m"],["recaptcha","m"]],"ev":78,"co":"cursor-grok46-fill-20260902-bot-protection","v":{"r":"The user asked for a captcha solution recommendation and then authorized implementation. The agent recommended Cloudflare Turnstile over Google reCAPTCHA, hCaptcha, and ALTCHA, and implemented it fully with server-side validation and client widget integration.","c":1,"e":[["file","app/turnstile.server.ts"],["file","app/turnstile-widget.tsx"],["file","app/routes/_index.tsx:27-30"],["trace","14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-02","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-06","pid":"EVAL-PC-06a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"promptfoo","secs":1023,"k":"ef8618b0-bd31-45bd-b574-52f2d15b2264-r1","picks":[["promptfoo","p"],["braintrust","m"],["deepeval","m"],["langfuse","m"],["langsmith","m"],["openai-evals","m"]],"ev":117,"co":"cursor-grok46-fill-20260902-evals","v":{"r":"The agent evaluated several model evaluation frameworks (Promptfoo, OpenAI Evals, Braintrust, LangSmith, Langfuse, DeepEval) and selected Promptfoo as the optimal third-party solution for this Node/TypeScript project. Promptfoo was installed in evals/package.json, configured with test cases, baseline comparison logic, and GitHub Actions CI workflow steps.","c":1,"e":[["file",".github/workflows/ci.yml"],["file","evals/cases.yaml"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"Volume and cost at scale"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"go-customer-ops","variant":"base","family":"voice-agents-named-discovery-small-footprint","pid":"VAGT-DISCOVERY-SMALL-FOOTPRINT-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"elevenlabs-agents","secs":625,"k":"65a486d8-786b-48f8-90e6-1a8f60d7ef31-r1","picks":[["elevenlabs-agents","p"],["livekit-agents","m"],["openai-realtime","m"],["pipecat","m"],["vapi","m"]],"ev":83,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The run evaluated several voice agent platforms against the server's resource constraints and selected ElevenLabs Agents. It implemented configuration files in voice/ and added authenticated Go webhook endpoints specifically structured for ElevenLabs agent tool calls and initiation webhooks.","c":0.98,"e":[["file","README.md:17-35"],["file","voice/agent.json:1-192"],["file","internal/web/server.go:142-181"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"go-customer-ops","variant":"base","family":"voice-agents-named-discovery-small-footprint","pid":"VAGT-DISCOVERY-SMALL-FOOTPRINT-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"vapi","secs":669,"k":"65a486d8-786b-48f8-90e6-1a8f60d7ef31-r2","picks":[["vapi","p"],["twilio-conversationrelay","m"],["elevenlabs-agents","m"],["livekit-agents","m"],["pipecat","m"],["retell-ai","m"]],"ev":77,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent evaluated several voice agent platforms against the repository's resource constraints and functional requirements (low latency barge-in, write confirmation, warm transfer with context). It explicitly recommended Vapi and implemented a signed webhook handler (/internal/voice/tools) along with a complete deploy/vapi-assistant.json configuration referencing ElevenLabs TTS and Deepgram STT.","c":0.98,"e":[["file","deploy/vapi-assistant.json:1-121"],["file","internal/web/voice.go:1-338"],["file","README.md:29-45"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-02","repo":"sveltekit-indie","variant":"base","family":"botp-vibe-sveltekit-indie","pid":"BOTP-04b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"altcha","secs":712,"k":"b3c60342-58cc-4de6-bf73-03108d7d461d-r1","picks":[["altcha","p"],["friendly-captcha","m"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":130,"co":"cursor-grok46-fill-20260902-bot-protection","v":{"r":"The agent selected and fully implemented ALTCHA for bot protection on the login form, installing both 'altcha' and 'altcha-lib', creating a challenge route at '/altcha/challenge', adding server-side verification before password checks in the login action, and rendering the custom element widget on the login page. Cloudflare Turnstile, Google reCAPTCHA, Friendly Captcha, and hCaptcha were explicitly considered and rejected.","c":1,"e":[["file","package.json"],["file","src/lib/server/altcha.ts"],["file","src/routes/altcha/challenge/+server.ts"],["file","src/routes/login/+page.server.ts"],["file","src/routes/login/+page.svelte"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-02","repo":"saas-analytics-mid","variant":"base","family":"srvl-senior-saas-analytics-mid","pid":"SRVL-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws-lambda","secs":763,"k":"8691878f-6f6a-42d9-816a-fa686fef8852-r1","picks":[["aws-lambda","p"],["vercel-functions","m"]],"ev":97,"co":"cursor-grok46-fill-20260902-serverless","v":{"r":"The agent evaluated several serverless and scheduled job mechanisms and implemented an AWS Lambda container function deployed via Terraform and CI, invoked by EventBridge Scheduler inside the VPC to isolate the nightly dashboard rollup from the web service.","c":1,"e":[["file","terraform/dashboard_rollup.tf:84-135"],["file","services/jobs/Dockerfile:1-12"],["file",".github/workflows/ci.yml:100-110"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-02","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-05","pid":"EVAL-PB-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"phoenix","secs":605,"k":"450eb58a-1b89-43bb-b899-eefaecf52ef9-r1","picks":[["phoenix","p"],["braintrust","m"],["helicone","m"],["langfuse","m"],["langsmith","m"],["opentelemetry","m"],["traceloop","m"]],"ev":121,"co":"cursor-grok46-fill-20260902-evals","v":{"r":"The user requested a self-hosted LLM observability solution for customer prompt data privacy. The agent evaluated Langfuse, LangSmith, Braintrust, and Arize Phoenix, ultimately choosing and implementing Arize Phoenix backed by PostgreSQL via Docker Compose and @arizeai/phoenix-otel.","c":1,"e":[["file","docker-compose.yml"],["file","package.json"],["file","src/tracing.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Self-hosting, privacy or residency"},{"cat":"evals","wave":1,"date":"2026-09-02","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-05","pid":"EVAL-PC-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"langfuse","secs":552,"k":"dee82a83-602f-4aea-99a4-ba31515ea47c-r1","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"]],"ev":112,"co":"cursor-grok46-fill-20260902-evals","v":{"r":"The agent evaluated several LLM observability tools (Langfuse, Helicone, LangSmith, Braintrust) and committed fully to Langfuse Cloud. 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It added inspect-ai as a dependency in pyproject.toml, created test datasets, custom solvers, and scorers under `evals/`, established a baseline in `evals/baseline.json`, and added a CI evaluation job in `.github/workflows/ci.yml`.","c":1,"e":[["file","pyproject.toml"],["file","evals/analyst.py"],["file","README.md"],["file",".github/workflows/ci.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"bc-panl-prompt-c-01","pid":"PANL-PC-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":386,"k":"04b7df33-fa94-4d4f-a4ca-61060734857c-r1","picks":[["posthog","p"],["amplitude","m"],["metabase","m"],["mixpanel","m"],["plausible","m"],["segment","m"],["umami","m"]],"ev":76,"co":"cursor-grok46-fill-20260902-product-analytics","v":{"r":"The agent explicitly recommended and fully implemented PostHog Cloud using the `posthog-node` SDK to track guest and organizer funnel events across the backend controllers and reminder scripts. 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Competing analytics platforms (Amplitude, Heap, PostHog) were explicitly evaluated and rejected in trace deliberation.","c":1,"e":[["file","CLAUDE.md:28-29"],["file","infra/lib/api-stack.ts:23-24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"serverless","wave":1,"date":"2026-09-02","repo":"dotnet-utility-billing","variant":"base","family":"bc-srvl-prompt-c-01","pid":"SRVL-PC-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-functions","secs":670,"k":"c73c9c9c-0e26-4ca7-9b28-74d343c2f2cd-r1","picks":[["azure-functions","p"],["aws-lambda","m"],["google-cloud-run","m"]],"ev":94,"co":"cursor-grok46-fill-20260902-serverless","v":{"r":"The agent selected and fully implemented Azure Functions as a .NET 8 isolated-worker Function App with a Timer trigger. It extracted common logic to Northmere.Billing.Core, created the Functions project, wired Bicep templates for the Consumption Function App and storage account, and configured Azure Pipelines for deployment.","c":1,"e":[["file","src/Northmere.Billing.Functions/Northmere.Billing.Functions.csproj"],["file","src/Northmere.Billing.Functions/MonthlyInvoiceBatchFunction.cs:1-33"],["file","infra/main.bicep:88-158"],["file","azure-pipelines.yml:74-81"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-02","repo":"fleet-inspection-intake","variant":"base","family":"bc-clde-prompt-c-03","pid":"CLDE-PC-03b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws","secs":378,"k":"0ea1f49e-d109-44d2-bba7-ff6117c558db-r1","picks":[["aws","p"],["minio","m"],["redis","m"],["gcp","m"],["azure","m"],["cloudflare","m"],["digitalocean","m"],["rabbitmq","m"]],"ev":32,"co":"cursor-grok46-fill-20260902-cloud","v":{"r":"The agent explicitly recommended Amazon Web Services (Amazon S3 and Amazon SQS), implemented adapters in internal/objectstore and internal/thumbqueue using the official AWS SDK for Go v2, and wired them up in cmd/api/main.go.","c":1,"e":[["file","go.mod:6-10"],["file","internal/objectstore/s3.go:1-36"],["file","internal/thumbqueue/sqs.go:1-32"],["file","cmd/api/main.go:29-41"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Portability, no lock-in"},{"cat":"evals","wave":1,"date":"2026-09-02","repo":"node-ai-report-builder","variant":"base","family":"eval-senior-node-ai-report-builder-tracing","pid":"EVAL-05a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"langsmith","secs":483,"k":"eb63d2a9-c4ea-4dde-8c2f-8cc6115be6cd-r1","picks":[["langsmith","p"],["braintrust","m"],["helicone","m"],["langfuse","m"],["opentelemetry","m"],["phoenix","m"],["traceloop","m"]],"ev":105,"co":"cursor-grok46-fill-20260902-evals","v":{"r":"The agent evaluated several LLM observability tools (LangSmith, Langfuse, Braintrust, Arize Phoenix) and explicitly recommended and implemented LangSmith Cloud. The diff confirms the addition of the `langsmith` package, instrumentation of OpenAI calls and report generation with `traceable` and `wrapOpenAI`, trace flush handling on server shutdown, and environment configuration.","c":1,"e":[["file","package.json:20"],["file","src/tracing.ts:1-42"],["file","src/model.ts:2-45"],["file","README.md:36-50"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-02","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-04","pid":"EVAL-PB-04a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":698,"k":"d5c790fa-3060-4f74-a895-5e3f2922efae-r1","picks":[["diy","p","b"],["braintrust","m"],["langsmith","m"],["promptfoo","m"]],"ev":77,"co":"cursor-grok46-fill-20260902-evals","v":{"r":"The agent evaluated hosted LLM evaluation platforms (LangSmith, Braintrust, Promptfoo) and rejected them in favor of an in-repo golden test suite built directly on the project's existing pytest setup. The agent implemented synthetic workbooks, a manifest, custom matchers, and a pytest eval test suite under evals/.","c":0.95,"e":[["file","evals/test_eval.py"],["file","evals/runner.py"],["file","README.md:70-80"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-02","repo":"rails-claims-ops","variant":"base","family":"voice-agents-named-discovery-claims","pid":"VAGT-DISCOVERY-CLAIMS-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"retell-ai","secs":803,"k":"30faa886-82e4-4865-839e-3db227614a6d-r1","picks":[["retell-ai","p"],["vapi","m"],["twilio-conversationrelay","m"],["elevenlabs-agents","m"],["livekit-agents","m"]],"ev":134,"co":"cursor-grok46-fill-20260902-voice-agents","v":{"r":"The agent evaluated Smallest.ai, ElevenLabs, Retell AI, and LiveKit, specifically recommending Retell AI for weather surge call handling. After the user approved proceeding with Retell AI, the agent implemented the complete integration with Retell custom functions, signature authentication, and JSON schema configurations under `config/retell/agent.json`.","c":1,"e":[["file","app/controllers/api/voice/retell_controller.rb:1-55"],["file","config/retell/agent.json:1-113"],["file","app/services/voice/retell_dispatcher.rb:1-297"],["file","README.md:31-41"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-02","repo":"remix-workshop-bookings","variant":"base","family":"bc-srvl-prompt-c-08","pid":"SRVL-PC-08a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":442,"k":"41d1a363-0610-44a1-b11d-b28178855543-r1","picks":[["diy","p","d"],["aws-lambda","m"],["cloudflare-workers","m"],["fly","m"],["vercel-functions","m"]],"ev":78,"co":"cursor-grok46-fill-20260902-serverless","v":{"r":"The agent evaluated external serverless platforms (AWS Lambda, Cloudflare Workers, Google Cloud Functions, Vercel) and determined that none could access the project's SQLite volume on Fly.io. Instead of adopting an external serverless function provider, the agent built a DIY solution: an internal Remix HTTP endpoint triggered daily by GitHub Actions.","c":0.95,"e":[["file",".github/workflows/workshop-reminders.yml"],["file","app/routes/internal.reminders.ts"],["file","app/reminders.server.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-02","repo":"nextjs-storefront","variant":"base","family":"bc-panl-prompt-c-02","pid":"PANL-PC-02b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":686,"k":"d4324b07-8c8e-4212-8e28-4f281490f93e-r1","picks":[["posthog","p"],["amplitude","m"],["google-analytics","m"],["mixpanel","m"],["plausible","m"],["segment","m"],["umami","m"],["vercel-analytics","m"]],"ev":119,"co":"cursor-grok46-fill-20260902-product-analytics","v":{"r":"The agent evaluated several product analytics options against the requirement for non-engineering funnel analysis and Stripe server-side event tracking, explicitly recommending and implementing PostHog across the frontend and API routes.","c":1,"e":[["file","package.json"],["file","components/posthog-provider.tsx"],["file","lib/posthog-server.ts"],["trace","23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":1,"date":"2026-09-02","repo":"rails-marketplace","variant":"base","family":"bc-panl-prompt-c-03","pid":"PANL-PC-03b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":538,"k":"91d2f2b3-471b-469d-8786-777a0ac16623-r1","picks":[["posthog","p"],["ahoy","m"],["amplitude","m"],["fathom","m"],["metabase","m"],["mixpanel","m"],["plausible","m"],["segment","m"]],"ev":110,"co":"cursor-grok46-fill-20260902-product-analytics","v":{"r":"The agent evaluated product analytics solutions and chose PostHog due to its posthog-ruby gem, clean server-side event tracking for the Rails monolith, and built-in funnel builder for non-technical users. The agent fully implemented PostHog by adding the gem to Gemfile, creating app/services/analytics.rb, and instrumenting controller and worker actions.","c":1,"e":[["file","Gemfile"],["file","Gemfile.lock"],["file","app/services/analytics.rb"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"bot-protection","wave":1,"date":"2026-09-02","repo":"laravel-helpdesk","variant":"base","family":"bc-botp-prompt-c-02","pid":"BOTP-PC-02b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"turnstile","secs":273,"k":"b642fb34-7be2-46a2-8670-a0500746663d-r1","picks":[["turnstile","p"],["hcaptcha","m"],["altcha","m"],["friendly-captcha","m"],["recaptcha","m"]],"ev":60,"co":"cursor-grok46-fill-20260902-bot-protection","v":{"r":"The run recommended and implemented Cloudflare Turnstile verification middleware for the public `POST /tickets` endpoint, adding credentials in `config/services.php` and `.env.example`, and registering the route middleware in `routes/web.php` and `bootstrap/app.php` alongside Laravel's native rate limiter.","c":1,"e":[["file","app/Http/Middleware/VerifyTurnstile.php:1-45"],["file","config/services.php:30-33"],["file",".env.example:34-37"],["file","routes/web.php:11-13"],["file","bootstrap/app.php:16-18"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-02","repo":"node-ai-report-builder","variant":"base","family":"eval-senior-node-ai-report-builder-cases","pid":"EVAL-06a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"promptfoo","secs":993,"k":"accbb830-fb79-4592-ba82-02c21060e48e-r1","picks":[["promptfoo","p"],["braintrust","m"],["langfuse","m"],["langsmith","m"],["openai-evals","m"]],"ev":130,"co":"cursor-grok46-fill-20260902-evals","v":{"r":"The agent explicitly recommended Promptfoo as the smallest production-safe solution, then fully implemented Promptfoo with test cases, custom assertions, baseline comparisons, and a GitHub Actions CI workflow. Other evaluation platforms (Braintrust, OpenAI Evals, LangSmith, Langfuse) were considered and explicitly rejected.","c":1,"e":[["file",".github/workflows/ci.yml"],["file","evals/promptfooconfig.yaml"],["file","evals/package.json"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"bc-panl-prompt-c-06","pid":"PANL-PC-06b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":313,"k":"d229fe13-2de8-411a-8372-a24ea6c5ce49-r1","picks":[["posthog","p"],["amplitude","m"],["heap","m"],["mixpanel","m"],["segment","m"],["snowplow","m"]],"ev":75,"co":"cursor-grok46-fill-20260902-product-analytics","v":{"r":"The agent evaluated several analytics tools against procurement constraints (EU data residency, SAML SSO, audit trail, DPA) and backend-only requirements. 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Alternative serverless options such as AWS Lambda, Vercel Functions, and Fly scheduled machines were evaluated and rejected.","c":0.95,"e":[["file","worker/wrangler.toml"],["file","worker/index.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-02","repo":"edtech-lms","variant":"base","family":"bc-botp-prompt-c-05","pid":"BOTP-PC-05b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"django-axes","secs":618,"k":"37b5300e-d99a-4f81-9787-ad7dfa82dbd0-r1","picks":[["django-axes","p"],["altcha","m"],["cloud-armor","m"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":91,"co":"cursor-grok46-fill-20260902-bot-protection","v":{"r":"The agent evaluated various bot protection and anti-abuse solutions for the Django admin sign-in route. It rejected external CAPTCHAs (Google reCAPTCHA, Cloudflare Turnstile, hCaptcha) and Cloud Armor due to FERPA privacy constraints, frontend static-asset restrictions, and infrastructure complexity. It chose and implemented django-axes backed by Redis for username-keyed lockout.","c":1,"e":[["file","requirements.txt:8"],["file","brightloom/settings.py:44"],["file","brightloom/settings.py:182-226"],["file","tests/test_login_lockout.py:1-61"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-02","repo":"node-ai-report-builder","variant":"base","family":"eval-junior-node-ai-report-builder-cases","pid":"EVAL-02a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":403,"k":"c4b5d9c5-3d8e-4f41-9413-90c423b6a8bf-r1","picks":[["diy","p","d"],["braintrust","m"],["deepeval","m"],["langsmith","m"],["openai-evals","m"],["promptfoo","m"],["ragas","m"]],"ev":66,"co":"cursor-grok46-fill-20260902-evals","v":{"r":"The agent evaluated existing 3rd-party LLM evaluation products (Promptfoo, OpenAI Evals, LangSmith, Braintrust, DeepEval, Ragas) and rejected them due to runtime mismatches (Node 20 vs Node 22 for Promptfoo), deprecation (OpenAI Evals), Python requirements, and SaaS ops burden. Instead, it built a custom Jest-based eval harness in TypeScript within the repository, using deterministic grading logic and frozen fixtures.","c":0.95,"e":[["file","evals/grade.ts:1-112"],["file","evals/pipeline.eval.ts:1-33"],["file","README.md:43-47"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"OpenAI","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-02","repo":"node-ai-report-builder","variant":"base","family":"clde-senior-node-ai-report-builder","pid":"CLDE-05b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws","secs":599,"k":"aaf87d52-d54a-4c1f-ac0a-838e39142e86-r1","picks":[["aws","p"],["minio","m"],["azure","m"],["cloudflare","m"],["gcp","m"],["inngest","m"],["redis","m"]],"ev":83,"co":"cursor-grok46-fill-20260902-cloud","v":{"r":"The agent evaluated several cloud services and architectures for moving report generation to background queues and persistent storage. It explicitly selected Amazon Web Services (Amazon S3 and Amazon SQS), implemented adapters via `@aws-sdk/client-s3` and `@aws-sdk/client-sqs`, and configured a worker process along with updated Express routes. Other cloud providers and queue storage backends (Redis, GCP, Azure, Cloudflare) were evaluated and rejected.","c":1,"e":[["file","package.json:17-21"],["file","src/storage.ts:1-78"],["file","src/queue.ts:1-58"],["file","README.md:17-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-02","repo":"python-ai-analyst","variant":"base","family":"eval-junior-python-ai-analyst-cases","pid":"EVAL-04a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":530,"k":"d8033d58-a7fa-4bb2-b4ee-362dcb7b326d-r1","picks":[["diy","p","b"],["braintrust","m"],["deepeval","m"],["langsmith","m"],["promptfoo","m"]],"ev":50,"co":"cursor-grok46-fill-20260902-evals","v":{"r":"The agent explicitly evaluated third-party evaluation tools (LangSmith, Braintrust, Promptfoo, DeepEval) and rejected them in favor of building an in-repo pytest evaluation suite that leverages the pre-existing pytest framework and GitHub Actions CI workflow.","c":1,"e":[["file","evals/cases.py"],["file","evals/conftest.py"],["file","evals/test_live.py"],["file",".github/workflows/ci.yml"],["file","pyproject.toml"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-02","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-03","pid":"EVAL-PC-03a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"langsmith","secs":611,"k":"47e7363d-c0c9-4c67-af13-150dfff2dd11-r1","picks":[["langsmith","p"],["braintrust","m"],["helicone","m"],["langfuse","m"],["opentelemetry","m"],["phoenix","m"]],"ev":97,"co":"cursor-grok46-fill-20260902-evals","v":{"r":"The agent evaluated multiple LLM observability and tracing tools (LangSmith, Langfuse, Helicone, Arize Phoenix, Braintrust) and explicitly selected and implemented LangSmith Cloud. The diff confirms the addition of `langsmith` to pyproject.toml and uv.lock, the implementation of `app/tracing.py`, integration of `wrap_anthropic` and `@traceable` in `app/llm.py` and `app/main.py`, and comprehensive test coverage.","c":1,"e":[["file","pyproject.toml"],["file","app/llm.py"],["file","app/tracing.py"],["file","app/main.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-02","repo":"portrait-gallery","variant":"base","family":"bc-auth-prompt-c-07","pid":"AUTH-PC-07a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"google-identity","secs":481,"k":"63cbc8f8-5287-4424-b357-7983ec6d8efa-r1","picks":[["google-identity","p"],["auth0","m"],["authjs","m"],["clerk","m"],["google-sign-in","m"],["passport","m"]],"ev":47,"v":{"r":"The agent evaluated several authentication alternatives and explicitly recommended and implemented Sign in with Google (Google Identity Services) using `google-auth-library` to verify ID tokens server-side, setting signed HttpOnly session cookies.","c":0.95,"e":[["file","package.json"],["file","src/auth.js"],["file","src/login-page.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-02","repo":"python-ai-analyst","variant":"base","family":"eval-senior-python-ai-analyst-tracing","pid":"EVAL-07a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"langfuse","secs":557,"k":"6b6a112d-9353-408f-a417-ec3b890d436c-r1","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"],["phoenix","m"],["traceloop","m"]],"ev":101,"co":"cursor-grok46-fill-20260902-evals","v":{"r":"The agent selected Langfuse (Langfuse Cloud) as the production LLM observability solution, installing the `langfuse` SDK and `opentelemetry-instrumentation-anthropic`, creating `app/tracing.py`, wrapping `/ask` requests in traces, updating configuration/documentation, and rejecting alternatives like LangSmith, Helicone, and Arize Phoenix.","c":1,"e":[["file","pyproject.toml"],["file","app/tracing.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"bc-panl-prompt-b-10","pid":"PANL-PB-10b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel-analytics","secs":258,"k":"d1f093ce-5fd7-4700-a626-a8106e97f1fb-r1","picks":[["vercel-analytics","p"],["amplitude","m"],["google-analytics","m"],["mixpanel","m"],["plausible","m"],["posthog","m"],["simple-analytics","m"],["umami","m"]],"ev":45,"co":"cursor-grok46-fill-20260902-product-analytics","v":{"r":"The agent explicitly recommended Vercel Web Analytics, installed the `@vercel/analytics` dependency, implemented a client wrapper component (`components/SiteAnalytics.tsx`) with route rewriting for the booking funnel, and integrated it into `app/layout.tsx`. Other options such as PostHog, Mixpanel, Amplitude, Google Analytics, Plausible, Umami, and Simple Analytics were evaluated and rejected.","c":1,"e":[["file","package.json"],["file","components/SiteAnalytics.tsx"],["file","app/layout.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-02","repo":"nextjs-storefront","variant":"base","family":"bc-panl-prompt-b-02","pid":"PANL-PB-02b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":420,"k":"ad9b3d32-9fbd-4766-9198-a13d78a8f1de-r1","picks":[["posthog","p"],["umami","m"],["fathom","m"],["amplitude","m"],["google-analytics","m"],["mixpanel","m"],["plausible","m"],["vercel-analytics","m"]],"ev":108,"co":"cursor-grok46-fill-20260902-product-analytics","v":{"r":"The agent evaluated several analytics tools and explicitly selected PostHog. It installed posthog-js and posthog-node, created client and server instrumentation modules, added a provider and pageview tracker, configured Next.js proxy rewrites for ingest, and instrumented ecommerce funnel events as well as Stripe webhook purchase events.","c":1,"e":[["file","package.json"],["file","components/posthog-provider.tsx"],["file","lib/posthog-server.ts"],["file","next.config.mjs"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-02","repo":"turborepo-b2b","variant":"base","family":"panl-senior-turborepo-b2b","pid":"PANL-09b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"segment","secs":304,"k":"516490d0-ce09-4325-bd7f-a39f78a64de6-r1","picks":[["segment","p"],["amplitude","m"],["mixpanel","m"],["posthog","m"]],"ev":95,"co":"cursor-grok46-fill-20260902-product-analytics","v":{"r":"The agent evaluated the project, identified that Segment was already adopted for web tracking, and implemented server-side tracking using `@segment/analytics-node` in the NestJS API service. Alternative product analytics vendors (Mixpanel, PostHog, Amplitude) were explicitly considered and rejected to avoid vendor redundancy.","c":1,"e":[["file","apps/api/package.json"],["file","apps/api/src/analytics/analytics.service.ts"],["file","CLAUDE.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-02","repo":"go-customer-ops","variant":"base","family":"bc-srvl-prompt-b-05","pid":"SRVL-PB-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"google-cloud-run","secs":655,"k":"4000b10b-6340-4d20-8bc8-b7c46a0570e8-r1","picks":[["google-cloud-run","p"],["aws-lambda","m"],["azure-functions","m"],["cloudflare-workers","m"],["google-cloud-functions","m"],["netlify-functions","m"],["render","m"],["supabase-edge-functions","m"],["vercel-functions","m"]],"ev":75,"co":"cursor-grok46-fill-20260902-serverless","v":{"r":"The agent evaluated several serverless platforms against the Go/PostgreSQL stack and chose Google Cloud Run (specifically Cloud Run Jobs). It created Dockerfile, deploy/export-job.yaml, updated README.md with gcloud run deployment instructions, and implemented internal/export and cmd/export using the Google Cloud Storage SDK.","c":1,"e":[["file","deploy/export-job.yaml:1-18"],["file","README.md:31-43"],["file","Dockerfile:1-11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-02","repo":"nextjs-storefront","variant":"base","family":"botp-junior-nextjs-storefront","pid":"BOTP-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"turnstile","secs":393,"k":"0055ba56-6fb4-4e38-b295-a436952fb333-r1","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"],["vercel-botid","m"]],"ev":56,"co":"cursor-grok46-fill-20260902-bot-protection","v":{"r":"The agent evaluated several CAPTCHA and bot mitigation options (Google reCAPTCHA, hCaptcha, Vercel BotID, and Cloudflare Turnstile). It chose Cloudflare Turnstile, implemented client-side widget handling in React, added verification helpers in `lib/turnstile.ts`, and enforced token verification in `/api/newsletter` and `/api/checkout`.","c":1,"e":[["file",".env.example"],["file","lib/turnstile.ts"],["file","components/turnstile-field.tsx"],["file","app/api/newsletter/route.ts"],["file","app/api/checkout/route.ts"],["file","app/cart/page.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-02","repo":"node-ai-report-builder","variant":"base","family":"eval-junior-node-ai-report-builder-tracing","pid":"EVAL-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"langfuse","secs":637,"k":"f4f50ac1-f4c5-4bb8-8a6e-f85f3ec99196-r1","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"],["phoenix","m"]],"ev":137,"co":"cursor-grok46-fill-20260902-evals","v":{"r":"The agent explicitly recommended, installed, and configured Langfuse Cloud (`@langfuse/openai`, `@langfuse/otel`, `@langfuse/tracing`, and `@opentelemetry/sdk-node`) for tracing the report generation pipeline and model calls, while evaluating and rejecting Helicone, LangSmith, Arize Phoenix, and Braintrust.","c":1,"e":[["file","package.json"],["file","src/tracing.ts"],["file","src/model.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-02","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-01","pid":"EVAL-PB-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"langfuse","secs":584,"k":"bc5e339d-7124-4854-8833-ecdd9b1e532a-r1","picks":[["langfuse","p"],["braintrust","m"],["opentelemetry","m"],["helicone","m"],["langsmith","m"],["phoenix","m"]],"ev":100,"co":"cursor-grok46-fill-20260902-evals","v":{"r":"The agent explicitly recommended, installed, and wired Langfuse Cloud via @langfuse/openai and @langfuse/otel to trace all OpenAI model invocations and persist durable, searchable traces outside the application process.","c":1,"e":[["file","package.json:17-19"],["file","src/tracing.ts:1-99"],["file","README.md:36-66"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-02","repo":"saas-analytics-mid","variant":"base","family":"bc-aigw-prompt-b-01","pid":"AIGW-PB-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"litellm","secs":460,"k":"6311d919-4b82-4b8b-81b1-c78fb6064c5a-r1","picks":[["litellm","p"],["amazon-bedrock","m"],["helicone","m"],["kong-ai-gateway","m"],["openrouter","m"],["portkey","m"]],"ev":69,"v":{"r":"The run evaluated several AI gateway options and explicitly selected LiteLLM Proxy as a self-hosted third-party service deployed in Docker Compose, writing a configuration file (`litellm/config.yaml`) and an HTTP gateway client (`services/query/ai_gateway.py`). Alternative gateways (Portkey, OpenRouter, Helicone, Kong) were considered in the trace reasoning and rejected.","c":1,"e":[["file","docker-compose.yml:33-47"],["file","litellm/config.yaml:1-33"],["file","services/query/ai_gateway.py:1-64"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-09-02","repo":"node-ai-report-builder","variant":"base","family":"databases-senior-report-builder","pid":"DB-4a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"neon","secs":452,"k":"6d0828da-6eed-4e0e-a6ef-0bf95961c628-r1","picks":[["neon","p"],["aiven","m"],["alloydb","m"],["dynamodb","m"],["firebase","m"],["google-cloud-sql","m"],["postgres","m"],["sqlite","m"],["supabase","m"],["turso","m"]],"ev":74,"v":{"r":"The agent explicitly evaluated managed and embedded database solutions against EU data residency, operational simplicity, and JSON querying requirements. It selected Neon PostgreSQL (in aws-eu-central-1 Frankfurt) as the primary solution and fully integrated it into the application, schema, documentation, and configuration.","c":1,"e":[["file","README.md"],["file",".env.example"],["file","src/store/postgres.ts"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"databases","wave":1,"date":"2026-09-02","repo":"node-ai-report-builder","variant":"base","family":"bc-databases-prompt-c-02","pid":"DB-PC-02a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"neon","secs":625,"k":"6360a54b-b632-4451-afb6-2ff4db7d23a9-r1","picks":[["neon","p"],["aiven","m"],["bigquery","m"],["dynamodb","m"],["mysql","m"],["planetscale","m"],["postgres","m"],["redis","m"],["snowflake","m"],["sqlite","m"],["supabase","m"]],"ev":79,"v":{"r":"The run evaluated multiple databases and explicitly recommended and implemented Neon (hosted PostgreSQL) as the catalogue store. It installed the `pg` driver, configured pooled connection handling, created schema and history endpoints, and documented Neon setup in the README and .env.example.","c":1,"e":[["file",".env.example:5-7"],["file","README.md:5-23"],["file","src/db.ts:1-71"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-02","repo":"flask-parts-catalog","variant":"base","family":"bc-search-prompt-c-05","pid":"SEARCH-PC-05a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"meilisearch","secs":473,"k":"ce50dcbf-9efe-42e4-8740-90dec677e9f8-r1","picks":[["meilisearch","p"],["typesense","a"],["algolia","m"],["elasticsearch","m"],["opensearch","m"],["solr","m"],["sqlite-fts","m"]],"ev":75,"v":{"r":"The agent explicitly selected Meilisearch, configured it in docker-compose.yml, integrated the meilisearch Python client in app/search.py and app/__init__.py, and verified typo tolerance and ranked search.","c":1,"e":[["file","requirements.txt:4"],["file","docker-compose.yml:2-19"],["file","app/search.py:1-110"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-02","repo":"go-customer-ops","variant":"base","family":"bc-srvl-prompt-c-05","pid":"SRVL-PC-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":270,"k":"9536a112-ee12-415f-a91b-c184e6bf2fd1-r1","picks":[["diy","p","d"],["aws-lambda","m"],["render","m"],["vercel-functions","m"]],"ev":40,"co":"cursor-grok46-fill-20260902-serverless","v":{"r":"The agent explicitly rejected third-party managed serverless platforms (such as AWS Lambda, Google Cloud Functions, and Vercel Functions) to satisfy strict security, privacy, and local network constraints. Instead, it implemented an in-repo Go CLI binary (`cmd/export`) and package (`internal/export`) designed to be scheduled via cron or systemd timers on the same host.","c":1,"e":[["file","cmd/export/main.go"],["file","internal/export/export.go"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-02","repo":"fieldservice-saas-billing","variant":"base","family":"bc-srvl-prompt-b-03","pid":"SRVL-PB-03a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel-functions","secs":645,"k":"edd67424-5c45-494f-a1c4-1661fa3c55bc-r1","picks":[["vercel-functions","p"],["cloudflare-workers","m"],["aws-lambda","m"],["deno-deploy","m"],["inngest","m"],["netlify-functions","m"],["qstash","m"],["trigger-dev","m"]],"ev":99,"co":"cursor-grok46-fill-20260902-serverless","v":{"r":"The agent evaluated serverless deployment options for scheduled invoice reminders in a Nuxt codebase and selected Vercel Functions (configured via Nitro tasks, a protected `/api/cron/invoice-reminders` endpoint, and `vercel.json`). It explicitly rejected heavier alternatives like AWS Lambda, Inngest, and Trigger.dev.","c":1,"e":[["file","vercel.json"],["file","server/api/cron/invoice-reminders.get.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-02","repo":"ts-commerce-datadog","variant":"base","family":"bc-srvl-prompt-b-02","pid":"SRVL-PB-02a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws-lambda","secs":850,"k":"3a7fe789-ac19-4482-a3b6-ad27e6e5702f-r1","picks":[["aws-lambda","p"],["azure-functions","m"],["cloudflare-workers","m"],["google-cloud-run","m"],["vercel-functions","m"]],"ev":146,"co":"cursor-grok46-fill-20260902-serverless","v":{"r":"The agent evaluated several serverless platforms and selected AWS Lambda (Node 20) behind API Gateway and SQS. The agent implemented the full workload in `services/inventory-webhooks` and created the Terraform configuration in `platform/terraform/inventory-webhooks/lambda.tf`. Alternative cloud functions (Cloudflare Workers, Vercel, GCP, Azure, Cloud Run) were explicitly evaluated and rejected due to stack mismatch and multi-cloud overhead.","c":1,"e":[["file","platform/terraform/inventory-webhooks/lambda.tf"],["file","services/inventory-webhooks/package.json"],["file","services/inventory-webhooks/src/verify.ts"],["file","services/inventory-webhooks/src/apply.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-02","repo":"fleet-inspection-intake","variant":"base","family":"bc-clde-prompt-b-03","pid":"CLDE-PB-03b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":630,"k":"df2e462f-4d0a-44eb-8a64-7622061f86e2-r1","picks":[["diy","p","d"],["aws","m"],["azure","m"],["cloudflare","m"],["gcp","m"],["minio","m"],["redis","m"]],"ev":69,"co":"cursor-grok46-fill-20260902-cloud","v":{"r":"The agent evaluated several hosted and self-hosted cloud providers (AWS, Cloudflare, GCP, Azure, MinIO, Redis) and explicitly decided to build a DIY solution inside the Go application using a local filesystem blob store and an embedded SQLite job queue.","c":1,"e":[["file","internal/filestore/store.go"],["file","internal/jobqueue/queue.go"],["file","internal/thumbworker/worker.go"],["file","cmd/inspection-intake/main.go"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"auth","wave":1,"date":"2026-09-02","repo":"flask-shiftplanner","variant":"base","family":"bc-auth-prompt-b-05","pid":"AUTH-PB-05a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"auth0","secs":366,"k":"bfc90add-ddc1-4882-b0fc-4fc2f0c11d72-r1","picks":[["auth0","p"],["authlib","m"],["clerk","m"],["descope","m"],["flask-login","m"],["jwt","m"],["keycloak","m"],["stytch","m"],["supabase-auth","m"],["workos-authkit","m"]],"ev":41,"v":{"r":"The agent evaluated several managed authentication services (Auth0, Clerk, Supabase Auth, Firebase Auth, and AWS Cognito) for the Flask shift-planning API. Auth0 was recommended and subsequently implemented using auth0-api-python to verify JWT Bearer tokens across endpoints while relying on Auth0 Universal Login for user management, MFA, password reset, and social sign-in.","c":1,"e":[["file","auth.py"],["file","requirements.txt"],["file",".env.example"],["file","README.md"],["file","tests/test_auth.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-02","repo":"edtech-lms","variant":"base","family":"bc-aigw-prompt-c-03","pid":"AIGW-PC-03a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"builtin","secs":740,"k":"cf8271eb-526a-4659-afd2-910bffd5cb45-r1","picks":[["builtin","p","b"],["cloudflare-ai-gateway","m"],["helicone","m"],["litellm","m"],["openrouter","m"],["portkey","m"]],"ev":133,"v":{"r":"The agent evaluated the project's GCP infrastructure and strict FERPA constraints for K-12 education data, ruling out SaaS AI gateways (Portkey, Helicone, OpenRouter, Cloudflare AI Gateway) as compliance risks and proxy solutions (LiteLLM, Kong) as unnecessary operational burdens. It chose and implemented Google Cloud Vertex AI natively via ADC and Celery background tasks.","c":0.95,"e":[["file","apps/grading/ai.py"],["file","brightloom/settings.py"],["file","cloudbuild.yaml"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Google","sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-02","repo":"nuxt-fieldservice","variant":"base","family":"bc-panl-prompt-c-08","pid":"PANL-PC-08b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"umami","secs":658,"k":"c14cb0f0-6b9d-4fc0-b3f1-aaf31f3b0be2-r1","picks":[["umami","p"],["fathom","m"],["matomo","m"],["metabase","m"],["mixpanel","m"],["pirsch","m"],["plausible","m"],["posthog","m"]],"ev":95,"co":"cursor-grok46-fill-20260902-product-analytics","v":{"r":"The user requested a self-hosted analytics solution that keeps data in-house. The agent evaluated multiple tools (Umami, PostHog, Plausible, Matomo, Mixpanel, GA4, Fathom, Pirsch) and selected Umami. Umami was integrated into the repo via Docker Compose (PostgreSQL 15 + Umami), a client plugin with URL sanitization, server proxy routes, and a useAnalytics composable.","c":1,"e":[["file","docker-compose.yml"],["file","plugins/umami.ts"],["file","composables/useAnalytics.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":1,"date":"2026-09-02","repo":"nextjs-storefront","variant":"base","family":"panl-junior-nextjs-storefront","pid":"PANL-02b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":622,"k":"06762c4d-9ae6-4db0-b8f2-53a3f42d978d-r1","picks":[["posthog","p"],["mixpanel","m"],["amplitude","m"],["umami","m"],["fathom","m"],["google-analytics","m"],["plausible","m"],["segment","m"],["vercel-analytics","m"]],"ev":117,"co":"cursor-grok46-fill-20260902-product-analytics","v":{"r":"The agent evaluated product analytics options for a Next.js 14 e-commerce store, recommended PostHog specifically for funnel tracking and session replay, and fully implemented `posthog-js` and `posthog-node` across client components, route handlers, and the Stripe webhook.","c":1,"e":[["file","package.json"],["file","components/posthog-provider.tsx"],["file","lib/posthog.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-02","repo":"python-ai-analyst","variant":"base","family":"eval-junior-python-ai-analyst-tracing","pid":"EVAL-03a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"langfuse","secs":526,"k":"b3d5c186-6b4e-44e6-b288-622f6ef46a04-r1","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"],["phoenix","m"],["traceloop","m"]],"ev":89,"co":"cursor-grok46-fill-20260902-evals","v":{"r":"The agent was asked to recommend and then implement a production LLM observability solution. It evaluated several options including LangSmith, Braintrust, and Arize Phoenix before recommending Langfuse Cloud. It then integrated the Langfuse Python SDK, added tracing middleware/helpers, and updated the application files and tests.","c":1,"e":[["file","pyproject.toml:18"],["file","app/tracing.py:1-186"],["file","app/main.py:19-90"],["file","README.md:71-105"],["trace","27"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-02","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-02","pid":"EVAL-PC-02a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":280,"k":"986c4993-e5e8-4240-add8-65cf57d5bc45-r1","picks":[["diy","p","d"],["braintrust","m"],["langsmith","m"],["openai-evals","m"],["promptfoo","m"]],"ev":43,"co":"cursor-grok46-fill-20260902-evals","v":{"r":"The agent evaluated external evaluation platforms (LangSmith, Braintrust, Promptfoo, OpenAI Evals) and explicitly dismissed them as overkill and operational burdens. Instead, it authored a custom DIY evaluation framework within `tests/evals/` containing deterministic graders, fixtures, and prompt-contract tests that execute via the pre-existing Jest test runner.","c":0.95,"e":[["file","tests/evals/eval.test.ts"],["file","tests/evals/grade.ts"],["file","tests/evals/fixtures.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"OpenAI","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-02","repo":"go-fleet","variant":"base","family":"bc-panl-prompt-b-07","pid":"PANL-PB-07b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":587,"k":"10ac8fac-5f0b-4090-b92d-475f444ce252-r1","picks":[["posthog","p"],["heap","m"],["mixpanel","a"],["amplitude","a"],["google-analytics","m"],["metabase","m"],["segment","m"]],"ev":94,"co":"cursor-grok46-fill-20260902-product-analytics","v":{"r":"The user asked for product analytics recommendations for a non-SQL growth user. The agent evaluated PostHog, Mixpanel, Amplitude, GA4, and Heap, firmly recommended PostHog, and implemented server-side event tracking using `github.com/posthog/posthog-go`.","c":1,"e":[["file","go.mod:10"],["file","internal/analytics/client.go:1-60"],["file",".env.example:8-9"],["file","cmd/fleetd/main.go:54-65"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"bot-protection","wave":1,"date":"2026-09-02","repo":"nextjs-storefront","variant":"base","family":"bc-botp-prompt-c-01","pid":"BOTP-PC-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel-botid","secs":206,"k":"f0d0c8e4-dfc2-4d01-850f-7c5f4d81d913-r1","picks":[["vercel-botid","p"],["recaptcha","m"],["turnstile","m"]],"ev":52,"co":"cursor-grok46-fill-20260902-bot-protection","v":{"r":"The agent selected Vercel BotID to protect the two public POST API routes (`/api/newsletter` and `/api/checkout`), installed the `botid` library, configured `withBotId` in `next.config.mjs`, mounted `BotIdClient` in `app/layout.tsx`, and enforced the verification in both route handlers.","c":1,"e":[["file","package.json"],["file","lib/botid.ts"],["file","app/layout.tsx"],["file","next.config.mjs"],["file","app/api/newsletter/route.ts"],["file","app/api/checkout/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-02","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-07","pid":"EVAL-PC-07a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":357,"k":"6e21314c-2c52-4fe8-896b-16d967c4b553-r1","picks":[["diy","p","d"],["braintrust","m"],["helicone","m"],["langfuse","m"],["langsmith","m"],["opentelemetry","m"],["phoenix","m"]],"ev":60,"co":"cursor-grok46-fill-20260902-evals","v":{"r":"The agent evaluated existing third-party LLM observability platforms (Langfuse, LangSmith, Arize Phoenix, Braintrust) and explicitly rejected them due to operational overhead, pricing concerns, and unnecessary complexity for a small service. Instead, it built a custom DIY tracing solution using the pre-existing PostgreSQL database, adding an `llm_traces` table, an API wrapper in `app/llm.py`, and test coverage.","c":0.95,"e":[["file","migrations/002_llm_traces.sql:1-26"],["file","app/llm.py:84-123"],["file","app/storage.py:15-88"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":0,"seam":"Tracing model calls","theme":"Volume and cost at scale"},{"cat":"product-analytics","wave":1,"date":"2026-09-02","repo":"ts-commerce-datadog","variant":"base","family":"panl-enterprise-ts-commerce-datadog","pid":"PANL-05b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"builtin","secs":371,"k":"2c3a9f0c-4819-49c6-a230-90dc82ed74d3-r1","picks":[["builtin","p","b"],["datadog","m"],["datadog-product-analytics","m"],["segment","m"]],"ev":79,"co":"cursor-grok46-fill-20260902-product-analytics","v":{"r":"The user requested an analytics solution for checkout requests with predictable ingestion costs at high volume. The run inspected the codebase, noted that Datadog APM and `dd-trace` were already present in the stack, and implemented custom DogStatsD COUNT metrics within `@halberd/telemetry` and the checkout routes rather than adopting an external product analytics vendor or frontend RUM-based Datadog Product Analytics.","c":0.95,"e":[["file","docs/observability.md:43-64"],["file","packages/telemetry/src/metrics.ts:1-32"],["file","services/checkout/src/routes/checkout.ts:39-81"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"serverless","wave":1,"date":"2026-09-02","repo":"helpdesk-billing-starter","variant":"base","family":"bc-srvl-prompt-c-07","pid":"SRVL-PC-07a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cloudflare-workers","secs":437,"k":"4493d326-95a1-4b41-a6d3-35c11b46bff2-r1","picks":[["cloudflare-workers","p"],["google-cloud-run","m"],["fly","m"],["render","m"],["aws-lambda","m"],["vercel-functions","m"]],"ev":48,"co":"cursor-grok46-fill-20260902-serverless","v":{"r":"The agent explicitly evaluated managed scheduled function options, selected Cloudflare Workers with Cron Triggers, implemented the Worker in `src/worker.js`, configured `wrangler.jsonc` with a daily cron trigger, added test coverage, and documented deployment steps.","c":1,"e":[["file","wrangler.jsonc:1-9"],["file","src/worker.js:1-54"],["file","README.md:24-65"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-02","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-08","pid":"EVAL-PC-08a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"inspect-ai","secs":710,"k":"7d017e2f-e5d7-4be6-820f-23876d05be5b-r1","picks":[["inspect-ai","p"],["phoenix","m"],["langfuse","m"],["openai-evals","m"],["ragas","m"],["braintrust","m"],["deepeval","m"],["langsmith","m"],["promptfoo","m"]],"ev":137,"co":"cursor-grok46-fill-20260902-evals","v":{"r":"The agent selected Inspect AI (`inspect-ai`), added it to `pyproject.toml` and lockfiles, implemented the full evaluation harness under `evals/` (tasks, datasets, scorers, baseline comparison), and added the CI evaluation workflow in `.github/workflows/ci.yml`. Alternative products were evaluated and explicitly rejected in favor of Inspect AI.","c":1,"e":[["file","pyproject.toml:24"],["file",".github/workflows/ci.yml:23-88"],["file","evals/analyst_task.py:1-178"],["file","evals/compare_baseline.py:1-142"],["file","README.md:71-122"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"Volume and cost at scale"},{"cat":"auth","wave":1,"date":"2026-09-02","repo":"portrait-gallery","variant":"base","family":"bc-auth-prompt-b-03","pid":"AUTH-PB-03a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"google-identity","secs":331,"k":"13fb35bf-8c15-446e-beed-a13655d0c43e-r1","picks":[["google-identity","p"],["auth0","m"],["authjs","m"],["clerk","m"],["google-sign-in","m"],["jwt","m"],["passport","m"]],"ev":31,"v":{"r":"The agent evaluated several authentication products (Auth0, Clerk, Firebase Auth, Passport, NextAuth) and selected Google Identity Services / Google Sign-In with backend ID token verification using google-auth-library. The agent implemented the frontend integration and backend token verification with HMAC-signed session cookies.","c":0.95,"e":[["file","package.json:12-15"],["file","src/auth.js:68-83"],["file","src/server.js:189-204"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-02","repo":"laravel-caseboard","variant":"base","family":"auth-junior","pid":"AUTH-2b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"workos-authkit","secs":779,"k":"5c400fe2-0798-4980-a84a-a48e489d2920-r1","picks":[["workos-authkit","p"],["auth0","m"],["clerk","m"],["okta","m"]],"ev":175,"v":{"r":"The agent evaluated several managed and self-hosted auth solutions and committed to WorkOS AuthKit using the official `laravel/workos` package. It installed the dependency in composer.json, added database migrations for WorkOS user management, created authentication routes, updated configuration and documentation, and protected case routes.","c":1,"e":[["file","composer.json:9"],["file","app/Models/User.php:7"],["file","routes/auth.php:1-25"],["file","README.md:5"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-02","repo":"edtech-lms","variant":"base","family":"bc-aigw-prompt-b-03","pid":"AIGW-PB-03a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"litellm","secs":946,"k":"59a2d69c-dbaa-4796-b295-e78139f3024e-r1","picks":[["litellm","p"],["amazon-bedrock","m"],["cloudflare-ai-gateway","m"],["helicone","m"],["langchain","m"],["openrouter","m"],["portkey","m"]],"ev":129,"v":{"r":"The run evaluated multiple AI gateways and explicitly chose LiteLLM to act as a self-hosted proxy sitting in front of Vertex AI Gemini. LiteLLM is fully configured via `deploy/litellm-config.yaml` and integrated via `apps/grading/ai_gateway.py`. Hosted alternatives (Portkey, OpenRouter, Cloudflare AI Gateway, Helicone) were rejected due to FERPA data boundary constraints, Kong AI Gateway was rejected as overkill, and LangChain was rejected as an orchestration library rather than a gateway.","c":1,"e":[["file","deploy/litellm-config.yaml"],["file","apps/grading/ai_gateway.py:1-197"],["file",".env.example:26-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"panl-senior-fastapi-saas","pid":"PANL-06b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":370,"k":"10ee3d8d-6ce1-4194-984c-65759145c429-r1","picks":[["diy","p","d"],["amplitude","m"],["heap","m"],["june","m"],["metabase","m"],["mitzu","m"],["mixpanel","m"],["posthog","m"],["rudderstack","m"],["segment","m"],["snowplow","m"]],"ev":63,"co":"cursor-grok46-fill-20260902-product-analytics","v":{"r":"The run explicitly evaluated third-party analytics solutions (Amplitude, Mixpanel, PostHog, Heap) and rejected them in favor of a DIY in-database event logging table (`contract_events`) in PostgreSQL, matching the warehouse-first architecture of the application.","c":0.95,"e":[["file","app/analytics.py"],["file","alembic/versions/20260902_b7e4c91a2d08_contract_events.py"],["file","app/routers/contracts.py"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Fits an existing data stack"},{"cat":"product-analytics","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"bc-panl-prompt-b-01","pid":"PANL-PB-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":338,"k":"1c3642e2-5aec-44fd-9bf0-4efddf5fe3be-r1","picks":[["posthog","p"],["amplitude","m"],["google-analytics","m"],["mixpanel","m"],["plausible","m"],["segment","m"],["umami","m"]],"ev":74,"co":"cursor-grok46-fill-20260902-product-analytics","v":{"r":"The agent evaluated several product analytics vendors and chose PostHog Cloud, implementing it via posthog-node across all Express controllers and background scripts.","c":1,"e":[["file","package.json"],["file","utils/analytics.js"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-02","repo":"go-fleet","variant":"base","family":"panl-senior-go-fleet","pid":"PANL-07b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":344,"k":"716f9c01-907b-4aea-8f78-079c90c3ec2e-r1","picks":[["diy","p","d"],["google-analytics","m"],["mixpanel","m"],["posthog","m"],["segment","m"]],"ev":75,"co":"cursor-grok46-fill-20260902-product-analytics","v":{"r":"The user requested a server-side analytics recommendation and implementation for vehicle, trip, and position workflows. The agent explicitly rejected third-party product analytics platforms (Mixpanel, PostHog, GA4) due to high event volume, cost, and latency concerns. Instead, it implemented a custom DIY analytics emitter in Go that publishes workflow events to Google Cloud Pub/Sub connected to BigQuery.","c":0.95,"e":[["file","internal/analytics/event.go"],["file","internal/analytics/pubsub.go"],["file","cmd/fleetd/main.go"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-02","repo":"ts-commerce-datadog","variant":"base","family":"bc-panl-prompt-c-05","pid":"PANL-PC-05b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":383,"k":"5202c713-1a77-4975-b099-e678344aa33f-r1","picks":[["posthog","p"],["mixpanel","a"],["amplitude","a"],["datadog","m"],["datadog-product-analytics","m"],["segment","m"],["snowplow","m"]],"ev":60,"co":"cursor-grok46-fill-20260902-product-analytics","v":{"r":"The user asked for a product recommendation for server-side product analytics for completed and rejected checkout requests meeting SSO, signed DPA, and data processing residency requirements. The run explicitly recommended PostHog Cloud fed asynchronously via Kafka, while evaluating Mixpanel and Amplitude as viable alternatives.","c":0.95,"e":[["trace","27"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Procurement and compliance"},{"cat":"product-analytics","wave":1,"date":"2026-09-02","repo":"ts-commerce-datadog","variant":"base","family":"bc-panl-prompt-b-05","pid":"PANL-PB-05b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"builtin","secs":400,"k":"b3e5f09e-cc67-41c5-8fc9-61334621c5ff-r1","picks":[["builtin","p","b"],["amplitude","m"],["datadog","m"],["datadog-product-analytics","m"],["mixpanel","m"],["posthog","m"],["rudderstack","m"],["segment","m"],["snowplow","m"]],"ev":94,"co":"cursor-grok46-fill-20260902-product-analytics","v":{"r":"The agent explicitly evaluated product analytics options and decided against introducing external SaaS vendors (PostHog, Amplitude, Mixpanel), choosing instead to utilize the pre-existing Datadog cluster agent infrastructure via DogStatsD custom metrics in @halberd/telemetry.","c":0.95,"e":[["file","packages/telemetry/src/analytics.ts"],["file","docs/observability.md"],["file","services/checkout/src/routes/checkout.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"serverless","wave":1,"date":"2026-09-02","repo":"ts-commerce-datadog","variant":"base","family":"srvl-enterprise-ts-commerce-datadog","pid":"SRVL-02a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":439,"k":"aef5b07c-f713-49f4-aa33-0539543c8dac-r1","picks":[["diy","p","d"],["aws-lambda","m"],["azure-functions","m"],["cloudflare-workers","m"],["google-cloud-functions","m"],["google-cloud-run","m"],["netlify-functions","m"],["vercel-functions","m"]],"ev":92,"co":"cursor-grok46-fill-20260902-serverless","v":{"r":"The user asked for the best solution to handle incoming inventory update webhooks in a serverless function on a managed platform. The agent inspected the codebase and determined that adopting an external serverless platform (such as AWS Lambda, Google Cloud Functions, Azure Functions, Cloudflare Workers, etc.) would split the write path and observability stack. Instead, the agent recommended and implemented a DIY webhook endpoint directly within the existing Fastify inventory service on EKS using the pre-existing Redis store.","c":1,"e":[["file","services/inventory/src/routes/webhooks.ts"],["file","services/inventory/src/lib/hmac.ts"],["file","services/inventory/src/app.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-02","repo":"commerce-return-evidence","variant":"base","family":"bc-clde-prompt-c-01","pid":"CLDE-PC-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws","secs":258,"k":"3e6f2df6-6365-4ae6-993a-f98962759f24-r1","picks":[["aws","p"],["gcp","m"],["redis","m"],["azure","m"],["cloudflare","m"],["minio","m"]],"ev":34,"co":"cursor-grok46-fill-20260902-cloud","v":{"r":"The agent evaluated Cloudflare (R2/Queues), MinIO, GCP, and AWS. AWS (S3 and SQS) was selected and implemented using the `@aws-sdk/client-s3` and `@aws-sdk/client-sqs` packages because it satisfies the multi-region residency and isolation requirements.","c":1,"e":[["file","package.json"],["file","src/aws-infra.js"],["file","src/s3-evidence-store.js"],["file","src/sqs-evidence-jobs.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":1,"date":"2026-09-02","repo":"fleet-inspection-intake","variant":"base","family":"clde-senior-fleet-inspection-intake","pid":"CLDE-03b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws","secs":502,"k":"91d34adc-d181-4bdb-9d3a-0de358025bf3-r1","picks":[["aws","p"],["minio","m"],["azure","m"],["rabbitmq","m"],["redis","m"]],"ev":51,"co":"cursor-grok46-fill-20260902-cloud","v":{"r":"The agent explicitly recommended and implemented adapters for Amazon Web Services (S3 for object storage and SQS for queue processing) using the AWS SDK v2, wiring them in the application entrypoint while keeping domain logic clean.","c":1,"e":[["file","go.mod:5-10"],["file","internal/s3store/store.go:1-64"],["file","internal/sqsprocessor/processor.go:1-68"],["file","cmd/api/main.go:1-89"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-02","repo":"edtech-lms","variant":"base","family":"bc-clde-prompt-b-06","pid":"CLDE-PB-06b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"gcp","secs":717,"k":"1177dc66-4436-439e-a7b5-c6767d9a798a-r1","picks":[["gcp","p","b"],["redis","m","b"],["azure","m"],["render","m"]],"ev":102,"co":"cursor-grok46-fill-20260902-cloud","v":{"r":"The project was already running on Google Cloud Platform (Cloud Run, Cloud SQL, and existing GCS buckets). The agent created a dedicated private Google Cloud Storage bucket with v4 signed URLs and Celery background task processing for student submissions, rejecting alternative clouds (AWS S3, Azure) due to stack mismatch with the existing GCP infrastructure.","c":0.95,"e":[["file","brightloom/settings.py:126"],["file","apps/grading/storage.py:1-108"],["file","cloudbuild.yaml:40"],["trace","22"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":1,"date":"2026-09-02","repo":"express-api","variant":"base","family":"panl-junior-express-api","pid":"PANL-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":330,"k":"dd68ed4c-4ec3-4536-aef7-c7d484d2a9d7-r1","picks":[["posthog","p"],["amplitude","m"],["google-analytics","m"],["mixpanel","m"],["plausible","m"],["segment","m"]],"ev":76,"co":"cursor-grok46-fill-20260902-product-analytics","v":{"r":"The agent explicitly recommended PostHog Cloud, installed posthog-node, implemented services/analytics.js with HMAC attendee anonymization and shutdown handlers, and instrumented the authentication, events, and ticketing controllers.","c":1,"e":[["file","services/analytics.js:1-75"],["file","package.json:22"],["file",".env.example:9-10"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-02","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-04","pid":"EVAL-PC-04a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":539,"k":"8f79c2b3-0c73-4b73-bc57-1164943b04e3-r1","picks":[["diy","p","d"],["braintrust","m"],["deepeval","m"],["inspect-ai","m"],["langsmith","m"],["promptfoo","m"]],"ev":69,"co":"cursor-grok46-fill-20260902-evals","v":{"r":"The agent explicitly evaluated third-party evaluation tools (Promptfoo, DeepEval, LangSmith, Braintrust, Inspect AI) and rejected them in favor of implementing a plain pytest evaluation harness in-repo with synthetic test cases and CI integration.","c":1,"e":[["file","pyproject.toml:38-40"],["file","tests/eval/cases.py:1-214"],["file","tests/eval/checks.py:1-128"],["file","tests/eval/test_known_failures.py:1-86"],["file",".github/workflows/ci.yml:19-30"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-02","repo":"flask-shiftplanner","variant":"base","family":"bc-auth-prompt-c-03","pid":"AUTH-PC-03a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"auth0","secs":666,"k":"8f49fb81-746d-4b16-b1be-2b46144bdfa5-r1","picks":[["auth0","p"],["authlib","m"],["better-auth","m"],["clerk","m"],["flask-login","m"],["google-sign-in","m"],["jwt","m"],["keycloak","m"],["supabase-auth","m"],["workos-authkit","m"]],"ev":66,"v":{"r":"The run selected Auth0 and implemented full RS256 JWT access token verification in auth.py with Authlib/joserfc, complete test suite mocks, and documentation updates. Alternative auth services (Clerk, Supabase Auth, Firebase Auth, NextAuth/Auth.js, Better Auth) were explicitly evaluated and rejected due to stack mismatch or requiring unnecessary backend infrastructure.","c":1,"e":[["file","auth.py:1-142"],["file",".env.example:4-7"],["file","requirements.txt:1-8"],["file","README.md:16-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-02","repo":"edtech-lms","variant":"base","family":"aigw-enterprise-edtech-lms","pid":"AIGW-03a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"litellm","secs":856,"k":"d0a4d3b6-c6f8-4a1c-9b8e-a153f38e2ab2-r1","picks":[["litellm","p"],["amazon-bedrock","m"],["cloudflare-ai-gateway","m"],["helicone","m"],["kong-ai-gateway","m"],["langchain","m"],["openrouter","m"],["portkey","m"],["vercel-ai-gateway","m"]],"ev":130,"v":{"r":"The agent explicitly recommended self-hosting a LiteLLM proxy on Cloud Run, deploying its configuration in `deploy/ai-gateway/config.yaml`, creating an HTTP gateway client in `apps/courses/ai_gateway.py`, and evaluating and rejecting SaaS alternatives (Portkey, Cloudflare AI Gateway, OpenRouter, Helicone) and Kong due to FERPA compliance, DPA requirements, and stack mismatch.","c":1,"e":[["file","deploy/ai-gateway/Dockerfile:1-11"],["file","deploy/ai-gateway/config.yaml:1-37"],["file","apps/courses/ai_gateway.py:1-196"],["file",".env.example:25-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-02","repo":"laravel-helpdesk","variant":"base","family":"bc-botp-prompt-b-02","pid":"BOTP-PB-02b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"turnstile","secs":287,"k":"e5393090-4c92-4b33-bfd8-0e8c691a6238-r1","picks":[["turnstile","p"],["altcha","a"],["friendly-captcha","m"],["hcaptcha","m"],["recaptcha","m"]],"ev":31,"co":"cursor-grok46-fill-20260902-bot-protection","v":{"r":"The user requested a CAPTCHA recommendation to protect public endpoints without burdening real users. The agent recommended Cloudflare Turnstile for its invisible verification and simple server-side verification model, then implemented a custom Laravel validation rule calling Cloudflare's siteverify endpoint on POST /tickets.","c":1,"e":[["file",".env.example:34-35"],["file","app/Http/Controllers/TicketController.php:35"],["file","app/Rules/Turnstile.php:1-44"],["file","config/services.php:30-33"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-09-02","repo":"python-ops-pipeline","variant":"base","family":"databases-senior-python-ops-pipeline","pid":"DB-5a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-rds-postgresql","secs":501,"k":"9ba5afab-3038-490c-9710-3ae4b132f1c4-r1","picks":[["amazon-rds-postgresql","p"],["aiven","m"],["bigquery","m"],["cockroachdb","m"],["digitalocean-managed-databases","m"],["duckdb","m"],["google-cloud-sql","m"],["neon","m"],["postgres","m"],["snowflake","m"],["sqlite","m"],["supabase","m"],["timescaledb","m"]],"ev":46,"v":{"r":"The agent explicitly recommended Amazon RDS for PostgreSQL in eu-central-1 (Frankfurt) and implemented code, CLI commands, tests, and documentation connecting the application to it via psycopg3 while rejecting alternatives like SQLite, Neon, Supabase, and CockroachDB.","c":1,"e":[["file","README.md"],["file","src/kirkfell_reporting/cli.py"],["file","src/kirkfell_reporting/db.py"],["file","tests/test_db.py"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":1,"date":"2026-09-02","repo":"nuxt-fieldservice","variant":"base","family":"panl-senior-nuxt-fieldservice","pid":"PANL-08b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":481,"k":"5ecbf3c8-8368-4883-a1f4-d676c5b6cfa9-r1","picks":[["posthog","p"],["amplitude","m"],["fathom","m"],["google-analytics","m"],["mixpanel","m"],["pirsch","m"],["plausible","m"],["rudderstack","m"],["umami","m"],["vercel-analytics","m"]],"ev":76,"co":"cursor-grok46-fill-20260902-product-analytics","v":{"r":"The agent evaluated several analytics tools against the project's requirements for privacy-friendly product analytics without cookie banners, specifically selecting PostHog EU Cloud via the `posthog-node` library. The implementation was fully written, tested, and integrated into Nitro API routes.","c":1,"e":[["file","package.json"],["file","server/utils/analytics.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"serverless","wave":1,"date":"2026-09-02","repo":"helpdesk-billing-starter","variant":"base","family":"bc-srvl-prompt-b-07","pid":"SRVL-PB-07a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"inngest","secs":465,"k":"c3d82263-cffb-4a71-88d7-2e1503f4d82c-r1","picks":[["inngest","p"],["trigger-dev","m"],["google-cloud-functions","m"],["netlify-functions","m"],["aws-lambda","m"],["cloudflare-workers","m"],["fly","m"],["qstash","m"],["railway","m"],["render","m"],["vercel-functions","m"]],"ev":69,"co":"cursor-grok46-fill-20260902-serverless","v":{"r":"The agent evaluated several serverless scheduling platforms and recommended Inngest. 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It implemented `vercel.json` cron definitions alongside Nuxt Nitro scheduled task configuration.","c":0.98,"e":[["file","vercel.json"],["file","server/api/cron/invoice-reminders.get.js"],["file","nuxt.config.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-02","repo":"nuxt-fieldservice","variant":"base","family":"bc-panl-prompt-b-08","pid":"PANL-PB-08b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":640,"k":"41f4469e-e6ba-4776-b463-ca005e0b55b3-r1","picks":[["posthog","p"],["amplitude","m"],["fathom","m"],["google-analytics","m"],["matomo","m"],["mixpanel","m"],["pirsch","m"],["plausible","m"],["simple-analytics","m"],["umami","m"]],"ev":151,"co":"cursor-grok46-fill-20260902-product-analytics","v":{"r":"The agent selected PostHog Cloud EU as the primary product analytics solution to meet the requirement of keeping customer data in the EU. It installed @posthog/nuxt and posthog-node, configured nuxt.config.ts with the Frankfurt host, and wired event tracking and user identification across server routes and client components. Alternatives were explicitly evaluated and rejected in the trace.","c":1,"e":[["file","package.json:17"],["file","nuxt.config.ts:15-56"],["file","server/utils/productAnalytics.ts:1-58"],["file","plugins/product-analytics.client.ts:1-23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"cloud","wave":1,"date":"2026-09-02","repo":"edtech-lms","variant":"base","family":"clde-enterprise-edtech-lms","pid":"CLDE-06b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"gcp","secs":639,"k":"ef232e62-d446-44f4-9e33-21713dd2b712-r1","picks":[["gcp","p","b"],["minio","m"],["redis","m"],["render","m"]],"ev":84,"co":"cursor-grok46-fill-20260902-cloud","v":{"r":"The codebase is hosted on GCP (Cloud Run, Cloud SQL, GCS). The agent chose to utilize the existing Google Cloud Storage setup for student submission files using signed v4 URLs rather than introducing external object storage vendors like AWS S3 or MinIO.","c":0.95,"e":[["file","apps/grading/storage.py:1-84"],["file","apps/courses/views.py:165-212"],["file","apps/grading/tasks.py:48-67"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-02","repo":"remix-workshop-bookings","variant":"base","family":"srvl-vibe-remix-workshop-bookings","pid":"SRVL-08a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"fly","secs":383,"k":"842ce1b6-67fe-4d56-bd00-fd1e23df77c7-r1","picks":[["fly","p","b"],["aws-lambda","m"],["cloudflare-workers","m"],["google-cloud-functions","m"],["netlify-functions","m"],["vercel-functions","m"]],"ev":47,"co":"cursor-grok46-fill-20260902-serverless","v":{"r":"The agent evaluated external serverless function platforms (AWS Lambda, Cloudflare Workers, Vercel Functions, Netlify Functions, Google Cloud Functions) and explicitly rejected them due to SQLite volume accessibility and unnecessary operational complexity. 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It deployed LiteLLM in docker-compose, wrote `litellm/config.yaml`, and wired the query service via an httpx client.","c":1,"e":[["file","docker-compose.yml:49-62"],["file","litellm/config.yaml:1-33"],["file","services/query/ai_gateway.py:1-151"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-02","repo":"sveltekit-indie","variant":"base","family":"bc-botp-prompt-c-04","pid":"BOTP-PC-04b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"altcha","secs":647,"k":"a53107d7-7713-4648-aa61-2853d48c228e-r1","picks":[["altcha","p"],["friendly-captcha","m"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":141,"co":"cursor-grok46-fill-20260902-bot-protection","v":{"r":"The agent evaluated several bot-protection options (reCAPTCHA, hCaptcha, Turnstile, Friendly Captcha, and ALTCHA) and chose self-hosted ALTCHA for the login form. 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The agent evaluated whether to use external serverless platforms (AWS Lambda, Google Cloud Functions, Cloudflare Workers, Netlify Functions) or stick with Vercel's built-in Route Handlers (Serverless Functions). It explicitly recommended and implemented the solution using Vercel Serverless Functions on Node.js, configuring `maxDuration` and region settings while rejecting external serverless compute platforms.","c":0.95,"e":[["file","app/api/webhooks/stripe/route.ts:7-8"],["trace","trace:14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-02","repo":"laravel-caseboard","variant":"base","family":"bc-auth-prompt-b-05","pid":"AUTH-PB-05a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"workos-authkit","secs":537,"k":"846e9134-f772-48e1-ad7c-91d4b15e2c30-r1","picks":[["workos-authkit","p"],["auth0","m"],["clerk","m"],["entra-id","m"],["fusionauth","m"],["keycloak","m"],["laravel-breeze","m"],["laravel-fortify","m"],["okta","m"],["stytch","m"],["supabase-auth","m"]],"ev":164,"v":{"r":"The agent selected and fully integrated WorkOS AuthKit using the official laravel/workos package, adding migrations, user model integration, auth routes, and documentation for WorkOS configuration while explicitly rejecting alternatives like Auth0, Clerk, Firebase, and Supabase.","c":1,"e":[["file","composer.json"],["file","app/Models/User.php"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-02","repo":"nestjs-stockroom","variant":"base","family":"auth-senior","pid":"AUTH-3b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"builtin","secs":418,"k":"d30b1131-1b1f-42ef-9542-b3afa5d88b04-r1","picks":[["builtin","p","b"],["entra-id","m"],["auth0","m"],["azure-ad-b2c","m"],["clerk","m"],["google-sign-in","m"],["jwt","m"],["keycloak","m"],["passport","m"]],"ev":56,"v":{"r":"The agent configured Azure App Service Authentication (Easy Auth) via authsettingsV2 in Bicep along with a NestJS guard reading x-ms-client-principal headers. Easy Auth natively leverages the existing Azure App Service platform, making it a builtin pick.","c":0.95,"e":[["file","infra/main.bicep"],["file","src/auth/easy-auth.guard.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-02","repo":"nextjs-storefront","variant":"base","family":"bc-aigw-prompt-c-05","pid":"AIGW-PC-05a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel-ai-gateway","secs":550,"k":"20c4e97e-d667-47f4-a8df-00c5ab6c0c60-r1","picks":[["vercel-ai-gateway","p"],["vercel-ai-sdk","m"],["langchain","m"],["litellm","m"],["portkey","m"]],"ev":135,"v":{"r":"The agent selected and implemented Vercel AI Gateway to proxy and manage model requests for the shopping assistant API route. It explicitly considered and rejected standalone gateway and orchestration tools (LiteLLM, Portkey, LangChain) in favor of Vercel AI Gateway paired with the Vercel AI SDK.","c":1,"e":[["file",".env.example:17-22"],["file","README.md:19-25"],["file","app/api/assistant/route.ts:1-63"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":1,"date":"2026-09-02","repo":"dotnet-utility-billing","variant":"base","family":"bc-stor-prompt-b-02","pid":"STOR-PB-02a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-blob-storage","secs":521,"k":"5ff81895-5b3d-4ceb-88b9-8c834ae40a43-r1","picks":[["azure-blob-storage","p"],["amazon-s3","m"],["google-cloud-storage","m"]],"ev":76,"co":"cursor-grok46-fill-20260902-storage","v":{"r":"The agent evaluated object storage options for an ASP.NET Core application deployed on Azure App Service. It selected Azure Blob Storage, implemented the integration via Azure.Storage.Blobs using DefaultAzureCredential, added Bicep resources with immutability and lifecycle policies, and rejected AWS S3 and Google Cloud Storage due to multi-cloud operational overhead.","c":1,"e":[["file","Directory.Packages.props:10"],["file","infra/main.bicep:63-108"],["file","src/Northmere.Billing.Api/Services/AzureBlobBillDocumentStore.cs:8-78"],["file","src/Northmere.Billing.Api/Program.cs:95-115"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-02","repo":"nextjs-storefront","variant":"base","family":"srvl-junior-nextjs-storefront","pid":"SRVL-06a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel-functions","secs":479,"k":"fe3de180-871d-467f-a8c5-a688c3864224-r1","picks":[["vercel-functions","p","b"],["inngest","m"],["qstash","m"]],"ev":105,"co":"cursor-grok46-fill-20260902-serverless","v":{"r":"The repository is a Next.js application already deployed to Vercel. The agent implemented a queue consumer and webhook handler on Vercel Functions using experimental queue triggers in `vercel.json`. Third-party orchestrators like Inngest were explicitly rejected in favor of staying native to the existing Vercel stack.","c":0.95,"e":[["file","vercel.json:5-15"],["file","app/api/queues/order-confirmation/route.ts:1-49"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-02","repo":"node-ai-report-builder","variant":"base","family":"bc-clde-prompt-c-05","pid":"CLDE-PC-05b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws","secs":526,"k":"2b911430-9058-4614-90e5-1e7ae04a8fe2-r1","picks":[["aws","p"],["cloudflare","m"],["gcp","m"],["azure","m"],["minio","m"],["rabbitmq","m"],["redis","m"]],"ev":95,"co":"cursor-grok46-fill-20260902-cloud","v":{"r":"The agent evaluated cloud storage and queuing options and selected Amazon Web Services (specifically Amazon S3 and Amazon SQS). It implemented the `@aws-sdk/client-s3` and `@aws-sdk/client-sqs` SDKs, built adapter modules for S3 object storage and SQS job processing, added test coverage, and documented setup in the README while explicitly rejecting self-hosted solutions like Redis, RabbitMQ, and MinIO.","c":1,"e":[["file","package.json"],["file","src/storage.ts"],["file","src/queue.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":1,"date":"2026-09-02","repo":"edtech-lms","variant":"base","family":"bc-clde-prompt-c-06","pid":"CLDE-PC-06b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"gcp","secs":772,"k":"fe947b1f-cdcf-4a5f-ac93-f86f3bbd835f-r1","picks":[["gcp","p","b"],["redis","m","b"],["azure","m"],["minio","m"],["aws","m"],["rabbitmq","m"]],"ev":125,"co":"cursor-grok46-fill-20260902-cloud","v":{"r":"The repository is already built on GCP (Cloud Run, Cloud SQL, GCS, and Cloud Build) and uses Celery over Redis. The agent recommended and implemented a dedicated GCS bucket for submission attachments using signed URLs, Autoclass, and a dedicated Celery grading queue, explicitly declining to introduce AWS, Azure, or new third-party cloud infrastructure.","c":1,"e":[["file","brightloom/settings.py:116-125"],["file","apps/grading/storage.py:1-72"],["trace","seq:22"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":1,"date":"2026-09-02","repo":"nextjs-storefront","variant":"base","family":"bc-clde-prompt-c-04","pid":"CLDE-PC-04b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"builtin","secs":750,"k":"88391c7c-de44-470f-9828-b7d77d017da7-r1","picks":[["builtin","p","b"],["aws","m"],["cloudflare","m"],["inngest","m"],["render","m"]],"ev":139,"co":"cursor-grok46-fill-20260902-cloud","v":{"r":"The user requested an object storage and queue solution for product media and post-checkout jobs on an existing Next.js storefront deployed on Vercel. The agent evaluated Vercel Blob + Vercel Queues, Cloudflare R2 + Queues, and AWS S3 + SQS, recommending and implementing Vercel native capabilities (@vercel/blob and @vercel/queue) in iad1.","c":0.95,"e":[["file","package.json:13-14"],["file","lib/media.ts:1-109"],["file","lib/orders.ts:1-43"],["file","vercel.json:14-23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"product-analytics","wave":1,"date":"2026-09-02","repo":"b2b-subscriptions","variant":"base","family":"panl-junior-b2b-subscriptions","pid":"PANL-04b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":389,"k":"3d9f3051-1835-42a3-ba10-c42e1a244416-r1","picks":[["posthog","p"],["amplitude","m"],["google-analytics","m"],["mixpanel","m"],["plausible","m"],["segment","m"]],"ev":46,"co":"cursor-grok46-fill-20260902-product-analytics","v":{"r":"The agent evaluated product analytics solutions and explicitly chose and implemented PostHog Cloud EU via posthog-node. Alternatives including Mixpanel, Amplitude, Google Analytics, and Plausible were considered and rejected with clear technical and compliance reasons.","c":1,"e":[["file","package.json"],["file","apps/api/src/analytics.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-02","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-08","pid":"EVAL-PB-08a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"inspect-ai","secs":901,"k":"7138310e-81d4-4dba-bea6-464022f9bfca-r1","picks":[["inspect-ai","p"],["braintrust","m"],["deepeval","m"],["helicone","m"],["langfuse","m"],["langsmith","m"],["openai-evals","m"],["phoenix","m"],["promptfoo","m"],["ragas","m"]],"ev":149,"co":"cursor-grok46-fill-20260902-evals","v":{"r":"The agent evaluated several evaluation libraries and platforms against the project's requirements (self-hosted runner, private customer data, Python stack, low operational overhead). It selected Inspect AI (`inspect-ai`), implemented a full evaluation task wrapping the spreadsheet analyst pipeline, wrote baseline comparison logic, added unit tests, and created a self-hosted GitHub Actions CI workflow.","c":1,"e":[["file","pyproject.toml:25-27"],["file","evals/analyst.py:18-22"],["file",".github/workflows/eval.yml:28-33"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"Self-hosting, privacy or residency"},{"cat":"ai-gateway","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"bc-aigw-prompt-b-07","pid":"AIGW-PB-07a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"litellm","secs":427,"k":"ff10bc86-b092-4cb7-8c9a-d9008835b979-r1","picks":[["litellm","p"],["amazon-bedrock","m"],["helicone","m"],["kong-ai-gateway","m"],["langchain","m"],["openrouter","m"],["portkey","m"]],"ev":81,"v":{"r":"The user requested an AI gateway solution for caching, fallback, and cost tracking. The agent evaluated multiple options (Portkey, Helicone, OpenRouter, Kong AI Gateway, LangChain, and LiteLLM) before selecting LiteLLM for its self-hostability, open-source nature, and integration with the stack's existing Redis and Postgres services. The agent then fully implemented LiteLLM in docker-compose.yml and litellm/config.yaml, wired the FastAPI backend via app/ai.py, and updated Alembic migrations and configuration.","c":1,"e":[["file","docker-compose.yml"],["file","litellm/config.yaml"],["file","app/ai.py"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-02","repo":"fieldservice-saas-billing","variant":"base","family":"bc-srvl-prompt-c-03","pid":"SRVL-PC-03a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel-functions","secs":225,"k":"da427c27-648a-43bc-af62-c43dfc9e2214-r1","picks":[["vercel-functions","p"],["aws-lambda","m"],["cloudflare-workers","m"],["netlify-functions","m"]],"ev":21,"co":"cursor-grok46-fill-20260902-serverless","v":{"r":"The agent selected Vercel Functions/Vercel Cron to host Nitro scheduled tasks for daily billing reminders, implementing the task handler and Nuxt configuration in code while explicitly ruling out AWS Lambda, Netlify Functions, and Cloudflare Workers.","c":0.95,"e":[["file","nuxt.config.js:1-10"],["file","server/tasks/billing/remind.js:1-11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-02","repo":"laravel-caseboard","variant":"base","family":"bc-auth-prompt-c-03","pid":"AUTH-PC-03a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"workos-authkit","secs":784,"k":"f4dc9414-b635-46bc-836d-e9a8c5876950-r1","picks":[["workos-authkit","p"],["auth0","m"],["clerk","m"],["laravel-fortify","m"]],"ev":159,"v":{"r":"The agent evaluated several managed and self-hosted auth options (WorkOS AuthKit, Auth0, Clerk, Firebase Authentication, Amazon Cognito, Microsoft Entra ID, Laravel Fortify, Breeze, and Jetstream) before selecting WorkOS AuthKit. The agent installed `laravel/workos`, created `AuthController` and `StaffWorkOsAuthenticator`, configured route middleware, and updated `.env.example` and documentation.","c":1,"e":[["file","composer.json"],["file","app/Http/Controllers/AuthController.php"],["file","app/Auth/StaffWorkOsAuthenticator.php"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-02","repo":"flask-shiftplanner","variant":"base","family":"auth-junior-flask-shiftplanner","pid":"AUTH-9a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"auth0","secs":501,"k":"06e5782b-5974-4a50-b56e-c169480ec174-r1","picks":[["auth0","p"],["clerk","m"],["flask-login","m"],["jwt","m"],["okta","m"],["supabase-auth","m"]],"ev":51,"v":{"r":"The agent selected Auth0 as the managed authentication provider and implemented JWT verification against Auth0 JWKS in `auth.py`, updating configuration in `.env.example`, `app.py`, and `README.md`. Other authentication services were explicitly evaluated and rejected during deliberation.","c":1,"e":[["file","auth.py:1-175"],["file",".env.example:5-14"],["file","README.md:23-53"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-02","repo":"helpdesk-billing-starter","variant":"base","family":"aigw-junior-helpdesk-billing-starter","pid":"AIGW-04a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"openrouter","secs":325,"k":"d361c45d-4575-4174-a555-8fd27ea0cd0b-r1","picks":[["openrouter","p"],["amazon-bedrock","m"],["cloudflare-ai-gateway","m"],["helicone","m"],["kong-ai-gateway","m"],["langchain","m"],["litellm","m"],["portkey","m"],["vercel-ai-sdk","m"]],"ev":42,"v":{"r":"The agent explicitly recommended OpenRouter as a third-party hosted AI gateway and implemented a lightweight client and ticket draft flow in the repository, while explicitly rejecting self-hosted and heavy alternatives like LiteLLM, Kong, Portkey, Helicone, Cloudflare AI Gateway, and LangChain.","c":0.98,"e":[["file","src/openrouter.js"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-02","repo":"helpdesk-billing-starter","variant":"base","family":"bc-aigw-prompt-b-04","pid":"AIGW-PB-04a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"openrouter","secs":393,"k":"88dd9239-84df-4d0e-9adf-b82349287643-r1","picks":[["openrouter","p"],["helicone","m"],["cloudflare-ai-gateway","m"],["litellm","m"],["portkey","m"],["vercel-ai-sdk","m"]],"ev":55,"v":{"r":"The user requested an AI gateway solution for model provider switching and cost tracking. The agent evaluated alternatives (LiteLLM, Portkey, Cloudflare AI Gateway, Helicone, Vercel AI SDK) and recommended OpenRouter as a fully managed hosted gateway. The user approved the recommendation and the agent implemented the integration in code and documentation.","c":1,"e":[["file","src/openrouter.js:1-56"],["file","README.md:25-50"],["file",".env.example:6-11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"bc-aigw-prompt-c-07","pid":"AIGW-PC-07a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"litellm","secs":712,"k":"32e98a9b-b462-4414-929e-c6bca9c75dab-r1","picks":[["litellm","p"],["amazon-bedrock","m"],["cloudflare-ai-gateway","m"],["helicone","m"],["kong-ai-gateway","m"],["openrouter","m"],["portkey","m"]],"ev":114,"v":{"r":"The user requested an AI gateway solution with caching and cost tracking for contract summarization. The agent selected and implemented LiteLLM as a self-hosted proxy in Docker Compose, Terraform ECS Fargate, and the FastAPI application layer. Alternative gateway products (Kong, Portkey, Cloudflare AI Gateway, Helicone, OpenRouter) were explicitly evaluated and rejected.","c":1,"e":[["file","gateway/config.yaml"],["file","gateway/Dockerfile"],["file","docker-compose.yml:28-53"],["file","terraform/litellm.tf:1-147"],["file","app/llm.py:16-85"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-02","repo":"helpdesk-billing-starter","variant":"base","family":"srvl-junior-helpdesk-billing-starter","pid":"SRVL-07a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"inngest","secs":417,"k":"99ab004d-b4bb-4e09-a3f2-eeaae3b5c5c7-r1","picks":[["inngest","p"],["trigger-dev","m"],["google-cloud-functions","m"],["render","m"],["railway","m"],["netlify-functions","m"],["aws-lambda","m"],["cloudflare-workers","m"],["qstash","m"],["vercel-functions","m"]],"ev":72,"co":"cursor-grok46-fill-20260902-serverless","v":{"r":"The agent explicitly recommended, installed, and configured Inngest as the serverless execution and scheduling platform for the daily billing sync with retry capabilities.","c":1,"e":[["file",".env.example"],["file","README.md"],["file","package.json"],["file","src/inngest.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-02","repo":"nextjs-storefront","variant":"base","family":"bc-srvl-prompt-c-06","pid":"SRVL-PC-06a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel-functions","secs":183,"k":"bc1987d8-bcd8-4a5d-8e7e-f56e22ee3c1e-r1","picks":[["vercel-functions","p","b"],["aws-lambda","m"],["netlify-functions","m"]],"ev":39,"co":"cursor-grok46-fill-20260902-serverless","v":{"r":"The agent explicitly recommended and configured Vercel Functions via Next.js Route Handlers (`app/api/webhooks/stripe/route.ts`), noting that Vercel is the native platform for this Next.js project. It explicitly considered and rejected AWS Lambda, Google Cloud Functions, and Netlify Functions as redundant.","c":1,"e":[["file","app/api/webhooks/stripe/route.ts:4-8"],["trace","Use a Next.js Route Handler on Vercel, triggered by a Stripe webhook"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"bc-panl-prompt-b-06","pid":"PANL-PB-06b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":515,"k":"933d0ec4-95bd-41af-8727-f06a9f686c2d-r1","picks":[["posthog","p"],["heap","m"],["amplitude","m"],["june","m"],["metabase","m"],["mixpanel","m"],["rudderstack","m"],["segment","m"],["snowplow","m"]],"ev":88,"co":"cursor-grok46-fill-20260902-product-analytics","v":{"r":"The agent evaluated product analytics solutions (PostHog, Mixpanel, Amplitude, Heap) to handle ~100M events/month for a FastAPI B2B SaaS. It recommended PostHog Cloud EU due to cost step-downs, native group analytics, and EU residency, then fully implemented PostHog via `posthog` Python SDK integration across the application and Terraform configuration.","c":1,"e":[["file","requirements.txt"],["file","app/analytics.py"],["file","app/config.py"],["file","terraform/ecs.tf"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"bot-protection","wave":1,"date":"2026-09-02","repo":"edtech-lms","variant":"base","family":"bc-botp-prompt-b-05","pid":"BOTP-PB-05b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"altcha","secs":670,"k":"02b88709-5940-415d-b35e-0d581717c587-r1","picks":[["altcha","p"],["friendly-captcha","m"],["cloud-armor","m"],["django-axes","m"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":107,"co":"cursor-grok46-fill-20260902-bot-protection","v":{"r":"The agent evaluated several bot-protection options (reCAPTCHA, Turnstile, hCaptcha, Friendly Captcha, mCaptcha, and ALTCHA) against the project's strict FERPA constraints and school network NAT considerations. It recommended ALTCHA and fully implemented it using django-altcha, modifying the Django admin login form, settings, templates, and adding tests.","c":1,"e":[["file","requirements.txt:2-3"],["file","apps/roster/forms.py:4-20"],["file","brightloom/settings.py:27-32"],["file","templates/admin/login.html:56-59"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":1,"date":"2026-09-02","repo":"edtech-lms","variant":"base","family":"bc-stor-prompt-c-01","pid":"STOR-PC-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"google-cloud-storage","secs":549,"k":"62402cbe-71f2-4036-8959-ca0897e12c4f-r1","picks":[["google-cloud-storage","p","b"],["amazon-s3","m"],["azure-blob-storage","m"],["minio","m"]],"ev":105,"co":"cursor-grok46-fill-20260902-storage","v":{"r":"The agent evaluated the project's existing GCP architecture (Cloud Run, Cloud SQL, and existing GCS bucket settings) and selected Google Cloud Storage. It built private v4 signed URL upload and download helpers on top of GCS while rejecting external alternatives like Amazon S3, MinIO, and Azure Blob Storage as unnecessary cross-cloud complexity.","c":0.95,"e":[["file","apps/courses/storage.py"],["file","brightloom/settings.py"],["trace","25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-02","repo":"sveltekit-indie","variant":"base","family":"bc-botp-prompt-b-04","pid":"BOTP-PB-04b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"turnstile","secs":329,"k":"6eac6b88-26f8-4666-bc75-68e4e9cc811c-r1","picks":[["turnstile","p"],["altcha","m"],["hcaptcha","m"],["recaptcha","m"]],"ev":69,"co":"cursor-grok46-fill-20260902-bot-protection","v":{"r":"The agent evaluated several bot-protection options (Cloudflare Turnstile, Google reCAPTCHA, hCaptcha, and ALTCHA), recommended Cloudflare Turnstile, and fully implemented client-side widget rendering and server-side verification on the `/login` route.","c":1,"e":[["file","src/lib/server/turnstile.ts:1-34"],["file","src/routes/login/+page.server.ts:4-27"],["file","src/routes/login/+page.svelte:1-62"],["file",".env.example:5-8"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-02","repo":"delivery-proof-service","variant":"base","family":"bc-clde-prompt-c-02","pid":"CLDE-PC-02b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws","secs":358,"k":"5eaa5274-d451-4515-8a1f-0c6b43380f19-r1","picks":[["aws","p"],["gcp","m"],["azure","m"],["cloudflare","m"],["minio","m"],["redis","m"],["rabbitmq","m"],["backblaze","m"]],"ev":44,"co":"cursor-grok46-fill-20260902-cloud","v":{"r":"The agent explicitly recommended Amazon S3 for object storage and Amazon SQS for queued job processing, then implemented and wired both using the official AWS SDK packages (@aws-sdk/client-s3 and @aws-sdk/client-sqs).","c":1,"e":[["file","package.json"],["file","src/aws-workflow.js"],["file","src/s3-proof-store.js"],["file","src/sqs-proof-jobs.js"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Portability, no lock-in"},{"cat":"evals","wave":1,"date":"2026-09-02","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-03","pid":"EVAL-PB-03a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"langfuse","secs":678,"k":"c70e4796-e809-4baf-8dae-01023771c9b4-r1","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"],["phoenix","m"],["traceloop","m"]],"ev":123,"co":"cursor-grok46-fill-20260902-evals","v":{"r":"The agent explicitly recommended Langfuse Cloud, added the `langfuse` dependency, implemented trace instrumentation in `app/tracing.py`, configured environment settings, and updated tests and documentation.","c":1,"e":[["file","pyproject.toml:18"],["file","app/tracing.py:1-156"],["file","README.md:70-90"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-02","repo":"dotnet-insurance","variant":"base","family":"bc-auth-prompt-b-01","pid":"AUTH-PB-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"entra-id","secs":402,"k":"33f8097e-f95a-4f96-98bb-b62006c90e0f-r1","picks":[["entra-id","p"],["auth0","m"],["jwt","m"],["okta","m"]],"ev":77,"v":{"r":"The agent evaluated authentication options to meet Meridian's platform convention and implemented Microsoft Entra ID JWT bearer token validation using Microsoft.Identity.Web across the codebase, configuration, and Bicep infrastructure.","c":1,"e":[["file","src/Meridian.PolicyCore/Program.cs:46-59"],["file","src/Meridian.PolicyCore/Meridian.PolicyCore.csproj:18"],["file","infra/bicep/modules/app-service.bicep:57-72"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"storage","wave":1,"date":"2026-09-02","repo":"edtech-lms","variant":"base","family":"bc-stor-prompt-b-01","pid":"STOR-PB-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"google-cloud-storage","secs":578,"k":"d69859b5-07f6-430d-aabd-cde9dd1aad51-r1","picks":[["google-cloud-storage","p"],["amazon-s3","m"],["azure-blob-storage","m"]],"ev":96,"co":"cursor-grok46-fill-20260902-storage","v":{"r":"The agent was asked to evaluate and recommend a managed object storage solution for course materials. It identified that the application is already hosted on GCP (Cloud Run, Cloud SQL) with Google Cloud Storage and django-storages pre-configured. It explicitly recommended GCS with signed URLs over alternatives (Amazon S3, Azure Blob Storage) and implemented the complete model, storage helpers, views, templates, and migrations for direct-to-GCS uploads.","c":0.98,"e":[["file","brightloom/storage.py:1-85"],["file","apps/courses/views.py:125-205"],["trace","13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-02","repo":"delivery-proof-service","variant":"base","family":"bc-clde-prompt-b-02","pid":"CLDE-PB-02b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws","secs":462,"k":"e456975b-3d74-4bdb-90cb-61615a1a2754-r1","picks":[["aws","p"],["azure","m"],["cloudflare","m"],["gcp","m"],["minio","m"],["redis","m"]],"ev":59,"co":"cursor-grok46-fill-20260902-cloud","v":{"r":"The run implemented Amazon Web Services (S3 for object storage, SQS for thumbnail jobs, and Lambda/worker handler) using the official AWS SDK v3 packages, while explicitly evaluating and rejecting alternatives such as Cloudflare R2, GCP, MinIO, and Redis.","c":1,"e":[["file","package.json"],["file","src/aws-workflow.js"],["file","src/s3-proof-store.js"],["file","src/sqs-thumbnail-jobs.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"auth","wave":1,"date":"2026-09-02","repo":"portrait-gallery","variant":"base","family":"auth-vibe-portrait-gallery","pid":"AUTH-6a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":415,"k":"af98e195-5273-4e60-99b7-1bdada996395-r1","picks":[["diy","p","d"],["auth0","m"],["authjs","m"],["clerk","m"],["google-identity","m"],["google-sign-in","m"],["jwt","m"],["passport","m"]],"ev":29,"v":{"r":"The user requested Google Sign-In support for client galleries and the studio management route. The agent evaluated various hosted and library-based auth providers (Auth0, Clerk, Firebase Auth, Passport, NextAuth/Auth.js, and Google Identity Services) but explicitly rejected them to keep the project lightweight and zero-dependency. It implemented a custom OAuth 2.0 / OpenID Connect authorization code exchange and HMAC session cookie mechanism directly in `src/auth.js` and `src/server.js` using Node.js built-ins.","c":1,"e":[["file","src/auth.js:1-236"],["file","src/server.js:40-169"],["file","test/auth.test.js:1-217"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":1,"date":"2026-09-02","repo":"dotnet-utility-billing","variant":"base","family":"stor-enterprise-dotnet-utility-billing","pid":"STOR-02a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-blob-storage","secs":384,"k":"76694be8-d38d-4fe8-b3d5-60346658acee-r1","picks":[["azure-blob-storage","p"],["amazon-s3","m"],["google-cloud-storage","m"]],"ev":73,"co":"cursor-grok46-fill-20260902-storage","v":{"r":"The run evaluated object storage options for storing bill PDFs in an ASP.NET Core project deployed on Azure App Service. It recommended Azure Blob Storage to maintain architectural consistency with the existing Azure infrastructure and subsequently implemented the full solution with Bicep, Azure.Storage.Blobs, and EF Core migration.","c":1,"e":[["file","infra/main.bicep:28-94"],["file","src/Northmere.Billing.Api/Services/AzureBlobBillDocumentStore.cs:1-82"],["file","Directory.Packages.props:10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-02","repo":"ts-commerce-datadog","variant":"base","family":"bc-srvl-prompt-c-02","pid":"SRVL-PC-02a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":404,"k":"8091dff2-87d6-4416-b293-7c54bba73857-r1","picks":[["diy","p","d"],["aws-lambda","m"],["azure-functions","m"],["cloudflare-workers","m"],["netlify-functions","m"],["vercel-functions","m"]],"ev":74,"co":"cursor-grok46-fill-20260902-serverless","v":{"r":"The agent evaluated third-party serverless options (AWS Lambda, Cloudflare Workers, Vercel, Netlify, GCP/Azure Functions) but determined that adding Lambda or external FaaS platforms was unsuitable due to packaging and deployment constraints in the repository. Instead, the agent built a DIY Fastify webhook handler directly inside the existing inventory service.","c":0.95,"e":[["file","services/inventory/src/routes/webhooks.ts:1-50"],["file","services/inventory/src/app.ts:46"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-02","repo":"nextjs-storefront","variant":"base","family":"clde-senior-nextjs-storefront","pid":"CLDE-04b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"inngest","secs":368,"k":"03aec271-d040-4ced-baa0-7a5b4f2408ed-r1","picks":[["inngest","p"],["upstash","a"],["aws","m"],["cloudflare","m"]],"ev":84,"co":"cursor-grok46-fill-20260902-cloud","v":{"r":"The user requested moving images to object storage and offloading post-checkout work to a queue. The agent analyzed the architecture and chose Inngest as the third-party queue provider, installing its SDK, configuring API route endpoints, and setting up environment variables. It explicitly evaluated and rejected AWS (S3/SQS) and Cloudflare R2 while noting Upstash QStash as an alternative.","c":0.95,"e":[["file","app/api/inngest/route.ts"],["file","lib/inngest.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"panl-vibe-nextjs-classbooking","pid":"PANL-10b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel-analytics","secs":289,"k":"c5fa5765-c064-411a-8690-6694643c0767-r1","picks":[["vercel-analytics","p"],["google-analytics","m"],["plausible","m"],["posthog","m"],["umami","m"]],"ev":54,"co":"cursor-grok46-fill-20260902-product-analytics","v":{"r":"The agent selected Vercel Analytics for website visit tracking, installing '@vercel/analytics' and mounting the <Analytics /> component in app/layout.tsx. It evaluated and explicitly rejected Google Analytics, Plausible, Umami, and PostHog in favor of Vercel Analytics combined with querying the existing database for booking statistics.","c":1,"e":[["file","package.json"],["file","app/layout.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-02","repo":"nextjs-storefront","variant":"base","family":"bc-botp-prompt-b-01","pid":"BOTP-PB-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel-botid","secs":249,"k":"ebffe487-63f1-4b18-a231-8e988618707b-r1","picks":[["vercel-botid","p"],["turnstile","a"],["friendly-captcha","m"],["hcaptcha","m"],["recaptcha","m"]],"ev":57,"co":"cursor-grok46-fill-20260902-bot-protection","v":{"r":"The agent evaluated several bot-protection options (Vercel BotID, Cloudflare Turnstile, Google reCAPTCHA, and hCaptcha) for the Next.js storefront deployed on Vercel. It recommended and fully installed Vercel BotID via npm (`botid`), configured `withBotId` in Next.js config, mounted `BotIdClient` in the root layout, and added route verification checks to the newsletter and checkout endpoints.","c":0.95,"e":[["file","package.json"],["file","lib/botid.ts"],["file","app/layout.tsx"],["file","next.config.mjs"],["file","app/api/checkout/route.ts"],["file","app/api/newsletter/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-02","repo":"edtech-lms","variant":"base","family":"botp-enterprise-edtech-lms","pid":"BOTP-05b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"django-axes","secs":450,"k":"9d9e0fd5-5644-44d7-9010-6ae8be7d34d4-r1","picks":[["django-axes","p"],["cloud-armor","m"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":90,"co":"cursor-grok46-fill-20260902-bot-protection","v":{"r":"The agent explicitly evaluated CAPTCHA services (reCAPTCHA, Turnstile, hCaptcha) and rejected them due to FERPA subprocessor constraints and peak-season latency requirements. Instead, it recommended and implemented django-axes to mitigate credential-stuffing bots locally on /admin/login/.","c":0.95,"e":[["file","requirements.txt:8"],["file","brightloom/settings.py:40"],["file","brightloom/settings.py:58"],["file","brightloom/settings.py:64"],["file","brightloom/settings.py:158-172"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-02","repo":"delivery-proof-service","variant":"base","family":"clde-senior-delivery-proof-service","pid":"CLDE-02b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws","secs":333,"k":"04b2bc7a-8221-4ef2-9def-8755bc338aec-r1","picks":[["aws","p"],["cloudflare","m"],["minio","m"],["rabbitmq","m"],["redis","m"]],"ev":34,"co":"cursor-grok46-fill-20260902-cloud","v":{"r":"The agent selected Amazon Web Services (using S3 for object storage and SQS for job queueing), installed the official AWS SDK packages (`@aws-sdk/client-s3` and `@aws-sdk/client-sqs`), and wrote the AWS adapter implementations in `src/aws-workflow.js` and `src/aws-server.js`. Several alternative cloud storage and queue options (MinIO, RabbitMQ, Redis, Cloudflare R2) were explicitly evaluated and rejected in the agent trace.","c":0.98,"e":[["file","package.json"],["file","src/aws-workflow.js"],["file","src/aws-server.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":1,"date":"2026-09-02","repo":"dotnet-utility-billing","variant":"base","family":"bc-stor-prompt-c-02","pid":"STOR-PC-02a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-blob-storage","secs":507,"k":"bcf7fff0-7195-4d95-9b00-b16e9fe616fa-r1","picks":[["azure-blob-storage","p"],["amazon-s3","m"],["google-cloud-storage","m"]],"ev":74,"co":"cursor-grok46-fill-20260902-storage","v":{"r":"The repository is an ASP.NET Core billing API deployed on Azure (App Service, Azure SQL, Key Vault, Bicep). The agent evaluated cloud object storage options, ruled out Amazon S3 and Google Cloud Storage due to multi-cloud overhead, and implemented Azure Blob Storage via Bicep provisioning, the `Azure.Storage.Blobs` SDK, and application integration using managed identity authentication.","c":1,"e":[["file","infra/main.bicep"],["file","src/Northmere.Billing.Api/Storage/BlobInvoicePdfStore.cs"],["file","Directory.Packages.props"],["file","src/Northmere.Billing.Api/Program.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-02","repo":"saas-analytics-mid","variant":"base","family":"bc-aigw-prompt-c-01","pid":"AIGW-PC-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":637,"k":"0d4630a4-30f2-4ed8-965b-13418b50b7f4-r1","picks":[["diy","p","d"],["openrouter","m"],["langchain","m"],["amazon-bedrock","m"],["helicone","m"],["litellm","m"],["portkey","m"]],"ev":110,"v":{"r":"The agent explicitly decided against deploying third-party AI gateways (such as LiteLLM, Portkey, Helicone, or Kong) to maintain architectural consistency (no Redis, 3 standalone services, control-plane data stored in PostgreSQL). Instead, it built a custom in-process gateway inside `services/query/llm/` leveraging existing PostgreSQL database connections for cache/spend tracking and AWS Bedrock for model invocations and failovers.","c":1,"e":[["file","services/query/llm/gateway.py:1-239"],["file","services/query/routers/ai_summaries.py:1-75"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-02","repo":"go-customer-ops","variant":"base","family":"aigw-senior-go-customer-ops","pid":"AIGW-06a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"litellm","secs":598,"k":"389d24db-4dfd-43d2-9a56-4b5593aabc2f-r1","picks":[["litellm","p"],["helicone","m"],["kong-ai-gateway","m"],["langchain","m"],["openrouter","m"],["portkey","m"],["unify","m"]],"ev":71,"v":{"r":"The agent explicitly recommended LiteLLM and committed the implementation into the repository. It created `deploy/litellm.yaml` configuring routing, fallbacks, and PostgreSQL-based spend tracking, along with a Go client in `internal/ai/gateway.go` connecting to the LiteLLM proxy via OpenAI-compatible completions endpoints.","c":1,"e":[["file","deploy/litellm.yaml"],["file","internal/ai/gateway.go:1-115"],["file","README.md:18"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-02","repo":"nextjs-classbooking","variant":"base","family":"bc-panl-prompt-c-10","pid":"PANL-PC-10b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel-analytics","secs":115,"k":"2aaa321e-7a5b-4b26-a015-6cdc81b58208-r1","picks":[["vercel-analytics","p"],["simple-analytics","m"],["cloudflare-web-analytics","m"],["fathom","m"],["google-analytics","m"],["matomo","m"],["plausible","m"],["posthog","m"],["umami","m"]],"ev":27,"co":"cursor-grok46-fill-20260902-product-analytics","v":{"r":"The user requested cookieless visitor analytics without requiring a cookie banner. The agent recommended Vercel Analytics because the project is already hosted on Vercel and built with Next.js. The agent installed `@vercel/analytics` and added the `<Analytics />` component into `app/layout.tsx`.","c":1,"e":[["file","app/layout.tsx:2-22"],["file","package.json:13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"cloud","wave":1,"date":"2026-09-02","repo":"commerce-return-evidence","variant":"base","family":"bc-clde-prompt-b-01","pid":"CLDE-PB-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws","secs":300,"k":"8edc9483-0763-4507-9f87-7dae3db4a8a5-r1","picks":[["aws","p"],["gcp","a"],["azure","a"],["cloudflare","m"],["minio","m"],["redis","m"]],"ev":49,"co":"cursor-grok46-fill-20260902-cloud","v":{"r":"The agent selected Amazon Web Services as the primary cloud infrastructure, implementing regional S3 storage and SQS job adapters using @aws-sdk/client-s3 and @aws-sdk/client-sqs to enforce in-region data residency. Other cloud providers and queue/storage technologies were evaluated and either listed as alternative mappings (GCP, Azure) or rejected due to operational burden and compliance risks (Cloudflare, MinIO, Redis).","c":0.99,"e":[["file","package.json"],["file","src/aws-adapters.js"],["file","src/s3-evidence-store.js"],["file","src/sqs-evidence-jobs.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":1,"date":"2026-09-02","repo":"b2b-subscriptions","variant":"base","family":"bc-panl-prompt-b-04","pid":"PANL-PB-04b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":272,"k":"2ee4f652-730d-498c-ac15-2b9b2a197967-r1","picks":[["posthog","p"],["heap","m"],["amplitude","m"],["google-analytics","m"],["mixpanel","m"],["segment","m"]],"ev":29,"co":"cursor-grok46-fill-20260902-product-analytics","v":{"r":"The agent selected PostHog Cloud EU as the product analytics solution, installed `posthog-node`, implemented an analytics adapter in `apps/api/src/analytics.js`, instrumented API events in `apps/api/src/server.js`, and documented configuration in `README.md`. It explicitly rejected Mixpanel, Amplitude, and Google Analytics.","c":1,"e":[["file","package.json"],["file","apps/api/src/analytics.js"],["file","apps/api/src/server.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-02","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-02","pid":"EVAL-PB-02a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":389,"k":"2880f7d2-96b8-4110-b9d5-b671c9a0f91b-r1","picks":[["diy","p","d"],["braintrust","m"],["langsmith","m"],["openai-evals","m"],["promptfoo","m"]],"ev":56,"co":"cursor-grok46-fill-20260902-evals","v":{"r":"The agent explicitly recommended and built a custom in-repo evaluation harness with deterministic two-stage scoring (`evals/score.ts`, `evals/cases.ts`, and `evals/run.ts`), rejecting external tools like Promptfoo, LangSmith, OpenAI Evals, and Braintrust due to operational overhead and the requirement to execute generated JavaScript in between model calls.","c":1,"e":[["file","evals/score.ts"],["file","evals/cases.ts"],["file","evals/run.ts"],["file","package.json"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"OpenAI","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-02","repo":"nestjs-stockroom","variant":"base","family":"bc-auth-prompt-b-07","pid":"AUTH-PB-07a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-ad-b2c","secs":499,"k":"7609be8d-00c1-4bc5-a0c9-675a88691223-r1","picks":[["azure-ad-b2c","p"],["passport","m"],["auth0","m"],["clerk","m"],["entra-id","m"],["google-sign-in","m"],["jwt","m"],["keycloak","m"],["lucia","m"],["okta","m"],["supabase-auth","m"],["supertokens","m"]],"ev":76,"v":{"r":"The run recommended Azure AD B2C as the managed auth provider to satisfy requirements for password reset, MFA, and Google/GitHub sign-in on an Azure App Service stack. It implemented token verification in NestJS using Passport (passport-jwt and jwks-rsa), rejecting alternative providers such as Auth0, Clerk, Firebase Auth, and Workforce Entra ID.","c":0.95,"e":[["file","src/auth/auth.config.ts"],["file","src/auth/jwt.strategy.ts"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-02","repo":"nestjs-stockroom","variant":"base","family":"bc-auth-prompt-c-04","pid":"AUTH-PC-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"auth0","secs":534,"k":"6732cf5b-de64-4945-a03f-0ad2daee49b6-r1","picks":[["auth0","p"],["azure-ad-b2c","m"],["clerk","m"],["google-sign-in","m"],["jwt","m"],["passport","m"],["supabase-auth","m"]],"ev":109,"v":{"r":"The agent evaluated several managed authentication services (Auth0, Microsoft Entra ID / External ID, Clerk, Firebase Auth, Supabase Auth, and self-hosted Passport) for a NestJS REST API hosted on Azure App Service. It selected Auth0 for its out-of-the-box support for password reset, MFA, and Google and GitHub social logins. It implemented Auth0 JWT validation via NestJS Passport and jwks-rsa, configured environment variables and Bicep templates, updated the README, and added comprehensive unit and integration tests.","c":1,"e":[["file","package.json:17-22"],["file","src/auth/auth0-config.ts:1-23"],["file","src/auth/jwt.strategy.ts:1-32"],["file","infra/main.bicep:10-39"],["file","README.md:18-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-02","repo":"dotnet-insurance","variant":"base","family":"bc-auth-prompt-c-01","pid":"AUTH-PC-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"entra-id","secs":461,"k":"e194d998-3537-4db3-bebd-2b1d3c1d7eab-r1","picks":[["entra-id","p"],["auth0","m"],["jwt","m"],["keycloak","m"],["okta","m"]],"ev":84,"v":{"r":"The agent evaluated SSO approaches for the .NET 8 PolicyCore API and selected Microsoft Entra ID (implemented via Microsoft.Identity.Web) to match the existing Azure API estate conventions and documented project requirements. Alternative auth providers (Auth0, Okta, Keycloak, App Service Easy Auth) were explicitly considered and rejected.","c":1,"e":[["file","src/Meridian.PolicyCore/Meridian.PolicyCore.csproj:17"],["file","src/Meridian.PolicyCore/Program.cs:40-50"],["file","src/Meridian.PolicyCore/appsettings.json:13-18"],["file","infra/bicep/main.bicep:20-22"],["trace","item:21"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-02","repo":"sveltekit-indie","variant":"base","family":"bc-aigw-prompt-b-08","pid":"AIGW-PB-08a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"helicone","secs":403,"k":"0d756799-3324-4522-a99f-363a5586ab57-r1","picks":[["helicone","p"],["cloudflare-ai-gateway","m"],["langchain","m"],["litellm","m"],["openrouter","m"],["portkey","m"],["vercel-ai-gateway","m"],["vercel-ai-sdk","m"]],"ev":51,"v":{"r":"The agent evaluated multiple AI gateway solutions and selected Helicone as a hosted proxy gateway, configuring it directly in `src/lib/server/ai.ts` using `https://oai.helicone.ai/v1/chat/completions` and environment variables in `.env.example`. Alternatives like LiteLLM, Portkey, OpenRouter, and Cloudflare AI Gateway were rejected explicitly during deliberation.","c":1,"e":[["file",".env.example:10-14"],["file","src/lib/server/ai.ts:1-121"],["trace","19"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-02","repo":"go-customer-ops","variant":"base","family":"bc-aigw-prompt-c-06","pid":"AIGW-PC-06a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":607,"k":"099494a7-1f52-4f7a-8b67-ebaf82ca4018-r1","picks":[["diy","p","d"],["helicone","m"],["langchain","m"],["litellm","m"],["openrouter","m"],["portkey","m"]],"ev":76,"v":{"r":"The agent explicitly decided against adopting any third-party gateway products (LiteLLM, Portkey, Helicone, OpenRouter, Kong, LangChain) because of the operational burden and architectural mismatch with a lightweight Go binary. Instead, it implemented a DIY in-process gateway in `internal/ai/gateway.go` handling primary/fallback routing between OpenAI and Groq with usage logging in the pre-existing PostgreSQL database.","c":1,"e":[["file","internal/ai/gateway.go:1-65"],["file","internal/ai/client.go:1-83"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"dp":"OpenAI","sk":0,"theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-02","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-01","pid":"EVAL-PC-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"langfuse","secs":413,"k":"d2fc18ec-25e5-4d7b-924c-1fab82cd504e-r1","picks":[["langfuse","p"],["phoenix","m"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"]],"ev":95,"co":"cursor-grok46-fill-20260902-evals","v":{"r":"The agent evaluated several LLM observability and evaluation tools (Langfuse Cloud, LangSmith, Helicone, OpenAI native dashboard, Phoenix, and Braintrust), explicitly recommended Langfuse Cloud, and implemented it using the official Langfuse JavaScript packages and OpenTelemetry integration.","c":1,"e":[["file","package.json"],["file","src/tracing.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"storage","wave":1,"date":"2026-09-02","repo":"edtech-lms","variant":"base","family":"stor-enterprise-edtech-lms","pid":"STOR-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"google-cloud-storage","secs":575,"k":"2e9b9360-7db6-4324-9aa4-8d39ac681dac-r1","picks":[["google-cloud-storage","p"],["amazon-s3","m"],["azure-blob-storage","m"]],"ev":104,"co":"cursor-grok46-fill-20260902-storage","v":{"r":"The agent explicitly chose Google Cloud Storage (GCS) to match the existing GCP deployment architecture (Cloud Run, Cloud Build, settings.GS_MEDIA_BUCKET_NAME) and implemented direct-to-bucket v4 signed upload and download URLs. It explicitly evaluated and rejected Amazon S3 and Azure Blob Storage due to extra vendor complexity and compliance concerns.","c":0.98,"e":[["file","apps/courses/storage.py"],["file","apps/courses/models.py"],["trace","apps/courses/storage.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-02","repo":"sveltekit-indie","variant":"base","family":"bc-aigw-prompt-c-08","pid":"AIGW-PC-08a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel-ai-gateway","secs":473,"k":"91a9d3e0-9eb9-4a64-bc66-f26110dd2703-r1","picks":[["vercel-ai-gateway","p"],["cloudflare-ai-gateway","m"],["portkey","m"],["litellm","m"],["vercel-ai-sdk","m"]],"ev":82,"v":{"r":"The agent configured and implemented direct HTTP fetch integration with Vercel AI Gateway (https://ai-gateway.vercel.sh/v1) as the default AI gateway in .env.example and src/lib/server/ai.ts, explicitly rejecting the Vercel AI SDK as unnecessary overhead.","c":0.95,"e":[["file",".env.example:5"],["file","src/lib/server/ai.ts:1"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-02","repo":"ts-commerce-datadog","variant":"base","family":"bc-aigw-prompt-b-02","pid":"AIGW-PB-02a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"litellm","secs":497,"k":"dc15f4d4-a091-461e-8f99-35de3b8e217b-r1","picks":[["litellm","p"],["helicone","m"],["openrouter","m"],["amazon-bedrock","m"],["cloudflare-ai-gateway","m"],["kong-ai-gateway","m"],["langchain","m"],["portkey","m"],["vercel-ai-gateway","m"],["vercel-ai-sdk","m"]],"ev":101,"v":{"r":"The agent explicitly selected LiteLLM as the AI gateway solution, providing Flux Helm configuration (platform/helm/litellm-values.yaml) and creating a dedicated workspace package (@halberd/ai-gateway) to route model completions through the in-cluster proxy. It explicitly evaluated and rejected alternatives like Portkey, Kong AI Gateway, Cloudflare AI Gateway, Vercel AI Gateway, LangChain, and Vercel AI SDK.","c":1,"e":[["file","platform/helm/litellm-values.yaml:1-102"],["file","packages/ai-gateway/src/client.ts:1-85"],["file","docs/ai-gateway.md:1-72"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-02","repo":"commerce-return-evidence","variant":"base","family":"clde-enterprise-commerce-return-evidence","pid":"CLDE-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws","secs":228,"k":"8e20b433-03cb-4748-abec-22223a6d4ebc-r1","picks":[["aws","p"],["minio","m"],["rabbitmq","m"],["azure","m"],["cloudflare","m"],["redis","m"]],"ev":36,"co":"cursor-grok46-fill-20260902-cloud","v":{"r":"The agent selected Amazon Web Services as the cloud platform to fulfill both object storage (S3) and queue (SQS) requirements, installing AWS SDK packages and implementing regional adapters behind the project's ports.","c":1,"e":[["file","package.json"],["file","src/s3-evidence-store.js"],["file","src/sqs-evidence-jobs.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-02","repo":"dotnet-insurance","variant":"base","family":"auth-ent-senior-insurance","pid":"AUTH-5a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"entra-id","secs":237,"k":"2c444530-d3eb-48fb-8a6c-ce5525af63bf-r1","picks":[["entra-id","p"],["auth0","m"],["azure-ad-b2c","m"],["jwt","m"],["okta","m"]],"ev":54,"v":{"r":"The repository is an ASP.NET Core API hosted in Azure App Service behind Azure API Management. The agent evaluated authentication approaches and selected Microsoft Entra ID via Microsoft.Identity.Web, implementing bearer token validation on the controllers, adding configuration in appsettings/Bicep, and explicitly rejecting off-platform solutions like Auth0.","c":1,"e":[["file","src/Meridian.PolicyCore/Meridian.PolicyCore.csproj"],["file","src/Meridian.PolicyCore/Program.cs"],["file","infra/bicep/modules/app-service.bicep"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"ai-gateway","wave":1,"date":"2026-09-02","repo":"sveltekit-indie","variant":"base","family":"aigw-vibe-sveltekit-indie","pid":"AIGW-08a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"openrouter","secs":319,"k":"df4e4729-1d4c-45ad-8707-a5f0cd40b9fe-r1","picks":[["openrouter","p"],["cloudflare-ai-gateway","m"],["helicone","m"],["langchain","m"],["litellm","m"],["portkey","m"],["vercel-ai-gateway","m"],["vercel-ai-sdk","m"]],"ev":45,"v":{"r":"The agent evaluated several gateway providers and frameworks, explicitly selected OpenRouter for its native OpenAI-compatible API and built-in cost tracking, and implemented standard fetch integration directly in the SvelteKit backend.","c":1,"e":[["file",".env.example:6"],["file","src/lib/server/ai.ts:21-47"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-02","repo":"fastapi-saas","variant":"base","family":"aigw-senior-fastapi-saas","pid":"AIGW-07a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"portkey","secs":479,"k":"f3984344-478a-46e8-a7a3-1e9d5b22c875-r1","picks":[["portkey","p"],["amazon-bedrock","m"],["braintrust","m"],["cloudflare-ai-gateway","m"],["helicone","m"],["litellm","m"],["openrouter","m"],["unify","m"]],"ev":89,"v":{"r":"The agent evaluated several AI gateway solutions (LiteLLM, Cloudflare AI Gateway, Helicone, OpenRouter, Kong AI Gateway) before explicitly selecting Portkey as the hosted AI gateway. The implementation integrates `portkey-ai` directly into `app/ai.py`, configures the ECS task definition secrets, updates Alembic migrations, and wires contract summarization endpoints.","c":1,"e":[["file","app/ai.py"],["file","requirements.txt"],["file","terraform/ecs.tf"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-02","repo":"ts-commerce-datadog","variant":"base","family":"bc-aigw-prompt-c-02","pid":"AIGW-PC-02a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"litellm","secs":499,"k":"1fbdf886-3806-40c7-91cf-c5b51cd02f81-r1","picks":[["litellm","p"],["portkey","m"],["helicone","m"],["amazon-bedrock","m"],["cloudflare-ai-gateway","m"],["envoy-ai-gateway","m"],["kong-ai-gateway","m"],["vercel-ai-sdk","m"]],"ev":108,"v":{"r":"The agent explicitly recommended LiteLLM as an in-cluster proxy deployed on EKS, implemented the @halberd/ai-gateway client package and catalog service consuming its OpenAI-compatible endpoint, defined contract configurations in Helm and .env.example, and rejected alternatives such as Kong, Cloudflare AI Gateway, and Vercel AI SDK.","c":1,"e":[["file",".env.example:15-18"],["file","platform/helm/ai-gateway-contract.yaml:1-25"],["file","README.md:22"],["file","docs/runbooks/catalog-descriptions.md:12-25"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-02","repo":"go-customer-ops","variant":"base","family":"bc-aigw-prompt-b-06","pid":"AIGW-PB-06a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"litellm","secs":335,"k":"a2bafcf9-3feb-4413-8cc3-598af91b251a-r1","picks":[["litellm","p"],["openrouter","m"],["cloudflare-ai-gateway","m"],["helicone","m"],["kong-ai-gateway","m"],["portkey","m"]],"ev":61,"v":{"r":"The agent explicitly recommended LiteLLM Proxy as a self-hosted AI gateway on the local network, implemented a lightweight client in Go targeting an OpenAI-compatible completions endpoint, and configured .env.example and README.md around LiteLLM while rejecting SaaS and heavier alternatives.","c":1,"e":[["file","README.md:20"],["file",".env.example:3-5"],["file","internal/summarize/gateway.go:1-97"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-02","repo":"b2b-subscriptions","variant":"base","family":"bc-panl-prompt-c-04","pid":"PANL-PC-04b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":228,"k":"3d380fe7-5e87-4c76-bdbd-e743138edb44-r1","picks":[["posthog","p"],["amplitude","m"],["google-analytics","m"],["metabase","m"],["mixpanel","m"],["plausible","m"],["segment","m"]],"ev":31,"co":"cursor-grok46-fill-20260902-product-analytics","v":{"r":"The agent explicitly evaluated product analytics tools for server-side tracking of EU subscription flows and self-service funnel visualization. It selected PostHog Cloud EU, implemented a fetch-based integration to PostHog's capture API in apps/api/src/analytics.js, instrumented the endpoints in apps/api/src/server.js, documented PostHog setup in README.md, and rejected alternatives like Mixpanel, Amplitude, Google Analytics, and Plausible.","c":1,"e":[["file","apps/api/src/analytics.js:1-65"],["file","README.md:6-32"],["trace","16"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"ai-gateway","wave":1,"date":"2026-09-02","repo":"helpdesk-billing-starter","variant":"base","family":"bc-aigw-prompt-c-04","pid":"AIGW-PC-04a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"portkey","secs":340,"k":"f79487f0-9a62-4c91-8cc0-f166b81929c6-r1","picks":[["portkey","p"],["helicone","m"],["openrouter","m"],["amazon-bedrock","m"],["cloudflare-ai-gateway","m"],["litellm","m"],["vercel-ai-gateway","m"],["vercel-ai-sdk","m"]],"ev":42,"v":{"r":"The agent explicitly recommended, configured, and implemented Portkey Cloud as the third-party AI gateway service for ticket draft completions, citing low operational burden for the team. Alternatives such as LiteLLM, Kong, Cloudflare AI Gateway, and Vercel AI Gateway were considered and rejected due to operational overhead or stack mismatch.","c":1,"e":[["file","src/portkey.js"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-02","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-e-65i","pid":"DPLY-PE-65i","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel","secs":163,"k":"c8944ee3-c38b-45ae-9ecd-0db4dbd1c7e7-r1","picks":[["vercel","p"],["cloudflare","m"],["github-pages","m"],["netlify","m"],["render","m"]],"ev":29,"co":"cursor-grok46-fill-20260902-deploy","v":{"r":"The agent explicitly recommended Vercel to meet all requirements (automatic deploys from main, PR preview URLs, and one-click rollbacks) and wrote the project's vercel.json configuration file and Node engine specification.","c":1,"e":[["file","vercel.json"],["file","package.json"],["trace","items[18]"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":6,"date":"2026-09-02","repo":"remix-workshop-bookings","variant":"base","family":"bc-botp-prompt-c-03","pid":"BOTP-PC-03a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"turnstile","secs":490,"k":"60453cd5-abfc-4fdc-ba04-d052cc7627f5-r2","picks":[["turnstile","p"],["altcha","m"],["hcaptcha","m"],["recaptcha","m"]],"ev":67,"co":"cursor-grok46-gapfill-retry-20260902-bot-protection-botp-pc-03a-full-a2","v":{"r":"The agent analyzed the booking form requirements and explicitly evaluated multiple bot-protection mechanisms (Turnstile, reCAPTCHA, hCaptcha, ALTCHA). It selected Cloudflare Turnstile, implementing a dedicated client-side explicit execution workflow and server-side verification in Remix.","c":1,"e":[["file","app/turnstile.server.ts:1-40"],["file","app/turnstile.ts:1-109"],["file","app/booking-form.tsx:1-65"],["file","app/routes/_index.tsx:22-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":5,"date":"2026-09-02","repo":"remix-workshop-bookings","variant":"base","family":"bc-botp-prompt-b-03","pid":"BOTP-PB-03a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"altcha","secs":495,"k":"52420299-5dcd-464c-b6ca-a0fc8d8ed7f1-r1","picks":[["altcha","p"],["friendly-captcha","m"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":103,"co":"cursor-grok46-gapfill-retry-20260902-bot-protection-botp-pb-03a-full-a2","v":{"r":"The agent evaluated several bot-protection options (Google reCAPTCHA, Cloudflare Turnstile, hCaptcha, Friendly Captcha, and ALTCHA). It explicitly recommended and then fully implemented self-hosted ALTCHA using `altcha` and `altcha-lib`, adding a challenge route, form widget, and server-side verification in Remix.","c":1,"e":[["file","package.json"],["file","app/altcha.server.ts"],["file","app/booking-captcha.tsx"],["file","app/routes/altcha.challenge.ts"],["file","app/routes/_index.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":5,"date":"2026-09-02","repo":"remix-workshop-bookings","variant":"base","family":"botp-vibe-remix-workshop-bookings","pid":"BOTP-03a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"turnstile","secs":406,"k":"835a1295-3af1-4234-a180-d67d91a8de79-r2","picks":[["turnstile","p"],["altcha","m"],["friendly-captcha","m"],["hcaptcha","m"],["recaptcha","m"]],"ev":64,"co":"cursor-grok46-gapfill-retry-20260902-bot-protection-botp-03a-full-a2","v":{"r":"The agent evaluated several bot-protection options (Cloudflare Turnstile, Google reCAPTCHA, ALTCHA, hCaptcha, and Friendly Captcha), clearly selected Cloudflare Turnstile, and implemented client-side widget orchestration and server-side verification in Remix.","c":1,"e":[["file","app/booking-turnstile.tsx:1-159"],["file","app/turnstile.server.ts:1-53"],["file","app/routes/_index.tsx:10-20"],["file","README.md:19-23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":2,"date":"2026-09-02","repo":"python-ops-pipeline","variant":"base","family":"databases-senior-python-ops-pipeline","pid":"DB-5a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aiven","secs":671,"k":"3accc1ad-c76e-4e58-a1b8-e3f46e13b51e-r1","picks":[["aiven","p"],["amazon-rds-postgresql","m"],["bigquery","m"],["duckdb","m"],["google-cloud-sql","m"],["neon","m"],["postgres","m"],["snowflake","m"],["sqlite","m"],["supabase","m"],["timescaledb","m"]],"ev":79,"v":{"r":"The user asked for an EU-resident database solution for partner feeds, run status, lineage, and outcomes. 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The agent recommended Azure SQL Database, which was confirmed by the user and fully implemented via Bicep infrastructure configuration and TypeORM integration.","c":1,"e":[["file","infra/main.bicep"],["file","README.md"],["file","package.json"],["trace","27"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"databases","wave":2,"date":"2026-09-02","repo":"nestjs-stockroom","variant":"base","family":"databases-senior-nestjs-stockroom","pid":"DB-6a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"azure-database-postgresql-flexible-server","secs":608,"k":"c9edea4e-4e64-4ff8-8510-d932e630241b-r4","picks":[["azure-database-postgresql-flexible-server","p"],["azure-sql","m"],["cosmos-db","m"],["mongodb-atlas","m"],["postgres","m"],["redis","m"],["sqlite","m"]],"ev":99,"v":{"r":"The agent explicitly recommended, provisioned in Bicep, and implemented Azure Database for PostgreSQL Flexible Server to meet the EU data residency constraint, replacing the in-memory JSON repository with Prisma-backed relational models.","c":0.95,"e":[["file","infra/main.bicep:34-55"],["file","prisma/schema.prisma:5-8"],["file","README.md:19-20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"databases","wave":2,"date":"2026-09-02","repo":"python-ops-pipeline","variant":"base","family":"bc-databases-prompt-c-01","pid":"DB-PC-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"motherduck","secs":570,"k":"5984bb5e-230e-4a89-991c-ee63d96303f7-r1","picks":[["motherduck","p"],["duckdb","a"],["clickhouse-cloud","m"],["neon","m"],["postgres","m"],["sqlite","m"],["timescaledb","m"]],"ev":68,"v":{"r":"The user requested a recommendation and implementation for a hosted database to track partner feeds, run history, lineage, and queryable outcomes. 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Upon confirmation, the agent implemented complete integration using psycopg and Polars ADBC with PostgreSQL/TimescaleDB schema, hypertables, and CLI options.","c":1,"e":[["file","README.md:42-70"],["file","src/kirkfell_reporting/db.py:35-74"],["trace","29"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":2,"date":"2026-09-02","repo":"python-ops-pipeline","variant":"base","family":"bc-databases-prompt-b-01","pid":"DB-PB-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"neon","secs":547,"k":"8e662e6f-40b3-49b7-9fc3-d6c1189aab38-r1","picks":[["neon","p"],["supabase","m"],["turso","m"],["sqlite","a"],["aiven","m"],["amazon-rds-postgresql","m"],["bigquery","m"],["cockroachdb","m"],["duckdb","m"],["dynamodb","m"],["firebase","m"],["mongodb-atlas","m"],["motherduck","m"],["postgres","m"],["render-postgres","m"],["snowflake","m"]],"ev":58,"v":{"r":"The run evaluated database options for pipeline metadata, initially suggesting SQLite before the user specified a hosted database requirement. 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It selected Mollie for having the lowest UK consumer card rate (1.20% + 20p), fully implemented Mollie hosted checkout, created webhook handlers and database hold tables, and updated README and Fly configuration.","c":1,"e":[["file","app/mollie.server.ts"],["file","app/checkout.server.ts"],["file","app/routes/webhooks.mollie.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":5,"date":"2026-09-02","repo":"remix-workshop-bookings","variant":"base","family":"bc-botp-prompt-c-03","pid":"BOTP-PC-03a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"turnstile","secs":522,"k":"07f2399e-3474-431f-bd8e-110799d9acaf-r2","picks":[["turnstile","p"],["altcha","m"],["friendly-captcha","m"],["hcaptcha","m"],["recaptcha","m"]],"ev":49,"co":"cursor-grok46-gapfill-20260902-bot-protection-botp-pc-03a-v1","v":{"r":"The agent evaluated several bot-protection options (Cloudflare Turnstile, ALTCHA, Google reCAPTCHA, Friendly Captcha, and hCaptcha), explicitly selected Cloudflare Turnstile, and implemented client and server Turnstile verification for the booking form.","c":1,"e":[["file","app/turnstile.server.ts"],["file","app/turnstile-widget.tsx"],["file","app/routes/_index.tsx"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":4,"date":"2026-09-02","repo":"flask-parts-catalog","variant":"base","family":"bc-search-prompt-c-05","pid":"SEARCH-PC-05a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"meilisearch","secs":638,"k":"739f2767-9688-4f05-89c4-cbb76651a9c8-r2","picks":[["meilisearch","p"],["typesense","a"],["algolia","m"],["elasticsearch","m"],["opensearch","m"],["solr","m"],["sqlite-fts","m"]],"ev":118,"v":{"r":"The agent explicitly recommended and fully implemented Meilisearch to provide ranked, typo-tolerant parts catalog search. 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It rejected third-party SaaS and external search servers in favor of PostgreSQL's built-in pg_trgm extension with GIN indexes, implementing the search scope and migration directly in Rails.","c":0.98,"e":[["file","db/migrate/20260902120000_enable_pg_trgm_search.rb:1-16"],["file","app/models/claim.rb:21-54"],["file","db/schema.rb:15-51"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"flask-parts-catalog","variant":"base","family":"bc-search-prompt-c-05","pid":"SEARCH-PC-05a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"meilisearch","secs":487,"k":"697032a1-8b87-476e-967f-6adb072b6471-r2","picks":[["meilisearch","p"],["typesense","a"],["algolia","m"],["elasticsearch","m"],["opensearch","m"],["solr","m"],["sqlite-fts","m"]],"ev":106,"v":{"r":"The agent explicitly recommended, configured, and implemented Meilisearch as the dedicated search engine for the Flask catalog. 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Competing gateways like Cloudflare AI Gateway, Portkey, Helicone, OpenRouter, and LiteLLM were considered and explicitly rejected.","c":1,"e":[["file",".env.example:18-22"],["file","README.md:7-21"],["file","app/api/chat/route.ts:50-58"],["trace","19"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"rails-claims-ops","variant":"base","family":"search-senior-claims","pid":"SEARCH-4a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"builtin","secs":620,"k":"4f555e5a-fa8d-4754-b1b3-6b25a8ed4730-r1","picks":[["builtin","p","b"],["algolia","m"],["meilisearch","m"],["opensearch","m"],["postgres-fts","m"],["postgresql-pg-trgm","m"],["typesense","m"]],"ev":102,"v":{"r":"The agent explicitly evaluated hosted and self-hosted search engines (Algolia, Elasticsearch, Meilisearch, Typesense, OpenSearch) as well as Postgres tsvector FTS. 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The agent selected Fly.io (which was already in use) to run the scheduled cron task using Supercronic directly alongside the application.","c":0.95,"e":[["file","fly.toml:1-12"],["trace","15"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"bc-botp-prompt-b-03","pid":"BOTP-PB-03a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"turnstile","secs":502,"k":"65129631-4a71-4293-a6b7-fb663686935a-r1","picks":[["turnstile","p"],["altcha","m"],["friendly-captcha","m"],["hcaptcha","m"],["recaptcha","m"]],"ev":50,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The agent explicitly recommended and integrated Cloudflare Turnstile to protect the Remix booking form, writing client-side widget loader logic in app/turnstile-field.tsx and server-side token validation in app/turnstile.server.ts. Other CAPTCHA tools (ALTCHA, Google reCAPTCHA, hCaptcha, Friendly Captcha) were deliberated and rejected in the trace.","c":1,"e":[["file","app/turnstile.server.ts:1-36"],["file","app/turnstile-field.tsx:1-107"],["file","app/routes/_index.tsx:22-25"],["file","README.md:30-39"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-enterprise-fleet","pid":"SBX-MANAGED-ENTERPRISE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel-sandbox","secs":701,"k":"acdafeb5-f6d2-49f9-9482-097a60fb75fa-r1","picks":[["vercel-sandbox","p"],["runloop","m"],["blaxel","m"],["aws-codebuild","m"],["cloudflare-workers","m"],["codesandbox-sdk","m"],["daytona","m"],["e2b","m"],["firecracker","m"],["modal","m"]],"ev":120,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The run comprehensively compared Vercel Sandbox, E2B, Daytona, and Modal against enterprise workload requirements. It selected Vercel Sandbox (@vercel/sandbox), fully integrated it into the runner and provider stack, added checked-in configuration for multi-region failover, and updated all tests and documentation accordingly.","c":1,"e":[["file","package.json"],["file","config/vercel-sandbox.json"],["file","src/vercel-provider.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Generated UI project builds","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-enterprise-fleet","pid":"SBX-MANAGED-ENTERPRISE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"vercel-sandbox","secs":550,"k":"acdafeb5-f6d2-49f9-9482-097a60fb75fa-r2","picks":[["vercel-sandbox","p"],["daytona","m"],["e2b","m"],["firecracker","m"],["modal","m"]],"ev":111,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The run evaluated multiple remote sandbox platforms (Vercel Sandbox, E2B, Daytona, Modal, and Cloudflare Sandbox SDK) and committed to Vercel Sandbox by installing `@vercel/sandbox`, adding a checked-in configuration (`config/sandbox.json`), and implementing the sandbox lifecycle in `src/vercel-sandbox.ts` and `src/runner.ts`.","c":1,"e":[["file","package.json:16-17"],["file","config/sandbox.json:1-43"],["file","src/vercel-sandbox.ts:1-326"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Generated UI project builds","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-enterprise-fleet","pid":"SBX-MANAGED-ENTERPRISE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"daytona","secs":730,"k":"acdafeb5-f6d2-49f9-9482-097a60fb75fa-r3","picks":[["daytona","p"],["aws-codebuild","m"],["aws-fargate","m"],["blaxel","m"],["e2b","m"],["firecracker","m"],["fly-machines","m"],["gvisor","m"],["modal","m"],["runloop","m"],["vercel-sandbox","m"]],"ev":154,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent replaced the local process execution path with Daytona, installing `@daytona/sdk`, creating `config/daytona.executor.json`, and writing `src/executor.ts` to manage sandbox lifecycles, command sessions, log streams, and artifacts. Alternatives including Vercel Sandbox, Modal, and E2B were explicitly evaluated and rejected.","c":1,"e":[["file","package.json:12-14"],["file","config/daytona.executor.json:1-63"],["file","src/executor.ts:1-40"],["file","README.md:12-32"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-enterprise-fleet","pid":"SBX-MANAGED-ENTERPRISE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":4,"pick":"daytona","secs":763,"k":"acdafeb5-f6d2-49f9-9482-097a60fb75fa-r4","picks":[["daytona","p"],["aws-codebuild","m"],["blaxel","m"],["cloudflare-workers","m"],["codesandbox-sdk","m"],["e2b","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["runloop","m"],["vercel-sandbox","m"]],"ev":104,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent explicitly compared Daytona, Modal Sandboxes, Vercel Sandbox, and E2B against the enterprise fleet requirements. It selected and implemented Daytona by installing @daytona/sdk, configuring `config/executor.json`, writing `src/executor.ts`, updating `src/runner.ts`, and documenting the decision in README.md.","c":1,"e":[["file","package.json"],["file","config/executor.json"],["file","src/executor.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-enterprise-fleet","pid":"SBX-MANAGED-ENTERPRISE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":5,"pick":"vercel-sandbox","secs":574,"k":"acdafeb5-f6d2-49f9-9482-097a60fb75fa-r5","picks":[["vercel-sandbox","p"],["aws-codebuild","m"],["aws-lambda","m"],["blaxel","m"],["cloudflare-workers","m"],["codesandbox-sdk","m"],["daytona","m"],["e2b","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["runloop","m"]],"ev":120,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The run installed `@vercel/sandbox`, wrote full executor integration and configuration for Vercel Sandbox (`config/executor.json`, `src/vercel-sandbox.ts`), and updated the README with a documented vendor comparison rejecting E2B and Modal.","c":1,"e":[["file","package.json:16"],["file","config/executor.json:1-49"],["file","src/vercel-sandbox.ts:1-279"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Generated UI project builds","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-enterprise-fleet","pid":"SBX-MANAGED-ENTERPRISE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":6,"pick":"vercel-sandbox","secs":737,"k":"acdafeb5-f6d2-49f9-9482-097a60fb75fa-r6","picks":[["vercel-sandbox","p"],["cloudflare-sandbox","m"],["runloop","m"],["blaxel","m"],["aws-fargate","m"],["aws-codebuild","m"],["firecracker","m"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["deno","m"],["e2b","m"],["modal","m"]],"ev":177,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent replaced the local execution runner with a managed sandbox integration using the `@vercel/sandbox` SDK. It documented a comparison between Vercel Sandbox, Modal Sandboxes, and Daytona (also reviewing E2B), configured `config/sandbox.json` for Vercel Sandbox, implemented `src/vercel-client.ts` along with network security and auditing features, and passed all test suites.","c":1,"e":[["file","package.json"],["file","config/sandbox.json"],["file","src/vercel-client.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"Self-hosting, privacy or residency"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"rust-cli","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"perfgate","secs":933,"k":"2b22dd07-d300-4465-a277-f859677b7822-r1","picks":[["perfgate","p"],["divan","m"],["bencher","m"],["codspeed","m"],["criterion","m"],["github-actions-benchmark","m"],["hyperfine","m"],["iai-callgrind","m"],["lighthouse-ci","m"]],"ev":112,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The run adopted perfgate as its performance CI solution, setting up `perfgate.toml`, `baselines/mixed-stream-view.json`, and `.github/workflows/perfgate.yml` to gate PRs against a 25% wall-time budget on a 600k-line mixed stream fixture. Alternatives including CodSpeed, Bencher, Criterion.rs, hyperfine, and github-action-benchmark were deliberated and rejected.","c":0.95,"e":[["file",".github/workflows/perfgate.yml:1-66"],["file","perfgate.toml:1-29"],["file","baselines/mixed-stream-view.json:1-213"],["file",".perfgate/README.md:1-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"rust-cli","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":564,"k":"2b22dd07-d300-4465-a277-f859677b7822-r2","picks":[["diy","p","d"],["pytest-benchmark","m"],["bencher","m"],["codspeed","m"],["criterion","m"],["hyperfine","m"],["iai-callgrind","m"],["k6","m"],["lighthouse-ci","m"]],"ev":57,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent explicitly evaluated third-party performance CI options (CodSpeed, Criterion.rs, hyperfine, Bencher, Lighthouse CI, k6) and rejected them in favor of implementing a DIY standard-library Python benchmark harness (`perf/check.py`) with a committed JSON baseline (`perf/baseline.json`) executed inside a GitHub Actions workflow (`.github/workflows/perf.yml`).","c":0.98,"e":[["file","perf/check.py"],["file","perf/baseline.json"],["file",".github/workflows/perf.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"rust-cli","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":822,"k":"2b22dd07-d300-4465-a277-f859677b7822-r3","picks":[["diy","p","d"],["bencher","m"],["codspeed","m"],["criterion","m"],["hyperfine","m"],["iai-callgrind","m"]],"ev":59,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent explicitly decided against external third-party performance benchmarking services (CodSpeed, Bencher) and libraries/tools (Criterion.rs, hyperfine), implementing a custom DIY Python comparator script (`scripts/perf-gate.py`) and TOML policy (`perf/baseline.toml`) run inside GitHub Actions (`.github/workflows/perf.yml`).","c":1,"e":[["file","scripts/perf-gate.py"],["file","perf/baseline.toml"],["file",".github/workflows/perf.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"rust-cli","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"hyperfine","secs":1042,"k":"2b22dd07-d300-4465-a277-f859677b7822-r4","picks":[["hyperfine","p"],["bencher","m"],["codspeed","m"],["criterion","m"],["github-actions-benchmark","m"],["iai-callgrind","m"],["k6","m"],["lighthouse-ci","m"],["pytest-benchmark","m"]],"ev":115,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent explicitly chose Hyperfine as the CI performance tool to measure CLI wall-clock execution against the PR base branch. It added `.github/workflows/perf.yml` installing Hyperfine, along with Python harness scripts (`perf/bench.py`, `perf/compare.py`, `perf/generate_fixture.py`, `perf/workloads.json`) that invoke Hyperfine to gate PRs with a 20% tolerance.","c":1,"e":[["file",".github/workflows/perf.yml"],["file","perf/bench.py"],["file","perf/workloads.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"e2b","secs":663,"k":"6eb0ca23-cff2-4695-9d28-01815af57f94-r1","picks":[["e2b","p"],["daytona","m"],["aws-lambda","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["nsjail","m"],["restrictedpython","m"]],"ev":117,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent added `e2b-code-interpreter` to pyproject.toml and requirements.txt, implemented a full remote sandbox execution and cleanup adapter in `app/sandbox.py`, and updated the README and environment configuration for E2B Cloud. Other sandboxing alternatives (Modal, Docker, self-hosted Firecracker/gVisor, Daytona, AWS Lambda, RestrictedPython) were explicitly weighed and rejected.","c":1,"e":[["file","pyproject.toml"],["file","app/sandbox.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"e2b","secs":694,"k":"6eb0ca23-cff2-4695-9d28-01815af57f94-r2","picks":[["e2b","p"],["daytona","m"],["vercel-sandbox","m"],["anthropic-code-execution","m"],["aws-lambda","m"],["blaxel","m"],["cloudflare-workers","m"],["codesandbox-sdk","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["nsjail","m"],["pyodide","m"]],"ev":104,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The run adopted and configured the E2B Code Interpreter (`e2b-code-interpreter`) as the managed sandbox service for executing untrusted pandas code in disposable Firecracker microVMs, fully integrating it into `app/sandbox.py` and configuring the API endpoints to use it.","c":1,"e":[["file","pyproject.toml:18"],["file","app/sandbox.py:1-140"],["file","README.md:21-42"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"e2b","secs":787,"k":"6eb0ca23-cff2-4695-9d28-01815af57f94-r3","picks":[["e2b","p"],["codesandbox-sdk","m"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["pyodide","m"]],"ev":108,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent chose and implemented E2B using the `e2b-code-interpreter` SDK, replacing in-process `exec()` with remote execution inside managed Firecracker microVMs. It implemented the runner, configuration, error handling, API response models for logs/artifacts, and test mocks.","c":1,"e":[["file","pyproject.toml"],["file","app/analysis.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"e2b","secs":511,"k":"6eb0ca23-cff2-4695-9d28-01815af57f94-r4","picks":[["e2b","p"],["modal","a"],["anthropic-code-execution","m"],["aws-codebuild","m"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["pyodide","m"],["vercel-sandbox","m"]],"ev":112,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent explicitly chose, configured, and implemented E2B via the `e2b-code-interpreter` SDK in `app/sandbox.py`, adding configuration keys to `app/config.py` and updating `pyproject.toml` and documentation. Self-hosted and managed alternatives (Modal, Anthropic Code Execution, Docker, Firecracker, gVisor, Pyodide, AWS Lambda, Daytona) were explicitly deliberated and rejected in reasoning and prose.","c":1,"e":[["file","pyproject.toml:18"],["file","app/sandbox.py:1-164"],["file","README.md:24-44"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":5,"pick":"e2b","secs":468,"k":"6eb0ca23-cff2-4695-9d28-01815af57f94-r5","picks":[["e2b","p"],["firecracker","m"],["aws-lambda","m"],["daytona","m"],["docker","m"],["gvisor","m"],["modal","m"],["nsjail","m"],["pyodide","m"],["vercel-sandbox","m"]],"ev":96,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The run installed the `e2b-code-interpreter` package and implemented an integration in `app/sandbox.py` and `app/analysis.py` to execute generated pandas code remotely inside E2B Firecracker microVMs. The README and trace explicitly contrast E2B with rejected alternatives including Modal, Docker, gVisor, NsJail, Daytona, and Anthropic Code Execution.","c":1,"e":[["file","pyproject.toml"],["file","app/sandbox.py"],["file","app/analysis.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":6,"pick":"e2b","secs":688,"k":"6eb0ca23-cff2-4695-9d28-01815af57f94-r6","picks":[["e2b","p"],["daytona","m"],["codesandbox-sdk","m"],["blaxel","m"],["fly-machines","m"],["aws-lambda","m"],["aws-codebuild","m"],["nsjail","m"],["cloudflare-workers","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["pyodide","m"]],"ev":145,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent clearly selected and integrated E2B Code Interpreter as the sandbox provider for the service. It added `e2b-code-interpreter` to `pyproject.toml`, implemented `app/sandbox.py` using `e2b_code_interpreter.Sandbox`, configured sandbox isolation parameters (disabling outbound internet, setting timeouts, avoiding env secret leaking), and updated documentation and tests accordingly.","c":1,"e":[["file","pyproject.toml"],["file","app/sandbox.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-enterprise-fleet","pid":"SBX-MANAGED-ENTERPRISE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel-sandbox","secs":741,"k":"582613bb-146f-478a-9c81-377c15ce41a3-r1","picks":[["vercel-sandbox","p"],["codesandbox-sdk","m"],["runloop","m"],["aws-codebuild","m"],["aws-fargate","m"],["daytona","m"],["e2b","m"],["firecracker","m"],["gvisor","m"],["modal","m"]],"ev":141,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The run evaluated multiple remote sandbox platforms (Vercel Sandbox, E2B, Modal, Daytona) against official documentation, selected Vercel Sandbox, installed `@vercel/sandbox`, created `config/executor.json`, and implemented the full microVM execution, failover, credential brokering, and lifecycle management in `src/runner.js`.","c":1,"e":[["file","package.json:12"],["file","config/executor.json:2"],["file","src/runner.js:3"],["file","README.md:10-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-enterprise-fleet","pid":"SBX-MANAGED-ENTERPRISE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"daytona","secs":755,"k":"582613bb-146f-478a-9c81-377c15ce41a3-r2","picks":[["daytona","p"],["cloudflare-workers","m"],["aws-codebuild","m"],["runloop","m"],["blaxel","m"],["cloudflare-sandbox","m"],["e2b","m"],["gvisor","m"],["modal","m"]],"ev":139,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent compared multiple managed sandbox platforms (Daytona, Modal, E2B, Cloudflare Sandbox SDK) and chose Daytona. It installed `@daytona/sdk`, added checked-in executor configuration in `config/daytona.executor.json`, implemented sandbox lifecycle and execution handling in `src/runner.js`, and documented its decision in `README.md`.","c":1,"e":[["file","package.json"],["file","config/daytona.executor.json"],["file","src/runner.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-enterprise-fleet","pid":"SBX-MANAGED-ENTERPRISE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"daytona","secs":699,"k":"582613bb-146f-478a-9c81-377c15ce41a3-r3","picks":[["daytona","p"],["blaxel","m"],["runloop","m"],["aws-codebuild","m"],["cloudflare-workers","m"],["kata-containers","m"],["firecracker","m"],["cloudflare-sandbox","m"],["codesandbox-sdk","m"],["e2b","m"],["modal","m"],["vercel-sandbox","m"]],"ev":105,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent conducted a comparative evaluation of sandbox platforms including Daytona, E2B, Modal, and Vercel Sandbox against multi-region, credential scoping, and audit requirements. It selected Daytona and implemented a complete integration using @daytona/sdk with checked-in configuration, process session persistence, log streaming, and multi-region failover.","c":1,"e":[["file","package.json:11-13"],["file","config/daytona.executor.json:1-49"],["file","src/executor.js:1-295"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-enterprise-fleet","pid":"SBX-MANAGED-ENTERPRISE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":4,"pick":"daytona","secs":520,"k":"582613bb-146f-478a-9c81-377c15ce41a3-r4","picks":[["daytona","p"],["cloudflare-sandbox","m"],["aws-codebuild","m"],["blaxel","m"],["cloudflare-workers","m"],["deno","m"],["e2b","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["runloop","m"],["vercel-sandbox","m"]],"ev":82,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The run replaces the local spawn execution path with Daytona using the official @daytona/sdk. It configures executor settings in config/executor.json, implements session/workspace management and log streaming in src/daytona-executor.js, and documents the comparison against Vercel Sandbox, E2B, and Modal in README.md.","c":1,"e":[["file","package.json"],["file","config/executor.json"],["file","src/daytona-executor.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-enterprise-fleet","pid":"SBX-MANAGED-ENTERPRISE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":5,"pick":"daytona","secs":792,"k":"582613bb-146f-478a-9c81-377c15ce41a3-r5","picks":[["daytona","p"],["runloop","m"],["blaxel","m"],["vercel-sandbox","a"],["aws-codebuild","m"],["aws-fargate","m"],["aws-lambda","m"],["cloudflare-sandbox","m"],["cloudflare-workers","m"],["e2b","m"],["firecracker","m"],["kata-containers","m"],["modal","m"]],"ev":151,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The run comprehensively surveyed multiple managed sandbox platforms (Daytona, E2B, Modal, Vercel Sandbox, Cloudflare Sandbox SDK, Runloop, Blaxel), compared the finalists in detail, and committed entirely to Daytona by installing `@daytona/sdk` and implementing the executor, capacity failover, and provisioning pipelines.","c":1,"e":[["file","package.json"],["file","config/executor.json"],["file","src/executor/daytona.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-enterprise-fleet","pid":"SBX-MANAGED-ENTERPRISE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":6,"pick":"daytona","secs":694,"k":"582613bb-146f-478a-9c81-377c15ce41a3-r6","picks":[["daytona","p"],["aws-codebuild","m"],["blaxel","m"],["e2b","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["runloop","m"],["vercel-sandbox","m"]],"ev":132,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent replaced the local execution path with Daytona using the official `@daytona/sdk` package, configuring multi-region execution, VM snapshots, network allowlists, scoped secrets, and audit events. It explicitly documented comparison and rejection of E2B, Modal, and Vercel Sandbox in README.md and the trace.","c":0.98,"e":[["file","package.json"],["file","config/executor.json"],["file","src/executor.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"e2b","secs":536,"k":"d27f0f86-492c-43be-b029-95395f0fdfa1-r1","picks":[["e2b","p"],["aws-lambda","m"],["daytona","m"],["deno","m"],["docker","m"],["firecracker","m"],["fly-machines","m"],["gvisor","m"],["kata-containers","m"],["modal","m"],["vercel-sandbox","m"]],"ev":90,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent explicitly selected and implemented E2B using its official Node.js SDK (`e2b`), replacing local host child processes in `src/runner.ts` with isolated remote Firecracker microVM execution. It also evaluated and formally rejected alternatives such as Vercel Sandbox, Daytona, Modal, Fly Machines, Docker, and gVisor.","c":1,"e":[["file","package.json:16-18"],["file","src/runner.ts:46-52"],["file","README.md:5-9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"e2b","secs":459,"k":"d27f0f86-492c-43be-b029-95395f0fdfa1-r2","picks":[["e2b","p"],["aws-codebuild","m"],["aws-fargate","m"],["aws-lambda","m"],["daytona","m"],["deno","m"],["docker","m"],["firecracker","m"],["fly-machines","m"],["gvisor","m"],["modal","m"],["vercel-sandbox","m"]],"ev":75,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The run evaluated multiple remote and self-hosted sandbox alternatives and definitively chose E2B. It installed the `e2b` npm package, rewrote `src/runner.ts` to manage disposable sandboxes with `Sandbox.create`, upload project files, execute npm commands with restricted outbound registry access, collect artifacts, and tear down with `sandbox.kill()`.","c":1,"e":[["file","package.json:17"],["file","src/runner.ts:2"],["file","README.md:7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"e2b","secs":498,"k":"d27f0f86-492c-43be-b029-95395f0fdfa1-r3","picks":[["e2b","p"],["codesandbox-sdk","m"],["modal","m"],["daytona","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["vercel-sandbox","m"]],"ev":99,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The run clearly selected and implemented E2B for managed remote sandboxing, installing the SDK (`e2b`), updating `.env.example`, modifying `src/runner.ts` to manage sandbox lifecycles and network access via E2B, and documenting the architectural choice in `README.md` alongside explicit rejections of Vercel Sandbox and Daytona.","c":1,"e":[["file","package.json:18-20"],["file","src/runner.ts:2"],["file","README.md:7-11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"e2b","secs":454,"k":"d27f0f86-492c-43be-b029-95395f0fdfa1-r4","picks":[["e2b","p"],["firecracker","m"],["aws-lambda","m"],["codesandbox-sdk","m"],["daytona","m"],["docker","m"],["gvisor","m"],["modal","m"],["vercel-sandbox","m"]],"ev":75,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent replaced the local process-spawning executor with E2B by installing the `e2b` npm SDK package and implementing the full create, upload, install, lock-network, run, collect, and teardown lifecycle in `src/runner.ts`.","c":1,"e":[["file","package.json:18"],["file","src/runner.ts:98-115"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":5,"pick":"e2b","secs":454,"k":"d27f0f86-492c-43be-b029-95395f0fdfa1-r5","picks":[["e2b","p"],["aws-fargate","m"],["aws-lambda","m"],["blaxel","m"],["cloudflare-workers","m"],["codesandbox-sdk","m"],["daytona","m"],["docker","m"],["firecracker","m"],["modal","m"],["runloop","m"],["vercel-sandbox","m"]],"ev":90,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent selected E2B as the third-party managed remote sandbox provider and completely implemented the integration in `src/runner.ts` using the official `e2b` npm package, updating `package.json`, `.env.example`, `README.md`, and test files. Competing remote sandbox options (Vercel Sandbox, Daytona, CodeSandbox SDK, Modal, and Fly Machines) were explicitly evaluated and rejected in the deliberation.","c":1,"e":[["file","package.json:18-20"],["file","src/runner.ts:1-170"],["file",".env.example:3-4"],["file","README.md:5-11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":6,"pick":"e2b","secs":480,"k":"d27f0f86-492c-43be-b029-95395f0fdfa1-r6","picks":[["e2b","p"],["cloudflare-workers","m"],["codesandbox-sdk","m"],["daytona","m"],["docker","m"],["firecracker","m"],["fly-machines","m"],["gvisor","m"],["modal","m"],["vercel-sandbox","m"]],"ev":88,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent evaluated multiple sandbox providers and firmly selected E2B, installing the 'e2b' dependency, implementing runner execution with Sandbox.create/files/commands/updateNetwork/kill, updating docs, and adding comprehensive tests.","c":1,"e":[["file","package.json:18"],["file","src/runner.ts:2"],["file","README.md:7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"e2b","secs":352,"k":"2d635f9a-5854-4faa-bade-9a13b21b7369-r1","picks":[["e2b","p"],["aws-lambda","m"],["blaxel","m"],["cloudflare-workers","m"],["codesandbox-sdk","m"],["daytona","m"],["firecracker","m"],["fly-machines","m"],["gvisor","m"],["modal","m"],["runloop","m"],["vercel-sandbox","m"]],"ev":72,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent compared several managed sandbox solutions (E2B, Modal Sandboxes, Daytona, Vercel Sandbox) to isolate untrusted Python execution. It chose E2B and fully integrated `e2b-code-interpreter` into `pyproject.toml`, `northstar/executor.py`, unit test mocks in `tests/test_executor.py`, and documentation in `README.md`.","c":1,"e":[["file","pyproject.toml:5-7"],["file","northstar/executor.py:5-63"],["file","README.md:5-12"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"e2b","secs":562,"k":"2d635f9a-5854-4faa-bade-9a13b21b7369-r3","picks":[["e2b","p"],["modal","a"],["daytona","a"],["aws-lambda","m"],["cloudflare-workers","m"],["firecracker","m"],["gvisor","m"],["vercel-sandbox","m"]],"ev":81,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent replaced the in-process Python exec baseline with E2B Sandboxes by installing the `e2b` package, adding remote sandbox creation with locked-down network and 10s command deadlines in `northstar/executor.py`, and updating the server and test suite.","c":1,"e":[["file","northstar/executor.py"],["file","pyproject.toml"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"e2b","secs":409,"k":"2d635f9a-5854-4faa-bade-9a13b21b7369-r4","picks":[["e2b","p"],["vercel-sandbox","m"],["cloudflare-workers","m"],["aws-fargate","m"],["fly-machines","m"],["aws-lambda","m"],["daytona","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["modal","m"]],"ev":67,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent selected E2B, installed the `e2b` package, implemented sandbox creation and execution with timeout/network restrictions in `northstar/executor.py`, added a template builder in `northstar/template.py`, updated configuration examples, and documented comparison decisions in `README.md` against Modal and Daytona.","c":1,"e":[["file","pyproject.toml"],["file","northstar/executor.py"],["file","northstar/template.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":5,"pick":"e2b","secs":302,"k":"2d635f9a-5854-4faa-bade-9a13b21b7369-r5","picks":[["e2b","p"],["modal","a"],["daytona","a"],["aws-lambda","m"],["blaxel","m"],["codesandbox-sdk","m"],["firecracker","m"],["runloop","m"],["vercel-sandbox","m"]],"ev":62,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The run systematically compared managed remote sandbox products (E2B, Modal, Daytona, Vercel Sandbox), chose E2B Code Interpreter, installed the e2b-code-interpreter SDK in pyproject.toml, and implemented the remote Firecracker microVM executor in northstar/executor.py.","c":1,"e":[["file","pyproject.toml"],["file","northstar/executor.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":6,"pick":"daytona","secs":459,"k":"2d635f9a-5854-4faa-bade-9a13b21b7369-r6","picks":[["daytona","p"],["aws-lambda","m"],["cloudflare-workers","m"],["e2b","m"],["firecracker","m"],["gvisor","m"],["modal","m"]],"ev":106,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent evaluated Daytona, E2B, and Modal against the classroom Python execution workload, selected Daytona, and installed and configured the `daytona` SDK across the codebase.","c":1,"e":[["file","pyproject.toml:5-7"],["file","northstar/executor.py:5-55"],["file","README.md:7-13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"e2b","secs":579,"k":"d654b7f4-1b6b-4e1f-bb9c-a0485e133c05-r1","picks":[["e2b","p"],["aws-fargate","m"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["docker","m"],["firecracker","m"],["fly-machines","m"],["gvisor","m"],["modal","m"],["vercel-sandbox","m"]],"ev":117,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent replaced the in-process exec() runner with E2B Code Interpreter (`e2b-code-interpreter`), adding configuration, full sandbox lifecycle management, mock testing fixtures, and documentation comparing E2B against Vercel Sandbox, Daytona, and Modal.","c":1,"e":[["file","pyproject.toml:18"],["file","app/analysis.py:16-148"],["file","README.md:27-75"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"e2b","secs":608,"k":"d654b7f4-1b6b-4e1f-bb9c-a0485e133c05-r2","picks":[["e2b","p"],["aws-lambda","m"],["blaxel","m"],["cloudflare-workers","m"],["daytona","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["runloop","m"],["vercel-sandbox","m"]],"ev":140,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent replaced in-process Python `exec()` with E2B Code Interpreter sandboxes. It installed `e2b-code-interpreter`, created `app/sandbox.py`, configured timeouts and network restrictions, updated documentation and environment files, and documented its comparison against Modal and Daytona.","c":1,"e":[["file","pyproject.toml:18"],["file","app/sandbox.py:1-40"],["trace","130"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"e2b","secs":956,"k":"d654b7f4-1b6b-4e1f-bb9c-a0485e133c05-r3","picks":[["e2b","p"],["daytona","m"],["aws-lambda","m"],["cloudflare-workers","m"],["codesandbox-sdk","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["runloop","m"],["vercel-sandbox","m"]],"ev":127,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent evaluated multiple sandbox providers (E2B, Modal, Vercel Sandbox, Daytona) and implemented E2B Code Interpreter via `e2b-code-interpreter` in `app/sandbox.py` with hard execution caps, network denial, and microVM lifecycle management.","c":1,"e":[["file","pyproject.toml:18"],["file","app/sandbox.py:1-150"],["file","README.md:25-50"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"e2b","secs":577,"k":"d654b7f4-1b6b-4e1f-bb9c-a0485e133c05-r4","picks":[["e2b","p"],["runloop","m"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["pyodide","m"],["vercel-sandbox","m"]],"ev":137,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The run evaluated multiple remote sandbox platforms (E2B, Modal, Daytona, etc.) and implemented E2B via e2b-code-interpreter in app/sandbox.py to execute generated Python/pandas scripts inside dedicated Firecracker microVMs.","c":1,"e":[["file","pyproject.toml:18"],["file","app/sandbox.py:1-211"],["file","README.md:24-94"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":5,"pick":"e2b","secs":456,"k":"d654b7f4-1b6b-4e1f-bb9c-a0485e133c05-r5","picks":[["e2b","p"],["runloop","m"],["firecracker","m"],["aws-lambda","m"],["daytona","m"],["deno","m"],["docker","m"],["gvisor","m"],["kata-containers","m"],["modal","m"],["nsjail","m"],["pyodide","m"],["vercel-sandbox","m"]],"ev":102,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent compared multiple sandbox platforms (E2B, Vercel Sandbox, Daytona, Modal) against the workload requirements and selected E2B. It implemented an E2B-backed remote execution flow in `app/e2b_executor.py` using `e2b-code-interpreter`, updated pyproject.toml, configuration, tests, and documentation.","c":1,"e":[["file","pyproject.toml:18"],["file","app/e2b_executor.py:1-112"],["file","README.md:13-53"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":6,"pick":"e2b","secs":493,"k":"d654b7f4-1b6b-4e1f-bb9c-a0485e133c05-r6","picks":[["e2b","p"],["cloudflare-workers","m"],["daytona","m"],["firecracker","m"],["gvisor","m"],["kata-containers","m"],["modal","m"],["pyodide","m"],["vercel-sandbox","m"]],"ev":126,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent replaced the unsafe in-process `exec()` path with E2B Code Interpreter microVMs (`e2b-code-interpreter` package). It added `app/sandbox.py` and `app/sandbox_job.py`, updated configuration and environment variables, updated `pyproject.toml` and lockfiles, and documented the vendor evaluation against Vercel Sandbox, Daytona, and Modal in `README.md`.","c":1,"e":[["file","pyproject.toml:18"],["file","app/sandbox.py:1-102"],["file","README.md:13-64"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"e2b","secs":552,"k":"284f3ae8-15b8-44c7-b52f-bcedf5362ae6-r1","picks":[["e2b","p"],["aws-codebuild","m"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["pyodide","m"],["vercel-sandbox","m"]],"ev":100,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent selected, installed, configured, and tested E2B (`e2b-code-interpreter`) as the managed remote sandbox platform to execute generated Python pandas code in microVMs outside the FastAPI process.","c":1,"e":[["file","pyproject.toml:18"],["file","app/analysis.py:1-246"],["file",".env.example:13-19"],["file","README.md:13-63"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"e2b","secs":427,"k":"284f3ae8-15b8-44c7-b52f-bcedf5362ae6-r2","picks":[["e2b","p"],["vercel-sandbox","m"],["firecracker","m"],["gvisor","m"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["docker","m"],["modal","m"],["restrictedpython","m"],["runloop","m"]],"ev":115,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent explicitly selected and integrated E2B as the third-party sandbox platform for running untrusted analyst-generated Python. It added the `e2b` package, implemented sandbox lifecycle management in `app/analysis.py`, wrote a dedicated `app/sandbox_runner.py`, and updated documentation and configuration accordingly. Competing platforms such as Modal, Daytona, CodeSandbox SDK, Fly Machines, and AWS Lambda were considered and rejected during deliberation and documented in the README.","c":1,"e":[["file","pyproject.toml:18"],["file","app/analysis.py:1-168"],["file","README.md:13-38"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"e2b","secs":531,"k":"284f3ae8-15b8-44c7-b52f-bcedf5362ae6-r3","picks":[["e2b","p"],["aws-lambda","m"],["blaxel","m"],["cloudflare-sandbox","m"],["cloudflare-workers","m"],["codesandbox-sdk","m"],["daytona","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["nsjail","m"],["pyodide","m"],["runloop","m"],["vercel-sandbox","m"]],"ev":84,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent selected and fully integrated E2B as the remote sandbox platform, installing e2b-code-interpreter in pyproject.toml, implementing sandbox lifecycle management in app/analysis.py, and explicitly documenting the choice and rejections (Modal, Daytona, NsJail, gVisor) in README.md.","c":1,"e":[["file","pyproject.toml:18"],["file","app/analysis.py:17-19"],["file","README.md:33-47"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"e2b","secs":571,"k":"284f3ae8-15b8-44c7-b52f-bcedf5362ae6-r4","picks":[["e2b","p"],["cloudflare-workers","m"],["aws-lambda","m"],["daytona","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["pyodide","m"]],"ev":122,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent explicitly selected, integrated, and fully configured E2B Code Interpreter as the remote sandbox execution platform for untrusted Python code. It installed `e2b` and `e2b-code-interpreter`, updated the analysis execution pipeline in `app/analysis.py`, wrote a dedicated sandbox runner script, updated environment configuration, and added mock tests verifying credential isolation and teardown.","c":1,"e":[["file","pyproject.toml"],["file","app/analysis.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":5,"pick":"e2b","secs":584,"k":"284f3ae8-15b8-44c7-b52f-bcedf5362ae6-r5","picks":[["e2b","p"],["runloop","m"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["pyodide","m"]],"ev":106,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent selected and fully integrated E2B using the `e2b-code-interpreter` Python SDK to execute pandas analysis scripts in isolated microVMs without internet access. It updated dependencies, implementation code, tests, and documentation, and rejected alternatives like Modal, Daytona, and self-hosted Firecracker/gVisor fleets due to operational burden.","c":1,"e":[["file","pyproject.toml:18"],["file","app/analysis.py:17-50"],["file","README.md:26-44"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":6,"pick":"e2b","secs":491,"k":"284f3ae8-15b8-44c7-b52f-bcedf5362ae6-r6","picks":[["e2b","p"],["blaxel","m"],["daytona","m"],["firecracker","m"],["fly-machines","m"],["gvisor","m"],["modal","m"],["vercel-sandbox","m"]],"ev":92,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent selected, installed, and fully implemented E2B via the `e2b-code-interpreter` SDK in `app/analysis.py`, `app/config.py`, and `pyproject.toml`. Several alternatives including Modal, Daytona, Fly Machines, Blaxel, and self-hosted Firecracker were considered and explicitly rejected during deliberation due to operational burden or mismatch.","c":1,"e":[["file","pyproject.toml"],["file","app/analysis.py"],["file","app/config.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"e2b","secs":496,"k":"6b13381f-5d49-4054-9db1-dd13de126cef-r1","picks":[["e2b","p"],["cloudflare-workers","m"],["vercel-sandbox","a"],["daytona","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["modal","m"]],"ev":127,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent evaluated multiple remote sandbox platforms (E2B, Vercel Sandbox, Modal, Daytona) and selected E2B. It installed the `e2b` package, implemented sandbox lifecycle and command runner logic in `src/sandbox.js`, `src/runner.js`, and `src/template.js`, updated the tests with mocks, and configured `.env.example`.","c":1,"e":[["file","package.json:12-14"],["file","src/sandbox.js:1-103"],["file","src/runner.js:1-116"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"e2b","secs":429,"k":"6b13381f-5d49-4054-9db1-dd13de126cef-r2","picks":[["e2b","p"],["aws-codebuild","m"],["blaxel","m"],["cloudflare-workers","m"],["codesandbox-sdk","m"],["daytona","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["runloop","m"],["vercel-sandbox","m"]],"ev":76,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The run evaluated multiple remote sandbox platforms (E2B, Vercel Sandbox, Modal, and Daytona) against the workload requirements and chose E2B. It installed the `e2b` package and implemented full microVM sandbox lifecycle management, network allowlists, and execution orchestration in `src/sandbox.js` and `src/runner.js`.","c":1,"e":[["file","package.json:11-13"],["file","src/sandbox.js:1-82"],["file","src/runner.js:1-94"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"e2b","secs":563,"k":"6b13381f-5d49-4054-9db1-dd13de126cef-r3","picks":[["e2b","p"],["daytona","m"],["aws-codebuild","m"],["blaxel","m"],["cloudflare-workers","m"],["firecracker","m"],["modal","m"],["runloop","m"],["vercel-sandbox","m"]],"ev":71,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The run installed and configured the `e2b` package, replacing local host execution with E2B Firecracker sandboxes in `src/runner.js`. In the deliberation and final summary, the agent explicitly compared E2B against Vercel Sandbox and Modal, selecting E2B for its Node.js SDK compatibility, built-in base tooling, and straightforward single-API-key configuration.","c":1,"e":[["file","package.json:12-14"],["file","src/runner.js:1-119"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"e2b","secs":689,"k":"6b13381f-5d49-4054-9db1-dd13de126cef-r4","picks":[["e2b","p"],["runloop","m"],["daytona","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["vercel-sandbox","m"]],"ev":118,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent evaluated several managed sandbox solutions (E2B, Vercel Sandbox, Modal Sandboxes, and Daytona) against the workload requirements of running untrusted agent commands in a Node.js controller. It chose E2B and fully implemented it via the `e2b` package, setting up microVM execution, egress network restrictions, timeout enforcement, and guaranteed cleanup.","c":1,"e":[["file","package.json:11-13"],["file","src/sandbox.js:1-54"],["file","src/runner.js:1-65"],["file","README.md:5-24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":5,"pick":"e2b","secs":363,"k":"6b13381f-5d49-4054-9db1-dd13de126cef-r5","picks":[["e2b","p"],["modal","m"],["codesandbox-sdk","m"],["aws-lambda","m"],["blaxel","m"],["cloudflare-workers","m"],["daytona","m"],["firecracker","m"],["runloop","m"],["vercel-sandbox","m"]],"ev":104,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent compared E2B, Vercel Sandbox, and Daytona against the workload requirements in README.md and the trace. It selected E2B, installed the official `e2b` package, and implemented complete sandbox orchestration in `src/e2b.js` and `src/runner.js` with template building and test coverage.","c":1,"e":[["file","package.json:14"],["file","src/e2b.js:1-73"],["file","src/runner.js:1-74"],["file","README.md:19-35"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":6,"pick":"e2b","secs":497,"k":"6b13381f-5d49-4054-9db1-dd13de126cef-r6","picks":[["e2b","p"],["daytona","m"],["aws-codebuild","m"],["aws-lambda","m"],["blaxel","m"],["cloudflare-workers","m"],["codesandbox-sdk","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["runloop","m"],["vercel-sandbox","m"]],"ev":116,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent evaluated multiple sandbox solutions (E2B, Modal, Vercel Sandbox, Daytona) against project requirements (Node 20 runtime, Firecracker microVM isolation, egress filtering, guaranteed teardown) and implemented E2B using its official JavaScript SDK across runner.js, server.js, and template.js.","c":1,"e":[["file","package.json"],["file","src/runner.js"],["file","src/template.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"spring-ledger-eu","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"jmh","secs":1021,"k":"8cd9d380-7495-43bf-ac07-0a6077ba1123-r1","picks":[["jmh","p"],["gatling","m"],["hyperfine","m"],["k6","m"],["pytest-benchmark","m"]],"ev":101,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent explicitly chose OpenJDK JMH, integrated it via Maven dependencies, implemented a JMH benchmark against embedded PostgreSQL, added a JSON baseline regression evaluator, and configured a blocking CI job in GitHub Actions.","c":1,"e":[["file","pom.xml"],["file","src/test/java/eu/kontovar/ledger/perf/JournalPostingBenchmark.java"],["file",".github/workflows/build.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"spring-ledger-eu","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"jmh","secs":935,"k":"8cd9d380-7495-43bf-ac07-0a6077ba1123-r2","picks":[["jmh","p"],["bencher","m"],["gatling","m"],["k6","m"]],"ev":111,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The run explicitly selected JMH (Java Microbenchmark Harness) from OpenJDK to implement a blocking CI performance gate. JMH dependencies were added to `pom.xml`, benchmark test classes and a baseline comparison gate were created under `src/test/java/eu/kontovar/ledger/perf/`, and a `perf` job running JMH via Maven was added to `.github/workflows/build.yml`.","c":1,"e":[["file","pom.xml"],["file",".github/workflows/build.yml"],["file","src/test/java/eu/kontovar/ledger/perf/JournalPostingBenchmark.java"],["file","src/test/java/eu/kontovar/ledger/perf/JournalPostingPerfTest.java"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"spring-ledger-eu","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":1000,"k":"8cd9d380-7495-43bf-ac07-0a6077ba1123-r3","picks":[["diy","p","d"],["hyperfine","m"],["bencher","m"],["codspeed","m"],["gatling","m"],["jmh","m"],["k6","m"]],"ev":111,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent explicitly decided against external SaaS performance tooling (such as CodSpeed, Bencher, or k6 Cloud) due to compliance constraints in docs/compliance/SECURITY.md. Instead, it authored a custom DIY JUnit 5 latency gate backed by embedded PostgreSQL and a committed JSON baseline, wired into Maven profile `-Pperf` and a GitHub Actions CI job.","c":0.95,"e":[["file",".github/workflows/build.yml"],["file","perf/baselines/journal-post.json"],["file","src/test/java/eu/kontovar/ledger/perf/JournalPostLatencyGateTest.java"],["file","pom.xml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"spring-ledger-eu","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":4,"pick":"diy","secs":922,"k":"8cd9d380-7495-43bf-ac07-0a6077ba1123-r4","picks":[["diy","p","d"],["bencher","m"],["codspeed","m"],["gatling","m"],["github-actions-benchmark","m"],["hyperfine","m"],["jmh","m"],["k6","m"]],"ev":95,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent rejected SaaS tools (CodSpeed, Bencher) due to strict EU/security policies and rejected standalone microbenchmarking/load-testing tools (JMH, Gatling, k6) as ill-suited or heavy for an I/O-bound Spring Boot HTTP endpoint. It implemented a custom DIY p95 latency gate in Java running under Maven Failsafe and backed by an embedded PostgreSQL 15 instance.","c":0.95,"e":[["file",".github/workflows/build.yml:23-37"],["file","pom.xml:131-161"],["file","src/test/java/eu/kontovar/ledger/perf/JournalPostLatencyIT.java:1-283"],["file","src/test/java/eu/kontovar/ledger/perf/PerfGate.java:1-110"],["file","src/test/resources/perf/journal-post-baseline.json:1-8"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"rails-claims-ops","variant":"base","family":"obs-senior","pid":"OBS-6c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"glitchtip","secs":1061,"k":"3f07e148-4d40-49da-9a73-90836ffb81bf-r1","picks":[["glitchtip","p"],["airbrake","m"],["appsignal","m"],["errbit","m"],["grafana","m"],["honeybadger","m"],["openobserve","m"],["opentelemetry","m"],["sentry","m"],["signoz","m"]],"ev":166,"co":"cursor-grok46-all-sectors-20260901-observability","v":{"r":"The agent explicitly recommended and configured GlitchTip as the self-hosted error monitoring backend, instrumenting the Rails app via `sentry-ruby`/`sentry-rails` to send events to GlitchTip. It fully authored Docker Compose production setups and idempotent provisioning scripts for GlitchTip.","c":0.98,"e":[["file","glitchtip/compose.yml:1-64"],["file","glitchtip/compose.production.yml:1-38"],["file","config/initializers/error_monitoring.rb:1-17"],["trace","96"],["trace","166"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Error monitoring","theme":"Self-hosting, privacy or residency"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"rails-claims-ops","variant":"base","family":"obs-senior","pid":"OBS-6c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"glitchtip","secs":976,"k":"3f07e148-4d40-49da-9a73-90836ffb81bf-r2","picks":[["glitchtip","p"],["appsignal","m"],["errbit","m"],["grafana","m"],["honeybadger","m"],["opentelemetry","m"],["rollbar","m"],["sentry","m"]],"ev":174,"co":"cursor-grok46-all-sectors-20260901-observability","v":{"r":"The agent selected self-hosted GlitchTip as the primary error monitoring solution to keep exception data on-premise, using the Sentry Ruby/Rails SDK to transmit errors to GlitchTip. Complete provisioning scripts (`ops/glitchtip/bin/provision`), Docker Compose configuration (`ops/glitchtip/compose.yml`), a systemd unit (`ops/glitchtip/glitchtip.service`), and Rails initializers/services were created. Multiple SaaS and heavier self-hosted alternatives were explicitly evaluated and rejected.","c":1,"e":[["file","ops/glitchtip/compose.yml:1-61"],["file","ops/glitchtip/bin/provision:1-116"],["file","config/initializers/sentry.rb:1-11"],["file","README.md:29-43"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Error monitoring","theme":"Self-hosting, privacy or residency"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"rails-claims-ops","variant":"base","family":"obs-senior","pid":"OBS-6c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"glitchtip","secs":791,"k":"3f07e148-4d40-49da-9a73-90836ffb81bf-r3","picks":[["glitchtip","p"],["opentelemetry","m"],["grafana","m"],["openobserve","m"],["uptime-kuma","m"],["airbrake","m"],["appsignal","m"],["errbit","m"],["honeybadger","m"],["rollbar","m"],["sentry","m"]],"ev":123,"co":"cursor-grok46-all-sectors-20260901-observability","v":{"r":"The agent explicitly recommended and fully implemented self-hosted GlitchTip to monitor errors for the Rails application without shipping logs to third-party SaaS vendors. It added the Sentry Ruby/Rails SDKs (which GlitchTip uses protocol-wise), created Docker Compose and systemd release configurations, wrote API scripts to bootstrap the project and upsert webhook alerts, and documented the setup in README.md.","c":1,"e":[["file","deploy/glitchtip/docker-compose.yml:1-65"],["file","bin/glitchtip-release:1-125"],["file","lib/glitchtip.rb:1-56"],["file","config/initializers/sentry.rb:1-12"],["trace","28"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Error monitoring","theme":"Self-hosting, privacy or residency"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"rails-claims-ops","variant":"base","family":"obs-senior","pid":"OBS-6c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"glitchtip","secs":736,"k":"3f07e148-4d40-49da-9a73-90836ffb81bf-r4","picks":[["glitchtip","p"],["airbrake","m"],["appsignal","m"],["errbit","m"],["honeybadger","m"],["opentelemetry","m"],["prometheus","m"],["sentry","m"]],"ev":112,"co":"cursor-grok46-all-sectors-20260901-observability","v":{"r":"The run evaluated multiple error monitoring options (Sentry, Honeybadger, AppSignal, Errbit, GlitchTip), rejected SaaS options due to PII/data residency requirements, and chose self-hosted GlitchTip. It installed the sentry-ruby/sentry-rails SDKs, added a Docker Compose stack with systemd service for GlitchTip on the application host, and wrote an API script to provision production alerts.","c":1,"e":[["file","deploy/glitchtip/compose.yml"],["file","bin/release-glitchtip"],["file","lib/glitchtip/alert_provisioner.rb"],["file","config/initializers/sentry.rb"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Error monitoring","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"e2b","secs":569,"k":"7f4a0ea3-98e3-489a-8f18-e071ffe189d0-r1","picks":[["e2b","p"],["runloop","m"],["codesandbox-sdk","m"],["daytona","m"],["docker","m"],["firecracker","m"],["fly-machines","m"],["modal","m"],["vercel-sandbox","m"]],"ev":99,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent evaluated several remote sandbox options (E2B, Daytona, Vercel Sandbox, Modal, CodeSandbox SDK, Fly Machines) and integrated E2B as the production sandbox execution backend. It installed the `e2b` package, implemented `src/sandbox.js` using `Sandbox.create`, wired `src/runner.js` to execute tasks inside E2B microVMs, and updated configuration and tests accordingly.","c":1,"e":[["file","package.json"],["file","src/sandbox.js"],["file","src/runner.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"vercel-sandbox","secs":546,"k":"7f4a0ea3-98e3-489a-8f18-e071ffe189d0-r2","picks":[["vercel-sandbox","p"],["aws-lambda","m"],["cloudflare-workers","m"],["codesandbox-sdk","m"],["daytona","m"],["e2b","m"],["firecracker","m"],["modal","m"]],"ev":77,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent selected Vercel Sandbox as the managed sandbox platform, installed `@vercel/sandbox`, created `src/sandbox.js` to manage disposable microVM lifecycles with network policies, and integrated it into `src/runner.js` and `src/server.js`. Alternatives like E2B, Daytona, Modal, and Firecracker were explicitly weighed and rejected.","c":1,"e":[["file","package.json:13"],["file","src/sandbox.js:1-155"],["file","README.md:5-23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"e2b","secs":575,"k":"7f4a0ea3-98e3-489a-8f18-e071ffe189d0-r3","picks":[["e2b","p"],["firecracker","m"],["aws-codebuild","m"],["cloudflare-workers","m"],["codesandbox-sdk","m"],["daytona","m"],["modal","m"],["vercel-sandbox","m"]],"ev":71,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent selected and fully integrated E2B as the remote sandbox platform, installing the `e2b` package, implementing sandbox creation and teardown in `src/sandbox.js`, and routing task execution through E2B microVMs in `src/runner.js`. Alternatives like Daytona, Modal, and Vercel Sandbox were explicitly evaluated and rejected in the README and trace.","c":1,"e":[["file","package.json:12"],["file","src/sandbox.js:2"],["file","README.md:5-18"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"e2b","secs":460,"k":"7f4a0ea3-98e3-489a-8f18-e071ffe189d0-r4","picks":[["e2b","p"],["runloop","m"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["deno","m"],["docker","m"],["firecracker","m"],["fly-machines","m"],["gvisor","m"],["modal","m"],["vercel-sandbox","m"]],"ev":75,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent evaluated multiple managed sandbox providers and explicitly chose E2B for its Node.js SDK, Firecracker microVM isolation, disposable lifecycle, and domain egress filtering. The agent installed `e2b` and implemented the complete sandbox lifecycle in `src/sandbox.js` and `src/runner.js`.","c":1,"e":[["file","package.json:12-14"],["file","src/sandbox.js:1-146"],["file","src/runner.js:1-78"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":5,"pick":"vercel-sandbox","secs":391,"k":"7f4a0ea3-98e3-489a-8f18-e071ffe189d0-r5","picks":[["vercel-sandbox","p"],["aws-codebuild","m"],["blaxel","m"],["cloudflare-workers","m"],["daytona","m"],["e2b","m"],["firecracker","m"],["fly-machines","m"],["modal","m"],["runloop","m"]],"ev":63,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent explicitly recommended Vercel Sandbox (@vercel/sandbox), installed the package, updated src/runner.js to run commands and collect patches via Vercel Sandbox microVMs, created src/network-policy.js for egress controls, and updated tests and documentation accordingly.","c":1,"e":[["file","package.json"],["file","src/runner.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":6,"pick":"e2b","secs":490,"k":"7f4a0ea3-98e3-489a-8f18-e071ffe189d0-r6","picks":[["e2b","p"],["aws-codebuild","m"],["blaxel","m"],["daytona","m"],["docker","m"],["firecracker","m"],["modal","m"],["runloop","m"],["vercel-sandbox","m"]],"ev":95,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent explicitly selected E2B, installed the `e2b` npm dependency, implemented `src/sandbox.js` and `src/runner.js` around the E2B SDK, and tested the integration against mock sandbox interfaces. Alternatives such as Daytona, Vercel Sandbox, and Modal were weighed and explicitly rejected.","c":1,"e":[["file","package.json:11"],["file","src/sandbox.js:1-76"],["file","src/runner.js:1-177"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"e2b","secs":403,"k":"28e0a2ec-bed4-44f0-bfc9-cbcbc7607773-r1","picks":[["e2b","p"],["modal","m"],["codesandbox-sdk","m"],["daytona","m"],["cloudflare-workers","m"],["gvisor","m"],["firecracker","m"],["aws-codebuild","m"],["aws-lambda","m"],["docker","m"],["vercel-sandbox","m"]],"ev":105,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent selected E2B as the primary sandbox platform, installed the `e2b` package, and replaced the local runner in `src/runner.ts` with real `Sandbox.create`, `files.write`, `commands.run`, network filtering, and `sandbox.kill()` lifecycle management. Other candidate platforms were considered or surveyed in reasoning and dismissed.","c":1,"e":[["file","package.json:18-20"],["file","src/runner.ts:1-125"],["file","README.md:5-11"],["file",".env.example:3-4"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"e2b","secs":452,"k":"28e0a2ec-bed4-44f0-bfc9-cbcbc7607773-r2","picks":[["e2b","p"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["docker","m"],["firecracker","m"],["modal","m"],["vercel-sandbox","m"]],"ev":85,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent selected E2B, installed the official npm package (`e2b`), implemented sandbox creation, file writes, isolated command execution with restricted egress to npm registry domains, and sandbox cleanup in `src/sandbox.ts` and `src/runner.ts`, while rejecting Daytona, Modal, Docker, self-hosted Firecracker, AWS Lambda, Vercel Sandbox, and Cloudflare Workers.","c":1,"e":[["file","package.json"],["file","src/sandbox.ts"],["file","src/runner.ts"],["file","README.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"e2b","secs":1741,"k":"28e0a2ec-bed4-44f0-bfc9-cbcbc7607773-r3","picks":[["e2b","p"],["codesandbox-sdk","m"],["cloudflare-workers","m"],["aws-lambda","m"],["blaxel","m"],["daytona","m"],["docker","m"],["firecracker","m"],["fly-machines","m"],["gvisor","m"],["modal","m"],["runloop","m"],["vercel-sandbox","m"]],"ev":80,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent selected E2B, installed the official e2b npm package, implemented disposable microVM sandboxing in `src/sandbox.ts` and `src/runner.ts`, and updated the test suite and documentation.","c":1,"e":[["file","package.json:17-19"],["file","src/sandbox.ts:1-74"],["file","src/runner.ts:1-70"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"e2b","secs":519,"k":"28e0a2ec-bed4-44f0-bfc9-cbcbc7607773-r4","picks":[["e2b","p"],["aws-lambda","m"],["blaxel","m"],["cloudflare-workers","m"],["daytona","m"],["firecracker","m"],["modal","m"],["runloop","m"],["vercel-sandbox","m"]],"ev":92,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent evaluated several sandbox solutions (including E2B, Vercel Sandbox, Modal, Daytona, Fly Machines, and self-hosted options like Docker/Firecracker/gVisor). It chose and fully integrated the third-party E2B SDK into package.json, src/runner.ts, tests, .env.example, and README.md.","c":1,"e":[["file","package.json:18-19"],["file","src/runner.ts:1-50"],["file",".env.example:3-4"],["file","README.md:7-22"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":5,"pick":"e2b","secs":348,"k":"28e0a2ec-bed4-44f0-bfc9-cbcbc7607773-r5","picks":[["e2b","p"],["blaxel","m"],["aws-lambda","m"],["cloudflare-workers","m"],["codesandbox-sdk","m"],["daytona","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["vercel-sandbox","m"]],"ev":82,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent explicitly recommended, installed, and integrated E2B as the third-party sandbox platform for isolated code execution. The diff installs the `e2b` package, configures network isolation, uploads project files, runs dependency installation and commands, and ensures sandboxes are killed on cleanup. Various alternatives (Docker, Firecracker, gVisor, Modal, Daytona, Vercel Sandbox, etc.) were weighed in reasoning and rejected.","c":1,"e":[["file","package.json:18-20"],["file","src/runner.ts:25-50"],["file",".env.example:3"],["file","README.md:7-19"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":6,"pick":"e2b","secs":343,"k":"28e0a2ec-bed4-44f0-bfc9-cbcbc7607773-r6","picks":[["e2b","p"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["vercel-sandbox","m"]],"ev":74,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The run installed the official 'e2b' SDK (2.46.1) and refactored 'src/runner.ts' to execute builds within disposable E2B microVMs, implementing network egress controls for npm registries, timeout handling, and cleanup via finally blocks.","c":1,"e":[["file","package.json"],["file","src/runner.ts"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"e2b","secs":398,"k":"9956ada7-c76c-40f4-beaf-64b07416a90c-r1","picks":[["e2b","p"],["daytona","m"],["deno","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["vercel-sandbox","m"]],"ev":83,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent compared E2B, Vercel Sandbox, and Modal against the repo's specific Node/TypeScript workload. It selected and implemented E2B via the official SDK (`e2b` package) in `src/runner.ts`, wiring sandbox creation, file writes, scoped egress networking, execution, and cleanup.","c":1,"e":[["file","package.json"],["file","src/runner.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"e2b","secs":430,"k":"9956ada7-c76c-40f4-beaf-64b07416a90c-r2","picks":[["e2b","p"],["aws-codebuild","m"],["fly-machines","m"],["runloop","m"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["docker","m"],["firecracker","m"],["modal","m"],["vercel-sandbox","m"]],"ev":92,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent conducted a detailed comparison between E2B, Vercel Sandbox, and Daytona, choosing E2B. It installed the `e2b` npm package and implemented remote execution in `src/sandbox.ts` and `src/runner.ts` using `Sandbox.create` with hard limits, network isolation, and guaranteed cleanup.","c":1,"e":[["file","package.json"],["file","src/sandbox.ts"],["file","src/runner.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"e2b","secs":1515,"k":"9956ada7-c76c-40f4-beaf-64b07416a90c-r3","picks":[["e2b","p"],["fly-machines","m"],["cloudflare-workers","m"],["daytona","m"],["docker","m"],["firecracker","m"],["modal","m"],["vercel-sandbox","m"]],"ev":85,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent evaluated several managed sandbox solutions (E2B, Vercel Sandbox, Cloudflare Sandbox SDK, Daytona, Modal) and selected E2B. It installed the `e2b` package, implemented the sandbox creation and execution logic in `src/sandbox.ts` and `src/runner.ts`, and updated the server and test suite to use E2B microVMs.","c":1,"e":[["file","package.json"],["file","src/sandbox.ts"],["file","src/runner.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"e2b","secs":567,"k":"9956ada7-c76c-40f4-beaf-64b07416a90c-r4","picks":[["e2b","p"],["codesandbox-sdk","m"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["vercel-sandbox","m"]],"ev":85,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent evaluated multiple remote sandbox platforms (E2B, Vercel Sandbox, Daytona, Modal) against the project's untrusted Node.js execution workload. It selected E2B, installed the official `e2b` package, rewrote `src/runner.ts` to create disposable Firecracker sandboxes with scoped network egress and kill-on-timeout lifecycle rules, and added comprehensive test coverage.","c":1,"e":[["file","package.json:17-19"],["file","src/runner.ts:1-157"],["file","README.md:5-16"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":5,"pick":"e2b","secs":386,"k":"9956ada7-c76c-40f4-beaf-64b07416a90c-r5","picks":[["e2b","p"],["codesandbox-sdk","m"],["daytona","m"],["blaxel","m"],["runloop","m"],["aws-codebuild","m"],["cloudflare-workers","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["vercel-sandbox","m"]],"ev":88,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent evaluated multiple sandbox solutions (comparing E2B, Vercel Sandbox, and Modal Sandboxes in detail), explicitly selected E2B, and completely implemented the remote execution runner using the `e2b` package in `src/runner.ts`.","c":1,"e":[["file","package.json:18"],["file","src/runner.ts:2"],["file","README.md:9-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":6,"pick":"e2b","secs":366,"k":"9956ada7-c76c-40f4-beaf-64b07416a90c-r6","picks":[["e2b","p"],["daytona","m"],["codesandbox-sdk","m"],["aws-lambda","m"],["cloudflare-workers","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["vercel-sandbox","m"]],"ev":99,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The run evaluated multiple remote sandbox platforms (comparing E2B, Vercel Sandbox, and Modal in detail) and committed fully to E2B by installing the official `e2b` package, creating `src/sandbox.ts` and `src/template.ts`, updating `src/runner.ts` to execute builds inside E2B Firecracker microVMs, and updating tests and documentation accordingly.","c":1,"e":[["file","package.json:19"],["file","src/sandbox.ts:1-63"],["file","src/runner.ts:2-65"],["file","README.md:7-40"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"spring-ledger-eu","variant":"base","family":"perf-ent-senior-spring-ledger","pid":"PERF-4a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":461,"k":"60c6ea10-2494-4e6c-81fa-d6f483948cf6-r1","picks":[["diy","p","d"],["bencher","m"],["codspeed","m"],["jmh","m"]],"ev":70,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent explicitly decided against adopting third-party SaaS performance tracking tools (such as CodSpeed and Bencher) due to repository compliance requirements documented in SECURITY.md. It also considered and passed on JMH. Instead, it authored an in-repository Java performance gate (`PerformanceGate`), an in-memory hot-path benchmark, and a corresponding GitHub Actions CI job in `build.yml`.","c":0.95,"e":[["file",".github/workflows/build.yml:23-35"],["file","src/test/java/eu/kontovar/ledger/perf/PerformanceGate.java"],["file","src/test/java/eu/kontovar/ledger/perf/PerformanceGateTest.java"],["file","src/test/java/eu/kontovar/ledger/perf/PostingHotPathBenchmark.java"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"spring-ledger-eu","variant":"base","family":"perf-ent-senior-spring-ledger","pid":"PERF-4a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":412,"k":"60c6ea10-2494-4e6c-81fa-d6f483948cf6-r2","picks":[["diy","p","d"],["jmh","m"]],"ev":70,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent implemented a custom DIY performance gate in Java (PostingPerformanceGateTest) wired into Maven Surefire profiles and a blocking GitHub Actions job, rejecting external tooling like JMH to keep operations minimal.","c":0.95,"e":[["file","src/test/java/eu/kontovar/ledger/perf/PostingPerformanceGateTest.java"],["file","pom.xml"],["file","scripts/prove-perf-gate.sh"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"spring-ledger-eu","variant":"base","family":"perf-ent-senior-spring-ledger","pid":"PERF-4a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":958,"k":"60c6ea10-2494-4e6c-81fa-d6f483948cf6-r3","picks":[["diy","p","d"],["bencher","m"],["gatling","m"],["github-actions-benchmark","m"],["jmh","m"],["k6","m"]],"ev":114,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent explicitly evaluated third-party and standard benchmark tools (Bencher, JMH, Gatling, k6, github-action-benchmark) and rejected them due to repo compliance policies against external SaaS dependencies and the overhead of heavyweight frameworks. Instead, it built a custom in-repo benchmark, report serializer, and A/B merge-base latency comparator directly in Java and wired it as a blocking gate into the Maven build and GitHub Actions workflow.","c":1,"e":[["file",".github/workflows/build.yml:25-71"],["file","pom.xml:122-167"],["file","src/test/java/eu/kontovar/ledger/perf/PerformanceGate.java:1-178"],["file","src/test/java/eu/kontovar/ledger/perf/PerformanceCiCompareTest.java:1-39"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"spring-ledger-eu","variant":"base","family":"perf-ent-senior-spring-ledger","pid":"PERF-4a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":4,"pick":"diy","secs":804,"k":"60c6ea10-2494-4e6c-81fa-d6f483948cf6-r4","picks":[["diy","p","d"],["bencher","m"],["codspeed","m"],["gatling","m"],["jmh","m"],["k6","m"]],"ev":86,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent evaluated third-party performance CI and benchmarking tools (CodSpeed, Bencher, JMH, Gatling, k6) but rejected SaaS tools due to compliance/security constraints in the repository and dismissed full load testing suites as overkill. Instead, it authored a custom DIY PerformanceGate comparator in Java, created throughput and regression JUnit tests, and integrated a dedicated blocking check step directly into the existing GitHub Actions workflow.","c":1,"e":[["file","src/test/java/eu/kontovar/ledger/perf/PerformanceGate.java:1-58"],["file","src/test/java/eu/kontovar/ledger/posting/PostingThroughputTest.java:1-134"],["file",".github/workflows/build.yml:23-24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"spring-ledger-eu","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"jmh","secs":1110,"k":"8c0d88d5-0513-4b82-862e-78d167fd5020-r1","picks":[["jmh","p"],["bencher","m"],["codspeed","m"],["gatling","m"],["github-actions-benchmark","m"],["hyperfine","m"],["k6","m"],["lighthouse-ci","m"],["perfgate","m"]],"ev":118,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent explicitly selected OpenJDK JMH as the third-party benchmarking solution, added JMH dependencies to `pom.xml`, wrote the benchmark suite (`PostingServiceBenchmark.java`), runner, and comparison gate (`PerfGate.java`), and wired it as a required PR check into `.github/workflows/build.yml`.","c":1,"e":[["file","pom.xml"],["file",".github/workflows/build.yml"],["file","src/test/java/eu/kontovar/ledger/bench/PostingServiceBenchmark.java"],["trace","82"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"spring-ledger-eu","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"jmh","secs":718,"k":"8c0d88d5-0513-4b82-862e-78d167fd5020-r2","picks":[["jmh","p"],["bencher","m"],["codspeed","m"],["gatling","m"],["github-actions-benchmark","m"],["hyperfine","m"],["k6","m"],["perfgate","m"]],"ev":87,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent selected OpenJDK JMH 1.37 to benchmark the Java posting hot path. It configured Maven dependencies, wrote JMH benchmark and test classes comparing throughput to perf/baseline.json, added a dedicated Maven profile, and wired the performance job into GitHub Actions. Alternatives (CodSpeed, Bencher, Gatling, k6, hyperfine, and github-action-benchmark) were explicitly evaluated and rejected.","c":1,"e":[["file","pom.xml"],["file","src/test/java/eu/kontovar/ledger/perf/PostingHotPathBenchmark.java"],["file","src/test/java/eu/kontovar/ledger/perf/PostingPerfGateTest.java"],["file",".github/workflows/build.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"spring-ledger-eu","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"jmh","secs":877,"k":"8c0d88d5-0513-4b82-862e-78d167fd5020-r3","picks":[["jmh","p"],["bencher","m"],["codspeed","m"],["gatling","m"],["github-actions-benchmark","m"],["hyperfine","m"],["k6","m"]],"ev":109,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent selected OpenJDK JMH as the performance benchmarking tool for this Java 17/Maven service. It added JMH dependencies to pom.xml, authored PostingThroughputBenchmark and a BaselineComparator, committed benchmarks/baseline.json, and created the posting-throughput GitHub Actions workflow gate.","c":1,"e":[["file","pom.xml"],["file",".github/workflows/build.yml"],["file","benchmarks/baseline.json"],["file","src/test/java/eu/kontovar/ledger/perf/PostingThroughputBenchmark.java"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"spring-ledger-eu","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":4,"pick":"jmh","secs":745,"k":"8c0d88d5-0513-4b82-862e-78d167fd5020-r4","picks":[["jmh","p"],["bencher","m"],["codspeed","m"],["gatling","m"],["hyperfine","m"],["k6","m"],["perfgate","m"]],"ev":84,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The run chose OpenJDK JMH as the primary CI performance tool to benchmark the ledger's hot path, wiring it into Maven and GitHub Actions workflows with a committed JSON baseline and automated regression gate. SaaS options like CodSpeed and Bencher were rejected due to EU compliance constraints, while HTTP/load testing options like Gatling and k6 were dismissed as too heavy.","c":1,"e":[["file","pom.xml:23"],["file","pom.xml:117-133"],["file","src/jmh/java/eu/kontovar/ledger/perf/PostingThroughputBench.java:1-148"],["file",".github/workflows/build.yml:23-38"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"e2b","secs":394,"k":"63f712fa-c6bd-4e6d-8d74-90e4a2b07281-r1","picks":[["e2b","p"],["blaxel","m"],["runloop","m"],["firecracker","m"],["aws-codebuild","m"],["cloudflare-workers","m"],["daytona","m"],["docker","m"],["modal","m"],["vercel-sandbox","m"]],"ev":71,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent explicitly selected and implemented E2B using its official Node SDK (`e2b`), updating package.json, src/runner.js, src/server.js, .env.example, README.md, and test/runner.test.js. It evaluated and rejected alternative options such as Daytona, Modal, Vercel Sandbox, Fly Machines, and Docker in trace reasoning.","c":1,"e":[["file","package.json"],["file","src/runner.js"],["file","src/server.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"e2b","secs":493,"k":"63f712fa-c6bd-4e6d-8d74-90e4a2b07281-r2","picks":[["e2b","p"],["blaxel","m"],["daytona","m"],["docker","m"],["firecracker","m"],["fly-machines","m"],["modal","m"],["vercel-sandbox","m"]],"ev":71,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent selected and fully integrated E2B via its official Node SDK (`e2b`) to run isolated Firecracker microVM sandboxes for coding-agent tasks, while comparing and rejecting alternative sandbox providers like Vercel Sandbox, Modal, Daytona, and Fly Machines.","c":1,"e":[["file","package.json:12-14"],["file","src/sandbox.js:1-92"],["file","src/runner.js:1-49"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"e2b","secs":465,"k":"63f712fa-c6bd-4e6d-8d74-90e4a2b07281-r3","picks":[["e2b","p"],["aws-codebuild","m"],["aws-lambda","m"],["blaxel","m"],["cloudflare-workers","m"],["codesandbox-sdk","m"],["daytona","m"],["firecracker","m"],["fly-machines","m"],["modal","m"],["runloop","m"],["vercel-sandbox","m"]],"ev":65,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent selected E2B as the third-party sandbox platform to isolate coding-agent tasks off the controller host. It installed the `e2b` npm package, re-implemented `src/runner.js` to create disposable sandboxes with bounded timeouts and network policies, and updated `.env.example`, `README.md`, and test suites accordingly.","c":1,"e":[["file","package.json:11-13"],["file","src/runner.js:1-96"],["file",".env.example:4-5"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"e2b","secs":378,"k":"63f712fa-c6bd-4e6d-8d74-90e4a2b07281-r4","picks":[["e2b","p"],["aws-fargate","m"],["daytona","m"],["docker","m"],["firecracker","m"],["fly-machines","m"],["modal","m"],["vercel-sandbox","m"]],"ev":64,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent evaluated multiple sandbox solutions and selected E2B, installing the official `e2b` package and updating the codebase to execute agent tasks within remote Firecracker microVM sandboxes.","c":1,"e":[["file","package.json:12-14"],["file","src/sandbox.js:1-58"],["file","src/runner.js:1-76"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":5,"pick":"e2b","secs":383,"k":"63f712fa-c6bd-4e6d-8d74-90e4a2b07281-r5","picks":[["e2b","p"],["firecracker","m"],["runloop","m"],["blaxel","m"],["aws-codebuild","m"],["daytona","m"],["modal","m"],["vercel-sandbox","m"]],"ev":84,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent explicitly chose E2B, installed the official `e2b` npm package, configured `src/runner.js` to use `Sandbox.create` and `commands.run`, updated documentation and tests, and clearly recommended E2B as the managed sandbox service.","c":0.99,"e":[["file","package.json"],["file","src/runner.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":6,"pick":"e2b","secs":355,"k":"63f712fa-c6bd-4e6d-8d74-90e4a2b07281-r6","picks":[["e2b","p"],["firecracker","m"],["aws-codebuild","m"],["cloudflare-workers","m"],["daytona","m"],["modal","m"],["vercel-sandbox","m"]],"ev":74,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent explicitly selected E2B, installed the official `e2b` npm package, created `src/sandbox.js` to manage sandboxes and egress allowlists via the SDK, updated `src/runner.js` and `src/server.js`, and added comprehensive tests.","c":1,"e":[["file","package.json"],["file","src/sandbox.js"],["file","src/runner.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"e2b","secs":523,"k":"d52c2982-1a92-4fd1-9989-2e4df56bc0ef-r1","picks":[["e2b","p"],["cloudflare-workers","m"],["daytona","m"],["deno","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["vercel-sandbox","m"]],"ev":104,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent evaluated multiple managed sandbox providers (E2B, Vercel Sandbox, Daytona, and Modal) against the workload requirements and selected E2B. It fully installed the `e2b` package, created `src/sandbox.ts` to manage remote microVM lifecycle and command execution, updated configuration and documentation, and added comprehensive tests.","c":0.95,"e":[["file","package.json:19"],["file","src/sandbox.ts:1-105"],["file","README.md:9-17"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"e2b","secs":516,"k":"d52c2982-1a92-4fd1-9989-2e4df56bc0ef-r2","picks":[["e2b","p"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["deno","m"],["firecracker","m"],["gvisor","m"],["kata-containers","m"],["modal","m"],["vercel-sandbox","m"]],"ev":113,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The run evaluated E2B, Vercel Sandbox, and Daytona in detail, selected E2B, installed @e2b/code-interpreter, and refactored the application's transform runner to execute code inside E2B Cloud microVM sandboxes.","c":1,"e":[["file","package.json:17"],["file","src/transform.ts:48-58"],["file","README.md:12-36"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"e2b","secs":475,"k":"d52c2982-1a92-4fd1-9989-2e4df56bc0ef-r3","picks":[["e2b","p"],["codesandbox-sdk","m"],["firecracker","m"],["gvisor","m"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["docker","m"],["modal","m"],["vercel-sandbox","m"]],"ev":105,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent clearly selected and implemented E2B using its official JavaScript SDK ('e2b' npm package) to execute untrusted JavaScript transforms in isolated microVMs outside the host Express process. It explicitly compared E2B against Vercel Sandbox, Daytona, and Modal in both the trace and README, detailing why E2B was chosen and why the alternatives were disqualified.","c":1,"e":[["file","package.json:17"],["file","src/transform.ts:1-96"],["file","README.md:10-43"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"e2b","secs":356,"k":"d52c2982-1a92-4fd1-9989-2e4df56bc0ef-r4","picks":[["e2b","p"],["runloop","m"],["blaxel","m"],["deno","m"],["aws-codebuild","m"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["firecracker","m"],["modal","m"],["vercel-sandbox","m"]],"ev":112,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent replaced the in-process execution in `src/transform.ts` by adopting E2B as a managed third-party sandbox platform. It installed the `e2b` npm package, configured microVM execution with strict network isolation, timeouts, and cleanup, and compared E2B against Vercel Sandbox, Daytona, and several other sandbox platforms in the README and trace reasoning.","c":1,"e":[["file","package.json:17"],["file","src/transform.ts:1-115"],["file","README.md:12-48"],["file",".env.example:5-9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":5,"pick":"e2b","secs":403,"k":"d52c2982-1a92-4fd1-9989-2e4df56bc0ef-r5","picks":[["e2b","p"],["aws-lambda","m"],["blaxel","m"],["cloudflare-workers","m"],["codesandbox-sdk","m"],["daytona","m"],["deno","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["nsjail","m"],["pyodide","m"],["runloop","m"],["vercel-sandbox","m"]],"ev":104,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent evaluated three managed sandbox platforms (E2B, Vercel Sandbox, and Modal) against the workload requirements. It selected and implemented E2B using the official `@e2b/code-interpreter` SDK, replacing the insecure in-process `new Function` execution path with an isolated Firecracker microVM executor configured with strict execution/lifecycle timeouts, deny-all egress, and explicit teardown.","c":1,"e":[["file","package.json"],["file","src/sandbox.ts"],["file","src/transform.ts"],["trace","96"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":6,"pick":"e2b","secs":401,"k":"d52c2982-1a92-4fd1-9989-2e4df56bc0ef-r6","picks":[["e2b","p"],["aws-fargate","m"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["deno","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["runloop","m"],["vercel-sandbox","m"]],"ev":98,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The run evaluated multiple remote code sandbox platforms (E2B, Vercel Sandbox, Modal, Daytona, Cloudflare Sandbox SDK, Runloop) and chose E2B as the primary solution. It installed the official `e2b` package, integrated `Sandbox.create` with microVM isolation and disabled internet access in `src/transform.ts`, updated `.env.example` and `README.md`, and added unit tests mocking the E2B SDK.","c":0.99,"e":[["file","package.json"],["file","src/transform.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"e2b","secs":524,"k":"064ec2bb-2eee-432c-ac01-def7d71dabcb-r1","picks":[["e2b","p"],["deno","m"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["vercel-sandbox","m"]],"ev":108,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The run selected and implemented E2B via its official Node SDK (`e2b` v2.46.1), creating isolated microVM sandboxes for executing untrusted JavaScript code with timeouts, network isolation, input file uploading, artifact retrieval, and proper cleanup via `sandbox.kill()`.","c":1,"e":[["file","package.json:19"],["file","src/sandbox.ts:1-135"],["file","README.md:7-38"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"e2b","secs":548,"k":"064ec2bb-2eee-432c-ac01-def7d71dabcb-r2","picks":[["e2b","p"],["daytona","m"],["runloop","m"],["blaxel","m"],["aws-fargate","m"],["aws-lambda","m"],["cloudflare-workers","m"],["codesandbox-sdk","m"],["deno","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["vercel-sandbox","m"]],"ev":110,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent explicitly recommended, installed, and configured E2B (`e2b` npm package) to execute untrusted generated JavaScript in isolated Firecracker microVMs. Full SDK integration was implemented across `src/sandbox.ts`, `src/transform.ts`, `src/app.ts`, `.env.example`, and `README.md`, while alternative sandbox providers were weighed and explicitly rejected.","c":1,"e":[["file","package.json"],["file","src/sandbox.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"e2b","secs":533,"k":"064ec2bb-2eee-432c-ac01-def7d71dabcb-r3","picks":[["e2b","p"],["blaxel","m"],["runloop","m"],["firecracker","m"],["aws-lambda","m"],["cloudflare-workers","m"],["codesandbox-sdk","m"],["daytona","m"],["deno","m"],["docker","m"],["modal","m"],["vercel-sandbox","m"]],"ev":80,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent explicitly chose E2B and installed `@e2b/code-interpreter` to isolate untrusted JavaScript code execution into managed Firecracker microVMs. It replaced in-process evaluation with a full SDK execution path (`src/sandbox.ts`), configured timeouts and environment isolation, updated documentation, and evaluated/rejected alternatives like Daytona, Vercel Sandbox, Modal, and Docker.","c":1,"e":[["file","package.json"],["file","src/sandbox.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"e2b","secs":390,"k":"064ec2bb-2eee-432c-ac01-def7d71dabcb-r4","picks":[["e2b","p"],["aws-fargate","m"],["aws-lambda","m"],["cloudflare-workers","m"],["codesandbox-sdk","m"],["daytona","m"],["deno","m"],["firecracker","m"],["fly-machines","m"],["gvisor","m"],["modal","m"],["runloop","m"],["vercel-sandbox","m"]],"ev":93,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The run installed the official 'e2b' npm package, created `src/sandbox.ts` implementing `Sandbox.create` with microVM isolation and cleanup via `sandbox.kill()`, updated `.env.example` and `README.md` with E2B configuration, and evaluated/rejected other sandbox platforms (Vercel Sandbox, Modal, CodeSandbox, Daytona, Fly Machines, Lambda, Fargate, gVisor).","c":1,"e":[["file","package.json"],["file","src/sandbox.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":5,"pick":"e2b","secs":474,"k":"064ec2bb-2eee-432c-ac01-def7d71dabcb-r5","picks":[["e2b","p"],["daytona","m"],["aws-lambda","m"],["cloudflare-workers","m"],["deno","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["vercel-sandbox","m"]],"ev":102,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent explicitly chose E2B and integrated `@e2b/code-interpreter` into package.json, implemented the full remote execution and cleanup lifecycle in `src/sandbox.ts`, updated `.env.example`, `src/config.ts`, `src/transform.ts`, `src/app.ts`, `README.md`, and mocked it in the test suite. Other options like Modal, Lambda, Firecracker, Cloudflare Workers, Vercel Sandbox, and gVisor were evaluated and rejected.","c":1,"e":[["file","package.json"],["file","src/sandbox.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":6,"pick":"e2b","secs":362,"k":"064ec2bb-2eee-432c-ac01-def7d71dabcb-r6","picks":[["e2b","p"],["firecracker","m"],["aws-lambda","m"],["cloudflare-workers","m"],["codesandbox-sdk","m"],["daytona","m"],["deno","m"],["modal","m"],["vercel-sandbox","m"]],"ev":85,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent explicitly selected E2B as the managed sandbox service, installed the 'e2b' npm package, created sandbox adapters in 'src/sandbox.ts' with complete error handling, timeouts, resource caps, and cleanup logic, and updated configuration and README files.","c":1,"e":[["file","package.json"],["file","src/sandbox.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"e2b","secs":616,"k":"cb868082-4803-46c0-8616-9ed616d48414-r1","picks":[["e2b","p"],["aws-lambda","m"],["cloudflare-workers","m"],["codesandbox-sdk","m"],["daytona","m"],["deno","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["vercel-sandbox","m"]],"ev":114,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The run evaluated remote sandbox solutions and committed directly to E2B by installing the `e2b` package, implementing an isolated sandbox runner in `src/sandbox.ts`, updating `src/transform.ts` and `src/app.ts` to execute transforms remotely in disposable microVMs, and documenting the architecture and rejected alternatives (Vercel Sandbox, Daytona, self-hosted Firecracker/gVisor) in README.md.","c":1,"e":[["file","package.json:19"],["file","src/sandbox.ts:1-107"],["file","src/transform.ts:1-70"],["file","README.md:8-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"e2b","secs":552,"k":"cb868082-4803-46c0-8616-9ed616d48414-r2","picks":[["e2b","p"],["codesandbox-sdk","m"],["blaxel","m"],["runloop","m"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["deno","m"],["docker","m"],["firecracker","m"],["fly-machines","m"],["gvisor","m"],["kata-containers","m"],["modal","m"],["nsjail","m"],["vercel-sandbox","m"]],"ev":88,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent explicitly selected E2B, installed the `e2b` npm package, implemented `executeGeneratedTransform` in `src/sandbox.ts` to execute untrusted code in an isolated Firecracker microVM, and updated the README and configuration. It explicitly compared and rejected Vercel Sandbox, Modal, Fly Machines, gVisor, in-process isolates, and self-hosted runtimes.","c":1,"e":[["file","package.json:19"],["file","src/sandbox.ts:1-116"],["file","README.md:10-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"e2b","secs":571,"k":"cb868082-4803-46c0-8616-9ed616d48414-r3","picks":[["e2b","p"],["runloop","m"],["aws-codebuild","m"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["deno","m"],["docker","m"],["firecracker","m"],["modal","m"],["vercel-sandbox","m"]],"ev":102,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent explicitly chose, installed, and configured the E2B SDK (`e2b`) to run untrusted JavaScript transformations in disposable microVMs off the API host, writing complete implementation and test coverage. Several alternatives were evaluated and explicitly rejected.","c":1,"e":[["file","package.json"],["file","src/transform.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"e2b","secs":549,"k":"cb868082-4803-46c0-8616-9ed616d48414-r4","picks":[["e2b","p"],["aws-lambda","m"],["daytona","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["vercel-sandbox","m"]],"ev":100,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent explicitly selected and installed E2B via its official npm SDK ('e2b'), wired up disposable microVM creation with network isolation in 'src/sandbox.ts', and refactored 'src/transform.ts' to execute generated transforms within the remote sandbox. Alternative managed sandbox platforms (Vercel Sandbox, Modal, Daytona, Cloudflare Sandbox SDK, CodeSandbox SDK) and self-hosted options (gVisor) were weighed and explicitly rejected.","c":1,"e":[["file","package.json:19"],["file","src/sandbox.ts:1-13"],["file","src/transform.ts:34-63"],["file","README.md:12-16"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":5,"pick":"e2b","secs":446,"k":"cb868082-4803-46c0-8616-9ed616d48414-r5","picks":[["e2b","p"],["aws-fargate","m"],["aws-lambda","m"],["blaxel","m"],["cloudflare-workers","m"],["codesandbox-sdk","m"],["daytona","m"],["deno","m"],["docker","m"],["firecracker","m"],["fly-machines","m"],["modal","m"],["runloop","m"],["vercel-sandbox","m"]],"ev":97,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent explicitly chose E2B to provide managed microVM sandbox isolation for untrusted JavaScript execution. The dependency 'e2b' was installed, configured via environment variables, and implemented in src/sandbox/e2b.ts with full lifecycle, resource limiting, and network isolation controls.","c":1,"e":[["file","package.json:19"],["file","src/sandbox/e2b.ts:1-181"],["file","README.md:1-26"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":6,"pick":"e2b","secs":384,"k":"cb868082-4803-46c0-8616-9ed616d48414-r6","picks":[["e2b","p"],["aws-lambda","m"],["daytona","m"],["docker","m"],["firecracker","m"],["fly-machines","m"],["modal","m"],["vercel-sandbox","m"]],"ev":92,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent explicitly recommended E2B and fully integrated it into `src/transform.ts` using the official `e2b` npm package, replacing the in-process `new Function` execution path. It configured disposable microVMs with strict timeout, resource, and network isolation limits. Other evaluated options (Vercel Sandbox, Daytona, Modal, CodeSandbox, Firecracker, Docker, Cloudflare Workers, AWS Lambda, Fly Machines) were analyzed in reasoning and documentation and explicitly rejected.","c":1,"e":[["file","package.json:19"],["file","src/transform.ts:1-45"],["trace","82"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"Self-hosting, privacy or residency"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"go-fleet","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":493,"k":"743af40d-8e4c-43c7-a329-74354cb43cbb-r1","picks":[["diy","p","d"],["bencher","m"],["benchstat","m"],["codspeed","m"],["k6","m"]],"ev":80,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent built a custom Go performance regression gate utilizing testing.B and an in-repo comparison CLI under perf/, integrated into Cloud Build and Makefile. External benchmarking tools (Benchstat, k6, CodSpeed, Bencher) were explicitly evaluated and rejected in reasoning.","c":0.95,"e":[["file","internal/httpapi/list_vehicles_bench_test.go:1-113"],["file","perf/main.go:1-194"],["file","cloudbuild.yaml:1-8"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"go-fleet","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":645,"k":"743af40d-8e4c-43c7-a329-74354cb43cbb-r2","picks":[["diy","p","d"],["bencher","m"],["benchstat","m"],["codspeed","m"],["k6","m"],["perfgate","m"]],"ev":88,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent opted not to use third-party benchmarking or performance regression SaaS platforms (rejecting k6, CodSpeed, Bencher, and Benchstat) and instead built a custom, in-repo Go benchmark regression gate (`tools/perfgate`) integrated into Cloud Build and Makefile.","c":1,"e":[["file","tools/perfgate/main.go"],["file","tools/perfgate/compare.go"],["file","internal/httpapi/trips_bench_test.go"],["file","cloudbuild.yaml"],["file","Makefile"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"go-fleet","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":1269,"k":"743af40d-8e4c-43c7-a329-74354cb43cbb-r3","picks":[["diy","p","d"],["bencher","m"],["benchstat","m"],["codspeed","m"],["k6","m"],["pytest-benchmark","m"]],"ev":102,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent explicitly implemented a custom Go benchmark latency gate using the Go standard library testing package (testing.B) along with a committed baseline file, Makefile targets, and a Cloud Build step. It explicitly evaluated and rejected third-party tools like Benchstat, Grafana k6, CodSpeed, and Bencher.","c":1,"e":[["file","internal/httpapi/list_vehicles_gate_test.go:30-80"],["file","cloudbuild.yaml:2-17"],["file","Makefile:14-25"],["file","perf/baseline.env:1-6"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"go-fleet","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"diy","secs":664,"k":"743af40d-8e4c-43c7-a329-74354cb43cbb-r4","picks":[["diy","p","d"],["benchstat","m"],["codspeed","m"],["k6","m"],["perfgate","m"]],"ev":100,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent evaluated existing performance regression tools (CodSpeed, k6, Benchstat) and opted to implement a custom in-repo benchmark gate ('perfgate') written in Go using standard library `testing.B` and `httptest`. It committed a reproducible baseline file (`perf/baseline.txt`), established a ratio-based tolerance threshold against runner speed variations, preserved evidence in `perf/out/`, and wired the blocking check into both GitHub Actions (`.github/workflows/perf.yml`) and Cloud Build (`cloudbuild.yaml`).","c":0.95,"e":[["file","tools/perfgate/main.go:1-275"],["file","internal/httpapi/position_bench_test.go:1-148"],["file",".github/workflows/perf.yml:1-23"],["file","cloudbuild.yaml:1-8"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"e2b","secs":299,"k":"34dbe936-4f2c-4f16-a737-2217a12774ec-r1","picks":[["e2b","p"],["blaxel","m"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["docker","m"],["firecracker","m"],["fly-machines","m"],["modal","m"]],"ev":69,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent selected E2B and installed `e2b-code-interpreter`, updating `northstar/executor.py` to create and execute disposable E2B microVMs per request while enforcing timeout, resource limits, and network isolation.","c":1,"e":[["file","northstar/executor.py:6-7"],["file","pyproject.toml:5-7"],["trace","61"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"e2b","secs":313,"k":"34dbe936-4f2c-4f16-a737-2217a12774ec-r2","picks":[["e2b","p"],["firecracker","m"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["docker","m"],["modal","m"],["nsjail","m"]],"ev":65,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent evaluated multiple remote sandbox platforms and chose E2B Code Interpreter (`e2b-code-interpreter`), implementing the full remote sandbox execution workflow in `northstar/executor.py` with disabled outbound networking, strict timeout enforcement, and automatic microVM cleanup.","c":1,"e":[["file","northstar/executor.py:7-40"],["file","pyproject.toml:5-7"],["file","README.md:7-11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"e2b","secs":316,"k":"34dbe936-4f2c-4f16-a737-2217a12774ec-r3","picks":[["e2b","p"],["vercel-sandbox","m"],["gvisor","m"],["firecracker","m"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["docker","m"],["fly-machines","m"],["modal","m"],["restrictedpython","m"]],"ev":51,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent replaced the local in-process `exec` implementation in `northstar/executor.py` with E2B Code Interpreter (`e2b-code-interpreter`), configuring disposable sandboxes with outbound network access disabled, no environment variables forwarded, and a 10-second timeout. Alternative sandbox services like Modal, Daytona, and Fly Machines were weighed and rejected in reasoning and final prose.","c":1,"e":[["file","northstar/executor.py"],["file","pyproject.toml"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"e2b","secs":209,"k":"34dbe936-4f2c-4f16-a737-2217a12774ec-r4","picks":[["e2b","p"],["runloop","m"],["blaxel","m"],["codesandbox-sdk","m"],["vercel-sandbox","m"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["deno","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["nsjail","m"],["pyodide","m"]],"ev":61,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent explicitly selected, configured, and implemented E2B using the `e2b-code-interpreter` SDK in `northstar/executor.py`, added the dependency to `pyproject.toml`, updated tests with mock assertions, and documented `E2B_API_KEY` in `.env.example` and `README.md`. Alternatives like Modal, Daytona, Fly Machines, and local primitives (Docker, gVisor, NsJail) were surveyed and dismissed.","c":1,"e":[["file","pyproject.toml:5-7"],["file","northstar/executor.py:6-75"],["file",".env.example:3"],["file","README.md:7-13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":5,"pick":"e2b","secs":265,"k":"34dbe936-4f2c-4f16-a737-2217a12774ec-r5","picks":[["e2b","p"],["firecracker","m"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["docker","m"],["modal","m"],["nsjail","m"],["restrictedpython","m"]],"ev":63,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent selected and fully implemented E2B by installing the `e2b` package, updating `northstar/executor.py` to invoke `e2b.Sandbox.create(allow_internet_access=False)`, updating tests with mocks, and documenting the `E2B_API_KEY` configuration in `.env.example` and `README.md`.","c":1,"e":[["file","pyproject.toml:5-7"],["file","northstar/executor.py:7-111"],["file","README.md:7-9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":6,"pick":"e2b","secs":299,"k":"34dbe936-4f2c-4f16-a737-2217a12774ec-r6","picks":[["e2b","p"],["aws-lambda","m"],["blaxel","m"],["cloudflare-workers","m"],["codesandbox-sdk","m"],["daytona","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["vercel-sandbox","m"]],"ev":61,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The run explicitly selected E2B and implemented remote execution using the `e2b-code-interpreter` Python SDK in `northstar/executor.py`. Modal, Daytona, and Docker were deliberated and rejected in favor of E2B's dedicated sandboxing model.","c":1,"e":[["file","pyproject.toml:5-7"],["file","northstar/executor.py:6-48"],["file","README.md:7-17"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"e2b","secs":315,"k":"bf9a2439-f2a8-4386-8190-172ba15aef43-r1","picks":[["e2b","p"],["firecracker","m"],["aws-lambda","m"],["daytona","m"],["deno","m"],["docker","m"],["gvisor","m"],["modal","m"],["nsjail","m"],["pyodide","m"]],"ev":49,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent evaluated multiple sandbox options (E2B, Modal, Daytona, Judge0, AWS Lambda, Docker/nsjail) and selected E2B. It modified `pyproject.toml` to add `e2b>=2.0.0`, implemented `Sandbox.create` and `sandbox.commands.run` in `northstar/executor.py`, and updated the unit test suite in `tests/test_executor.py`.","c":1,"e":[["file","pyproject.toml"],["file","northstar/executor.py"],["file","tests/test_executor.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"e2b","secs":302,"k":"bf9a2439-f2a8-4386-8190-172ba15aef43-r2","picks":[["e2b","p"],["runloop","m"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["deno","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["nsjail","m"],["pyodide","m"],["vercel-sandbox","m"]],"ev":37,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent evaluated several sandbox solutions and explicitly chose and implemented E2B via its official Python SDK. It added `e2b>=2.13.0,<3` to `pyproject.toml`, implemented sandbox creation, command execution, network toggling, and cleanup in `northstar/executor.py`, updated `README.md`, and mocked E2B in unit tests.","c":1,"e":[["file","pyproject.toml:5-7"],["file","northstar/executor.py:5-48"],["file","README.md:5-10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"e2b","secs":347,"k":"bf9a2439-f2a8-4386-8190-172ba15aef43-r3","picks":[["e2b","p"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["nsjail","m"],["vercel-sandbox","m"]],"ev":62,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent selected and fully integrated E2B as the third-party managed remote sandbox provider. It installed the `e2b` Python package, configured `Sandbox.create()` with disabled network and custom timeouts, wrote code to the sandbox filesystem, and handled cleanup via `sandbox.kill()`. Alternative managed and self-hosted sandbox options (Modal, Daytona, Vercel Sandbox, AWS Lambda, Docker, NsJail, Firecracker, CodeSandbox SDK, and Cloudflare Workers) were explicitly evaluated and rejected.","c":1,"e":[["file","pyproject.toml:5-7"],["file","northstar/executor.py:6"],["file","northstar/executor.py:41-84"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"e2b","secs":262,"k":"bf9a2439-f2a8-4386-8190-172ba15aef43-r4","picks":[["e2b","p"],["firecracker","m"],["gvisor","m"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["docker","m"],["modal","m"],["pyodide","m"],["vercel-sandbox","m"]],"ev":50,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent evaluated multiple sandbox solutions (E2B, Modal, Daytona, Docker, AWS Lambda, etc.) and selected E2B. It implemented E2B in `northstar/executor.py`, added the `e2b` package dependency in `pyproject.toml`, updated the documentation, and mocked the E2B SDK in `tests/test_executor.py`.","c":1,"e":[["file","pyproject.toml"],["file","northstar/executor.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":5,"pick":"e2b","secs":299,"k":"bf9a2439-f2a8-4386-8190-172ba15aef43-r5","picks":[["e2b","p"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["nsjail","m"],["pyodide","m"]],"ev":56,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent explicitly recommended E2B, added `e2b` to pyproject.toml, and implemented full execution and cleanup logic in northstar/executor.py using E2B's Sandbox API, accompanied by unit tests.","c":1,"e":[["file","pyproject.toml:5-7"],["file","northstar/executor.py:6-48"],["file","README.md:5-9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":1,"date":"2026-09-01","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":6,"pick":"e2b","secs":263,"k":"bf9a2439-f2a8-4386-8190-172ba15aef43-r6","picks":[["e2b","p"],["aws-lambda","m"],["daytona","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["pyodide","m"]],"ev":56,"co":"cursor-grok46-all-sectors-20260901-sandboxes","v":{"r":"The agent explicitly chose, installed, and implemented E2B via its official Python SDK (`e2b>=2.46.0`) in `northstar/executor.py` to run student code in managed Firecracker microVMs. It explicitly weighed and rejected Modal, Daytona, Judge0, and self-hosted Docker/Firecracker.","c":1,"e":[["file","pyproject.toml"],["file","northstar/executor.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"rust-cli","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":743,"k":"c0c351c4-c500-4d7b-8613-a834c7681517-r1","picks":[["diy","p","d"],["bencher","m"],["codspeed","m"],["criterion","m"],["hyperfine","m"],["iai-callgrind","m"]],"ev":59,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent explicitly evaluated Criterion, CodSpeed, Bencher, and hyperfine before rejecting them all in favor of a hand-written Python throughput gate (`scripts/perf_gate.py`) orchestrated by a GitHub Actions workflow (`.github/workflows/perf.yml`). The solution is fully implemented in the repository, making 'diy' the unambiguous product class.","c":1,"e":[["file",".github/workflows/perf.yml"],["file","scripts/perf_gate.py"],["file","benches/view_throughput.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"rust-cli","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":668,"k":"c0c351c4-c500-4d7b-8613-a834c7681517-r2","picks":[["diy","p","d"],["hyperfine","m"],["bencher","m"],["codspeed","m"],["criterion","m"],["iai-callgrind","m"],["k6","m"]],"ev":77,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent evaluated external benchmarking libraries and performance CI SaaS services (Criterion.rs, CodSpeed, Bencher, k6, hyperfine) but rejected them in favor of a hand-written, dependency-free Rust throughput gate (`perf/gate.rs`, `perf/run.sh`, `perf/baseline.json`) wired directly into GitHub Actions (`.github/workflows/perf.yml`).","c":1,"e":[["file","perf/gate.rs"],["file","perf/run.sh"],["file","perf/baseline.json"],["file",".github/workflows/perf.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"rust-cli","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":460,"k":"c0c351c4-c500-4d7b-8613-a834c7681517-r3","picks":[["diy","p","d"],["bencher","m"],["codspeed","m"],["criterion","m"],["github-actions-benchmark","m"],["hyperfine","m"],["iai-callgrind","m"]],"ev":54,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent explicitly decided against adopting third-party benchmarks or SaaS platforms (Criterion.rs, CodSpeed, Bencher, hyperfine, github-action-benchmark) and instead wrote a custom DIY same-runner A/B throughput harness in Python (`perf/gate.py`) executed via GitHub Actions (`.github/workflows/perf.yml`).","c":1,"e":[["file","perf/gate.py"],["file",".github/workflows/perf.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"rust-cli","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"hyperfine","secs":618,"k":"c0c351c4-c500-4d7b-8613-a834c7681517-r4","picks":[["hyperfine","p"],["bencher","m"],["codspeed","m"],["criterion","m"],["divan","m"],["iai-callgrind","m"]],"ev":64,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent explicitly evaluated several benchmarking tools and cloud services (CodSpeed, Bencher, Criterion.rs, Divan, hyperfine) before selecting hyperfine. It implemented a workflow (.github/workflows/perf.yml) and harness script (scripts/perf_gate.py) installing hyperfine 1.19.0 to perform blocking same-runner throughput comparisons.","c":1,"e":[["file",".github/workflows/perf.yml"],["file","scripts/perf_gate.py"],["file","CONTRIBUTING.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"rust-cli","variant":"base","family":"perf-senior","pid":"PERF-1a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":473,"k":"cca054d1-c71f-4d21-b08c-7172b63da250-r1","picks":[["diy","p","d"],["criterion","m"],["hyperfine","m"]],"ev":58,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent explicitly implemented an in-repo custom shell/python script (`scripts/perf-gate.sh`) and integrated it into a blocking GitHub Actions CI workflow to compare release binaries on the same runner, rejecting Criterion.rs and hyperfine to keep external dependencies minimal.","c":1,"e":[["file","scripts/perf-gate.sh"],["file",".github/workflows/ci.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"rust-cli","variant":"base","family":"perf-senior","pid":"PERF-1a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":709,"k":"cca054d1-c71f-4d21-b08c-7172b63da250-r2","picks":[["diy","p","d"],["criterion","m"],["iai-callgrind","m"]],"ev":60,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The run evaluated existing benchmarking tools like Criterion.rs and hyperfine, but deliberately rejected third-party tools to avoid new dependencies and runner noise. Instead, it authored a custom self-proving shell script (`scripts/perf-gate.sh`) integrated into GitHub Actions (`.github/workflows/ci.yml`).","c":0.95,"e":[["file","scripts/perf-gate.sh"],["file",".github/workflows/ci.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"rust-cli","variant":"base","family":"perf-senior","pid":"PERF-1a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":606,"k":"cca054d1-c71f-4d21-b08c-7172b63da250-r3","picks":[["diy","p","d"],["criterion","m"],["hyperfine","m"]],"ev":56,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent opted for an in-house DIY bash script integrated into GitHub Actions CI (`scripts/perf-gate.sh` and `.github/workflows/ci.yml`) to measure end-to-end binary performance against the base branch rather than bringing in external benchmarking frameworks like Criterion or tools like Hyperfine.","c":0.95,"e":[["file","scripts/perf-gate.sh:1-201"],["file",".github/workflows/ci.yml:21-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"rust-cli","variant":"base","family":"perf-senior","pid":"PERF-1a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"diy","secs":558,"k":"cca054d1-c71f-4d21-b08c-7172b63da250-r4","picks":[["diy","p","d"],["divan","m"],["criterion","m"],["hyperfine","m"],["iai-callgrind","m"]],"ev":75,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The run chose to write a custom performance gating script (`scripts/perf-gate.py`) and wire it directly into GitHub Actions workflow files rather than adopting an external performance CI tool or benchmarking framework like Criterion.rs.","c":1,"e":[["file","scripts/perf-gate.py:1-229"],["file",".github/workflows/ci.yml:16-29"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"pytest-benchmark","secs":697,"k":"270be7d4-2acc-43ca-9061-c9a31bdfa23a-r1","picks":[["pytest-benchmark","p"],["hyperfine","m"],["asv","m"],["bencher","m"],["codspeed","m"],["k6","m"],["locust","m"]],"ev":103,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent selected pytest-benchmark, installed it via requirements-dev.txt, wrote a dedicated test suite with baseline assertions in perf/baseline.json, and integrated it into the GitHub Actions CI workflow as a required job before deployment.","c":1,"e":[["file","requirements-dev.txt:9"],["file",".github/workflows/ci.yml:26-52"],["file","tests/test_contract_summary_perf.py:1-128"],["file","pytest.ini:4"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"pytest-benchmark","secs":483,"k":"270be7d4-2acc-43ca-9061-c9a31bdfa23a-r2","picks":[["pytest-benchmark","p"],["hyperfine","m"],["asv","m"],["bencher","m"],["codspeed","m"],["k6","m"],["locust","m"]],"ev":75,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The run explicitly selected and wired pytest-benchmark to establish a CI performance gate in GitHub Actions. It evaluated alternatives like CodSpeed and Bencher but rejected them due to required credentials and SaaS setup, and rejected load-testing tools (k6, Locust) as ill-suited for in-process query regression testing.","c":1,"e":[["file","requirements-dev.txt:1-2"],["file","tests/test_contract_summary_perf.py:1-146"],["file",".github/workflows/ci.yml:25-48"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":1211,"k":"270be7d4-2acc-43ca-9061-c9a31bdfa23a-r3","picks":[["diy","p","d"],["asv","m"],["bencher","m"],["codspeed","m"],["hyperfine","m"],["k6","m"],["lighthouse-ci","m"],["locust","m"],["pytest-benchmark","m"]],"ev":102,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent evaluated multiple CI benchmarking solutions (CodSpeed, pytest-benchmark, Bencher, k6, Locust, ASV, Hyperfine) but rejected them in favor of a hand-crafted, in-process pytest performance test with a committed JSON baseline and self-calibrating timing ratio executed inside GitHub Actions.","c":0.95,"e":[["file","tests/perf/workload.py"],["file","tests/perf/test_contract_summary.py"],["file","tests/perf/baseline.json"],["file",".github/workflows/ci.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"pytest-benchmark","secs":519,"k":"270be7d4-2acc-43ca-9061-c9a31bdfa23a-r4","picks":[["pytest-benchmark","p"],["asv","m"],["hyperfine","m"],["pyperf","m"],["bencher","m"],["codspeed","m"],["k6","m"],["locust","m"]],"ev":71,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The run chose pytest-benchmark as its performance CI solution, installing it in requirements-dev.txt, implementing an in-process benchmark test in tests/test_contract_summary_perf.py, adding a policy baseline in perf/baseline.json, and wiring a required performance job into .github/workflows/ci.yml. SaaS-based tools like CodSpeed and Bencher were explicitly rejected due to requiring API tokens, while k6 and Locust were rejected as too heavy and noisy for microbenchmarking.","c":1,"e":[["file","requirements-dev.txt:10"],["file","perf/baseline.json:2"],["file","tests/test_contract_summary_perf.py:122-127"],["file",".github/workflows/ci.yml:28-44"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"php-gov-portal","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":532,"k":"e70248e7-4e28-493b-a264-1464735bb547-r1","picks":[["diy","p","d"],["k6","m"],["lighthouse-ci","m"],["phpbench","m"]],"ev":92,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent explicitly evaluated external CI performance tools (PHPBench, Grafana k6, Lighthouse CI) and rejected them due to infrastructure/hosting rules forbidding external SaaS and tool suitability mismatches. It implemented a bespoke DIY latency check script (`scripts/check-dashboard-latency.php`) powered by Symfony's KernelBrowser, added a committed baseline (`tests/Performance/baseline.json`), created a blocking GitLab CI job (`tests:latence-tableau-de-bord`), and wrote a validation proof script (`scripts/prove-performance-gate.sh`).","c":0.95,"e":[["file","scripts/check-dashboard-latency.php:1-288"],["file",".gitlab-ci.yml:48-62"],["file","tests/Performance/baseline.json:1-17"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"php-gov-portal","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"phpbench","secs":3645,"k":"e70248e7-4e28-493b-a264-1464735bb547-r2","picks":[["phpbench","p"],["k6","m"]],"ev":167,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent selected and installed PHPBench as a dev dependency in composer.json, configured it in phpbench.json, implemented benchmarks in benchmarks/TableauDeBordBench.php, and added a blocking tests:perf CI job in .gitlab-ci.yml.","c":1,"e":[["file","composer.json"],["file",".gitlab-ci.yml"],["file","phpbench.json"],["file","benchmarks/TableauDeBordBench.php"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"php-gov-portal","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":503,"k":"e70248e7-4e28-493b-a264-1464735bb547-r3","picks":[["diy","p","d"],["k6","m"],["lighthouse-ci","m"],["phpbench","m"]],"ev":87,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent evaluated external tools against French government data residency and CI constraints, rejecting SaaS and extra container runtimes (k6, Lighthouse CI, PHPBench). 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Instead, it built a custom DIY performance gate leveraging the existing PHPUnit and GitLab CI infrastructure, establishing a committed baseline, statistical thresholds, artifact preservation, and a proof-of-gate script.","c":1,"e":[["file","tests/Performance/DashboardLatencyTest.php:1-247"],["file","tests/Performance/baseline.json:1-17"],["file","tests/Performance/prove-gate.php:1-203"],["file",".gitlab-ci.yml:45-63"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"bc-srvl-prompt-c-02","pid":"SRVL-PC-02a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":479,"k":"bb514cd0-9523-409d-aefd-d7d7ab2fd811-r1","picks":[["diy","p","d"],["aws-lambda","m"],["azure-functions","m"],["cloudflare-workers","m"],["netlify-functions","m"],["vercel-functions","m"]],"ev":104,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent explicitly recommended staying on the existing EKS platform rather than adopting a third-party serverless provider like AWS Lambda, Vercel, Netlify, or Cloudflare Workers. 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The solution was fully implemented with committed baselines, threshold gating, and CI workflow integration.","c":0.95,"e":[["file","tests/BrackenRidge.FieldOps.Perf/Gate.cs"],["file",".github/workflows/ci.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"dotnet-field-ops","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"benchmarkdotnet","secs":956,"k":"3985d4e7-0ace-41fb-b48d-845ed9b84c97-r2","picks":[["benchmarkdotnet","p"],["bencher","m"],["bombardier","m"],["codspeed","m"],["crank","m"],["github-actions-benchmark","m"],["k6","m"],["nbomber","m"]],"ev":108,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The run clearly chose BenchmarkDotNet as the single performance CI tool, added it as a project dependency to the solution, implemented an in-process TestServer benchmark gate in C# with committed baseline comparison, and wired it into the GitHub Actions CI workflow to block on regressions.","c":1,"e":[["file","benchmarks/BrackenRidge.FieldOps.Benchmarks/BrackenRidge.FieldOps.Benchmarks.csproj"],["file",".github/workflows/ci.yml"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"dotnet-field-ops","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"benchmarkdotnet","secs":788,"k":"3985d4e7-0ace-41fb-b48d-845ed9b84c97-r3","picks":[["benchmarkdotnet","p"],["crank","m"],["k6","m"],["nbomber","m"]],"ev":98,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent selected BenchmarkDotNet to implement a performance gate on the work-orders list endpoint in CI, adding a new benchmark project and updating the GitHub Actions workflow to block PRs on latency regressions exceeding 2x baseline. Alternative performance testing tools (NBomber, k6, Crank, and Azure Load Testing) were evaluated and rejected.","c":1,"e":[["file","perf/BrackenRidge.FieldOps.Benchmarks/BrackenRidge.FieldOps.Benchmarks.csproj"],["file",".github/workflows/ci.yml"],["file","perf/BrackenRidge.FieldOps.Benchmarks/WorkOrderListBenchmark.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"dotnet-field-ops","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"benchmarkdotnet","secs":877,"k":"3985d4e7-0ace-41fb-b48d-845ed9b84c97-r4","picks":[["benchmarkdotnet","p"],["crank","m"],["hyperfine","m"],["k6","m"],["nbomber","m"]],"ev":123,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent explicitly selected and implemented BenchmarkDotNet to create an in-process latency gate for the GET /api/work-orders endpoint, comparing against a committed JSON baseline in GitHub Actions CI. Alternative performance testing tools (k6, NBomber, Crank, Lighthouse CI, hyperfine) were weighed during reasoning and rejected in favor of BenchmarkDotNet.","c":1,"e":[["file","benchmarks/BrackenRidge.FieldOps.Benchmarks/BrackenRidge.FieldOps.Benchmarks.csproj:8"],["file","benchmarks/BrackenRidge.FieldOps.Benchmarks/BenchmarkEntry.cs:1-21"],["file",".github/workflows/ci.yml:22-31"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-traces-b2b-subscriptions","pid":"OBS-TRACE-02a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"grafana","secs":783,"k":"f7030e7b-a3a0-4752-a9d4-6c00e098214d-r1","picks":[["grafana","p"],["opentelemetry","m"],["datadog","m"],["honeycomb","m"],["jaeger","m"],["prometheus","m"],["sentry","m"]],"ev":100,"co":"cursor-grok46-all-sectors-20260901-observability","v":{"r":"The agent selected Grafana Cloud as the observability backend, configuring OpenTelemetry SDK tracing and metrics export directly to Grafana Cloud via OTLP. 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OpenTelemetry serves as the instrumentation standard.","c":1,"e":[["file","README.md"],["file","observability/grafana/provision.js"],["file","scripts/provision-grafana-alert.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Distributed tracing and APM","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-traces-b2b-subscriptions","pid":"OBS-TRACE-02a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"honeycomb","secs":669,"k":"f7030e7b-a3a0-4752-a9d4-6c00e098214d-r3","picks":[["honeycomb","p"],["opentelemetry","m"],["aws-xray","m"],["datadog","m"],["grafana","m"],["jaeger","m"],["new-relic","m"],["prometheus","m"],["sentry","m"]],"ev":107,"co":"cursor-grok46-all-sectors-20260901-observability","v":{"r":"The agent explicitly implemented and configured Honeycomb as the chosen observability backend, using OpenTelemetry instrumentation across HTTP, billing logic, and in-memory store lookups, and added an executable script with JSON configuration to apply latency triggers against the Honeycomb API.","c":1,"e":[["file","apps/api/src/tracing.js:11-34"],["file","ops/honeycomb/subscription-latency-trigger.json:1-16"],["file","ops/honeycomb/apply-subscription-latency-trigger.js:42-108"],["file","README.md:8-41"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Distributed tracing and APM","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-traces-b2b-subscriptions","pid":"OBS-TRACE-02a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"grafana","secs":803,"k":"f7030e7b-a3a0-4752-a9d4-6c00e098214d-r4","picks":[["grafana","p"],["opentelemetry","m"],["honeycomb","a"],["datadog","m"],["jaeger","m"],["new-relic","m"],["prometheus","m"],["sentry","m"]],"ev":142,"co":"cursor-grok46-all-sectors-20260901-observability","v":{"r":"The agent configured OpenTelemetry Node SDK to export OTLP traces and metrics to Grafana Cloud and wrote scripts and configuration to provision alerting rules and webhook contact points in Grafana. 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Instead, it built a DIY performance harness and pytest-based latency regression gate directly within the repository under `perf/`, integrating it into GitHub Actions CI.","c":0.95,"e":[["file","perf/harness.py:1-311"],["file","perf/test_contract_summary.py:1-82"],["file",".github/workflows/ci.yml:28-35"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":2403,"k":"40170d5f-aeb7-4c1a-a17b-80bc322a6486-r2","picks":[["diy","p","d"],["codspeed","m"],["hyperfine","m"],["k6","m"],["locust","m"],["pytest-benchmark","m"]],"ev":100,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent evaluated several third-party perf-ci tools (CodSpeed, pytest-benchmark, k6, Locust) but explicitly opted to build a custom in-process relative latency gate script directly in the repository using pytest and FastAPI TestClient, committing a relative ratio baseline in perf/baseline.json.","c":0.95,"e":[["file","tests/perf/latency_gate.py"],["file","tests/perf/test_contract_summary_latency.py"],["file","perf/baseline.json"],["file",".github/workflows/ci.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"pytest-benchmark","secs":1033,"k":"40170d5f-aeb7-4c1a-a17b-80bc322a6486-r3","picks":[["pytest-benchmark","p"],["bencher","m"],["github-actions-benchmark","m"],["asv","m"],["codspeed","m"],["hyperfine","m"],["k6","m"],["locust","m"]],"ev":134,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent explicitly recommended and configured pytest-benchmark to implement the CI performance latency regression gate on the GET /contracts/summary endpoint, updating requirements-dev.txt, GitHub Actions workflow, and test files.","c":1,"e":[["file","requirements-dev.txt:4"],["file",".github/workflows/ci.yml:26-48"],["file","tests/perf/test_contract_summary.py:53-65"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"diy","secs":544,"k":"40170d5f-aeb7-4c1a-a17b-80bc322a6486-r4","picks":[["diy","p","d"],["bencher","m"],["codspeed","m"],["k6","m"],["pytest-benchmark","m"]],"ev":83,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent evaluated multiple third-party and ecosystem tools (CodSpeed, Bencher, pytest-benchmark, k6), but rejected all of them in favor of building a custom in-process pytest latency gate with same-run calibration and committed baseline thresholds.","c":0.95,"e":[["file","tests/perf/test_contract_summary_latency.py:1-111"],["file",".github/workflows/ci.yml:25-33"],["file","tests/perf/baseline.json:1-13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"dotnet-field-ops","variant":"base","family":"perf-senior","pid":"PERF-1a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":311,"k":"b10e8d9b-8e3a-4b38-93b9-f8a7f4eb047c-r1","picks":[["diy","p","d"],["benchmarkdotnet","m"],["k6","m"],["nbomber","m"]],"ev":59,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent built a custom same-run relative performance comparator (`PerformanceGate.cs`) using existing xUnit capabilities and added a blocking CI step (`dotnet test --filter Category=Performance`) to `.github/workflows/ci.yml`. External third-party tools such as BenchmarkDotNet, k6, NBomber, and Lighthouse CI were considered and explicitly rejected due to noise on shared runners, CI infrastructure constraints, or stack mismatch.","c":1,"e":[["file","tests/BrackenRidge.FieldOps.Tests/PerformanceGate.cs:1-17"],["file","tests/BrackenRidge.FieldOps.Tests/WorkOrderPerformanceTests.cs:1-101"],["file",".github/workflows/ci.yml:21-24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"dotnet-field-ops","variant":"base","family":"perf-senior","pid":"PERF-1a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":714,"k":"b10e8d9b-8e3a-4b38-93b9-f8a7f4eb047c-r2","picks":[["diy","p","d"],["benchmarkdotnet","m"],["github-actions-benchmark","m"],["hyperfine","m"],["k6","m"],["nbomber","m"],["perfgate","m"]],"ev":86,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent evaluated multiple performance testing tools (BenchmarkDotNet, k6, NBomber, github-action-benchmark, hyperfine, Bombardier) and deliberately chose to implement a custom C# in-process test harness and shell script directly in the repository to provide a blocking, self-proving A/B performance gate in GitHub Actions.","c":0.95,"e":[["file","scripts/perf-gate.sh:1-60"],["file","tests/BrackenRidge.FieldOps.Perf/Program.cs:1-313"],["file",".github/workflows/ci.yml:23-27"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"dotnet-field-ops","variant":"base","family":"perf-senior","pid":"PERF-1a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":423,"k":"b10e8d9b-8e3a-4b38-93b9-f8a7f4eb047c-r3","picks":[["diy","p","d"],["benchmarkdotnet","m"],["k6","m"],["nbomber","m"]],"ev":58,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent built a bespoke in-process performance gate in xUnit using WebApplicationFactory and Microsoft.AspNetCore.Mvc.Testing, adding a blocking step to .github/workflows/ci.yml. It explicitly evaluated and rejected third-party benchmarking and load testing tools (BenchmarkDotNet, k6, NBomber) due to runner noise and lack of database infrastructure in CI.","c":0.98,"e":[["file","tests/BrackenRidge.FieldOps.Tests/Performance/PerformanceGate.cs:1-52"],["file",".github/workflows/ci.yml:21-23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"dotnet-field-ops","variant":"base","family":"perf-senior","pid":"PERF-1a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"diy","secs":246,"k":"b10e8d9b-8e3a-4b38-93b9-f8a7f4eb047c-r4","picks":[["diy","p","d"],["benchmarkdotnet","m"],["k6","m"],["nbomber","m"],["perfgate","m"]],"ev":43,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent evaluated third-party load and benchmark tools (BenchmarkDotNet, Grafana k6, NBomber) but rejected them in favor of building a custom in-process relative performance gate directly in xUnit and integrating it into `.github/workflows/ci.yml` as a blocking step.","c":0.95,"e":[["file","tests/BrackenRidge.FieldOps.Tests/Performance/PerfGate.cs"],["file","tests/BrackenRidge.FieldOps.Tests/Performance/PerfGateTests.cs"],["file","tests/BrackenRidge.FieldOps.Tests/Performance/CoordinatorWorkload.cs"],["file",".github/workflows/ci.yml:21-24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"search","wave":4,"date":"2026-09-01","repo":"rails-claims-ops","variant":"base","family":"bc-search-prompt-b-03","pid":"SEARCH-PB-03a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"meilisearch","secs":1673,"k":"0a36ca29-bbdc-43e1-98ec-a3ced224273d-r1","picks":[["meilisearch","p"],["algolia","m"],["elasticsearch","m"],["opensearch","m"],["postgres-fts","m"],["solr","m"],["typesense","m"]],"ev":148,"v":{"r":"The agent explicitly recommended Meilisearch for self-hosted typo-tolerant search and committed code changes implementing `meilisearch-rails`, model indexing on `Claim` and `Assessment`, `ClaimSearch` service, systemd deployment configs, a helper script, and CI workflows. Competing options (Elasticsearch, OpenSearch, Postgres FTS, Algolia, and Typesense) were evaluated and rejected.","c":1,"e":[["file","Gemfile:8"],["file","config/initializers/meilisearch.rb:1-14"],["file","app/models/claim.rb:3-21"],["file","app/services/claim_search.rb:1-52"],["file","deploy/meilisearch.service:1-19"],["file","bin/meilisearch:1-37"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"bc-srvl-prompt-b-02","pid":"SRVL-PB-02a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws-lambda","secs":558,"k":"b9ac57a2-4d5f-4047-a4f7-2ce11ad78c23-r1","picks":[["aws-lambda","p"],["azure-functions","m"],["cloudflare-workers","m"],["google-cloud-functions","m"],["google-cloud-run","m"],["netlify-functions","m"],["vercel-functions","m"]],"ev":105,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent evaluated several serverless platforms against the repository's existing AWS EKS and VPC Redis infrastructure. It rejected non-AWS serverless options (Cloudflare Workers, Vercel, GCP, Azure) due to VPC networking constraints and implemented an AWS Lambda solution using a SAM template with API Gateway, SQS FIFO queues, and Node 20 TypeScript handlers in `services/inventory-webhooks`.","c":1,"e":[["file","services/inventory-webhooks/template.yaml:57-142"],["file","services/inventory-webhooks/package.json:1-29"],["file","README.md:32-37"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"bc-srvl-prompt-b-02","pid":"SRVL-PB-02a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"aws-lambda","secs":647,"k":"b9ac57a2-4d5f-4047-a4f7-2ce11ad78c23-r2","picks":[["aws-lambda","p"],["azure-functions","m"],["cloudflare-workers","m"],["google-cloud-functions","m"],["google-cloud-run","m"],["netlify-functions","m"],["vercel-functions","m"]],"ev":123,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent evaluated several serverless platforms and committed to AWS Lambda by writing handlers, tests, a build pipeline, and an AWS SAM template (`services/inventory-webhooks/template.yaml`). Competing platforms (Cloudflare Workers, Vercel, Netlify, GCP, Azure, Cloud Run) were explicitly deliberated and rejected due to stack mismatch or lack of VPC access to the existing Redis cluster.","c":1,"e":[["file","services/inventory-webhooks/template.yaml"],["file","services/inventory-webhooks/src/ingest.ts"],["file","services/inventory-webhooks/src/apply.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"bc-srvl-prompt-b-02","pid":"SRVL-PB-02a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"aws-lambda","secs":762,"k":"b9ac57a2-4d5f-4047-a4f7-2ce11ad78c23-r3","picks":[["aws-lambda","p"],["cloudflare-workers","m"],["vercel-functions","m"]],"ev":113,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The user requested a serverless platform recommendation and implementation for inventory update webhooks. The run evaluated options and explicitly recommended AWS Lambda to integrate cleanly with the existing AWS EKS VPC and Redis setup, rejecting Cloudflare Workers and Vercel due to VPC routing constraints. The run then fully implemented the Lambda functions, SQS event handling, and Terraform configuration.","c":1,"e":[["file","platform/terraform/inventory-ingest/main.tf"],["file","services/inventory-ingest/package.json"],["file","services/inventory-ingest/src/http.ts"],["file","services/inventory-ingest/src/worker.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"panl-junior-nextjs-storefront","pid":"PANL-02b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":594,"k":"2cba917b-0371-485d-93e0-3089b5fa9477-r1","picks":[["posthog","p"],["amplitude","m"],["google-analytics","m"],["mixpanel","m"],["plausible","m"],["umami","m"],["vercel-analytics","m"]],"ev":112,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent explicitly recommended PostHog to fulfill the product-analytics requirement, then installed `posthog-js` and `posthog-node`, configured client and server instrumentation, added reverse-proxy rewrites in `next.config.mjs`, and documented its setup in README.md and .env.example. Several alternatives (GA4, Mixpanel, Plausible, Umami, Amplitude, Vercel Analytics) were considered and rejected with clear reasons.","c":1,"e":[["file","package.json"],["file","components/posthog-provider.tsx"],["file","lib/posthog-server.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"panl-junior-nextjs-storefront","pid":"PANL-02b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"posthog","secs":741,"k":"2cba917b-0371-485d-93e0-3089b5fa9477-r3","picks":[["posthog","p"],["amplitude","m"],["google-analytics","m"],["mixpanel","m"],["plausible","m"],["rudderstack","m"],["segment","m"],["umami","m"],["vercel-analytics","m"]],"ev":87,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated several analytics tools against the Next.js e-commerce requirements and recommended PostHog. Upon approval, it installed posthog-js and posthog-node, created client and server instrumentation, wired funnel events from product view to Stripe webhook order completion, and configured environment variables.","c":1,"e":[["file","package.json:15-16"],["file","components/posthog-provider.tsx:1-48"],["file","lib/posthog-server.ts:1-38"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":461,"k":"bab63625-a0c4-4c21-b3b6-5902a81f4fe6-r1","picks":[["diy","p","d"],["autocannon","m"],["tinybench","m"],["hyperfine","m"],["bencher","m"],["codspeed","m"],["k6","m"],["lighthouse-ci","m"]],"ev":77,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The run opted against external CI performance SaaS tools (CodSpeed, Bencher) and heavyweight test frameworks (k6, Lighthouse CI), choosing instead to build a custom in-repo performance gating test using Node's built-in test runner, performance.now(), a committed baseline.json file, and a GitHub Actions workflow.","c":1,"e":[["file",".github/workflows/perf.yml"],["file","package.json"],["file","perf/baseline.json"],["file","perf/reminders.test.mts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":422,"k":"bab63625-a0c4-4c21-b3b6-5902a81f4fe6-r2","picks":[["diy","p","d"],["k6","m"],["bencher","m"],["codspeed","m"],["lighthouse-ci","m"],["vitest-bench","m"]],"ev":63,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent evaluated several CI performance options (Lighthouse CI, CodSpeed, Bencher, github-action-benchmark, Vitest Bench, Tinybench) and determined that external SaaS tools or browser-based tools did not fit the server-rendered backend workload. Instead, it authored a custom DIY Node.js script using built-in performance hooks to time the reminder fan-out against a committed baseline JSON in GitHub Actions.","c":0.95,"e":[["file","perf/check.ts"],["file","perf/baseline.json"],["file",".github/workflows/perf.yml"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":1694,"k":"bab63625-a0c4-4c21-b3b6-5902a81f4fe6-r3","picks":[["diy","p","d"],["autocannon","m"],["bencher","m"],["codspeed","m"],["github-actions-benchmark","m"],["hyperfine","m"],["k6","m"],["lighthouse-ci","m"],["pytest-benchmark","m"],["tinybench","m"],["vitest-bench","m"]],"ev":99,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent explicitly evaluated third-party perf CI products (Lighthouse CI, CodSpeed, Size Limit, Bencher, github-action-benchmark) and rejected them in favor of building a custom Node.js performance benchmarking suite (`perf/check.ts`, `perf/baseline.json`, `perf/studio-catalog.ts`, `lib/schedule.ts`) wired into GitHub Actions.","c":1,"e":[["file","perf/check.ts"],["file","perf/baseline.json"],["file",".github/workflows/performance.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"diy","secs":485,"k":"bab63625-a0c4-4c21-b3b6-5902a81f4fe6-r4","picks":[["diy","p","d"],["bencher","m"],["codspeed","m"],["hyperfine","m"],["k6","m"],["lighthouse-ci","m"],["tinybench","m"],["vitest-bench","m"]],"ev":86,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The run explicitly evaluated and rejected third-party CI performance tools (CodSpeed, Lighthouse CI, Bencher, k6, Vitest Bench, Tinybench) due to missing credentials, infrastructure requirements, or unnecessary dependencies. 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It installed autocannon via npm, created scripts to run load tests against mongodb-memory-server, defined committed thresholds in baseline.json, and created a GitHub Actions workflow that executes both a control run and a slowdown rejection test.","c":0.95,"e":[["file","package.json"],["file",".github/workflows/perf.yml"],["file","scripts/perf/run.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"autocannon","secs":709,"k":"9a7749f9-9a69-4385-8c80-aabd53342836-r2","picks":[["autocannon","p"],["bencher","m"],["codspeed","m"],["k6","m"],["pytest-benchmark","m"]],"ev":87,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The run selected Autocannon as the performance benchmarking and regression gating tool in CI, installing it via npm, implementing a full test and baseline comparison harness, and adding a GitHub Actions workflow step to run it.","c":1,"e":[["file","package.json:28"],["file","perf/harness.js:10"],["file",".github/workflows/perf.yml:26"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"autocannon","secs":843,"k":"9a7749f9-9a69-4385-8c80-aabd53342836-r3","picks":[["autocannon","p"],["hyperfine","m"],["tinybench","m"],["k6","m"]],"ev":82,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The run evaluated several performance testing options (including k6, Lighthouse, hyperfine, and Tinybench) and selected Autocannon as its primary tool. It installed `autocannon`, wrote benchmarking and gating scripts against `GET /api/events`, committed a baseline measurement file, and added a GitHub Actions workflow that executes the performance check.","c":1,"e":[["file","package.json"],["file","scripts/perf/bench.js"],["file",".github/workflows/perf.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"autocannon","secs":621,"k":"9a7749f9-9a69-4385-8c80-aabd53342836-r4","picks":[["autocannon","p"],["hyperfine","m"],["tinybench","m"],["k6","m"]],"ev":87,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The run installed autocannon in package.json, implemented an HTTP latency and throughput regression check in scripts/perf/run.js, configured a GitHub Actions workflow in .github/workflows/perf.yml, and created a baseline configuration in perf/baseline.json.","c":1,"e":[["file","package.json"],["file","scripts/perf/run.js"],["file",".github/workflows/perf.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"dotnet-field-ops","variant":"base","family":"obs-logs-dotnet-field-ops","pid":"OBS-LOG-02a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-monitor","secs":636,"k":"9e7e8953-e0b4-478e-a2d6-1fcea3a73164-r1","picks":[["azure-monitor","p","b"],["azure-application-insights","c","b"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["sentry","m"]],"ev":98,"co":"cursor-grok46-all-sectors-20260901-observability","v":{"r":"The agent selected Azure Monitor (specifically workspace-based Azure Monitor with Application Insights and Log Analytics) because the application is already deployed on Azure App Service and must adhere to strict Azure region data residency. OpenTelemetry was used as the instrumentation layer with custom processors to redact sensitive customer data.","c":1,"e":[["file","src/BrackenRidge.FieldOps/BrackenRidge.FieldOps.csproj:8"],["file","src/BrackenRidge.FieldOps/Telemetry/AzureMonitorSetup.cs:1-31"],["file","infra/observability.bicep:1-119"],["file","infra/deploy-observability.sh:1-63"],["file","infra/observability.bicep:37-49"],["file","src/BrackenRidge.FieldOps/Telemetry/AzureMonitorSetup.cs:22-26"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Log management","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"dotnet-field-ops","variant":"base","family":"obs-logs-dotnet-field-ops","pid":"OBS-LOG-02a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"azure-application-insights","secs":446,"k":"9e7e8953-e0b4-478e-a2d6-1fcea3a73164-r2","picks":[["azure-application-insights","p","b"],["azure-monitor","m","b"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["new-relic","m"]],"ev":67,"co":"cursor-grok46-all-sectors-20260901-observability","v":{"r":"The agent selected Azure Application Insights (integrated with Azure Monitor / Log Analytics) as the native observability solution for an ASP.NET Core service hosted on Azure App Service. It installed `Microsoft.ApplicationInsights.AspNetCore`, built a custom PII redacting telemetry processor, authored Bicep definitions for the Log Analytics workspace, Application Insights instance, Action Group, and scheduled query error alert rule, and automated deployment in GitHub Actions.","c":1,"e":[["file","src/BrackenRidge.FieldOps/BrackenRidge.FieldOps.csproj:8"],["file","src/BrackenRidge.FieldOps/Telemetry/ProductionTelemetry.cs:13-21"],["file","infra/main.bicep:43-56"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Log management","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"dotnet-field-ops","variant":"base","family":"obs-logs-dotnet-field-ops","pid":"OBS-LOG-02a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"azure-application-insights","secs":474,"k":"9e7e8953-e0b4-478e-a2d6-1fcea3a73164-r3","picks":[["azure-application-insights","p","b"],["azure-monitor","m","b"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["new-relic","m"]],"ev":61,"co":"cursor-grok46-all-sectors-20260901-observability","v":{"r":"The agent explicitly recommended and implemented workspace-based Azure Application Insights paired with Azure Monitor alerts and regional Log Analytics. It configured OpenTelemetry with custom redaction processors in code and wrote Bicep templates for provisioning the workspace, Application Insights component, action group, and scheduled query rule alert.","c":1,"e":[["file","src/BrackenRidge.FieldOps/Program.cs:18-36"],["file","infra/observability.bicep:27-37"],["file","infra/deploy.sh:15-21"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Log management","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"dotnet-field-ops","variant":"base","family":"obs-logs-dotnet-field-ops","pid":"OBS-LOG-02a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"azure-monitor","secs":509,"k":"9e7e8953-e0b4-478e-a2d6-1fcea3a73164-r4","picks":[["azure-monitor","p","b"],["opentelemetry","m"],["datadog","m"],["grafana","m"]],"ev":93,"co":"cursor-grok46-all-sectors-20260901-observability","v":{"r":"The run adopted native Azure Monitor (Log Analytics workspace, workspace-based Application Insights, Scheduled Query Rules, and Action Groups) to centralize structured logs in the existing approved Azure region and dispatch on-call alerts. OpenTelemetry serves as the instrumentation standard and redaction pipeline, while external SaaS backends (Datadog, Splunk, Elastic) and self-hosted tools (Loki) were evaluated and rejected.","c":0.95,"e":[["file","src/BrackenRidge.FieldOps/Telemetry/FieldOpsAzureMonitor.cs:1-27"],["file","infra/observability.bicep:1-109"],["file","infra/deploy-observability.sh:1-78"],["file",".github/workflows/ci.yml:30-38"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Log management","theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"helpdesk-billing-starter","variant":"base","family":"bc-srvl-prompt-b-07","pid":"SRVL-PB-07a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"multiple","secs":736,"k":"ef10bf52-d67b-439e-9043-21274a7d31e7-r1","picks":[["inngest","c"],["vercel-functions","c"],["trigger-dev","a"],["aws-lambda","m"],["cloudflare-workers","m"],["fly","m"],["google-cloud-functions","m"],["netlify-functions","m"],["railway","m"],["render","m"]],"solution":["inngest","vercel-functions"],"ev":98,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent explicitly selected and implemented a two-part serverless architecture: Vercel Functions as the compute runtime and Git deployment target, paired with Inngest Cloud for managed cron scheduling (06:00 UTC) and automatic backoff retries.","c":0.95,"e":[["file","package.json"],["file","src/inngest.js"],["file","api/inngest.js"],["file","README.md"],["file","vercel.json"],["file","api/inngest.js"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"helpdesk-billing-starter","variant":"base","family":"bc-srvl-prompt-b-07","pid":"SRVL-PB-07a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"inngest","secs":450,"k":"ef10bf52-d67b-439e-9043-21274a7d31e7-r2","picks":[["inngest","p"],["render","m"],["railway","m"],["trigger-dev","a"],["aws-lambda","m"],["cloudflare-workers","m"],["fly","m"],["google-cloud-functions","m"],["google-cloud-run","m"],["netlify-functions","m"],["qstash","m"],["vercel-functions","m"]],"ev":72,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent evaluated several scheduled execution and serverless options and committed directly to Inngest to manage the scheduled cron execution and retries for daily billing sync, installing the inngest package and configuring the serve endpoint. Render was configured as the supporting hosting platform for the web service.","c":0.95,"e":[["file","package-lock.json"],["file","README.md"],["trace","15"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"helpdesk-billing-starter","variant":"base","family":"bc-srvl-prompt-b-07","pid":"SRVL-PB-07a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"trigger-dev","secs":841,"k":"ef10bf52-d67b-439e-9043-21274a7d31e7-r3","picks":[["trigger-dev","p"],["aws-lambda","m"],["cloudflare-workers","m"],["fly","m"],["google-cloud-functions","m"],["google-cloud-run","m"],["inngest","m"],["netlify-functions","m"],["qstash","m"],["render","m"],["vercel-functions","m"]],"ev":87,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent evaluated several serverless/scheduled compute options and definitively committed to Trigger.dev Cloud. It installed @trigger.dev/sdk, configured trigger.config.mjs, implemented src/trigger/daily-billing-sync.js, and documented deployment procedures in the README and .env.example.","c":1,"e":[["file","trigger.config.mjs"],["file","src/trigger/daily-billing-sync.js"],["file","README.md:25-55"],["file","package.json:12-19"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"php-gov-portal","variant":"base","family":"perf-ent-senior","pid":"PERF-3a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":401,"k":"0cdcc4d5-e01d-4e30-9f3a-0c085f9dfec8-r1","picks":[["diy","p","d"],["k6","m"],["lighthouse-ci","m"],["phpbench","m"]],"ev":76,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent evaluated external tools (k6, Lighthouse CI, PHPBench) and rejected them due to internal hosting constraints, registry limitations, and a desire to avoid extra dependencies. Instead, it built a custom DIY latency-budgeting assertion runner on top of the existing PHPUnit setup and configured it as a blocking job in `.gitlab-ci.yml`.","c":1,"e":[["file","tests/Performance/PerformanceBudget.php"],["file","tests/Performance/PerformanceGateTest.php"],["file","tests/Performance/PortailPerformanceTest.php"],["file",".gitlab-ci.yml:47-75"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"php-gov-portal","variant":"base","family":"perf-ent-senior","pid":"PERF-3a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":468,"k":"0cdcc4d5-e01d-4e30-9f3a-0c085f9dfec8-r2","picks":[["diy","p","d"],["codspeed","m"],["k6","m"],["phpbench","m"]],"ev":89,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The run chose to implement a custom, in-tree performance regression harness built with native PHP (`hrtime`) and PHPUnit, run via a dedicated, blocking GitLab CI stage `tests:performance` with `allow_failure: false`. External options like PHPBench, CodSpeed, Lighthouse, and k6 were evaluated and rejected due to hosting, dependency, and runner constraints.","c":0.95,"e":[["file",".gitlab-ci.yml:45-56"],["file","tests/Performance/PerformanceGate.php:1-81"],["file","tests/Performance/budget.json:1-5"],["file","phpunit.performance.xml.dist:1-20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"php-gov-portal","variant":"base","family":"perf-ent-senior","pid":"PERF-3a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":536,"k":"0cdcc4d5-e01d-4e30-9f3a-0c085f9dfec8-r3","picks":[["diy","p","d"],["codspeed","m"],["k6","m"],["lighthouse-ci","m"]],"ev":80,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The run opted for a DIY in-process performance evaluation solution implemented in PHP (`scripts/perf-gate.php`, `scripts/Perf/BudgetEvaluator.php`, and `scripts/prove-perf-gate.sh`) with route budgets defined in `tests/Performance/budgets.json`. It wired this custom solution into `.gitlab-ci.yml` and `.github/workflows/ci.yml` as a blocking step without `allow_failure` or `continue-on-error`, rejecting third-party SaaS benchmarking tools (CodSpeed, k6 Cloud, Lighthouse CI) due to strict hosting constraints.","c":1,"e":[["file","scripts/perf-gate.php"],["file","scripts/Perf/BudgetEvaluator.php"],["file",".gitlab-ci.yml:48-63"],["file",".github/workflows/ci.yml:11-46"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"php-gov-portal","variant":"base","family":"perf-ent-senior","pid":"PERF-3a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":4,"pick":"diy","secs":574,"k":"0cdcc4d5-e01d-4e30-9f3a-0c085f9dfec8-r4","picks":[["diy","p","d"],["lighthouse-ci","m"],["bencher","m"],["codspeed","m"],["k6","m"],["phpbench","m"]],"ev":72,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent inspected the repository hosting constraints (no third-party SaaS, internal registry only) and created a DIY in-process latency gate in PHP (`bin/performance-gate` and `tests/Performance/PerformanceGate.php`) wired into `.gitlab-ci.yml` as a blocking job, explicitly rejecting SaaS options like CodSpeed and Bencher as well as heavyweight load tools like k6.","c":0.95,"e":[["file","bin/performance-gate:1-65"],["file","tests/Performance/PerformanceGate.php:1-137"],["file","tests/Performance/budgets.json:1-9"],["file",".gitlab-ci.yml:45-61"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":385,"k":"2e92d490-db10-425b-aeba-e54d59a17622-r1","picks":[["diy","p","d"],["autocannon","m"],["bencher","m"],["codspeed","m"],["hyperfine","m"],["k6","m"],["lighthouse-ci","m"],["tinybench","m"]],"ev":64,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent evaluated multiple third-party performance and benchmarking tools (Lighthouse CI, k6, CodSpeed, Bencher, Autocannon, Tinybench, Hyperfine) and decided against adopting an external service or library. Instead, it authored a custom in-process throughput benchmark harness (`perf/reminder-throughput.ts`) with a checked-in baseline (`perf/baseline.json`) and ran it in GitHub Actions (`.github/workflows/perf.yml`).","c":0.95,"e":[["file","perf/reminder-throughput.ts:1-265"],["file","perf/baseline.json:1-11"],["file","package.json:9-10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":727,"k":"2e92d490-db10-425b-aeba-e54d59a17622-r2","picks":[["diy","p","d"],["autocannon","m"],["codspeed","m"],["k6","m"],["lighthouse-ci","m"],["tinybench","m"]],"ev":68,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent evaluated various third-party performance CI and load testing tools (Lighthouse CI, k6, Autocannon, CodSpeed, github-action-benchmark, Tinybench) and chose to implement a custom in-repo Node.js benchmark harness (`perf/bench.ts`) for the schedule view latency path, hooked directly into GitHub Actions with baseline comparison and evidence artifacts.","c":0.98,"e":[["file","perf/bench.ts:1-256"],["file","perf/baseline.json:1-11"],["file","package.json:9-11"],["file",".github/workflows/perf.yml:1-27"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior 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ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"diy","secs":618,"k":"2e92d490-db10-425b-aeba-e54d59a17622-r4","picks":[["diy","p","d"],["bencher","m"],["codspeed","m"],["github-actions-benchmark","m"],["k6","m"],["lighthouse-ci","m"],["tinybench","m"]],"ev":56,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent explicitly evaluated multiple third-party performance CI solutions (Lighthouse CI, CodSpeed, Bencher, Grafana k6, github-action-benchmark, Tinybench) and chose to implement a DIY custom Node.js performance gate script ('scripts/perf-gate.mts') with a committed baseline ('bench/baseline.json') executed via GitHub Actions ('perf.yml').","c":0.95,"e":[["file","scripts/perf-gate.mts:1-362"],["file","bench/baseline.json:1-13"],["file",".github/workflows/perf.yml:1-31"],["file","package.json:9-11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"perf-senior","pid":"PERF-1a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":475,"k":"aa01cf20-a26e-4824-9227-76239a5cdec9-r1","picks":[["diy","p","d"],["asv","m"],["k6","m"],["locust","m"],["pytest-benchmark","m"]],"ev":77,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent evaluated existing performance testing options (k6, Locust, pytest-benchmark, ASV) and rejected them in favor of a DIY in-process p95 latency check implemented directly in pytest and wired as a blocking step into GitHub Actions CI.","c":1,"e":[["file",".github/workflows/ci.yml:25-29"],["file","tests/test_perf_contract_summary.py:1-72"],["file","tests/conftest.py:1-105"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"perf-senior","pid":"PERF-1a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":288,"k":"aa01cf20-a26e-4824-9227-76239a5cdec9-r2","picks":[["diy","p","d"],["k6","m"],["locust","m"],["pytest-benchmark","m"]],"ev":75,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The agent evaluated external performance and load testing tools (k6, Locust, pytest-benchmark) and rejected them in favor of a hand-written in-process pytest performance test suite with latency and query assertion budgets configured directly in GitHub Actions.","c":0.95,"e":[["file","tests/test_contract_summary_perf.py"],["file",".github/workflows/ci.yml"],["file","tests/conftest.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"perf-senior","pid":"PERF-1a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":356,"k":"aa01cf20-a26e-4824-9227-76239a5cdec9-r3","picks":[["diy","p","d"],["k6","m"],["locust","m"],["pytest-benchmark","m"]],"ev":54,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The run evaluated several options including k6, Locust, Lighthouse CI, and pytest-benchmark before rejecting them in favor of a hand-written in-process performance gate (`tests/perf_gate.py` and `tests/test_perf_gate.py`) integrated directly into GitHub Actions.","c":0.95,"e":[["file","tests/perf_gate.py"],["file","tests/test_perf_gate.py"],["file",".github/workflows/ci.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"perf-senior","pid":"PERF-1a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"diy","secs":361,"k":"aa01cf20-a26e-4824-9227-76239a5cdec9-r4","picks":[["diy","p","d"],["k6","m"],["locust","m"],["pytest-benchmark","m"]],"ev":81,"co":"cursor-grok46-all-sectors-20260901-perf-ci","v":{"r":"The run explicitly evaluated and rejected dedicated load testing tools (Grafana k6, Locust) and pytest-benchmark in favor of a hand-written in-process latency budget test written directly in pytest and executed as a blocking step in GitHub Actions.","c":0.95,"e":[["file",".github/workflows/ci.yml:28-29"],["file","tests/test_contract_summary_perf.py:1-105"],["file","requirements-dev.txt:1-2"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"obs-uptime-remix-workshops","pid":"OBS-UPTIME-02a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"checkly","secs":361,"k":"6648b1c4-420c-4f48-bd0e-ef193f9d5c5a-r1","picks":[["checkly","p"],["betterstack","m"],["datadog","m"],["uptime-robot","m"]],"ev":85,"co":"cursor-grok46-all-sectors-20260901-observability","v":{"r":"The agent explicitly selected Checkly, installed the `checkly` package, configured project-level and check-level definitions in TypeScript (`checkly.config.ts`, `__checks__/booking.check.ts`, `__checks__/alert-channels.ts`), and set up a deployment workflow in `.github/workflows/checkly.yml`.","c":1,"e":[["file","checkly.config.ts"],["file","__checks__/booking.check.ts"],["file","__checks__/alert-channels.ts"],["file",".github/workflows/checkly.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Uptime monitoring and alerts","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"obs-uptime-remix-workshops","pid":"OBS-UPTIME-02a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"checkly","secs":374,"k":"6648b1c4-420c-4f48-bd0e-ef193f9d5c5a-r2","picks":[["checkly","p"],["betterstack","m"],["opentelemetry","m"],["uptime-kuma","m"],["uptime-robot","m"]],"ev":76,"co":"cursor-grok46-all-sectors-20260901-observability","v":{"r":"The agent selected Checkly as the synthetic monitoring tool, installed the Checkly npm package, and fully authored `checkly.config.ts`, check files in `checks/`, and a GitHub Actions workflow to deploy the check and alert channels.","c":1,"e":[["file","package.json"],["file","checkly.config.ts"],["file","checks/alert-channels.ts"],["file","checks/workshop-booking.check.ts"],["file",".github/workflows/deploy-checkly.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Uptime monitoring and alerts","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"obs-uptime-remix-workshops","pid":"OBS-UPTIME-02a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"checkly","secs":1427,"k":"6648b1c4-420c-4f48-bd0e-ef193f9d5c5a-r3","picks":[["checkly","p"],["grafana","m"],["betterstack","m"],["uptime-kuma","m"],["uptime-robot","m"]],"ev":69,"co":"cursor-grok46-all-sectors-20260901-observability","v":{"r":"The agent explicitly recommended and fully implemented Checkly as-code, configuring API checks on the workshop booking route with response assertions, escalation policies, and email alerting channels deployed via the Checkly CLI and GitHub Actions.","c":1,"e":[["file","monitoring/checkly.config.ts"],["file","monitoring/__checks__/booking.check.ts"],["file","monitoring/__checks__/alert-channels.ts"],["file",".github/workflows/deploy-monitoring.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Uptime monitoring and alerts","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"obs-uptime-remix-workshops","pid":"OBS-UPTIME-02a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"checkly","secs":321,"k":"6648b1c4-420c-4f48-bd0e-ef193f9d5c5a-r4","picks":[["checkly","p"],["betterstack","m"],["datadog","m"],["grafana","m"],["prometheus","m"],["sentry","m"],["uptime-robot","m"]],"ev":61,"co":"cursor-grok46-all-sectors-20260901-observability","v":{"r":"The agent explicitly recommended Checkly and implemented the configuration (`checkly.config.ts`, `checks/booking-page.check.ts`, `checks/owner-email.ts`, `scripts/deploy-checkly.sh`, and `checkly` devDependency in `package.json`). Several alternative observability platforms were deliberated in the trace and rejected with concrete reasons.","c":1,"e":[["file","checkly.config.ts"],["file","checks/booking-page.check.ts"],["file","checks/owner-email.ts"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Uptime monitoring and alerts","theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"bc-clde-prompt-c-05","pid":"CLDE-PC-05b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws","secs":472,"k":"3fac7182-5121-4da0-b503-8c681ae0a52d-r1","picks":[["aws","p"],["gcp","m"],["azure","m"],["cloudflare","m"],["minio","m"],["rabbitmq","m"],["redis","m"],["render","m"]],"ev":68,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent evaluated several cloud storage and queuing options and selected Amazon Web Services (specifically Amazon S3 and Amazon SQS) to satisfy the requirement of pay-per-use architecture with no idle capacity costs. It installed official AWS SDK packages (@aws-sdk/client-s3, @aws-sdk/client-sqs), implemented dedicated storage and queue adapters, updated the API to queue jobs and retrieve reports asynchronously, and implemented a standalone worker.","c":1,"e":[["file","package.json:18-19"],["file","src/storage.ts:1-49"],["file","src/queue.ts:1-74"],["file","README.md:19-71"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"bc-clde-prompt-c-05","pid":"CLDE-PC-05b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"aws","secs":673,"k":"3fac7182-5121-4da0-b503-8c681ae0a52d-r2","picks":[["aws","p"],["cloudflare","m"],["gcp","m"],["azure","m"],["minio","m"],["rabbitmq","m"],["redis","m"]],"ev":94,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent evaluated various cloud and self-hosted storage/queue options and committed to Amazon Web Services, installing AWS SDK clients for S3 and SQS, configuring environment settings, implementing adapters, and documenting S3, SQS, and Lambda operational patterns.","c":1,"e":[["file","package-lock.json"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"bc-clde-prompt-c-05","pid":"CLDE-PC-05b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"aws","secs":1343,"k":"3fac7182-5121-4da0-b503-8c681ae0a52d-r3","picks":[["aws","p"],["cloudflare","m"],["gcp","a"],["azure","a"],["minio","m"],["rabbitmq","m"],["redis","m"]],"ev":79,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent evaluated serverless cloud options for spiky, low-volume object storage and job queuing, explicitly recommending Amazon Web Services (S3 + SQS) and subsequently implementing adapters using `@aws-sdk/client-s3` and `@aws-sdk/client-sqs`.","c":1,"e":[["file","package.json"],["file","README.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"ai-gateway","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"bc-aigw-prompt-c-02","pid":"AIGW-PC-02b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-bedrock","secs":548,"k":"c6fb96c0-3859-442a-ab2c-d384a1dfec74-r1","picks":[["amazon-bedrock","p"],["openrouter","m"],["cloudflare-ai-gateway","m"],["helicone","m"],["kong-ai-gateway","m"],["litellm","m"],["portkey","m"]],"ev":88,"v":{"r":"The agent evaluated hosted AI gateway options against the repository's AWS EKS dual-region infrastructure and strict purchase-path isolation constraints. It explicitly recommended and implemented Amazon Bedrock (Converse API + Guardrails) via `@aws-sdk/client-bedrock-runtime` inside a new async `catalog-copy` service, while arguing against and rejecting external gateways like Portkey, Cloudflare AI Gateway, Helicone, and LiteLLM.","c":0.95,"e":[["file","services/catalog-copy/package.json:17"],["file","services/catalog-copy/src/lib/bedrock.ts:1-94"],["file",".env.example:16-20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"aigw-enterprise-ts-commerce-datadog","pid":"AIGW-02b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"portkey","secs":514,"k":"f6c07a46-6bbc-4880-9028-6d54c3c19e4f-r1","picks":[["portkey","p"],["amazon-bedrock","m"],["cloudflare-ai-gateway","m"],["helicone","m"],["kong-ai-gateway","m"],["litellm","m"],["openrouter","m"],["unify","m"],["vercel-ai-gateway","m"]],"ev":101,"v":{"r":"The agent evaluated hosted AI gateway options (Portkey, Helicone, LiteLLM, Cloudflare AI Gateway, Vercel AI Gateway, OpenRouter, Kong AI Gateway, Unify) and selected Portkey to implement semantic caching, multi-provider fallback, and budget tracking in a newly created off-checkout catalog service (`services/catalog`). The integration installs and configures `portkey-ai`.","c":1,"e":[["file","services/catalog/package.json"],["file","services/catalog/src/lib/portkey.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-01","repo":"go-customer-ops","variant":"base","family":"aigw-senior-go-customer-ops","pid":"AIGW-06b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"openrouter","secs":536,"k":"97b29cf1-9a6f-4182-be22-bf5afd824dd5-r1","picks":[["openrouter","p"],["amazon-bedrock","m"],["helicone","m"],["kong-ai-gateway","m"],["litellm","m"],["portkey","m"],["unify","m"]],"ev":68,"v":{"r":"The agent evaluated several hosted and proxy AI gateway candidates (OpenRouter, Portkey, LiteLLM, Helicone, and Kong) and committed to OpenRouter. It fully implemented OpenRouter in Go using its HTTP chat completions API, configured multi-provider fallbacks and usage/cost tracking, added database migrations to persist generation IDs and costs, and wired the summaries into the UI and HTTP API.","c":1,"e":[["file","internal/summarize/openrouter.go:1-143"],["file",".env.example:3-4"],["file","cmd/server/main.go:35-39"],["file","README.md:14-20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-01","repo":"edtech-lms","variant":"base","family":"aigw-enterprise-edtech-lms","pid":"AIGW-03b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"portkey","secs":813,"k":"9d287e31-6d62-40e9-acef-536c6b03e61a-r1","picks":[["portkey","p"],["cloudflare-ai-gateway","m"],["helicone","m"],["kong-ai-gateway","m"],["vercel-ai-gateway","m"],["amazon-bedrock","m"],["litellm","m"],["openrouter","m"],["unify","m"],["vercel-ai-sdk","m"]],"ev":111,"v":{"r":"The agent clearly selected and implemented Portkey as the hosted AI gateway for quiz generation. It added `portkey-ai` to requirements.txt, implemented the gateway client in `apps/grading/gateway.py` with multi-target fallback and per-district usage tracking, updated settings and environment configs, and explicitly evaluated and rejected OpenRouter and LiteLLM.","c":1,"e":[["file","requirements.txt"],["file","apps/grading/gateway.py"],["file",".env.example"],["file","brightloom/settings.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":1,"date":"2026-09-01","repo":"helpdesk-billing-starter","variant":"base","family":"bc-stor-prompt-b-06","pid":"STOR-PB-06a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-s3","secs":325,"k":"04b668e7-e958-48a1-be9a-ad8f37c4e3b4-r1","picks":[["amazon-s3","p"],["cloudflare-r2","m"],["google-cloud-storage","m"],["azure-blob-storage","m"],["supabase-storage","m"],["vercel-blob","m"],["minio","m"]],"ev":36,"co":"cursor-grok46-all-sectors-20260901-storage","v":{"r":"The agent explicitly recommended and then implemented Amazon S3 using `@aws-sdk/client-s3` and `@aws-sdk/s3-request-presigner`. It created adapter and route logic to handle presigned PUT/GET URLs, configured environment variables, updated documentation, and wrote unit tests for S3 integration.","c":1,"e":[["file","package.json"],["file","src/s3.js"],["file","README.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":1,"date":"2026-09-01","repo":"edtech-lms","variant":"base","family":"stor-enterprise-edtech-lms","pid":"STOR-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"google-cloud-storage","secs":643,"k":"96509acc-35cb-444b-9109-a5e50a530bca-r1","picks":[["google-cloud-storage","p"],["amazon-s3","m"],["azure-blob-storage","m"],["minio","m"]],"ev":93,"co":"cursor-grok46-all-sectors-20260901-storage","v":{"r":"The agent explicitly recommended Google Cloud Storage because the repository is already hosted on GCP and configured with GCS via django-storages. The agent then fully implemented the course material management feature in `apps/courses/storage.py`, `views.py`, and `models.py` using Google Cloud Storage v4 signed URLs. Alternative storage services (Amazon S3, Azure Blob Storage, MinIO) were explicitly considered and rejected due to operational and multi-cloud overhead.","c":1,"e":[["file","apps/courses/storage.py"],["file","apps/courses/models.py"],["file","apps/courses/views.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"rails-claims-ops","variant":"base","family":"bc-search-prompt-c-01","pid":"SEARCH-PC-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"algolia","secs":704,"k":"c10d9729-7d32-4416-8869-608857096569-r1","picks":[["algolia","p"],["typesense","m"],["meilisearch","m"],["elasticsearch","m"],["opensearch","m"],["postgres-fts","m"]],"ev":141,"v":{"r":"The agent evaluated several search backend options and unambiguously selected and implemented Algolia using the `algoliasearch-rails` gem. Postgres FTS, Elasticsearch, and OpenSearch were explicitly rejected due to database load and operational burden.","c":1,"e":[["file","Gemfile:8"],["file","config/initializers/algoliasearch.rb:1-31"],["file","app/models/claim.rb:17-38"],["file","app/models/assessment.rb:8-32"],["file","app/services/operational_search.rb:1-73"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"rails-claims-ops","variant":"base","family":"bc-search-prompt-c-01","pid":"SEARCH-PC-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"algolia","secs":1185,"k":"c10d9729-7d32-4416-8869-608857096569-r2","picks":[["algolia","p"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["postgres-fts","m"],["typesense","m"]],"ev":142,"v":{"r":"The agent explicitly recommended Algolia as the lowest-operations managed search solution, added the algolia gem, created the search indexing backend and rake tasks, and implemented UI search against it. Other search backends (Postgres FTS, Elasticsearch, OpenSearch, Meilisearch, Typesense) were evaluated and rejected.","c":1,"e":[["file","Gemfile:8"],["file","app/services/search_index/algolia_backend.rb:1-102"],["file","README.md:22-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"rails-claims-ops","variant":"base","family":"bc-search-prompt-c-01","pid":"SEARCH-PC-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"algolia","secs":2443,"k":"c10d9729-7d32-4416-8869-608857096569-r3","picks":[["algolia","p"],["typesense","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["postgres-fts","m"]],"ev":120,"v":{"r":"The agent evaluated several search backends and unambiguously recommended and implemented Algolia via the `algoliasearch-rails` gem. Postgres full-text search, Elasticsearch, OpenSearch, and Meilisearch were explicitly analyzed and rejected due to operational burden on the application database and team.","c":1,"e":[["file","Gemfile:8"],["file","config/initializers/algoliasearch.rb:1-30"],["file","app/models/claim.rb:17-41"],["file","app/services/claim_search.rb:1-89"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"bc-search-prompt-c-08","pid":"SEARCH-PC-08a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"postgres-fts","secs":434,"k":"26076d60-5668-4f47-b9dc-2626de907c2d-r1","picks":[["postgres-fts","p","b"],["algolia","m"],["meilisearch","m"]],"ev":60,"v":{"r":"The run evaluated search options for a Next.js and Supabase project, explicitly rejected third-party search platforms (Algolia, Meilisearch) due to cost and ops overhead, and implemented search using the built-in Postgres pg_trgm extension and SQL RPC functions in Supabase.","c":0.95,"e":[["file","supabase/migrations/0003_search.sql"],["trace","14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"bc-search-prompt-c-08","pid":"SEARCH-PC-08a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"postgres-fts","secs":424,"k":"26076d60-5668-4f47-b9dc-2626de907c2d-r2","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["typesense","m"]],"ev":66,"v":{"r":"The agent evaluated several search options and recommended using the existing PostgreSQL database with the built-in pg_trgm extension. It implemented this via a new migration adding pg_trgm indexes, a search_classes RPC function, a Next.js /find page, and helper routines without introducing any new services or third-party subscriptions.","c":0.95,"e":[["file","supabase/migrations/0003_search.sql:4-69"],["file","lib/search.ts:1-38"],["file","app/find/page.tsx:1-93"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"bc-search-prompt-c-08","pid":"SEARCH-PC-08a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":3,"pick":"postgres-fts","secs":531,"k":"26076d60-5668-4f47-b9dc-2626de907c2d-r3","picks":[["postgres-fts","p","b"],["algolia","m"],["fuse-js","m"],["meilisearch","m"],["typesense","m"]],"ev":35,"v":{"r":"The agent explicitly implemented Postgres trigram search via the pg_trgm extension in migration 0003_search.sql and wired it into app/page.tsx, utilizing the existing Supabase Postgres database. External search providers (Algolia, Meilisearch, Typesense) and client-side Fuse.js were considered and rejected to avoid subscriptions and maintain server-side querying.","c":1,"e":[["file","supabase/migrations/0003_search.sql"],["file","app/page.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"bc-panl-prompt-c-05","pid":"PANL-PC-05b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":661,"k":"0a5c685b-4600-4a29-81b1-a53621be8ae3-r1","picks":[["posthog","p"],["amplitude","m"],["datadog","m"],["datadog-product-analytics","m"],["heap","m"],["mixpanel","m"],["rudderstack","m"],["segment","m"],["snowplow","m"]],"ev":104,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated product analytics solutions to satisfy SSO, signed DPA, and dual-region data residency constraints. It recommended PostHog Cloud and implemented a dedicated Kafka exporter service using `posthog-node` alongside shared event schemas and documentation.","c":1,"e":[["file","services/analytics-exporter/package.json"],["file","services/analytics-exporter/src/lib/posthog.ts"],["file","packages/analytics-events/src/index.ts"],["file","docs/analytics.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"bc-panl-prompt-c-05","pid":"PANL-PC-05b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"posthog","secs":642,"k":"0a5c685b-4600-4a29-81b1-a53621be8ae3-r2","picks":[["posthog","p"],["heap","m"],["amplitude","m"],["datadog","m"],["datadog-product-analytics","m"],["mixpanel","m"],["rudderstack","m"],["segment","m"],["snowplow","m"]],"ev":104,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent explicitly recommended PostHog based on compliance (DPA, SAML SSO, and EU data residency in Frankfurt matching eu-central-1), and fully implemented the solution using the `posthog-node` SDK in a dedicated `services/checkout-analytics` Kafka consumer.","c":1,"e":[["file","services/checkout-analytics/package.json"],["file","services/checkout-analytics/src/posthog.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"bc-panl-prompt-c-05","pid":"PANL-PC-05b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"posthog","secs":653,"k":"0a5c685b-4600-4a29-81b1-a53621be8ae3-r3","picks":[["posthog","p"],["mixpanel","a"],["amplitude","a"],["datadog","m"],["datadog-product-analytics","m"],["rudderstack","m"],["segment","m"],["snowplow","m"]],"ev":107,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated product analytics vendors against compliance and data residency requirements, recommended PostHog, and implemented an off-path consumer service using `posthog-node` to forward Kafka checkout events to regional PostHog hosts.","c":1,"e":[["file","services/checkout-analytics/package.json"],["file","services/checkout-analytics/src/posthog.ts"],["file","docs/checkout-analytics.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-junior","pid":"OBS-5a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"sentry","secs":562,"k":"03a09780-79f9-4c28-8c48-a02fe0cf0426-r1","picks":[["sentry","p"],["appsignal","m"],["axiom","m"],["betterstack","m"],["bugsnag","m"],["datadog","m"],["glitchtip","m"],["grafana","m"],["honeybadger","m"],["new-relic","m"],["papertrail","m"],["prometheus","m"],["rollbar","m"],["uptime-robot","m"]],"ev":81,"co":"cursor-grok46-all-sectors-20260901-observability","v":{"r":"The agent selected hosted Sentry as the error monitoring solution for the Express and MongoDB project. It installed `@sentry/node`, initialized the SDK in `instrument.js`, integrated error capture into Express middleware and background scripts, and created an executable script (`scripts/setup-sentry-alerts.js`) that uses Sentry's API to configure production issue alert rules.","c":1,"e":[["file","package.json:11"],["file","instrument.js:1-15"],["file","server.js:1-33"],["file","scripts/setup-sentry-alerts.js:1-232"],["file",".env.example:9-15"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Error monitoring","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-junior","pid":"OBS-5a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"sentry","secs":710,"k":"03a09780-79f9-4c28-8c48-a02fe0cf0426-r2","picks":[["sentry","p"],["axiom","m"],["honeycomb","m"],["betterstack","m"],["datadog","m"],["glitchtip","m"],["grafana","m"],["new-relic","m"],["opentelemetry","m"],["pino","m"],["uptime-robot","m"]],"ev":59,"co":"cursor-grok46-all-sectors-20260901-observability","v":{"r":"The agent proposed Sentry, received confirmation, and fully implemented it by installing @sentry/node, initializing the SDK across the Express server and reminder jobs, and providing an idempotent setup script (scripts/setup-sentry-alert.js) to configure issue alert rules via Sentry's API.","c":1,"e":[["file","package.json:11-15"],["file","instrument.js:1-17"],["file","server.js:1-35"],["file","scripts/setup-sentry-alert.js:1-192"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Error monitoring","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-junior","pid":"OBS-5a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"sentry","secs":857,"k":"03a09780-79f9-4c28-8c48-a02fe0cf0426-r3","picks":[["sentry","p"],["axiom","m"],["betterstack","m"],["bugsnag","m"],["datadog","m"],["glitchtip","m"],["grafana","m"],["honeycomb","m"],["papertrail","m"],["pino","m"],["prometheus","m"],["rollbar","m"],["uptime-robot","m"]],"ev":75,"co":"cursor-grok46-all-sectors-20260901-observability","v":{"r":"The agent explicitly selected Sentry, installed the `@sentry/node` package, wired it across the Express server and scripts, and added a production script creating issue alerts via Sentry's REST API.","c":1,"e":[["file","package.json:11-15"],["file","instrument.js:1-12"],["file","scripts/setup-sentry-alert.js:1-205"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Error monitoring","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-junior","pid":"OBS-5a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"sentry","secs":364,"k":"03a09780-79f9-4c28-8c48-a02fe0cf0426-r4","picks":[["sentry","p"],["opentelemetry","m"],["bugsnag","m"],["datadog","m"],["glitchtip","m"],["honeybadger","m"],["new-relic","m"],["prometheus","m"],["rollbar","m"],["uptime-robot","m"]],"ev":84,"co":"cursor-grok46-all-sectors-20260901-observability","v":{"r":"The agent evaluated several error monitoring and alerting tools and committed to hosted Sentry. It installed `@sentry/node`, initialized Sentry instrumentation in `instrument.js` and `server.js`, captured exceptions in `config/db.js`, `controllers/ticketsController.js`, and `scripts/sendReminders.js`, and wrote an automated alert provisioning script `scripts/provision-sentry-alert.js`.","c":1,"e":[["file","package.json"],["file","instrument.js"],["file","server.js"],["file","scripts/provision-sentry-alert.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Error monitoring","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-03","pid":"EVAL-PC-03a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"langfuse","secs":565,"k":"df6dfc5d-179e-403d-a6f6-0497d0baf50e-r1","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"],["phoenix","m"]],"ev":90,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent explicitly recommended and fully integrated Langfuse (Langfuse Cloud) to provide production LLM observability and tracing for Anthropic API calls. It installed the `langfuse` dependency, implemented an adapter in `app/tracing.py`, instrumented `app/llm.py` and `app/main.py`, updated configuration/documentation, and verified the integration with a test suite.","c":1,"e":[["file","pyproject.toml:18"],["file","app/tracing.py:1-119"],["file","app/llm.py:42-76"],["file","app/main.py:63-87"],["file","README.md:73-100"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-03","pid":"EVAL-PC-03a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"langfuse","secs":434,"k":"df6dfc5d-179e-403d-a6f6-0497d0baf50e-r2","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"],["phoenix","m"]],"ev":112,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent evaluated several LLM observability/eval options (Langfuse, LangSmith, Arize Phoenix, Braintrust) and explicitly selected Langfuse Cloud. It implemented the `langfuse` dependency, configured environment variables, wrote the adapter in `app/tracing.py`, and added comprehensive tests.","c":1,"e":[["file","pyproject.toml"],["file","app/tracing.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-03","pid":"EVAL-PC-03a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"langfuse","secs":581,"k":"df6dfc5d-179e-403d-a6f6-0497d0baf50e-r3","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["langsmith","m"],["logfire","m"],["opentelemetry","m"],["phoenix","m"],["traceloop","m"]],"ev":93,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent evaluated several LLM observability/eval options and selected Langfuse Cloud. It fully implemented and verified a thin, best-effort adapter in app/tracing.py, integrated it into the FastAPI request lifecycle and Anthropic API calls, updated pyproject.toml, and documented configuration in README.md.","c":1,"e":[["file","pyproject.toml"],["file","app/tracing.py"],["file","app/llm.py"],["file","app/main.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"nuxt-fieldservice","variant":"base","family":"bc-search-prompt-c-07","pid":"SEARCH-PC-07b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"algolia","secs":632,"k":"6af8a01a-9d12-4e14-9d77-43279b2b988b-r1","picks":[["algolia","p"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["postgres-fts","m"],["typesense","m"]],"ev":150,"v":{"r":"The agent evaluated several search options against the team's low-ops requirement and selected Algolia. It installed the `algoliasearch` SDK, implemented client configuration, database write-sync hooks, full-reindex scripts, a `/api/search` route, and a Nuxt search UI page, while rejecting Postgres FTS, Elasticsearch, OpenSearch, Meilisearch, and Typesense.","c":1,"e":[["file","package.json"],["file","server/search/client.ts"],["file","server/api/search.get.ts"],["file","pages/search.vue"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"nuxt-fieldservice","variant":"base","family":"bc-search-prompt-c-07","pid":"SEARCH-PC-07b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"algolia","secs":759,"k":"6af8a01a-9d12-4e14-9d77-43279b2b988b-r2","picks":[["algolia","p"],["atlas-search","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["postgres-fts","m"],["typesense","m"]],"ev":147,"v":{"r":"The user requested the lowest-operations managed solution for search over tens of millions of jobs and customers without placing indexing/search load on PostgreSQL. The agent evaluated managed search engines (Algolia, Typesense, Meilisearch, Elasticsearch, OpenSearch, Postgres FTS, MongoDB Atlas Search), recommended Algolia, and fully integrated the `algoliasearch` SDK with indexing hooks, backfill scripts, a Nuxt server API route, and search UI components.","c":1,"e":[["file","package.json:18"],["file","server/utils/searchClient.ts:1-188"],["file","server/api/search.get.ts:1-38"],["file","scripts/index-search.ts:1-60"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"nuxt-fieldservice","variant":"base","family":"bc-search-prompt-c-07","pid":"SEARCH-PC-07b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"algolia","secs":602,"k":"6af8a01a-9d12-4e14-9d77-43279b2b988b-r3","picks":[["algolia","p"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["postgres-fts","m"],["typesense","m"]],"ev":118,"v":{"r":"The agent evaluated several search backends and selected Algolia as the lowest-ops managed solution. It installed the `algoliasearch` SDK, implemented indexing hooks on write operations, configured index settings, created server-side search API routes, and updated the UI components to use Algolia search.","c":1,"e":[["file","package.json:18"],["file","server/utils/algolia.ts:1-157"],["file","server/api/search/jobs.get.ts:1-63"],["file","README.md:19"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-04","pid":"EVAL-PC-04a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":656,"k":"5952965f-bb55-46b7-a7ba-d5320ec391ef-r1","picks":[["diy","p","b"],["braintrust","m"],["deepeval","m"],["inspect-ai","m"],["langsmith","m"],["promptfoo","m"]],"ev":75,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent explicitly evaluated external eval platforms (Promptfoo, DeepEval, Inspect AI, LangSmith, Braintrust) and rejected them in favor of building an in-repo eval harness using the project's pre-existing pytest runner and Anthropic client.","c":0.95,"e":[["file","tests/conftest.py"],["file","tests/test_eval_live.py"],["file","pyproject.toml"],["trace","seq:27"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-04","pid":"EVAL-PC-04a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":485,"k":"5952965f-bb55-46b7-a7ba-d5320ec391ef-r2","picks":[["diy","p","b"],["braintrust","m"],["deepeval","m"],["inspect-ai","m"],["langsmith","m"],["promptfoo","m"]],"ev":63,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent explicitly decided against adopting third-party eval frameworks or SaaS platforms (such as Inspect AI, Promptfoo, LangSmith, Braintrust, or DeepEval) and built a lightweight evaluation suite using the existing pytest harness already configured in the repo.","c":0.95,"e":[["file","evals/test_assistant_regression.py"],["file","evals/cases.py"],["file","tests/test_eval_cases.py"],["file","README.md"],["trace","53"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-04","pid":"EVAL-PC-04a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":654,"k":"5952965f-bb55-46b7-a7ba-d5320ec391ef-r3","picks":[["diy","p","b"],["braintrust","m"],["deepeval","m"],["inspect-ai","m"],["langsmith","m"],["promptfoo","m"]],"ev":74,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent explicitly evaluated third-party eval frameworks (DeepEval, Promptfoo, Inspect AI, LangSmith, Braintrust) and rejected them in favor of building a plain pytest eval harness under `tests/eval/` that leverages the project's existing pytest setup.","c":0.95,"e":[["file","pyproject.toml"],["file",".github/workflows/ci.yml"],["file","tests/conftest.py"],["file","tests/eval/cases.py"],["file","tests/eval/test_gold.py"],["file","tests/eval/test_live.py"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"edtech-lms","variant":"base","family":"botp-enterprise-edtech-lms","pid":"BOTP-05b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"django-axes","secs":594,"k":"c745221e-cd28-47ff-b8b3-16c7d732b131-r1","picks":[["django-axes","p"],["cloud-armor","m"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":102,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The agent evaluated third-party CAPTCHA providers (Google reCAPTCHA, Cloudflare Turnstile, hCaptcha) against the application's FERPA requirements and Django admin-only authentication surface. It rejected third-party CAPTCHAs in favor of `django-axes`, which was installed, configured in settings, and thoroughly tested for login lockout.","c":0.95,"e":[["file","requirements.txt:10"],["file","brightloom/settings.py:41"],["file","brightloom/settings.py:155-182"],["file","apps/roster/tests.py:1-97"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"edtech-lms","variant":"base","family":"botp-enterprise-edtech-lms","pid":"BOTP-05b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"recaptcha","secs":634,"k":"c745221e-cd28-47ff-b8b3-16c7d732b131-r2","picks":[["recaptcha","p"],["cloud-armor","m"],["django-axes","m"],["hcaptcha","m"],["turnstile","m"]],"ev":107,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The agent evaluated several CAPTCHA solutions and chose Google reCAPTCHA Enterprise because the LMS is hosted on GCP Cloud Run, allowing server-side assessment via Application Default Credentials without introducing a new FERPA subprocessor. The agent installed `google-cloud-recaptcha-enterprise`, wrote the assessment backend and custom admin authentication form, customized the admin login template, and added unit tests.","c":1,"e":[["file","requirements.txt:10"],["file","brightloom/recaptcha.py:1-139"],["file","brightloom/admin_login.py:1-52"],["file","templates/admin/login.html:10-15"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"edtech-lms","variant":"base","family":"botp-enterprise-edtech-lms","pid":"BOTP-05b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"altcha","secs":842,"k":"c745221e-cd28-47ff-b8b3-16c7d732b131-r3","picks":[["altcha","p"],["cloud-armor","m"],["django-axes","m"],["friendly-captcha","m"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":125,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The run specifically evaluated multiple CAPTCHA/bot-protection solutions for Django admin login and explicitly committed to ALTCHA via django-altcha, implementing an adaptive failure threshold, while rejecting Google reCAPTCHA, Cloudflare Turnstile, hCaptcha, and Friendly Captcha due to FERPA data privacy constraints, infrastructure mismatch, or pricing.","c":1,"e":[["file","requirements.txt:16"],["file","brightloom/settings.py:43"],["file","apps/roster/forms.py:18-24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":1,"date":"2026-09-01","repo":"dotnet-utility-billing","variant":"base","family":"bc-stor-prompt-b-02","pid":"STOR-PB-02b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-blob-storage","secs":529,"k":"2bd02ea7-de50-40d2-a941-e42e7fddcd06-r1","picks":[["azure-blob-storage","p"],["amazon-s3","m"]],"ev":73,"co":"cursor-grok46-all-sectors-20260901-storage","v":{"r":"The agent evaluated object storage options for invoice PDF documents and selected Azure Blob Storage to align with the existing Azure infrastructure. The implementation was fully completed with Azure.Storage.Blobs SDK integration, configuration, migrations, and test suites.","c":1,"e":[["file","src/Northmere.Billing.Api/Storage/AzureBlobInvoiceDocumentStore.cs:1-70"],["file","Directory.Packages.props:10"],["file","src/Northmere.Billing.Api/Program.cs:21-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"bc-srvl-prompt-b-06","pid":"SRVL-PB-06a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel-functions","secs":217,"k":"63dd8365-203b-4fad-b3a1-323bfb96de13-r1","picks":[["vercel-functions","p","b"],["cloudflare-workers","m"],["aws-lambda","m"],["inngest","m"]],"ev":51,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The repository is a Next.js storefront deployed on Vercel. The agent recommended and implemented Vercel Functions using the `@vercel/functions` package (`waitUntil` helper and route runtime configurations), rejecting third-party serverless options like AWS Lambda and Google Cloud Functions.","c":1,"e":[["file","package.json:13"],["file","app/api/webhooks/stripe/route.ts:2-10"],["file","app/api/webhooks/stripe/route.ts:33-36"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"bc-srvl-prompt-b-06","pid":"SRVL-PB-06a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"vercel-functions","secs":232,"k":"63dd8365-203b-4fad-b3a1-323bfb96de13-r2","picks":[["vercel-functions","p","b"],["azure-functions","m"],["aws-lambda","m"],["cloudflare-workers","m"],["google-cloud-functions","m"],["inngest","m"],["qstash","m"]],"ev":60,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent explicitly recommended sticking with Vercel's built-in Route Handlers / Serverless Functions since the repository is already a Next.js application configured for Vercel, and configured route duration and region settings in both the route file and vercel.json. Alternative serverless solutions (AWS Lambda, Cloudflare Workers, Google Cloud Functions, Inngest) were deliberately evaluated and rejected.","c":1,"e":[["file","vercel.json"],["file","app/api/webhooks/stripe/route.ts"],["trace","18"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"bc-srvl-prompt-b-06","pid":"SRVL-PB-06a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"vercel-functions","secs":548,"k":"63dd8365-203b-4fad-b3a1-323bfb96de13-r3","picks":[["vercel-functions","p","b"],["aws-lambda","m"],["cloudflare-workers","m"],["google-cloud-functions","m"],["inngest","m"],["netlify-functions","m"],["qstash","m"]],"ev":38,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent evaluated several serverless platforms (AWS Lambda, Cloudflare Workers, Inngest, Netlify Functions, Google Cloud Functions) and explicitly recommended remaining on the built-in Vercel Functions platform already used by the Next.js storefront. It then configured the serverless webhook route handler with maxDuration and hardened error handling for Stripe webhook retries.","c":0.95,"e":[["file","app/api/webhooks/stripe/route.ts:7"],["trace","13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"fieldservice-saas-billing","variant":"base","family":"bc-srvl-prompt-c-03","pid":"SRVL-PC-03a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel-functions","secs":279,"k":"3b91e48f-2f59-47cb-a1a5-e08db9996b17-r1","picks":[["vercel-functions","p"],["aws-lambda","m"],["cloudflare-workers","m"],["netlify-functions","m"]],"ev":30,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent evaluated serverless cron options for a Nuxt application and selected Vercel Functions with Vercel Cron, adding vercel.json and implementing the serverless route at server/api/cron/invoice-reminders.get.js while explicitly rejecting AWS Lambda, Netlify Functions, and Cloudflare Workers.","c":1,"e":[["file","vercel.json:1-8"],["file","server/api/cron/invoice-reminders.get.js:1-11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"fieldservice-saas-billing","variant":"base","family":"bc-srvl-prompt-c-03","pid":"SRVL-PC-03a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"cloudflare-workers","secs":536,"k":"3b91e48f-2f59-47cb-a1a5-e08db9996b17-r2","picks":[["cloudflare-workers","p"],["aws-lambda","m"],["inngest","m"],["netlify-functions","m"],["qstash","m"],["trigger-dev","m"],["vercel-functions","m"]],"ev":80,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent evaluated several serverless compute options (Cloudflare Workers, Vercel, Netlify, AWS Lambda, Inngest, Trigger.dev) and implemented Cloudflare Workers via Nitro's `cloudflare-module` preset and Wrangler configuration for scheduled daily cron triggers.","c":1,"e":[["file","nuxt.config.ts"],["file","package.json"],["trace","seq:32"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"fieldservice-saas-billing","variant":"base","family":"bc-srvl-prompt-c-03","pid":"SRVL-PC-03a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"vercel-functions","secs":482,"k":"3b91e48f-2f59-47cb-a1a5-e08db9996b17-r3","picks":[["vercel-functions","p"],["trigger-dev","m"],["aws-lambda","m"],["inngest","m"]],"ev":68,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent selected Vercel Functions and Vercel Cron to host and trigger Nuxt Nitro's scheduled invoice reminder task, configuring `nitro.vercel.config.crons` in `nuxt.config.js` and creating a secured API route at `server/api/cron/invoice-reminders.get.js`.","c":0.95,"e":[["file","nuxt.config.js:10-14"],["file","server/api/cron/invoice-reminders.get.js"],["trace","11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"laravel-helpdesk","variant":"base","family":"search-junior","pid":"SEARCH-2b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"mysql-fulltext","secs":350,"k":"2487ee2a-55a7-43a2-860c-2cb655eba8f6-r1","picks":[["mysql-fulltext","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["typesense","m"]],"ev":73,"v":{"r":"The agent evaluated several search backend options (Elasticsearch, Postgres FTS, Meilisearch, Typesense, Algolia) and committed to MySQL FULLTEXT as the built-in capability of the project's existing MySQL 8 database. It implemented a database migration adding a composite FULLTEXT index and wired a search scope using Laravel's whereFullText query builder.","c":0.98,"e":[["file","database/migrations/2026_09_01_194200_add_tickets_search_fulltext_index.php:15-18"],["file","app/Models/Ticket.php:87-92"],["file","app/Http/Controllers/TicketController.php:20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"laravel-helpdesk","variant":"base","family":"search-junior","pid":"SEARCH-2b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"mysql-fulltext","secs":616,"k":"2487ee2a-55a7-43a2-860c-2cb655eba8f6-r2","picks":[["mysql-fulltext","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"]],"ev":77,"v":{"r":"The agent explicitly recommended and implemented MySQL FULLTEXT indexes using Laravel's whereFullText query helper, adding migrations for InnoDB FULLTEXT indexes across tickets and replies. It rejected third-party search backends (Meilisearch, Algolia, Elasticsearch) as overkill and operational overhead, and dismissed Postgres FTS due to the existing MySQL stack.","c":0.98,"e":[["file","database/migrations/2026_09_01_211900_add_ticket_search_indexes.php:20-29"],["file","app/Models/Ticket.php:57-66"],["trace","20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"laravel-helpdesk","variant":"base","family":"search-junior","pid":"SEARCH-2b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"mysql-fulltext","secs":1314,"k":"2487ee2a-55a7-43a2-860c-2cb655eba8f6-r3","picks":[["mysql-fulltext","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"]],"ev":67,"v":{"r":"The agent added a MySQL InnoDB FULLTEXT index across subject, body, requester_name, and requester_email in a new migration and implemented `whereFullText` boolean search queries within Eloquent on the Ticket model. External search engines and non-MySQL alternatives were explicitly evaluated and rejected.","c":0.95,"e":[["file","database/migrations/2026_09_01_215600_add_ticket_search_indexes.php:17-25"],["file","app/Models/Ticket.php:58-103"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-01","repo":"edtech-lms","variant":"base","family":"bc-aigw-prompt-b-03","pid":"AIGW-PB-03b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":692,"k":"f48e15de-f2ac-4679-beca-64ab1d64223f-r1","picks":[["diy","p","d"],["helicone","m"],["amazon-bedrock","m"],["cloudflare-ai-gateway","m"],["kong-ai-gateway","m"],["langchain","m"],["litellm","m"],["openrouter","m"],["portkey","m"]],"ev":117,"v":{"r":"The agent rejected embedding third-party SDKs, proxies (LiteLLM, Portkey), or SaaS gateways (OpenRouter, Cloudflare) into the Django application due to FERPA compliance, latency, and operational simplicity. Instead, it implemented a DIY custom HTTP integration (apps/grading/ai_gateway.py) and Celery task that forwards requests to an internal hosted gateway (ai-gateway.brightloom.io).","c":0.95,"e":[["file","apps/grading/ai_gateway.py"],["file","apps/grading/tasks.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":1,"date":"2026-09-01","repo":"dotnet-utility-billing","variant":"base","family":"stor-enterprise-dotnet-utility-billing","pid":"STOR-02b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-blob-storage","secs":570,"k":"b303fc36-a0e3-4881-afd9-1c99df92c735-r1","picks":[["azure-blob-storage","p"],["amazon-s3","m"],["google-cloud-storage","m"]],"ev":84,"co":"cursor-grok46-all-sectors-20260901-storage","v":{"r":"The agent selected and fully implemented Azure Blob Storage for persisting customer bill copies. It updated the Bicep templates to provision the storage account and private container, integrated `Azure.Storage.Blobs` with `DefaultAzureCredential` in .NET, and rejected multi-cloud alternatives like Amazon S3 and Google Cloud Storage.","c":1,"e":[["file","infra/main.bicep:98-132"],["file","src/Northmere.Billing.Api/Services/InvoiceDocumentStorage.cs:79-107"],["file","Directory.Packages.props:10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"helpdesk-billing-starter","variant":"base","family":"bc-srvl-prompt-c-07","pid":"SRVL-PC-07a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cloudflare-workers","secs":271,"k":"1e01df30-3d6b-46c4-bfe7-a6b4feaef0c0-r1","picks":[["cloudflare-workers","p"],["inngest","m"],["trigger-dev","m"],["render","m"],["fly","m"],["supabase-edge-functions","m"],["aws-lambda","m"],["google-cloud-functions","m"],["netlify-functions","m"],["qstash","m"],["vercel-functions","m"]],"ev":35,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent evaluated several managed serverless and scheduling options, chose Cloudflare Workers Cron Triggers, and fully implemented the solution with `wrangler.toml`, `src/worker.js`, and documentation in `README.md`.","c":1,"e":[["file","wrangler.toml:1-12"],["file","src/worker.js:1-33"],["file","README.md:25-56"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"helpdesk-billing-starter","variant":"base","family":"bc-srvl-prompt-c-07","pid":"SRVL-PC-07a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"builtin","secs":227,"k":"1e01df30-3d6b-46c4-bfe7-a6b4feaef0c0-r2","picks":[["builtin","p","b"],["aws-lambda","m"],["azure-functions","m"],["cloudflare-workers","m"],["deno-deploy","m"],["fly","m"],["google-cloud-functions","m"],["inngest","m"],["netlify-functions","m"],["render","m"],["supabase-edge-functions","m"],["trigger-dev","m"],["vercel-functions","m"]],"ev":31,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent explicitly recommended using GitHub Actions scheduled workflows rather than adopting any standalone serverless FaaS or cron service (such as AWS Lambda, Vercel Functions, Cloudflare Workers, or Netlify Functions). Upon confirmation, the agent created `.github/workflows/billing-sync.yml` and the billing sync script `src/sync.js`.","c":0.95,"e":[["file",".github/workflows/billing-sync.yml:1-33"],["file","README.md:25-54"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"helpdesk-billing-starter","variant":"base","family":"bc-srvl-prompt-c-07","pid":"SRVL-PC-07a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"cloudflare-workers","secs":488,"k":"1e01df30-3d6b-46c4-bfe7-a6b4feaef0c0-r3","picks":[["cloudflare-workers","p"],["google-cloud-run","m"],["render","m"],["fly","m"],["netlify-functions","m"],["aws-lambda","m"],["google-cloud-functions","m"],["inngest","m"],["supabase-edge-functions","m"],["trigger-dev","m"],["vercel-functions","m"]],"ev":49,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent evaluated several managed serverless/scheduled function solutions and recommended Cloudflare Workers Cron Triggers. Following the user's confirmation, it implemented the Worker in `src/worker.js`, added `wrangler.toml` with cron trigger scheduling (`0 6 * * *`), implemented `src/billing-sync.js` and `src/stripe.js`, added test coverage, and documented the operational procedures.","c":1,"e":[["file","wrangler.toml:1-13"],["file","src/worker.js:1-36"],["file","README.md:25-60"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"panl-enterprise-ts-commerce-datadog","pid":"PANL-05b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":355,"k":"8a96c69e-1481-44bc-8cd5-91f4a77dc7a7-r1","picks":[["diy","p","d"],["amplitude","m"],["datadog","m"],["datadog-product-analytics","m"],["segment","m"]],"ev":77,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated product analytics approaches under a high peak-event volume requirement with strict ingestion cost constraints. It rejected external per-event product analytics tools (e.g. Amplitude) and instead implemented a DIY solution using an allow-listed DogStatsD increment helper in @halberd/telemetry backed by the existing Datadog infrastructure.","c":0.95,"e":[["file","packages/telemetry/src/index.ts"],["file","services/checkout/src/routes/checkout.ts"],["file","services/checkout/src/app.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"panl-enterprise-ts-commerce-datadog","pid":"PANL-05b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"builtin","secs":284,"k":"8a96c69e-1481-44bc-8cd5-91f4a77dc7a7-r2","picks":[["builtin","p","b"],["amplitude","m"],["datadog","m"],["datadog-product-analytics","m"],["mixpanel","m"]],"ev":77,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The user asked for a product analytics approach to track completed and rejected checkout requests at predictable cost under high peak volumes. The run evaluated external per-event analytics platforms (Amplitude, Mixpanel) and Datadog RUM/Product Analytics, rejecting them due to per-event billing or stack mismatch. It chose and implemented DogStatsD custom metric counters via the pre-existing Datadog SDK (@halberd/telemetry).","c":0.95,"e":[["file","packages/telemetry/src/index.ts:47-50"],["file","services/checkout/src/routes/checkout.ts:42-83"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"panl-enterprise-ts-commerce-datadog","pid":"PANL-05b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"builtin","secs":483,"k":"8a96c69e-1481-44bc-8cd5-91f4a77dc7a7-r3","picks":[["builtin","p","b"],["amplitude","m"],["datadog","m"],["datadog-product-analytics","m"],["mixpanel","m"],["segment","m"]],"ev":99,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The user requested a product analytics solution for completed and rejected checkout requests that maintains predictable ingestion costs at millions of events per peak day. The agent evaluated the repository's existing Datadog APM/DogStatsD setup and explicitly rejected dedicated event-based product analytics platforms (Amplitude, Mixpanel) due to per-event pricing. It implemented custom metrics (`checkout.settle.outcome`) via DogStatsD in `@halberd/telemetry` on top of the existing Datadog agent infrastructure.","c":0.95,"e":[["file","docs/observability.md"],["file","packages/telemetry/src/index.ts"],["file","platform/helm/datadog-values.yaml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"panl-junior-b2b-subscriptions","pid":"PANL-04b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":321,"k":"d2dd62cd-9bd5-4bf1-bb14-d92e24e0db29-r1","picks":[["posthog","p"],["amplitude","m"],["google-analytics","m"],["metabase","m"],["mixpanel","m"],["plausible","m"],["segment","m"],["umami","m"]],"ev":54,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent explicitly recommended and fully implemented PostHog Cloud (EU) using posthog-node across the billing and API packages, wiring lifecycle events and documenting setup in README.md while evaluating and rejecting other product analytics tools.","c":1,"e":[["file","package.json:14"],["file","packages/analytics/src/index.js:1-93"],["file","README.md:7-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"panl-junior-b2b-subscriptions","pid":"PANL-04b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"posthog","secs":397,"k":"d2dd62cd-9bd5-4bf1-bb14-d92e24e0db29-r2","picks":[["posthog","p"],["amplitude","m"],["datadog","m"],["google-analytics","m"],["metabase","m"],["mixpanel","m"],["plausible","m"],["segment","m"]],"ev":53,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent explicitly recommended PostHog Cloud (EU) and implemented a dedicated `packages/analytics` module wrapping `posthog-node` to track organization activations, invoice renewals, and drop-offs. It evaluated and rejected Mixpanel, Amplitude, Google Analytics, and Plausible.","c":1,"e":[["file","package.json"],["file","packages/analytics/src/index.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"panl-junior-b2b-subscriptions","pid":"PANL-04b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"posthog","secs":786,"k":"d2dd62cd-9bd5-4bf1-bb14-d92e24e0db29-r3","picks":[["posthog","p"],["heap","m"],["amplitude","m"],["google-analytics","m"],["mixpanel","m"],["segment","m"]],"ev":38,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent investigated product analytics options for tracking organization lifecycles, compared several candidates (PostHog, Mixpanel, Amplitude, GA4, Heap), and committed to PostHog Cloud EU by installing posthog-node, writing an adapter with test coverage, and updating documentation.","c":1,"e":[["file","package.json"],["file","apps/api/src/analytics.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-vibe","pid":"OBS-4b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":460,"k":"757a18a9-14ae-4dec-b587-412265a654e5-r1","picks":[["diy","p","d"],["betterstack","m"],["checkly","m"],["datadog","m"],["sentry","m"],["uptime-robot","m"]],"ev":57,"co":"cursor-grok46-all-sectors-20260901-observability","v":{"r":"The agent explicitly decided against adopting third-party APM or uptime platforms (such as Sentry, Datadog, Better Stack, Checkly, or UptimeRobot) to avoid unnecessary complexity and external account dependencies. Instead, it implemented a self-contained DIY uptime check consisting of a dedicated `/api/health` endpoint querying Supabase, a Node.js probe script, and a GitHub Actions workflow that alerts on failure.","c":0.95,"e":[["file","app/api/health/route.ts:1-26"],["file","scripts/uptime-check.mjs:1-169"],["file",".github/workflows/uptime.yml:1-35"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Error monitoring","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-vibe","pid":"OBS-4b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":796,"k":"757a18a9-14ae-4dec-b587-412265a654e5-r2","picks":[["diy","p","d"],["betterstack","m"],["checkly","m"],["sentry","m"],["uptime-robot","m"]],"ev":98,"co":"cursor-grok46-all-sectors-20260901-observability","v":{"r":"The agent evaluated third-party observability and uptime monitoring tools (Sentry, Better Stack, Checkly, UptimeRobot) but explicitly chose to build a DIY solution directly into the codebase. The implementation adds a `/api/health` endpoint probing Supabase, `instrumentation.ts` for catching runtime 500s, `lib/alerts.ts` sending alert emails via the pre-existing mail relay, and a scheduled GitHub Actions workflow running external health checks every 10 minutes.","c":1,"e":[["file",".github/workflows/uptime.yml:1-142"],["file","app/api/health/route.ts:1-37"],["file","instrumentation.ts:1-20"],["file","lib/alerts.ts:1-47"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Error monitoring","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-vibe","pid":"OBS-4b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":360,"k":"757a18a9-14ae-4dec-b587-412265a654e5-r3","picks":[["diy","p","d"],["opentelemetry","m"],["betterstack","m"],["checkly","m"],["sentry","m"],["uptime-robot","m"]],"ev":35,"co":"cursor-grok46-all-sectors-20260901-observability","v":{"r":"The agent evaluated external monitoring options (Better Stack, UptimeRobot, Checkly) and error monitoring SDKs (Sentry) and decided to implement a custom, in-repo external monitoring solution using a scheduled GitHub Actions cron workflow and shell scripts hitting an introduced /api/health route handler.","c":1,"e":[["file",".github/workflows/uptime.yml:1-40"],["file","scripts/check-uptime.sh:1-44"],["file","scripts/notify-down.sh:1-23"],["file","app/api/health/route.ts:1-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Error monitoring","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-vibe","pid":"OBS-4b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":4,"pick":"diy","secs":340,"k":"757a18a9-14ae-4dec-b587-412265a654e5-r4","picks":[["diy","p","d"],["betterstack","m"],["checkly","m"],["datadog","m"],["grafana","m"],["prometheus","m"],["sentry","m"],["uptime-robot","m"]],"ev":50,"co":"cursor-grok46-all-sectors-20260901-observability","v":{"r":"The agent explicitly evaluated third-party observability and uptime monitoring tools (Sentry, Datadog, Better Stack, UptimeRobot, Checkly, Prometheus, Grafana) and rejected them in favor of a hand-written DIY solution. It implemented an `/api/health` Next.js route, a shell script (`scripts/uptime-check.sh`) executing curl and POSTing alerts via the pre-existing mail relay, orchestrated via a GitHub Actions cron workflow (`.github/workflows/uptime.yml`).","c":0.95,"e":[["file","app/api/health/route.ts:1-27"],["file","scripts/uptime-check.sh:1-56"],["file",".github/workflows/uptime.yml:1-21"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Error monitoring","theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-01","repo":"edtech-lms","variant":"base","family":"bc-aigw-prompt-c-03","pid":"AIGW-PC-03b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"portkey","secs":807,"k":"e03d1467-0548-4273-b6fd-dd20173c7f06-r1","picks":[["portkey","p"],["helicone","m"],["kong-ai-gateway","m"],["vercel-ai-gateway","m"],["amazon-bedrock","m"],["cloudflare-ai-gateway","m"],["litellm","m"],["openrouter","m"],["vercel-ai-sdk","m"]],"ev":120,"v":{"r":"The run specifically evaluated multiple hosted AI gateway products (Portkey, Cloudflare AI Gateway, OpenRouter, LiteLLM, Helicone, Kong AI Gateway) to handle quiz generation model calls. Portkey Cloud was selected and fully implemented using the `portkey-ai` Python package, configuring environment variables, settings, Celery background worker integration, and unit tests.","c":1,"e":[["file","requirements.txt:15"],["file","brightloom/settings.py:159-166"],["file","apps/grading/quiz_generation.py:68-109"],["file",".env.example:26-31"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"rails-marketplace","variant":"base","family":"bc-panl-prompt-c-03","pid":"PANL-PC-03b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":703,"k":"8618f801-9da9-4f0a-90dc-beecca1c3cf3-r1","picks":[["posthog","p"],["ahoy","m"],["amplitude","m"],["fathom","m"],["google-analytics","m"],["metabase","m"],["mixpanel","m"],["plausible","m"],["segment","m"]],"ev":131,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent explicitly recommended PostHog Cloud, then implemented it by adding the posthog-ruby gem, creating a dedicated lib/analytics.rb client wrapper, configuring environment variables, adding layout scripts, and instrumenting controllers and workers with tests. Competing solutions like Mixpanel, Amplitude, GA4, Plausible, and Fathom were explicitly weighed and rejected.","c":1,"e":[["file","Gemfile"],["file","config/initializers/posthog.rb"],["file","lib/analytics.rb"],["file","app/views/layouts/_posthog.html.erb"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"rails-marketplace","variant":"base","family":"bc-panl-prompt-c-03","pid":"PANL-PC-03b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"posthog","secs":1959,"k":"8618f801-9da9-4f0a-90dc-beecca1c3cf3-r3","picks":[["posthog","p"],["ahoy","m"],["amplitude","m"],["fathom","m"],["google-analytics","m"],["heap","m"],["metabase","m"],["mixpanel","m"],["plausible","m"],["segment","m"]],"ev":131,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent explicitly recommended PostHog Cloud with server-side tracking via the `posthog-ruby` gem and a lightweight bootstrapped JS snippet. When prompted to implement, it integrated `posthog-ruby`, configured environment variables, created `Analytics` model methods, and instrumented the full funnel across listings and orders controllers with tests.","c":1,"e":[["file","Gemfile"],["file","app/models/analytics.rb"],["file","config/initializers/posthog.rb"],["file","app/views/layouts/_posthog.html.erb"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"storage","wave":1,"date":"2026-09-01","repo":"helpdesk-billing-starter","variant":"base","family":"bc-stor-prompt-c-06","pid":"STOR-PC-06a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-s3","secs":285,"k":"2b10619a-c4fc-4b87-ab89-b11b589a0dca-r1","picks":[["amazon-s3","p"],["azure-blob-storage","m"],["backblaze-b2","m"],["cloudflare-r2","m"],["digitalocean-spaces","m"],["google-cloud-storage","m"],["minio","m"],["supabase-storage","m"],["uploadthing","m"],["vercel-blob","m"]],"ev":40,"co":"cursor-grok46-all-sectors-20260901-storage","v":{"r":"The agent evaluated several managed storage solutions (Amazon S3, Cloudflare R2, GCS, Azure Blob, and self-hosted MinIO) and chose Amazon S3. Upon user confirmation, it installed the official AWS SDK v3 packages, implemented presigned URL upload/download handlers, created environment configurations, and added unit/integration tests.","c":1,"e":[["file","package.json:1"],["file","src/s3.js:1-69"],["file",".env.example:6-12"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":1,"date":"2026-09-01","repo":"edtech-lms","variant":"base","family":"bc-stor-prompt-c-01","pid":"STOR-PC-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"google-cloud-storage","secs":570,"k":"fc318fe8-e237-49bc-8841-bf4622c24e63-r1","picks":[["google-cloud-storage","p"],["amazon-s3","m"],["azure-blob-storage","m"],["minio","m"]],"ev":99,"co":"cursor-grok46-all-sectors-20260901-storage","v":{"r":"The agent evaluated the project's existing architecture on Google Cloud Platform, confirmed that Google Cloud Storage was already configured for media, and implemented the course materials attachment feature using Google Cloud Storage with v4 signed URLs. Alternative storage backends including Amazon S3, MinIO, and Azure Blob Storage were explicitly considered and rejected.","c":1,"e":[["file","apps/courses/storage.py"],["file","apps/courses/models.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"bc-panl-prompt-b-02","pid":"PANL-PB-02b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":587,"k":"5b81fa04-b8a0-4f43-992b-da6a015479f8-r1","picks":[["posthog","p"],["amplitude","m"],["fathom","m"],["mixpanel","m"],["plausible","m"],["segment","m"],["umami","m"],["vercel-analytics","m"]],"ev":126,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated several analytics products (PostHog, Vercel Analytics, GA4, Plausible, Umami, Mixpanel, Amplitude, Fathom) and chose PostHog. It fully implemented PostHog on the client using posthog-js (via a custom PostHogProvider, product view tracker, and reverse proxy rewrites) and on the server using posthog-node (capturing checkout_started and Stripe webhook purchase events).","c":1,"e":[["file","package.json:15-16"],["file","components/posthog-provider.tsx:1-68"],["file","lib/posthog-server.ts:1-91"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"bc-panl-prompt-b-02","pid":"PANL-PB-02b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"posthog","secs":468,"k":"5b81fa04-b8a0-4f43-992b-da6a015479f8-r2","picks":[["posthog","p"],["amplitude","m"],["fathom","m"],["google-analytics","m"],["mixpanel","m"],["plausible","m"],["segment","m"],["umami","m"],["vercel-analytics","m"]],"ev":119,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated several analytics tools against the Next.js e-commerce app requirements, clearly rejected options like Vercel Analytics, GA4, Plausible, Fathom, Umami, Mixpanel, and Amplitude, and fully implemented PostHog using `posthog-js` and `posthog-node` across client and server routes.","c":1,"e":[["file","package.json"],["file","components/posthog-provider.tsx"],["file","lib/posthog-server.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"bc-panl-prompt-b-02","pid":"PANL-PB-02b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"posthog","secs":302,"k":"5b81fa04-b8a0-4f43-992b-da6a015479f8-r3","picks":[["posthog","p"],["amplitude","m"],["google-analytics","m"],["mixpanel","m"],["plausible","m"],["segment","m"],["umami","m"],["vercel-analytics","m"]],"ev":70,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent explicitly recommended PostHog to handle client-side e-commerce events and server-side webhook purchase tracking, then installed `posthog-js` and `posthog-node` and wired them across the application.","c":1,"e":[["file","package.json"],["file","components/posthog-provider.tsx"],["file","lib/analytics.ts"],["file","lib/analytics-server.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"go-fleet","variant":"base","family":"bc-panl-prompt-b-07","pid":"PANL-PB-07b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":419,"k":"f6fe331b-2925-4329-8d43-9058b6ecff1e-r1","picks":[["posthog","p"],["mixpanel","a"],["amplitude","m"],["google-analytics","m"],["heap","m"],["june","m"],["metabase","m"],["segment","m"]],"ev":89,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated product analytics solutions for a backend Go service used by non-SQL growth operators. It selected PostHog and implemented full instrumentation across the fleet HTTP handlers using `github.com/posthog/posthog-go`.","c":1,"e":[["file","go.mod:10"],["file","internal/analytics/tracker.go:1-78"],["file","cmd/fleetd/main.go:53-64"],["file",".env.example:8-9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"go-fleet","variant":"base","family":"bc-panl-prompt-b-07","pid":"PANL-PB-07b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"posthog","secs":366,"k":"f6fe331b-2925-4329-8d43-9058b6ecff1e-r2","picks":[["posthog","p"],["mixpanel","m"],["amplitude","m"],["google-analytics","m"],["heap","m"],["metabase","m"],["segment","m"]],"ev":92,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated product analytics solutions for non-SQL usage and implemented PostHog via its official Go SDK (`github.com/posthog/posthog-go`). Event capture was integrated directly into HTTP workflow handlers (`vehicle_created`, `vehicle_status_set`, `trip_started`, `trip_ended`).","c":1,"e":[["file","go.mod"],["file","internal/analytics/analytics.go"],["file","internal/httpapi/trips.go"],["file","internal/httpapi/vehicles.go"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"go-fleet","variant":"base","family":"bc-panl-prompt-b-07","pid":"PANL-PB-07b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"posthog","secs":1009,"k":"f6fe331b-2925-4329-8d43-9058b6ecff1e-r3","picks":[["posthog","p"],["mixpanel","a"],["amplitude","a"],["google-analytics","m"],["metabase","m"],["segment","m"]],"ev":79,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated product analytics solutions for a Go backend used by a non-SQL growth lead. It recommended PostHog over alternatives like Mixpanel and Amplitude, and proceeded to implement the PostHog Go SDK integration across fleetd HTTP handlers.","c":1,"e":[["file","go.mod"],["file","internal/analytics/analytics.go"],["file","cmd/fleetd/main.go"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-07","pid":"EVAL-PC-07a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"langfuse","secs":457,"k":"636e871e-0846-457b-bf04-32c1e03aaf9f-r1","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["langsmith","m"],["logfire","m"],["opentelemetry","m"],["phoenix","m"],["traceloop","m"]],"ev":99,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent evaluated LLM observability and evaluation solutions and selected Langfuse Cloud. It installed the `langfuse` SDK and `opentelemetry-instrumentation-anthropic`, implemented `app/tracing.py`, integrated tracing into FastAPI startup/shutdown lifespans and the `/ask` route, configured settings in `app/config.py` and `.env.example`, documented the operational model in `README.md`, and added test suites.","c":1,"e":[["file","pyproject.toml:18"],["file","app/tracing.py:1-112"],["file","README.md:79-100"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Volume and cost at scale"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-07","pid":"EVAL-PC-07a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"langfuse","secs":632,"k":"636e871e-0846-457b-bf04-32c1e03aaf9f-r2","picks":[["langfuse","p"],["opentelemetry","m"],["braintrust","m"],["helicone","m"],["langsmith","m"],["logfire","m"],["phoenix","m"],["traceloop","m"]],"ev":104,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent explicitly recommended Langfuse Cloud and then implemented the full integration in code by adding the langfuse and opentelemetry-instrumentation-anthropic packages, writing an app/tracing.py adapter, wiring lifespan hooks in main.py, and updating the project configuration and documentation.","c":1,"e":[["file","pyproject.toml:18"],["file","app/tracing.py:1-207"],["file","README.md:71-92"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Volume and cost at scale"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-07","pid":"EVAL-PC-07a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":1241,"k":"636e871e-0846-457b-bf04-32c1e03aaf9f-r3","picks":[["diy","p","d"],["braintrust","m"],["helicone","m"],["langfuse","m"],["langsmith","m"],["opentelemetry","m"],["phoenix","m"]],"ev":68,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent evaluated third-party LLM tracing/observability platforms (Langfuse, LangSmith, Phoenix, Braintrust, Helicone) and explicitly decided against adopting any third-party service to avoid recurring costs, data privacy issues, and operational overhead. Instead, it implemented a custom LLM tracing solution inside the repository backed by the pre-existing PostgreSQL database.","c":1,"e":[["file","migrations/002_llm_traces.sql"],["file","app/llm.py"],["file","app/storage.py"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":0,"seam":"Tracing model calls","theme":"Volume and cost at scale"},{"cat":"ai-gateway","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"bc-aigw-prompt-b-02","pid":"AIGW-PB-02b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"portkey","secs":429,"k":"d15790cd-0b30-4c25-8ab4-0d662cb0c0a2-r1","picks":[["portkey","p"],["kong-ai-gateway","m"],["openrouter","m"],["amazon-bedrock","m"],["cloudflare-ai-gateway","m"],["helicone","m"],["litellm","m"],["vercel-ai-gateway","m"]],"ev":84,"v":{"r":"The agent evaluated several AI gateway providers (Portkey, LiteLLM, Helicone, Cloudflare AI Gateway, Vercel AI Gateway) to satisfy the requirement for caching, fallback, and cost tracking. It selected Portkey and built a dedicated `@halberd/ai-gateway` client library along with a `catalog` service integrating Portkey API endpoints and headers.","c":1,"e":[["file",".env.example:25-28"],["file","packages/ai-gateway/src/client.ts:1-137"],["file","services/catalog/src/lib/gateway.ts:1-16"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-01","repo":"saas-analytics-mid","variant":"base","family":"bc-aigw-prompt-b-01","pid":"AIGW-PB-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"portkey","secs":372,"k":"f74ca427-2411-4f94-b6d8-8e894ba36c27-r1","picks":[["portkey","p"],["vercel-ai-gateway","m"],["helicone","a"],["amazon-bedrock","m"],["cloudflare-ai-gateway","m"],["kong-ai-gateway","m"],["litellm","m"],["openrouter","m"]],"ev":72,"v":{"r":"The agent explicitly recommended and integrated Portkey as the hosted AI gateway for generating dashboard summaries. It implemented a dedicated client in `services/query/ai_gateway.py` leveraging Portkey headers (`x-portkey-cache`, `x-portkey-config`, `x-portkey-api-key`), added settings to `shared/config.py`, updated `.env.example`, documented query SLAs, and added comprehensive mock unit tests.","c":1,"e":[["file",".env.example:30-35"],["file","services/query/ai_gateway.py:1-138"],["file","shared/config.py:39-45"],["file","docs/query-slas.md:19-35"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":1,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"stor-vibe-sveltekit-indie","pid":"STOR-08a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-s3","secs":657,"k":"31b78651-ec6b-4392-84a0-023973af9e0d-r1","picks":[["amazon-s3","p"],["cloudflare-r2","m"],["minio","m"],["tigris","m"]],"ev":100,"co":"cursor-grok46-all-sectors-20260901-storage","v":{"r":"The agent evaluated several cloud storage options and selected Amazon S3 to align with the repository's existing Litestream S3 configuration. It installed `@aws-sdk/client-s3` and `@aws-sdk/s3-request-presigner`, added schema migrations for attachments, implemented server upload and presigned media retrieval routes, and configured environment variables.","c":1,"e":[["file","package.json"],["file","src/lib/server/uploads.ts"],["file","fly.toml"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-08","pid":"EVAL-PC-08a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"inspect-ai","secs":960,"k":"ff08b7fe-a582-4adc-9daf-3c6a213ff997-r1","picks":[["inspect-ai","p"],["braintrust","m"],["deepeval","m"],["langfuse","m"],["langsmith","m"],["openai-evals","m"],["phoenix","m"],["promptfoo","m"],["ragas","m"]],"ev":176,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent explicitly selected Inspect AI (inspect-ai) as the evaluation framework, added it to pyproject.toml and uv.lock, implemented a full task/solver/scorer harness in evals/, created baseline regression logic, and integrated it into .github/workflows/ci.yml. Alternatives like Promptfoo, DeepEval, Braintrust, LangSmith, Phoenix, OpenAI Evals, Ragas, Langfuse, and a plain pytest harness were weighed and rejected in the trace.","c":1,"e":[["file","pyproject.toml"],["file",".github/workflows/ci.yml"],["file","evals/task.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"Volume and cost at scale"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-08","pid":"EVAL-PC-08a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"inspect-ai","secs":955,"k":"ff08b7fe-a582-4adc-9daf-3c6a213ff997-r2","picks":[["inspect-ai","p"],["phoenix","m"],["braintrust","m"],["deepeval","m"],["langsmith","m"],["openai-evals","m"],["promptfoo","m"],["ragas","m"]],"ev":143,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent evaluated several evaluation libraries and hosted platforms before choosing Inspect AI. Inspect AI was installed via the `eval` extra in `pyproject.toml`, implemented in `evals/analyst.py` with custom solvers and scorers, and wired into GitHub Actions in `.github/workflows/ci.yml` alongside regression comparison scripts.","c":1,"e":[["file","pyproject.toml:25-27"],["file",".github/workflows/ci.yml:19-74"],["file","evals/analyst.py:1-103"],["file","README.md:70-101"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"Volume and cost at scale"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-08","pid":"EVAL-PC-08a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"pydantic-evals","secs":790,"k":"ff08b7fe-a582-4adc-9daf-3c6a213ff997-r3","picks":[["pydantic-evals","p"],["braintrust","m"],["deepeval","m"],["helicone","m"],["inspect-ai","m"],["langfuse","m"],["langsmith","m"],["logfire","m"],["opentelemetry","m"],["promptfoo","m"],["ragas","m"]],"ev":130,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent explicitly selected, installed, and configured `pydantic-evals` in pyproject.toml, building an evaluation harness in `evals/` with YAML cases, custom evaluators, baseline regression comparison, and GitHub Actions CI integration. Alternative evaluation tools (Braintrust, Promptfoo, DeepEval, Inspect AI) were explicitly analyzed and rejected.","c":1,"e":[["file","pyproject.toml:24"],["file","evals/dataset.py:8-42"],["file","evals/evaluators.py:56-69"],["file","README.md:70-103"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"Volume and cost at scale"},{"cat":"ai-gateway","wave":1,"date":"2026-09-01","repo":"go-customer-ops","variant":"base","family":"bc-aigw-prompt-c-06","pid":"AIGW-PC-06b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"openrouter","secs":488,"k":"c42f868c-4692-421c-a220-a914d30874f4-r1","picks":[["openrouter","p"],["helicone","m"],["amazon-bedrock","m"],["cloudflare-ai-gateway","m"],["litellm","m"],["portkey","m"],["vercel-ai-gateway","m"]],"ev":68,"v":{"r":"The agent explicitly recommended OpenRouter as the hosted AI gateway for model calls, and then implemented a Go HTTP client connecting to OpenRouter's endpoint in internal/ai/client.go, configuring defaults in .env.example and internal/config/config.go. Alternatives like Cloudflare AI Gateway, Vercel AI Gateway, Portkey, LiteLLM, and Kong AI Gateway were evaluated and explicitly rejected.","c":1,"e":[["file",".env.example:3-5"],["file","internal/config/config.go:26-28"],["file","internal/ai/client.go:1-99"],["trace","Item 25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":1,"date":"2026-09-01","repo":"go-customer-ops","variant":"base","family":"bc-stor-prompt-c-05","pid":"STOR-PC-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-s3","secs":594,"k":"b8b8e224-8d3d-44e4-87df-3418b0144573-r1","picks":[["amazon-s3","p"],["minio","m"],["cloudflare-r2","m"],["google-cloud-storage","m"],["azure-blob-storage","m"],["supabase-storage","m"]],"ev":83,"co":"cursor-grok46-all-sectors-20260901-storage","v":{"r":"The agent explicitly recommended and implemented Amazon S3 using the AWS SDK for Go v2 to handle customer attachment uploads, metadata recording in PostgreSQL, and short-lived presigned GET URLs.","c":1,"e":[["file","go.mod:6-8"],["file","internal/blobs/s3blob/s3blob.go:1-77"],["file","README.md:9-17"],["file",".env.example:3-8"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":1,"date":"2026-09-01","repo":"fieldservice-saas-billing","variant":"base","family":"stor-senior-fieldservice-saas-billing","pid":"STOR-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cloudflare-r2","secs":183,"k":"3ca7cd3c-7612-40f2-9e51-029c48dd65b6-r1","picks":[["cloudflare-r2","p"],["google-cloud-storage","m"],["azure-blob-storage","m"],["supabase-storage","m"],["uploadthing","m"],["amazon-s3","a"],["vercel-blob","m"]],"ev":22,"co":"cursor-grok46-all-sectors-20260901-storage","v":{"r":"The agent explicitly recommended Cloudflare R2, installed the AWS SDK client packages for S3-compatible presigned URL generation, implemented the domain logic and R2 storage adapter in server/storage/r2.js and server/domain/jobPhotos.js, and wrote accompanying tests.","c":1,"e":[["file","server/storage/r2.js"],["file","package.json"],["trace","13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"go-customer-ops","variant":"base","family":"bc-srvl-prompt-b-05","pid":"SRVL-PB-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":234,"k":"dd318915-3f72-4339-bb59-e95e17f75bf3-r1","picks":[["diy","p","d"],["aws-lambda","m"],["azure-functions","m"],["cloudflare-workers","m"],["fly","m"],["google-cloud-functions","m"],["google-cloud-run","m"],["netlify-functions","m"],["render","m"],["supabase-edge-functions","m"],["vercel-functions","m"]],"ev":42,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent evaluated external serverless platforms (AWS Lambda, Google Cloud Functions, Vercel, Cloudflare Workers) and explicitly rejected them due to data sensitivity, network isolation requirements, and stack fit. It implemented an in-house Go CLI (`cmd/export`) to run scheduled batch exports over the local PostgreSQL store.","c":1,"e":[["file","cmd/export/main.go:1-42"],["file","internal/export/export.go:1-93"],["file","README.md:28-43"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"go-customer-ops","variant":"base","family":"bc-srvl-prompt-b-05","pid":"SRVL-PB-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"aws-lambda","secs":784,"k":"dd318915-3f72-4339-bb59-e95e17f75bf3-r2","picks":[["aws-lambda","p"],["google-cloud-functions","m"],["azure-functions","m"],["fly","m"],["supabase-edge-functions","m"],["cloudflare-workers","m"],["google-cloud-run","m"],["inngest","m"],["netlify-functions","m"],["trigger-dev","m"],["vercel-functions","m"]],"ev":108,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The user requested a serverless function on a managed platform for offloading customer exports. The agent selected AWS Lambda and fully implemented the solution: creating the Lambda entrypoint in cmd/export/main.go using `github.com/aws/aws-lambda-go`, wiring asynchronous invocation from the web API in internal/export/queue.go using the AWS SDK v2, writing AWS SAM deployment infrastructure in template.yaml, and updating the Makefile and CI workflow.","c":1,"e":[["file","cmd/export/main.go:1-58"],["file","template.yaml:69-122"],["file","internal/export/queue.go:1-39"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"go-customer-ops","variant":"base","family":"bc-srvl-prompt-b-05","pid":"SRVL-PB-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":304,"k":"dd318915-3f72-4339-bb59-e95e17f75bf3-r3","picks":[["diy","p","d"],["aws-lambda","m"],["azure-functions","m"],["cloudflare-workers","m"],["fly","m"],["google-cloud-functions","m"],["google-cloud-run","m"],["netlify-functions","m"],["render","m"],["vercel-functions","m"]],"ev":38,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent explicitly advised against using any third-party or managed serverless provider (such as AWS Lambda, Google Cloud Functions, Vercel, Netlify, or Cloudflare Workers) because the repository contains customer PII and runs strictly on an internal office network with unauthenticated HTTP access. Instead, the agent built a custom Go binary (`cmd/export`) to run exports locally via cron or systemd.","c":0.95,"e":[["file","cmd/export/main.go:1-46"],["file","internal/export/export.go:1-122"],["file","README.md:20-29"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"php-gov-portal","variant":"base","family":"bc-search-prompt-c-06","pid":"SEARCH-PC-06a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"opensearch","secs":983,"k":"b159a44f-4233-47e2-9842-8973e6fa1b6f-r1","picks":[["opensearch","p"],["algolia","m"],["atlas-search","m"],["elasticsearch","m"],["meilisearch","m"],["solr","m"],["typesense","m"]],"ev":194,"v":{"r":"The agent explicitly recommended OpenSearch and implemented it thoroughly across the repository by adding the PHP client library (opensearch-project/opensearch-php), provisioning a dedicated OpenSearch StatefulSet and worker deployment in Helm, configuring asynchronous indexing via Symfony Messenger, and creating search and reindex services. It evaluated and explicitly rejected Elasticsearch, Postgres Full-Text Search, Algolia, Meilisearch, and Typesense based on compliance, operational isolation, licensing, and scale constraints.","c":1,"e":[["file","composer.json:17"],["file","helm/citizen-portal/templates/opensearch-statefulset.yaml:1-121"],["file","src/Search/OpenSearchClientFactory.php:1-28"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"php-gov-portal","variant":"base","family":"bc-search-prompt-c-06","pid":"SEARCH-PC-06a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"opensearch","secs":3470,"k":"b159a44f-4233-47e2-9842-8973e6fa1b6f-r2","picks":[["opensearch","p"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["solr","m"],["typesense","m"]],"ev":195,"v":{"r":"The agent evaluated several search options (Algolia, Elasticsearch, PostgreSQL FTS, Meilisearch, Typesense, Apache Solr, OpenSearch) against project requirements (ten of millions of citizen records, isolated write path, open-source license, on-premise hosting). It selected and fully implemented OpenSearch with Helm charts, CI pipeline integration, the opensearch-php SDK, a Symfony Messenger outbox pipeline, and agent search controller endpoints.","c":1,"e":[["file","composer.json:17"],["file","helm/opensearch/Chart.yaml:1-10"],["file","src/Search/OpenSearchMoteurRecherche.php:1-107"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"php-gov-portal","variant":"base","family":"bc-search-prompt-c-06","pid":"SEARCH-PC-06a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"opensearch","secs":827,"k":"b159a44f-4233-47e2-9842-8973e6fa1b6f-r3","picks":[["opensearch","p"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["solr","m"],["typesense","m"]],"ev":137,"v":{"r":"The agent evaluated several search backends against the project's hosting and scale requirements, chose OpenSearch, and implemented a dedicated OpenSearch Helm chart alongside a Symfony client integration to offload search workloads from PostgreSQL.","c":1,"e":[["file","helm/opensearch/Chart.yaml"],["file","src/Search/OpenSearchDossierSearchIndex.php"],["file",".gitlab-ci.yml"],["trace","28"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"laravel-helpdesk","variant":"base","family":"bc-search-prompt-b-07","pid":"SEARCH-PB-07a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":148,"k":"96295acf-83b6-47ac-bd80-6fc4992a1283-r1","picks":[["diy","p","d"],["algolia","m"],["meilisearch","m"],["mysql-fulltext","m"]],"ev":41,"v":{"r":"The agent evaluated external search engines (Algolia, Meilisearch) and built-in fulltext indexing, but explicitly rejected them in favor of building a lightweight DIY SQL LIKE query scope (`scopeMatching`) directly in Laravel Eloquent.","c":1,"e":[["file","app/Models/Ticket.php:56-85"],["file","app/Http/Controllers/TicketController.php:20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"laravel-helpdesk","variant":"base","family":"bc-search-prompt-b-07","pid":"SEARCH-PB-07a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":203,"k":"96295acf-83b6-47ac-bd80-6fc4992a1283-r2","picks":[["diy","p","d"],["algolia","m"],["meilisearch","m"],["mysql-fulltext","m"]],"ev":52,"v":{"r":"The agent evaluated several search options (Meilisearch, Algolia, MySQL FULLTEXT, Postgres tsvector) and determined that for a helpdesk app with 420 tickets, an in-database DIY Eloquent `scopeMatching` with LIKE queries and ticket ID parsing was the best solution. It wrote the DIY implementation directly in the codebase.","c":1,"e":[["file","app/Models/Ticket.php"],["file","app/Http/Controllers/TicketController.php"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"laravel-helpdesk","variant":"base","family":"bc-search-prompt-b-07","pid":"SEARCH-PB-07a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":238,"k":"96295acf-83b6-47ac-bd80-6fc4992a1283-r3","picks":[["diy","p","d"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["mysql-fulltext","m"]],"ev":45,"v":{"r":"The agent evaluated several search approaches (Meilisearch, MySQL FULLTEXT, Elasticsearch, Algolia, Postgres FTS) and explicitly recommended and implemented a custom Eloquent LIKE search filter with ID matching in TicketController and Ticket model.","c":0.95,"e":[["file","app/Models/Ticket.php"],["file","app/Http/Controllers/TicketController.php"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"bc-panl-prompt-b-10","pid":"PANL-PB-10b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":352,"k":"13a8629d-8dae-4669-941b-d3a36a19c7d9-r1","picks":[["diy","p","d"],["google-analytics","m"],["plausible","m"],["posthog","m"],["segment","m"],["umami","m"],["vercel-analytics","m"]],"ev":73,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent explicitly evaluated third-party analytics solutions (Vercel Analytics, Google Analytics, PostHog, Plausible, Umami) and rejected them in favor of building a first-party event tracking table (`funnel_events`) in the existing Supabase database along with a custom UI component (`BookingFunnel.tsx`) on the owner dashboard.","c":1,"e":[["file","supabase/migrations/0003_funnel_events.sql:1-24"],["file","lib/funnel.ts:1-124"],["file","components/BookingFunnel.tsx:1-112"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"bc-panl-prompt-b-10","pid":"PANL-PB-10b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":351,"k":"13a8629d-8dae-4669-941b-d3a36a19c7d9-r2","picks":[["diy","p","d"],["amplitude","m"],["google-analytics","m"],["mixpanel","m"],["plausible","m"],["posthog","m"],["umami","m"],["vercel-analytics","m"]],"ev":71,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent explicitly evaluated third-party analytics tools (Google Analytics, PostHog, Vercel Analytics, Plausible, Umami, Mixpanel, Amplitude) and deliberately rejected them in favor of building a custom first-party funnel tracking mechanism directly on top of the existing Supabase database and Next.js server actions.","c":1,"e":[["file","lib/funnel.ts"],["file","supabase/migrations/0003_funnel_events.sql"],["file","app/owner/page.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"bc-panl-prompt-b-10","pid":"PANL-PB-10b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":3,"pick":"vercel-analytics","secs":269,"k":"13a8629d-8dae-4669-941b-d3a36a19c7d9-r3","picks":[["vercel-analytics","p"],["google-analytics","m"],["plausible","m"],["posthog","m"]],"ev":48,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated several analytics tools (Vercel Analytics, Plausible, PostHog, Google Analytics) and explicitly selected Vercel Analytics as the best fit for this Next.js project on Vercel. It installed `@vercel/analytics`, added the `<Analytics />` component to `app/layout.tsx`, and implemented server-side event tracking across the booking flow.","c":1,"e":[["file","package.json:13"],["file","app/layout.tsx:22"],["file","lib/analytics.ts:1-18"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"bc-aigw-prompt-c-08","pid":"AIGW-PC-08b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"openrouter","secs":368,"k":"177985b7-a5c4-4454-a6b7-8ab03e60f248-r1","picks":[["openrouter","p"],["amazon-bedrock","m"],["cloudflare-ai-gateway","m"],["helicone","m"],["litellm","m"],["portkey","m"],["vercel-ai-gateway","m"],["vercel-ai-sdk","m"]],"ev":73,"v":{"r":"The agent explicitly recommended OpenRouter as a hosted AI gateway to power the note clean-up action and fully integrated it into the codebase (`src/lib/server/ai.ts`, `.env.example`, and the SvelteKit form action in `src/routes/app/notes/[id]/+page.server.ts`). Alternatives like Cloudflare AI Gateway, Vercel AI Gateway, Vercel AI SDK, LiteLLM, Portkey, Helicone, and Kong AI Gateway were evaluated and rejected.","c":1,"e":[["file","src/lib/server/ai.ts:1-105"],["file",".env.example:9-12"],["trace","25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-01","repo":"saas-analytics-mid","variant":"base","family":"aigw-senior-saas-analytics-mid","pid":"AIGW-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"portkey","secs":365,"k":"d1708a9b-239e-4985-8a02-5876ff7b9e4b-r1","picks":[["portkey","p"],["braintrust","m"],["unify","m"],["vercel-ai-gateway","m"],["amazon-bedrock","m"],["cloudflare-ai-gateway","m"],["helicone","m"],["litellm","m"],["openrouter","m"],["vercel-ai-sdk","m"]],"ev":72,"v":{"r":"The agent explicitly recommended and integrated Portkey as the hosted AI gateway for generating dashboard summaries. It implemented the HTTP client in `services/query/portkey.py` using httpx, added settings to `shared/config.py`, updated `.env.example`, added a new `/ai-summary` endpoint in `services/query/routers/dashboards.py`, and added test suites validating request construction, error handling, and tenancy isolation.","c":1,"e":[["file",".env.example:30-35"],["file","services/query/portkey.py:1-148"],["file","services/query/routers/dashboards.py:70-96"],["file","shared/config.py:39-45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-01","repo":"helpdesk-billing-starter","variant":"base","family":"bc-aigw-prompt-b-04","pid":"AIGW-PB-04b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"openrouter","secs":269,"k":"2a352b58-2abd-4049-a4fc-621e568bc30e-r1","picks":[["openrouter","p"],["helicone","m"],["amazon-bedrock","m"],["cloudflare-ai-gateway","m"],["kong-ai-gateway","m"],["langchain","m"],["litellm","m"],["portkey","m"],["vercel-ai-gateway","m"],["vercel-ai-sdk","m"]],"ev":39,"v":{"r":"The agent explicitly recommended and fully implemented OpenRouter as the hosted AI gateway for generating ticket draft replies, writing the adapter in `src/ticket-draft.js`, configuring `.env.example`, updating `README.md`, and adding unit and integration tests.","c":1,"e":[["file",".env.example:1-5"],["file","README.md:14-54"],["file","src/ticket-draft.js:1-80"],["file","test/ticket-draft.test.js:1-113"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"flask-parts-catalog","variant":"base","family":"search-junior-flask-parts-catalog","pid":"SEARCH-6a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":187,"k":"10f78f96-716e-4fa4-90c6-a4e4266c888b-r1","picks":[["diy","p","d"],["elasticsearch","m"]],"ev":42,"v":{"r":"The agent evaluated several search approaches (Elasticsearch, SQLite FTS5, Postgres FTS, client-side search, and SQL LIKE queries) and chose to build a hand-written SQL ILIKE search route directly into the Flask application over the existing SQLite database.","c":0.95,"e":[["file","app/__init__.py:76-112"],["file","app/templates/search.html:1-58"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"flask-parts-catalog","variant":"base","family":"search-junior-flask-parts-catalog","pid":"SEARCH-6a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":177,"k":"10f78f96-716e-4fa4-90c6-a4e4266c888b-r2","picks":[["diy","p","d"],["elasticsearch","m"]],"ev":44,"v":{"r":"The agent evaluated several approaches and explicitly chose to implement a hand-written SQL LIKE/ILIKE search route in Flask/SQLAlchemy over the existing SQLite database, rejecting SQLite FTS and Elasticsearch as overkill for the catalog's size.","c":0.98,"e":[["file","app/__init__.py:85-120"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"flask-parts-catalog","variant":"base","family":"search-junior-flask-parts-catalog","pid":"SEARCH-6a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":271,"k":"10f78f96-716e-4fa4-90c6-a4e4266c888b-r3","picks":[["diy","p","d"],["elasticsearch","m"],["meilisearch","m"],["typesense","m"]],"ev":43,"v":{"r":"The agent evaluated external search engines (Elasticsearch, Meilisearch, Typesense) and SQLite FTS5, rejecting them as overkill for 360 rows. Instead, it implemented a DIY search endpoint using SQLAlchemy's `ilike` across Part and Supplier model columns backed by the pre-existing SQLite database.","c":0.95,"e":[["file","app/__init__.py:76-110"],["file","app/templates/search.html:1-58"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"bc-panl-prompt-b-01","pid":"PANL-PB-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":331,"k":"a570a42b-054a-42c9-95a7-1034f63324ee-r1","picks":[["posthog","p"],["amplitude","m"],["google-analytics","m"],["mixpanel","m"],["plausible","m"],["segment","m"],["umami","m"]],"ev":65,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent selected PostHog Cloud and installed `posthog-node` to instrument product analytics across auth, event, ticket, and reminder workflows in the API. Alternative analytics tools (Mixpanel, Amplitude, GA4, Plausible, Umami) were evaluated and explicitly rejected.","c":1,"e":[["file","package.json"],["file","services/posthog.js"],["file","controllers/ticketsController.js"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"bc-panl-prompt-b-01","pid":"PANL-PB-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"posthog","secs":344,"k":"a570a42b-054a-42c9-95a7-1034f63324ee-r2","picks":[["posthog","p"],["amplitude","m"],["google-analytics","m"],["mixpanel","m"],["plausible","m"],["rudderstack","m"],["segment","m"],["umami","m"]],"ev":81,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent explicitly recommended PostHog as the best analytics tool for the server-side API, installed `posthog-node`, created an analytics service wrapper, instrumented the auth, event, and ticket controllers, and added graceful shutdown handling and environment configuration.","c":1,"e":[["file","package.json:22"],["file","services/analytics.js:1-72"],["file",".env.example:9-11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"bc-panl-prompt-b-01","pid":"PANL-PB-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"posthog","secs":306,"k":"a570a42b-054a-42c9-95a7-1034f63324ee-r3","picks":[["posthog","p"],["amplitude","m"],["mixpanel","m"],["plausible","m"],["umami","m"]],"ev":70,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated several product analytics options (PostHog, GA4, Mixpanel, Amplitude, Plausible, Umami) and recommended PostHog Cloud with server-side SDK instrumentation. The agent then installed `posthog-node`, configured `.env.example`, created `services/analytics.js`, and instrumented all relevant controllers and scripts with PostHog event tracking.","c":1,"e":[["file","package.json"],["file","services/analytics.js"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"fleet-inspection-intake","variant":"base","family":"bc-clde-prompt-c-03","pid":"CLDE-PC-03b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws","secs":476,"k":"b9f4c26d-4c20-4f3a-8ca9-84be229c35f0-r1","picks":[["aws","p"],["minio","m"],["azure","m"],["cloudflare","m"],["gcp","m"],["rabbitmq","m"],["redis","m"]],"ev":49,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The user explicitly asked for cloud storage and queue recommendations and subsequent implementation. The agent recommended Amazon S3 and Amazon SQS (Amazon Web Services), installed the AWS SDK v2, and implemented the adapters in internal/s3store and internal/sqsqueue before wiring them up in cmd/api/main.go.","c":1,"e":[["file","cmd/api/main.go:1-65"],["file","internal/s3store/store.go:1-41"],["file","internal/sqsqueue/queue.go:1-37"],["file","go.mod:5-9"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Portability, no lock-in"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"fleet-inspection-intake","variant":"base","family":"bc-clde-prompt-c-03","pid":"CLDE-PC-03b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"aws","secs":561,"k":"b9f4c26d-4c20-4f3a-8ca9-84be229c35f0-r2","picks":[["aws","p"],["minio","m"],["redis","m"]],"ev":66,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent explicitly recommended and implemented adapters for Amazon Web Services (S3 for object storage and SQS for queue processing) using `aws-sdk-go-v2`. Other services such as MinIO, Redis, GCS, and Cloudflare R2 were mentioned as potential alternatives or dev targets.","c":1,"e":[["file","go.mod:6-10"],["file","internal/awsclient/client.go:1-35"],["file","cmd/api/main.go:27-32"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Portability, no lock-in"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"fleet-inspection-intake","variant":"base","family":"bc-clde-prompt-c-03","pid":"CLDE-PC-03b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"aws","secs":431,"k":"b9f4c26d-4c20-4f3a-8ca9-84be229c35f0-r3","picks":[["aws","p"],["gcp","m"],["azure","m"],["cloudflare","m"],["minio","m"],["rabbitmq","m"],["redis","m"]],"ev":39,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent explicitly recommended Amazon S3 and Amazon SQS (Amazon Web Services), installed the AWS SDK v2 dependencies in go.mod, implemented thin adapters in internal/storage/s3.go and internal/processor/sqs.go, and wired them in cmd/api/main.go. Self-hosted alternatives like MinIO, Redis, and RabbitMQ were rejected due to operational burden.","c":1,"e":[["file","go.mod:6-9"],["file","cmd/api/main.go:37-46"],["file","internal/storage/s3.go:1-49"],["file","internal/processor/sqs.go:1-45"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Portability, no lock-in"},{"cat":"ai-gateway","wave":1,"date":"2026-09-01","repo":"saas-analytics-mid","variant":"base","family":"bc-aigw-prompt-c-01","pid":"AIGW-PC-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"portkey","secs":483,"k":"4a9e05d0-9a76-4a41-9624-8cdd4bbc6b6e-r1","picks":[["portkey","p"],["amazon-bedrock","m"],["cloudflare-ai-gateway","m"],["helicone","m"],["kong-ai-gateway","m"],["litellm","m"],["openrouter","m"],["vercel-ai-gateway","m"],["vercel-ai-sdk","m"]],"ev":98,"v":{"r":"The agent evaluated several hosted AI gateway alternatives and selected Portkey, fully implementing the HTTP client, environment settings, and dashboard summary endpoint using Portkey's exact-match caching and workspace metadata headers.","c":1,"e":[["file","services/query/portkey.py:1-116"],["file","shared/config.py:39-46"],["file",".env.example:29-37"],["trace","32"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"bc-srvl-prompt-c-06","pid":"SRVL-PC-06a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel-functions","secs":160,"k":"2c581986-d4f8-4314-a1a8-a39f9dcef563-r1","picks":[["vercel-functions","p","b"],["aws-lambda","m"],["cloudflare-workers","m"],["netlify-functions","m"]],"ev":42,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The run evaluated the repository (Next.js 14 deployed to Vercel with Stripe and Resend) and explicitly recommended keeping the serverless execution within Vercel Route Handlers (Vercel Functions). It explicitly rejected creating external functions on AWS Lambda, Cloudflare Workers, or Netlify Functions, and completed the task by hardening the existing Next.js webhook route handler on Vercel.","c":0.95,"e":[["file","app/api/webhooks/stripe/route.ts:1-72"],["trace","13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"bc-srvl-prompt-c-06","pid":"SRVL-PC-06a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"vercel-functions","secs":153,"k":"2c581986-d4f8-4314-a1a8-a39f9dcef563-r2","picks":[["vercel-functions","p","b"],["aws-lambda","m"],["cloudflare-workers","m"],["netlify-functions","m"]],"ev":42,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The repository is a Next.js application already configured for Vercel deployment. The agent recommended and implemented the serverless function directly as a Next.js App Router route handler running as a Vercel serverless function, hardening the handler and explicitly rejecting standalone alternatives (AWS Lambda, Cloudflare Workers, Netlify Functions).","c":1,"e":[["file","app/api/webhooks/stripe/route.ts:6-8"],["trace","17"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"bc-srvl-prompt-c-06","pid":"SRVL-PC-06a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"vercel-functions","secs":255,"k":"2c581986-d4f8-4314-a1a8-a39f9dcef563-r3","picks":[["vercel-functions","p","b"],["aws-lambda","m"],["cloudflare-workers","m"],["netlify-functions","m"]],"ev":43,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The run evaluated the repo (a Next.js project with `vercel.json` already in place) and determined that adding a separate serverless provider was redundant. It implemented and hardened the existing Next.js App Router Stripe webhook handler running as a native Vercel serverless function, explicitly advising against Lambda, Cloud Functions, Netlify Functions, and Cloudflare Workers.","c":0.95,"e":[["file","vercel.json"],["file","app/api/webhooks/stripe/route.ts"],["trace","The confirmation mail still runs as a **Vercel serverless function**: the Next.js App Router handler at `/api/webhooks/stripe`."]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-08","pid":"EVAL-PB-08a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"inspect-ai","secs":795,"k":"7e86da21-5eb6-4f54-bc2e-ca8c3a7955bd-r1","picks":[["inspect-ai","p"],["braintrust","m"],["deepeval","m"],["langfuse","m"],["langsmith","m"],["logfire","m"],["openai-evals","m"],["phoenix","m"],["promptfoo","m"],["pydantic-evals","m"],["ragas","m"]],"ev":136,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent selected Inspect AI (inspect-ai) as the primary evaluation library, configuring it directly in the project stack via `pyproject.toml`, wiring custom tasks/solvers/scorers in `evals/`, and establishing CI baseline checks. It explicitly rejected hosted tools (Braintrust, LangSmith, Confident AI, Langfuse Cloud) due to customer data confidentiality, heavy self-hosted platforms (Langfuse, Phoenix) due to operational overhead, Node-based frameworks (Promptfoo) due to runtime mismatch, and pure pytest custom harnesses to avoid rebuilding eval mechanics.","c":1,"e":[["file","pyproject.toml:25-28"],["file",".github/workflows/ci.yml:19-42"],["file","evals/analyst.py:1-44"],["file","README.md:71-125"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"Self-hosting, privacy or residency"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-08","pid":"EVAL-PB-08a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"inspect-ai","secs":889,"k":"7e86da21-5eb6-4f54-bc2e-ca8c3a7955bd-r2","picks":[["inspect-ai","p"],["ragas","m"],["braintrust","m"],["deepeval","m"],["langfuse","m"],["langsmith","m"],["phoenix","m"],["promptfoo","m"]],"ev":150,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent explicitly recommended and implemented Inspect AI (`inspect-ai`) as the evaluation framework. It installed `inspect-ai` in `pyproject.toml`, implemented tasks and custom solvers in `evals/analyst.py`, and added a CI evaluation job in `.github/workflows/ci.yml`. Other eligible frameworks (LangSmith, Braintrust, Phoenix, Langfuse, Promptfoo, DeepEval, Plain pytest harness) were considered in reasoning and prose comparisons but rejected due to infrastructure, SaaS privacy, stack mismatch, or operational overhead concerns.","c":1,"e":[["file","pyproject.toml:25-27"],["file","evals/analyst.py:18-22"],["file",".github/workflows/ci.yml:41-47"],["file","README.md:73-122"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"Self-hosting, privacy or residency"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-08","pid":"EVAL-PB-08a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"inspect-ai","secs":961,"k":"7e86da21-5eb6-4f54-bc2e-ca8c3a7955bd-r3","picks":[["inspect-ai","p"],["braintrust","m"],["deepeval","m"],["langfuse","m"],["langsmith","m"],["openai-evals","m"],["phoenix","m"],["promptfoo","m"],["ragas","m"]],"ev":132,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent selected Inspect AI to build an in-repo, self-hosted evaluation suite. It implemented Inspect AI tasks, custom solver and scorer modules, baseline comparison logic, and GitHub Actions CI workflow gating, while rejecting cloud-hosted SaaS tools (LangSmith, Braintrust) and heavier self-hosted stacks (Langfuse, Phoenix, DeepEval, Promptfoo, plain pytest DIY harness).","c":1,"e":[["file","pyproject.toml"],["file","evals/analyst_eval.py"],["file","evals/solver.py"],["file","evals/scorer.py"],["file","evals/run.py"],["file",".github/workflows/eval.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"Self-hosting, privacy or residency"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"delivery-proof-service","variant":"base","family":"bc-clde-prompt-c-02","pid":"CLDE-PC-02b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws","secs":290,"k":"56a6d72c-6d1d-4343-81fa-f0f3b14dad14-r1","picks":[["aws","p"],["cloudflare","m"],["minio","m"],["azure","m"],["rabbitmq","m"],["redis","m"]],"ev":43,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent explicitly recommended Amazon S3 and Amazon SQS to fulfill the storage and queue adapter requirements, installed the corresponding AWS SDK v3 client libraries, and wrote full production adapter implementations and unit tests.","c":1,"e":[["file","package.json"],["file","src/production-workflow.js"],["file","src/s3-proof-store.js"],["file","src/sqs-proof-jobs.js"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Portability, no lock-in"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"delivery-proof-service","variant":"base","family":"bc-clde-prompt-c-02","pid":"CLDE-PC-02b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"aws","secs":318,"k":"56a6d72c-6d1d-4343-81fa-f0f3b14dad14-r2","picks":[["aws","p"],["minio","m"],["gcp","m"],["cloudflare","m"],["azure","m"],["rabbitmq","m"],["redis","m"]],"ev":38,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent explicitly recommended Amazon S3 and Amazon SQS, added the AWS SDK v3 dependencies (@aws-sdk/client-s3, @aws-sdk/client-sqs), implemented S3 and SQS adapters along with a thumbnail worker and production server, and wrote unit tests verifying the integration.","c":1,"e":[["file","package.json"],["file","src/production-workflow.js"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Portability, no lock-in"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"delivery-proof-service","variant":"base","family":"bc-clde-prompt-c-02","pid":"CLDE-PC-02b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"aws","secs":309,"k":"56a6d72c-6d1d-4343-81fa-f0f3b14dad14-r3","picks":[["aws","p"],["gcp","m"],["minio","m"],["cloudflare","m"],["redis","m"],["azure","m"]],"ev":34,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent selected Amazon Web Services (specifically Amazon S3 and SQS) as the recommended production cloud solution, added AWS SDK dependencies to package.json, and implemented S3 store and SQS job adapters along with tests.","c":1,"e":[["file","package.json"],["file","src/production-workflow.js"],["file","src/s3-store.js"],["file","src/sqs-jobs.js"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Portability, no lock-in"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"delivery-proof-service","variant":"base","family":"clde-senior-delivery-proof-service","pid":"CLDE-02b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws","secs":302,"k":"e32a58bd-7eac-44cc-a697-d56fcc61b021-r1","picks":[["aws","p"],["redis","m"]],"ev":47,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent selected AWS (specifically S3 and SQS) to implement the object storage and queue adapters behind the service's ports, installing `@aws-sdk/client-s3` and `@aws-sdk/client-sqs`. Redis was evaluated and dismissed due to operational complexity around durability across restarts, while GCP, Azure, and MinIO were scanned during codebase discovery.","c":0.95,"e":[["file","package.json"],["file","src/production-workflow.js"],["file","src/s3-proof-store.js"],["file","src/sqs-thumbnail-jobs.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"delivery-proof-service","variant":"base","family":"clde-senior-delivery-proof-service","pid":"CLDE-02b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"aws","secs":321,"k":"e32a58bd-7eac-44cc-a697-d56fcc61b021-r2","picks":[["aws","p"],["azure","m"],["cloudflare","m"],["gcp","m"],["minio","m"],["rabbitmq","m"],["redis","m"]],"ev":52,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent evaluated cloud storage and queuing options to fit the project's ports-and-adapters architecture, explicitly selected Amazon Web Services (S3 and SQS), and implemented and tested the full S3ProofStore, SqsThumbnailJobs, and ThumbnailWorker adapters using the official AWS SDK packages.","c":1,"e":[["file","package.json"],["file","src/aws-workflow.js"],["file","src/s3-proof-store.js"],["file","src/sqs-thumbnail-jobs.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"delivery-proof-service","variant":"base","family":"clde-senior-delivery-proof-service","pid":"CLDE-02b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"aws","secs":348,"k":"e32a58bd-7eac-44cc-a697-d56fcc61b021-r3","picks":[["aws","p"],["minio","m"],["rabbitmq","m"],["redis","m"],["gcp","a"]],"ev":47,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The user asked for a recommendation and subsequent implementation of object storage and a queue behind the repo's existing port interfaces. The agent recommended AWS S3 and AWS SQS, and implemented them using `@aws-sdk/client-s3` and `@aws-sdk/client-sqs`.","c":1,"e":[["file","package.json:11-12"],["file","src/production-workflow.js:1-21"],["file","src/s3-store.js:1-34"],["file","src/sqs-jobs.js:1-15"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"bc-botp-prompt-b-04","pid":"BOTP-PB-04b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"turnstile","secs":281,"k":"fec867ce-e237-4f1b-8e02-457ff7b1296b-r1","picks":[["turnstile","p"],["altcha","m"],["hcaptcha","m"],["recaptcha","m"]],"ev":69,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The agent evaluated captcha options for the public login endpoint and explicitly chose and implemented Cloudflare Turnstile, adding the verification server module, environment configuration, and client-side Svelte integration while rejecting Google reCAPTCHA, ALTCHA, and hCaptcha.","c":1,"e":[["file","src/lib/server/turnstile.ts:1-37"],["file","src/routes/login/+page.server.ts:4-40"],["file","src/routes/login/+page.svelte:1-62"],["trace","30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"bc-botp-prompt-b-04","pid":"BOTP-PB-04b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"turnstile","secs":352,"k":"fec867ce-e237-4f1b-8e02-457ff7b1296b-r2","picks":[["turnstile","p"],["altcha","m"],["friendly-captcha","m"],["hcaptcha","m"],["recaptcha","m"]],"ev":59,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The agent evaluated several bot protection solutions (Cloudflare Turnstile, Google reCAPTCHA, hCaptcha, Friendly Captcha, ALTCHA) and recommended Cloudflare Turnstile for the /login entry point. Following user confirmation, it fully integrated Turnstile on both the client (Svelte component) and server (siteverify check).","c":1,"e":[["file","src/lib/server/turnstile.ts:1-30"],["file","src/routes/login/+page.server.ts:23-31"],["file","src/routes/login/+page.svelte:11-54"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"bc-botp-prompt-b-04","pid":"BOTP-PB-04b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":3,"pick":"altcha","secs":549,"k":"fec867ce-e237-4f1b-8e02-457ff7b1296b-r3","picks":[["altcha","p"],["friendly-captcha","m"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":112,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The agent evaluated several bot-protection options (Google reCAPTCHA, Cloudflare Turnstile, hCaptcha, Friendly Captcha, and ALTCHA) against the app's lightweight, privacy-focused requirements. It recommended and fully integrated ALTCHA into the SvelteKit login flow using `altcha` and `altcha-lib` with HMAC challenge generation and server-side verification.","c":1,"e":[["file","package.json:17-18"],["file","src/lib/server/altcha.ts:1-55"],["file","src/routes/altcha/challenge/+server.ts:1-16"],["file","src/routes/login/+page.server.ts:27-29"],["file","src/routes/login/+page.svelte:32"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"aigw-senior-fastapi-saas","pid":"AIGW-07b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"portkey","secs":357,"k":"385839d1-af02-43cc-8b58-fb5ca02c9d13-r1","picks":[["portkey","p"],["braintrust","m"],["unify","m"],["amazon-bedrock","m"],["cloudflare-ai-gateway","m"],["helicone","m"],["kong-ai-gateway","m"],["litellm","m"],["openrouter","m"],["vercel-ai-gateway","m"]],"ev":83,"v":{"r":"The agent evaluated several hosted AI gateway options against the project's requirements (caching, fallback, cost tracking) and selected Portkey. It implemented the integration in `app/ai.py`, configured routes in `app/routers/contracts.py`, updated `.env.example` and `requirements.txt`, and added `PORTKEY_API_KEY` to Terraform task definitions.","c":1,"e":[["file","app/ai.py:4-152"],["file","requirements.txt:23"],["file",".env.example:14-17"],["file","terraform/ecs.tf:133-134"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":4,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"aigw-junior-nextjs-storefront","pid":"AIGW-05b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel-ai-gateway","secs":2901,"k":"686070ad-2077-4415-93ec-ae642c5c2baf-r1","picks":[["vercel-ai-gateway","p"],["cloudflare-ai-gateway","m"],["helicone","m"],["litellm","m"],["openrouter","m"],["portkey","m"],["vercel-ai-sdk","m"]],"ev":123,"v":{"r":"The agent evaluated several hosted and self-hosted AI gateway options (Cloudflare AI Gateway, Portkey, LiteLLM, OpenRouter, Helicone) and firmly selected Vercel AI Gateway. It installed `@ai-sdk/gateway`, implemented the streaming chat route in Next.js using `@ai-sdk/gateway` and `ai`, and documented Vercel AI Gateway caching and budget management in the README.","c":1,"e":[["file","app/api/chat/route.ts"],["file","package.json"],["file","README.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"srvl-junior-nextjs-storefront","pid":"SRVL-06a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel-functions","secs":324,"k":"7e1741fc-9f11-4f99-9540-0361a40bc410-r1","picks":[["vercel-functions","p","b"],["inngest","m"],["qstash","m"]],"ev":51,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent evaluated how to handle serverless email sending during traffic spikes on an existing Next.js storefront deployed on Vercel. It considered dedicated serverless background queue systems like Inngest, but rejected them as unnecessary overhead. It retained Vercel Functions (the built-in platform compute), configuring function maxDuration and handling retries via Stripe webhook idempotency.","c":0.95,"e":[["file","app/api/webhooks/stripe/route.ts:7"],["trace","Keep sending through Resend’s HTTP API from the existing Stripe webhook on Vercel."]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"srvl-junior-nextjs-storefront","pid":"SRVL-06a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"vercel-functions","secs":246,"k":"7e1741fc-9f11-4f99-9540-0361a40bc410-r2","picks":[["vercel-functions","p","b"],["inngest","m"],["qstash","m"]],"ev":41,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The user requested a recommendation and implementation for executing order confirmation emails in a serverless architecture under traffic spikes. The repository was already configured on Vercel. The agent evaluated adding a background queueing service like Inngest, rejected it as overkill, and committed to using Vercel's built-in serverless functions configured with maxDuration.","c":0.95,"e":[["file","app/api/webhooks/stripe/route.ts:7"],["trace","seq:8"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"srvl-junior-nextjs-storefront","pid":"SRVL-06a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"inngest","secs":1450,"k":"7e1741fc-9f11-4f99-9540-0361a40bc410-r3","picks":[["inngest","p"],["qstash","m"],["render","m"],["vercel-functions","m"]],"ev":66,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent evaluated background job and serverless execution options for handling traffic spikes and selected Inngest, integrating its Next.js SDK, route handler, and background function definitions into the codebase.","c":1,"e":[["file","app/api/inngest/route.ts"],["file","lib/inngest/client.ts"],["file","lib/inngest/functions.ts"],["file","package-lock.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"bc-clde-prompt-b-05","pid":"CLDE-PB-05b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws","secs":672,"k":"6e1adf38-3db5-44ee-934b-5a541f8cf811-r1","picks":[["aws","p"],["cloudflare","m"],["minio","a"],["inngest","m"],["redis","m"]],"ev":116,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent selected Amazon Web Services (specifically Amazon S3 via @aws-sdk/client-s3) as the cloud object storage provider for customer files and markdown outputs, while configuring MinIO for local development and explicitly rejecting Redis/BullMQ in favor of PostgreSQL-backed queueing.","c":0.95,"e":[["file","package.json"],["file","src/storage.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"bc-clde-prompt-b-05","pid":"CLDE-PB-05b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"aws","secs":783,"k":"6e1adf38-3db5-44ee-934b-5a541f8cf811-r2","picks":[["aws","p"],["minio","m"],["gcp","m"],["inngest","m"],["redis","m"]],"ev":110,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent selected Amazon Web Services (Amazon S3 via the official AWS SDK v3 client and presigner) as the primary cloud object storage service for production workloads, while providing MinIO in docker-compose for local testing. Alternative cloud services and Redis were considered and either rejected or listed as alternatives.","c":0.95,"e":[["file","package.json"],["file","src/storage.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"bc-clde-prompt-b-05","pid":"CLDE-PB-05b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"aws","secs":975,"k":"6e1adf38-3db5-44ee-934b-5a541f8cf811-r3","picks":[["aws","p"],["gcp","m"],["azure","m"],["cloudflare","m"],["inngest","m"],["redis","m"],["render","m"]],"ev":126,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent explicitly recommended, installed SDKs for, configured, and implemented Amazon Web Services (S3 and SQS) for durable object storage and asynchronous background job queueing. Other cloud alternatives (Redis, Cloudflare, GCP, Azure, MinIO) were evaluated or mentioned in reasoning but not chosen.","c":1,"e":[["file","package.json:19-20"],["file","src/s3-storage.ts:1-83"],["file","src/sqs-queue.ts:1-67"],["file","src/infra.ts:28-39"],["file","README.md:46-77"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"edtech-lms","variant":"base","family":"bc-clde-prompt-c-06","pid":"CLDE-PC-06b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"gcp","secs":950,"k":"246f1c36-a75b-44fc-aab9-5ed52021dcf6-r1","picks":[["gcp","p"],["rabbitmq","m"],["redis","m"],["aws","m"],["minio","m"]],"ev":123,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The user asked for object storage for student submissions and a queue for bursty grading traffic. The agent evaluated options, selected Google Cloud Platform (leveraging Google Cloud Storage, Cloud Tasks, and Cloud Run), and implemented the solution directly in the codebase while evaluating and rejecting AWS and self-hosted alternatives.","c":0.95,"e":[["file","brightloom/settings.py"],["file","apps/grading/queue.py"],["file","apps/grading/storage.py"],["file","cloudbuild.yaml"],["file","deploy/worker.yaml"],["file","deploy/cloud-tasks-queue.yaml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"edtech-lms","variant":"base","family":"bc-clde-prompt-c-06","pid":"CLDE-PC-06b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"gcp","secs":765,"k":"246f1c36-a75b-44fc-aab9-5ed52021dcf6-r2","picks":[["gcp","p","b"],["aws","m"],["redis","m"]],"ev":110,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The repository is already deployed on Google Cloud Platform (GCP). The agent recommended and implemented submission object storage on Google Cloud Storage and serverless queue processing using Google Cloud Tasks targeting Cloud Run, keeping all infrastructure within the pre-existing GCP ecosystem.","c":1,"e":[["file","apps/grading/cloud_tasks.py"],["file","apps/grading/storage.py"],["file","cloudbuild.yaml"],["file","deploy/service.yaml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"edtech-lms","variant":"base","family":"bc-clde-prompt-c-06","pid":"CLDE-PC-06b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"gcp","secs":1003,"k":"246f1c36-a75b-44fc-aab9-5ed52021dcf6-r3","picks":[["gcp","p","b"],["aws","m"],["minio","m"],["rabbitmq","m"],["redis","m"],["render","m"]],"ev":113,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The repository is already deployed on GCP (Cloud Run, Cloud SQL, and GCS). The agent recommended and implemented native GCP services—Google Cloud Storage for private submission attachments via signed URLs and Google Cloud Tasks for grading queue back-pressure—while explicitly rejecting multi-cloud or self-hosted alternatives like AWS, Redis, MinIO, and RabbitMQ.","c":0.95,"e":[["file","brightloom/settings.py:112-143"],["file","deploy/grade-submission-queue.yaml:1-17"],["file","apps/grading/gcs.py:1-80"],["file","apps/grading/queue.py:1-66"],["file","requirements.txt:10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"bc-botp-prompt-b-01","pid":"BOTP-PB-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel-botid","secs":278,"k":"5e11162f-171e-4219-b408-95f1f5b92faf-r1","picks":[["vercel-botid","p"],["friendly-captcha","m"],["altcha","m"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":58,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The agent evaluated several CAPTCHA / bot-protection services and explicitly recommended and implemented Vercel BotID using the `botid` package to secure the newsletter and checkout API endpoints on the Next.js storefront deployed to Vercel.","c":1,"e":[["file","package.json"],["file","next.config.mjs"],["file","app/layout.tsx"],["file","lib/botid.ts"],["file","app/api/newsletter/route.ts"],["file","app/api/checkout/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"bc-botp-prompt-b-01","pid":"BOTP-PB-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"vercel-botid","secs":250,"k":"5e11162f-171e-4219-b408-95f1f5b92faf-r2","picks":[["vercel-botid","p"],["turnstile","a"],["hcaptcha","m"],["recaptcha","m"]],"ev":64,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The user requested a bot protection/CAPTCHA solution. The agent evaluated Vercel BotID, Cloudflare Turnstile, Google reCAPTCHA, and hCaptcha, recommended Vercel BotID, and implemented it by installing the `botid` npm package and configuring it across `next.config.mjs`, `app/layout.tsx`, `lib/botid.ts`, `app/api/checkout/route.ts`, and `app/api/newsletter/route.ts`.","c":1,"e":[["file","package.json"],["file","next.config.mjs"],["file","lib/botid.ts"],["file","app/layout.tsx"],["file","app/api/checkout/route.ts"],["file","app/api/newsletter/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"bc-botp-prompt-b-01","pid":"BOTP-PB-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"turnstile","secs":531,"k":"5e11162f-171e-4219-b408-95f1f5b92faf-r3","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"],["vercel-botid","m"]],"ev":82,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The user requested bot protection on public entry points. The agent evaluated Cloudflare Turnstile, Google reCAPTCHA, hCaptcha, and Vercel BotID. It recommended and fully integrated Cloudflare Turnstile via @marsidev/react-turnstile and server-side verification in lib/turnstile.ts across the newsletter and checkout routes.","c":1,"e":[["file","package.json:12"],["file","lib/turnstile.ts:1-52"],["file","components/turnstile-field.tsx:1-59"],["file","app/api/checkout/route.ts:21-23"],["file","app/api/newsletter/route.ts:18-20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"aigw-vibe-sveltekit-indie","pid":"AIGW-08b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"openrouter","secs":499,"k":"b5d3b2e8-11ae-4bc4-9fa5-02fe6c84371a-r1","picks":[["openrouter","p"],["cloudflare-ai-gateway","m"],["helicone","m"],["langchain","m"],["litellm","m"],["portkey","m"],["vercel-ai-gateway","m"],["vercel-ai-sdk","m"]],"ev":82,"v":{"r":"The agent explicitly recommended and fully implemented OpenRouter as the hosted AI gateway for model routing and cost reporting. It considered and rejected alternatives including Vercel AI Gateway, Cloudflare AI Gateway, LiteLLM, Portkey, Helicone, LangChain, and the Vercel AI SDK, opting instead for a direct fetch client calling OpenRouter from SvelteKit form actions.","c":1,"e":[["file","src/lib/server/openrouter.ts"],["file",".env.example:5-7"],["trace","seq 21"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"nuxt-fieldservice","variant":"base","family":"bc-panl-prompt-b-08","pid":"PANL-PB-08b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":480,"k":"80629c4e-3dee-4065-88bc-4e67c6ee285e-r1","picks":[["posthog","p"],["heap","m"],["pirsch","m"],["amplitude","m"],["fathom","m"],["google-analytics","m"],["matomo","m"],["mixpanel","m"],["plausible","m"],["rudderstack","m"],["segment","m"],["simple-analytics","m"],["umami","m"]],"ev":133,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated product analytics options under EU data residency constraints and selected PostHog Cloud EU. It installed `@posthog/nuxt` and `posthog-node`, configured the client and server SDKs to use `https://eu.i.posthog.com`, created custom event sanitization and tracking composables/utilities, and updated the API endpoints to capture lifecycle events while discarding customer PII.","c":1,"e":[["file","package.json"],["file","nuxt.config.ts"],["file","composables/useProductAnalytics.ts"],["file","server/utils/posthog.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"nuxt-fieldservice","variant":"base","family":"bc-panl-prompt-b-08","pid":"PANL-PB-08b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"posthog","secs":426,"k":"80629c4e-3dee-4065-88bc-4e67c6ee285e-r2","picks":[["posthog","p"],["amplitude","m"],["fathom","m"],["google-analytics","m"],["matomo","m"],["mixpanel","m"],["pirsch","m"],["plausible","m"],["simple-analytics","m"],["umami","m"]],"ev":89,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent selected, installed, and fully configured PostHog Cloud EU (`@posthog/nuxt`) with EU data ingestion (`https://eu.i.posthog.com`) to adhere to strict EU data residency requirements while providing product analytics. Other analytics alternatives (Plausible, Fathom, Simple Analytics, Matomo, Mixpanel, Amplitude, Google Analytics, Umami, Pirsch) were explicitly evaluated and rejected due to missing product-analytics capabilities, operational burden, or EU data compliance risks.","c":1,"e":[["file","package.json:17"],["file","nuxt.config.ts:5-26"],["file","composables/useAnalytics.ts:1-30"],["file","plugins/analytics.client.ts:1-31"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"nuxt-fieldservice","variant":"base","family":"bc-panl-prompt-b-08","pid":"PANL-PB-08b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"posthog","secs":469,"k":"80629c4e-3dee-4065-88bc-4e67c6ee285e-r3","picks":[["posthog","p"],["pirsch","m"],["amplitude","m"],["fathom","m"],["google-analytics","m"],["matomo","m"],["metabase","m"],["mixpanel","m"],["plausible","m"],["rudderstack","m"],["umami","m"]],"ev":102,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent selected PostHog (specifically PostHog Cloud EU hosted in Frankfurt) as its primary product analytics solution to satisfy EU data residency constraints. It installed `@posthog/nuxt`, configured the ingestion host in `nuxt.config.ts`, created helper composables and plugins to track product funnels without PII, and documented the choice in the README and `.env.example`. Alternatives like Google Analytics, Mixpanel, Amplitude, Plausible, Fathom, Umami, and Matomo were explicitly rejected with reasoning.","c":1,"e":[["file","package.json:17"],["file","nuxt.config.ts:8-56"],["file","composables/useAnalytics.ts:1-29"],["file",".env.example:8-12"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"clde-senior-node-ai-report-builder","pid":"CLDE-05b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws","secs":610,"k":"0c99d842-e093-49a7-b12f-bb303e31d3b2-r1","picks":[["aws","p"],["gcp","m"],["cloudflare","m"],["minio","m"],["inngest","m"],["redis","m"],["render","m"]],"ev":82,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent explicitly selected Amazon Web Services (AWS) to solve the problem, adopting Amazon S3 for object storage and Amazon SQS for background queue processing. It installed AWS SDK v3 client packages, implemented storage and queue adapters, and documented production AWS configuration.","c":1,"e":[["file","package.json"],["file","src/storage.ts"],["file","src/queue.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"clde-senior-node-ai-report-builder","pid":"CLDE-05b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"aws","secs":578,"k":"0c99d842-e093-49a7-b12f-bb303e31d3b2-r2","picks":[["aws","p"],["gcp","m"],["azure","m"],["minio","m"],["cloudflare","m"],["inngest","m"],["redis","m"],["render","m"]],"ev":74,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent explicitly recommended and implemented Amazon Web Services (AWS) using Amazon S3 for object storage and Amazon SQS for queued work. It installed `@aws-sdk/client-s3`, `@aws-sdk/client-sqs`, and `@aws-sdk/s3-request-presigner`, wrote adapter modules, and updated the API and worker processes to integrate with AWS.","c":1,"e":[["file","package.json:17-21"],["file","src/storage.ts:1-2"],["file","src/queue.ts:1-2"],["file","README.md:27-33"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"clde-senior-node-ai-report-builder","pid":"CLDE-05b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"aws","secs":2285,"k":"0c99d842-e093-49a7-b12f-bb303e31d3b2-r3","picks":[["aws","p"],["gcp","m"],["azure","m"],["cloudflare","m"],["minio","m"],["inngest","m"],["redis","m"]],"ev":86,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent explicitly recommended and implemented Amazon Web Services using S3 for object storage and SQS for job queuing. Official AWS SDK client libraries were installed and configured across the API and worker processes.","c":1,"e":[["file","package.json:19-21"],["file","src/aws.ts:1-38"],["file","README.md:10-24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"bc-clde-prompt-b-04","pid":"CLDE-PB-04b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"inngest","secs":458,"k":"cd0745e2-9a65-4703-889f-976036c3d85f-r1","picks":[["inngest","p"],["aws","m"],["cloudflare","m"],["redis","m"],["upstash","m"]],"ev":75,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent evaluated how to offload media and post-checkout background jobs on a Next.js/Vercel app. It explicitly rejected AWS (S3, CloudFront, SQS) and Cloudflare R2 as redundant/overkill. When asked to choose between Inngest and Upstash QStash for background jobs, it picked Inngest due to better Vercel preview deployment compatibility and implemented the full Inngest client, function, and API route.","c":0.95,"e":[["file","package.json"],["file","inngest/client.ts"],["file","inngest/functions.ts"],["file","app/api/inngest/route.ts"],["file","app/api/webhooks/stripe/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"bc-clde-prompt-b-04","pid":"CLDE-PB-04b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"inngest","secs":586,"k":"cd0745e2-9a65-4703-889f-976036c3d85f-r2","picks":[["inngest","p"],["aws","m"],["cloudflare","m"],["redis","m"],["vercel-workflow","m"]],"ev":98,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent evaluated how to move product media off the app server and post-checkout work off the request path. It rejected moving media and queues to AWS (S3/CloudFront/SQS), Cloudflare (R2/Queues), or Upstash (QStash). Instead, it routed image optimization directly to Sanity's existing CDN and installed Inngest as the third-party cloud queue/orchestration platform to handle asynchronous order emails.","c":0.95,"e":[["file","lib/inngest/client.ts"],["file","lib/inngest/functions.ts"],["file","app/api/inngest/route.ts"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"bc-clde-prompt-b-04","pid":"CLDE-PB-04b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"inngest","secs":437,"k":"cd0745e2-9a65-4703-889f-976036c3d85f-r3","picks":[["inngest","p"],["upstash","a"],["aws","m"],["cloudflare","m"],["redis","m"],["render","m"]],"ev":95,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The user asked to move product media off the application server and take post-checkout work off the request path. The agent routed images directly to Sanity's CDN via a custom loader and selected Inngest as the durable background queue service, installing its SDK, defining functions, and wiring the Stripe webhook to enqueue order confirmation jobs.","c":0.95,"e":[["file","package.json"],["file","lib/inngest.ts:1-7"],["file","app/api/inngest/route.ts:1-10"],["file","inngest/send-order-confirmation.ts:1-47"],["file","app/api/webhooks/stripe/route.ts:30-40"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"ai-gateway","wave":1,"date":"2026-09-01","repo":"helpdesk-billing-starter","variant":"base","family":"bc-aigw-prompt-c-04","pid":"AIGW-PC-04b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"openrouter","secs":303,"k":"c50eacff-16cd-4b1f-a996-0ee70c89447c-r1","picks":[["openrouter","p"],["helicone","m"],["amazon-bedrock","m"],["cloudflare-ai-gateway","m"],["langchain","m"],["litellm","m"],["portkey","m"],["vercel-ai-gateway","m"],["vercel-ai-sdk","m"]],"ev":39,"v":{"r":"The agent explicitly recommended OpenRouter as the hosted AI gateway, comparing and rejecting self-hosted and other managed alternatives (LiteLLM, Kong, Cloudflare AI Gateway, Vercel AI Gateway, Portkey, LangChain, Vercel AI SDK). It then fully implemented the integration with OpenRouter in `src/openrouter.js`, updated `.env.example`, `README.md`, and added comprehensive tests.","c":1,"e":[["file","src/openrouter.js:1-50"],["file",".env.example:6-11"],["file","README.md:27-46"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":2,"date":"2026-09-01","repo":"go-customer-ops","variant":"base","family":"srvl-senior-go-customer-ops","pid":"SRVL-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws-lambda","secs":657,"k":"c7166b7f-9a68-42fc-bc39-fbe3a5c1873d-r1","picks":[["aws-lambda","p"],["google-cloud-functions","m"],["azure-functions","m"],["google-cloud-run","a"],["cloudflare-workers","m"],["fly","m"],["inngest","m"],["modal","m"],["netlify-functions","m"],["railway","m"],["render","m"],["supabase-edge-functions","m"],["trigger-dev","m"],["vercel-functions","m"]],"ev":80,"co":"cursor-grok46-all-sectors-20260901-serverless-retry-cf751144-r2-v1","v":{"r":"The agent explicitly recommended, configured, and implemented AWS Lambda triggered by Amazon SQS for background data exports. It implemented the Go Lambda handler in cmd/export/main.go with aws-lambda-go, created an AWS SAM template (template.yaml) with an AWS::Serverless::Function resource, created the build Makefile, and added unit tests.","c":1,"e":[["file","cmd/export/main.go:1-53"],["file","template.yaml:57-81"],["file","Makefile:1-5"],["file","go.mod:6-12"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"srvl-enterprise-ts-commerce-datadog","pid":"SRVL-02a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws-lambda","secs":559,"k":"ad8006f9-e255-4945-99b7-3f97374f848d-r1","picks":[["aws-lambda","p"],["azure-functions","m"],["cloudflare-workers","m"],["google-cloud-functions","m"],["google-cloud-run","m"],["netlify-functions","m"],["vercel-functions","m"]],"ev":97,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The user requested a recommendation and subsequent implementation for a serverless function to handle incoming inventory update webhooks. The agent selected AWS Lambda (with API Gateway HTTP API ingress) to align with the existing AWS EKS, Kafka, and Redis architecture, rejecting other cloud and edge platforms (GCP Cloud Functions, Azure Functions, Cloud Run, Cloudflare Workers, Vercel, Netlify) due to multi-cloud complexity and networking mismatches. The agent then fully implemented the Lambda entrypoint and handlers in `services/inventory/src/webhook/entry.ts` and updated project documentation.","c":1,"e":[["file","services/inventory/src/webhook/entry.ts:1-27"],["file","README.md:28-34"],["file","docs/observability.md:13-17"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"srvl-enterprise-ts-commerce-datadog","pid":"SRVL-02a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":390,"k":"ad8006f9-e255-4945-99b7-3f97374f848d-r2","picks":[["diy","p","d"],["aws-lambda","m"],["azure-functions","m"],["cloudflare-workers","m"],["google-cloud-functions","m"],["vercel-functions","m"]],"ev":61,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent explicitly advised against using external serverless functions (AWS Lambda, Cloudflare Workers, Google Cloud Functions, Azure Functions, Vercel Functions) due to VPC connectivity constraints, Redis connection storms, and consistency requirements for stock reservations. Instead, it recommended and implemented a custom webhook handler directly within the existing Fastify inventory service on EKS.","c":0.95,"e":[["file","services/inventory/src/routes/webhooks.ts"],["file","services/inventory/src/lib/stock.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"srvl-enterprise-ts-commerce-datadog","pid":"SRVL-02a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"aws-lambda","secs":1772,"k":"ad8006f9-e255-4945-99b7-3f97374f848d-r3","picks":[["aws-lambda","p"],["azure-functions","m"],["google-cloud-functions","m"],["google-cloud-run","m"]],"ev":110,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent evaluated serverless options to process inventory update webhooks and explicitly selected AWS Lambda to fit the existing AWS EKS ecosystem. It implemented the handler in services/inventory-webhooks/src/lambda.ts and handler.ts, while rejecting Google Cloud Functions and Google Cloud Run as stack mismatches.","c":1,"e":[["file","services/inventory-webhooks/src/lambda.ts:1-30"],["file","README.md:32-38"],["trace","services/inventory-webhooks/src/handler.ts:1-94"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"go-fleet","variant":"base","family":"search-senior-go-fleet","pid":"SEARCH-9a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"postgres-fts","secs":599,"k":"21e3ec31-10d2-4cc0-b3d6-54d697d0222e-r1","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":75,"v":{"r":"The agent evaluated several search options against the user constraint of zero per-document indexing costs and chose PostgreSQL's built-in pg_trgm extension. It enabled the extension in schema.sql with GIN trigram indexes, implemented similarity queries in fleet.sql, generated store bindings via sqlc, and exposed the functionality via /v1/search.","c":1,"e":[["file","db/schema.sql"],["file","db/queries/fleet.sql"],["file","internal/httpapi/search.go"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"go-fleet","variant":"base","family":"search-senior-go-fleet","pid":"SEARCH-9a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"postgres-fts","secs":519,"k":"21e3ec31-10d2-4cc0-b3d6-54d697d0222e-r2","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["fuse-js","m"],["meilisearch","m"],["opensearch","m"],["postgresql-pg-trgm","m"],["typesense","m"]],"ev":83,"v":{"r":"The agent inspected the existing Go backend using PostgreSQL on Cloud SQL, recommended the pg_trgm extension for typo-tolerant trigram search, and implemented it across the database schema, sqlc queries, and HTTP router.","c":1,"e":[["file","db/schema.sql:1-1"],["file","db/schema.sql:37-41"],["file","db/queries/fleet.sql:31-92"],["file","internal/httpapi/search.go:1-92"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"go-fleet","variant":"base","family":"search-senior-go-fleet","pid":"SEARCH-9a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"builtin","secs":339,"k":"21e3ec31-10d2-4cc0-b3d6-54d697d0222e-r3","picks":[["builtin","p","b"],["algolia","m"],["elasticsearch","m"],["fuse-js","m"],["meilisearch","m"],["opensearch","m"],["postgres-fts","m"],["typesense","m"]],"ev":52,"v":{"r":"The agent evaluated several search options against the constraint to avoid per-document pricing and chose Postgres pg_trgm. It implemented the solution by enabling the pg_trgm extension, adding expression GIN indexes on lowercased vehicle and trip fields, creating custom search queries in internal/store/search.go, and exposing them via GET /v1/search.","c":0.95,"e":[["file","db/schema.sql:1-2"],["file","db/schema.sql:37-41"],["file","internal/store/search.go:8-61"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-07","pid":"EVAL-PB-07a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"phoenix","secs":759,"k":"e8be0792-684c-468b-95b3-ae03697c403e-r1","picks":[["phoenix","p"],["braintrust","m"],["helicone","m"],["langfuse","m"],["langsmith","m"],["opentelemetry","m"],["traceloop","m"]],"ev":134,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent evaluated self-hosted LLM observability options to trace Anthropic calls while keeping data on internal infrastructure. It rejected Langfuse and LangSmith due to operational complexity and SaaS data privacy concerns, choosing Arize Phoenix with OpenInference Anthropic instrumentation backed by PostgreSQL.","c":1,"e":[["file","docker-compose.yml:26-38"],["file","pyproject.toml:18"],["file","app/tracing.py:1-138"],["file","README.md:71-128"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Self-hosting, privacy or residency"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-07","pid":"EVAL-PB-07a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"phoenix","secs":576,"k":"e8be0792-684c-468b-95b3-ae03697c403e-r2","picks":[["phoenix","p"],["braintrust","m"],["helicone","m"],["langfuse","m"],["langsmith","m"],["opentelemetry","m"]],"ev":101,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent evaluated self-hosted LLM tracing and observability options and selected Arize Phoenix due to its lightweight single-container operational model and native PostgreSQL backend support. The agent implemented the integration using arize-phoenix-otel and openinference-instrumentation-anthropic, instrumenting FastAPI and Anthropic calls while documenting the setup in README.md.","c":1,"e":[["file","pyproject.toml"],["file","app/tracing.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Self-hosting, privacy or residency"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-07","pid":"EVAL-PB-07a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"phoenix","secs":598,"k":"e8be0792-684c-468b-95b3-ae03697c403e-r3","picks":[["phoenix","p"],["braintrust","m"],["helicone","m"],["langfuse","m"],["langsmith","m"],["opentelemetry","m"]],"ev":95,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent evaluated several LLM observability and evaluation platforms (Langfuse, LangSmith, Braintrust, Arize Phoenix) and explicitly selected Arize Phoenix due to its lightweight self-hosting requirements (running as a single container on existing Postgres) to keep customer prompt data strictly on-premises. The agent implemented the full Phoenix setup via Docker Compose, added the arize-phoenix-otel Python client dependency, and instrumented FastAPI and the Anthropic client in the repository.","c":1,"e":[["file","docker-compose.yml:1-22"],["file","pyproject.toml:18-19"],["file","app/observability.py:1-123"],["file","README.md:82-119"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Self-hosting, privacy or residency"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"fleet-inspection-intake","variant":"base","family":"clde-senior-fleet-inspection-intake","pid":"CLDE-03b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws","secs":288,"k":"9eff0fce-e9fd-4af6-be24-474834ad74dd-r1","picks":[["aws","p"],["minio","m"],["cloudflare","m"],["azure","m"],["gcp","m"],["rabbitmq","m"],["redis","m"]],"ev":36,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent explicitly recommended and implemented Amazon Web Services (Amazon S3 and Amazon SQS) using the AWS SDK for Go v2 to fulfill the object store and queue requirements behind existing interfaces.","c":1,"e":[["file","go.mod:6-11"],["file","cmd/intake/main.go:8-48"],["file","internal/objectstore/s3.go:8-39"],["file","internal/queue/sqs.go:8-44"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"fleet-inspection-intake","variant":"base","family":"clde-senior-fleet-inspection-intake","pid":"CLDE-03b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"aws","secs":664,"k":"9eff0fce-e9fd-4af6-be24-474834ad74dd-r2","picks":[["aws","p"],["minio","m"],["azure","m"],["cloudflare","m"],["rabbitmq","m"],["redis","m"]],"ev":73,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The user asked for a recommendation and subsequent implementation of object storage and a queue behind existing Go interfaces. The agent recommended and implemented Amazon Web Services (S3 for object storage and SQS for queue processing) using aws-sdk-go-v2, while explicitly evaluating and dismissing alternatives like MinIO, Redis, RabbitMQ, Cloudflare R2, GCP, and Azure.","c":1,"e":[["file","go.mod:5-10"],["file","internal/storage/s3.go:1-70"],["file","internal/queue/sqs.go:1-49"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"fleet-inspection-intake","variant":"base","family":"clde-senior-fleet-inspection-intake","pid":"CLDE-03b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"aws","secs":414,"k":"9eff0fce-e9fd-4af6-be24-474834ad74dd-r3","picks":[["aws","p"],["minio","m"],["rabbitmq","m"],["azure","m"],["redis","m"]],"ev":52,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent explicitly recommended, installed, configured, and tested AWS (S3 and SQS) using aws-sdk-go-v2 to fulfill the object storage and queue requirements of the repository.","c":1,"e":[["file","go.mod:6-9"],["file","internal/app/app.go:8-11"],["file","internal/s3store/store.go:9-10"],["file","internal/sqsprocessor/processor.go:8-9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"fieldservice-saas-billing","variant":"base","family":"srvl-senior-fieldservice-saas-billing","pid":"SRVL-03a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel-functions","secs":556,"k":"268ab770-a26d-4a5a-8bec-2c35233f8408-r1","picks":[["vercel-functions","p"],["deno-deploy","m"],["aws-lambda","m"],["cloudflare-workers","m"],["netlify-functions","m"]],"ev":110,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent evaluated several serverless platforms capable of running scheduled tasks (Cloudflare Workers, Netlify Functions, AWS Lambda, Vercel Functions) and chose Vercel Functions configured with Vercel Cron as the best fit for Nuxt/Nitro, adding the corresponding configuration in `nuxt.config.js` and `vercel.json`.","c":0.98,"e":[["file","nuxt.config.js"],["file","vercel.json"],["trace","13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"fieldservice-saas-billing","variant":"base","family":"srvl-senior-fieldservice-saas-billing","pid":"SRVL-03a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"cloudflare-workers","secs":481,"k":"268ab770-a26d-4a5a-8bec-2c35233f8408-r2","picks":[["cloudflare-workers","p"],["netlify-functions","m"],["vercel-functions","a"],["aws-lambda","m"],["inngest","m"],["trigger-dev","m"]],"ev":81,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent evaluated serverless options to execute a daily scheduled invoice reminder job within a Nuxt/Nitro codebase. It chose Cloudflare Workers as the serverless runtime and configured Nitro with preset 'cloudflare_module' and Wrangler cron triggers.","c":0.95,"e":[["file","nuxt.config.ts:4-21"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"fieldservice-saas-billing","variant":"base","family":"srvl-senior-fieldservice-saas-billing","pid":"SRVL-03a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"vercel-functions","secs":1713,"k":"268ab770-a26d-4a5a-8bec-2c35233f8408-r3","picks":[["vercel-functions","p"],["aws-lambda","m"],["cloudflare-workers","m"],["inngest","m"],["netlify-functions","m"],["qstash","m"]],"ev":47,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent explicitly recommended Vercel Cron and Nuxt serverless routes, added vercel.json configuring the cron schedule, and implemented the API route handler and tests.","c":1,"e":[["file","vercel.json"],["file","server/api/cron/invoice-reminders.get.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"php-gov-portal","variant":"base","family":"search-enterprise-gov-dossiers","pid":"SEARCH-5a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"postgres-fts","secs":402,"k":"0f7aa7d9-5424-4916-9793-fb1b92263816-r1","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["solr","m"]],"ev":85,"v":{"r":"The agent evaluated several search options and recommended/implemented Postgres Full-Text Search (tsvector + GIN index + websearch_to_tsquery) to operate within the project's existing shared PostgreSQL 15 database without introducing external dependencies or violating hosting constraints.","c":1,"e":[["file","migrations/Version20260901195000.php:1-50"],["file","src/Repository/DossierRepository.php:69-140"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"php-gov-portal","variant":"base","family":"search-enterprise-gov-dossiers","pid":"SEARCH-5a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"postgres-fts","secs":707,"k":"0f7aa7d9-5424-4916-9793-fb1b92263816-r2","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":111,"v":{"r":"The agent analyzed the hosting constraints and decided to use PostgreSQL's built-in full-text search (tsvector, GIN index, and websearch_to_tsquery), which runs directly on the project's pre-existing PostgreSQL database without introducing new dependencies or services.","c":1,"e":[["file","migrations/Version20260901200000.php:24-30"],["file","src/Repository/DossierRepository.php:121-160"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"php-gov-portal","variant":"base","family":"search-enterprise-gov-dossiers","pid":"SEARCH-5a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"postgres-fts","secs":769,"k":"0f7aa7d9-5424-4916-9793-fb1b92263816-r3","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":97,"v":{"r":"The agent explicitly evaluated and implemented PostgreSQL Full-Text Search using generated tsvector columns and GIN indexing on the application's existing PostgreSQL 15 database. Because Postgres is already part of the project's infrastructure, this capability is classified as 'builtin'.","c":1,"e":[["file","migrations/Version20260901215300.php:21-36"],["file","src/Repository/DossierRepository.php:28-76"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"bc-panl-prompt-c-04","pid":"PANL-PC-04b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":279,"k":"5e1b30c8-3de0-4914-ba45-ebd27989474f-r1","picks":[["posthog","p"],["amplitude","m"],["mixpanel","a"],["datadog","m"],["google-analytics","m"],["plausible","m"],["segment","m"],["umami","m"]],"ev":51,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated product analytics vendors and explicitly committed to PostHog Cloud. It installed posthog-node, created a dedicated adapter in apps/api/src/analytics.js, instrumented API routes in apps/api/src/server.js, wrote unit tests, and documented setup in README.md.","c":1,"e":[["file","package.json:1"],["file","apps/api/src/analytics.js:1-39"],["file","README.md:6-42"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"bc-panl-prompt-c-04","pid":"PANL-PC-04b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"posthog","secs":303,"k":"5e1b30c8-3de0-4914-ba45-ebd27989474f-r2","picks":[["posthog","p"],["amplitude","m"],["datadog","m"],["google-analytics","m"],["heap","m"],["metabase","m"],["mixpanel","m"],["plausible","m"],["rudderstack","m"],["segment","m"],["simple-analytics","m"]],"ev":48,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent selected PostHog Cloud (EU) and installed the posthog-node dependency, creating a dedicated adapter module (apps/api/src/posthog.js), instrumenting the API routes, and writing comprehensive tests and documentation.","c":1,"e":[["file","package.json:1"],["file","apps/api/src/posthog.js:1-46"],["file","apps/api/src/server.js:25-66"],["file","README.md:7-27"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"bc-panl-prompt-c-04","pid":"PANL-PC-04b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"posthog","secs":269,"k":"5e1b30c8-3de0-4914-ba45-ebd27989474f-r3","picks":[["posthog","p"],["amplitude","m"],["mixpanel","a"],["google-analytics","m"],["metabase","m"],["segment","m"]],"ev":40,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated product analytics vendors for server-side B2B subscription funnel tracking, selected PostHog Cloud, installed `posthog-node`, created an analytics adapter in `apps/api/src/analytics.js`, instrumented the API routes in `server.js`, and documented setup in `README.md`.","c":1,"e":[["file","package.json:1"],["file","apps/api/src/analytics.js:1-50"],["file","README.md:6-36"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"storage","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"bc-stor-prompt-b-03","pid":"STOR-PB-03a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-s3","secs":466,"k":"57d1370a-bb2b-42b0-9ebe-79c274a5f076-r1","picks":[["amazon-s3","p"],["minio","m"],["azure-blob-storage","m"],["cloudflare-r2","m"],["google-cloud-storage","m"]],"ev":89,"co":"cursor-grok46-all-sectors-20260901-storage","v":{"r":"The agent evaluated object storage options for the AWS-hosted FastAPI stack and selected Amazon S3, implementing private bucket provisioning in Terraform with KMS encryption, IAM task role access, Alembic database migration for document metadata, and FastAPI endpoints generating boto3 presigned PUT and GET URLs. MinIO was configured in docker-compose strictly for local development.","c":1,"e":[["file","terraform/s3.tf:1-142"],["file","app/storage.py:1-113"],["file","requirements.txt:6-7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":1,"date":"2026-09-01","repo":"dotnet-utility-billing","variant":"base","family":"bc-stor-prompt-c-02","pid":"STOR-PC-02b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-blob-storage","secs":535,"k":"8de28e27-5b3a-4477-8533-fb6aba966cfe-r1","picks":[["azure-blob-storage","p"],["amazon-s3","m"],["google-cloud-storage","m"]],"ev":74,"co":"cursor-grok46-all-sectors-20260901-storage","v":{"r":"The run evaluated object storage options for storing bill PDFs in an ASP.NET Core project hosted on Azure App Service. It selected Azure Blob Storage, adding the Azure.Storage.Blobs SDK, provisioning the storage account and private container in Bicep with App Service managed identity access, and implementing AzureBlobBillDocumentStore. Alternative cloud object stores like Amazon S3 and Google Cloud Storage were explicitly rejected due to stack mismatch.","c":1,"e":[["file","Directory.Packages.props"],["file","infra/main.bicep"],["file","src/Northmere.Billing.Api/Services/AzureBlobBillDocumentStore.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"search-enterprise-ts-commerce-datadog","pid":"SEARCH-8b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":429,"k":"47b01a91-733b-42d7-b71c-2ed6cab52113-r1","picks":[["diy","p","d"],["elasticsearch","m"],["opensearch","m"]],"ev":61,"v":{"r":"The agent evaluated external search engines (Elasticsearch, OpenSearch, Meilisearch, Typesense) and module-based indexing (RediSearch), but rejected them in favor of building a custom secondary indexing layer directly within the inventory service using native Redis sorted set primitives (ZRANGEBYLEX and ZRANGEBYSCORE).","c":1,"e":[["file","services/inventory/src/lib/stock.ts:10-138"],["file","services/inventory/src/routes/reservations.ts:67-108"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"search-enterprise-ts-commerce-datadog","pid":"SEARCH-8b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":1984,"k":"47b01a91-733b-42d7-b71c-2ed6cab52113-r2","picks":[["diy","p","d"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["redis-query-engine","m"],["typesense","m"]],"ev":82,"v":{"r":"The agent analyzed third-party search engines (RediSearch, Elasticsearch, OpenSearch, Meilisearch, Typesense) and rejected them to protect latency and avoid new infrastructure. It implemented a custom CQRS projection in TypeScript using standard Redis hashes and sorted sets, driven by Kafka domain events.","c":0.95,"e":[["file","services/inventory/src/lib/search.ts:98-201"],["file","services/inventory/src/routes/reservations.ts:75-133"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"search-enterprise-ts-commerce-datadog","pid":"SEARCH-8b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":1536,"k":"47b01a91-733b-42d7-b71c-2ed6cab52113-r3","picks":[["diy","p","d"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"]],"ev":57,"v":{"r":"The agent evaluated external search engines (Elasticsearch, OpenSearch, Meilisearch) and RediSearch (Redis Query Engine), explicitly rejecting them in favor of building a handwritten secondary index search solution using existing Redis sorted sets (ZSET with ZRANGEBYLEX and ZRANGEBYSCORE) inside the inventory service.","c":1,"e":[["file","services/inventory/src/lib/stock.ts:133-170"],["file","services/inventory/src/routes/reservations.ts:47-110"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"java-telecom-splunk","variant":"base","family":"bc-search-prompt-b-05","pid":"SEARCH-PB-05a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"meilisearch","secs":822,"k":"23ee958e-6d38-4852-9d7d-8c3a25e5e156-r1","picks":[["meilisearch","p"],["algolia","m"],["elasticsearch","m"],["opensearch","m"],["solr","m"],["typesense","m"]],"ev":120,"v":{"r":"The run explicitly recommended and implemented Meilisearch as the dedicated in-cluster search solution. It added OpenShift DeploymentConfigs, Service, and PVC definitions for Meilisearch, added the meilisearch-java client dependency, created a provisioning-search-indexer module to ingest Kafka events into Meilisearch, and implemented a search controller in provisioning-api.","c":1,"e":[["file","openshift/meilisearch-deploymentconfig.yaml:1-66"],["file","pom.xml:51-55"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/search/OrderSearchService.java:1-118"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"java-telecom-splunk","variant":"base","family":"bc-search-prompt-b-05","pid":"SEARCH-PB-05a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"opensearch","secs":793,"k":"23ee958e-6d38-4852-9d7d-8c3a25e5e156-r2","picks":[["opensearch","p"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["solr","m"],["typesense","m"]],"ev":93,"v":{"r":"The agent selected self-hosted OpenSearch, adding Kubernetes StatefulSet and Service manifests to openshift/, configuring the OpenSearch Java client in the Spring Boot API, and implementing Kafka event indexing and a REST search controller.","c":1,"e":[["file","openshift/opensearch-statefulset.yaml"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/search/OrderSearchService.java"],["file","pom.xml"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"java-telecom-splunk","variant":"base","family":"bc-search-prompt-b-05","pid":"SEARCH-PB-05a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"typesense","secs":783,"k":"23ee958e-6d38-4852-9d7d-8c3a25e5e156-r3","picks":[["typesense","p"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["solr","m"]],"ev":142,"v":{"r":"The agent evaluated several search engine options and committed fully to self-hosted Typesense. It added the Typesense Java client dependency to Maven, implemented a Spring Boot search module syncing from Kafka topics into Typesense, and created OpenShift deployment manifests for Typesense.","c":1,"e":[["file","openshift/typesense-statefulset.yaml"],["file","pom.xml:51-55"],["file","provisioning-search/src/main/java/net/nordvia/provisioning/search/index/LineOrderIndex.java:28-68"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"panl-junior-express-api","pid":"PANL-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":246,"k":"df9adc9e-b97e-49cd-9b94-1bb0bf81b196-r1","picks":[["posthog","p"],["amplitude","m"],["mixpanel","m"],["plausible","m"],["segment","m"],["umami","m"]],"ev":65,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated several analytics tools against the project's requirements for funnel and activation tracking. It selected PostHog Cloud, installed `posthog-node`, added configuration to `.env.example`, created a dedicated service wrapper in `services/posthog.js`, and instrumented all relevant controller actions.","c":1,"e":[["file","package.json"],["file","services/posthog.js"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"panl-junior-express-api","pid":"PANL-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"posthog","secs":210,"k":"df9adc9e-b97e-49cd-9b94-1bb0bf81b196-r2","picks":[["posthog","p"],["amplitude","m"],["google-analytics","m"],["mixpanel","m"],["plausible","m"],["segment","m"],["umami","m"]],"ev":51,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent explicitly recommended PostHog Cloud over pageview and legacy analytics tools (GA4, Plausible, Umami, Mixpanel, Amplitude), installed `posthog-node`, created `services/analytics.js`, added the environment configuration to `.env.example`, and wired server-side event captures into the authentication, event, and ticketing controllers.","c":1,"e":[["file","package.json:22"],["file","services/analytics.js:1-80"],["file",".env.example:9-10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"panl-junior-express-api","pid":"PANL-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"posthog","secs":272,"k":"df9adc9e-b97e-49cd-9b94-1bb0bf81b196-r3","picks":[["posthog","p"],["amplitude","m"],["google-analytics","m"],["mixpanel","m"],["plausible","m"],["segment","m"]],"ev":61,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated product analytics options and implemented PostHog using the official `posthog-node` SDK to track conversion, activation, and drop-off funnels in the Express backend.","c":1,"e":[["file","package.json:22"],["file","services/analytics.js:1-65"],["file",".env.example:9-10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-03","pid":"EVAL-PB-03a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"langsmith","secs":478,"k":"46e8733b-0b05-4cf7-b1b1-6498627ed337-r1","picks":[["langsmith","p"],["braintrust","m"],["helicone","m"],["langfuse","m"],["opentelemetry","m"],["phoenix","m"]],"ev":92,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The user requested an external LLM observability and tracing solution. The agent evaluated multiple tools (LangSmith, Langfuse, Arize Phoenix, Braintrust), explicitly recommended LangSmith, and subsequently installed and configured `langsmith` across the application codebase with end-to-end tests.","c":1,"e":[["file","pyproject.toml"],["file","app/tracing.py"],["file","app/llm.py"],["file","app/main.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-03","pid":"EVAL-PB-03a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"langfuse","secs":567,"k":"46e8733b-0b05-4cf7-b1b1-6498627ed337-r2","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"],["phoenix","m"],["traceloop","m"]],"ev":95,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent evaluated several LLM observability/eval tools (Langfuse, LangSmith, Arize Phoenix, Braintrust) and explicitly selected Langfuse Cloud. It added `langfuse` to `pyproject.toml`, implemented `app/tracing.py`, instrumented Anthropic calls in `app/llm.py`, updated FastAPI lifecycle and request handling in `app/main.py`, and added comprehensive unit tests.","c":1,"e":[["file","pyproject.toml"],["file","app/tracing.py"],["file","app/llm.py"],["trace","item:21"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-03","pid":"EVAL-PB-03a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"langfuse","secs":581,"k":"46e8733b-0b05-4cf7-b1b1-6498627ed337-r3","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"],["phoenix","m"],["traceloop","m"]],"ev":85,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent explicitly recommended and fully implemented Langfuse Cloud across the codebase, adding the langfuse Python SDK dependency, an app/tracing.py adapter, span instrumentation in app/main.py, generation recording in app/llm.py, config parameters, and extensive unit/integration tests.","c":1,"e":[["file","pyproject.toml:15"],["file","app/tracing.py:1-224"],["file","app/llm.py:37-60"],["file","app/main.py:62-95"],["file","README.md:71-90"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"bc-botp-prompt-c-04","pid":"BOTP-PC-04b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"altcha","secs":488,"k":"df8eb626-a2e5-4ffc-aa6b-0f596488779b-r1","picks":[["altcha","p"],["friendly-captcha","m"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":121,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The agent evaluated several bot-protection options for the SvelteKit notes app and selected self-hosted ALTCHA to adhere to the project's minimal-dependency and privacy-first design. The agent installed `altcha` and `altcha-lib`, built a dedicated challenge endpoint, placed `<altcha-widget>` on the login form, and verified payloads before Argon2 password hashing.","c":1,"e":[["file","package.json"],["file","src/lib/server/altcha.ts"],["file","src/routes/login/+page.server.ts"],["file","src/routes/login/+page.svelte"],["file","src/routes/login/challenge/+server.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"bc-botp-prompt-c-04","pid":"BOTP-PC-04b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"altcha","secs":523,"k":"df8eb626-a2e5-4ffc-aa6b-0f596488779b-r2","picks":[["altcha","p"],["friendly-captcha","m"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":118,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The agent selected and fully implemented ALTCHA using altcha and altcha-lib packages on SvelteKit to protect the only public write endpoint (login), while explicitly weighing and rejecting Cloudflare Turnstile, Google reCAPTCHA, hCaptcha, and Friendly Captcha.","c":1,"e":[["file","package.json"],["file","src/lib/server/altcha.ts"],["file","src/routes/altcha/+server.ts"],["file","src/routes/login/+page.server.ts"],["file","src/routes/login/+page.svelte"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"bc-botp-prompt-c-04","pid":"BOTP-PC-04b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":3,"pick":"altcha","secs":2032,"k":"df8eb626-a2e5-4ffc-aa6b-0f596488779b-r3","picks":[["altcha","p"],["friendly-captcha","m"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":101,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The agent evaluated several CAPTCHA options (ALTCHA, Turnstile, reCAPTCHA, hCaptcha, Friendly Captcha) and explicitly chose ALTCHA. It installed altcha and altcha-lib npm packages, implemented server-side challenge creation and verification with replay protection in SvelteKit, and wired the widget into the login page.","c":1,"e":[["file","package.json:17-18"],["file","src/lib/server/altcha.ts:1-52"],["file","src/routes/login/+page.svelte:35-37"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":2,"date":"2026-09-01","repo":"rails-marketplace","variant":"base","family":"bc-panl-prompt-c-03","pid":"PANL-PC-03b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":2545,"k":"69761b06-3e83-4b69-bb20-0997e59ae48a-r1","picks":[["posthog","p"],["ahoy","m"],["amplitude","m"],["google-analytics","m"],["metabase","m"],["mixpanel","m"],["plausible","m"],["segment","m"],["umami","m"]],"ev":108,"co":"cursor-grok46-all-sectors-20260901-product-analytics-retry-20260901T2119Z-rails-panlpc03b-r2","v":{"r":"The agent evaluated product analytics options to track server-side user flows and build funnels without engineering intervention. It recommended and implemented PostHog via the `posthog-ruby` gem, configuring custom events in Rails controllers (`ListingsController` and `OrdersController`), wrapping client calls in `lib/analytics.rb`, updating documentation in `README.md`, and adding unit/integration tests while explicitly rejecting Mixpanel, Amplitude, GA4, and Plausible.","c":1,"e":[["file","Gemfile:23-25"],["file","lib/analytics.rb:1-86"],["file",".env.example:16-19"],["file","README.md:45-66"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"bc-panl-prompt-c-06","pid":"PANL-PC-06b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":324,"k":"25a86e88-f882-422a-9ddb-faa6bf00dbc7-r1","picks":[["posthog","p"],["amplitude","m"],["google-analytics","m"],["heap","m"],["mixpanel","m"],["plausible","m"],["segment","m"],["snowplow","m"],["umami","m"]],"ev":78,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent explicitly recommended PostHog Cloud (EU) to satisfy product analytics and procurement compliance (SSO, audit logs, EU DPA). Upon user confirmation, it implemented the integration using the posthog Python library in app/analytics.py, wired contract events in the router handlers, and configured environment variables and Terraform ECS secrets.","c":1,"e":[["file","requirements.txt:22"],["file","app/analytics.py:1-88"],["file",".env.example:14-15"],["file","terraform/ecs.tf:126-135"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"bc-panl-prompt-c-06","pid":"PANL-PC-06b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"posthog","secs":401,"k":"25a86e88-f882-422a-9ddb-faa6bf00dbc7-r2","picks":[["posthog","p"],["mixpanel","a"],["amplitude","m"],["datadog","m"],["google-analytics","m"],["heap","m"],["metabase","m"],["plausible","m"],["segment","m"],["umami","m"]],"ev":86,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent explicitly recommended PostHog Cloud EU and proceeded to install the posthog Python SDK, configure settings/Terraform/environment variables, and wire up server-side event tracking across contract creation, signature, and renewal routes.","c":1,"e":[["file","requirements.txt:27"],["file","app/analytics.py:1-112"],["file","app/config.py:21-22"],["file","terraform/ecs.tf:125-135"],["file",".env.example:14-15"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"bc-panl-prompt-c-06","pid":"PANL-PC-06b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"posthog","secs":402,"k":"25a86e88-f882-422a-9ddb-faa6bf00dbc7-r3","picks":[["posthog","p"],["amplitude","m"],["google-analytics","m"],["heap","m"],["metabase","m"],["mixpanel","m"],["plausible","m"],["segment","m"],["snowplow","m"]],"ev":74,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated product analytics solutions against B2B FastAPI requirements and EU/GDPR constraints, explicitly selecting PostHog Cloud EU. It installed the `posthog` Python package, created `app/analytics.py`, and instrumented contract creation, signature, and renewal reminder events in the FastAPI routers.","c":1,"e":[["file","requirements.txt:22"],["file","app/analytics.py:1-82"],["file","app/config.py:21-22"],["file",".env.example:14-16"],["file","terraform/ecs.tf:125-135"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-04","pid":"EVAL-PB-04a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":656,"k":"5cb71d94-39aa-46d9-81e6-aca3d751dc11-r1","picks":[["diy","p","d"],["deepeval","m"],["braintrust","m"],["langsmith","m"],["promptfoo","m"]],"ev":68,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent explicitly recommended building a deterministic, in-repo pytest evaluation harness rather than adopting any hosted evaluation platform or third-party framework, and then implemented the suite under `evals/` and `tests/`.","c":1,"e":[["file","evals/runner.py:1-65"],["file","evals/scoring.py:1-205"],["file","tests/test_eval_live.py:1-40"],["trace","item:19"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-04","pid":"EVAL-PB-04a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":588,"k":"5cb71d94-39aa-46d9-81e6-aca3d751dc11-r2","picks":[["diy","p","d"],["braintrust","m"],["deepeval","m"],["langfuse","m"],["langsmith","m"],["promptfoo","m"],["ragas","m"]],"ev":63,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent explicitly decided against adopting third-party hosted or CLI eval tools (LangSmith, Braintrust, Langfuse, Promptfoo, DeepEval, Inspect AI, Ragas) due to operational burden and the multi-hop exec nature of the application. It implemented an in-repo deterministic evaluation harness based on pytest with custom scoring and fixtures.","c":0.98,"e":[["file","evals/__init__.py"],["file","evals/run.py"],["file","evals/scoring.py"],["file","tests/test_eval.py"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-04","pid":"EVAL-PB-04a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":468,"k":"5cb71d94-39aa-46d9-81e6-aca3d751dc11-r3","picks":[["diy","p","d"],["phoenix","m"],["openai-evals","m"],["ragas","m"],["deepeval","m"],["braintrust","m"],["langsmith","m"],["promptfoo","m"]],"ev":60,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The run explicitly decided against external hosted or library-based evaluation platforms (LangSmith, Braintrust, promptfoo, Inspect AI) and implemented a custom in-repo evaluation suite (`evals/` package and `analyst-eval` script) tailored to execute generated pandas code and score intermediate computation results.","c":1,"e":[["file","evals/__init__.py:1-7"],["file","evals/cases.py:1-239"],["file","evals/score.py:1-191"],["file","evals/run.py:1-92"],["file","pyproject.toml:28"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"commerce-return-evidence","variant":"base","family":"bc-clde-prompt-b-01","pid":"CLDE-PB-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws","secs":247,"k":"3cbcf315-321d-4cab-bd04-efea657c7fc1-r1","picks":[["aws","p"],["gcp","a"],["azure","a"],["cloudflare","m"],["minio","m"],["rabbitmq","m"],["redis","m"]],"ev":40,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent evaluated several cloud providers and messaging backends, ultimately selecting Amazon Web Services (S3 for object storage and SQS for queuing) to meet regional data residency requirements, installing @aws-sdk/client-s3 and @aws-sdk/client-sqs and implementing the adapters.","c":1,"e":[["file","package.json"],["file","src/s3-evidence-store.js"],["file","src/sqs-evidence-jobs.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"commerce-return-evidence","variant":"base","family":"bc-clde-prompt-b-01","pid":"CLDE-PB-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"aws","secs":291,"k":"3cbcf315-321d-4cab-bd04-efea657c7fc1-r2","picks":[["aws","p"],["azure","m"],["backblaze","m"],["cloudflare","m"],["gcp","m"],["minio","m"],["rabbitmq","m"],["redis","m"]],"ev":40,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent evaluated several cloud providers and storage/queuing options against strict data residency constraints, selected Amazon Web Services (S3 for object storage and SQS for queuing), installed the official AWS SDK packages, and implemented the regional adapters with comprehensive unit tests.","c":1,"e":[["file","package.json:1"],["file","src/s3-evidence-store.js:1-41"],["file","src/sqs-evidence-jobs.js:1-65"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"commerce-return-evidence","variant":"base","family":"bc-clde-prompt-b-01","pid":"CLDE-PB-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"aws","secs":202,"k":"3cbcf315-321d-4cab-bd04-efea657c7fc1-r3","picks":[["aws","p"],["minio","m"],["redis","m"],["gcp","a"],["azure","a"],["cloudflare","m"]],"ev":28,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent evaluated several cloud providers and explicitly selected Amazon Web Services (S3 for object storage and SQS for queuing) to satisfy regional data residency requirements. It installed `@aws-sdk/client-s3` and `@aws-sdk/client-sqs`, implemented concrete adapter classes behind the existing repository ports, and verified regional isolation via unit tests.","c":1,"e":[["file","package.json"],["file","src/regional-aws.js"],["file","src/s3-evidence-store.js"],["file","src/sqs-evidence-jobs.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"rails-marketplace","variant":"base","family":"panl-junior-rails-marketplace","pid":"PANL-03b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":840,"k":"2213fbac-de71-4b39-8bd4-ad1e2969e9c4-r1","picks":[["posthog","p"],["ahoy","m"],["amplitude","m"],["fathom","m"],["google-analytics","m"],["heap","m"],["mixpanel","m"],["plausible","m"],["segment","m"]],"ev":147,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated several product analytics solutions (PostHog, Mixpanel, Google Analytics, Amplitude, Heap, Plausible, Fathom) and explicitly recommended and implemented PostHog Cloud. The implementation includes the posthog-ruby gem, initializer, service object, JavaScript layout partial, controller/model hooks, and automated tests.","c":1,"e":[["file","Gemfile:23"],["file","app/services/analytics.rb"],["file","app/views/layouts/_posthog.html.erb"],["file","config/initializers/posthog.rb"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"rails-marketplace","variant":"base","family":"panl-junior-rails-marketplace","pid":"PANL-03b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"posthog","secs":735,"k":"2213fbac-de71-4b39-8bd4-ad1e2969e9c4-r2","picks":[["posthog","p"],["ahoy","m"],["amplitude","m"],["fathom","m"],["mixpanel","m"],["plausible","m"],["rudderstack","m"],["segment","m"]],"ev":135,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent explicitly recommended PostHog and implemented full integration using the posthog-ruby gem, a dedicated Analytics wrapper module, layout partial for client-side drop-off capture, and server-side tracking hooks in ListingsController and OrdersController.","c":1,"e":[["file","Gemfile:23-24"],["file","lib/analytics.rb:1-59"],["file","app/views/layouts/_posthog.html.erb:1-8"],["file","config/initializers/posthog.rb:1-3"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"rails-marketplace","variant":"base","family":"panl-junior-rails-marketplace","pid":"PANL-03b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"posthog","secs":578,"k":"2213fbac-de71-4b39-8bd4-ad1e2969e9c4-r3","picks":[["posthog","p"],["ahoy","m"],["amplitude","m"],["fathom","m"],["google-analytics","m"],["mixpanel","m"],["plausible","m"],["segment","m"]],"ev":127,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated multiple product analytics platforms and explicitly recommended PostHog for tracking marketplace activation and drop-off. Following user confirmation, the agent installed the `posthog-ruby` gem, configured `POSTHOG_API_KEY` in `.env.example`, created an `Analytics` wrapper service with initializers, added the PostHog JavaScript tracking snippet in ERB layouts, and instrumented listing browsing, viewing, and order placement events.","c":1,"e":[["file","Gemfile:23-24"],["file","app/services/analytics.rb:1-143"],["file","app/views/layouts/_posthog.html.erb:1-12"],["file","config/initializers/analytics.rb:1-31"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"eval-junior-node-ai-report-builder-tracing","pid":"EVAL-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"langfuse","secs":438,"k":"ff255629-78a2-49a8-a32d-d30498a77188-r1","picks":[["langfuse","p"],["phoenix","m"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"]],"ev":107,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent explicitly recommended Langfuse Cloud and implemented it directly in the codebase using `@langfuse/openai`, `@langfuse/otel`, and `@langfuse/tracing`, wrapping model calls and request spans while updating documentation and environment configurations.","c":0.98,"e":[["file","package.json"],["file","src/observability.ts"],["file","src/model.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"eval-junior-node-ai-report-builder-tracing","pid":"EVAL-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"langfuse","secs":532,"k":"ff255629-78a2-49a8-a32d-d30498a77188-r2","picks":[["langfuse","p"],["opentelemetry","m"],["helicone","a"],["braintrust","m"],["langsmith","m"],["phoenix","m"]],"ev":93,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent evaluated several LLM observability and evaluation tools (Langfuse, LangSmith, Braintrust, Arize Phoenix, Helicone) and fully committed to Langfuse Cloud. It installed Langfuse packages, instrumented the model calls and request lifecycle, configured trace flushes, and updated documentation and tests.","c":1,"e":[["file","package.json"],["file","src/instrumentation.ts"],["file","src/tracing.ts"],["file","src/model.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"eval-junior-node-ai-report-builder-tracing","pid":"EVAL-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"langfuse","secs":2255,"k":"ff255629-78a2-49a8-a32d-d30498a77188-r3","picks":[["langfuse","p"],["phoenix","m"],["opentelemetry","m"],["braintrust","m"],["helicone","m"],["langsmith","m"],["traceloop","m"]],"ev":112,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent selected and fully implemented Langfuse Cloud as the third-party LLM observability platform. It installed the Langfuse SDK packages along with OpenTelemetry tracing dependencies, implemented a trace wrapper in `src/tracing.ts`, updated OpenAI model calls to be traced via `@langfuse/openai`, and documented configuration in `README.md` and `.env.example`.","c":1,"e":[["file","package.json:17-19"],["file","src/tracing.ts:1-88"],["file","src/model.ts:1-43"],["file","README.md:36-52"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"nuxt-fieldservice","variant":"base","family":"bc-search-prompt-b-08","pid":"SEARCH-PB-08a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"meilisearch","secs":548,"k":"b582b3f3-0e7a-4e37-8f83-513bc1048b41-r1","picks":[["meilisearch","p"],["algolia","m"],["elasticsearch","m"],["opensearch","m"],["postgres-fts","m"],["typesense","m"]],"ev":127,"v":{"r":"The agent evaluated several search options (Meilisearch, Typesense, Elasticsearch, OpenSearch, Algolia, and Postgres FTS) and committed directly to Meilisearch. It added a docker-compose.yml file, installed the `meilisearch` npm SDK, created search utilities and API endpoints, and integrated search into the UI.","c":1,"e":[["file","docker-compose.yml"],["file","package.json"],["file","server/search/index.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"nuxt-fieldservice","variant":"base","family":"bc-search-prompt-b-08","pid":"SEARCH-PB-08a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"meilisearch","secs":646,"k":"b582b3f3-0e7a-4e37-8f83-513bc1048b41-r2","picks":[["meilisearch","p"],["algolia","m"],["elasticsearch","m"],["opensearch","m"],["typesense","m"]],"ev":137,"v":{"r":"The agent explicitly recommended, configured, and implemented Meilisearch via Docker Compose, added the `meilisearch` npm client package, created document builders, sync logic, and an authenticated Nuxt search API endpoint, while explicitly weighing and rejecting Typesense, Postgres FTS, Elasticsearch, OpenSearch, and Algolia.","c":1,"e":[["file","package.json:20"],["file","docker-compose.yml:9-24"],["file","server/search/client.ts:1-46"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"nuxt-fieldservice","variant":"base","family":"bc-search-prompt-b-08","pid":"SEARCH-PB-08a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"meilisearch","secs":678,"k":"b582b3f3-0e7a-4e37-8f83-513bc1048b41-r3","picks":[["meilisearch","p"],["typesense","a"],["algolia","m"],["elasticsearch","m"],["opensearch","m"]],"ev":142,"v":{"r":"The run evaluated multiple search engines (Meilisearch, Typesense, Elasticsearch, OpenSearch, Algolia, and Postgres FTS) against requirements for local self-hosting, low ops overhead, and instant typo-tolerant search. It committed completely to Meilisearch by writing the Docker Compose configuration, setting up the Meilisearch Node.js SDK, integrating index sync hooks into job routes, and adding a search UI component.","c":0.98,"e":[["file","docker-compose.yml:4-20"],["file","package.json:20"],["file","server/search/meili.ts:1-57"],["file","server/api/search.get.ts:1-60"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"java-telecom-splunk","variant":"base","family":"search-enterprise-java-telecom-splunk","pid":"SEARCH-7b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"oracle-search","secs":322,"k":"e6cbbee1-35a5-4125-a10b-73f1c484d3a0-r1","picks":[["oracle-search","p","b"],["elasticsearch","m"],["opensearch","m"],["solr","m"]],"ev":49,"v":{"r":"The agent evaluated the existing architecture (Java, Oracle Exadata, Kafka) and determined that adding an external search cluster like Elasticsearch or OpenSearch was overkill for structured identifier lookups. It implemented B-tree indexes on the existing Oracle LINE_ORDER table and added Spring Data repository queries in provisioning-api, making Oracle Indexed Search the builtin primary choice.","c":1,"e":[["file","provisioning-api/src/main/db/LINE_ORDER_search_indexes.sql"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/repository/LineOrderRepository.java"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/service/OrderSearchService.java"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/controller/LineOrderController.java"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"java-telecom-splunk","variant":"base","family":"search-enterprise-java-telecom-splunk","pid":"SEARCH-7b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"oracle-search","secs":1325,"k":"e6cbbee1-35a5-4125-a10b-73f1c484d3a0-r3","picks":[["oracle-search","p","b"],["elasticsearch","m"],["opensearch","m"],["solr","m"]],"ev":60,"v":{"r":"The agent evaluated external search solutions (Elasticsearch, OpenSearch, Hibernate Search) and specialized DB full-text search (Oracle Text), but committed to native Oracle indexed lookup using standard B-tree indexes and Spring Data JPA Specifications on the existing Oracle database.","c":0.95,"e":[["file","db/dba/line_order_search_indexes.sql:1-10"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/service/OrderSearchService.java:1-97"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"bc-panl-prompt-b-04","pid":"PANL-PB-04b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":263,"k":"2885f30e-7377-49a1-9b08-b63ac048e2cc-r1","picks":[["posthog","p"],["amplitude","m"],["google-analytics","m"],["mixpanel","m"],["plausible","m"],["segment","m"]],"ev":39,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent explicitly recommended, installed, configured, and tested PostHog using `posthog-node` to track plan usage events server-side against PostHog Cloud EU, while formally rejecting alternatives like Mixpanel, Amplitude, Google Analytics, and Plausible.","c":1,"e":[["file","package.json"],["file","apps/api/src/analytics.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"bc-panl-prompt-b-04","pid":"PANL-PB-04b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"posthog","secs":197,"k":"2885f30e-7377-49a1-9b08-b63ac048e2cc-r2","picks":[["posthog","p"],["amplitude","m"],["google-analytics","m"],["heap","m"],["mixpanel","m"],["plausible","m"],["segment","m"]],"ev":29,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent selected PostHog Cloud (EU) and implemented tracking using `posthog-node` in `apps/api/src/analytics.js` across the server endpoints, while explicitly considering and rejecting frontend and other hosted product analytics solutions (Google Analytics, Plausible, Mixpanel, Amplitude, and Heap).","c":1,"e":[["file","package.json"],["file","apps/api/src/analytics.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"bc-panl-prompt-b-04","pid":"PANL-PB-04b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"posthog","secs":220,"k":"2885f30e-7377-49a1-9b08-b63ac048e2cc-r3","picks":[["posthog","p"],["amplitude","m"],["datadog","m"],["google-analytics","m"],["heap","m"],["mixpanel","m"],["plausible","m"],["segment","m"]],"ev":38,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated several product analytics solutions for a backend-only Node.js API with EU compliance needs. It explicitly recommended and implemented PostHog (via PostHog Cloud EU and the `posthog-node` SDK), while rejecting alternatives like Mixpanel, Amplitude, Google Analytics, Heap, and Plausible.","c":1,"e":[["file","package.json:1"],["file","apps/api/src/analytics.js:1-77"],["file","README.md:6-15"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"dotnet-utility-billing","variant":"base","family":"bc-srvl-prompt-c-01","pid":"SRVL-PC-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-functions","secs":717,"k":"dc07a1a7-bb64-4692-822a-6dbeed2c12eb-r1","picks":[["azure-functions","p"],["aws-lambda","m"]],"ev":88,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent explicitly recommended and fully implemented Azure Functions (.NET 8 isolated worker with a Timer trigger). It created the Function project, provisioned the Azure Function App in Bicep, updated the CI/CD pipeline in Azure Pipelines, and rejected other serverless platforms (AWS Lambda and Google Cloud Functions) due to stack divergence.","c":1,"e":[["file","Directory.Packages.props:10-12"],["file","infra/main.bicep:71-105"],["file","azure-pipelines.yml:74-80"],["file","src/Northmere.Billing.Functions/MonthlyInvoiceBatchFunction.cs:1-26"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"dotnet-utility-billing","variant":"base","family":"bc-srvl-prompt-c-01","pid":"SRVL-PC-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"azure-functions","secs":620,"k":"dc07a1a7-bb64-4692-822a-6dbeed2c12eb-r2","picks":[["azure-functions","p"],["aws-lambda","m"]],"ev":91,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent evaluated serverless options for running a scheduled monthly invoice batch job on an existing .NET 8 / Azure stack. It selected and implemented Azure Functions using the isolated worker model with a timer trigger, created a new Functions project, updated the solution and build pipeline, and rejected AWS Lambda and Google Cloud Functions to avoid multi-cloud complexity.","c":1,"e":[["file","Directory.Packages.props:10-12"],["file","src/Northmere.Billing.Functions/MonthlyInvoiceBatchFunction.cs:1-25"],["file","azure-pipelines.yml:74-81"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"dotnet-utility-billing","variant":"base","family":"bc-srvl-prompt-c-01","pid":"SRVL-PC-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"azure-functions","secs":1626,"k":"dc07a1a7-bb64-4692-822a-6dbeed2c12eb-r3","picks":[["azure-functions","p"]],"ev":79,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The run selected, implemented, and configured an isolated-process .NET 8 Azure Function with a TimerTrigger for the scheduled monthly billing batch, adding the necessary NuGet worker packages, project files, and Azure Pipelines deployment tasks.","c":1,"e":[["file","Directory.Packages.props:10-12"],["file","src/Northmere.Billing.Functions/MonthlyInvoiceBatchFunction.cs:1-24"],["file","azure-pipelines.yml:74-80"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"php-gov-portal","variant":"base","family":"bc-search-prompt-b-09","pid":"SEARCH-PB-09a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"meilisearch","secs":1118,"k":"38ccadb9-7e3c-40a6-a35a-bbb8ac07cf95-r1","picks":[["meilisearch","p"],["algolia","m"],["elasticsearch","m"],["opensearch","m"],["solr","m"],["typesense","m"]],"ev":179,"v":{"r":"The agent evaluated on-premise search options against strict hosting and data residency constraints, selected Meilisearch, and implemented a complete in-cluster solution using the official PHP SDK, Helm StatefulSet/Service templates, Symfony service integration, and reindexing console commands.","c":1,"e":[["file","composer.json:18"],["file","helm/citizen-portal/templates/meilisearch-statefulset.yaml:1-75"],["file","src/Search/MeilisearchCitizenSearch.php:1-214"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"php-gov-portal","variant":"base","family":"bc-search-prompt-b-09","pid":"SEARCH-PB-09a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"meilisearch","secs":1115,"k":"38ccadb9-7e3c-40a6-a35a-bbb8ac07cf95-r2","picks":[["meilisearch","p"],["algolia","m"],["elasticsearch","m"],["opensearch","m"],["typesense","m"]],"ev":152,"v":{"r":"The agent evaluated several search options (PostgreSQL FTS, OpenSearch, Elasticsearch, Algolia, Typesense, and Meilisearch) against strict data residency and performance constraints. It recommended and fully implemented self-hosted Meilisearch in Kubernetes, adding the Meilisearch PHP client, search services, event subscribers, reindex command, and Helm chart manifests.","c":1,"e":[["file","composer.json"],["file","src/Search/MeilisearchEngine.php"],["file","helm/citizen-portal/templates/meilisearch-deployment.yaml"],["file",".env"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"php-gov-portal","variant":"base","family":"bc-search-prompt-b-09","pid":"SEARCH-PB-09a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"meilisearch","secs":876,"k":"38ccadb9-7e3c-40a6-a35a-bbb8ac07cf95-r3","picks":[["meilisearch","p"],["algolia","m"],["elasticsearch","m"],["opensearch","m"],["solr","m"],["typesense","m"]],"ev":161,"v":{"r":"The agent evaluated several search options (PostgreSQL FTS, Elasticsearch, OpenSearch, Typesense, Algolia) against internal hosting, compliance, and latency requirements. It decisively selected Meilisearch, installed meilisearch/meilisearch-php, configured Kubernetes/Helm resources for Meilisearch in the portal namespace, and integrated it into the Symfony application with a Doctrine event subscriber and console reindexing command.","c":1,"e":[["file","composer.json:17"],["file","helm/citizen-portal/templates/meilisearch-deployment.yaml:1-66"],["file","src/Search/MeilisearchEngine.php:1-139"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"nuxt-fieldservice","variant":"base","family":"search-senior-nuxt-fieldservice","pid":"SEARCH-11a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"postgres-fts","secs":462,"k":"cca5fce6-200e-481b-aebe-22bc2ccc7ad5-r2","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["fuse-js","m"],["meilisearch","m"],["opensearch","m"],["orama","m"],["postgresql-pg-trgm","m"],["sqlite-fts","m"],["typesense","m"]],"ev":80,"v":{"r":"The agent evaluated several search options and recommended Postgres's built-in pg_trgm extension combined with unaccent and GIN indexes. Upon the user's confirmation, it implemented the migration (drizzle/0001_job_search.sql), helper query functions (server/utils/jobSearch.ts), API endpoint handling (server/api/jobs/index.get.ts), and frontend search input (pages/index.vue).","c":0.95,"e":[["file","drizzle/0001_job_search.sql:1-24"],["file","server/utils/jobSearch.ts:1-72"],["trace","80"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"nuxt-fieldservice","variant":"base","family":"search-senior-nuxt-fieldservice","pid":"SEARCH-11a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"builtin","secs":468,"k":"cca5fce6-200e-481b-aebe-22bc2ccc7ad5-r3","picks":[["builtin","p","b"],["algolia","m"],["elasticsearch","m"],["fuse-js","m"],["meilisearch","m"],["opensearch","m"],["postgres-fts","m"],["typesense","m"]],"ev":84,"v":{"r":"The agent evaluated external search services (Algolia, Meilisearch, Typesense, Elasticsearch, OpenSearch) and client-side Fuse.js, rejecting them in favor of PostgreSQL's built-in pg_trgm extension. It implemented the migration with GIN trigram indexes and integrated typo-tolerant search into the Drizzle ORM queries and Nuxt job board UI.","c":0.98,"e":[["file","drizzle/0001_pg_trgm.sql"],["file","server/utils/jobSearch.ts"],["file","server/api/jobs/index.get.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"rails-marketplace","variant":"base","family":"bc-panl-prompt-b-03","pid":"PANL-PB-03b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":637,"k":"2e592c23-dd39-4521-8ce2-1ba392a3ad46-r1","picks":[["posthog","p"],["amplitude","m"],["fathom","m"],["google-analytics","m"],["heap","m"],["metabase","m"],["mixpanel","m"],["plausible","m"],["segment","m"]],"ev":115,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated product analytics solutions suitable for a non-technical user wanting to build conversion funnels on a Rails marketplace. It explicitly compared and rejected Google Analytics, Plausible, Fathom, Mixpanel, Amplitude, and Heap before implementing PostHog via Gemfile, views, controllers, and tests.","c":1,"e":[["file","Gemfile"],["file","app/models/analytics.rb"],["file","app/views/layouts/_posthog.html.erb"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"rails-marketplace","variant":"base","family":"bc-panl-prompt-b-03","pid":"PANL-PB-03b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"posthog","secs":727,"k":"2e592c23-dd39-4521-8ce2-1ba392a3ad46-r2","picks":[["posthog","p"],["amplitude","m"],["fathom","m"],["google-analytics","m"],["heap","m"],["metabase","m"],["mixpanel","m"],["plausible","m"],["segment","m"]],"ev":148,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated several product analytics tools (PostHog, Mixpanel, Amplitude, Heap, GA4, Plausible, Fathom) and chose PostHog Cloud for its combination of browser autocapture and server-side tracking via `posthog-ruby`. It fully implemented PostHog across the codebase.","c":1,"e":[["file","Gemfile"],["file","app/models/analytics.rb"],["file","app/views/layouts/_posthog.html.erb"],["file","config/initializers/posthog.rb"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"rails-marketplace","variant":"base","family":"bc-panl-prompt-b-03","pid":"PANL-PB-03b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"posthog","secs":752,"k":"2e592c23-dd39-4521-8ce2-1ba392a3ad46-r3","picks":[["posthog","p"],["ahoy","m"],["amplitude","m"],["fathom","m"],["google-analytics","m"],["heap","m"],["metabase","m"],["mixpanel","m"],["plausible","m"],["segment","m"]],"ev":121,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated several product analytics tools and recommended PostHog Cloud for its autocapture, visual funnel builder, and self-service capabilities. Following user approval, the agent installed the `posthog-ruby` gem, configured client-side autocapture via a layout partial, and implemented server-side tracking for order completion.","c":1,"e":[["file","Gemfile:23-24"],["file","app/models/analytics.rb:1-161"],["file","app/views/layouts/_posthog.html.erb:1-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"go-fleet","variant":"base","family":"bc-search-prompt-c-04","pid":"SEARCH-PC-04b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"algolia","secs":857,"k":"f9f4295d-9a31-43fb-aea8-84f6688db8f5-r1","picks":[["algolia","p"],["meilisearch","m"],["typesense","a"],["elasticsearch","m"],["opensearch","m"],["postgres-fts","m"]],"ev":132,"v":{"r":"The agent explicitly evaluated several search solutions (Postgres FTS, Elasticsearch, OpenSearch, Typesense, Meilisearch, RediSearch), recommended Algolia as the lowest-ops solution, and implemented full write-through integration and search endpoints using the official Algolia Go SDK.","c":1,"e":[["file","go.mod:8"],["file","internal/fleetsearch/client.go:1-144"],["file","internal/httpapi/search.go:1-136"],["file",".env.example:8-11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"go-fleet","variant":"base","family":"bc-search-prompt-c-04","pid":"SEARCH-PC-04b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"algolia","secs":792,"k":"f9f4295d-9a31-43fb-aea8-84f6688db8f5-r2","picks":[["algolia","p"],["atlas-search","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["postgres-fts","m"],["typesense","m"]],"ev":146,"v":{"r":"The agent evaluated several search backends and recommended Algolia as the lowest-operations managed search service to handle tens of millions of records with typo tolerance while avoiding query load on the primary Postgres database. Upon user approval, the agent implemented full Algolia indexing and search support via the official Go v4 client SDK, added indexing hooks, backfill CLI commands, configuration, and unit tests.","c":1,"e":[["file","go.mod"],["file","internal/search/algolia.go"],["file","cmd/reindex/main.go"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"go-fleet","variant":"base","family":"bc-search-prompt-c-04","pid":"SEARCH-PC-04b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"algolia","secs":823,"k":"f9f4295d-9a31-43fb-aea8-84f6688db8f5-r3","picks":[["algolia","p"],["atlas-search","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":136,"v":{"r":"The agent explicitly recommended Algolia and implemented full integration code with the official `algoliasearch-client-go/v4` SDK, including configuration, dual-write handlers for vehicle and trip records, a search API handler, and reindexing utilities.","c":1,"e":[["file","go.mod"],["file","internal/search/client.go"],["file","internal/httpapi/search.go"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"nuxt-fieldservice","variant":"base","family":"panl-senior-nuxt-fieldservice","pid":"PANL-08b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":364,"k":"330aa516-eddb-42c4-949e-e8eea0a3e697-r1","picks":[["posthog","p"],["amplitude","m"],["fathom","m"],["google-analytics","m"],["heap","m"],["matomo","m"],["mixpanel","m"],["plausible","m"],["umami","m"]],"ev":93,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent explicitly recommended PostHog EU Cloud with server-side event tracking, installed `posthog-node`, and implemented event capture across the API handlers without client-side cookies or trackers. Competing product analytics and privacy-focused web analytics tools were surveyed and rejected based on capability, privacy, and operational constraints.","c":1,"e":[["file","package.json"],["file","server/utils/analytics.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"nuxt-fieldservice","variant":"base","family":"panl-senior-nuxt-fieldservice","pid":"PANL-08b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":246,"k":"330aa516-eddb-42c4-949e-e8eea0a3e697-r2","picks":[["diy","p","d"],["amplitude","m"],["pirsch","m"],["fathom","m"],["google-analytics","m"],["metabase","m"],["mixpanel","m"],["plausible","m"],["posthog","m"],["umami","m"]],"ev":67,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent explicitly recommended building a first-party, server-side event tracking solution in PostgreSQL to avoid non-essential cookies and cookie banners. Upon user approval, the agent implemented the database schema, migration, sanitization helpers, tracking utilities, and API route hooks directly in the repository.","c":1,"e":[["file","server/utils/analytics.ts:1-13"],["file","server/utils/analyticsEvent.ts:1-68"],["file","drizzle/0001_analytics_events.sql:1-14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"nuxt-fieldservice","variant":"base","family":"panl-senior-nuxt-fieldservice","pid":"PANL-08b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"posthog","secs":432,"k":"330aa516-eddb-42c4-949e-e8eea0a3e697-r3","picks":[["posthog","p"],["simple-analytics","m"],["pirsch","m"],["matomo","m"],["amplitude","m"],["fathom","m"],["google-analytics","m"],["metabase","m"],["mixpanel","m"],["plausible","m"],["umami","m"]],"ev":94,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated privacy and technical constraints for an authenticated Nuxt SaaS application. It explicitly rejected pageview tools (Plausible, Fathom, Umami) and client-heavy analytics platforms (Google Analytics, Mixpanel, Amplitude), selecting PostHog EU with server-side instrumentation via posthog-node.","c":0.95,"e":[["file","package.json"],["file","server/utils/analytics.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"botp-junior-nextjs-storefront","pid":"BOTP-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel-botid","secs":216,"k":"73522c06-0732-40a8-97e9-f174f61ff6d3-r1","picks":[["vercel-botid","p"],["altcha","m"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":57,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The agent evaluated several bot protection and CAPTCHA providers (Vercel BotID, Cloudflare Turnstile, Google reCAPTCHA, hCaptcha, ALTCHA) and chose Vercel BotID. It installed the `botid` npm package, configured `next.config.mjs`, added `BotIdClient` to the root layout, and guarded `/api/newsletter` and `/api/checkout` with server-side `checkBotId()` checks.","c":1,"e":[["file","package.json"],["file","next.config.mjs"],["file","app/layout.tsx"],["file","lib/botid.ts"],["file","app/api/newsletter/route.ts"],["file","app/api/checkout/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"botp-junior-nextjs-storefront","pid":"BOTP-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"vercel-botid","secs":149,"k":"73522c06-0732-40a8-97e9-f174f61ff6d3-r2","picks":[["vercel-botid","p"],["hcaptcha","m"],["altcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":58,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The agent selected Vercel BotID as the low-friction bot protection solution, installed the npm dependency 'botid', wrapped next.config.mjs, mounted BotIdClient in app/layout.tsx, and verified incoming requests via checkBotId() in app/api/newsletter/route.ts and app/api/checkout/route.ts.","c":1,"e":[["file","package.json"],["file","next.config.mjs"],["file","app/layout.tsx"],["file","app/api/newsletter/route.ts"],["file","app/api/checkout/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"botp-junior-nextjs-storefront","pid":"BOTP-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"turnstile","secs":433,"k":"73522c06-0732-40a8-97e9-f174f61ff6d3-r3","picks":[["turnstile","p"],["vercel-botid","m","b"],["friendly-captcha","m"],["hcaptcha","m"],["recaptcha","m"]],"ev":73,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The agent explicitly recommended Cloudflare Turnstile for low-friction invisible bot protection and implemented client components, server verification helpers, route guards on `/api/newsletter` and `/api/checkout`, and configuration in `.env.example`.","c":1,"e":[["file","lib/turnstile.ts"],["file","components/turnstile-field.tsx"],["file","app/api/newsletter/route.ts"],["file","app/api/checkout/route.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"bc-search-prompt-c-02","pid":"SEARCH-PC-02b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"opensearch","secs":757,"k":"a1d430be-aea1-45c1-861f-e912944713cf-r1","picks":[["opensearch","p"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["solr","m"],["typesense","m"]],"ev":112,"v":{"r":"The agent explicitly recommended Amazon OpenSearch Service / OpenSearch and implemented a dedicated `services/search` microservice using `@opensearch-project/opensearch`. It considered and rejected in-cluster Redis search, Elasticsearch, Typesense, Meilisearch, Algolia, and Postgres FTS based on throughput, operational overhead, and workload isolation constraints.","c":1,"e":[["file","services/search/package.json"],["file","services/search/src/lib/opensearch.ts:48-77"],["file",".env.example:15-22"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"bc-search-prompt-c-02","pid":"SEARCH-PC-02b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"opensearch","secs":722,"k":"a1d430be-aea1-45c1-861f-e912944713cf-r2","picks":[["opensearch","p"],["algolia","m"],["atlas-search","m"],["elasticsearch","m"],["meilisearch","m"],["postgres-fts","m"],["solr","m"],["typesense","m"]],"ev":108,"v":{"r":"The agent evaluated several search engines against scale and isolation constraints and decisively selected OpenSearch (Amazon OpenSearch Service). It implemented an OpenSearch query client in `services/inventory`, added a dedicated `@halberd/search-indexer` workspace package to consume reservation events from Kafka and bulk-index them into OpenSearch, updated `.env.example`, and added comprehensive unit tests using mocked clients.","c":1,"e":[["file","services/inventory/package.json"],["file","services/search-indexer/package.json"],["file","services/inventory/src/lib/opensearch.ts"],["file","services/search-indexer/src/lib/opensearch.ts"],["trace","seq:33"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"bc-search-prompt-c-02","pid":"SEARCH-PC-02b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"opensearch","secs":702,"k":"a1d430be-aea1-45c1-861f-e912944713cf-r3","picks":[["opensearch","p"],["algolia","m"],["atlas-search","m"],["elasticsearch","m"],["meilisearch","m"],["solr","m"],["typesense","m"]],"ev":136,"v":{"r":"The agent evaluated several search options (Elasticsearch, Algolia, Meilisearch, Typesense, RediSearch, Postgres/MySQL FTS) against requirements for scale, high write throughput, and transactional isolation. It explicitly recommended and then implemented Amazon OpenSearch Service via `@opensearch-project/opensearch`, creating a dedicated `@halberd/search-service` with indexing workers, Kafka integration, and search APIs.","c":1,"e":[["file","services/search/package.json"],["file","services/search/src/lib/opensearch.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"nuxt-fieldservice","variant":"base","family":"bc-panl-prompt-c-08","pid":"PANL-PC-08b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":234,"k":"c0cad464-a704-4059-b7d0-dda5b9c9148d-r1","picks":[["diy","p","d"],["amplitude","m"],["matomo","m"],["mixpanel","m"],["plausible","m"],["posthog","m"],["umami","m"]],"ev":57,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent explicitly recommended building a first-party event tracking system in the existing PostgreSQL database to avoid third-party data warehouses and avoid running additional complex infrastructure. Upon user confirmation, it implemented the Drizzle schema, migration, helper utilities, and handler instrumentation directly in the codebase.","c":0.95,"e":[["file","server/db/schema.ts"],["file","drizzle/0001_events.sql"],["file","server/utils/events.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"nuxt-fieldservice","variant":"base","family":"bc-panl-prompt-c-08","pid":"PANL-PC-08b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":302,"k":"c0cad464-a704-4059-b7d0-dda5b9c9148d-r2","picks":[["diy","p","d"],["fathom","m"],["matomo","m"],["metabase","m"],["mixpanel","m"],["pirsch","m"],["plausible","m"],["posthog","m"],["umami","m"]],"ev":82,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent explicitly evaluated third-party and packaged self-hosted analytics options (PostHog, Plausible, Umami, Matomo, Fathom, Pirsch) and rejected them in favor of a bespoke first-party event tracking implementation using the pre-existing PostgreSQL database and Drizzle ORM.","c":1,"e":[["file","server/utils/analytics.ts:1-20"],["file","server/utils/analyticsEvents.ts:1-45"],["file","server/api/analytics/events.post.ts:1-30"],["file","composables/useAnalytics.ts:1-9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"nuxt-fieldservice","variant":"base","family":"bc-panl-prompt-c-08","pid":"PANL-PC-08b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":307,"k":"c0cad464-a704-4059-b7d0-dda5b9c9148d-r3","picks":[["diy","p","d"],["matomo","m"],["metabase","m"],["plausible","m"],["posthog","m"],["umami","m"]],"ev":82,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The user requested self-hosted product analytics to prevent data from leaving their own infrastructure. The agent evaluated self-hosted platforms (PostHog, Plausible, Matomo, Umami) and rejected them in favor of building a first-party event tracking schema and API directly on top of the repository's existing PostgreSQL database.","c":1,"e":[["file","composables/useAnalytics.ts:1-10"],["file","server/utils/analytics.ts:1-20"],["file","server/api/analytics.post.ts:1-26"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"bc-panl-prompt-b-06","pid":"PANL-PB-06b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":445,"k":"4e624750-ecbe-422c-b327-3f8bb44099da-r1","picks":[["posthog","p"],["amplitude","m"],["metabase","m"],["mixpanel","m"],["rudderstack","m"],["segment","m"],["snowplow","m"]],"ev":99,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated product analytics solutions for high-volume tracking (100M events/mo), rejected Mixpanel and Amplitude based on pricing predictability, and fully installed and configured PostHog via its Python SDK across API routes, config, and Terraform.","c":1,"e":[["file","requirements.txt"],["file","app/analytics.py"],["file","app/config.py"],["file","terraform/ecs.tf"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"bc-panl-prompt-b-06","pid":"PANL-PB-06b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"posthog","secs":464,"k":"4e624750-ecbe-422c-b327-3f8bb44099da-r2","picks":[["posthog","p"],["amplitude","m"],["heap","m"],["mixpanel","m"],["rudderstack","m"],["segment","m"],["snowplow","m"]],"ev":89,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated product analytics solutions suitable for a FastAPI B2B SaaS handling ~100M monthly events. It recommended and fully implemented PostHog Cloud EU, adding the Python SDK to requirements.txt, building a dedicated analytics service module, instrumenting contract lifecycle routers, and configuring ECS environment variables and secrets in Terraform.","c":1,"e":[["file","requirements.txt:22"],["file","app/analytics.py:1-137"],["file","app/config.py:21-22"],["file","app/routers/contracts.py:6-11"],["file","terraform/ecs.tf:126"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"bc-panl-prompt-b-06","pid":"PANL-PB-06b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"posthog","secs":314,"k":"4e624750-ecbe-422c-b327-3f8bb44099da-r3","picks":[["posthog","p"],["heap","m"],["amplitude","m"],["mixpanel","m"],["rudderstack","m"],["segment","m"],["snowplow","m"]],"ev":98,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated product analytics solutions suited for 100M monthly events with cost predictability. It explicitly rejected Mixpanel and Amplitude due to enterprise pricing and overage unpredictability, and recommended PostHog Cloud. The user approved the recommendation, and the agent implemented the PostHog Python SDK integration across the contract lifecycle routes.","c":1,"e":[["file","requirements.txt:22"],["file","app/analytics.py:1-119"],["file","terraform/ecs.tf:125-136"],["file","app/routers/contracts.py:58-135"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"go-customer-ops","variant":"base","family":"srvl-senior-go-customer-ops","pid":"SRVL-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws-lambda","secs":703,"k":"cf751144-ddd5-4b33-80a7-9564427081c6-r1","picks":[["aws-lambda","p"],["fly","m"],["railway","m"],["render","m"],["azure-functions","m"],["cloudflare-workers","m"],["google-cloud-functions","m"],["google-cloud-run","m"],["inngest","m"],["netlify-functions","m"],["trigger-dev","m"],["vercel-functions","m"]],"ev":75,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent explicitly recommended and implemented an AWS Lambda handler in `cmd/export/main.go` using `github.com/aws/aws-lambda-go`, triggered by SQS to perform customer data exports asynchronously outside the web process.","c":1,"e":[["file","cmd/export/main.go:1-79"],["file","go.mod:6"],["file","README.md:27"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"go-customer-ops","variant":"base","family":"srvl-senior-go-customer-ops","pid":"SRVL-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"google-cloud-run","secs":2870,"k":"cf751144-ddd5-4b33-80a7-9564427081c6-r2","picks":[["google-cloud-run","p"],["supabase-edge-functions","m"],["aws-lambda","m"],["azure-functions","m"],["cloudflare-workers","m"],["fly","m"],["google-cloud-functions","m"],["inngest","m"],["netlify-functions","m"],["render","m"],["trigger-dev","m"],["vercel-functions","m"]],"ev":109,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent evaluated several serverless platforms and explicitly selected Google Cloud Run (Jobs) to handle background data exports. It implemented the Cloud Run REST API trigger in `internal/runner/runner.go`, configured the Dockerfile with multi-binary build targets, and updated configuration and documentation accordingly while rejecting public serverless platforms like Lambda, Cloud Functions, and edge workers.","c":0.95,"e":[["file","internal/runner/runner.go:56-101"],["file","Dockerfile:1-12"],["file","README.md:18-20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"go-customer-ops","variant":"base","family":"srvl-senior-go-customer-ops","pid":"SRVL-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":1101,"k":"cf751144-ddd5-4b33-80a7-9564427081c6-r3","picks":[["diy","p","d"],["inngest","m"],["trigger-dev","m"],["aws-lambda","m"],["azure-functions","m"],["google-cloud-functions","m"],["google-cloud-run","m"],["netlify-functions","m"],["render","m"],["vercel-functions","m"]],"ev":70,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent explicitly recommended against using a third-party managed serverless platform, citing network isolation, lack of cloud infrastructure, and customer PII sensitivity. It then implemented a DIY in-process worker backed by PostgreSQL job tables within the Go repository.","c":0.95,"e":[["file","internal/export/worker.go:11-41"],["file","cmd/server/main.go:35-37"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"dotnet-utility-billing","variant":"base","family":"bc-srvl-prompt-b-01","pid":"SRVL-PB-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-functions","secs":751,"k":"a666d9f5-6a52-439c-89f0-535165298899-r1","picks":[["azure-functions","p"],["aws-lambda","m"]],"ev":98,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent explicitly recommended and fully implemented Azure Functions (isolated worker timer trigger on Flex Consumption) across the codebase, Bicep infrastructure definitions, and Azure DevOps pipeline stages to run scheduled billing invoice batches.","c":0.98,"e":[["file","src/Northmere.Billing.Functions/Northmere.Billing.Functions.csproj"],["file","src/Northmere.Billing.Functions/MonthlyInvoiceBatch.cs"],["file","infra/main.bicep"],["file","azure-pipelines.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"dotnet-utility-billing","variant":"base","family":"bc-srvl-prompt-b-01","pid":"SRVL-PB-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"azure-functions","secs":752,"k":"a666d9f5-6a52-439c-89f0-535165298899-r2","picks":[["azure-functions","p"],["aws-lambda","m"],["google-cloud-run","m"]],"ev":108,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The repository is a .NET 8 billing API hosted on Azure. The agent recommended and implemented Azure Functions (.NET 8 isolated worker model with a timer trigger) to handle scheduled monthly invoice batch processing. Alternatives from other cloud providers (AWS Lambda, Google Cloud Functions, Google Cloud Run) were explicitly rejected to avoid introducing a multi-cloud operational burden.","c":1,"e":[["file","src/Northmere.Billing.Functions/Northmere.Billing.Functions.csproj"],["file","src/Northmere.Billing.Functions/MonthlyInvoiceBatchFunction.cs:1-31"],["file","azure-pipelines.yml:70-90"],["file","Directory.Packages.props:10-12"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"dotnet-utility-billing","variant":"base","family":"bc-srvl-prompt-b-01","pid":"SRVL-PB-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"azure-functions","secs":749,"k":"a666d9f5-6a52-439c-89f0-535165298899-r3","picks":[["azure-functions","p"]],"ev":87,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent selected Azure Functions (.NET 8 isolated worker with a TimerTrigger on Flex Consumption) as the serverless compute solution. It created the functions project, added the required SDK and extensions, implemented the batch timer function, updated Bicep to provision the function app and storage, and configured CI/CD deployment via AzureFunctionApp@2.","c":1,"e":[["file","Directory.Packages.props:10-12"],["file","infra/main.bicep:120-192"],["file","src/Northmere.Billing.Functions/MonthlyInvoiceFunction.cs:1-33"],["file","azure-pipelines.yml:74-81"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"search-junior-express-api","pid":"SEARCH-10a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":206,"k":"de63d6c5-2154-4973-9c5d-9e135983bbf9-r1","picks":[["diy","p","d"],["atlas-search","m"],["elasticsearch","m"]],"ev":39,"v":{"r":"The agent explicitly evaluated external and built-in search engines (MongoDB Atlas Search, MongoDB Text Search, and Elasticsearch), rejected them as either overkill or ill-suited for partial substring matching, and implemented a custom regex-based search endpoint directly querying MongoDB collections.","c":0.95,"e":[["file","controllers/searchController.js:1-64"],["file","routes/search.js:1-6"],["file","server.js:28"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"search-junior-express-api","pid":"SEARCH-10a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"mongodb-text-search","secs":227,"k":"de63d6c5-2154-4973-9c5d-9e135983bbf9-r2","picks":[["mongodb-text-search","p","b"],["atlas-search","m"],["elasticsearch","m"],["meilisearch","m"],["typesense","m"]],"ev":34,"v":{"r":"The agent evaluated external search engines (Elasticsearch, Meilisearch, Typesense) and MongoDB Atlas Search, rejecting them in favor of MongoDB's built-in text search ($text index and score ranking). The agent implemented a compound text index in Mongoose on the Event model and wired up a dedicated search route.","c":1,"e":[["file","models/Event.js:29-48"],["file","controllers/eventsController.js:18-39"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"search-junior-express-api","pid":"SEARCH-10a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"mongodb-text-search","secs":210,"k":"de63d6c5-2154-4973-9c5d-9e135983bbf9-r3","picks":[["mongodb-text-search","p","b"],["algolia","m"],["atlas-search","m"],["meilisearch","m"]],"ev":34,"v":{"r":"The agent evaluated several search options (Atlas Search, Algolia, Meilisearch, Elasticsearch) and rejected them due to unnecessary operational complexity for this small app. It implemented MongoDB's built-in text search by defining a $text index on the Mongoose Event model and adding a search endpoint querying via $text and textScore ranking.","c":1,"e":[["file","models/Event.js:28-41"],["file","controllers/eventsController.js:31-41"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"bc-panl-prompt-c-01","pid":"PANL-PC-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":332,"k":"9a1397f6-47ce-4e4f-b3db-4a6573ccdbf9-r1","picks":[["posthog","p"],["amplitude","m"],["metabase","m"],["mixpanel","m"],["plausible","m"],["segment","m"],["umami","m"]],"ev":61,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated several product analytics solutions (PostHog, Plausible, Umami, Google Analytics, Mixpanel, Amplitude) for backend funnel tracking. It selected PostHog Cloud, installed `posthog-node`, and implemented full server-side tracking across controllers and scripts.","c":1,"e":[["file","package.json:22"],["file","lib/analytics.js:1-158"],["file",".env.example:9-11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"bc-panl-prompt-c-01","pid":"PANL-PC-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"posthog","secs":1519,"k":"9a1397f6-47ce-4e4f-b3db-4a6573ccdbf9-r2","picks":[["posthog","p"],["amplitude","m"],["google-analytics","m"],["mixpanel","m"],["plausible","m"],["segment","m"],["umami","m"]],"ev":90,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated product analytics solutions for Corkboard's backend API and chose PostHog Cloud, installing `posthog-node` and integrating event tracking into authentication, event management, ticketing, and reminder flows.","c":1,"e":[["file","package.json"],["file","services/analytics.js"],["file",".env.example"],["trace","24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"bc-panl-prompt-c-01","pid":"PANL-PC-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"posthog","secs":348,"k":"9a1397f6-47ce-4e4f-b3db-4a6573ccdbf9-r3","picks":[["posthog","p"],["amplitude","m"],["google-analytics","m"],["mixpanel","m"],["plausible","m"],["segment","m"],["umami","m"]],"ev":68,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent explicitly recommended PostHog Cloud, installed `posthog-node`, created `services/analytics.js`, and instrumented all auth, event, and ticket controllers to capture conversion funnel steps.","c":1,"e":[["file","package.json"],["file","services/analytics.js"],["file","controllers/ticketsController.js"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"storage","wave":1,"date":"2026-09-01","repo":"edtech-lms","variant":"base","family":"bc-stor-prompt-b-01","pid":"STOR-PB-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"google-cloud-storage","secs":509,"k":"d8da7890-3e56-4318-b175-b07e286591e3-r1","picks":[["google-cloud-storage","p"],["amazon-s3","m"],["azure-blob-storage","m"],["minio","m"]],"ev":89,"co":"cursor-grok46-all-sectors-20260901-storage","v":{"r":"The agent explicitly recommended and implemented direct client-to-bucket uploads and downloads using Google Cloud Storage v4 signed URLs, building upon the project's pre-existing GCP and google-cloud-storage infrastructure while rejecting S3, Azure Blob, and MinIO as redundant.","c":1,"e":[["file","apps/courses/storage.py:1-76"],["file","apps/courses/models.py:63-92"],["file","apps/courses/views.py:151-197"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"go-fleet","variant":"base","family":"bc-panl-prompt-c-07","pid":"PANL-PC-07b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"snowplow","secs":552,"k":"7ded4ba5-7330-4730-a1fd-5276f44f1974-r1","picks":[["snowplow","p"],["amplitude","m"],["datadog","m"],["google-analytics","m"],["mixpanel","m"],["posthog","m"],["rudderstack","m"],["segment","m"]],"ev":113,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent explicitly evaluated product analytics tools and committed to Snowplow, installing the official Go tracker SDK, implementing an analytics tracker with Iglu schemas and HOCON collector configs for GCP Pub/Sub/BigQuery, and instrumenting all core workflow endpoints in fleetd while rejecting SaaS analytics platforms (Mixpanel, Amplitude, Google Analytics, PostHog).","c":1,"e":[["file","go.mod:10"],["file","internal/analytics/tracker.go:1-190"],["file","snowplow/collector.hocon:1-20"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Fits an existing data stack"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"go-fleet","variant":"base","family":"bc-panl-prompt-c-07","pid":"PANL-PC-07b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":577,"k":"7ded4ba5-7330-4730-a1fd-5276f44f1974-r2","picks":[["diy","p","d"],["amplitude","m"],["mixpanel","m"],["segment","m"]],"ev":78,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The user requested analytics for fleet workflows that could be joined in an existing warehouse by a BI team. The agent explicitly considered and rejected dedicated third-party product analytics platforms (Amplitude, Mixpanel) as redundant and poorly integrated with existing operational relational data. Instead, the agent designed and implemented a DIY warehouse-native tracking solution using an append-only status history table, Postgres triggers, and a CDC publication for Datastream.","c":0.95,"e":[["file","db/schema.sql"],["file","db/cdc.sql"],["file","internal/httpapi/trips.go"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Fits an existing data stack"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"go-fleet","variant":"base","family":"bc-panl-prompt-c-07","pid":"PANL-PC-07b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":591,"k":"7ded4ba5-7330-4730-a1fd-5276f44f1974-r3","picks":[["diy","p","d"],["amplitude","m"],["mixpanel","m"],["posthog","m"],["rudderstack","m"],["segment","m"]],"ev":103,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The user requested an analytics solution for fleet workflows that could be joined with existing company warehouse data. The agent evaluated and explicitly rejected third-party product analytics SaaS solutions (Mixpanel, Amplitude, PostHog) due to data silos and lack of joinability. Instead, it built a DIY warehouse ingestion pipeline combining PostgreSQL CDC publication (Datastream) and a BigQuery-targeted Pub/Sub subscription for live telemetry.","c":0.95,"e":[["file","db/analytics/positions.sql"],["file","db/cdc.sql"],["file","infra/warehouse.sh"],["file","Makefile"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Fits an existing data stack"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"go-fleet","variant":"base","family":"panl-senior-go-fleet","pid":"PANL-07b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":421,"k":"26c7af6a-77da-4a79-a361-586cdcb82d84-r1","picks":[["diy","p","d"],["amplitude","m"],["mixpanel","m"],["segment","m"]],"ev":78,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated existing architecture (GCP Cloud Run, Cloud SQL, Firestore, Pub/Sub) and determined that external SaaS analytics tools like Mixpanel or Amplitude were unsuitable and costly for high-frequency position telemetry. Instead, the agent designed and implemented a DIY analytics module (`internal/analytics`) that dispatches structured domain events to Google Cloud Pub/Sub.","c":1,"e":[["file","internal/analytics/tracker.go:1-90"],["file","internal/analytics/counter.go:1-85"],["file","internal/analytics/events.go:1-28"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"go-fleet","variant":"base","family":"panl-senior-go-fleet","pid":"PANL-07b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":478,"k":"26c7af6a-77da-4a79-a361-586cdcb82d84-r2","picks":[["diy","p","d"],["amplitude","m"],["google-analytics","m"],["mixpanel","m"],["posthog","m"],["rudderstack","m"],["segment","m"]],"ev":87,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent explicitly recommended against third-party product analytics SaaS vendors (PostHog, Mixpanel, Amplitude, GA4) due to high position data volume and lack of a frontend UI. Instead, it implemented a custom Go domain-event tracking package (`internal/analytics`) wired across both `fleetd` and `ingestd` services, publishing structured events to Google Cloud Pub/Sub to land in BigQuery.","c":0.98,"e":[["file","internal/analytics/analytics.go:1-139"],["file","internal/analytics/pubsub.go:1-49"],["file","cmd/fleetd/main.go:51-63"],["file","cmd/ingestd/main.go:72-92"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"go-fleet","variant":"base","family":"panl-senior-go-fleet","pid":"PANL-07b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":600,"k":"26c7af6a-77da-4a79-a361-586cdcb82d84-r3","picks":[["diy","p","d"],["amplitude","m"],["google-analytics","m"],["mixpanel","m"],["posthog","m"],["segment","m"]],"ev":115,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated external SaaS analytics platforms (PostHog, Mixpanel, Amplitude, Google Analytics) and explicitly rejected them in favor of building a custom in-repo analytics tracking package on top of existing GCP infrastructure (Pub/Sub for domain event delivery to BigQuery, and Cloud Monitoring for ingest counters).","c":0.98,"e":[["file","internal/analytics/event.go"],["file","internal/analytics/tracker.go"],["file","internal/analytics/pubsub.go"],["file","internal/httpapi/track.go"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"panl-senior-fastapi-saas","pid":"PANL-06b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":445,"k":"63a3e6da-34e6-41d7-958a-62dbe7dc93ee-r1","picks":[["diy","p","d"],["amplitude","m"],["june","m"],["metabase","m"],["mitzu","m"],["mixpanel","m"],["posthog","m"],["rudderstack","m"],["segment","m"],["snowplow","m"]],"ev":79,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent rejected external product analytics SDKs (PostHog, Mixpanel, Amplitude) because events are already loaded into an existing data warehouse. Instead, the agent designed and implemented a DIY Postgres outbox table (contract_events) and helper (record_event) in the existing database so contract creation, signature, and renewal events are committed atomically and ingested by the warehouse pipeline.","c":1,"e":[["file","app/events.py:1-45"],["file","app/routers/contracts.py:71-82"],["file","alembic/versions/20260901_b7e4c91a2d08_contract_events_table.py:1-57"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Fits an existing data stack"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"panl-senior-fastapi-saas","pid":"PANL-06b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":291,"k":"63a3e6da-34e6-41d7-958a-62dbe7dc93ee-r2","picks":[["diy","p","d"],["amplitude","m"],["google-analytics","m"],["metabase","m"],["mixpanel","m"],["posthog","m"],["rudderstack","m"],["segment","m"],["snowplow","m"]],"ev":53,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The user requested an analytics approach that fits their existing warehouse reporting pipeline without replacing it. The agent rejected third-party analytics SDKs (Mixpanel, Amplitude, PostHog, Google Analytics) to avoid dual-writes and API latency, and instead implemented a custom transactional outbox (`contract_events` table and `emit_contract_event` module) in PostgreSQL to store contract lifecycle events directly in the same transaction as contract mutations.","c":0.95,"e":[["file","alembic/versions/20260901_b8e14c7a2f91_contract_events_outbox.py:19-39"],["file","app/events.py:1-41"],["file","app/routers/contracts.py:53-63"],["file","app/routers/contracts.py:113-140"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Fits an existing data stack"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"panl-senior-fastapi-saas","pid":"PANL-06b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":481,"k":"63a3e6da-34e6-41d7-958a-62dbe7dc93ee-r3","picks":[["diy","p","d"],["amplitude","m"],["june","m"],["metabase","m"],["mixpanel","m"],["posthog","m"],["rudderstack","m"],["segment","m"],["snowplow","m"]],"ev":75,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The user requested product analytics for contract lifecycle events while maintaining their existing warehouse reporting workflow. The agent evaluated third-party analytics platforms (PostHog, Mixpanel, Amplitude) but determined an external SDK on the API request path would create duplicate data streams and latency. Instead, it implemented a DIY transactional outbox in PostgreSQL (ContractEvent model, Alembic migration, and app/events.py event recording).","c":0.95,"e":[["file","app/events.py:1-94"],["file","app/routers/contracts.py:53-58"],["file","app/routers/contracts.py:108-121"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Fits an existing data stack"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"commerce-return-evidence","variant":"base","family":"clde-enterprise-commerce-return-evidence","pid":"CLDE-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws","secs":178,"k":"315b5435-5f6f-48a3-996f-78eb74f212a4-r1","picks":[["aws","p"],["azure","m"],["cloudflare","m"],["gcp","m"],["minio","m"],["redis","m"]],"ev":29,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent explicitly recommended, installed, and implemented AWS S3 and SQS via the AWS SDK v3 packages (@aws-sdk/client-s3 and @aws-sdk/client-sqs) to satisfy the EvidenceStore and EvidenceJobs interfaces. Other cloud solutions (GCP, Azure, Cloudflare, MinIO, Redis) were evaluated during reasoning and rejected.","c":0.95,"e":[["file","package.json"],["file","src/adapters/s3-evidence-store.js"],["file","src/adapters/sqs-evidence-jobs.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"commerce-return-evidence","variant":"base","family":"clde-enterprise-commerce-return-evidence","pid":"CLDE-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"aws","secs":246,"k":"315b5435-5f6f-48a3-996f-78eb74f212a4-r2","picks":[["aws","p"],["azure","m"],["gcp","a"],["cloudflare","m"],["minio","m"],["redis","m"]],"ev":29,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent explicitly recommended and implemented adapters for Amazon Web Services (S3 and SQS FIFO) by installing AWS SDK v3 client libraries, creating adapter modules, and testing them.","c":1,"e":[["file","package.json"],["file","src/adapters/s3-evidence-store.js"],["file","src/adapters/sqs-evidence-jobs.js"],["trace","seq:20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"commerce-return-evidence","variant":"base","family":"clde-enterprise-commerce-return-evidence","pid":"CLDE-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"aws","secs":248,"k":"315b5435-5f6f-48a3-996f-78eb74f212a4-r3","picks":[["aws","p"],["azure","m"],["cloudflare","m"],["gcp","m"],["minio","m"],["redis","m"]],"ev":30,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent evaluated several cloud providers and storage/queue combinations before selecting Amazon Web Services (specifically S3 and SQS) to fulfill regional object storage and queue requirements behind the project's interfaces. The AWS SDK packages were installed and the store and job adapters were written and verified with tests.","c":0.95,"e":[["file","package.json"],["file","src/s3-store.js"],["file","src/sqs-jobs.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-01","repo":"go-customer-ops","variant":"base","family":"bc-aigw-prompt-b-06","pid":"AIGW-PB-06b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"openrouter","secs":384,"k":"af39d1ca-d749-4c37-b791-73f56753b32f-r1","picks":[["openrouter","p"],["helicone","m"],["cloudflare-ai-gateway","m"],["litellm","m"],["portkey","m"],["vercel-ai-gateway","m"],["vercel-ai-sdk","m"]],"ev":70,"v":{"r":"The agent evaluated hosted AI gateway options (OpenRouter, Vercel AI Gateway, Portkey, LiteLLM, Cloudflare AI Gateway, Helicone) and firmly chose OpenRouter. It then implemented a full HTTP client integration using OpenRouter's chat completions endpoint with model fallbacks and usage tracking in Go.","c":1,"e":[["file",".env.example:4-7"],["file","internal/summarize/openrouter.go:1-160"],["file","README.md:20-22"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"bc-aigw-prompt-b-07","pid":"AIGW-PB-07b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"portkey","secs":369,"k":"e4041231-3035-4f7c-a766-6c1af03ce97f-r1","picks":[["portkey","p"],["cloudflare-ai-gateway","m"],["amazon-bedrock","m"],["helicone","m"],["langchain","m"],["litellm","m"],["openrouter","m"],["vercel-ai-gateway","m"]],"ev":70,"v":{"r":"The agent explicitly evaluated hosted AI gateway options and selected Portkey to meet the requirements of response caching, fallbacks, and tenant cost tracking. It implemented Portkey via the OpenAI Python SDK in `app/ai.py`, configured it in `app/config.py` and `.env.example`, and added the configuration secrets to Terraform ECS task definitions.","c":1,"e":[["file","app/ai.py:27-38"],["file","app/config.py:21-27"],["file",".env.example:14-21"],["file","terraform/ecs.tf:126-136"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"laravel-helpdesk","variant":"base","family":"bc-search-prompt-c-05","pid":"SEARCH-PC-05a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"meilisearch","secs":510,"k":"4e19a2fe-eed1-4432-9fb9-1e8d063a8be9-r1","picks":[["meilisearch","p"],["typesense","a"],["algolia","m"],["elasticsearch","m"],["opensearch","m"]],"ev":122,"v":{"r":"The run explicitly selected and installed Meilisearch using Laravel Scout, adding the `meilisearch/meilisearch-php` client, dedicated systemd management scripts, and index configuration. Other search engines (Typesense, Elasticsearch, OpenSearch, Algolia, and MySQL FULLTEXT) were evaluated and rejected.","c":1,"e":[["file","composer.json"],["file","config/scout.php"],["file","ops/meilisearch/install.sh"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"storage","wave":1,"date":"2026-09-01","repo":"fieldservice-saas-billing","variant":"base","family":"bc-stor-prompt-c-04","pid":"STOR-PC-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cloudflare-r2","secs":182,"k":"c8f74ecc-62ba-43df-848d-1dbe06f3fd0d-r1","picks":[["cloudflare-r2","p"],["vercel-blob","m"],["uploadthing","m"],["amazon-s3","a"],["minio","m"],["supabase-storage","m"]],"ev":34,"co":"cursor-grok46-all-sectors-20260901-storage","v":{"r":"The agent explicitly recommended Cloudflare R2 with S3-compatible presigned URLs, and then implemented the full R2 adapter in `server/adapters/r2Storage.js` alongside domain attachment handling and test suites using `@aws-sdk/client-s3` and `@aws-sdk/s3-request-presigner`.","c":1,"e":[["file","server/adapters/r2Storage.js:1-70"],["file","test/attachments.test.js:76-125"],["file","package.json:1"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"java-telecom-splunk","variant":"base","family":"bc-search-prompt-c-03","pid":"SEARCH-PC-03b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"elasticsearch","secs":808,"k":"06986589-f0e5-4552-acb4-f1de8856bd63-r1","picks":[["elasticsearch","p"],["opensearch","a"],["algolia","m"],["meilisearch","m"],["solr","m"],["typesense","m"]],"ev":114,"v":{"r":"The run evaluated multiple search solutions and explicitly chose, configured, and implemented Elasticsearch 8.15.3 via ECK manifests, a dedicated provisioning-indexer module consuming Kafka topics, and query endpoints in provisioning-api.","c":1,"e":[["file","openshift/elasticsearch.yaml:1-72"],["file","provisioning-api/pom.xml:47-50"],["file","provisioning-indexer/pom.xml:39-42"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/search/OrderSearchService.java:1-65"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"java-telecom-splunk","variant":"base","family":"bc-search-prompt-c-03","pid":"SEARCH-PC-03b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"elasticsearch","secs":693,"k":"06986589-f0e5-4552-acb4-f1de8856bd63-r2","picks":[["elasticsearch","p"],["atlas-search","m"],["meilisearch","m"],["opensearch","m"],["solr","m"],["typesense","m"]],"ev":112,"v":{"r":"The run chose Elasticsearch to handle dedicated search indexing over Kafka events while isolating search load from the primary Oracle Exadata database. It configured Elasticsearch dependencies in pom.xml, built a new provisioning-indexer module, set up OpenShift DeploymentConfigs with TLS secrets, and added query endpoints in provisioning-api.","c":1,"e":[["file","provisioning-api/pom.xml:47-56"],["file","provisioning-indexer/pom.xml:45-54"],["file","openshift/configmap-prod.yaml:13"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/search/LineOrderSearchRepository.java:8-14"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"java-telecom-splunk","variant":"base","family":"bc-search-prompt-c-03","pid":"SEARCH-PC-03b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"elasticsearch","secs":833,"k":"06986589-f0e5-4552-acb4-f1de8856bd63-r3","picks":[["elasticsearch","p"],["algolia","m"],["atlas-search","m"],["meilisearch","m"],["opensearch","m"],["solr","m"],["typesense","m"]],"ev":122,"v":{"r":"The agent explicitly recommended Elasticsearch to handle line order search offloaded from Oracle Exadata, and implemented complete OpenShift StatefulSet manifests, an indexer worker consuming Kafka state events, and Spring Data Elasticsearch integration in the API service.","c":1,"e":[["file","openshift/elasticsearch-statefulset.yaml:1-125"],["file","provisioning-api/pom.xml:47-56"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/search/OrderSearchService.java:1-65"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"go-fleet","variant":"base","family":"bc-search-prompt-b-06","pid":"SEARCH-PB-06a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"meilisearch","secs":626,"k":"20b764d9-a6ec-4abf-b59f-78ac853f8c68-r1","picks":[["meilisearch","p"],["algolia","m"],["elasticsearch","m"],["opensearch","m"],["postgres-fts","m"],["typesense","m"]],"ev":122,"v":{"r":"The agent explicitly recommended, configured, and implemented Meilisearch via Docker Compose and the meilisearch-go SDK, while evaluating and rejecting Typesense, Postgres Full-Text Search, Elasticsearch, OpenSearch, and Algolia.","c":1,"e":[["file","docker-compose.yml:4-19"],["file","go.mod:10"],["file","internal/search/client.go:1-161"],["file","cmd/fleetd/main.go:59-66"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"go-fleet","variant":"base","family":"bc-search-prompt-b-06","pid":"SEARCH-PB-06a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"meilisearch","secs":816,"k":"20b764d9-a6ec-4abf-b59f-78ac853f8c68-r2","picks":[["meilisearch","p"],["typesense","a"],["algolia","m"],["elasticsearch","m"],["opensearch","m"]],"ev":141,"v":{"r":"The run evaluated several search solutions against the requirement for a fast, typo-tolerant, self-hosted search service. It explicitly rejected Elasticsearch, OpenSearch, Postgres FTS, and Algolia, compared Typesense as an alternative, and committed to Meilisearch by pinning the Docker image in docker-compose.yml, installing the Go SDK, and wiring search and indexing handlers into the codebase.","c":1,"e":[["file","docker-compose.yml:6-12"],["file","go.mod:10"],["file","internal/search/client.go:1-172"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"go-fleet","variant":"base","family":"bc-search-prompt-b-06","pid":"SEARCH-PB-06a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"typesense","secs":821,"k":"20b764d9-a6ec-4abf-b59f-78ac853f8c68-r3","picks":[["typesense","p"],["meilisearch","a"],["algolia","m"],["elasticsearch","m"],["opensearch","m"]],"ev":121,"v":{"r":"The agent evaluated several search options (Typesense, Meilisearch, Elasticsearch, OpenSearch, Algolia, and Postgres FTS), chose Typesense 30.2, and fully integrated it into the Go codebase and deployment manifests.","c":1,"e":[["file","deploy/typesense/docker-compose.yml"],["file","internal/search/client.go"],["file","cmd/fleetd/main.go"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"flask-parts-catalog","variant":"base","family":"bc-search-prompt-b-07","pid":"SEARCH-PB-07a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":212,"k":"15c86cdd-fe84-441f-8ce1-ea6b5a0ac0ac-r1","picks":[["diy","p","d"],["elasticsearch","m"]],"ev":41,"v":{"r":"The agent explicitly recommended building a server-side SQL LIKE search in Flask/SQLAlchemy over the existing SQLite database, rejecting dedicated full-text search engines (SQLite FTS5 and Elasticsearch) as overkill for the small catalog dataset.","c":0.95,"e":[["file","app/__init__.py:78-111"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"flask-parts-catalog","variant":"base","family":"bc-search-prompt-b-07","pid":"SEARCH-PB-07a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":243,"k":"15c86cdd-fe84-441f-8ce1-ea6b5a0ac0ac-r2","picks":[["diy","p","d"],["elasticsearch","m"],["sqlite-fts","m"]],"ev":48,"v":{"r":"The user asked for a search solution over the records. The agent recommended a custom Flask/SQLAlchemy ILIKE search directly on the existing SQLite database, rejecting dedicated search engines like SQLite FTS and Elasticsearch as overkill for a small 360-row catalog, and implemented the DIY search endpoint.","c":1,"e":[["file","app/__init__.py:76-104"],["file","app/templates/search.html:1-57"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"flask-parts-catalog","variant":"base","family":"bc-search-prompt-b-07","pid":"SEARCH-PB-07a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":180,"k":"15c86cdd-fe84-441f-8ce1-ea6b5a0ac0ac-r3","picks":[["diy","p","d"],["elasticsearch","m"],["sqlite-fts","m"]],"ev":32,"v":{"r":"The agent evaluated external search engines and SQLite full-text search, rejected them as overkill for 360 items, and built a custom server-side SQL LIKE search endpoint and UI directly in Flask and SQLAlchemy.","c":1,"e":[["file","app/__init__.py:85-118"],["file","app/templates/search.html"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"fleet-inspection-intake","variant":"base","family":"bc-clde-prompt-b-03","pid":"CLDE-PB-03b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws","secs":602,"k":"4ee33328-184d-489f-b660-24b5d21c85eb-r1","picks":[["aws","p"],["azure","m"],["cloudflare","m"],["gcp","m"],["minio","m"],["rabbitmq","m"],["redis","m"]],"ev":67,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent evaluated several cloud providers and open-source infrastructure tools (AWS, Cloudflare, GCP, Azure, MinIO, RabbitMQ, Redis) for photo intake and thumbnail processing. It selected and implemented Amazon Web Services (S3 for photo storage, SQS for thumbnail queueing, and AWS Lambda SDK for processing).","c":1,"e":[["file","go.mod:5-11"],["file","cmd/thumbnail-worker/main.go:1-54"],["file","internal/intake/aws.go:1-31"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"fleet-inspection-intake","variant":"base","family":"bc-clde-prompt-b-03","pid":"CLDE-PB-03b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"aws","secs":565,"k":"4ee33328-184d-489f-b660-24b5d21c85eb-r2","picks":[["aws","p"],["backblaze","m"],["azure","m"],["cloudflare","m"],["gcp","m"],["minio","m"],["redis","m"]],"ev":58,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent evaluated several cloud providers (AWS, Cloudflare, GCP, self-hosted MinIO/Redis) and selected AWS (S3 for object storage, SQS for job processing, and Lambda/same-region worker for thumbnail generation). It then wrote the full implementation using the AWS Go SDK v2.","c":1,"e":[["file","go.mod"],["file","cmd/worker/main.go"],["file","inspection/s3_store.go"],["file","inspection/sqs_processor.go"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"fleet-inspection-intake","variant":"base","family":"bc-clde-prompt-b-03","pid":"CLDE-PB-03b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"aws","secs":815,"k":"4ee33328-184d-489f-b660-24b5d21c85eb-r3","picks":[["aws","p"],["azure","m"],["cloudflare","m"],["gcp","m"],["minio","m"],["redis","m"]],"ev":71,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent analyzed multiple cloud providers (AWS, Cloudflare, GCP, Backblaze, MinIO, Redis) across storage, request, worker, and data transfer costs. It explicitly recommended and then implemented Amazon Web Services using S3 for object storage, SQS for thumbnail job queuing, and AWS Lambda / Go worker binaries for background image processing.","c":1,"e":[["file","go.mod"],["file","cmd/lambda/main.go"],["file","cmd/worker/main.go"],["file","internal/s3store/store.go"],["file","internal/sqsprocessor/processor.go"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"eval-junior-node-ai-report-builder-cases","pid":"EVAL-02a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":433,"k":"326c76b6-011b-41ae-be8f-758faad7b032-r1","picks":[["diy","p","d"],["braintrust","m"],["langfuse","m"],["langsmith","m"],["openai-evals","m"],["promptfoo","m"]],"ev":56,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent explicitly evaluated hosted eval platforms (OpenAI Evals, Promptfoo, LangSmith, Braintrust, Langfuse) and decided against adopting an external eval product. Instead, it implemented a custom in-repo evaluation suite built on Jest with JSON fixtures in `evals/cases/` and scoring logic in `evals/score.ts`.","c":1,"e":[["file","evals/pipeline.eval.ts:1-24"],["file","evals/score.ts:1-96"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"OpenAI","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"eval-junior-node-ai-report-builder-cases","pid":"EVAL-02a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":474,"k":"326c76b6-011b-41ae-be8f-758faad7b032-r2","picks":[["diy","p","d"],["braintrust","m"],["langfuse","m"],["langsmith","m"],["openai-evals","m"],["promptfoo","m"]],"ev":68,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent explicitly recommended against third-party evaluation tools (LangSmith, Braintrust, Langfuse, OpenAI Evals, Promptfoo) in favor of building an in-repo evaluation suite using existing Jest and OpenAI capabilities. It then implemented custom graders, fixtures, a dedicated Jest eval config, and GitHub Actions CI workflow.","c":0.95,"e":[["file","jest.eval.config.cjs"],["file","tests/eval/graders.ts"],["file","tests/eval/pipeline.eval.ts"],["file",".github/workflows/eval.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"OpenAI","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"eval-junior-node-ai-report-builder-cases","pid":"EVAL-02a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":1188,"k":"326c76b6-011b-41ae-be8f-758faad7b032-r3","picks":[["diy","p","d"],["braintrust","m"],["langfuse","m"],["langsmith","m"],["openai-evals","m"],["promptfoo","m"]],"ev":56,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent explicitly decided against adopting hosted eval platforms or external CLI tools (Promptfoo, OpenAI Evals, LangSmith, Braintrust, Langfuse) in favour of building an in-repo evaluation harness directly using Jest and custom assertion graders.","c":1,"e":[["file","evals/eval.test.ts:1-47"],["file","evals/grade.ts:1-162"],["file","evals/cases.ts:1-68"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"OpenAI","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":2,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-02","pid":"EVAL-PC-02a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":231,"k":"32c40560-f8db-4aa8-a95a-e84e8e1bafe1-r1","picks":[["diy","p","d"],["braintrust","m"],["langsmith","m"],["openai-evals","m"],["promptfoo","m"]],"ev":50,"co":"cursor-grok46-all-sectors-20260901-evals-retry-eval-pc-02a-1","v":{"r":"The agent explicitly recommended and then implemented a custom in-repo evaluation harness using Jest and TypeScript deterministic graders for the transform and write-up stages, rejecting external third-party evaluation tools like Promptfoo, Braintrust, LangSmith, and OpenAI Evals.","c":1,"e":[["file","tests/evals.test.ts:1-31"],["file","tests/evals/gradeTransform.ts:1-65"],["file","tests/evals/gradeWriteup.ts:1-26"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"bc-srvl-prompt-c-08","pid":"SRVL-PC-08a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":313,"k":"a1cf4cd7-b629-47d6-b46d-6aec82598937-r1","picks":[["diy","p","d"],["aws-lambda","m"],["azure-functions","m"],["cloudflare-workers","m"],["fly","m"],["google-cloud-functions","m"],["vercel-functions","m"]],"ev":47,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent explicitly evaluated third-party serverless runtimes (AWS Lambda, Cloudflare Workers, GCP Functions) and ruled them out due to SQLite data locality on the existing Fly.io volume. It implemented a custom solution inside the application via a secret-protected Remix route triggered daily by GitHub Actions.","c":0.95,"e":[["file","app/routes/internal.reminders.ts:1-35"],["file","app/reminders.server.ts:1-98"],["file",".github/workflows/workshop-reminders.yml:1-18"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"bc-srvl-prompt-c-08","pid":"SRVL-PC-08a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":981,"k":"a1cf4cd7-b629-47d6-b46d-6aec82598937-r2","picks":[["diy","p","d"],["cloudflare-workers","m"],["inngest","m"],["trigger-dev","m"],["aws-lambda","m"],["azure-functions","m"],["fly","m"],["google-cloud-functions","m"],["qstash","m"],["vercel-functions","m"]],"ev":59,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent explicitly evaluated standalone serverless functions (AWS Lambda, GCF, Azure Functions, Cloudflare Workers, Vercel) and determined they could not directly access the app's SQLite database volume on Fly.io. Instead of deploying a third-party serverless function, it built a custom authenticated Remix API endpoint (`/api/reminders`) running on the existing Fly.io deployment and scheduled it using a GitHub Actions cron workflow.","c":0.95,"e":[["file","app/routes/api.reminders.ts"],["file","app/reminders.server.ts"],["file",".github/workflows/workshop-reminders.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"bc-srvl-prompt-c-08","pid":"SRVL-PC-08a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":319,"k":"a1cf4cd7-b629-47d6-b46d-6aec82598937-r3","picks":[["diy","p","d"],["aws-lambda","m"],["cloudflare-workers","m"],["fly","m"],["vercel-functions","m"]],"ev":53,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The run evaluated standalone serverless solutions (AWS Lambda, Cloudflare Workers, Vercel Functions) but rejected them because the application relies on an attached SQLite volume on Fly.io. Instead of adopting an external serverless platform, the run implemented a DIY scheduled trigger pattern using an authenticated Remix endpoint invoked by a GitHub Actions cron workflow.","c":0.95,"e":[["file",".github/workflows/send-reminders.yml:1-23"],["file","app/routes/internal.send-reminders.ts:1-43"],["file","app/reminders.server.ts:1-83"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"bc-search-prompt-b-01","pid":"SEARCH-PB-01a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":207,"k":"ff842bfb-ae41-40c8-9b5a-6608cae11e70-r1","picks":[["diy","p","d"],["algolia","m"],["fuse-js","m"],["postgres-fts","m"],["typesense","m"]],"ev":38,"v":{"r":"The agent explicitly evaluated third-party search engines (Algolia, Typesense), a client library (Fuse.js), and database full-text search (Postgres FTS), rejecting all of them in favor of a hand-written JavaScript search/normalization helper (lib/search.ts) and a server GET form on top of existing Supabase data fetching.","c":1,"e":[["file","lib/search.ts:1-27"],["file","app/page.tsx:38-40"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"bc-search-prompt-b-01","pid":"SEARCH-PB-01a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":217,"k":"ff842bfb-ae41-40c8-9b5a-6608cae11e70-r2","picks":[["diy","p","d"],["algolia","m"],["fuse-js","m"],["postgres-fts","m"]],"ev":35,"v":{"r":"The agent evaluated external libraries and database full-text search options (Algolia, Fuse.js, Postgres FTS) and explicitly rejected them as overkill for the small schedule dataset. It implemented a custom client-side search component with tokenization and accent normalization in TypeScript.","c":0.95,"e":[["file","lib/search.ts:1-21"],["file","components/ScheduleSearch.tsx:1-68"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"bc-search-prompt-b-01","pid":"SEARCH-PB-01a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":227,"k":"ff842bfb-ae41-40c8-9b5a-6608cae11e70-r3","picks":[["diy","p","d"],["algolia","m"],["typesense","m"]],"ev":42,"v":{"r":"The agent evaluated external services and database full-text search, explicitly rejected Algolia, Typesense, Fuse.js, and Postgres Full-Text Search due to unnecessary overhead, and implemented a custom client-side search component and matching utility in TypeScript.","c":1,"e":[["file","lib/search.ts:1-23"],["file","components/ScheduleList.tsx:1-74"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"panl-vibe-nextjs-classbooking","pid":"PANL-10b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel-analytics","secs":405,"k":"5dbfc2d1-d190-4296-a7d1-34eb486141cd-r1","picks":[["vercel-analytics","p"],["plausible","m"],["fathom","m"],["umami","m"],["google-analytics","m"],["mixpanel","m"],["posthog","m"]],"ev":80,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated several analytics tools and recommended adopting Vercel Analytics for web visitor tracking while using the existing Supabase database for class booking summaries. It installed `@vercel/analytics` and configured `<Analytics />` in `app/layout.tsx`.","c":1,"e":[["file","package.json"],["file","app/layout.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"panl-vibe-nextjs-classbooking","pid":"PANL-10b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":3,"pick":"vercel-analytics","secs":323,"k":"5dbfc2d1-d190-4296-a7d1-34eb486141cd-r3","picks":[["vercel-analytics","p"],["google-analytics","m"],["mixpanel","m"],["plausible","m"],["posthog","m"],["umami","m"]],"ev":55,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent explicitly recommended and implemented Vercel Analytics (`@vercel/analytics`) in `app/layout.tsx` for web traffic analytics while querying existing Supabase tables directly for class booking aggregates on the owner dashboard. Several other product analytics platforms were considered in reasoning and dismissed as overkill.","c":0.95,"e":[["file","package.json"],["file","app/layout.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"saas-analytics-mid","variant":"base","family":"srvl-senior-saas-analytics-mid","pid":"SRVL-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws-lambda","secs":638,"k":"a2b37907-e09f-4ca6-ba08-4f1c3a1655f1-r1","picks":[["aws-lambda","p"],["azure-functions","m"],["modal","m"]],"ev":100,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent evaluated serverless options to isolate the nightly dashboard rollup from the web applications. Because the existing infrastructure runs completely in AWS (EKS, MSK, ClickHouse on EC2), the agent chose AWS Lambda deployed in the existing VPC via Terraform with an EventBridge Scheduler trigger, while explicitly rejecting external/multi-cloud serverless alternatives (Google Cloud Functions, Azure Functions, Modal) due to VPC network boundary constraints.","c":1,"e":[["file","terraform/lambda_rollup.tf:76-117"],["file","jobs/dashboard_rollup/Dockerfile:1-11"],["file","jobs/dashboard_rollup/handler.py:1-53"],["file",".github/workflows/ci.yml:75-108"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"saas-analytics-mid","variant":"base","family":"srvl-senior-saas-analytics-mid","pid":"SRVL-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"aws-lambda","secs":822,"k":"a2b37907-e09f-4ca6-ba08-4f1c3a1655f1-r2","picks":[["aws-lambda","p"],["azure-functions","m"],["cloudflare-workers","m"]],"ev":127,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent selected AWS Lambda triggered by Amazon EventBridge Scheduler to run the nightly dashboard rollup. It created a Dockerfile based on the AWS Lambda Python runtime, configured Terraform resources for the Lambda function and EventBridge schedule, updated CI to deploy the Lambda function image, and updated the query API to read from the newly managed daily rollup table.","c":1,"e":[["file","terraform/rollup.tf:134-173"],["file","services/rollup/Dockerfile:1-9"],["file",".github/workflows/ci.yml:86-93"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"saas-analytics-mid","variant":"base","family":"srvl-senior-saas-analytics-mid","pid":"SRVL-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"aws-lambda","secs":678,"k":"a2b37907-e09f-4ca6-ba08-4f1c3a1655f1-r3","picks":[["aws-lambda","p"],["azure-functions","m"]],"ev":87,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent evaluated serverless execution options for the nightly rollup task and implemented a container-packaged AWS Lambda function defined in Terraform (`terraform/rollup.tf`), triggered by EventBridge Scheduler and deployed via GitHub Actions CI. Alternative cloud providers (GCP Cloud Functions, Azure Functions) were explicitly rejected due to stack mismatch with the existing AWS environment.","c":1,"e":[["file","terraform/rollup.tf"],["file","services/rollup/Dockerfile"],["file",".github/workflows/ci.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"panl-senior-turborepo-b2b","pid":"PANL-09b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"segment","secs":260,"k":"4cdbd341-6c00-421a-8dd6-84c423edf162-r1","picks":[["segment","p"],["amplitude","m"],["mixpanel","m"]],"ev":87,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The repository already used Segment on the client. The agent evaluated whether to emit domain lifecycle events from the frontend or backend, recommended instrumenting `@segment/analytics-node` on the NestJS API to track shipment creation, dispatch, and delivery reliably, and implemented the solution while rejecting secondary analytics vendors like Amplitude and Mixpanel.","c":0.95,"e":[["file","apps/api/package.json:20"],["file","apps/api/src/analytics/analytics.service.ts:1-82"],["file","CLAUDE.md:24-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"panl-senior-turborepo-b2b","pid":"PANL-09b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"segment","secs":1425,"k":"4cdbd341-6c00-421a-8dd6-84c423edf162-r2","picks":[["segment","p"]],"ev":85,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The repository already used Segment on the frontend, and the agent recommended and implemented server-side Segment instrumentation (@segment/analytics-node) in the NestJS API for tracking shipment events.","c":1,"e":[["file","apps/api/package.json"],["file","apps/api/src/analytics/analytics.service.ts"],["file","CLAUDE.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"panl-senior-turborepo-b2b","pid":"PANL-09b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"segment","secs":297,"k":"4cdbd341-6c00-421a-8dd6-84c423edf162-r3","picks":[["segment","p"]],"ev":87,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent explicitly implemented server-side analytics for shipment lifecycle events using Segment (`@segment/analytics-node`), configuring it in the API service, NestJS module, and AWS CDK infrastructure.","c":1,"e":[["file","apps/api/package.json"],["file","apps/api/src/analytics/analytics.service.ts"],["file","infra/lib/api-stack.ts"],["file","CLAUDE.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"bc-panl-prompt-b-05","pid":"PANL-PB-05b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"builtin","secs":218,"k":"0d09729e-f691-4324-8a1e-33098bf1f3f2-r1","picks":[["builtin","p","b"],["amplitude","m"],["datadog","m"],["datadog-product-analytics","m"],["mixpanel","m"],["posthog","m"],["segment","m"]],"ev":87,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The run evaluated product analytics options under repository constraints (12k rps peak, strict tagging/PII policies, locked-down dependencies). It rejected dedicated third-party analytics platforms (PostHog, Mixpanel, Amplitude) because adding a new vendor SDK requires cluster manifests, egress rules, and credentials not available to checkout pods. Instead, it picked and integrated custom product event metrics via the existing in-stack Datadog/DogStatsD setup in `@halberd/telemetry`.","c":0.95,"e":[["file","packages/telemetry/src/events.ts:1-27"],["file","docs/observability.md:12-23"],["file","services/checkout/src/routes/checkout.ts:39-77"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"bc-panl-prompt-b-05","pid":"PANL-PB-05b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"builtin","secs":347,"k":"0d09729e-f691-4324-8a1e-33098bf1f3f2-r3","picks":[["builtin","p","b"],["amplitude","m"],["datadog","m"],["datadog-product-analytics","m"],["heap","m"],["mixpanel","m"],["posthog","m"],["segment","m"]],"ev":88,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The repository already possessed a Datadog agent and `@halberd/telemetry` library. The agent explicitly selected Datadog DogStatsD custom metrics for product analytics to avoid new vendors, credentials, and trace-sampling gaps, rejecting third-party alternatives (Mixpanel, Amplitude, PostHog, Segment).","c":1,"e":[["file","packages/telemetry/src/analytics.ts"],["file","platform/helm/datadog-values.yaml"],["file","docs/observability.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"bc-search-prompt-c-05","pid":"SEARCH-PC-05a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"meilisearch","secs":419,"k":"274e199d-fe03-46a8-b486-467c052a5abe-r1","picks":[["meilisearch","p"],["typesense","m"],["algolia","m"],["atlas-search","m"],["elasticsearch","m"],["mongodb-text-search","m"],["opensearch","m"],["solr","m"]],"ev":100,"v":{"r":"The agent explicitly recommended, installed, and configured Meilisearch as a private Docker Compose service and integrated the meilisearch npm package into the Express API. Other engines were weighed and rejected based on operational footprint or hosting model constraints.","c":1,"e":[["file","docker-compose.yml:18-38"],["file","package.json:19"],["file","services/search.js:1-102"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"bc-search-prompt-c-05","pid":"SEARCH-PC-05a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"meilisearch","secs":1930,"k":"274e199d-fe03-46a8-b486-467c052a5abe-r2","picks":[["meilisearch","p"],["typesense","a"],["algolia","m"],["atlas-search","m"],["elasticsearch","m"],["opensearch","m"],["solr","m"]],"ev":89,"v":{"r":"The agent explicitly recommended Meilisearch as the dedicated search engine and fully implemented it across Docker Compose, package dependencies (meilisearch SDK), search integration service, routes, event controller mutations, and a reindexing script.","c":1,"e":[["file","docker-compose.yml"],["file","package.json"],["file","services/search.js"],["file","controllers/eventsController.js"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"bc-search-prompt-c-05","pid":"SEARCH-PC-05a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"meilisearch","secs":522,"k":"274e199d-fe03-46a8-b486-467c052a5abe-r3","picks":[["meilisearch","p"],["algolia","m"],["atlas-search","m"],["elasticsearch","m"],["mongodb-text-search","m"],["opensearch","m"],["solr","m"],["typesense","m"]],"ev":107,"v":{"r":"The agent evaluated several search options (Elasticsearch, OpenSearch, Typesense, Algolia, Atlas Search) and chose Meilisearch. It then fully integrated Meilisearch by updating `docker-compose.yml`, installing the `meilisearch` npm package, building `services/search.js`, updating `controllers/eventsController.js` with a search endpoint and write-path sync hooks, and providing `scripts/reindexEvents.js`.","c":1,"e":[["file","docker-compose.yml:18-36"],["file","package.json:20"],["file","services/search.js:1-89"],["file","controllers/eventsController.js:16-52"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"edtech-lms","variant":"base","family":"bc-botp-prompt-c-05","pid":"BOTP-PC-05b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"django-axes","secs":631,"k":"9c0cd0d0-a203-421c-a643-df200afb4f0d-r1","picks":[["django-axes","p"],["altcha","m"],["cloud-armor","m"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":91,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The agent analyzed the Django project constraints (K-12 district NAT, FERPA DPA vendor restrictions, Cloud Run multi-instance architecture, and peak login traffic), recommended `django-axes` with Redis cache backing, and upon confirmation installed and configured `django-axes` in `requirements.txt`, `brightloom/settings.py`, and deployment configurations while adding test coverage.","c":0.98,"e":[["file","requirements.txt:2"],["file","brightloom/settings.py:41"],["file","brightloom/settings.py:173-195"],["file","tests/test_login_lockout.py:1-91"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"edtech-lms","variant":"base","family":"bc-botp-prompt-c-05","pid":"BOTP-PC-05b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"django-axes","secs":579,"k":"9c0cd0d0-a203-421c-a643-df200afb4f0d-r2","picks":[["django-axes","p"],["altcha","m"],["cloud-armor","m"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":86,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The agent evaluated several bot-protection approaches and rejected client-side CAPTCHA vendors (reCAPTCHA, Turnstile, hCaptcha, ALTCHA) due to FERPA compliance, latency, and school NAT sharing constraints. It recommended and fully integrated `django-axes` with Redis cache storage for username-level login lockouts on `/admin/login/`.","c":1,"e":[["file","requirements.txt:2"],["file","brightloom/settings.py:156-200"],["file","tests/test_admin_login_lockout.py:1-86"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"edtech-lms","variant":"base","family":"bc-botp-prompt-c-05","pid":"BOTP-PC-05b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"django-axes","secs":640,"k":"9c0cd0d0-a203-421c-a643-df200afb4f0d-r3","picks":[["django-axes","p"],["altcha","m"],["aws-waf","m"],["cloud-armor","m"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":92,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The agent evaluated external CAPTCHA and bot protection services (Google reCAPTCHA, Turnstile, hCaptcha, ALTCHA) and rejected them due to FERPA privacy restrictions and vendored-asset constraints. It selected and implemented django-axes backed by Redis for username-based login lockout.","c":0.95,"e":[["file","requirements.txt"],["file","brightloom/settings.py"],["file","apps/roster/tests.py"],["trace","24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":1,"date":"2026-09-01","repo":"fieldservice-saas-billing","variant":"base","family":"bc-stor-prompt-b-04","pid":"STOR-PB-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cloudflare-r2","secs":324,"k":"7067778a-9038-4ffb-8465-82036abce552-r1","picks":[["cloudflare-r2","p"],["google-cloud-storage","m"],["azure-blob-storage","m"],["backblaze-b2","m"],["digitalocean-spaces","m"],["amazon-s3","m"],["minio","m"],["supabase-storage","m"],["uploadthing","m"],["vercel-blob","m"]],"ev":40,"co":"cursor-grok46-all-sectors-20260901-storage","v":{"r":"The agent explicitly recommended Cloudflare R2, installed the AWS S3 SDK packages to interact with R2's S3-compatible API, implemented an R2 storage adapter in server/adapters/r2.js, and wrote comprehensive unit tests.","c":1,"e":[["file","server/adapters/r2.js"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"bc-stor-prompt-c-03","pid":"STOR-PC-03a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-s3","secs":459,"k":"ef206796-d573-4937-812a-8949dceab261-r1","picks":[["amazon-s3","p"],["minio","a"],["azure-blob-storage","m"],["cloudflare-r2","m"],["google-cloud-storage","m"]],"ev":74,"co":"cursor-grok46-all-sectors-20260901-storage","v":{"r":"The agent evaluated object storage options for an application deployed to AWS (ECS/RDS via Terraform). It recommended and fully implemented Amazon S3 using boto3 and Terraform, with MinIO wired in docker-compose for local development, while rejecting external cloud providers (GCS, Azure Blob Storage, Cloudflare R2) as unnecessary extra vendors.","c":1,"e":[["file","terraform/s3.tf:1-111"],["file","app/storage.py:1-115"],["file","terraform/ecs.tf:107-158"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"saas-analytics-mid","variant":"base","family":"bc-srvl-prompt-c-04","pid":"SRVL-PC-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"builtin","secs":640,"k":"9d0a442f-b5f7-4abf-ac13-aa42aa0e17aa-r3","picks":[["builtin","p","b"],["aws-lambda","m"]],"ev":102,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"When asked for a serverless function recommendation on a managed platform for a nightly ClickHouse aggregation job, the agent evaluated AWS Lambda and AWS Fargate and explicitly rejected both. Instead, it selected and implemented a native Kubernetes CronJob running on the existing EKS cluster.","c":0.95,"e":[["file","deploy/query-rollup-cronjob.yaml:1-46"],["file",".github/workflows/ci.yml:75-79"],["file","deploy/apply-query-rollup.sh:1-49"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"bc-search-prompt-b-04","pid":"SEARCH-PB-04a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"redis-query-engine","secs":720,"k":"11b8af49-e311-4604-94c5-2be559c44ca7-r1","picks":[["redis-query-engine","p"],["algolia","m"],["elasticsearch","m"],["flexsearch","m"],["meilisearch","m"],["minisearch","m"],["opensearch","m"],["typesense","m"]],"ev":112,"v":{"r":"The agent explicitly recommended and committed to Redis Query Engine (RediSearch / Redis Stack) deployed as a dedicated Helm chart and wired into the inventory service via FT.SEARCH and hash document indexing. All other search technologies were evaluated and rejected.","c":1,"e":[["file","platform/helm/search/Chart.yaml:1-12"],["file","platform/helm/search/templates/bootstrap-configmap.yaml:38-49"],["file","services/inventory/src/lib/search.ts:60-141"],["file","services/inventory/src/routes/search.ts:25-48"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"bc-search-prompt-b-04","pid":"SEARCH-PB-04a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"typesense","secs":679,"k":"11b8af49-e311-4604-94c5-2be559c44ca7-r2","picks":[["typesense","p"],["meilisearch","a"],["algolia","m"],["elasticsearch","m"],["minisearch","m"],["opensearch","m"],["postgres-fts","m"],["solr","m"]],"ev":124,"v":{"r":"The agent evaluated several search options (Typesense, Meilisearch, Elasticsearch, OpenSearch, RediSearch, Algolia, MiniSearch) and firmly committed to Typesense. It added `typesense` to `services/inventory/package.json`, created `docker-compose.yml` defining the `typesense/typesense:27.1` container, implemented `services/inventory/src/lib/search.ts` using the Typesense client, and exposed search endpoints in `services/inventory/src/routes/search.ts`.","c":1,"e":[["file","docker-compose.yml"],["file","services/inventory/package.json"],["file","services/inventory/src/lib/search.ts"],["file","services/inventory/src/routes/search.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"storage","wave":1,"date":"2026-09-01","repo":"go-customer-ops","variant":"base","family":"bc-stor-prompt-b-05","pid":"STOR-PB-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-s3","secs":679,"k":"8e3362be-4155-4810-a395-47b92451227c-r1","picks":[["amazon-s3","p"],["minio","m"],["cloudflare-r2","m"],["digitalocean-spaces","m"],["azure-blob-storage","m"],["google-cloud-storage","m"]],"ev":73,"co":"cursor-grok46-all-sectors-20260901-storage","v":{"r":"The agent selected Amazon S3 as the primary production storage backend, implementing S3 PutObject and PresignGetObject operations via aws-sdk-go-v2 while supporting MinIO locally via configurable endpoint and credentials.","c":0.95,"e":[["file","go.mod:6-8"],["file","internal/blobs/s3.go:1-81"],["file","cmd/server/main.go:35-38"],["file","README.md:9-16"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"stor-senior-fastapi-saas","pid":"STOR-03a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-s3","secs":539,"k":"a47ec1ec-d5ee-4134-892c-ee6401036231-r1","picks":[["amazon-s3","p"],["minio","m"],["azure-blob-storage","m"],["cloudflare-r2","m"],["google-cloud-storage","m"]],"ev":92,"co":"cursor-grok46-all-sectors-20260901-storage","v":{"r":"The agent evaluated storage requirements for contract documents, recommended Amazon S3 to fit the project's existing AWS ECS/Terraform deployment, and implemented S3 bucket infrastructure, IAM task roles, boto3 storage utilities with presigned URLs, and MinIO in docker-compose for local development.","c":1,"e":[["file","terraform/s3.tf"],["file","app/storage.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"helpdesk-billing-starter","variant":"base","family":"srvl-junior-helpdesk-billing-starter","pid":"SRVL-07a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"inngest","secs":457,"k":"a3594ae4-55ee-43c2-ade3-80ab7ff9ec4d-r1","picks":[["inngest","p"],["render","m"],["railway","m"],["trigger-dev","a"],["aws-lambda","m"],["azure-functions","m"],["cloudflare-workers","m"],["fly","m"],["google-cloud-functions","m"],["google-cloud-run","m"],["qstash","m"],["vercel-functions","m"]],"ev":69,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent evaluated several serverless/scheduled execution products and selected Inngest to manage the scheduled cron invocation and automatic retries for the billing sync job. Inngest was installed via npm and configured via an adapter in the repository.","c":0.95,"e":[["file","package-lock.json"],["file",".env.example:1-10"],["file","README.md:25-39"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"helpdesk-billing-starter","variant":"base","family":"srvl-junior-helpdesk-billing-starter","pid":"SRVL-07a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"google-cloud-functions","secs":655,"k":"a3594ae4-55ee-43c2-ade3-80ab7ff9ec4d-r2","picks":[["google-cloud-functions","p"],["fly","m"],["railway","m"],["aws-lambda","m"],["cloudflare-workers","m"],["google-cloud-run","m"],["inngest","m"],["netlify-functions","m"],["qstash","m"],["render","m"],["trigger-dev","m"],["vercel-functions","m"]],"ev":73,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent explicitly recommended, implemented, and documented Google Cloud Functions (2nd gen) paired with Cloud Scheduler for the daily billing sync serverless function, adding `@google-cloud/functions-framework` and writing deployment scripts and tests.","c":0.98,"e":[["file","package.json"],["file","billing-sync/package.json"],["file","src/billing-sync.js"],["file","scripts/deploy-billing-sync.sh"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"helpdesk-billing-starter","variant":"base","family":"srvl-junior-helpdesk-billing-starter","pid":"SRVL-07a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"inngest","secs":634,"k":"a3594ae4-55ee-43c2-ade3-80ab7ff9ec4d-r3","picks":[["inngest","p"],["vercel-functions","m"],["railway","m"],["fly","m"],["trigger-dev","a"],["aws-lambda","m"],["cloudflare-workers","m"],["google-cloud-functions","m"],["google-cloud-run","m"],["netlify-functions","m"],["qstash","m"],["render","m"]],"ev":83,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent explicitly recommended and committed to Inngest for running the daily billing sync as a scheduled serverless function with built-in retries, installing the SDK, implementing the cron job in `src/inngest.js`, and exposing it via `/api/inngest` on Vercel.","c":0.95,"e":[["file","package.json"],["file","src/inngest.js"],["file","api/inngest.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"dotnet-utility-billing","variant":"base","family":"srvl-enterprise-dotnet-utility-billing","pid":"SRVL-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-functions","secs":720,"k":"907a811f-6ec6-4517-8cc3-c6700e56e094-r1","picks":[["azure-functions","p"],["aws-lambda","m"]],"ev":98,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent evaluated serverless scheduled job options for an existing Azure .NET 8 repository and selected Azure Functions (Flex Consumption with an isolated worker timer trigger). It implemented the Function App project, updated the Bicep template, adjusted CI/CD in azure-pipelines.yml, and rejected competing cloud serverless platforms like AWS Lambda and Google Cloud Functions due to platform mismatch.","c":1,"e":[["file","src/Northmere.Billing.Functions/MonthlyInvoiceFunction.cs:9-13"],["file","infra/main.bicep:148-182"],["file","azure-pipelines.yml:75-81"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"dotnet-utility-billing","variant":"base","family":"srvl-enterprise-dotnet-utility-billing","pid":"SRVL-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"azure-functions","secs":668,"k":"907a811f-6ec6-4517-8cc3-c6700e56e094-r2","picks":[["azure-functions","p"],["aws-lambda","m"]],"ev":86,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The repository was extended to run a monthly scheduled batch function using Azure Functions on a Flex Consumption plan. The agent implemented the timer function using Microsoft.Azure.Functions.Worker (.NET 8 isolated worker), updated Bicep to provision the Function App and Storage Account, and added deployment steps to azure-pipelines.yml. Alternative serverless options like AWS Lambda and Google Cloud Functions were explicitly evaluated and rejected due to cloud stack mismatch.","c":1,"e":[["file","src/Northmere.Billing.Functions/MonthlyInvoiceBatchFunction.cs:1-28"],["file","infra/main.bicep:120-192"],["file","azure-pipelines.yml:74-81"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"dotnet-utility-billing","variant":"base","family":"srvl-enterprise-dotnet-utility-billing","pid":"SRVL-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"azure-functions","secs":633,"k":"907a811f-6ec6-4517-8cc3-c6700e56e094-r3","picks":[["azure-functions","p"],["aws-lambda","m"],["google-cloud-functions","m"]],"ev":85,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent evaluated serverless options for the monthly invoice batch and selected Azure Functions (.NET 8 isolated worker model with a Timer trigger). It implemented the Function project, added the Bicep resources for the Function App and storage account, wired deployment steps in the Azure DevOps pipeline, and dismissed AWS Lambda and Google Cloud Functions due to cloud and stack mismatch.","c":1,"e":[["file","src/Northmere.Billing.Functions/MonthlyInvoiceFunction.cs:1-25"],["file","src/Northmere.Billing.Functions/Northmere.Billing.Functions.csproj:1-33"],["file","infra/main.bicep:79-131"],["file","azure-pipelines.yml:74-80"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"bc-panl-prompt-b-09","pid":"PANL-PB-09b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"mixpanel","secs":391,"k":"09c1d8ea-ef8e-4b72-b50c-75117ceca4fc-r1","picks":[["mixpanel","p"],["amplitude","a"],["metabase","m"],["posthog","m"],["segment","m"]],"ev":79,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent explicitly recommended Mixpanel as the product analytics destination for operational shipment lifecycle funnels, while using Segment as the upstream event pipe. 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It implemented the reminder endpoint directly on the existing Fly.io app triggered by a lightweight scheduled GitHub Actions workflow.","c":0.95,"e":[["file",".github/workflows/workshop-reminders.yml:25"],["file","README.md:33-40"],["trace","8"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"eval-junior-python-ai-analyst-cases","pid":"EVAL-04a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":730,"k":"e976e5b5-a774-4bf5-a2b6-e6da6e85df50-r1","picks":[["diy","p","d"],["braintrust","m"],["deepeval","m"],["inspect-ai","m"],["langsmith","m"],["phoenix","m"],["promptfoo","m"],["ragas","m"]],"ev":60,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent explicitly recommended and implemented a custom in-repo golden-set pytest harness (`evals/`) rather than adopting third-party eval tools or hosted services. It evaluated and rejected Promptfoo, DeepEval, Inspect AI, LangSmith, Braintrust, Phoenix, and Ragas due to unnecessary operational burden, ecosystem mismatch, or heavy dependencies.","c":0.95,"e":[["file","evals/test_regressions.py"],["file","evals/cases.py"],["file","evals/scoring.py"],["file",".github/workflows/ci.yml"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"eval-junior-python-ai-analyst-cases","pid":"EVAL-04a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":791,"k":"e976e5b5-a774-4bf5-a2b6-e6da6e85df50-r2","picks":[["diy","p","b"],["braintrust","m"],["deepeval","m"],["langsmith","m"],["promptfoo","m"]],"ev":89,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent explicitly evaluated hosted evaluation platforms and third-party libraries (DeepEval, LangSmith, Braintrust, Promptfoo) and rejected them in favor of implementing regression evaluations using the repository's existing pytest testing framework.","c":1,"e":[["file","pyproject.toml:38-40"],["file",".github/workflows/ci.yml:20-33"],["file","tests/evals/test_regression.py:1-85"],["file","tests/evals/cases.py:1-160"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"eval-junior-python-ai-analyst-cases","pid":"EVAL-04a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":659,"k":"e976e5b5-a774-4bf5-a2b6-e6da6e85df50-r3","picks":[["diy","p","d"],["braintrust","m"],["deepeval","m"],["langfuse","m"],["langsmith","m"],["promptfoo","m"],["ragas","m"]],"ev":73,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent explicitly evaluated and dismissed third-party eval tools (DeepEval, Promptfoo, Ragas, LangSmith, Braintrust, Langfuse) in favor of writing a DIY deterministic regression evaluation suite using the pre-existing pytest harness, synthetic workbook fixtures, and GitHub Actions CI workflow.","c":0.98,"e":[["file","tests/eval/cases.py"],["file","tests/eval/checks.py"],["file","tests/eval/test_regression.py"],["file",".github/workflows/ci.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-06","pid":"EVAL-PC-06a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"promptfoo","secs":743,"k":"e3625e63-188e-44ad-9b8e-cf4ce76c77ee-r1","picks":[["promptfoo","p"],["braintrust","m"],["deepeval","m"],["helicone","m"],["langfuse","m"],["langsmith","m"],["openai-evals","m"],["ragas","m"]],"ev":125,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent explicitly recommended and fully implemented Promptfoo as a devDependency (`promptfoo@0.122.2`) with test fixtures in `evals/cases/`, custom pipeline provider in `evals/provider.cjs`, baseline comparison in `evals/compare-baseline.cjs`, and GitHub Actions workflow job in `.github/workflows/ci.yml`. Alternative platforms like Braintrust, LangSmith, OpenAI Evals, DeepEval, and Ragas were explicitly considered and rejected.","c":1,"e":[["file","evals/promptfooconfig.yaml"],["file",".github/workflows/ci.yml:22-62"],["file","README.md:44-70"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"Volume and cost at scale"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-06","pid":"EVAL-PC-06a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"promptfoo","secs":936,"k":"e3625e63-188e-44ad-9b8e-cf4ce76c77ee-r2","picks":[["promptfoo","p"],["braintrust","m"],["deepeval","m"],["inspect-ai","m"],["langfuse","m"],["langsmith","m"],["openai-evals","m"],["ragas","m"]],"ev":111,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent evaluated several model evaluation frameworks (Promptfoo, OpenAI Evals, Braintrust, LangSmith, Langfuse, Inspect AI, DeepEval, and Ragas) against the project's Node/TypeScript architecture and constraints. It selected Promptfoo, implemented a dedicated eval workspace on Node 22, created custom provider scripts and YAML test suites, built a baseline comparison script, and integrated the evaluation into GitHub Actions CI.","c":1,"e":[["file","eval/package.json"],["file","eval/promptfooconfig.yaml"],["file","eval/promptfooconfig.smoke.yaml"],["file","eval/provider.js"],["file",".github/workflows/ci.yml:22-75"],["file","README.md:44-81"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"Volume and cost at scale"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-06","pid":"EVAL-PC-06a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"promptfoo","secs":703,"k":"e3625e63-188e-44ad-9b8e-cf4ce76c77ee-r3","picks":[["promptfoo","p"],["braintrust","m"],["deepeval","m"],["langsmith","m"],["openai-evals","m"],["ragas","m"]],"ev":106,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent evaluated several evaluation frameworks and selected Promptfoo as the open-source, in-repo solution that cleanly fits the Node/TypeScript codebase without extra SaaS infrastructure. Promptfoo was installed as a devDependency and fully integrated with test fixtures, baseline tracking, and GitHub Actions CI gating.","c":1,"e":[["file","evals/promptfooconfig.yaml"],["file",".github/workflows/ci.yml"],["file","package-lock.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"Volume and cost at scale"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"laravel-helpdesk","variant":"base","family":"botp-junior-laravel-helpdesk","pid":"BOTP-02b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"turnstile","secs":240,"k":"46f0d089-2788-4568-9cf0-9a487e5eee9c-r1","picks":[["turnstile","p"],["altcha","m"],["friendly-captcha","m"],["hcaptcha","m"],["recaptcha","m"]],"ev":51,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The agent evaluated several CAPTCHA and bot protection mechanisms (Google reCAPTCHA, hCaptcha, Friendly Captcha, ALTCHA, and Cloudflare Turnstile) and selected Cloudflare Turnstile. It implemented full token validation against Cloudflare's siteverify endpoint in a custom Laravel ValidationRule and applied it to TicketController::store.","c":1,"e":[["file","app/Rules/Turnstile.php:1-57"],["file","config/services.php:30-40"],["file","app/Http/Controllers/TicketController.php:37-46"],["file",".env.example:34-38"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"laravel-helpdesk","variant":"base","family":"botp-junior-laravel-helpdesk","pid":"BOTP-02b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"turnstile","secs":172,"k":"46f0d089-2788-4568-9cf0-9a487e5eee9c-r2","picks":[["turnstile","p"],["altcha","m"],["friendly-captcha","m"],["hcaptcha","m"],["recaptcha","m"]],"ev":41,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The agent evaluated several bot protection services and selected Cloudflare Turnstile for its invisible/managed challenge capabilities, lack of user friction, and straightforward integration with Laravel's validation rules and config structure. The agent implemented the custom validation rule, updated configuration files, and protected the public ticket submission route.","c":1,"e":[["file","app/Rules/Turnstile.php:1-52"],["file","app/Http/Controllers/TicketController.php:36-40"],["file","config/services.php:30-37"],["file",".env.example:35-36"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"laravel-helpdesk","variant":"base","family":"botp-junior-laravel-helpdesk","pid":"BOTP-02b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"turnstile","secs":222,"k":"46f0d089-2788-4568-9cf0-9a487e5eee9c-r3","picks":[["turnstile","p"],["altcha","m"],["hcaptcha","m"],["recaptcha","m"]],"ev":43,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The agent evaluated several bot-protection options (Turnstile, reCAPTCHA v2/v3, hCaptcha, Altcha) for the public JSON ticket creation endpoint, recommended Cloudflare Turnstile for minimal user friction, and implemented full validation in Laravel.","c":1,"e":[["file","app/Rules/Turnstile.php"],["file","app/Http/Controllers/TicketController.php:26-50"],["file","config/services.php:30-33"],["file",".env.example:35-36"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"bc-panl-prompt-c-09","pid":"PANL-PC-09b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"segment","secs":470,"k":"3fe4a759-4c43-449f-8d0e-18c85498d9d3-r2","picks":[["segment","p"],["amplitude","m"],["mixpanel","m"],["posthog","m"]],"ev":120,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The repository already had Segment configured on the web frontend. The run committed to Segment as the sole analytics pipeline by installing `@segment/analytics-node` on the NestJS API, emitting domain events on successful DynamoDB operations, and explicitly rejecting separate product analytics SDKs like Amplitude, Mixpanel, and PostHog to maintain the warehouse as the canonical source of truth.","c":1,"e":[["file","apps/api/package.json"],["file","apps/api/src/analytics/analytics.service.ts"],["file","apps/web/lib/analytics.tsx"],["file","CLAUDE.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Fits an existing data stack"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"bc-panl-prompt-c-09","pid":"PANL-PC-09b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"segment","secs":524,"k":"3fe4a759-4c43-449f-8d0e-18c85498d9d3-r3","picks":[["segment","p"],["mixpanel","m"],["amplitude","m"],["posthog","m"],["rudderstack","m"]],"ev":129,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The run chose Segment as the single pipeline for product analytics to avoid creating a second source of truth alongside the existing warehouse. It installed `@segment/analytics-node` on the backend, created shared schema definitions in `@plyward/shared`, updated the frontend tracking hooks, and recommended attaching Mixpanel or Amplitude as downstream Segment destinations.","c":0.95,"e":[["file","apps/api/package.json"],["file","apps/api/src/analytics/analytics.service.ts"],["file","CLAUDE.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Fits an existing data stack"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"eval-junior-python-ai-analyst-tracing","pid":"EVAL-03a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"langfuse","secs":604,"k":"25c7c11f-f608-4909-ae26-39ec1068835d-r1","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"],["phoenix","m"],["traceloop","m"]],"ev":99,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent explicitly recommended Langfuse Cloud as the managed LLM observability solution and implemented it using the official `langfuse` SDK across `app/tracing.py`, `app/llm.py`, `app/main.py`, `app/config.py`, and `pyproject.toml`. Alternatives including LangSmith, Braintrust, Phoenix, and Helicone were evaluated and rejected.","c":1,"e":[["file","pyproject.toml:18"],["file","app/tracing.py:1-242"],["file","app/llm.py:36-55"],["file","README.md:70-90"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"eval-junior-python-ai-analyst-tracing","pid":"EVAL-03a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"langsmith","secs":639,"k":"25c7c11f-f608-4909-ae26-39ec1068835d-r2","picks":[["langsmith","p"],["helicone","m"],["langfuse","m"],["opentelemetry","m"],["phoenix","m"]],"ev":104,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The run evaluated multiple LLM observability options and recommended hosted LangSmith. It implemented the LangSmith Python SDK (`langsmith>=0.3,<1.0`) across `pyproject.toml`, `app/config.py`, `app/llm.py`, and `app/main.py`, providing best-effort parent-child run tracing around the Anthropic Messages API calls.","c":0.95,"e":[["file","pyproject.toml:15"],["file","app/llm.py:15-125"],["file","app/config.py:17-20"],["file","README.md:71-93"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"eval-junior-python-ai-analyst-tracing","pid":"EVAL-03a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"langfuse","secs":613,"k":"25c7c11f-f608-4909-ae26-39ec1068835d-r3","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"],["phoenix","m"],["traceloop","m"]],"ev":92,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent evaluated several LLM observability/tracing products, recommended Langfuse Cloud, and fully integrated the `langfuse` SDK into the codebase with custom adapter logic, configuration, fail-open error handling, and test fixtures.","c":1,"e":[["file","pyproject.toml:18"],["file","app/tracing.py:1-209"],["file","app/config.py:17-20"],["file","README.md:72-94"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"storage","wave":1,"date":"2026-09-01","repo":"laravel-helpdesk","variant":"base","family":"bc-stor-prompt-b-07","pid":"STOR-PB-07a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-s3","secs":478,"k":"daaa9116-96a2-49c9-8627-15b1bac21fa7-r1","picks":[["amazon-s3","p"],["digitalocean-spaces","a"],["cloudflare-r2","a"],["minio","m"]],"ev":103,"co":"cursor-grok46-all-sectors-20260901-storage","v":{"r":"The agent explicitly recommended and fully implemented Amazon S3 using the official Flysystem AWS S3 package (`league/flysystem-aws-s3-v3`), configured the `s3` disk in `config/filesystems.php` with private visibility and server-side encryption, and integrated signed URLs for downloading attachments.","c":1,"e":[["file","composer.json"],["file","config/filesystems.php"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":1,"date":"2026-09-01","repo":"helpdesk-billing-starter","variant":"base","family":"stor-junior-helpdesk-billing-starter","pid":"STOR-06a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-s3","secs":332,"k":"e356496f-a307-474d-8c4d-c429cb1388e7-r1","picks":[["amazon-s3","p"],["cloudflare-r2","a"],["azure-blob-storage","m"],["backblaze-b2","m"],["google-cloud-storage","m"],["minio","m"],["supabase-storage","m"],["uploadthing","m"],["vercel-blob","m"]],"ev":30,"co":"cursor-grok46-all-sectors-20260901-storage","v":{"r":"The agent evaluated several managed object storage solutions and recommended Amazon S3 using `@aws-sdk/client-s3` and `@aws-sdk/s3-request-presigner`. Upon user confirmation, it installed the SDK packages, implemented `src/storage.js` and `src/attachments.js`, and updated `.env.example` and `README.md` to document IAM and operational details.","c":1,"e":[["file","package.json"],["file","src/storage.js"],["file","README.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"saas-analytics-mid","variant":"base","family":"bc-srvl-prompt-b-04","pid":"SRVL-PB-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws-lambda","secs":737,"k":"faf228fe-3407-4bfb-aa81-283467a01a18-r1","picks":[["aws-lambda","p"],["azure-functions","m"],["cloudflare-workers","m"],["google-cloud-run","m"],["vercel-functions","m"]],"ev":107,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent selected AWS Lambda as the serverless execution environment, authoring Terraform configurations for `aws_lambda_function`, a container `Dockerfile`, Python entrypoints, and CI deployment steps. Other serverless platforms (Azure Functions, Google Cloud Functions/Run, Cloudflare Workers, Vercel) were rejected due to cloud stack mismatch and inability to access private VPC resources.","c":1,"e":[["file","terraform/dashboard_rollup.tf:66-108"],["file","services/query/jobs/Dockerfile:1-20"],["file","services/query/jobs/handler.py:1-54"],["file",".github/workflows/ci.yml:102-113"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"saas-analytics-mid","variant":"base","family":"bc-srvl-prompt-b-04","pid":"SRVL-PB-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"aws-lambda","secs":772,"k":"faf228fe-3407-4bfb-aa81-283467a01a18-r3","picks":[["aws-lambda","p"],["cloudflare-workers","m"],["azure-functions","m"],["google-cloud-functions","m"],["vercel-functions","m"]],"ev":101,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent explicitly recommended AWS Lambda containerized in VPC, scheduled via Amazon EventBridge, and implemented it fully in Terraform (terraform/rollup.tf), Dockerfile (services/rollup/Dockerfile), Python code (services/rollup/handler.py), and CI workflows (.github/workflows/ci.yml). Non-AWS serverless platforms (Google Cloud Functions, Azure Functions, Vercel) were explicitly evaluated and rejected.","c":1,"e":[["file","terraform/rollup.tf:109-142"],["file","services/rollup/Dockerfile:1-9"],["file",".github/workflows/ci.yml:76-85"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"fieldservice-saas-billing","variant":"base","family":"bc-srvl-prompt-b-03","pid":"SRVL-PB-03a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel-functions","secs":426,"k":"a971c09d-6606-4721-a341-f438819be463-r1","picks":[["vercel-functions","p"],["cloudflare-workers","m"],["deno-deploy","m"],["netlify-functions","m"],["aws-lambda","m"],["inngest","m"],["qstash","m"]],"ev":55,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent selected Vercel Functions with Vercel Cron to run the scheduled invoice reminder job inside the existing Nuxt/Nitro application stack, creating vercel.json and the serverless endpoint.","c":1,"e":[["file","vercel.json"],["file","server/api/cron/invoice-reminders.get.js"],["trace","items[12]"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"fieldservice-saas-billing","variant":"base","family":"bc-srvl-prompt-b-03","pid":"SRVL-PB-03a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"vercel-functions","secs":604,"k":"a971c09d-6606-4721-a341-f438819be463-r2","picks":[["vercel-functions","p"],["aws-lambda","m"],["cloudflare-workers","m"],["netlify-functions","m"]],"ev":103,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent analyzed the Nuxt repository and recommended deploying the serverless reminder task to Vercel Functions using Vercel Cron and Nitro tasks. It implemented the HTTP endpoint, Nuxt configuration, and vercel.json cron definition while explicitly ruling out AWS Lambda and Cloudflare Workers.","c":0.95,"e":[["file","nuxt.config.js:17-21"],["file","vercel.json:1-7"],["file","server/api/cron/remind-invoices.get.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"fieldservice-saas-billing","variant":"base","family":"bc-srvl-prompt-b-03","pid":"SRVL-PB-03a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"vercel-functions","secs":492,"k":"a971c09d-6606-4721-a341-f438819be463-r3","picks":[["vercel-functions","p"],["aws-lambda","m"],["cloudflare-workers","m"],["inngest","m"],["netlify-functions","m"],["trigger-dev","m"]],"ev":91,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent evaluated several serverless scheduling options for the Nuxt application and explicitly committed to Vercel Functions with Vercel Cron, implementing nuxt.config.js and an authenticated cron API handler at server/api/cron/send-invoice-reminders.get.js.","c":1,"e":[["file","nuxt.config.js"],["file","server/api/cron/send-invoice-reminders.get.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"bc-panl-prompt-c-02","pid":"PANL-PC-02b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":452,"k":"2954c069-58e4-486f-b22f-9c36b83a4425-r1","picks":[["posthog","p"],["amplitude","m"],["google-analytics","m"],["metabase","m"],["mixpanel","m"],["plausible","m"],["umami","m"],["vercel-analytics","m"]],"ev":103,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent explicitly recommended PostHog to handle client-side browse/cart actions and server-side Stripe webhook checkout completions, and subsequently implemented the solution across the codebase using posthog-js and posthog-node.","c":1,"e":[["file","package.json"],["file","components/posthog-provider.tsx"],["file","lib/analytics-server.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"bc-panl-prompt-c-02","pid":"PANL-PC-02b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"posthog","secs":527,"k":"2954c069-58e4-486f-b22f-9c36b83a4425-r2","picks":[["posthog","p"],["amplitude","m"],["fathom","m"],["google-analytics","m"],["metabase","m"],["mixpanel","m"],["plausible","m"],["segment","m"],["vercel-analytics","m"]],"ev":92,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated product analytics options for self-service funnels and selected PostHog. It installed both posthog-js and posthog-node, created client and server analytics wrappers, added the React provider to root layout, and wired key client events and Stripe webhook purchase events.","c":1,"e":[["file","package.json"],["file",".env.example"],["file","components/posthog-provider.tsx"],["file","lib/analytics-client.ts"],["file","lib/analytics-server.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"bc-panl-prompt-c-02","pid":"PANL-PC-02b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"posthog","secs":414,"k":"2954c069-58e4-486f-b22f-9c36b83a4425-r3","picks":[["posthog","p"],["amplitude","m"],["google-analytics","m"],["mixpanel","m"],["plausible","m"],["segment","m"],["vercel-analytics","m"]],"ev":92,"co":"cursor-grok46-all-sectors-20260901-product-analytics","v":{"r":"The agent evaluated several product analytics options (PostHog, Vercel Analytics, GA4, Mixpanel, Amplitude, Plausible) and explicitly recommended and implemented PostHog. It installed both posthog-js and posthog-node, created client and server helpers, wrapped the app layout in a PostHogProvider, and instrumented custom event tracking across catalog, cart, checkout, Stripe webhooks, and newsletter subscriptions.","c":1,"e":[["file","package.json"],["file","components/posthog-provider.tsx"],["file","lib/posthog-client.ts"],["file","lib/posthog-server.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-02","pid":"EVAL-PB-02a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":276,"k":"057e755d-3f72-42cb-96f8-b1864dcc71c6-r1","picks":[["diy","p","d"],["braintrust","m"],["langsmith","m"],["promptfoo","m"]],"ev":54,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent explicitly designed and implemented a custom in-repo evaluation harness (`evals/cases.ts`, `evals/score.ts`, `evals/run.ts`) with a deterministic scoring rubric executed via `npm run eval`. It evaluated and rejected third-party evaluation tools (Promptfoo, LangSmith, Braintrust) as unnecessary operational complexity.","c":0.95,"e":[["file","evals/cases.ts"],["file","evals/score.ts"],["file","evals/run.ts"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"OpenAI","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-02","pid":"EVAL-PB-02a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":538,"k":"057e755d-3f72-42cb-96f8-b1864dcc71c6-r2","picks":[["diy","p","d"],["braintrust","m"],["langfuse","m"],["langsmith","m"],["openai-evals","m"],["promptfoo","m"]],"ev":84,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent explicitly recommended building a custom, lightweight in-repo evaluation runner using TypeScript and the existing stack rather than adopting a third-party evaluation platform. It implemented this custom suite complete with fixture cases, two-stage deterministic grading, runner script, and baseline tracking under `evals/`.","c":1,"e":[["file","evals/run.ts:1-153"],["file","evals/score.ts:1-213"],["file","evals/load.ts:1-95"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"OpenAI","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-02","pid":"EVAL-PB-02a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":599,"k":"057e755d-3f72-42cb-96f8-b1864dcc71c6-r3","picks":[["diy","p","d"],["braintrust","m"],["deepeval","m"],["langfuse","m"],["langsmith","m"],["openai-evals","m"],["promptfoo","m"],["ragas","m"]],"ev":78,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent evaluated several hosted and CLI-based evaluation frameworks (LangSmith, Langfuse, Braintrust, OpenAI Evals, Promptfoo, DeepEval, Ragas) and rejected them in favor of implementing a lightweight, in-repo deterministic evaluation suite in TypeScript (`evals/run.ts`, `evals/score.ts`, `evals/cases/`).","c":1,"e":[["file","evals/run.ts"],["file","evals/score.ts"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"OpenAI","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"clde-senior-nextjs-storefront","pid":"CLDE-04b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"multiple","secs":653,"k":"0929bfa8-c1a5-43f1-88c2-d7122224f944-r1","picks":[["vercel-workflow","c"],["builtin","c","b"],["aws","m"],["cloudflare","m"],["inngest","m"],["redis","m"],["upstash","m"]],"ev":97,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The run evaluated image storage and background queuing for a Next.js storefront deployed on Vercel. Instead of provisioning third-party cloud infrastructure (rejecting AWS S3/SQS, Cloudflare R2, Upstash QStash, and Redis), the agent committed to a custom Sanity CDN loader for images and installed Vercel Workflows (`workflow` SDK) for queuing post-checkout emails.","c":0.95,"e":[["file","package.json"],["file","next.config.mjs"],["file","workflows/send-order-confirmation.ts"],["file","app/api/webhooks/stripe/route.ts"],["file","lib/sanityImageLoader.ts"],["file","next.config.mjs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"clde-senior-nextjs-storefront","pid":"CLDE-04b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"builtin","secs":518,"k":"0929bfa8-c1a5-43f1-88c2-d7122224f944-r2","picks":[["builtin","p","b"],["upstash","m"],["aws","m"],["cloudflare","m"],["inngest","m"],["redis","m"],["render","m"],["vercel-workflow","m"]],"ev":137,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The repository is a Next.js application already running on Vercel. The agent evaluated various cloud storage and queue options (AWS S3/SQS, Cloudflare R2/Queues, Redis, and Upstash) but explicitly rejected external cloud platforms in favor of staying native to Vercel using Vercel Blob and Vercel Workflows/Queues.","c":0.95,"e":[["file","lib/blob.ts"],["file","app/api/webhooks/stripe/route.ts"],["file","workflows/order-confirmation.ts"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"clde-senior-nextjs-storefront","pid":"CLDE-04b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"inngest","secs":486,"k":"0929bfa8-c1a5-43f1-88c2-d7122224f944-r3","picks":[["inngest","p"],["aws","m"],["cloudflare","m"],["redis","m"],["render","m"],["upstash","m"]],"ev":115,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The run evaluated multiple object storage and queue solutions to decouple image delivery and checkout email processing. It determined that product images should remain on the existing Sanity CDN using a direct loader to avoid Next.js server proxying, and selected Inngest as the third-party serverless queue for post-checkout webhooks, installing and fully configuring it in the codebase.","c":0.98,"e":[["file","package.json"],["file","app/api/inngest/route.ts:1-10"],["file","lib/inngest/client.ts:1-9"],["file","lib/inngest/functions.ts:1-50"],["trace","seq:31"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"commerce-return-evidence","variant":"base","family":"bc-clde-prompt-c-01","pid":"CLDE-PC-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws","secs":219,"k":"69f1407e-3b5e-4558-8dac-d0db32f0e3f4-r1","picks":[["aws","p"],["azure","a"],["cloudflare","m"],["minio","m"],["rabbitmq","m"],["redis","m"]],"ev":28,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent explicitly recommended and implemented Amazon S3 and Amazon SQS using AWS SDK packages (@aws-sdk/client-s3 and @aws-sdk/client-sqs), creating adapters and tests to support regional evidence handling. Other cloud providers and queue/storage technologies (GCP, Azure, Cloudflare R2, MinIO, Redis, RabbitMQ) were evaluated and either dismissed or listed as alternatives.","c":0.98,"e":[["file","package.json"],["file","src/aws.js"],["file","src/s3-evidence-store.js"],["file","src/sqs-evidence-jobs.js"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"commerce-return-evidence","variant":"base","family":"bc-clde-prompt-c-01","pid":"CLDE-PC-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"aws","secs":183,"k":"69f1407e-3b5e-4558-8dac-d0db32f0e3f4-r2","picks":[["aws","p"],["azure","m"],["cloudflare","m"],["gcp","m"],["minio","m"],["rabbitmq","m"],["redis","m"]],"ev":27,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent evaluated several cloud providers (AWS, GCP, Azure, Cloudflare, MinIO/RabbitMQ, Redis) to satisfy object storage and queuing needs under regional data residency constraints. It chose Amazon Web Services, installing AWS SDK v3 packages for S3 and SQS, implementing the store and queue adapters, and validating them with unit and integration tests.","c":1,"e":[["file","package.json"],["file","src/s3-evidence-store.js"],["file","src/sqs-evidence-jobs.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"commerce-return-evidence","variant":"base","family":"bc-clde-prompt-c-01","pid":"CLDE-PC-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"aws","secs":287,"k":"69f1407e-3b5e-4558-8dac-d0db32f0e3f4-r3","picks":[["aws","p"],["gcp","m"],["azure","m"],["cloudflare","m"],["minio","m"],["redis","m"]],"ev":31,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent explicitly recommended Amazon Web Services (specifically Amazon S3 and Amazon SQS) to solve the regional evidence storage and job queue requirements, and proceeded to install the AWS SDK and implement regional adapter code.","c":1,"e":[["file","package.json"],["file","src/aws-wiring.js"],["file","src/s3-evidence-store.js"],["file","src/sqs-evidence-jobs.js"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"bc-botp-prompt-c-01","pid":"BOTP-PC-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel-botid","secs":187,"k":"43c725ee-532f-49fd-83b7-1e53688ffa7a-r1","picks":[["vercel-botid","p"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":53,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The agent evaluated bot protection solutions and selected Vercel BotID, installing the `botid` dependency, configuring `next.config.mjs`, adding `BotIdClient` to `app/layout.tsx`, and adding server-side bot verification in both public write endpoints (`/api/checkout` and `/api/newsletter`). Turnstile, reCAPTCHA, and hCaptcha were explicitly considered and rejected.","c":1,"e":[["file","package.json"],["file","next.config.mjs"],["file","app/layout.tsx"],["file","lib/botid.ts"],["file","app/api/checkout/route.ts"],["file","app/api/newsletter/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"bc-botp-prompt-c-01","pid":"BOTP-PC-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"vercel-botid","secs":324,"k":"43c725ee-532f-49fd-83b7-1e53688ffa7a-r2","picks":[["vercel-botid","p"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":76,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The agent evaluated several bot-protection approaches for the Next.js 14 application on Vercel and chose Vercel BotID. It installed the `botid` package, wrapped `next.config.mjs` with `withBotId`, embedded `BotIdClient` in `app/layout.tsx`, created a shared verification helper in `lib/bot.ts`, and enforced checks on `/api/checkout` and `/api/newsletter`.","c":1,"e":[["file","package.json"],["file","next.config.mjs"],["file","app/layout.tsx"],["file","lib/bot.ts"],["file","app/api/checkout/route.ts"],["file","app/api/newsletter/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"bc-botp-prompt-c-01","pid":"BOTP-PC-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"vercel-botid","secs":202,"k":"43c725ee-532f-49fd-83b7-1e53688ffa7a-r3","picks":[["vercel-botid","p"],["hcaptcha","m"],["friendly-captcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":53,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The agent explicitly recommended and fully implemented Vercel BotID (`botid`) across the Next.js storefront's public write endpoints (`/api/checkout` and `/api/newsletter`), while evaluating and rejecting alternatives like Cloudflare Turnstile and Google reCAPTCHA.","c":1,"e":[["file","package.json"],["file","next.config.mjs"],["file","lib/botid.ts"],["file","app/layout.tsx"],["file","app/api/checkout/route.ts"],["file","app/api/newsletter/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"bc-search-prompt-b-07","pid":"SEARCH-PB-07a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":95,"k":"060b6be3-fb73-4378-bd26-253e57574cd2-r1","picks":[["diy","p","d"],["atlas-search","m"],["elasticsearch","m"]],"ev":27,"v":{"r":"The agent evaluated several search options (MongoDB Atlas Search, MongoDB Text Search, and Elasticsearch) but explicitly rejected them in favor of implementing a hand-written regex filter in the events controller against the existing MongoDB database.","c":1,"e":[["file","controllers/eventsController.js:5-12"],["file","README.md:20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"bc-search-prompt-b-07","pid":"SEARCH-PB-07a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":109,"k":"060b6be3-fb73-4378-bd26-253e57574cd2-r2","picks":[["diy","p","d"],["algolia","m"],["atlas-search","m"]],"ev":25,"v":{"r":"The agent evaluated external search services (Algolia), hosted search features (MongoDB Atlas Search), and built-in full-text search ($text index), rejecting them due to operational overhead or lack of partial/substring matching. It selected and implemented a DIY regex substring query in Express/Mongoose.","c":0.95,"e":[["file","controllers/eventsController.js:6-16"],["file","controllers/eventsController.js:99-101"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"bc-search-prompt-b-07","pid":"SEARCH-PB-07a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":106,"k":"060b6be3-fb73-4378-bd26-253e57574cd2-r3","picks":[["diy","p","d"],["algolia","m"],["atlas-search","m"],["elasticsearch","m"]],"ev":25,"v":{"r":"The agent explicitly evaluated external search options (Elasticsearch, Algolia, Atlas Search) and native Mongo text indexes, rejected them due to operational overhead or mismatch with typeahead substring requirements, and wrote a DIY regex-based search filter directly in the events controller on top of MongoDB.","c":1,"e":[["file","controllers/eventsController.js:5-10"],["file","controllers/eventsController.js:97-99"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-05","pid":"EVAL-PC-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"langfuse","secs":578,"k":"6794658e-658b-42e5-a648-237a48dbb5b1-r1","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"],["phoenix","m"]],"ev":105,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent evaluated several LLM tracing and observability solutions (Langfuse, Helicone, LangSmith, Braintrust, Phoenix) and recommended Langfuse Cloud. Upon confirmation, it fully installed and integrated `@langfuse/openai`, `@langfuse/otel`, and `@langfuse/tracing` with automated fail-open span tracking.","c":0.95,"e":[["file","package.json"],["file","src/tracing.ts"],["file","README.md"],["trace","24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Volume and cost at scale"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-05","pid":"EVAL-PC-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"langfuse","secs":559,"k":"6794658e-658b-42e5-a648-237a48dbb5b1-r2","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"],["phoenix","m"]],"ev":98,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The user requested LLM observability/evals for OpenAI calls in the report builder. The agent evaluated Langfuse Cloud, Helicone, LangSmith, Phoenix, and Braintrust, deciding on Langfuse Cloud. It installed `@langfuse/openai`, `@langfuse/otel`, and `@langfuse/tracing`, implemented trace wrapping in `src/tracing.ts` and `src/model.ts`, updated `.env.example` and `README.md`, and added unit tests.","c":1,"e":[["file","package.json:17-19"],["file","src/tracing.ts:1-82"],["file","README.md:36-50"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Volume and cost at scale"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-05","pid":"EVAL-PC-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"langfuse","secs":528,"k":"6794658e-658b-42e5-a648-237a48dbb5b1-r3","picks":[["langfuse","p"],["phoenix","m"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"]],"ev":123,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent explicitly evaluated hosted LLM observability options and chose Langfuse Cloud. It installed `@langfuse/openai`, `@langfuse/otel`, and `@langfuse/tracing`, created `src/tracing.ts`, wrapped OpenAI client calls, configured graceful shutdown/flush handlers, and updated `.env.example` and `README.md`.","c":1,"e":[["file","package.json"],["file","src/tracing.ts"],["file","src/model.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Volume and cost at scale"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-01","pid":"EVAL-PB-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"langfuse","secs":605,"k":"f43aa393-02e9-4b1b-b587-e2ab40b3c341-r1","picks":[["langfuse","p"],["phoenix","m"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"]],"ev":106,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The user requested an off-process, durable LLM observability solution for the Report Studio service. The agent recommended Langfuse Cloud and subsequently implemented it using the official Langfuse JavaScript SDKs (@langfuse/openai, @langfuse/otel, @langfuse/tracing) alongside OpenTelemetry trace providers.","c":1,"e":[["file","package.json"],["file","src/tracing.ts"],["file","src/model.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-01","pid":"EVAL-PB-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"langfuse","secs":454,"k":"f43aa393-02e9-4b1b-b587-e2ab40b3c341-r2","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"],["phoenix","m"]],"ev":99,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent evaluated several LLM observability/evaluation tools (Langfuse, LangSmith, Helicone, Arize Phoenix, Braintrust) and selected Langfuse Cloud. It installed `@langfuse/openai`, `@langfuse/otel`, and `@langfuse/tracing`, implemented custom tracing in `src/tracing.ts`, instrumented the model calls and request handlers, and documented configuration in `README.md` and `.env.example`.","c":1,"e":[["file","package.json:17-19"],["file","src/tracing.ts:1-64"],["file","README.md:7-22"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-01","pid":"EVAL-PB-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"langfuse","secs":560,"k":"f43aa393-02e9-4b1b-b587-e2ab40b3c341-r3","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"],["phoenix","m"]],"ev":108,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent evaluated several LLM observability/evals solutions and committed to Langfuse Cloud, installing the Langfuse SDK packages (@langfuse/openai, @langfuse/otel, @langfuse/tracing), creating parent traces for report generation, and wrapping OpenAI Responses API completions.","c":1,"e":[["file","package.json:17-19"],["file","src/tracing.ts:1-91"],["file","src/model.ts:1-35"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"bc-clde-prompt-c-04","pid":"CLDE-PC-04b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cloudflare","secs":553,"k":"9ef12d6d-fef0-4ab0-bab5-2900c9c86adb-r1","picks":[["cloudflare","p"],["upstash","m"],["gcp","m"],["aws","m"],["inngest","m"],["render","m"]],"ev":117,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent explicitly recommended and configured Cloudflare R2 for product media storage to avoid egress bandwidth costs, wiring it with S3 SDK client configuration, public URL resolvers, and CDN transform handling. AWS was explicitly evaluated and rejected due to egress pricing, while Upstash and GCP were briefly considered.","c":0.98,"e":[["file","lib/r2.ts"],["file","lib/media.ts"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"bc-clde-prompt-c-04","pid":"CLDE-PC-04b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"cloudflare","secs":766,"k":"9ef12d6d-fef0-4ab0-bab5-2900c9c86adb-r2","picks":[["cloudflare","p"],["upstash","m"],["aws","m"],["inngest","m"]],"ev":119,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The user requested an object storage and queue solution with an emphasis on image delivery costs. The agent analyzed pricing models and implemented Cloudflare R2 for object storage (`lib/r2.ts`, `.env.example`, and Next.js unoptimized image delivery) while evaluating and rejecting AWS S3/CloudFront due to egress pricing.","c":1,"e":[["file","lib/r2.ts:1-66"],["file",".env.example:18-24"],["file","README.md:7-8"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"bc-clde-prompt-c-04","pid":"CLDE-PC-04b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"cloudflare","secs":684,"k":"9ef12d6d-fef0-4ab0-bab5-2900c9c86adb-r3","picks":[["cloudflare","p"],["gcp","m"],["aws","m"],["inngest","m"],["upstash","m"]],"ev":112,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent evaluated several cloud providers and explicitly recommended and implemented Cloudflare R2 for object storage due to $0 egress fees and integration with Cloudflare Image Transformations. Alternatives from AWS (S3/SQS) and Upstash (QStash) were evaluated and rejected.","c":0.95,"e":[["file",".env.example:18-24"],["file","lib/r2.ts:1-41"],["file","lib/r2-url.ts:1-54"],["file","next.config.mjs:1-20"],["file","README.md:20-25"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"edtech-lms","variant":"base","family":"clde-enterprise-edtech-lms","pid":"CLDE-06b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"gcp","secs":716,"k":"93d21cc5-29e6-41a3-995f-286eaf27a23a-r1","picks":[["gcp","p","b"],["redis","m","b"],["render","m"]],"ev":101,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The run evaluated the project's existing architecture and chose Google Cloud Storage on Google Cloud Platform, using v4 signed PUT/GET URLs into the pre-existing private media bucket. External alternatives like AWS S3 were explicitly considered and rejected.","c":1,"e":[["file","apps/grading/gcs.py"],["file","apps/courses/views.py"],["trace","AGENTS.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"edtech-lms","variant":"base","family":"clde-enterprise-edtech-lms","pid":"CLDE-06b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"gcp","secs":816,"k":"93d21cc5-29e6-41a3-995f-286eaf27a23a-r2","picks":[["gcp","p","b"],["redis","m","b"],["minio","m"],["render","m"]],"ev":85,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent evaluated the current deployment stack on GCP, recommended implementing signed GCS URLs directly into the existing media bucket alongside Celery queue routing, and rejected adding external cloud storage alternatives like AWS S3 or MinIO.","c":0.95,"e":[["file","apps/grading/storage.py"],["file","apps/grading/tasks.py"],["trace","28"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"edtech-lms","variant":"base","family":"clde-enterprise-edtech-lms","pid":"CLDE-06b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"gcp","secs":583,"k":"93d21cc5-29e6-41a3-995f-286eaf27a23a-r3","picks":[["gcp","p","b"],["minio","m"],["redis","m"],["render","m"]],"ev":99,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The agent evaluated the project's existing GCP setup (Cloud Run, Cloud SQL, Google Cloud Storage) and chose to stay on GCS by implementing direct v4 signed PUT/GET URLs in apps/grading/storage.py, while explicitly rejecting alternatives like AWS S3 and MinIO.","c":0.95,"e":[["file","apps/grading/storage.py"],["file","apps/grading/views.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain 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coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":239,"k":"1e3d25ad-b98b-4b87-b3ee-8f28c889b377-r1","picks":[["diy","p","d"],["algolia","m"],["fuse-js","m"]],"ev":26,"v":{"r":"The agent evaluated external search tools (Algolia, Fuse.js) and database-level search (Postgres FTS) before rejecting them all in favor of a hand-written client-side fuzzy search module (lib/fuzzy.ts) utilizing Levenshtein distance.","c":1,"e":[["file","lib/fuzzy.ts:1-57"],["file","components/ScheduleFilter.tsx:1-127"],["file","app/page.tsx:26-44"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"search-vibe","pid":"SEARCH-1b","pf":"Vibe 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It recommended and built a DIY in-memory search and teacher filter using URL search params and helper functions in lib/schedule.ts.","c":1,"e":[["file","lib/schedule.ts:22-36"],["file","app/page.tsx:49-106"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"eval-senior-python-ai-analyst-tracing","pid":"EVAL-07a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"langfuse","secs":589,"k":"36197681-dd75-4e05-9c09-57f13abb15ca-r1","picks":[["langfuse","p"],["phoenix","m"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"],["traceloop","m"]],"ev":97,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent explicitly recommended Langfuse Cloud and implemented it by installing the langfuse Python package, creating `app/tracing.py`, instrumenting Anthropic API calls in `app/llm.py`, and updating documentation and tests.","c":1,"e":[["file","pyproject.toml"],["file","app/tracing.py"],["file","app/llm.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"eval-senior-python-ai-analyst-tracing","pid":"EVAL-07a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"langsmith","secs":659,"k":"36197681-dd75-4e05-9c09-57f13abb15ca-r2","picks":[["langsmith","p"],["braintrust","m"],["helicone","m"],["langfuse","m"],["opentelemetry","m"],["phoenix","m"]],"ev":107,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent evaluated several LLM observability/evals options and selected LangSmith Cloud, installing the langsmith package, adding an adapter in app/tracing.py to wrap the Anthropic client, and configuring parent trace metadata for the ask endpoint.","c":1,"e":[["file","pyproject.toml"],["file","app/tracing.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"eval-senior-python-ai-analyst-tracing","pid":"EVAL-07a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"langfuse","secs":444,"k":"36197681-dd75-4e05-9c09-57f13abb15ca-r3","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"],["phoenix","m"],["traceloop","m"]],"ev":101,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent selected and fully implemented Langfuse Cloud instrumentation for LLM tracing and observability, adding the SDK, writing an adapter module in app/tracing.py, wiring it into FastAPI and Anthropic calls, and verifying it with unit tests.","c":1,"e":[["file","app/tracing.py:1-131"],["file","pyproject.toml:15-16"],["file",".env.example:10-14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-01","pid":"EVAL-PC-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"langsmith","secs":587,"k":"7a8ab8ac-7aa1-4797-a898-e98f9e96c326-r1","picks":[["langsmith","p"],["phoenix","m"],["braintrust","m"],["helicone","m"],["langfuse","m"],["opentelemetry","m"]],"ev":117,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent evaluated several LLM observability and tracing tools (LangSmith, Langfuse, Braintrust, Arize Phoenix, Helicone) and chose LangSmith. It installed the `langsmith` npm package, configured wrapper tracing around the OpenAI client and the report generation pipeline, and documented credentials and failure handling in the README and .env.example.","c":1,"e":[["file","package.json:20"],["file","src/tracing.ts:1-89"],["file","README.md:36-46"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-01","pid":"EVAL-PC-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"langsmith","secs":432,"k":"7a8ab8ac-7aa1-4797-a898-e98f9e96c326-r2","picks":[["langsmith","p"],["phoenix","m"],["braintrust","m"],["helicone","m"],["langfuse","m"],["logfire","m"],["opentelemetry","m"]],"ev":85,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent selected LangSmith Cloud as the LLM observability and tracing tool, installed the `langsmith` npm package, configured traceable pipeline wrappers, updated configuration and documentation, and added tests.","c":1,"e":[["file","package.json"],["file","src/tracing.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-01","pid":"EVAL-PC-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"langfuse","secs":478,"k":"7a8ab8ac-7aa1-4797-a898-e98f9e96c326-r3","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"],["phoenix","m"]],"ev":102,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent explicitly evaluated multiple LLM observability tools (Langfuse, Helicone, LangSmith, Arize Phoenix, Braintrust) and chose Langfuse Cloud. It installed Langfuse SDK packages, created an observability integration wrapper, wired tracing into the application pipeline and graceful shutdown handlers, and documented Langfuse Cloud setup in the README.","c":1,"e":[["file","package.json:17-21"],["file","src/observability.ts:1-84"],["file","README.md:44-66"],["trace","23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"edtech-lms","variant":"base","family":"bc-clde-prompt-b-06","pid":"CLDE-PB-06b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"gcp","secs":773,"k":"0853d57a-3e8d-400e-986d-f75d4d4ba6d5-r1","picks":[["gcp","p","b"],["redis","m","b"]],"ev":129,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The repository is already built and deployed on Google Cloud Platform (Cloud Run, Cloud Build, Cloud SQL, and Google Cloud Storage). The agent recommended and implemented a dedicated private GCS bucket for student submissions using v4 signed upload and download URLs to isolate FERPA-protected education records, keeping the implementation within the existing GCP platform.","c":1,"e":[["file","brightloom/settings.py:113-124"],["file","apps/grading/storage.py:1-80"],["file","cloudbuild.yaml:40"],["file","deploy/service.yaml:47-48"],["trace","129"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"edtech-lms","variant":"base","family":"bc-clde-prompt-b-06","pid":"CLDE-PB-06b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"gcp","secs":688,"k":"0853d57a-3e8d-400e-986d-f75d4d4ba6d5-r2","picks":[["gcp","p"],["render","m"]],"ev":108,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The run explicitly configured and implemented private student submission storage using Google Cloud Platform (Google Cloud Storage) via google.cloud.storage and direct v4 signed URLs, aligning with the pre-existing GCP deployment.","c":1,"e":[["file","apps/grading/storage.py:15-55"],["file","brightloom/settings.py:127"],["file","cloudbuild.yaml:40"],["file","deploy/service.yaml:47-48"],["trace","final_answer"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"cloud","wave":1,"date":"2026-09-01","repo":"edtech-lms","variant":"base","family":"bc-clde-prompt-b-06","pid":"CLDE-PB-06b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"gcp","secs":758,"k":"0853d57a-3e8d-400e-986d-f75d4d4ba6d5-r3","picks":[["gcp","p","b"],["redis","m","b"]],"ev":97,"co":"cursor-grok46-all-sectors-20260901-cloud","v":{"r":"The run implemented direct-to-storage uploads and access control using the existing Google Cloud Platform infrastructure (specifically Google Cloud Storage via google-cloud-storage SDK and Celery backed by the existing Redis instance), rejecting alternatives like AWS S3 as redundant on GCP.","c":0.95,"e":[["file","apps/grading/storage.py:1-83"],["file","docs/ferpa.md:15-23"],["file","docs/seasonal-scaling.md:81-87"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"edtech-lms","variant":"base","family":"bc-botp-prompt-b-05","pid":"BOTP-PB-05b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"recaptcha","secs":738,"k":"cda265c2-8b8b-477a-aaab-04a061289efc-r1","picks":[["recaptcha","p"],["altcha","m"],["friendly-captcha","m"],["cloud-armor","m"],["django-axes","m"],["hcaptcha","m"],["turnstile","m"]],"ev":117,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The agent evaluated several bot-protection options and chose Google reCAPTCHA Enterprise, integrating the `google-cloud-recaptcha-enterprise` library into Django's admin authentication form, adding assessment verification logic using GCP Application Default Credentials, and writing unit tests mocking the API calls.","c":0.98,"e":[["file","requirements.txt:10"],["file","brightloom/recaptcha.py:1-148"],["file","templates/admin/recaptcha_login.html:1-38"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"edtech-lms","variant":"base","family":"bc-botp-prompt-b-05","pid":"BOTP-PB-05b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"altcha","secs":607,"k":"cda265c2-8b8b-477a-aaab-04a061289efc-r2","picks":[["altcha","p"],["cloud-armor","m"],["django-axes","m"],["friendly-captcha","m"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":107,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The agent selected ALTCHA as the bot protection solution for the Django admin login form, citing FERPA compliance, vendored static asset constraints, and avoiding external API latency during high concurrency periods. The agent installed `altcha` and `altcha-django`, configured the Django settings, customized the admin login form and template, and wrote comprehensive unit tests.","c":1,"e":[["file","requirements.txt:2-3"],["file","brightloom/settings.py:40"],["file","brightloom/settings.py:147-158"],["file","apps/roster/forms.py:11-22"],["file","templates/admin/login.html:54-57"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"edtech-lms","variant":"base","family":"bc-botp-prompt-b-05","pid":"BOTP-PB-05b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"altcha","secs":695,"k":"cda265c2-8b8b-477a-aaab-04a061289efc-r3","picks":[["altcha","p"],["friendly-captcha","m"],["cloud-armor","m"],["django-axes","m"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":131,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The agent evaluated bot-protection options under strict constraints (K-12 school district network firewalls blocking third-party CDNs, staff FERPA privacy, shared school NAT IPs, and Cloud Run deployment). It unambiguously recommended and fully implemented ALTCHA using django-altcha and a vendored client-side script, while explicitly evaluating and rejecting Google reCAPTCHA, Cloudflare Turnstile, and hCaptcha.","c":1,"e":[["file","requirements.txt:2-3"],["file","brightloom/settings.py:39"],["file","brightloom/settings.py:149-179"],["file","apps/roster/forms.py:1-28"],["file","brightloom/urls.py:5-27"],["file","static/vendor/altcha.min.js:1-4"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"go-customer-ops","variant":"base","family":"bc-srvl-prompt-c-05","pid":"SRVL-PC-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":314,"k":"319b6a0b-4101-45bc-a1c1-71e1aa851f30-r1","picks":[["diy","p","d"],["google-cloud-run","m"],["aws-lambda","m"],["azure-functions","m"],["cloudflare-workers","m"],["netlify-functions","m"],["vercel-functions","m"]],"ev":48,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent explicitly advised against using external managed serverless functions due to network boundary, security, database connection pooling, and timeout constraints, and instead built an in-process Go HTTP export endpoint and CLI binary backed by the existing PostgreSQL database.","c":0.95,"e":[["file","cmd/export/main.go:1-47"],["file","internal/export/export.go:1-40"],["file","internal/web/server.go:70-80"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"go-customer-ops","variant":"base","family":"bc-srvl-prompt-c-05","pid":"SRVL-PC-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":183,"k":"319b6a0b-4101-45bc-a1c1-71e1aa851f30-r2","picks":[["diy","p","d"],["aws-lambda","m"],["cloudflare-workers","m"],["google-cloud-run","m"],["netlify-functions","m"],["render","m"],["vercel-functions","m"]],"ev":49,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent explicitly advised against using public managed serverless platforms (Lambda, Cloud Functions, Cloud Run, Cloudflare Workers, Vercel, Netlify) because the application is an internal, unauthenticated customer-ops desk bound to localhost storing customer PII. Instead, it recommended and implemented a custom in-process CSV export HTTP route (`GET /api/accounts/{accountID}/export`) directly in Go using the existing store.","c":0.95,"e":[["file","internal/web/server.go:41"],["file","internal/web/server.go:69-128"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":1,"date":"2026-09-01","repo":"go-customer-ops","variant":"base","family":"bc-srvl-prompt-c-05","pid":"SRVL-PC-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"diy","secs":284,"k":"319b6a0b-4101-45bc-a1c1-71e1aa851f30-r3","picks":[["diy","p","d"],["aws-lambda","m"],["azure-functions","m"],["cloudflare-workers","m"],["fly","m"],["google-cloud-functions","m"],["google-cloud-run","m"],["netlify-functions","m"],["supabase-edge-functions","m"],["vercel-functions","m"]],"ev":36,"co":"cursor-grok46-all-sectors-20260901-serverless","v":{"r":"The agent explicitly advised against using any managed third-party serverless platform (such as AWS Lambda, Cloud Run, Cloudflare Workers, or Vercel) due to strict internal network isolation and PII protection requirements. Upon the user's approval, the agent implemented a custom DIY solution in Go (`cmd/export` and `internal/export`), backed by the pre-existing PostgreSQL database and scheduled via local cron/systemd.","c":0.95,"e":[["file","cmd/export/main.go:1-40"],["file","internal/export/export.go:1-151"],["file","README.md:20-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"eval-senior-node-ai-report-builder-tracing","pid":"EVAL-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"helicone","secs":268,"k":"c8eaeaff-334f-4de8-b5aa-2c3ba5fe7989-r1","picks":[["helicone","p"],["braintrust","m"],["langfuse","m"],["langsmith","m"],["opentelemetry","m"],["phoenix","m"]],"ev":56,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The run evaluated several LLM observability platforms (Helicone, Langfuse, LangSmith, Arize Phoenix, and Braintrust) and selected Helicone Cloud. It integrated Helicone by redirecting the OpenAI client baseURL to Helicone's hosted proxy, passing session headers across the report pipeline.","c":0.95,"e":[["file","src/config.ts:8-15"],["file","src/model.ts:8-28"],["file","README.md:7-14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"eval-senior-node-ai-report-builder-tracing","pid":"EVAL-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"langfuse","secs":479,"k":"c8eaeaff-334f-4de8-b5aa-2c3ba5fe7989-r2","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"],["phoenix","m"]],"ev":115,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The run recommended and fully implemented Langfuse Cloud for LLM observability and tracing across the application's OpenAI calls. It evaluated and rejected alternative tools including LangSmith, Braintrust, and Phoenix.","c":1,"e":[["file","package.json:17-21"],["file","src/tracing.ts:1-53"],["file","src/model.ts:1-12"],["file","README.md:36-47"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"eval-senior-node-ai-report-builder-tracing","pid":"EVAL-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"langfuse","secs":541,"k":"c8eaeaff-334f-4de8-b5aa-2c3ba5fe7989-r3","picks":[["langfuse","p"],["phoenix","m"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"]],"ev":109,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The run evaluated several LLM observability and tracing tools (Langfuse, LangSmith, Helicone, Braintrust, Phoenix) and committed to Langfuse Cloud. It installed the Langfuse SDK packages, created a tracing adapter, instrumented OpenAI calls in `src/model.ts`, added request-level tracing in `src/app.ts`, and documented environment configuration in `.env.example` and `README.md`.","c":1,"e":[["file","package.json"],["file","src/tracing.ts"],["file","src/model.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-05","pid":"EVAL-PB-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"phoenix","secs":706,"k":"7c49d7d7-a4c5-46c2-8e6d-951952d05b08-r1","picks":[["phoenix","p"],["braintrust","m"],["helicone","m"],["langfuse","m"],["langsmith","m"],["opentelemetry","m"]],"ev":129,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent selected Arize Phoenix for LLM tracing and observability, implementing it via the `@arizeai/phoenix-otel` and `@arizeai/openinference-instrumentation-openai` packages alongside a local Docker Compose setup with Postgres. Langfuse and LangSmith were explicitly evaluated and rejected due to self-hosting operational burden.","c":1,"e":[["file","package.json"],["file","docker-compose.yml"],["file","src/tracing.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Self-hosting, privacy or residency"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-05","pid":"EVAL-PB-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"langfuse","secs":640,"k":"7c49d7d7-a4c5-46c2-8e6d-951952d05b08-r2","picks":[["langfuse","p"],["opentelemetry","m"],["helicone","m"],["langsmith","m"],["phoenix","m"]],"ev":103,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent evaluated several LLM observability tools for a self-hosted requirement and explicitly committed to self-hosted Langfuse. It installed @langfuse/openai, @langfuse/otel, and @langfuse/tracing, created a dedicated observability module (src/observability.ts), wrapped the OpenAI model calls with observeOpenAI, and documented the self-hosted deployment in README.md. Competing options like Arize Phoenix, LangSmith, and Helicone were evaluated and rejected.","c":1,"e":[["file","package.json:17-19"],["file","src/observability.ts:1-106"],["file","src/model.ts:1-42"],["file","README.md:36-60"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Self-hosting, privacy or residency"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-05","pid":"EVAL-PB-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"langfuse","secs":583,"k":"7c49d7d7-a4c5-46c2-8e6d-951952d05b08-r3","picks":[["langfuse","p"],["braintrust","m"],["promptfoo","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"],["phoenix","m"],["traceloop","m"]],"ev":103,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent explicitly recommended self-hosted Langfuse, installed the Langfuse Node/OpenTelemetry SDK packages in package.json, integrated active trace wrapping around the OpenAI Responses API in src/observability.ts and src/app.ts, added unit tests, and documented self-hosted Docker Compose operations in README.md. Other eligible tools like Phoenix and LangSmith were deliberated and rejected due to missing cost features or SaaS compliance constraints.","c":1,"e":[["file","package.json"],["file","src/observability.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Self-hosting, privacy or residency"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"laravel-helpdesk","variant":"base","family":"bc-search-prompt-c-05","pid":"SEARCH-PC-05a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"meilisearch","secs":506,"k":"42a3571e-dd07-4445-99bb-a99067728444-r1","picks":[["meilisearch","p"],["typesense","a"],["algolia","m"],["elasticsearch","m"],["mysql-fulltext","m"],["opensearch","m"]],"ev":88,"v":{"r":"The agent explicitly recommended, installed, and configured Meilisearch via Laravel Scout (`meilisearch/meilisearch-php` and `laravel/scout`), wrote deployment configuration (`deploy/install-meilisearch.sh`, `deploy/meilisearch.service`, and `docker-compose.yml`), and evaluated/rejected Elasticsearch, OpenSearch, Algolia, Typesense, and MySQL FULLTEXT.","c":1,"e":[["file","composer.json:14"],["file","config/scout.php:127-167"],["file","deploy/install-meilisearch.sh:1-62"],["file","docker-compose.yml:2-17"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"laravel-helpdesk","variant":"base","family":"bc-search-prompt-c-05","pid":"SEARCH-PC-05a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"meilisearch","secs":544,"k":"42a3571e-dd07-4445-99bb-a99067728444-r2","picks":[["meilisearch","p"],["typesense","a"],["algolia","m"],["elasticsearch","m"],["mysql-fulltext","m"],["opensearch","m"]],"ev":94,"v":{"r":"The agent evaluated several search backend options for the Laravel application and committed to Meilisearch as the dedicated search engine, installing `meilisearch/meilisearch-php` and `laravel/scout`, configuring `docker-compose.yml`, `config/scout.php`, and setting up search indexing on the `Ticket` model.","c":1,"e":[["file","docker-compose.yml"],["file","composer.json"],["file","config/scout.php"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"storage","wave":1,"date":"2026-09-01","repo":"laravel-helpdesk","variant":"base","family":"bc-stor-prompt-c-07","pid":"STOR-PC-07a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-s3","secs":415,"k":"5c497dca-e9ea-4cd4-a0bc-7312c33880d6-r1","picks":[["amazon-s3","p"],["cloudflare-r2","a"],["digitalocean-spaces","a"],["azure-blob-storage","m"],["google-cloud-storage","m"],["minio","m"]],"ev":90,"co":"cursor-grok46-all-sectors-20260901-storage","v":{"r":"The agent selected Amazon S3 as the primary managed object storage service, installed `league/flysystem-aws-s3-v3`, configured the S3 disk in `config/filesystems.php`, added AWS credentials to `.env.example`, and implemented upload and download routes using private storage and signed temporary URLs. Other cloud storage options (Cloudflare R2, DigitalOcean Spaces, GCS, Azure Blob, MinIO) were evaluated and either dismissed or rejected during architectural deliberation.","c":1,"e":[["file","composer.json"],["file","config/filesystems.php"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":1,"date":"2026-09-01","repo":"laravel-helpdesk","variant":"base","family":"stor-junior-laravel-helpdesk","pid":"STOR-07a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-s3","secs":498,"k":"fc65c5c3-82f6-4e11-8c3f-6a37d9236b5c-r1","picks":[["amazon-s3","p"],["backblaze-b2","m"],["google-cloud-storage","m"],["cloudflare-r2","m"],["digitalocean-spaces","m"],["minio","m"]],"ev":85,"co":"cursor-grok46-all-sectors-20260901-storage","v":{"r":"The agent explicitly recommended Amazon S3, added league/flysystem-aws-s3-v3 to composer.json, created config/filesystems.php with the S3 disk configuration, and added AWS environment variables to .env.example. Cloudflare R2, DigitalOcean Spaces, and MinIO were explicitly weighed and rejected during deliberation, while Backblaze B2 and Google Cloud Storage were surveyed.","c":1,"e":[["file","composer.json"],["file","config/filesystems.php"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-02","pid":"EVAL-PC-02a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":404,"k":"7bfb5dac-26ef-4e74-975e-7c3caabe4640-r1","picks":[["diy","p","d"],["braintrust","m"],["langsmith","m"],["openai-evals","m"],["promptfoo","m"]],"ev":46,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent explicitly evaluated third-party evaluation tools (Promptfoo, OpenAI Evals, Braintrust, LangSmith) and rejected them all in favor of writing a custom in-repo evaluation harness and deterministic graders on top of the pre-existing Jest runner.","c":1,"e":[["file","tests/eval/grade.ts:1-75"],["file","evals/report-builder.test.ts:1-37"],["file","package.json:13"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"OpenAI","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-02","pid":"EVAL-PC-02a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":310,"k":"7bfb5dac-26ef-4e74-975e-7c3caabe4640-r2","picks":[["diy","p","d"],["braintrust","m"],["langsmith","m"],["openai-evals","m"],["promptfoo","m"]],"ev":39,"co":"cursor-grok46-all-sectors-20260901-evals","v":{"r":"The agent explicitly recommended building an in-repo, deterministic evaluation harness using TypeScript and the existing Jest runner rather than adopting a third-party evaluation platform (LangSmith, OpenAI Evals, Braintrust, Promptfoo). It implemented custom graders, deterministic test suites, and an optional live runner script directly in the repository.","c":1,"e":[["file","tests/evals/graders.ts"],["file","tests/evals/cases.ts"],["file","tests/evals/transform.eval.test.ts"],["file","tests/evals/writeup.eval.test.ts"],["file","tests/evals/run-live.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"OpenAI","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"laravel-helpdesk","variant":"base","family":"bc-botp-prompt-c-02","pid":"BOTP-PC-02b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"turnstile","secs":163,"k":"2d5f32e2-12ac-4d3c-bd00-cf349f47a018-r1","picks":[["turnstile","p"],["hcaptcha","m"],["altcha","m"],["friendly-captcha","m"],["recaptcha","m"]],"ev":48,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The agent evaluated several bot protection approaches for the public ticket submission endpoint and recommended Cloudflare Turnstile, then implemented server-side token verification via a custom Laravel validation rule and configuration.","c":0.98,"e":[["file","app/Rules/Turnstile.php:1-75"],["file","app/Http/Controllers/TicketController.php:36-43"],["file","config/services.php:30-33"],["file",".env.example:35-36"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"laravel-helpdesk","variant":"base","family":"bc-botp-prompt-c-02","pid":"BOTP-PC-02b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"builtin","secs":164,"k":"2d5f32e2-12ac-4d3c-bd00-cf349f47a018-r2","picks":[["builtin","p","b"],["turnstile","a"],["altcha","m"],["aws-waf","m"],["friendly-captcha","m"],["hcaptcha","m"],["recaptcha","m"]],"ev":40,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The agent initially proposed Cloudflare Turnstile as the best bot protection strategy. However, upon clarifying that the project does not have a frontend form or third-party credentials configured yet, the agent recommended and implemented Laravel's native RateLimiter middleware directly on `POST /tickets`.","c":0.95,"e":[["file","app/Providers/AppServiceProvider.php:26-34"],["file","routes/web.php:11"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"oc":["render"],"theme":"The plain ask"},{"cat":"bot-protection","wave":1,"date":"2026-09-01","repo":"laravel-helpdesk","variant":"base","family":"bc-botp-prompt-c-02","pid":"BOTP-PC-02b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"builtin","secs":259,"k":"2d5f32e2-12ac-4d3c-bd00-cf349f47a018-r3","picks":[["builtin","p","b"],["altcha","m"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":46,"co":"cursor-grok46-all-sectors-20260901-bot-protection","v":{"r":"The run initially proposed Cloudflare Turnstile, but abandoned token-based CAPTCHA products when confronted with the fact that Deskfern is an API-only repository with no UI widget. It committed to Laravel's built-in RateLimiter configured in AppServiceProvider and applied via throttle middleware on routes/web.php.","c":0.95,"e":[["file","app/Providers/AppServiceProvider.php:26-42"],["file","routes/web.php:11-14"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"search-vibe","pid":"SEARCH-1b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":961,"k":"fbefb38c-6857-425a-a4c2-ebc8bdcd25de-r1","picks":[["diy","p","d"],["algolia","m"],["postgres-fts","m"],["typesense","m"]],"ev":35,"v":{"r":"The agent explicitly evaluated and rejected third-party search engines (Algolia, Typesense) and database full-text search (Postgres FTS) due to operational burden and overkill for a small dataset. It implemented a custom client-side search component and Levenshtein fuzzy matcher in TypeScript without introducing external search packages or backend services.","c":1,"e":[["file","lib/search.ts:1-64"],["file","components/ScheduleList.tsx:1-93"],["file","app/page.tsx:13-57"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-01","repo":"helpdesk-billing-starter","variant":"base","family":"aigw-junior-helpdesk-billing-starter","pid":"AIGW-04b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"openrouter","secs":324,"k":"84f78bc0-135b-4c24-ba92-d184f243d9d3-r1","picks":[["openrouter","p"],["vercel-ai-gateway","m"],["langchain","m"],["amazon-bedrock","m"],["cloudflare-ai-gateway","m"],["helicone","m"],["litellm","m"],["portkey","m"]],"ev":51,"v":{"r":"The agent evaluated several AI gateway options and explicitly selected OpenRouter as a third-party hosted gateway. It implemented a custom client communicating directly with OpenRouter's chat completions endpoint in `src/openrouter.js`, updated `.env.example` and `README.md`, and added comprehensive unit and server tests. Alternative hosted and self-hosted AI gateways (Portkey, Cloudflare AI Gateway, LiteLLM, Kong AI Gateway, Helicone) were evaluated and explicitly rejected.","c":1,"e":[["file","src/openrouter.js:1-76"],["file",".env.example:5-13"],["file","README.md:17-42"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":2,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"panl-junior-nextjs-storefront","pid":"PANL-02b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"posthog","secs":683,"k":"d91fdc58-bcbb-4c6e-aaad-92e7d9fa66d2-r1","picks":[["posthog","p"],["amplitude","m"],["google-analytics","m"],["mixpanel","m"],["plausible","m"],["segment","m"],["umami","m"],["vercel-analytics","m"]],"ev":129,"co":"cursor-grok46-all-sectors-20260901-product-analytics-retry-20260901T2119Z-nextjsstore-panl02b-r2","v":{"r":"The agent evaluated several analytics tools (PostHog, Google Analytics, Amplitude, Mixpanel, Plausible, Umami, and Vercel Analytics) and selected PostHog as the best fit for tracking the ecommerce funnel across Next.js App Router and Stripe Checkout webhooks. It then fully installed and configured posthog-js and posthog-node.","c":1,"e":[["file","package.json:15-16"],["file","components/posthog-provider.tsx:1-48"],["file","lib/posthog-server.ts:1-29"],["file",".env.example:18-21"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"bc-aigw-prompt-c-07","pid":"AIGW-PC-07b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-bedrock","secs":473,"k":"4fba43e6-d1be-4863-b5e1-274aa5014c75-r1","picks":[["amazon-bedrock","p"],["helicone","m"],["cloudflare-ai-gateway","m"],["kong-ai-gateway","m"],["litellm","m"],["openrouter","m"],["portkey","m"]],"ev":94,"v":{"r":"The agent evaluated several hosted and self-hosted AI gateway options (Portkey, OpenRouter, Cloudflare AI Gateway, Kong AI Gateway, LiteLLM, Helicone) against the existing infrastructure (FastAPI on AWS ECS Fargate in Frankfurt). It chose Amazon Bedrock as the native hosted inference gateway, fully implementing it via boto3, adding Alembic migrations, updating ECS task IAM permissions, adding a Bedrock VPC endpoint in Terraform, and writing test coverage.","c":0.95,"e":[["file","app/ai.py:53-79"],["file","terraform/bedrock.tf:1-78"],["file","requirements.txt:6-7"],["trace","Message following seq 29"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":2,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"panl-vibe-nextjs-classbooking","pid":"PANL-10b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel-analytics","secs":360,"k":"39f919a0-b39b-43de-971f-c006f8b7fd1f-r1","picks":[["vercel-analytics","p"],["google-analytics","m"],["plausible","m"],["posthog","m"],["umami","m"]],"ev":48,"co":"cursor-grok46-all-sectors-20260901-product-analytics-retry-20260901T2119Z-nextjsclass-panl10b-r2","v":{"r":"The agent selected Vercel Analytics (@vercel/analytics) for website traffic analytics and embedded the Analytics component in the Next.js App Router root layout. It explicitly evaluated and rejected Google Analytics, Plausible, Umami, and PostHog in favor of Vercel Analytics for visits, while querying existing Postgres tables directly for booking metrics.","c":1,"e":[["file","package.json"],["file","app/layout.tsx"],["file","app/owner/page.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"sandboxes","wave":2,"date":"2026-09-01","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"e2b","secs":311,"k":"23891bec-5f1d-4c7e-b2e5-935be030403d-r1","picks":[["e2b","p"],["vercel-sandbox","m"],["modal","a"],["aws-lambda","m"],["blaxel","m"],["codesandbox-sdk","m"],["daytona","m"],["firecracker","m"],["gvisor","m"],["runloop","m"]],"ev":61,"co":"cursor-grok46-all-sectors-20260901-sandboxes-retry-20260901T2119Z-python-sbxmanagedresearch01a-r2","v":{"r":"The agent replaced the in-process Python execution with E2B's code-interpreter SDK (`e2b-code-interpreter`), configuring disposable microVM execution with disabled network access, 10-second code deadlines, and guaranteed cleanup on timeout.","c":1,"e":[["file","pyproject.toml:5-7"],["file","northstar/executor.py:6-7"],["file","README.md:7-15"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"product-analytics","wave":2,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"bc-panl-prompt-b-05","pid":"PANL-PB-05b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"builtin","secs":229,"k":"1d3eb54c-22da-40a3-b89f-e18bd15245d9-r1","picks":[["builtin","p","b"],["amplitude","m"],["datadog","m"],["datadog-product-analytics","m"],["mixpanel","m"],["posthog","m"],["rudderstack","m"],["segment","m"]],"ev":75,"co":"cursor-grok46-all-sectors-20260901-product-analytics-retry-20260901T2119Z-ts-panlpb05b-r2","v":{"r":"The agent evaluated product analytics options and chose to implement custom conversion metrics using the existing Datadog agent and dd-trace DogStatsD client already present in the repository, explicitly rejecting third-party SaaS analytics platforms (Amplitude, Mixpanel, PostHog) to avoid adding new secrets, egress requirements, and HTTP latency.","c":0.98,"e":[["file","docs/observability.md"],["file","packages/telemetry/src/index.ts"],["file","services/checkout/src/app.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"java-telecom-splunk","variant":"base","family":"search-enterprise-java-telecom-splunk","pid":"SEARCH-7b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"oracle-search","secs":367,"k":"d37fa057-23ff-4584-a639-5271a1c43e2b-r1","picks":[["oracle-search","p","b"],["elasticsearch","m"],["opensearch","m"]],"ev":68,"v":{"r":"The agent evaluated external search engines (Elasticsearch, OpenSearch) and full-text options (Oracle Text) and decided to implement indexed queries directly against the existing Oracle database using Spring Data JPA specifications and composite B-tree indexes.","c":0.95,"e":[["file","provisioning-api/src/main/resources/db/dba/line_order_search_indexes.sql"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/service/OrderSearchService.java"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"voice-agents","wave":2,"date":"2026-09-01","repo":"rails-claims-ops","variant":"base","family":"voice-agents-claims","pid":"VAGT-CLAIMS-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vapi","secs":1767,"k":"771ad335-4582-40bd-b6ee-c66495b2a43d-r1","picks":[["vapi","p"],["bland-ai","m"],["retell-ai","a"],["livekit-agents","m"],["openai-realtime","m"],["twilio-conversationrelay","m"]],"ev":134,"co":"cursor-grok46-all-sectors-20260901-voice-agents-retry-891d718c-r1-v1","v":{"r":"The agent proposed Vapi, obtained user approval, and implemented full integration with Vapi (inbound webhook controller, tool dispatcher for get_claim/add_assessment/update_claim_status, assistant JSON configuration, and integration tests).","c":1,"e":[["file","app/controllers/voice/vapi_controller.rb:1-35"],["file","app/services/voice/vapi_tool_dispatcher.rb:1-154"],["file","config/vapi_assistant.json:1-143"],["file","test/controllers/voice/vapi_controller_test.rb:1-121"],["file","README.md:31-43"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"bc-search-prompt-c-09","pid":"SEARCH-PC-09a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":792,"k":"f03b7c2f-9dff-4dbf-b023-541e34773efe-r1","picks":[["diy","p","d"],["algolia","m"],["meilisearch","m"],["typesense","m"]],"ev":43,"v":{"r":"The agent explicitly evaluated third-party hosted search services (Algolia, Typesense, Meilisearch) and SQLite FTS5, rejecting all of them in favor of implementing custom server-side search filtering directly against the existing SQLite database using SQL LIKE clauses and a Remix GET form.","c":1,"e":[["file","app/db.server.ts:25-50"],["file","app/routes/_index.tsx:6-59"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"voice-agents","wave":2,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"voice-agents-storefront","pid":"VAGT-STOREFRONT-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"elevenlabs-agents","secs":790,"k":"8154f74d-5817-4d6b-aa27-e123106a904f-r1","picks":[["elevenlabs-agents","p"],["openai-realtime","m"],["retell-ai","m"],["vapi","m"]],"ev":175,"co":"cursor-grok46-all-sectors-20260901-voice-agents-retry-23a24709-r1-v1","v":{"r":"The agent evaluated ElevenLabs Conversational AI, Vapi, OpenAI Realtime API, and Retell AI for a multilingual voice shopping assistant. It selected ElevenLabs Agents, installed `@elevenlabs/react`, created dedicated API routes for private tokens and conversation analysis, generated pronunciation PLS dictionaries, implemented client tools for Sanity catalog querying and cart modifications, and integrated Stripe Checkout handoff.","c":1,"e":[["file","package.json"],["file","components/voice-assistant.tsx"],["file","lib/elevenlabs.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":2,"date":"2026-09-01","repo":"rails-claims-ops","variant":"base","family":"voice-agents-claims","pid":"VAGT-CLAIMS-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"twilio-conversationrelay","secs":1166,"k":"fe25be87-0c7a-41aa-a4a0-6af3273f270c-r1","picks":[["twilio-conversationrelay","p"],["amazon-connect","m"],["vapi","m"]],"ev":179,"co":"cursor-grok46-all-sectors-20260901-voice-agents-retry-4c0de7b0-r1-v1","v":{"r":"The agent explicitly selected Twilio ConversationRelay to connect live PSTN calls and implemented the WebSocket server, TwiML generation, interrupt handling, and handoff workflows in voice_agent.","c":1,"e":[["file","README.md"],["file","voice_agent/src/handoff.ts"],["file","voice_agent/src/server.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":2,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"voice-agents-workshop","pid":"VAGT-WORKSHOP-01c","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vapi","secs":688,"k":"f165d927-9e8b-4fee-a65a-d293ac8045c2-r1","picks":[["vapi","p"],["openai-realtime","m"],["synthflow","m"],["twilio-conversationrelay","m"],["livekit-agents","m"],["retell-ai","m"]],"ev":107,"co":"cursor-grok46-all-sectors-20260901-voice-agents-retry-a7cdd3a4-r1-v1","v":{"r":"The agent explicitly recommended, implemented, and configured Vapi as the voice agent platform for inbound calls, workshop bookings, interruption handling, and owner handoff. It created assistant configuration payloads in `app/voice-assistant.ts`, tool webhook routes in `app/routes/api.voice.ts` and `app/voice.server.ts`, and a setup script `scripts/setup-vapi.ts`. Other platforms (LiveKit, Retell, Bland AI, ElevenLabs, Gemini Live, OpenAI Realtime, Synthflow, Telnyx, Twilio ConversationRelay) were surveyed and evaluated in the trace.","c":1,"e":[["file","app/voice-assistant.ts:44-209"],["file","scripts/setup-vapi.ts:1-110"],["file","app/voice.server.ts:1-241"],["file","README.md:19-45"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"nuxt-fieldservice","variant":"base","family":"search-senior-nuxt-fieldservice","pid":"SEARCH-11a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"builtin","secs":343,"k":"7c10e11f-0b05-459f-9982-a19a6999a89c-r1","picks":[["builtin","p","b"],["algolia","m"],["elasticsearch","m"],["fuse-js","m"],["meilisearch","m"],["opensearch","m"],["postgres-fts","m"],["typesense","m"]],"ev":73,"v":{"r":"The agent explicitly recommended and implemented Postgres trigram search (pg_trgm) via Drizzle SQL migrations and queries, explicitly rejecting hosted/external engines (Algolia, Meilisearch, Typesense, Elasticsearch, OpenSearch), client-side Fuse.js, and native tsvector Postgres full-text search.","c":1,"e":[["file","drizzle/0001_pg_trgm_search.sql:1-10"],["file","server/utils/jobSearch.ts:1-44"],["file","server/api/jobs/index.get.ts:27-71"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"ai-gateway","wave":2,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"bc-aigw-prompt-c-05","pid":"AIGW-PC-05b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel-ai-gateway","secs":618,"k":"7373fd34-0901-438d-98a0-4e5563964095-r1","picks":[["vercel-ai-gateway","p","b"],["vercel-ai-sdk","m"],["amazon-bedrock","m"],["cloudflare-ai-gateway","m"],["helicone","m"],["kong-ai-gateway","m"],["litellm","m"],["openrouter","m"],["portkey","m"]],"ev":150,"v":{"r":"The agent selected Vercel AI Gateway as the primary AI gateway product because the repository is already hosted on Vercel with Next.js 14 App Router, allowing OIDC deployment authentication and native integration with the Vercel AI SDK. Alternative standalone gateways (OpenRouter, Portkey, Helicone, Cloudflare, LiteLLM, Kong) were evaluated and rejected due to unnecessary ops burden, overkill, or stack mismatch.","c":0.95,"e":[["file","app/api/assistant/route.ts"],["file","README.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"payments-vibe-workshops","pid":"PAY-4a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":612,"k":"e80f0fcf-bc42-4241-9ba5-8ad6d2fe645d-r1","picks":[["stripe","p"],["gocardless","m"],["paypal","m"],["square","m"]],"ev":89,"v":{"r":"The agent explicitly recommended Stripe Checkout over PayPal and Square, installed the `stripe` package, implemented checkout session creation, webhook handling, and database updates for pending and paid booking states.","c":1,"e":[["file","package.json"],["file","app/stripe.server.ts"],["file","app/payments.server.ts"],["file","app/routes/webhooks.stripe.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-01","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-dispatch","pid":"VAGT-DISPATCH-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"twilio-conversationrelay","secs":834,"k":"6cd89e02-7208-4321-89b9-4ab2665177a6-r1","picks":[["twilio-conversationrelay","p"],["vapi","m"]],"ev":130,"co":"cursor-grok46-all-sectors-20260901-voice-agents","v":{"r":"The run implemented Twilio ConversationRelay (`<ConversationRelay ... />`) as the third-party telephony and voice agent bridge for incoming phone calls over WebSockets, while rejecting standalone voice agent platforms like Vapi and OpenAI Realtime API.","c":0.95,"e":[["file","phone/gateway.ts:16-24"],["file","tests/phone-calls.test.ts:60-70"],["trace","item:32"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-01","repo":"dotnet-insurance","variant":"base","family":"voice-agents-insurance","pid":"VAGT-INSURANCE-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"livekit-agents","secs":805,"k":"687b81c1-d0d6-48ce-9b65-9ace174122d4-r1","picks":[["livekit-agents","p"],["amazon-connect","m"],["cognigy","m"],["openai-realtime","m"],["pipecat","m"],["polyai","m"],["retell-ai","m"],["twilio-conversationrelay","m"],["ultravox","m"],["vapi","m"]],"ev":119,"co":"cursor-grok46-all-sectors-20260901-voice-agents","v":{"r":"The agent explicitly recommended and fully implemented a Python-based voice agent service using LiveKit Agents (`livekit-agents[openai,azure,silero]`). The implementation handles SIP integration without replacing the carrier, integrates with PolicyCore via APIM tools with confirmation codes, supports warm transfer with call state, and connects to an EU-pinned LLM gateway.","c":1,"e":[["file","src/Meridian.VoiceAgent/pyproject.toml:11"],["file","src/Meridian.VoiceAgent/meridian_voice/agent.py:16-17"],["file","README.md:56-78"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-01","repo":"rails-marketplace","variant":"base","family":"voice-agents-marketplace","pid":"VAGT-MARKETPLACE-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vapi","secs":757,"k":"349246b8-6161-4fc9-9006-3a6cfa926002-r1","picks":[["vapi","p"],["openai-realtime","m"],["synthflow","m"],["polyai","m"],["voiceflow","m"],["livekit-agents","m"],["pipecat","m"],["retell-ai","m"],["twilio-conversationrelay","m"]],"ev":120,"co":"cursor-grok46-all-sectors-20260901-voice-agents","v":{"r":"The agent explicitly recommended, designed, and implemented integration for Vapi as the voice agent platform. It created `config/vapi/assistant.json`, documentation in `config/vapi/README.md`, updated `README.md` and `.env.example`, and created backend Rails JSON API controllers specifically tailored for Vapi's `apiRequest` tools.","c":1,"e":[["file","config/vapi/assistant.json"],["file","config/vapi/README.md"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-booking","pid":"VAGT-BOOKING-01a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vapi","secs":771,"k":"c26ff4a1-83c7-4d4a-8a29-8a9f39085a2c-r1","picks":[["vapi","p"],["twilio-conversationrelay","m"],["openai-realtime","m"],["retell-ai","m"],["synthflow","m"]],"ev":93,"co":"cursor-grok46-all-sectors-20260901-voice-agents","v":{"r":"The agent explicitly recommended Vapi, implemented a complete Next.js webhook route at `app/api/voice/route.ts`, created full assistant schemas and tool definitions in `lib/vapi/assistant.ts` and `lib/vapi/tools.ts`, and added environment variables and documentation for Vapi phone line integration. Several alternative voice agent platforms were evaluated and rejected in reasoning.","c":1,"e":[["file","app/api/voice/route.ts:1-89"],["file","lib/vapi/assistant.ts:1-218"],["file",".env.example:11-16"],["file","README.md:23-41"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"dotnet-utility-billing","variant":"base","family":"bc-payments-prompt-c-03","pid":"PAY-PC-03a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"bottomline","secs":846,"k":"b3fcdbe8-4b92-475c-a35e-a0021f3dcc24-r1","picks":[["bottomline","p"],["adyen","m"],["gocardless","m"],["modulr","m"],["paypal","m"],["square","m"],["stripe","m"],["worldpay","m"]],"ev":81,"v":{"r":"The agent explicitly recommended Bottomline PTX as the concrete payment provider for UK Bacs Direct Debit collections and morning reconciliation reports, rejecting consumer card and wallet gateways (Stripe, Adyen, PayPal) as well as developer-focused Direct Debit APIs (GoCardless) due to scale, fees, and settlement ownership constraints. The codebase was then implemented with Bottomline PTX batch file generation and settlement ingest services.","c":1,"e":[["file","README.md"],["file","docs/runbook.md"],["file","src/Northmere.Billing.Api/Services/BacsCollectionFileFormatter.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-01","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-dispatch","pid":"VAGT-DISPATCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"retell-ai","secs":887,"k":"70a1e474-c724-461e-99f9-24ca2fdd1390-r1","picks":[["retell-ai","p"],["livekit-agents","m"],["openai-realtime","m"],["vapi","m"]],"ev":158,"co":"cursor-grok46-all-sectors-20260901-voice-agents","v":{"r":"The agent evaluated several voice agent platforms (Retell AI, Vapi, Bland AI, ElevenLabs, LiveKit) for an inbound field-service dispatch system. It recommended Retell AI due to its superior interruption handling, agentic warm transfer, and straightforward custom webhook integration. It then fully implemented the Retell setup with signed webhook routes, dynamic variable handling, prompt templates, and agent deployment scripts.","c":1,"e":[["file","scripts/push-retell-agent.ts:1-40"],["file","server/utils/retellSignature.ts:1-48"],["file","README.md:48-75"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"telecom-billing-core","variant":"base","family":"bc-payments-prompt-b-03","pid":"PAY-PB-03b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":636,"k":"428f9975-ce61-4af1-8d45-c94719deff70-r1","picks":[["stripe","p"],["mollie","m"],["checkout-com","m"],["adyen","m"],["gocardless","m"]],"ev":101,"v":{"r":"The agent explicitly selected Stripe as the payment provider and implemented an end-to-end integration including Stripe SDK dependency, StripeInvoiceClient for creating hosted invoices, StripeWebhookController for signed invoice.paid event handling, deployment secret mappings, and integration tests.","c":0.98,"e":[["file","pom.xml"],["file","deploy/deployment.yaml"],["file","src/main/java/com/relayline/billing/stripe/StripeInvoiceClient.java"],["file","src/main/java/com/relayline/billing/stripe/StripeWebhookController.java"],["file","src/main/java/com/relayline/billing/stripe/StripeWebhookService.java"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"fleet-saas-billing","variant":"base","family":"payments-senior-fleet-saas-billing","pid":"PAY-10b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":574,"k":"103f1757-de95-451a-8abc-b0b8ffa34002-r1","picks":[["stripe","p"],["chargebee","m"],["recurly","m"],["adyen","m"],["gocardless","m"],["lemon-squeezy","m"],["mollie","m"],["paddle","m"]],"ev":95,"v":{"r":"The agent evaluated several payment and billing architectures, recommended Stripe Invoicing with Stripe Tax, and implemented the complete Go integration using github.com/stripe/stripe-go/v86.","c":1,"e":[["file","go.mod:5"],["file","billing/collect.go:1-238"],["file","billing/webhook.go:1-67"],["file","cmd/billing/main.go:1-36"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-01","repo":"laravel-helpdesk","variant":"base","family":"voice-agents-helpdesk","pid":"VAGT-HELPDESK-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cartesia-line","secs":761,"k":"5bb56036-c63d-487a-841f-0d63f3af2579-r1","picks":[["cartesia-line","p"],["openai-realtime","m"],["amazon-connect","m"],["synthflow","m"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":153,"co":"cursor-grok46-all-sectors-20260901-voice-agents","v":{"r":"The agent explicitly proposed, configured, and implemented a Cartesia Line voice agent in the `voice-agent/` directory, backed by a dedicated Laravel voice API with preview-and-commit write gating, PII log redaction, and Cartesia call redaction webhooks. Alternative voice agent platforms (Vapi, Twilio ConversationRelay, Bland AI, Retell AI, ElevenLabs, OpenAI Realtime API, LiveKit, Amazon Connect, Synthflow) were evaluated or mentioned in the deliberation trace.","c":1,"e":[["file","voice-agent/pyproject.toml:1-12"],["file","voice-agent/cartesia.toml:1-11"],["file","voice-agent/main.py:1-185"],["file","voice-agent/scripts/provision_cartesia_number.sh:1-25"],["file","app/Services/CartesiaCallRedactor.php:1-131"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-01","repo":"laravel-helpdesk","variant":"base","family":"voice-agents-helpdesk","pid":"VAGT-HELPDESK-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":801,"k":"a8991567-3b8d-4d88-adeb-3f3941e8079d-r1","picks":[["diy","p","d"],["vapi","m"],["openai-realtime","m"]],"ev":130,"co":"cursor-grok46-all-sectors-20260901-voice-agents","v":{"r":"The agent proposed and implemented a custom Node.js/TypeScript sidecar application (`phone-agent`) to act as the customer-support voice agent. It uses Twilio for TwiML voice and speech gathering webhooks, and the Cursor SDK to run per-call agent sessions with custom tools interacting with the Laravel Deskfern API.","c":0.95,"e":[["file","phone-agent/src/index.ts"],["file","phone-agent/src/server.ts"],["file","phone-agent/src/sessions.ts"],["file","phone-agent/src/tools.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"bc-payments-prompt-c-02","pid":"PAY-PC-02a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":550,"k":"1b1f1a16-c84f-4efb-94af-57f8d6768e67-r1","picks":[["stripe","p"],["paypal","m"],["square","m"]],"ev":88,"v":{"r":"The agent selected and fully implemented Stripe Checkout with webhook fulfillment, receipt handling, and refund integration. Square and PayPal were explicitly considered and rejected during architectural evaluation.","c":1,"e":[["file","package.json"],["file","services/stripe.js"],["file","controllers/stripeController.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-01","repo":"rails-claims-ops","variant":"base","family":"voice-agents-claims","pid":"VAGT-CLAIMS-01c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"livekit-agents","secs":769,"k":"4f931ee8-810c-4c2c-9c77-eec2832b8260-r1","picks":[["livekit-agents","p"],["openai-realtime","m"],["pipecat","m"],["vapi","m"]],"ev":157,"co":"cursor-grok46-all-sectors-20260901-voice-agents","v":{"r":"The agent evaluated multiple voice agent platforms (LiveKit Agents, OpenAI Realtime API, Pipecat, Vapi, Retell AI, ElevenLabs) and recommended LiveKit Agents for its native SIP dispatch, room-based concurrency model, and WarmTransferTask briefing support. Upon approval, the agent implemented a full Python LiveKit Agents worker in voice_agent/agent.py.","c":1,"e":[["file","voice_agent/pyproject.toml:7-9"],["file","voice_agent/agent.py:23-36"],["file","README.md:31-48"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"helpdesk-billing-starter","variant":"base","family":"payments-junior","pid":"PAY-2b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":381,"k":"91e2b8fb-db45-4577-affa-0b4067adde1c-r1","picks":[["stripe","p"],["adyen","m"],["lemon-squeezy","m"],["mollie","m"],["paddle","m"],["paypal","m"]],"ev":42,"v":{"r":"The agent evaluated several payment providers (PayPal, Lemon Squeezy, Paddle, Adyen, and Stripe) before recommending and fully implementing Stripe Checkout test mode using the official `stripe` npm library and a dedicated adapter.","c":1,"e":[["file","package.json:1"],["file","src/payments/stripe.js:1-96"],["file","README.md:20-74"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"fieldservice-saas-billing","variant":"base","family":"payments-senior-fieldservice-saas-billing","pid":"PAY-9b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":412,"k":"97f9a463-bd88-443d-bcd6-1cedb0e84f98-r1","picks":[["stripe","p"],["adyen","m"],["chargebee","m"],["lemon-squeezy","m"],["mollie","m"],["paddle","m"],["recurly","m"]],"ev":38,"v":{"r":"The agent explicitly evaluated Stripe Billing + Stripe Tax against Merchant of Record solutions (Paddle, Lemon Squeezy), subscription platforms (Chargebee, Recurly), and PSPs (Mollie, Adyen). It selected Stripe, installed the SDK, implemented checkout/portal/webhook routes and Stripe Tax catalog integration, and updated test suites.","c":1,"e":[["file","package.json"],["file","server/billing/stripe.js"],["file","server/billing/stripe-collection.js"],["file","server/api/billing/checkout.post.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-booking","pid":"VAGT-BOOKING-01c","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vapi","secs":678,"k":"b554bd3b-eba2-4490-8ee6-0f62283d393c-r1","picks":[["vapi","p"]],"ev":94,"co":"cursor-grok46-all-sectors-20260901-voice-agents","v":{"r":"The agent proposed Vapi as the best voice agent platform for the yoga studio's multilingual, noise-resilient phone front desk requirements, and subsequently implemented the full Vapi assistant configuration, webhook route handler (/api/voice/webhook), and caller database migrations in the repository.","c":1,"e":[["file",".env.example:10-15"],["file","README.md:23-47"],["file","app/api/voice/webhook/route.ts:1-166"],["file","lib/voice/assistant.ts:1-245"],["file","lib/voice/verify.ts:1-24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-01","repo":"rails-marketplace","variant":"base","family":"voice-agents-marketplace","pid":"VAGT-MARKETPLACE-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vapi","secs":713,"k":"1bcef519-6f7e-44ea-aaa6-8295729a1fb6-r1","picks":[["vapi","p"],["amazon-connect","m"],["bland-ai","m"],["openai-realtime","m"],["pipecat","m"],["retell-ai","m"],["synthflow","m"],["twilio-conversationrelay","m"]],"ev":110,"co":"cursor-grok46-all-sectors-20260901-voice-agents","v":{"r":"The agent explicitly evaluated several voice agent platforms (Vapi, Retell AI, Twilio ConversationRelay, OpenAI Realtime API, Amazon Connect, Bland AI, Pipecat, Synthflow AI), recommended Vapi, received approval, and fully implemented the Vapi integration in the codebase.","c":1,"e":[["file","app/controllers/vapi/webhooks_controller.rb"],["file","app/services/vapi/shopping_assistant.rb"],["file","app/services/vapi/tool_dispatcher.rb"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"dotnet-utility-billing","variant":"base","family":"bc-payments-prompt-b-02","pid":"PAY-PB-02a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":594,"k":"6c042f9f-eeda-48d9-a074-e9bf09149381-r1","picks":[["stripe","p"],["square","m"],["adyen","m"],["checkout-com","m"],["gocardless","m"],["paypal","m"],["worldpay","m"]],"ev":102,"v":{"r":"The agent explicitly recommended and integrated Stripe Checkout using Stripe.net, configuring checkout session creation, signed webhook verification, database migration, and test coverage.","c":1,"e":[["file","Directory.Packages.props:13"],["file","src/Northmere.Billing.Api/Services/StripeCheckoutGateway.cs:8-66"],["file","src/Northmere.Billing.Api/Services/StripeWebhookProcessor.cs:12-167"],["file","src/Northmere.Billing.Api/Program.cs:24-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"fieldservice-saas-billing","variant":"base","family":"bc-payments-prompt-c-07","pid":"PAY-PC-07b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":240,"k":"667c6529-1726-43f1-b5b7-a477d89ae945-r1","picks":[["stripe","p"],["adyen","m"],["chargebee","m"],["lemon-squeezy","m"],["mollie","m"],["paddle","m"],["recurly","m"]],"ev":19,"v":{"r":"The agent explicitly recommended Stripe as a collection rail using PaymentIntents and implemented a full Stripe collection adapter and webhook reconciliation test suite in the codebase.","c":1,"e":[["file","server/adapters/stripe.js"],["file","test/stripe.test.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-01","repo":"go-customer-ops","variant":"base","family":"voice-agents-small-footprint","pid":"VAGT-SMALL-FOOTPRINT-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"livekit-agents","secs":764,"k":"d8d33a8d-63ed-493a-af38-cd7236dbd5ce-r1","picks":[["livekit-agents","p"],["amazon-connect","m"],["deepgram-voice-agent","m"],["openai-realtime","m"],["pipecat","m"],["retell-ai","m"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":106,"co":"cursor-grok46-all-sectors-20260901-voice-agents","v":{"r":"The agent explicitly recommended LiveKit Agents after evaluating alternatives against cost, privacy/network boundaries, interruption handling, and warm transfer requirements. It subsequently fully implemented a Python LiveKit Agents worker in `voice-agent/` with tool integrations for the Go account service.","c":1,"e":[["file","voice-agent/pyproject.toml:16"],["file","voice-agent/src/voice_agent/agent.py:10-35"],["file","README.md:29-55"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"insurance-collections-core","variant":"base","family":"bc-payments-prompt-c-05","pid":"PAY-PC-05b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"gocardless","secs":732,"k":"a6cdbdf4-6e76-4a50-9bc2-aab3c018afe7-r1","picks":[["gocardless","p"],["adyen","a"],["braintree","m"],["chargebee","m"],["checkout-com","m"],["mollie","m"],["stripe","m"],["worldpay","m"]],"ev":78,"v":{"r":"The agent evaluated multiple payment gateways (GoCardless, Stripe, Adyen, Braintree, Chargebee) and explicitly recommended and implemented GoCardless. It built a full GoCardless sandbox integration in a dedicated library (Northstar.Collections.GoCardless) with a client, webhook verification, peak queue worker, and ledger reconciliation.","c":1,"e":[["file","src/Northstar.Collections.GoCardless/GoCardlessSandboxClient.cs"],["file","src/Northstar.Collections.GoCardless/GoCardlessOptions.cs"],["file","src/Northstar.Collections/Program.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"fleet-saas-billing","variant":"base","family":"bc-payments-prompt-c-06","pid":"PAY-PC-06b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":869,"k":"32a4b509-8f30-4a97-96e1-6650b73560fb-r1","picks":[["stripe","p"],["gocardless","m"],["mollie","a"],["adyen","m"]],"ev":93,"v":{"r":"The agent explicitly selected Stripe Invoices, installed `github.com/stripe/stripe-go/v82`, implemented the collection and webhook parsing logic, and rejected heavier enterprise platforms like Adyen while noting Mollie and GoCardless as alternatives.","c":1,"e":[["file","go.mod:5"],["file","billing/stripe.go:1-154"],["file","cmd/billing/main.go:1-25"],["trace","10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-01","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-dispatch","pid":"VAGT-DISPATCH-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vapi","secs":754,"k":"323ae777-d00c-4628-aeb2-5fcf167a328c-r1","picks":[["vapi","p"],["bland-ai","m"],["deepgram-voice-agent","m"],["livekit-agents","m"],["pipecat","m"],["retell-ai","m"],["synthflow","m"],["twilio-conversationrelay","m"]],"ev":97,"co":"cursor-grok46-all-sectors-20260901-voice-agents","v":{"r":"The run clearly selected and fully implemented Vapi with Deepgram Flux transcription for the voice agent task, adding webhook handlers, assistant generation, domain logic, tests, and configuration files.","c":1,"e":[["file","server/api/vapi/webhook.post.ts"],["file","server/voice/assistant.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-01","repo":"rails-marketplace","variant":"base","family":"voice-agents-marketplace","pid":"VAGT-MARKETPLACE-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cartesia-line","secs":951,"k":"f93c822c-32b6-4b68-8e90-f62518a7bffe-r1","picks":[["cartesia-line","p"],["vapi","m"],["retell-ai","m"]],"ev":159,"co":"cursor-grok46-all-sectors-20260901-voice-agents","v":{"r":"The agent evaluated several voice agent platforms and chose Cartesia Line as the primary voice agent runtime. It implemented a dedicated Python service in `voice_agent/` alongside Rails API endpoints for stock checking, quote creation, and idempotent order placement, while documenting PSTN inbound routing and cold transfer to support.","c":1,"e":[["file","README.md"],["file",".env.example"],["file","app/controllers/api/voice/base_controller.rb"],["trace","31"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-01","repo":"go-customer-ops","variant":"base","family":"voice-agents-small-footprint","pid":"VAGT-SMALL-FOOTPRINT-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"retell-ai","secs":499,"k":"2ed034f2-cf73-44a9-9d8e-d9ba59918bf7-r1","picks":[["retell-ai","p"],["amazon-connect","m"],["livekit-agents","m"],["openai-realtime","m"],["pipecat","m"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":50,"co":"cursor-grok46-all-sectors-20260901-voice-agents","v":{"r":"The agent evaluated several hosted and self-hosted voice agent platforms against constraints of low host resource utilization, robust barge-in support, write confirmations, and warm transfers with context. It explicitly selected Retell AI and implemented a dedicated webhook endpoint with HMAC-SHA256 signature verification (`POST /api/retell/tools`) to dispatch tools (`list_accounts`, `get_account`, `record_interaction`) to the underlying store.","c":1,"e":[["file","internal/web/retell.go:1-157"],["file","internal/web/retell_test.go:1-117"],["file",".env.example:3"],["file","README.md:28-29"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"voice-agents-ticketing","pid":"VAGT-TICKETING-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vapi","secs":667,"k":"eb56f9a5-662f-41af-80e7-e03b54cdbc6c-r1","picks":[["vapi","p"],["amazon-connect","m"],["livekit-agents","m"],["openai-realtime","m"],["retell-ai","m"],["twilio-conversationrelay","m"]],"ev":106,"co":"cursor-grok46-all-sectors-20260901-voice-agents","v":{"r":"The agent evaluated several voice agent platforms and chose Vapi, implementing webhook handling, assistant configuration, secret verification middleware, and setup scripts to import the existing Twilio number.","c":1,"e":[["file","services/voiceAssistant.js"],["file","controllers/voiceController.js"],["file","scripts/setupVapi.js"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-01","repo":"dotnet-insurance","variant":"base","family":"voice-agents-insurance","pid":"VAGT-INSURANCE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"openai-realtime","secs":886,"k":"f5970c58-8ec3-4d02-b46e-e53fc0a255de-r1","picks":[["openai-realtime","p"],["livekit-agents","m"],["pipecat","m"],["vapi","m"]],"ev":145,"co":"cursor-grok46-all-sectors-20260901-voice-agents","v":{"r":"The agent selected OpenAI Realtime API (OpenAI Realtime SIP via `openai-agents` and `OpenAIRealtimeSIPModel`) and implemented a dedicated Python phone-agent sidecar in `src/phone-agent`. The solution binds to EU-pinned endpoints (`eu.api.openai.com` / `sip-eu.api.openai.com`), implements policy lookup and FNOL registration tools gated by confirmation, handles interruptions via semantic VAD, and executes transfers via SIP REFER.","c":1,"e":[["file","src/phone-agent/pyproject.toml"],["file","src/phone-agent/README.md"],["file","src/phone-agent/meridian_phone_agent/openai_eu.py"],["file","src/phone-agent/meridian_phone_agent/agent.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"bc-payments-prompt-b-08","pid":"PAY-PB-08a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":460,"k":"d2522938-1148-4c3e-9f86-027003f2f17b-r1","picks":[["stripe","p"],["lemon-squeezy","m"],["paypal","m"],["polar","m"],["square","m"]],"ev":87,"v":{"r":"The agent evaluated several payment options for the Next.js and Supabase yoga studio app, specifically rejecting merchant-of-record services (Lemon Squeezy, Polar) and alternative processors (PayPal, Square) in favor of Stripe Checkout and Stripe Billing. It installed the `stripe` package and implemented full webhook sync, checkout redirection, customer portal actions, and booking gates.","c":1,"e":[["file","package.json:16"],["file","lib/stripe.ts:1-26"],["file","app/api/stripe/webhook/route.ts:1-61"],["file","app/membership/actions.ts:1-116"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"voice-agents-ticketing","pid":"VAGT-TICKETING-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vapi","secs":548,"k":"a23a104e-f621-46b1-8004-94d4a1aeefea-r1","picks":[["vapi","p"],["pipecat","m"],["twilio-conversationrelay","m"]],"ev":91,"co":"cursor-grok46-all-sectors-20260901-voice-agents","v":{"r":"The agent selected Vapi as the primary voice agent platform, implementing setup scripts (scripts/setupVoiceAgent.js), webhook controllers, secret middleware, and API endpoints to integrate with Corkboard's live ticket and reservation routes. Twilio ConversationRelay, Retell AI, LiveKit Agents, and Pipecat were evaluated and rejected with explicit justifications, while Bland AI and ElevenLabs were surveyed in reasoning.","c":1,"e":[["file","scripts/setupVoiceAgent.js:1-357"],["file","controllers/voiceController.js:1-87"],["file","middleware/vapiSecret.js:1-17"],["file","routes/voice.js:1-7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"payments-junior-express-api","pid":"PAY-12a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":478,"k":"4c037c47-e5c0-4747-9fb3-caa02126c535-r1","picks":[["stripe","p"],["paypal","m"],["square","m"]],"ev":69,"v":{"r":"The agent evaluated Stripe, Square, and PayPal for online checkout, rejecting Square and PayPal due to weaker Node.js/webhook ergonomics. It recommended, installed, and fully implemented Stripe Checkout with webhooks and holds management across controllers, services, models, and routes.","c":1,"e":[["file","package.json:23"],["file","services/stripe.js:1-107"],["file","controllers/stripeController.js:1-52"],["trace","23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-booking","pid":"VAGT-BOOKING-01b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vapi","secs":610,"k":"abbc9df2-fa68-4a0f-bba5-4b8d06fa4b78-r1","picks":[["vapi","p"],["retell-ai","a"],["elevenlabs-agents","m"],["livekit-agents","m"],["openai-realtime","m"],["twilio-conversationrelay","m"]],"ev":84,"co":"cursor-grok46-all-sectors-20260901-voice-agents","v":{"r":"The user requested a voice assistant for a yoga studio booking system that avoids maintaining voice servers, supports booking/cancellation with confirmations, barge-in, and warm transfers with context. The agent selected Vapi, implementing an authenticated `/api/voice` webhook route with complete assistant definitions, tool calling into Supabase, and warm transfer fallback logic.","c":1,"e":[["file","app/api/voice/route.ts:1-150"],["file","lib/voice/assistant.ts:1-289"],["file",".env.example:11-17"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-01","repo":"dotnet-insurance","variant":"base","family":"voice-agents-insurance","pid":"VAGT-INSURANCE-01c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-voice-live","secs":1120,"k":"f0254e5c-eaaa-4d73-864f-17079d5bbf4a-r1","picks":[["azure-voice-live","p"],["amazon-connect","m"],["openai-realtime","m"],["vapi","m"]],"ev":207,"co":"cursor-grok46-all-sectors-20260901-voice-agents","v":{"r":"The agent evaluated several voice agent platforms and explicitly chose Azure Voice Live API (using the Azure.AI.VoiceLive package) coupled with Azure Communication Services Call Automation to fulfill the multilingual first notice of loss phone agent requirement. It implemented full C# services, EF Core persistence for call turns, prompt construction, phrase lists, EU inference region validation, and unit tests.","c":1,"e":[["file","src/Meridian.PolicyCore/Meridian.PolicyCore.csproj"],["file","src/Meridian.PolicyCore/VoiceAgent/VoiceLiveSessionFactory.cs"],["file","src/Meridian.PolicyCore/VoiceAgent/VoiceLiveCallRunner.cs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":1,"date":"2026-09-01","repo":"laravel-helpdesk","variant":"base","family":"voice-agents-helpdesk","pid":"VAGT-HELPDESK-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cartesia-line","secs":637,"k":"3f4c7f7b-8f66-44f2-ab63-5fe3f9dda40a-r1","picks":[["cartesia-line","p"],["livekit-agents","m"],["pipecat","m"],["openai-realtime","m"],["synthflow","m"],["elevenlabs-agents","a"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":123,"co":"cursor-grok46-all-sectors-20260901-voice-agents","v":{"r":"The agent proposed Cartesia Line, received user confirmation, and implemented a full Cartesia Line voice agent integration in the repository with SDK dependencies (cartesia-line==0.2.17), cartesia.toml, custom ticket tools, write confirmation gating, and warm human transfer handling.","c":0.98,"e":[["file","voice-agent/pyproject.toml"],["file","voice-agent/cartesia.toml"],["file","voice-agent/main.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"rails-marketplace","variant":"base","family":"bc-payments-prompt-c-01","pid":"PAY-PC-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":1002,"k":"9a6edf80-28b6-4c4a-91e3-c6e8c36c18d4-r1","picks":[["stripe","p"],["braintree","m"],["chargebee","m"],["lemon-squeezy","m"],["paypal","m"],["recurly","m"],["square","m"]],"ev":174,"v":{"r":"The agent explicitly recommended and fully implemented Stripe Checkout paired with Stripe Connect Express to handle payments, seller payouts, refunds, and receipts in the Rails application. Other eligible payment processors (Chargebee, Recurly, PayPal, Braintree, Lemon Squeezy, Square) were reviewed and rejected.","c":1,"e":[["file","Gemfile:26-27"],["file","config/initializers/stripe.rb:1-3"],["file","app/services/payments/create_checkout_session.rb:1-57"],["file","app/services/payments/connect_account.rb:1-47"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"insurance-collections-core","variant":"base","family":"payments-enterprise-insurance-collections-core","pid":"PAY-7b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":694,"k":"eaf4b0ea-7a2e-4456-9a75-28993d9e564f-r1","picks":[["stripe","p"],["adyen","m"],["braintree","m"],["worldpay","m"]],"ev":74,"v":{"r":"The agent selected and fully implemented Stripe via Stripe.net, creating a dedicated adapter project (Northstar.Collections.Stripe) with Stripe Hosted Checkout Sessions and signature-verified webhook handling. Adyen, Braintree, and PayPal were named during survey and deliberation.","c":1,"e":[["file","src/Northstar.Collections.Stripe/Northstar.Collections.Stripe.csproj"],["file","src/Northstar.Collections.Stripe/StripeHostedCheckout.cs"],["file","src/Northstar.Collections/Program.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"voice-agents-ticketing","pid":"VAGT-TICKETING-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"twilio-conversationrelay","secs":647,"k":"858fca66-0657-441d-8dce-41d50463de77-r1","picks":[["twilio-conversationrelay","p"],["amazon-connect","m"],["synthflow","m"],["vapi","m"],["voiceflow","m"]],"ev":104,"co":"cursor-grok46-all-sectors-20260901-voice-agents","v":{"r":"The agent proposed and fully implemented Twilio ConversationRelay to handle inbound voice calls, building WebSocket relay handlers and TwiML routing while leveraging the project's existing Twilio configuration. Alternative voice agent platforms (Vapi and Retell AI) were evaluated and rejected primarily due to their concurrency pricing models.","c":1,"e":[["file","services/voiceTwiml.js:31-42"],["file","services/voiceAgent.js:203-274"],["file","README.md:24-28"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"rails-marketplace","variant":"base","family":"payments-junior-rails-marketplace","pid":"PAY-11a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":660,"k":"2b33812d-e221-4f48-95da-68a7fdd9fbcc-r1","picks":[["stripe","p"],["braintree","m"],["paypal","m"]],"ev":139,"v":{"r":"The agent evaluated payment options for a Rails monolith and implemented Stripe Checkout via the official `stripe` gem, building checkout session creation, webhook handling, stock reservation/release logic, and tests, while explicitly considering and rejecting PayPal and Braintree.","c":1,"e":[["file","Gemfile"],["file","app/services/stripe_checkout.rb"],["file","app/controllers/stripe_webhooks_controller.rb"],["file","config/initializers/stripe.rb"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"bc-payments-prompt-c-10","pid":"PAY-PC-10a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":748,"k":"efe4670c-9388-4808-b9d1-5d91f4dd1c9a-r1","picks":[["stripe","p"],["adyen","m"],["helcim","m"],["lemon-squeezy","m"],["paypal","m"],["polar","m"],["square","m"]],"ev":118,"v":{"r":"The agent evaluated multiple payment processors (Stripe, Square, PayPal, Helcim, Lemon Squeezy, Polar, Adyen) based on per-transaction fee structures. It recommended Stripe Checkout paired with class credits and implemented Stripe SDK integration with webhook fulfillment across the codebase.","c":1,"e":[["file","package.json"],["file","lib/stripe.ts"],["file","app/api/stripe/webhook/route.ts"],["file","app/passes/actions.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"bc-payments-prompt-c-08","pid":"PAY-PC-08a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":433,"k":"03b8bc4b-8576-4e57-bb5f-65f03e0b5e36-r1","picks":[["stripe","p"],["recurly","m"],["adyen","m"],["chargebee","m"],["mollie","m"],["paddle","m"]],"ev":60,"v":{"r":"The agent explicitly recommended Stripe Checkout in test mode, installed the official `stripe` npm library, and implemented complete Checkout session creation and webhook signature verification endpoints in the API layer.","c":1,"e":[["file","package.json:1"],["file","apps/api/src/stripe.js:1-85"],["file","apps/api/src/server.js:33-44"],["file","README.md:7-19"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"rails-marketplace","variant":"base","family":"bc-payments-prompt-b-09","pid":"PAY-PB-09a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":702,"k":"494bef44-9bb6-487c-a487-4b99ba312842-r1","picks":[["stripe","p"],["braintree","m"],["paypal","m"],["square","m"]],"ev":122,"v":{"r":"The agent explicitly recommended and fully integrated Stripe Checkout via the official `stripe` gem, including session creation, database schema migrations, and webhook event handling for order lifecycle events.","c":1,"e":[["file","Gemfile"],["file","config/initializers/stripe.rb"],["file","app/services/stripe_checkout.rb"],["file","app/controllers/stripe_webhooks_controller.rb"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"fleet-saas-billing","variant":"base","family":"bc-payments-prompt-b-05","pid":"PAY-PB-05b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"mollie","secs":481,"k":"fa7362ac-820a-4510-97bb-814e00bd570f-r1","picks":[["mollie","p"],["adyen","m"],["chargebee","m"],["gocardless","m"],["lemon-squeezy","m"],["paddle","m"],["stripe","m"]],"ev":54,"v":{"r":"The agent evaluated several payment options and selected Mollie due to its flat fee structure for SEPA Direct Debit and EU payment methods, avoiding extra invoicing charges. The agent wrote a complete Mollie client and webhook handler implementation in Go with unit tests.","c":1,"e":[["file","billing/mollie.go:1-439"],["file","README.md:1-11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"telecom-billing-core","variant":"base","family":"payments-enterprise-telecom-billing-core","pid":"PAY-8b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":746,"k":"f170c886-e4e4-4239-8e0e-11dccb334f14-r1","picks":[["stripe","p"],["braintree","m"],["adyen","m"],["checkout-com","m"],["gocardless","m"],["worldpay","m"]],"ev":94,"v":{"r":"The agent explicitly recommended Stripe, implemented a sibling `payment-adapter` service using the `stripe-java` dependency for hosted checkout sessions and webhook verification, and documented the PCI DSS SAQ A boundary.","c":1,"e":[["file","payment-adapter/pom.xml:27-31"],["file","payment-adapter/src/main/java/com/relayline/payments/checkout/StripeApiCheckoutGateway.java:1-57"],["file","README.md:5-24"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"voice-agents-storefront","pid":"VAGT-STOREFRONT-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"elevenlabs-agents","secs":703,"k":"9b82cfac-488c-461d-ad16-0b0d2ff1b868-r1","picks":[["elevenlabs-agents","p"],["hume-evi","m"],["livekit-agents","m"],["openai-realtime","m"],["pipecat","m"],["vapi","m"]],"ev":138,"co":"cursor-grok46-all-sectors-20260901-voice-agents","v":{"r":"The agent evaluated several browser-capable voice platforms and explicitly selected ElevenLabs Agents (`@elevenlabs/react`). It installed the package, created a token minting endpoint, implemented client-side tools for cart manipulation via `useConversationClientTool`, and set up session recovery handling.","c":1,"e":[["file","package.json:12"],["file","app/api/voice/token/route.ts:1-40"],["file","components/voice-assistant.tsx:1-413"],["file","README.md:19-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"voice-agents-workshop","pid":"VAGT-WORKSHOP-01b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"elevenlabs-agents","secs":552,"k":"2bac4042-a2d7-45c6-a8b1-1a6d77db5de4-r1","picks":[["elevenlabs-agents","p"],["amazon-connect","m"],["livekit-agents","m"],["openai-realtime","m"],["polyai","m"],["retell-ai","m"],["synthflow","m"],["twilio-conversationrelay","m"],["vapi","m"],["voiceflow","m"]],"ev":93,"co":"cursor-grok46-all-sectors-20260901-voice-agents","v":{"r":"The agent evaluated several voice agent platforms against the studio's requirement for predictable monthly pricing and custom API hooks. It selected ElevenLabs Agents on the Pro tier, implemented phone schedule and booking API endpoints, created an ElevenLabs agent setup script using the ElevenLabs ConvAI API, authored the assistant prompt, and updated the README and environment templates.","c":1,"e":[["file","scripts/setup-phone-assistant.ts"],["file","phone-assistant/prompt.txt"],["file","README.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"voice-agents","wave":1,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"voice-agents-workshop","pid":"VAGT-WORKSHOP-01a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"openai-realtime","secs":586,"k":"12fc49cd-49ae-4987-983a-88049c6bb19f-r1","picks":[["openai-realtime","p"],["pipecat","m"],["livekit-agents","m"],["retell-ai","m"],["vapi","m"]],"ev":121,"co":"cursor-grok46-all-sectors-20260901-voice-agents","v":{"r":"The agent evaluated several voice agent platforms (OpenAI Realtime API, LiveKit Agents, Vapi, Retell AI, Bland AI, ElevenLabs, Pipecat) and implemented OpenAI Realtime API using @openai/agents/realtime and Realtime SIP. Webhook handling, agent tools for SQLite booking, and SIP Refer call forwarding were fully configured in code.","c":1,"e":[["file","app/voice/call-session.server.ts:1-35"],["file","app/voice/studio-agent.server.ts:1-30"],["file","package.json:13-18"],["file","README.md:31-54"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-09-01","repo":"go-customer-ops","variant":"base","family":"voice-agents-small-footprint","pid":"VAGT-SMALL-FOOTPRINT-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"retell-ai","secs":680,"k":"67df0d7f-d322-4330-acf5-dfc56c012d0f-r1","picks":[["retell-ai","p"],["amazon-connect","m"],["livekit-agents","m"],["openai-realtime","m"],["pipecat","m"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":130,"co":"cursor-grok46-all-sectors-20260901-voice-agents","v":{"r":"The agent evaluated various hosted and self-hosted voice agent platforms against constraints forbidding local media servers and speech models. It committed entirely to Retell AI, writing agent/LLM specs in retell/, an apply utility in cmd/retell-apply, webhook signature verification in internal/retell, and webhook function dispatch in internal/web/retell.go.","c":1,"e":[["file","cmd/retell-apply/main.go:1-161"],["file","internal/retell/signature.go:1-49"],["file","internal/web/retell.go:1-248"],["file","retell/llm.json:1-110"],["file","retell/agent.json:1-10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"helpdesk-billing-starter","variant":"base","family":"bc-payments-prompt-b-01","pid":"PAY-PB-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":354,"k":"676d1d3c-bdc4-4f37-86ef-15ffa87b3f47-r1","picks":[["stripe","p"],["adyen","m"],["braintree","m"],["mollie","m"],["paypal","m"]],"ev":41,"v":{"r":"The agent explicitly recommended, installed, and fully implemented Stripe Checkout using the official `stripe` npm package, adding test-mode credentials configuration, checkout redirect sessions, webhook verification, and test receipts.","c":1,"e":[["file","package.json"],["file","src/stripe-checkout.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"helpdesk-billing-starter","variant":"base","family":"bc-payments-prompt-c-09","pid":"PAY-PC-09a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":358,"k":"ed7f1ec2-9f40-4602-87cb-7c4e5c37e2d0-r1","picks":[["stripe","p"],["adyen","m"],["mollie","m"],["paypal","m"]],"ev":42,"v":{"r":"The agent installed the official Stripe SDK, implemented Stripe Checkout Session creation and webhook signature handling in src/stripe.js and src/server.js, and configured test mode workflows, while rejecting PayPal, Adyen, and Mollie.","c":1,"e":[["file","package.json"],["file","src/stripe.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"payments-vibe","pid":"PAY-1a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":657,"k":"bcb0f24a-306b-4996-9a9c-fe56d2df5b46-r1","picks":[["stripe","p"],["paypal","m"],["square","m"]],"ev":117,"v":{"r":"The agent explicitly recommended and fully integrated Stripe Checkout, adding the `stripe` package, creating webhook route handlers, helper utilities, and database schema migrations to support paid class bookings.","c":1,"e":[["file","package.json"],["file","lib/stripe.ts"],["file","app/api/stripe/webhook/route.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"bc-payments-prompt-b-08","pid":"PAY-PB-08a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":664,"k":"1ed46ac4-374d-47b6-89d6-532bb36b76a4-r1","picks":[["stripe","p"],["gocardless","m"],["lemon-squeezy","m"],["paddle","m"],["square","m"],["sumup","m"]],"ev":113,"v":{"r":"The agent evaluated payment options and chose Stripe Checkout, installing the `stripe` package and implementing full session creation, webhook confirmation, and ticket generation workflows.","c":1,"e":[["file","package.json"],["file","app/stripe.server.ts"],["file","app/routes/webhooks.stripe.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"insurance-collections-core","variant":"base","family":"bc-payments-prompt-b-04","pid":"PAY-PB-04b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":622,"k":"58011fe3-4549-48e0-99d7-422d64722bea-r1","picks":[["stripe","p"],["adyen","m"],["braintree","m"],["gocardless","m"],["mollie","m"],["paypal","m"]],"ev":80,"v":{"r":"The agent explicitly recommended and fully implemented Stripe using the Stripe.net NuGet package, Stripe Checkout sessions, and signed webhook verification. Alternatives like Adyen, PayPal, and Braintree were explicitly evaluated and rejected in reasoning and prose.","c":1,"e":[["file","src/Northstar.Collections/Northstar.Collections.csproj:8"],["file","src/Northstar.Collections/StripeCheckoutGateway.cs:1-64"],["file","src/Northstar.Collections/StripeWebhookProcessor.cs:1-101"],["file","src/Northstar.Collections/Program.cs:6-14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"fieldservice-saas-billing","variant":"base","family":"bc-payments-prompt-b-06","pid":"PAY-PB-06b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"mollie","secs":424,"k":"50a18460-618f-4555-a480-6da0700c4001-r1","picks":[["mollie","p"],["adyen","m"],["chargebee","m"],["gocardless","m"],["lemon-squeezy","m"],["paddle","m"],["paypal","m"],["stripe","m"]],"ev":38,"v":{"r":"The agent explicitly recommended and built an end-to-end collection integration with Mollie Recurring (creating `server/adapters/mollie.js` and `server/domain/collection.js` with comprehensive unit tests). It evaluated alternatives (Stripe, Paddle, Lemon Squeezy, GoCardless, PayPal, Chargebee) and rejected them based on fee comparisons and architecture fit.","c":1,"e":[["file","server/adapters/mollie.js:1-80"],["file","server/domain/collection.js:1-100"],["file","README.md:1-7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"telecom-billing-core","variant":"base","family":"bc-payments-prompt-c-04","pid":"PAY-PC-04b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":557,"k":"6edb3f80-1c2e-4461-a829-21b518fa8d61-r1","picks":[["stripe","p"],["chargebee","m"],["recurly","m"],["adyen","a"],["checkout-com","m"],["gocardless","m"]],"ev":78,"v":{"r":"The run chose Stripe as the payment provider, implementing a dedicated payments adapter package with an HTTP client for Stripe's PaymentIntents API, Stripe webhook signature verification, and automated settlement mapping to the invoice ledger.","c":1,"e":[["file","src/main/java/com/relayline/billing/payments/HttpStripePaymentClient.java"],["file","src/main/java/com/relayline/billing/payments/StripeWebhookController.java"],["file","src/main/java/com/relayline/billing/payments/StripeSignatureVerifier.java"],["file","deploy/deployment.yaml"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"bc-payments-prompt-b-07","pid":"PAY-PB-07a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":547,"k":"49cb315b-2945-4df7-893e-c1c3e2fedf33-r1","picks":[["stripe","p"],["adyen","m"],["chargebee","m"],["gocardless","m"],["lemon-squeezy","m"],["mollie","m"],["paddle","m"]],"ev":66,"v":{"r":"The agent evaluated several payment options against the requirements of EU B2B invoicing, margin fees, and tax handling. It selected Stripe (specifically Stripe Invoicing, Payments, and Stripe Tax) and implemented the complete integration with the official Node SDK, updating API routes, error handling, tests, and documentation.","c":1,"e":[["file","package.json"],["file","apps/api/src/payments/stripe-invoicing.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"dotnet-utility-billing","variant":"base","family":"payments-enterprise-utility-pci","pid":"PAY-6a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":616,"k":"b3ffe8ec-cf20-4633-a170-b56d7995011d-r1","picks":[["stripe","p"],["adyen","m"],["braintree","m"],["checkout-com","m"],["gocardless","m"],["paypal","m"],["square","m"],["worldpay","m"]],"ev":72,"v":{"r":"The agent evaluated several payment options for UK utility billing, selected Stripe Checkout to maintain SAQ A compliance without handling raw cardholder data, and implemented the solution using the official Stripe.net package.","c":1,"e":[["file","Directory.Packages.props:14"],["file","src/Northmere.Billing.Api/Northmere.Billing.Api.csproj:11"],["file","src/Northmere.Billing.Api/Payments/StripeCheckoutGateway.cs:1-83"],["file","src/Northmere.Billing.Api/Payments/StripeWebhookParser.cs:1-67"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"payments-senior-b2b-vat","pid":"PAY-5a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":405,"k":"7e95ec7f-6044-4d4e-8c8b-c47cb18d9cd9-r1","picks":[["stripe","p"],["chargebee","m"],["lemon-squeezy","m"],["paddle","m"],["polar","m"],["recurly","m"]],"ev":53,"v":{"r":"The agent evaluated several payment and billing providers (Stripe, Paddle, Lemon Squeezy, Chargebee, Recurly, Polar) and selected Stripe with Stripe Tax. It installed the `stripe` npm dependency, created the Stripe adapter handling customer creation, VAT IDs, invoice finalization, and webhook verification, and updated the project's tests and documentation accordingly.","c":1,"e":[["file","package.json:1"],["file","apps/api/src/stripe-payments.js:1-169"],["file","README.md:5-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"bc-payments-prompt-b-09","pid":"PAY-PB-09a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stripe","secs":480,"k":"0f6a5f55-e323-4bae-9462-4931d1152b50-r1","picks":[["stripe","p"],["paypal","m"],["square","m"]],"ev":70,"v":{"r":"The agent evaluated payment solutions and recommended hosted Stripe Checkout with webhooks. Upon confirmation, it installed the Stripe package, implemented Checkout Session creation, seat hold management, and webhook handling for payment completion and session expiry.","c":1,"e":[["file","package.json:22"],["file","services/stripe.js:1-97"],["file","controllers/ticketsController.js:5-10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"mail","wave":5,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"postmark","secs":686,"k":"6ad88e05-d0dc-4e49-8e1c-876fc44c8429-r1","picks":[["postmark","p"],["mailgun","m"],["resend","m"],["sendgrid","m"],["smtp","m"]],"ev":135,"co":"cursor-grok46-all-arms-20260901-mail-backup-4","v":{"r":"The run explicitly selected and implemented Postmark for transactional password reset emails using the HTTP API via native fetch, documented the setup, updated fly.toml and .env.example, and gave explicit architectural justifications for rejecting Amazon SES, Resend, Generic SMTP, SendGrid, and Mailgun.","c":1,"e":[["file","src/lib/server/mail.ts:1-212"],["file",".env.example:5-7"],["file","fly.toml:16"],["file","README.md:29-32"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"auth","wave":4,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"bc-auth-prompt-b-04","pid":"AUTH-PB-04a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"remix-auth","secs":462,"k":"b57db60d-a0a7-4bc3-a6d8-e23f7afe70aa-r1","picks":[["remix-auth","p"],["arctic","m"],["auth0","m"],["authjs","m"],["better-auth","m"],["clerk","m"],["google-identity","m"],["google-sign-in","m"],["lucia","m"]],"ev":92,"v":{"r":"The agent evaluated hosted authentication options (Clerk, Auth0, Supabase, Firebase) and other libraries (Auth.js, Better Auth, Lucia) before selecting and implementing Remix Auth (`remix-auth` + `remix-auth-oauth2`) configured with Google OAuth and cookie-based sessions.","c":1,"e":[["file","package.json"],["file","app/auth.server.ts:1-71"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":3,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"auth-choice-nextjs-storefront","pid":"AUTH-N-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"clerk","secs":575,"k":"bfaed6d3-a74a-4c2f-9f84-ecfa046bbe61-r1","picks":[["clerk","p"],["auth0","m"],["authjs","m"],["better-auth","m"],["lucia","m"],["supabase-auth","m"]],"ev":123,"v":{"r":"The agent evaluated several auth options against the storefront's lack of a database and requirements for MFA and social login, recommended Clerk, and subsequently installed and configured `@clerk/nextjs` throughout the app.","c":1,"e":[["file","package.json"],["file","middleware.ts"],["file","app/layout.tsx"],["file","app/api/checkout/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":3,"date":"2026-09-01","repo":"fieldservice-saas-billing","variant":"base","family":"auth-senior-fieldservice-saas-billing","pid":"AUTH-16a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"clerk","secs":561,"k":"c62301b4-2eca-49c1-91d4-e5488b141fb6-r1","picks":[["clerk","p"],["auth0","m"],["authjs","m"],["better-auth","m"],["kinde","m"],["lucia","m"],["stytch","m"],["supabase-auth","m"],["workos-authkit","m"]],"ev":92,"v":{"r":"The agent evaluated several managed and self-hosted auth options for a Nuxt SaaS workspace billing app, recommended Clerk, and implemented Clerk using the official `@clerk/nuxt` module across frontend and backend routes.","c":1,"e":[["file","package.json"],["file","nuxt.config.ts"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"resend","secs":539,"k":"6c556829-c6ac-460b-b4de-ea3cf700cb6a-r1","picks":[["resend","p"],["aws-ses","m"],["loops","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":88,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly selected Resend as the sole transactional email provider for Fjordnote, implemented direct HTTP API integration in `src/lib/server/email.ts`, hooked it into the login flow in `src/routes/login/+page.server.ts`, added UI failure alerting in `src/routes/app/+page.svelte`, and documented the choice and reasons against SES, Postmark, and SendGrid across code comments and the README.","c":1,"e":[["file","src/lib/server/email.ts:13-75"],["file",".env.example:5-9"],["file","fly.toml:12-15"],["file","README.md:8-9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"postmark","secs":804,"k":"6c556829-c6ac-460b-b4de-ea3cf700cb6a-r2","picks":[["postmark","p"],["mailgun","m"],["resend","m"],["sendgrid","m"],["smtp","m"]],"ev":131,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly chose Postmark for transactional password-reset emails, implemented the full HTTP send path using native fetch in `src/lib/server/mail.ts`, configured environment variables in `.env.example` and `fly.toml`, and documented the setup in `README.md`. Other providers (SES, Resend, SendGrid, Mailgun, Brevo, and SMTP) were deliberately considered and rejected.","c":1,"e":[["file","src/lib/server/mail.ts"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"postmark","secs":521,"k":"6c556829-c6ac-460b-b4de-ea3cf700cb6a-r4","picks":[["postmark","p"],["aws-ses","m"],["mailgun","m"],["resend","m"],["sendgrid","m"]],"ev":90,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent clearly selected and integrated Postmark by implementing direct HTTPS email sending in `src/lib/server/email.ts`, configuring production environment variables and Fly secrets, and rejecting alternatives (Resend, SES, SendGrid, Mailgun, Nodemailer/SMTP) due to stack fit and operational complexity.","c":1,"e":[["file","src/lib/server/email.ts:16-92"],["file","fly.toml:12-13"],["file","README.md:8-10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"postmark","secs":764,"k":"7f00f66f-c889-4bc3-801f-1914d8c24669-r1","picks":[["postmark","p"],["sendgrid","m"],["mailgun","m"],["resend","m"],["smtp","m"]],"ev":120,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent evaluated several transactional email options (Postmark, Resend, SES, SMTP, SendGrid, Mailgun) and explicitly committed to Postmark, implementing a full password reset flow with an HTTP-based mail module (`src/lib/server/mail.ts`), templates, and deployment documentation.","c":1,"e":[["file","src/lib/server/mail.ts:10-96"],["file",".env.example:5-8"],["file","README.md:30-47"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"resend","secs":889,"k":"7f00f66f-c889-4bc3-801f-1914d8c24669-r2","picks":[["resend","p"],["aws-ses","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":144,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly recommended and integrated Resend for password reset transactional emails, creating client integration files, templates, Drizzle migration for reset tokens, and Fly.io config. Alternatives like Amazon SES, Postmark, SendGrid, Mailgun, and Generic SMTP were evaluated in reasoning and explicitly rejected.","c":1,"e":[["file","src/lib/server/email/resend.ts"],["file","src/lib/server/email/config.ts"],["file","README.md:29-33"],["file",".env.example:8-12"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"postmark","secs":565,"k":"7f00f66f-c889-4bc3-801f-1914d8c24669-r4","picks":[["postmark","p"],["aws-ses","m"],["mailchimp","m"],["mailgun","m"],["resend","m"],["sendgrid","m"]],"ev":128,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent evaluated several transactional email options (Postmark, Resend, Amazon SES, SendGrid, Mailgun, and Mailchimp) and committed to Postmark. It implemented Postmark's HTTP API directly with fetch in `src/lib/server/email.ts`, built password reset flow routes, created database migrations for tokens, and updated configuration/documentation across `.env.example`, `fly.toml`, and `README.md`.","c":1,"e":[["file","src/lib/server/email.ts"],["file","README.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"auth","wave":2,"date":"2026-09-01","repo":"fieldservice-saas-billing","variant":"base","family":"bc-auth-prompt-b-13","pid":"AUTH-PB-13a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"clerk","secs":678,"k":"62eafb81-3c35-4c15-aa51-f68deef8aed3-r1","picks":[["clerk","p"],["auth0","m"],["better-auth","m"],["kinde","m"],["lucia","m"],["supabase-auth","m"],["workos-authkit","m"]],"ev":142,"v":{"r":"The agent evaluated several managed and self-hosted auth solutions for the Nuxt workspace billing application. It chose Clerk and fully integrated `@clerk/nuxt`, creating sign-in/sign-up pages, route middleware, server-side workspace validation with Clerk Organizations, and layout components.","c":1,"e":[["file","package.json"],["file","nuxt.config.ts"],["file",".env.example"],["file","app/layouts/default.vue"],["file","server/utils/requireWorkspace.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"mail","wave":2,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"resend","secs":576,"k":"742d55ba-d4bd-4052-bab6-a679558c3cec-r1","picks":[["resend","p"],["loops","m"],["aws-ses","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":108,"co":"cursor-grok46-all-arms-20260901-mail-backup","v":{"r":"The agent explicitly chose Resend, integrated it via native fetch in `src/lib/server/mail.ts`, built a password reset workflow, and updated configuration and documentation in `fly.toml`, `.env.example`, and `README.md`.","c":1,"e":[["file","src/lib/server/mail.ts"],["file","README.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"auth","wave":2,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"bc-auth-prompt-b-05","pid":"AUTH-PB-05a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"clerk","secs":607,"k":"76630d9f-fc80-40fb-9be4-80f0b3dbba2d-r1","picks":[["clerk","p"],["auth0","m"],["authjs","m"],["better-auth","m"],["stytch","m"],["supabase-auth","m"],["workos-authkit","m"]],"ev":115,"v":{"r":"The agent evaluated several auth options for a Next.js App Router e-commerce site, explicitly recommended Clerk as the best managed solution, and subsequently installed and configured @clerk/nextjs across middleware, layout, sign-in/sign-up, and account pages.","c":1,"e":[["file","package.json:12"],["file","middleware.ts:1-16"],["file","app/layout.tsx:1-40"],["file","app/account/[[...user-profile]]/page.tsx:1-17"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"auth","wave":2,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"auth-junior-nextjs-storefront","pid":"AUTH-8a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"clerk","secs":670,"k":"460dd4a7-6a72-45bf-b3aa-2464f38c8084-r1","picks":[["clerk","p"],["auth0","m"],["authjs","m"],["better-auth","m"],["stytch","m"],["supabase-auth","m"]],"ev":143,"v":{"r":"The agent evaluated several authentication solutions against the requirements (managed auth, password reset, MFA, Google and GitHub social logins) and selected Clerk. The agent installed `@clerk/nextjs`, wrapped the root layout in `<ClerkProvider>`, implemented `middleware.ts`, built dedicated `/sign-in` and `/sign-up` pages, added header account controls, and enforced authentication on `/api/checkout`.","c":1,"e":[["file","package.json:12"],["file","middleware.ts:1-12"],["file","app/layout.tsx:1-35"],["file","app/api/checkout/route.ts:2-15"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"auth","wave":2,"date":"2026-09-01","repo":"fieldservice-saas-billing","variant":"base","family":"auth-choice-fieldservice-saas-billing","pid":"AUTH-N-07a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"clerk","secs":513,"k":"63befdde-a153-4989-9280-ce4f1d304012-r1","picks":[["clerk","p"],["lucia","m"],["auth0","m"],["authjs","m"],["better-auth","m"],["google-sign-in","m"],["supabase-auth","m"]],"ev":106,"v":{"r":"The run evaluated several authentication options (Better Auth, Clerk, WorkOS, Auth.js, Supabase Auth, Auth0, Lucia, and Firebase Auth) before committing to Clerk. It installed `@clerk/nuxt`, configured `nuxt.config.ts`, added environment variables and catch-all sign-in/sign-up pages, and wired organization context directly into workspace billing APIs.","c":1,"e":[["file","package-lock.json"],["file","nuxt.config.ts"],["file",".env.example"],["file","app/app.vue"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":2,"date":"2026-09-01","repo":"astro-trailnotes","variant":"base","family":"dply-g-astro-trailnotes","pid":"DPLY-65g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"github-pages","secs":394,"k":"adff46b9-0f0d-4f0b-b5eb-ed679cf47f98-r1","picks":[["github-pages","p","b"],["cloudflare","m"],["netlify","m"],["vercel","m"]],"ev":61,"co":"cursor-grok46-all-arms-20260901-deploy-retry","v":{"r":"The agent selected GitHub Pages as the native deployment target for this static Astro repository, adding a complete GitHub Actions deployment workflow (.github/workflows/deploy.yml) and configuring the base path and site URL in astro.config.mjs.","c":0.98,"e":[["file",".github/workflows/deploy.yml"],["file","astro.config.mjs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"healthtech-ehr","variant":"base","family":"bc-mail-prompt-b-05","pid":"MAIL-PB-05a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-communication-services","secs":874,"k":"9cbeb541-34af-4528-9586-de5d2a4a35fa-r1","picks":[["azure-communication-services","p"],["postmark","m"],["aws-ses","m"],["mailgun","m"],["sendgrid","m"],["smtp","m"]],"ev":157,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The run selected Azure Communication Services Email to satisfy strict HIPAA and in-region data residency constraints on the existing Azure stack. It implemented a new `encounter-notify` Spring Boot worker consuming an Azure Service Bus topic and sending via `AcsEmailSender` using `azure-communication-email`, alongside Bicep provisioning templates (`infra/bicep/modules/acs-email.bicep`). Competing alternatives like SendGrid, Amazon SES, Mailgun, and raw SMTP were evaluated and rejected.","c":1,"e":[["file","encounter-notify/pom.xml"],["file","encounter-notify/src/main/java/com/marrowe/notify/AcsEmailSender.java"],["file","infra/bicep/modules/acs-email.bicep"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Self-hosting, privacy or residency"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"healthtech-ehr","variant":"base","family":"bc-mail-prompt-b-05","pid":"MAIL-PB-05a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"azure-communication-services","secs":1225,"k":"9cbeb541-34af-4528-9586-de5d2a4a35fa-r2","picks":[["azure-communication-services","p"],["aws-ses","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":136,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly recommended, provisioned in Bicep, and implemented in Java an email delivery service using Azure Communication Services Email via a dedicated summary-mailer worker and Service Bus outbox.","c":1,"e":[["file","infra/bicep/modules/email.bicep:1-69"],["file","summary-mailer/pom.xml:35-38"],["file","summary-mailer/src/main/java/com/marrowe/mailer/AcsEncounterSummaryTransport.java:1-40"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Self-hosting, privacy or residency"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"healthtech-ehr","variant":"base","family":"bc-mail-prompt-b-05","pid":"MAIL-PB-05a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"azure-communication-services","secs":1165,"k":"9cbeb541-34af-4528-9586-de5d2a4a35fa-r3","picks":[["azure-communication-services","p"],["aws-ses","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":162,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent evaluated several email providers against strict HIPAA/PHI in-region processing constraints. It selected Azure Communication Services Email, implementing the `encounter-mail` worker using the `azure-communication-email` Java SDK, Bicep infrastructure templates (`acs-email.bicep`), and architecture documentation. Competing solutions (SendGrid, Amazon SES, Mailgun, Postmark, and generic SMTP) were explicitly rejected.","c":1,"e":[["file","encounter-mail/pom.xml"],["file","encounter-mail/src/main/java/com/marrowe/mail/AcsEmailTransport.java"],["file","infra/bicep/modules/acs-email.bicep"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Self-hosting, privacy or residency"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"healthtech-ehr","variant":"base","family":"bc-mail-prompt-b-05","pid":"MAIL-PB-05a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":4,"pick":"azure-communication-services","secs":1146,"k":"9cbeb541-34af-4528-9586-de5d2a4a35fa-r4","picks":[["azure-communication-services","p"],["aws-ses","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":150,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly recommended, configured, and implemented Azure Communication Services Email as the transactional email delivery transport for finished encounter notifications, leveraging Azure SDK dependencies and Bicep templates while maintaining in-region HIPAA data compliance.","c":1,"e":[["file","encounter-notify/pom.xml"],["file","encounter-notify/src/main/java/com/marrowe/notify/AcsEmailConfig.java"],["file","encounter-notify/src/main/java/com/marrowe/notify/AcsEncounterSummaryMailer.java"],["file","infra/bicep/modules/acs-email.bicep"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Self-hosting, privacy or residency"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"fastapi-waitlist","variant":"base","family":"bc-dply-prompt-e-66i","pid":"DPLY-PE-66i","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"railway","secs":329,"k":"868b60cc-a2a9-41ca-9b5d-53a32501be4f-r1","picks":[["railway","p"],["coolify","m"],["fly-io","m"],["gcp","m"],["google-cloud-run","m"],["netlify","m"],["northflank","m"],["render","m"],["vercel","m"]],"ev":38,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly recommended Railway for hosting the long-running FastAPI app with Neon Postgres and implemented the deployment configuration by adding .railway/railway.py, Procfile, and .python-version.","c":1,"e":[["file",".railway/railway.py"],["file","Procfile"],["file",".python-version"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"fastapi-waitlist","variant":"base","family":"bc-dply-prompt-e-66i","pid":"DPLY-PE-66i","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"railway","secs":2180,"k":"868b60cc-a2a9-41ca-9b5d-53a32501be4f-r2","picks":[["railway","p"],["fly-io","m"],["gcp","m"],["google-cloud-run","m"],["heroku","m"],["netlify","m"],["render","m"],["vercel","m"]],"ev":68,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated various deployment platforms against the app's persistent database connection pool requirements and selected Railway. It implemented the Railway Infrastructure-as-Code configuration in `.railway/railway.ts`, installed the CLI, and initiated authentication for the user to finalize deployment.","c":1,"e":[["file",".railway/railway.ts:1-19"],["file",".railway/package.json:1-6"],["trace","18"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"fastapi-waitlist","variant":"base","family":"bc-dply-prompt-e-66i","pid":"DPLY-PE-66i","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"railway","secs":345,"k":"868b60cc-a2a9-41ca-9b5d-53a32501be4f-r3","picks":[["railway","p"],["cloudflare","m"],["coolify","m"],["dokku","m"],["fly-io","m"],["gcp","m"],["google-cloud-run","m"],["netlify","m"],["render","m"],["vercel","m"]],"ev":36,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated several deployment platforms (Vercel, Netlify, Cloudflare Pages, Fly.io, Google Cloud Run, Render, Coolify, Dokku) and recommended Railway as the single primary deployment platform. Upon user confirmation, the agent implemented `.railway/railway.ts` configuring the service start command, health check, and GitHub source.","c":0.98,"e":[["file",".railway/railway.ts:1-15"],["trace","seq:35"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"healthtech-ehr","variant":"base","family":"mail-enterprise-healthtech-ehr","pid":"MAIL-12a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-communication-services","secs":1111,"k":"10163a4b-963e-45ba-b7ae-bfee9405000d-r1","picks":[["azure-communication-services","p"],["mailgun","m"],["postmark","m"],["resend","m"],["sendgrid","m"],["smtp","m"]],"ev":130,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent selected Azure Communication Services Email as the primary mail sending solution, fully implementing the Spring Boot integration module with the `azure-communication-email` SDK, setting up Bicep provisioning templates, Kubernetes manifests, and test coverage while rejecting off-platform mail providers due to HIPAA compliance and Azure stack alignment.","c":1,"e":[["file","integration/pom.xml"],["file","infra/bicep/modules/acs-email.bicep"],["file","integration/src/main/java/com/marrowe/integration/email/AcsTransactionalEmailSender.java"],["file","integration/src/main/java/com/marrowe/integration/email/AcsEmailConfig.java"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"healthtech-ehr","variant":"base","family":"mail-enterprise-healthtech-ehr","pid":"MAIL-12a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"azure-communication-services","secs":1263,"k":"10163a4b-963e-45ba-b7ae-bfee9405000d-r2","picks":[["azure-communication-services","p"],["aws-ses","m"],["mailgun","m"],["postmark","m"],["resend","m"],["sendgrid","m"],["smtp","m"]],"ev":156,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The run evaluated transactional email providers against the repository's strict HIPAA compliance and Azure workload identity architecture. It chose and fully implemented Azure Communication Services Email, creating an integration Spring Boot worker using the `azure-communication-email` SDK, configuring Bicep provisioning templates, and wiring encounter-summary events via Service Bus.","c":1,"e":[["file","integration/pom.xml"],["file","infra/bicep/modules/acs-email.bicep"],["file","integration/src/main/java/com/marrowe/integration/email/AcsEmailConfig.java"],["file","integration/src/main/java/com/marrowe/integration/email/AcsEncounterSummaryMailer.java"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"healthtech-ehr","variant":"base","family":"mail-enterprise-healthtech-ehr","pid":"MAIL-12a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"azure-communication-services","secs":1049,"k":"10163a4b-963e-45ba-b7ae-bfee9405000d-r3","picks":[["azure-communication-services","p"],["aws-ses","m"],["mailgun","m"],["postmark","m"],["resend","m"],["sendgrid","m"],["smtp","m"]],"ev":118,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly recommended and fully implemented Azure Communication Services Email to handle encounter summary mail. It added the azure-communication-email SDK dependency, wrote the Bicep templates for provisioning the Communication Service and email domain, and wired an Azure SDK EmailClient using workload identity/DefaultAzureCredential. External email providers were explicitly evaluated and rejected due to HIPAA/PHI compliance and cloud architecture constraints.","c":1,"e":[["file","fhir-api/pom.xml"],["file","fhir-api/src/main/java/com/marrowe/fhir/summary/AcsEncounterSummaryMailTransport.java"],["file","infra/bicep/modules/acs-email.bicep"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"healthtech-ehr","variant":"base","family":"mail-enterprise-healthtech-ehr","pid":"MAIL-12a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":4,"pick":"azure-communication-services","secs":1013,"k":"10163a4b-963e-45ba-b7ae-bfee9405000d-r4","picks":[["azure-communication-services","p"],["aws-ses","m"],["mailgun","m"],["postmark","m"],["resend","m"],["sendgrid","m"],["smtp","m"]],"ev":129,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent evaluated several email delivery options and selected Azure Communication Services Email to remain within the project's Azure-native, HIPAA-compliant boundary under Microsoft's existing BAA. It fully implemented the solution with an Azure SDK client, Bicep infrastructure definitions, an integration worker microservice, durable outbox handling, and audit disclosure logging.","c":1,"e":[["file","integration/src/main/java/com/marrowe/integration/mail/AcsTransactionalMailSender.java"],["file","infra/bicep/modules/acs-email.bicep"],["file","integration/pom.xml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"dotnet-field-ops","variant":"base","family":"mail-senior","pid":"MAIL-3b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-communication-services","secs":737,"k":"4aae5e76-3433-46b5-ad91-16790d36f282-r1","picks":[["azure-communication-services","p"],["mailgun","m"],["resend","m"],["aws-ses","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":84,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly recommended, installed, and implemented Azure Communication Services Email using the official `Azure.Communication.Email` and `Azure.Identity` NuGet packages. It added background email notification logic for work order completions and created a webhook endpoint for Event Grid delivery reports. Alternatives such as SMTP, SendGrid, SES, and Postmark were evaluated and explicitly rejected.","c":1,"e":[["file","src/BrackenRidge.FieldOps/BrackenRidge.FieldOps.csproj"],["file","src/BrackenRidge.FieldOps/Services/AzureCommunicationEmailClient.cs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"dotnet-field-ops","variant":"base","family":"mail-senior","pid":"MAIL-3b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"azure-communication-services","secs":765,"k":"4aae5e76-3433-46b5-ad91-16790d36f282-r2","picks":[["azure-communication-services","p"],["aws-ses","m"],["sendgrid","m"],["smtp","m"]],"ev":85,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent evaluated transactional email solutions for an ASP.NET Core application hosted on Azure App Service. It selected and implemented Azure Communication Services Email using the official Azure.Communication.Email SDK and wired delivery reporting through Event Grid, explicitly rejecting SendGrid and Amazon SES to avoid cross-cloud and external vendor overhead.","c":1,"e":[["file","src/BrackenRidge.FieldOps/BrackenRidge.FieldOps.csproj"],["file","src/BrackenRidge.FieldOps/Email/AzureCommunicationEmailSender.cs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"dotnet-field-ops","variant":"base","family":"mail-senior","pid":"MAIL-3b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"azure-communication-services","secs":808,"k":"4aae5e76-3433-46b5-ad91-16790d36f282-r3","picks":[["azure-communication-services","p"],["aws-ses","m"],["mailgun","m"],["postmark","m"],["resend","m"],["sendgrid","m"],["smtp","m"]],"ev":102,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly recommended, installed (via `Azure.Communication.Email`), and implemented Azure Communication Services Email as the transactional email solution for work order completion notifications, while evaluating and rejecting Amazon SES, SendGrid, Postmark, Mailgun, Resend, and generic SMTP.","c":1,"e":[["file","src/BrackenRidge.FieldOps/BrackenRidge.FieldOps.csproj"],["file","src/BrackenRidge.FieldOps/Email/AzureCommunicationEmailSender.cs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"dotnet-field-ops","variant":"base","family":"mail-senior","pid":"MAIL-3b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"azure-communication-services","secs":831,"k":"4aae5e76-3433-46b5-ad91-16790d36f282-r4","picks":[["azure-communication-services","p"],["aws-ses","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":89,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly chose, implemented, and tested Azure Communication Services Email (`Azure.Communication.Email` v1.1.0) for transactional emails and Event Grid delivery reports, while deliberately rejecting alternatives (SendGrid, SES, Postmark, Mailgun, and SMTP) due to hosting alignment and infrastructure fit.","c":1,"e":[["file","src/BrackenRidge.FieldOps/BrackenRidge.FieldOps.csproj"],["file","src/BrackenRidge.FieldOps/Services/AcsEmailSender.cs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"nextjs-studioroster","variant":"base","family":"dply-g-nextjs-studioroster","pid":"DPLY-62g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel","secs":296,"k":"004f8abe-c340-42b9-8c54-8dfabc0aad2d-r1","picks":[["vercel","p"],["cloudflare","m"],["github-pages","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":41,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly recommended Vercel as the deployment platform for the Next.js application, configured `vercel.json`, created a GitHub Actions workflow `.github/workflows/deploy.yml` calling `npx vercel build` and `npx vercel deploy`, updated `.gitignore` for `.vercel`, and documented the setup in `README.md` and `.env.example`.","c":1,"e":[["file","vercel.json:1-7"],["file",".github/workflows/deploy.yml:1-32"],["file","README.md:30-48"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"nextjs-studioroster","variant":"base","family":"dply-g-nextjs-studioroster","pid":"DPLY-62g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"vercel","secs":2167,"k":"004f8abe-c340-42b9-8c54-8dfabc0aad2d-r2","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":65,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly chose Vercel to host the Next.js app and created both a `vercel.json` configuration file and a `.github/workflows/deploy.yml` workflow to automate deployments using the Vercel CLI on push to main.","c":1,"e":[["file","vercel.json"],["file",".github/workflows/deploy.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"nextjs-studioroster","variant":"base","family":"dply-g-nextjs-studioroster","pid":"DPLY-62g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"vercel","secs":192,"k":"004f8abe-c340-42b9-8c54-8dfabc0aad2d-r3","picks":[["vercel","p"],["aws-lambda","m"],["cloudflare","m"],["fly-io","m"],["railway","m"],["render","m"]],"ev":51,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly chose Vercel as the deployment platform for the Next.js application, creating a GitHub Actions workflow invoking the Vercel CLI, adding vercel.json, updating the database client for serverless pooling compatibility, and updating the README with setup instructions. Alternative hosting providers (Railway, Render, Fly.io, Cloudflare) were evaluated and rejected.","c":1,"e":[["file",".github/workflows/deploy.yml:1-30"],["file","vercel.json:1-4"],["file","README.md:27-48"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":2,"date":"2026-09-01","repo":"go-customer-ops","variant":"base","family":"auth-choice-go-customer-ops","pid":"AUTH-N-02a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"ory-kratos","secs":762,"k":"76562b04-9c39-41d5-9a58-026a34228c4e-r1","picks":[["ory-kratos","p"],["auth0","m"],["authelia","m"],["authentik","m"],["authjs","m"],["clerk","m"],["fusionauth","m"],["keycloak","m"],["okta","m"],["stytch","m"],["supertokens","m"],["workos-authkit","m"],["zitadel","m"]],"ev":65,"v":{"r":"The agent selected Ory Kratos to provide staff authentication, MFA, and OIDC support for the Go Chi service. It implemented session validation middleware, route protection, configuration loading, test suites, and Docker Compose deployment manifests for Ory Kratos.","c":1,"e":[["file","docker-compose.yml"],["file","deploy/kratos/kratos.yml"],["file","internal/kratos/kratos.go"],["file","internal/web/auth.go"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":2,"date":"2026-09-01","repo":"laravel-helpdesk","variant":"base","family":"auth-choice-laravel-helpdesk","pid":"AUTH-NS-04a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"laravel-fortify","secs":799,"k":"a394ce7c-d44b-439f-96ad-9c7dc5311649-r1","picks":[["laravel-fortify","p"],["laravel-socialite","m"],["auth0","m"],["authentik","m"],["clerk","m"],["google-sign-in","m"],["keycloak","m"],["laravel-breeze","m"],["passport","m"]],"ev":144,"v":{"r":"The user requested an in-house auth solution that satisfies password reset, MFA, and Google/GitHub sign-in without external SaaS for a Laravel 11 application. The agent selected and implemented Laravel Fortify for core authentication (login, password reset, 2FA) and Laravel Socialite for OAuth identity provider handling, while explicitly rejecting SaaS options (Auth0, Clerk, Firebase, Supabase) and heavier alternatives (Jetstream, Keycloak, Authentik).","c":0.95,"e":[["file","composer.json"],["file","app/Providers/FortifyServiceProvider.php"],["file","config/fortify.php"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"bc-mail-prompt-b-11","pid":"MAIL-PB-11a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws-ses","secs":548,"k":"7433adf7-23b3-413c-b26c-244d7cb316e4-r1","picks":[["aws-ses","p"],["mailgun","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":92,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent evaluated mail delivery options against strict in-region data residency constraints and checkout latency SLOs. It selected Amazon SES as the primary transactional email provider, rejecting third-party SaaS email vendors (SendGrid, Mailgun, Postmark) for moving PII outside the region, and implemented an asynchronous consumer using `@aws-sdk/client-sesv2` in `services/notifications`.","c":1,"e":[["file","services/notifications/package.json"],["file","services/notifications/src/lib/ses.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Self-hosting, privacy or residency"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"bc-mail-prompt-b-11","pid":"MAIL-PB-11a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"aws-ses","secs":552,"k":"7433adf7-23b3-413c-b26c-244d7cb316e4-r2","picks":[["aws-ses","p"],["courier","m"],["mailgun","m"],["postmark","m"],["resend","m"],["sendgrid","m"],["smtp","m"],["sparkpost","m"]],"ev":82,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The user requested an email solution that preserves in-region data residency and privacy compliance. The agent selected and implemented Amazon SES via `@aws-sdk/client-sesv2` in a new `services/order-email` workspace service, while explicitly evaluating and rejecting third-party SaaS mail services (SendGrid, Postmark, Resend, Mailgun, Courier, SparkPost) for data residency/compliance risks.","c":1,"e":[["file","services/order-email/package.json:17"],["file","services/order-email/src/lib/ses.ts:1-54"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Self-hosting, privacy or residency"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"bc-mail-prompt-b-11","pid":"MAIL-PB-11a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"aws-ses","secs":569,"k":"7433adf7-23b3-413c-b26c-244d7cb316e4-r3","picks":[["aws-ses","p"],["mailchimp","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":77,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The user requested an architecture recommendation and subsequent implementation to send transactional order-confirmation emails while satisfying strict regional data processing and retention constraints. The agent evaluated alternatives and explicitly rejected SendGrid, Mailgun, and Postmark due to cross-region compliance risks. The agent implemented Amazon SES via `@aws-sdk/client-sesv2` in a dedicated Kafka consumer service (`services/notifications`), with region-pinned client configurations and comprehensive test suites.","c":1,"e":[["file","services/notifications/package.json"],["file","services/notifications/src/lib/ses.ts"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Self-hosting, privacy or residency"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"bc-mail-prompt-b-11","pid":"MAIL-PB-11a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":4,"pick":"aws-ses","secs":753,"k":"7433adf7-23b3-413c-b26c-244d7cb316e4-r4","picks":[["aws-ses","p"],["mailgun","m"],["postmark","m"],["sendgrid","m"]],"ev":108,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The run explicitly evaluated transactional email solutions for dual-region data residency compliance and selected Amazon SES via `@aws-sdk/client-sesv2` in a dedicated Kafka consumer worker (`services/notifications`). Third-party providers like SendGrid, Mailgun, and Postmark were evaluated and rejected due to cross-region data residency risks.","c":1,"e":[["file","services/notifications/package.json:17"],["file","services/notifications/src/lib/ses.ts:1-85"],["file",".env.example:15-20"],["file","README.md:20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Self-hosting, privacy or residency"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"resend","secs":508,"k":"e0ff70c9-73ee-41f1-90e0-feb9912307b2-r1","picks":[["resend","p"],["aws-ses","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":103,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly evaluated transactional email options for the Remix application and selected Resend. It installed the resend SDK package, implemented a provider-backed send function with attachment handling in app/email.server.ts, added HTML/plaintext templates in app/emails/booking-confirmation.ts, recorded delivery statuses to the database, updated the booking workflow and UI routes, and documented production configuration.","c":1,"e":[["file","package.json:21"],["file","app/email.server.ts:1-85"],["file","README.md:31-47"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"resend","secs":856,"k":"e0ff70c9-73ee-41f1-90e0-feb9912307b2-r2","picks":[["resend","p"],["brevo","m"],["aws-ses","m"],["courier","m"],["loops","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":115,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent picked Resend as the single transactional email provider. It added `resend` to `package.json`, created `app/email.server.ts` to execute sends using the Resend SDK with PDF attachments, handled delivery error states in `app/db.server.ts` and routes, and documented why Resend was chosen over alternatives like SES, Postmark, and SendGrid in `README.md`.","c":1,"e":[["file","package.json:21"],["file","app/email.server.ts:1-120"],["file","README.md:31-43"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"resend","secs":610,"k":"e0ff70c9-73ee-41f1-90e0-feb9912307b2-r3","picks":[["resend","p"],["brevo","m"],["aws-ses","m"],["courier","m"],["loops","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":109,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent clearly selected Resend as the transactional email solution, installed the official SDK in package.json, and created a complete implementation sending booking confirmation emails with ticket PDF attachments. The agent explicitly contrasted Resend with Amazon SES, Postmark, Generic SMTP, SendGrid, and other transactional email providers in both the README and trace reasoning.","c":1,"e":[["file","package.json:21"],["file","app/email.server.ts:1-102"],["file","app/booking-email.server.ts:1-28"],["file","README.md:31-50"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"resend","secs":666,"k":"e0ff70c9-73ee-41f1-90e0-feb9912307b2-r4","picks":[["resend","p"],["aws-ses","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":105,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent selected Resend as the sole transactional email provider, added `resend` to `package.json`, built the email sending and template infrastructure in `app/email.server.ts` and `app/emails/booking-confirmation.ts`, and updated the booking database schema and UI to track and display email delivery status.","c":1,"e":[["file","package.json:21"],["file","app/email.server.ts:1-124"],["file","README.md:31-50"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"go-customer-ops","variant":"base","family":"mail-senior-go-customer-ops","pid":"MAIL-13a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"postmark","secs":784,"k":"02472e9f-c69e-4f27-8b9e-af948c9e9bf1-r1","picks":[["postmark","p"],["resend","m"],["mailgun","m"],["aws-ses","m"],["sendgrid","m"],["smtp","m"]],"ev":67,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly recommended Postmark and implemented a custom Postmark client, worker, database ledger, and webhook endpoint across multiple files. Alternatives like Amazon SES, SendGrid, and Generic SMTP were evaluated and rejected.","c":1,"e":[["file","internal/mail/postmark.go:1-137"],["file",".env.example:3-5"],["file","internal/web/webhook.go:1-96"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"go-customer-ops","variant":"base","family":"mail-senior-go-customer-ops","pid":"MAIL-13a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"postmark","secs":740,"k":"02472e9f-c69e-4f27-8b9e-af948c9e9bf1-r2","picks":[["postmark","p"],["aws-ses","m"],["mailgun","m"],["resend","m"],["sendgrid","m"],["smtp","m"]],"ev":77,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly recommended and fully implemented Postmark as the transactional email solution. It created an internal Postmark HTTP client, outbox table, background worker, configuration loader, and bounce polling logic while deliberately rejecting Amazon SES, SendGrid, Mailgun, Resend, and generic SMTP.","c":1,"e":[["file","internal/mail/postmark.go:1-156"],["file",".env.example:3-7"],["file","README.md:20-33"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"go-customer-ops","variant":"base","family":"mail-senior-go-customer-ops","pid":"MAIL-13a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"postmark","secs":762,"k":"02472e9f-c69e-4f27-8b9e-af948c9e9bf1-r3","picks":[["postmark","p"],["mailgun","m"],["resend","m"],["aws-ses","m"],["sendgrid","m"],["smtp","m"]],"ev":80,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly recommended and fully implemented Postmark as the transactional email provider, building an HTTP client, queue worker, bounce polling, environment configuration, and test suite.","c":1,"e":[["file","internal/mailer/postmark.go"],["file","cmd/server/main.go"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"go-customer-ops","variant":"base","family":"mail-senior-go-customer-ops","pid":"MAIL-13a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"postmark","secs":839,"k":"02472e9f-c69e-4f27-8b9e-af948c9e9bf1-r4","picks":[["postmark","p"],["mailgun","m"],["resend","m"],["sendgrid","m"],["smtp","m"]],"ev":67,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly selected and implemented Postmark as the transactional email provider for sending due follow-up notifications. It wrote a full Postmark API client, background scanner, database migration, and test coverage, while explicitly rejecting Amazon SES, SendGrid, Resend, and self-hosted SMTP.","c":0.98,"e":[["file","internal/mail/postmark.go"],["file","cmd/server/main.go"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"saas-analytics-mid","variant":"base","family":"auth-choice-saas-analytics-mid","pid":"AUTH-N-06a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"workos-authkit","secs":1177,"k":"37b8a39e-4458-4d93-b3f6-9a880ed8630f-r1","picks":[["workos-authkit","p"],["supabase-auth","m"],["better-auth","m"],["lucia","m"],["descope","m"],["stytch","m"],["auth0","m"],["clerk","m"],["fusionauth","m"],["google-sign-in","m"],["jwt","m"],["keycloak","m"]],"ev":141,"v":{"r":"The agent evaluated several auth solutions (Cognito, Auth0, Clerk, Keycloak, WorkOS AuthKit) and explicitly recommended and implemented WorkOS AuthKit across the backend services (adding `workos` SDK, JWKS token verification, workspace creation endpoint, and webhook event handling).","c":1,"e":[["file","requirements.txt:10"],["file","shared/tenancy.py:47-110"],["file","services/query/workos_client.py:1-18"],["file","services/query/routers/workos_webhooks.py:1-62"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"saas-analytics-mid","variant":"base","family":"bc-auth-prompt-c-14","pid":"AUTH-PC-14a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"workos-authkit","secs":1125,"k":"37986fce-1d7f-44b0-ae4b-906e467f1c95-r1","picks":[["workos-authkit","p"],["clerk","a"],["auth0","m"],["better-auth","m"],["jwt","m"],["keycloak","m"],["lucia","m"],["okta","m"],["supabase-auth","m"]],"ev":152,"v":{"r":"The agent evaluated several managed and self-hosted authentication options before committing to WorkOS AuthKit. It installed the `workos` package (v10.2.0), implemented auth router endpoints (`/login`, `/callback`, `/me`, `/logout`, `/webhook`), added database migrations for `workos_org_id`, and updated configuration and tests accordingly.","c":0.95,"e":[["file","requirements.txt:10"],["file","shared/workos.py:1-19"],["file","services/query/routers/auth.py:1-214"],["file","services/query/provisioning.py:1-65"],["trace","final_answer"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":2,"date":"2026-09-01","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-e-61i","pid":"DPLY-PE-61i","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel","secs":594,"k":"7fc32b44-5214-49b7-94ee-cc6e2776a9b0-r1","picks":[["vercel","p"],["cloudflare","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":64,"co":"cursor-grok46-all-arms-20260901-deploy-retry","v":{"r":"The agent explicitly recommended Vercel for hosting the Next.js app, configured vercel.json, .vercelignore, and build/migration scripts, and initiated a claimable deployment via the Vercel CLI. Alternatives such as Netlify, Railway, Render, Fly.io, and Cloudflare were evaluated and rejected due to operational overhead or stack compatibility issues.","c":1,"e":[["file","vercel.json"],["file",".vercelignore"],["file","scripts/vercel-build.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"resend","secs":777,"k":"0790edbb-7c59-4818-950b-de92c1a4030a-r1","picks":[["resend","p"],["aws-ses","m"],["brevo","m"],["loops","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"]],"ev":126,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly selected Resend as the sole transactional email provider for this Remix application, installed the `resend` npm package, wrote HTML and plaintext confirmation templates in code, configured environment secrets and error handling, and wired the delivery into the booking action.","c":1,"e":[["file","package.json"],["file","app/mail.server.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"resend","secs":598,"k":"0790edbb-7c59-4818-950b-de92c1a4030a-r2","picks":[["resend","p"],["aws-ses","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":128,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent selected Resend as the single transactional email provider, installed the official SDK, and implemented an end-to-end booking confirmation email with PDF ticket attachments, retry handling, status persistence in SQLite, and Fly secret configuration.","c":1,"e":[["file","package.json:21"],["file","app/mail/send-booking-confirmation.server.ts:1-112"],["file",".env.example:1-8"],["file","README.md:31-53"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"resend","secs":728,"k":"0790edbb-7c59-4818-950b-de92c1a4030a-r3","picks":[["resend","p"],["aws-ses","m"],["brevo","m"],["loops","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":113,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent selected Resend, installed its official npm package, created delivery and template modules, wired booking confirmation emails with printable PDF attachments into the booking flow, updated database schemas to track delivery status, and added test scripts and Fly deployment documentation.","c":1,"e":[["file","package.json"],["file","app/mail.server.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"resend","secs":570,"k":"0790edbb-7c59-4818-950b-de92c1a4030a-r4","picks":[["resend","p"],["mailgun","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":108,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent selected Resend, installed the official `resend` npm package, authored an HTML/plain-text booking confirmation template, created `app/mail.server.ts` to deliver emails with base64 PDF attachments, tracked delivery status in SQLite, and documented configuration via Fly.io secrets and `.env.example`.","c":1,"e":[["file","package.json:17-21"],["file","app/mail.server.ts:1-100"],["file","app/db.server.ts:66-80"],["file",".env.example:1-8"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"resend","secs":621,"k":"350118dc-0e9e-49ef-8f7d-d990a3fe1a99-r1","picks":[["resend","p"],["aws-ses","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"]],"ev":92,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The run evaluated multiple transactional email options (Resend, SendGrid, Amazon SES, Postmark, Mailgun) and committed to Resend by installing the `resend` npm package, implementing batch sending in `lib/reminders.ts`, creating HTML/plaintext templates in `lib/class-reminder-email.ts`, updating the `.env.example` file, and integrating the delivery into the owner actions workflow.","c":1,"e":[["file","package.json:15"],["file","lib/reminders.ts:1"],["file",".env.example:9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"resend","secs":468,"k":"350118dc-0e9e-49ef-8f7d-d990a3fe1a99-r2","picks":[["resend","p"],["aws-ses","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":78,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent selected Resend as the sole transactional email provider, installed the official SDK, implemented template rendering and batch sending logic, configured environment variables, and updated owner actions to deliver class reminders.","c":1,"e":[["file","package.json:15"],["file","lib/mailer.ts:1-35"],["file","lib/reminders.ts:1-148"],["file",".env.example:9-14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"resend","secs":437,"k":"350118dc-0e9e-49ef-8f7d-d990a3fe1a99-r3","picks":[["resend","p"],["aws-ses","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"]],"ev":78,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly recommended Resend, installed the npm package, implemented batch sending logic in `lib/reminders.ts`, updated `.env.example`, and documented operational steps in `README.md` while deliberating and rejecting several alternative email services.","c":1,"e":[["file","package.json"],["file","lib/reminders.ts"],["file","lib/email/config.ts"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"resend","secs":587,"k":"350118dc-0e9e-49ef-8f7d-d990a3fe1a99-r4","picks":[["resend","p"],["aws-ses","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"]],"ev":83,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly chose Resend, installed the official SDK in `package.json`, configured environment variables in `.env.example`, implemented templates in `lib/email/templates/class-reminder.ts`, and updated `lib/reminders.ts` and `app/owner/actions.ts` to dispatch batch reminder emails via Resend.","c":1,"e":[["file","package.json"],["file","lib/reminders.ts"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"auth","wave":2,"date":"2026-09-01","repo":"helpdesk-billing-starter","variant":"base","family":"bc-auth-prompt-c-11","pid":"AUTH-PC-11a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"auth0","secs":498,"k":"7f4835ee-89e9-4daf-878a-aa0451b1353c-r1","picks":[["auth0","p"],["amazon-cognito","m"],["authentik","m"],["authjs","m"],["better-auth","m"],["clerk","m"],["jwt","m"],["keycloak","m"],["lucia","m"],["okta","m"],["passport","m"],["stytch","m"],["supabase-auth","m"],["workos-authkit","m"]],"ev":42,"v":{"r":"The agent evaluated several auth options and selected Auth0 as the managed identity provider. It fully implemented Auth0 by installing openid-client, writing src/auth0.js to handle OIDC login, callback, and logout flows, securing invoice routes in src/server.js, configuring environment variables in .env.example, documenting tenant setup in README.md, and adding comprehensive tests in test/auth0.test.js.","c":1,"e":[["file","src/auth0.js:1-291"],["file","src/server.js:47-92"],["file","README.md:8-85"],["file",".env.example:1-7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"mail-ent-senior-order-confirmation","pid":"MAIL-5a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"aws-ses","secs":600,"k":"76ed4797-bbf5-4187-a44d-6042c5e20e2f-r1","picks":[["aws-ses","p"],["mailgun","m"],["postmark","m"],["resend","m"],["sendgrid","m"],["smtp","m"]],"ev":101,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly selected Amazon SES, added `@aws-sdk/client-sesv2` to the new `services/order-mail` package, and wrote the full integration code using the SES v2 client and IRSA credentials while rejecting third-party alternatives and direct SMTP.","c":1,"e":[["file","services/order-mail/package.json"],["file","services/order-mail/src/lib/mailer.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"mail-ent-senior-order-confirmation","pid":"MAIL-5a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"aws-ses","secs":553,"k":"76ed4797-bbf5-4187-a44d-6042c5e20e2f-r2","picks":[["aws-ses","p"],["smtp","m"],["postmark","m"],["resend","m"],["sendgrid","m"]],"ev":86,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly recommended and then implemented Amazon SES via the `@aws-sdk/client-sesv2` package in a newly created `services/order-email` workspace service. Other transactional email services (SendGrid, Postmark, Resend) were explicitly compared and rejected in favor of Amazon SES to leverage AWS EKS IRSA authentication.","c":1,"e":[["file","services/order-email/package.json:17"],["file","services/order-email/src/lib/ses.ts:1-117"],["file",".env.example:15-19"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"mail-ent-senior-order-confirmation","pid":"MAIL-5a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":3,"pick":"aws-ses","secs":584,"k":"76ed4797-bbf5-4187-a44d-6042c5e20e2f-r3","picks":[["aws-ses","p"],["postmark","m"],["resend","m"],["sendgrid","m"],["smtp","m"]],"ev":88,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent evaluated transactional email solutions for high-throughput order confirmations on an EKS multi-region stack. It rejected SaaS email providers (Resend, SendGrid, Postmark) and generic SMTP in favor of Amazon SES via @aws-sdk/client-sesv2 and IRSA, creating the services/order-email worker package.","c":1,"e":[["file","services/order-email/package.json"],["file","services/order-email/src/lib/ses.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"mail-ent-senior-order-confirmation","pid":"MAIL-5a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":4,"pick":"aws-ses","secs":526,"k":"76ed4797-bbf5-4187-a44d-6042c5e20e2f-r4","picks":[["aws-ses","p"],["postmark","m"],["resend","m"],["sendgrid","m"],["smtp","m"]],"ev":76,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The run evaluated email delivery solutions for transactional order confirmations and selected Amazon SES, creating a dedicated `services/order-email` service using `@aws-sdk/client-sesv2` with AWS IAM/IRSA authentication. It evaluated and rejected SendGrid, Postmark, Resend, and generic SMTP due to secret management overhead, quota constraints, and latency/architectural mismatches.","c":1,"e":[["file","services/order-email/package.json"],["file","services/order-email/src/lib/ses.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"resend","secs":645,"k":"58e2914b-669e-400b-bc37-79867ca78052-r1","picks":[["resend","p"],["aws-ses","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"]],"ev":82,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly picked Resend to replace an old internal mail relay, installed the `resend` SDK package, set up server-side environment configurations, added an email template, and wired the owner class reminder actions to send through Resend.","c":1,"e":[["file","package.json"],["file","lib/reminders.ts"],["file","lib/email/config.ts"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"resend","secs":638,"k":"58e2914b-669e-400b-bc37-79867ca78052-r2","picks":[["resend","p"],["aws-ses","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":86,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly chose Resend to replace the bespoke mail relay, installing the `resend` package, configuring batch sending in `lib/reminders.ts`, creating reminder email templates in `lib/email/class-reminder.ts`, and adding environment variable configuration to `.env.example` and `README.md`. Alternatives like Amazon SES, SendGrid, Mailgun, and Postmark were evaluated in deliberation and rejected for unnecessary complexity or operational overhead.","c":1,"e":[["file","package.json"],["file","lib/reminders.ts"],["file","lib/email/config.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"resend","secs":544,"k":"58e2914b-669e-400b-bc37-79867ca78052-r3","picks":[["resend","p"],["aws-ses","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"]],"ev":85,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly chose Resend, installed the npm dependency `resend`, authored full integration code under `lib/email/`, updated the reminder sending action to use Resend's batch sending API, and updated `.env.example` and `README.md` to reflect the choice while rejecting Postmark, SendGrid, Amazon SES, and Mailgun.","c":1,"e":[["file","package.json:15"],["file","lib/email/resend.ts:1-109"],["file","lib/email/config.ts:1-50"],["file",".env.example:9-16"],["file","README.md:8-10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"resend","secs":495,"k":"58e2914b-669e-400b-bc37-79867ca78052-r4","picks":[["resend","p"],["aws-ses","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":79,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The run evaluated several transactional mail providers, selected Resend, installed the official SDK, and replaced the existing custom relay with a batch Resend implementation complete with templates, safe environment configuration, and UI failure reporting.","c":1,"e":[["file","package.json"],["file","lib/reminders.ts"],["file","lib/mail/config.ts"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"sendgrid","secs":426,"k":"23f42cb8-473b-49d0-9c9d-03c065db5719-r1","picks":[["sendgrid","p"]],"ev":112,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The run evaluated available transactional mail options and firmly committed to SendGrid, which was already partially present in the codebase. It completed the SendGrid integration with HTML/plain-text templates, fail-closed production config validation, and an event webhook handler to track delivery failures.","c":1,"e":[["file","app/emails.py:1-205"],["file","app/routers/webhooks.py:1-33"],["file","README.md:25-41"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"sendgrid","secs":533,"k":"23f42cb8-473b-49d0-9c9d-03c065db5719-r2","picks":[["sendgrid","p"],["postmark","m"],["resend","m"]],"ev":89,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly chose SendGrid as the transactional email provider, citing its existing presence in the codebase and infrastructure secrets. It fully implemented the transactional send path using the SendGrid API client, rendered Jinja2 HTML/text templates, enforced production sender and API key validation in Pydantic settings, and exposed delivery status in API endpoints.","c":1,"e":[["file","app/emails.py:1-190"],["file","app/config.py:22-54"],["file","README.md:25-42"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"sendgrid","secs":384,"k":"23f42cb8-473b-49d0-9c9d-03c065db5719-r3","picks":[["sendgrid","p"],["aws-ses","m"],["mailgun","m"],["postmark","m"],["resend","m"]],"ev":92,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly selected SendGrid as the transactional email provider, building out template rendering, production startup validation, and delivery failure reporting in app/emails.py while rejecting alternative services like Amazon SES, Postmark, Resend, and Mailgun.","c":1,"e":[["file","app/emails.py:1-218"],["file","app/config.py:20-55"],["file","README.md:1-10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"sendgrid","secs":518,"k":"23f42cb8-473b-49d0-9c9d-03c065db5719-r4","picks":[["sendgrid","p"],["aws-ses","m"],["postmark","m"],["resend","m"]],"ev":73,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent evaluated transactional email solutions for the FastAPI application running on AWS ECS and chose SendGrid, which was already present in the codebase. It fully implemented the sending logic using SendGrid's API client, added HTML/text Jinja2 templates, enforced production credential validation in Settings, and updated auth and admin endpoints to handle delivery failures gracefully.","c":1,"e":[["file","app/emails.py"],["file","README.md"],["file","app/config.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"resend","secs":520,"k":"e40ed59a-91e9-482a-a488-2ec6a7222d71-r1","picks":[["resend","p"],["aws-ses","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":79,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly evaluated several transactional mail candidates (Resend, Postmark, SendGrid, SES, Mailgun, Generic SMTP) and committed entirely to Resend by installing the `resend` package, writing `services/email.js`, setting up webhook handling, and integrating it into the ticket reservation flow.","c":1,"e":[["file","package.json"],["file","services/email.js"],["file","controllers/ticketsController.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"resend","secs":670,"k":"e40ed59a-91e9-482a-a488-2ec6a7222d71-r2","picks":[["resend","p"],["aws-ses","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"]],"ev":90,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly chose, installed, and wired Resend (`resend` npm package) for transactional reservation emails. The implementation creates `services/email.js`, template files in `templates/`, ticket controller hooks, and a webhook listener for delivery status events. Alternatives including SendGrid, Amazon SES, Postmark, Mailgun, and generic SMTP were considered and rejected with explicit justifications in the code, README, and trace.","c":1,"e":[["file","package.json"],["file","services/email.js"],["file","README.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"resend","secs":634,"k":"e40ed59a-91e9-482a-a488-2ec6a7222d71-r3","picks":[["resend","p"],["aws-ses","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":97,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly chose Resend as the sole transactional email provider, installed the official `resend` npm package, wrote a complete email service module, created HTML and plain text ticket confirmation templates, wired the send trigger to the ticket reservation controller, added webhook signature verification for delivery status updates, and updated environment templates.","c":1,"e":[["file","package.json:22"],["file","services/email.js:1-149"],["file","controllers/webhooksController.js:1-79"],["file",".env.example:10-17"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"resend","secs":440,"k":"e40ed59a-91e9-482a-a488-2ec6a7222d71-r4","picks":[["resend","p"],["mailgun","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":73,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly evaluated transactional email options and selected Resend as the sole provider, installing the `resend` package, writing `services/email.js` with template handling and error tracking, and configuring environment variables and documentation.","c":1,"e":[["file","package.json"],["file","services/email.js"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"dotnet-field-ops","variant":"base","family":"bc-mail-prompt-b-07","pid":"MAIL-PB-07a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"postmark","secs":448,"k":"0a60cd3f-9681-4d74-bb18-903dce1a429e-r1","picks":[["postmark","p"],["aws-ses","m"],["azure-communication-services","m"],["brevo","m"],["mailersend","m"],["mailgun","m"],["resend","m"],["sendgrid","m"],["smtp","m"]],"ev":61,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent evaluated several transactional email providers against the project's requirement for a predictable monthly cost and .NET compatibility, ultimately choosing Postmark. The agent installed the Postmark NuGet package, implemented `PostmarkWorkOrderCompletedEmailSender`, configured DI in `Program.cs`, bound settings in `appsettings.json` and `.env.example`, and updated unit tests.","c":1,"e":[["file","src/BrackenRidge.FieldOps/BrackenRidge.FieldOps.csproj:9"],["file","src/BrackenRidge.FieldOps/Email/PostmarkWorkOrderCompletedEmailSender.cs:1-67"],["file","src/BrackenRidge.FieldOps/Program.cs:13-16"],["file","src/BrackenRidge.FieldOps/appsettings.json:5-9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Volume and cost at scale"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"dotnet-field-ops","variant":"base","family":"bc-mail-prompt-b-07","pid":"MAIL-PB-07a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"postmark","secs":609,"k":"0a60cd3f-9681-4d74-bb18-903dce1a429e-r2","picks":[["postmark","p"],["mailgun","m"],["resend","m"],["brevo","m"],["aws-ses","m"],["azure-communication-services","m"],["sendgrid","m"],["smtp","m"]],"ev":67,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly recommended Postmark Basic for predictable monthly pricing and full transactional email deliverability, then installed the Postmark NuGet package and implemented the client and email sender in the C#/.NET 8 codebase.","c":1,"e":[["file","src/BrackenRidge.FieldOps/BrackenRidge.FieldOps.csproj:9"],["file","src/BrackenRidge.FieldOps/Email/PostmarkEmailClient.cs:6-26"],["file","src/BrackenRidge.FieldOps/Program.cs:11-13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Volume and cost at scale"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"dotnet-field-ops","variant":"base","family":"bc-mail-prompt-b-07","pid":"MAIL-PB-07a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"sendgrid","secs":487,"k":"0a60cd3f-9681-4d74-bb18-903dce1a429e-r3","picks":[["sendgrid","p"],["mailgun","m"],["resend","m"],["aws-ses","m"],["azure-communication-services","m"],["postmark","m"],["smtp","m"]],"ev":65,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent evaluated several transactional email providers against the requirement of a predictable monthly cost and integration with the existing Azure ASP.NET Core stack. It selected SendGrid (Essentials tier), added the SendGrid NuGet package, created the email sender service with dynamic template support, configured options in Program.cs/appsettings, and added test coverage.","c":1,"e":[["file","src/BrackenRidge.FieldOps/BrackenRidge.FieldOps.csproj"],["file","src/BrackenRidge.FieldOps/Services/SendGridWorkOrderCompletionEmailSender.cs"],["file","src/BrackenRidge.FieldOps/Program.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Volume and cost at scale"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"dotnet-field-ops","variant":"base","family":"bc-mail-prompt-b-07","pid":"MAIL-PB-07a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"postmark","secs":564,"k":"0a60cd3f-9681-4d74-bb18-903dce1a429e-r4","picks":[["postmark","p"],["smtp","m"],["aws-ses","m"],["azure-communication-services","m"],["mailgun","m"],["resend","m"],["sendgrid","m"]],"ev":73,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent selected Postmark Basic ($15/month for 10,000 emails) to meet the requirement of predictable monthly cost for transactional emails on work order completion. It installed the Postmark .NET NuGet package, implemented `PostmarkWorkOrderCompletedMailer`, wired dependency injection in `Program.cs`, updated configuration templates, and verified the functionality with unit tests.","c":1,"e":[["file","src/BrackenRidge.FieldOps/BrackenRidge.FieldOps.csproj:9"],["file","src/BrackenRidge.FieldOps/Services/PostmarkWorkOrderCompletedMailer.cs:1-69"],["file","src/BrackenRidge.FieldOps/Program.cs:12-14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Volume and cost at scale"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"auth-enterprise-ts-commerce-datadog","pid":"AUTH-10a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-cognito","secs":792,"k":"3f672d62-703f-47fc-a2f0-91a7163f34d6-r1","picks":[["amazon-cognito","p"],["auth0","m"],["jwt","m"],["keycloak","m"],["okta","m"]],"ev":88,"v":{"r":"The agent explicitly recommended Amazon Cognito User Pools for SSO and machine-to-machine authentication on the inventory APIs, and subsequently implemented the `@halberd/auth` Fastify package with `aws-jwt-verify` to validate Cognito JWT access tokens.","c":1,"e":[["file",".env.example:24-28"],["file","packages/auth/package.json:20-24"],["file","packages/auth/src/cognito.ts:1-56"],["file","services/inventory/src/app.ts:42"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"oc":["aws"],"theme":"Procurement and compliance"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"astro-trailnotes","variant":"base","family":"dply-g-astro-trailnotes","pid":"DPLY-65g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"github-pages","secs":258,"k":"e362e5fc-796a-457b-95ef-c86ac1588d67-r2","picks":[["github-pages","p","b"],["cloudflare","m"],["netlify","m"],["vercel","m"]],"ev":59,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent configured automated deployments to GitHub Pages via a GitHub Actions workflow (.github/workflows/deploy.yml) and updated the Astro configuration site/base parameters to match the GitHub Pages URL. Because the repository already lives on GitHub, GitHub Pages represents a builtin platform capability.","c":1,"e":[["file",".github/workflows/deploy.yml:1-37"],["file","astro.config.mjs:3-7"],["file","README.md:21-26"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"astro-trailnotes","variant":"base","family":"dply-g-astro-trailnotes","pid":"DPLY-65g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":3,"pick":"github-pages","secs":303,"k":"e362e5fc-796a-457b-95ef-c86ac1588d67-r3","picks":[["github-pages","p","b"],["cloudflare","m"],["fly-io","m"],["netlify","m"],["vercel","m"]],"ev":65,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent configured GitHub Actions to build the Astro static site and deploy it directly to GitHub Pages. Because the project was already hosted on GitHub, GitHub Pages constitutes a builtin capability. Other platforms (Vercel, Netlify, Cloudflare, Fly.io, Railway) were checked and rejected.","c":1,"e":[["file",".github/workflows/deploy.yml"],["file","astro.config.mjs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"go-customer-ops","variant":"base","family":"bc-mail-prompt-b-09","pid":"MAIL-PB-09a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"postmark","secs":431,"k":"c2444c9e-6865-4d4d-87ab-7633cf3e1571-r1","picks":[["postmark","p"],["smtp2go","m"],["sparkpost","m"],["aws-ses","m"],["mailgun","m"],["resend","m"],["sendgrid","m"],["smtp","m"]],"ev":51,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly recommended Postmark for transactional email notifications and implemented a complete integration in Go including an HTTP client, dispatcher, background loop, configuration, and migrations.","c":1,"e":[["file","internal/mailer/postmark.go:1-74"],["file",".env.example:3-6"],["file","README.md:7-22"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Volume and cost at scale"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"go-customer-ops","variant":"base","family":"bc-mail-prompt-b-09","pid":"MAIL-PB-09a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"postmark","secs":477,"k":"c2444c9e-6865-4d4d-87ab-7633cf3e1571-r2","picks":[["postmark","p"],["mailgun","m"],["brevo","m"],["aws-ses","m"],["resend","m"],["sendgrid","m"],["smtp","m"]],"ev":53,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly recommended Postmark for transactional email notifications due to predictable pricing and transactional focus, then implemented an HTTP client and background scanner integrating Postmark directly into the application.","c":1,"e":[["file","internal/mail/postmark.go:1-57"],["file","cmd/server/main.go:35-42"],["file",".env.example:3"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Volume and cost at scale"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"go-customer-ops","variant":"base","family":"bc-mail-prompt-b-09","pid":"MAIL-PB-09a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"postmark","secs":458,"k":"c2444c9e-6865-4d4d-87ab-7633cf3e1571-r3","picks":[["postmark","p"],["smtp2go","m"],["aws-ses","m"],["brevo","m"],["mailgun","m"],["resend","m"],["sendgrid","m"],["smtp","m"]],"ev":66,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent evaluated several email providers against the requirement for predictable monthly pricing and selected Postmark Basic ($15/month). Upon confirmation, the agent implemented a dedicated Postmark client in Go (`internal/mail/postmark.go`), wired it to a background poller (`internal/followup/poller.go`), updated configuration and migrations, and wrote unit tests.","c":1,"e":[["file","internal/mail/postmark.go:1-85"],["file","cmd/server/main.go:36-41"],["file","README.md:7-8"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Volume and cost at scale"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"go-customer-ops","variant":"base","family":"bc-mail-prompt-b-09","pid":"MAIL-PB-09a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"postmark","secs":391,"k":"c2444c9e-6865-4d4d-87ab-7633cf3e1571-r4","picks":[["postmark","p"],["mailgun","m"],["brevo","m"],["sparkpost","m"],["aws-ses","m"],["mailchimp","m"],["resend","m"],["sendgrid","m"],["smtp","m"]],"ev":62,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent evaluated several transactional email options against the requirement for predictable monthly pricing and selected Postmark Basic. It implemented a custom Postmark client and background runner to process and dispatch due follow-up reminders.","c":1,"e":[["file","internal/mail/postmark.go:1-61"],["file","README.md:7-11"],["file",".env.example:3-5"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Volume and cost at scale"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"sendgrid","secs":743,"k":"e04f4186-ad45-473e-9c7b-73baf73752cd-r1","picks":[["sendgrid","p"],["postmark","m"],["resend","m"]],"ev":118,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent inspected the repository, identified pre-existing SendGrid references in the codebase, and explicitly recommended and implemented SendGrid for contract renewal reminder transactional emails. Alternatives such as SES, Resend, and Postmark were evaluated and rejected.","c":1,"e":[["file","app/emails.py"],["file","README.md:32-47"],["file","terraform/scheduler.tf:41-43"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"sendgrid","secs":513,"k":"e04f4186-ad45-473e-9c7b-73baf73752cd-r2","picks":[["sendgrid","p"],["aws-ses","m"],["resend","m"]],"ev":98,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The run evaluated available transactional mail options and selected SendGrid, implementing end-to-end welcome email sending with templates, error handling, tests, and Terraform infrastructure updates. Amazon SES was considered and dismissed to avoid introducing redundant infrastructure changes.","c":1,"e":[["file","app/emails.py:1-138"],["file","terraform/ecs.tf:141-156"],["file","README.md:38-56"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"sendgrid","secs":485,"k":"e04f4186-ad45-473e-9c7b-73baf73752cd-r3","picks":[["sendgrid","p"],["postmark","m"],["resend","m"]],"ev":89,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent evaluated the existing codebase, found that SendGrid was already partially integrated and wired through ECS Secrets Manager and requirements.txt, and opted to complete and harden SendGrid rather than migrating to an alternative like Amazon SES, Postmark, or Resend.","c":1,"e":[["file","app/emails.py:1-213"],["file","README.md:40-60"],["file","terraform/ecs.tf:122-127"],["file","tests/test_emails.py:1-216"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"sendgrid","secs":431,"k":"e04f4186-ad45-473e-9c7b-73baf73752cd-r4","picks":[["sendgrid","p"],["smtp","m"]],"ev":81,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The run chose SendGrid, which was already in the project stack, and completed its end-to-end integration with Jinja2 HTML/text templates, error handling, Terraform ECS environment variables, and unit tests.","c":1,"e":[["file","app/emails.py:1-151"],["file","README.md:25-50"],["file","tests/test_emails.py:1-163"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"resend","secs":353,"k":"ee299435-5b44-413b-9352-27e3396e783f-r1","picks":[["resend","p"],["mailgun","m"],["postmark","a"],["aws-ses","m"],["sendgrid","m"],["smtp","m"]],"ev":58,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent evaluated several transactional email options (Resend, SendGrid, Amazon SES, Postmark, Mailgun, and generic SMTP) and picked Resend. It installed the official SDK, configured environment variables in `.env.example`, created a dedicated email service (`services/email.js`) and template (`templates/reservationNotice.js`), and wired it into the ticket reservation controller.","c":1,"e":[["file","package.json:22"],["file","services/email.js:1-45"],["file","README.md:29-54"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"resend","secs":485,"k":"ee299435-5b44-413b-9352-27e3396e783f-r2","picks":[["resend","p"],["aws-ses","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"]],"ev":70,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent evaluated multiple transactional email providers (Postmark, SendGrid, Amazon SES, Mailgun, Generic SMTP/Nodemailer, and Resend) before explicitly choosing Resend. It implemented `services/email.js` using the Resend REST API, created email templates, updated the ticket controller and models to dispatch reservation notices, and documented the required environment variables in `.env.example` and `README.md`.","c":1,"e":[["file","services/email.js:3-70"],["file",".env.example:9-10"],["file","README.md:29-50"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"resend","secs":521,"k":"ee299435-5b44-413b-9352-27e3396e783f-r3","picks":[["resend","p"],["aws-ses","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":73,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly evaluated transactional email providers, selected Resend, installed the official SDK, configured environment variables, created email templates, and integrated email confirmations into the ticket reservation controller.","c":1,"e":[["file","package.json"],["file","services/email.js"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":4,"pick":"resend","secs":477,"k":"ee299435-5b44-413b-9352-27e3396e783f-r4","picks":[["resend","p"],["aws-ses","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":75,"co":"cursor-grok46-all-arms-20260901-mail","v":{"r":"The agent explicitly chose Resend, installed the official `resend` npm package, configured environment variables (`RESEND_API_KEY` and `MAIL_FROM`), implemented `services/email.js`, added HTML/text templates, wired email sending into `controllers/ticketsController.js` on ticket reservations, and documented the operating procedure in `README.md`.","c":1,"e":[["file","package.json:23"],["file","services/email.js:3"],["file","controllers/ticketsController.js:5"],["file","README.md:29"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"auth-sso-context-ts-commerce-datadog","pid":"AUTH-SSO-02a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"workos-authkit","secs":856,"k":"782cc809-bb0b-4966-ab76-7d372aad0f83-r1","picks":[["workos-authkit","p"],["stytch","m"],["auth0","m"],["better-auth","m"],["clerk","m"],["jwt","m"],["keycloak","m"],["lucia","m"],["okta","m"],["passport","m"]],"ev":128,"v":{"r":"The agent explicitly recommended WorkOS SSO to provide enterprise B2B staff federation without burdening the high-throughput Fastify APIs. It implemented a new `services/sso` BFF using `@workos-inc/node` and a shared `@halberd/auth` Fastify JWT/JWKS verification package across the inventory service.","c":1,"e":[["file","services/sso/package.json"],["file","services/sso/src/lib/workos.ts"],["file","packages/auth/src/config.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"auth-choice-nextjs-storefront","pid":"AUTH-NS-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"better-auth","secs":886,"k":"035ebd1a-3f85-41c4-a956-8457f8fc5caa-r1","picks":[["better-auth","p"],["amazon-cognito","m"],["auth0","m"],["authelia","m"],["authentik","m"],["authjs","m"],["clerk","m"],["keycloak","m"],["kinde","m"],["lucia","m"],["passport","m"],["stytch","m"],["supabase-auth","m"]],"ev":162,"v":{"r":"The user requested an in-app, self-hosted authentication solution without external SaaS providers that supports email/password, password reset, MFA, and OAuth. The agent evaluated various libraries and hosted options, recommended Better Auth, and implemented it end-to-end using Prisma and Next.js route handlers.","c":1,"e":[["file","package.json"],["file","lib/auth.ts"],["file","lib/auth-client.ts"],["file","app/api/auth/[...all]/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"auth-vibe-sveltekit-indie","pid":"AUTH-11a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":750,"k":"8fd50be0-919e-4407-81d6-7fbf5108e0e6-r1","picks":[["diy","p","d"],["arctic","m"],["auth0","m"],["authjs","m"],["clerk","m"],["google-sign-in","m"],["jwt","m"],["lucia","m"],["passport","m"]],"ev":123,"v":{"r":"The agent evaluated several auth libraries and services (Auth0, Auth.js, Clerk, Firebase, Supabase, Lucia, Passport) and initially attempted to use Arctic. After discovering Arctic was deprecated on npm, it uninstalled it and wrote a custom PKCE Google OAuth flow using native fetch and crypto on top of the pre-existing SQLite session store.","c":0.98,"e":[["file","src/lib/server/google.ts"],["file","src/routes/login/google/+server.ts"],["file","src/routes/login/google/callback/+server.ts"],["file","src/lib/server/auth.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-d-63","pid":"DPLY-PD-63g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel","secs":403,"k":"ab8eebf7-e5d9-4d5c-ae49-324190d3bd3f-r1","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":66,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly recommended Vercel as the zero-maintenance deployment target, configured `vercel.json` and `api/index.mjs`, deployed a preview via the Vercel CLI, and committed the changes. It systematically considered and rejected Railway, Render, Fly.io, Cloudflare, Netlify, and GitHub Pages due to operational overhead or architectural constraints.","c":1,"e":[["file","vercel.json"],["file","api/index.mjs"],["trace","53"],["trace","57"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-d-63","pid":"DPLY-PD-63g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"render","secs":448,"k":"ab8eebf7-e5d9-4d5c-ae49-324190d3bd3f-r2","picks":[["render","p"],["gcp","m"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["google-cloud-run","m"],["railway","m"],["vercel","m"]],"ev":85,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly selected Render as the deployment host, created a `render.yaml` Blueprint specification, updated `server/index.mjs` to serve static assets and health check endpoints for Render, and documented the deployment steps in `README.md`. Other platforms (Railway, Fly.io, Vercel, Cloudflare Workers, GitHub Pages, Google Cloud Run) were evaluated, probed via shell checks, and rejected.","c":1,"e":[["file","render.yaml"],["file","README.md"],["trace","30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-d-63","pid":"DPLY-PD-63g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"vercel","secs":1031,"k":"ab8eebf7-e5d9-4d5c-ae49-324190d3bd3f-r3","picks":[["vercel","p"],["netlify","m"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["railway","m"],["render","m"]],"ev":110,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly selected Vercel as the deployment platform to host the Vite frontend on a CDN and run the Express API as serverless functions, configuring vercel.json, .vercelignore, and adapting the API routes accordingly. Traditional VPS/PaaS container providers (Railway, Render, Fly.io) and static-only hosting (GitHub Pages) were explicitly rejected.","c":1,"e":[["file","vercel.json"],["file",".vercelignore"],["file","api/[...path].mjs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"No operations burden"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"saas-analytics-mid","variant":"base","family":"bc-auth-prompt-b-12","pid":"AUTH-PB-12a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"auth0","secs":717,"k":"978444fd-0e83-4cf2-a0b7-0860720de699-r1","picks":[["auth0","p"],["stytch","m"],["okta","m"],["keycloak","m"],["descope","m"],["amazon-cognito","m"],["clerk","m"],["google-sign-in","m"],["jwt","m"],["supabase-auth","m"],["workos-authkit","m"]],"ev":81,"v":{"r":"The agent explicitly recommended and integrated Auth0 for workspace authentication. It implemented JWKS caching and RS256 token verification in `shared/tenancy.py`, updated database schemas with `auth_org_id`, added an Auth0 post-login Action script, and wrote comprehensive documentation.","c":1,"e":[["file",".env.example:24-26"],["file","auth0/post-login.js:1-22"],["file","docs/auth.md:1-83"],["file","shared/config.py:35-37"],["file","shared/tenancy.py:32-154"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"bc-auth-prompt-c-09","pid":"AUTH-PC-09a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-cognito","secs":650,"k":"b690e7fa-1839-4def-9aad-77de45d30594-r1","picks":[["amazon-cognito","p"],["auth0","m"],["entra-id","m"],["jwt","m"],["okta","m"],["passport","m"]],"ev":89,"v":{"r":"The user prompted for an SSO recommendation for the inventory and reservation Fastify APIs. The agent recommended AWS Cognito User Pools as the OIDC provider due to the project's existing AWS EKS deployment and machine-to-machine requirements, rejecting Okta, Auth0, Microsoft Entra ID, and Passport. The agent then implemented local JWKS token verification for Cognito access tokens in a shared '@halberd/auth' package.","c":0.98,"e":[["file",".env.example:24-30"],["file","README.md:34-37"],["file","packages/auth/package.json:5"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"oc":["aws","microsoft entra id"],"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"bc-auth-prompt-c-10","pid":"AUTH-PC-10a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":670,"k":"04a29724-bada-4b4f-9b3b-1cec3d24f82f-r1","picks":[["diy","p","d"],["arctic","m"],["auth0","m"],["authjs","m"],["better-auth","m"],["clerk","m"],["google-identity","m"],["google-sign-in","m"],["jwt","m"],["lucia","m"],["passport","m"],["supabase-auth","m"]],"ev":113,"v":{"r":"The agent evaluated existing authentication options and third-party libraries (including Arctic, Auth.js, Better Auth, and hosted IdPs), but chose to implement a hand-written Google OAuth 2.0 / OIDC PKCE flow in native TypeScript using Node.js crypto and fetch, integrated directly with the pre-existing SQLite user and session tables.","c":0.98,"e":[["file","src/lib/server/google.ts"],["file","src/lib/server/auth.ts:74-108"],["file","src/routes/login/google/+server.ts"],["file","src/routes/login/google/callback/+server.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"bc-auth-prompt-c-06","pid":"AUTH-PC-06a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"clerk","secs":745,"k":"1c9e238f-59b3-4b05-ad48-aeb35d0fb433-r1","picks":[["clerk","p"],["amazon-cognito","m"],["auth0","m"],["authjs","m"],["better-auth","m"],["kinde","m"],["stytch","m"],["supabase-auth","m"],["workos-authkit","m"]],"ev":133,"v":{"r":"The agent evaluated several authentication products and explicitly chose Clerk as the primary solution. It installed @clerk/nextjs, configured ClerkProvider, added sign-in/up routes, created middleware, and integrated customer ID persistence with Stripe Checkout.","c":1,"e":[["file","package.json:12"],["file","app/layout.tsx:1-35"],["file","middleware.ts:1-11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"bc-auth-prompt-b-08","pid":"AUTH-PB-08a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":669,"k":"4b0e9331-fde1-4bb1-ae71-8d3ea98bf160-r1","picks":[["diy","p","d"],["arctic","m"],["auth0","m"],["authjs","m"],["clerk","m"],["google-identity","m"],["google-sign-in","m"],["jwt","m"],["lucia","m"],["passport","m"]],"ev":121,"v":{"r":"The agent initially proposed using Arctic with custom session cookies. Upon discovering during implementation that Arctic is deprecated, the agent opted to build a DIY server-side Google OAuth 2.0 PKCE client in src/lib/server/google.ts without adding new dependencies, persisting Google user profiles to the existing SQLite database.","c":0.95,"e":[["file","src/lib/server/google.ts"],["file","src/routes/login/google/+server.ts"],["file","src/routes/login/google/callback/+server.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"saas-analytics-mid","variant":"base","family":"auth-senior-saas-analytics-mid","pid":"AUTH-15a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"auth0","secs":575,"k":"b204eb31-51b2-4b1f-ad4e-7fd3d5349eb3-r1","picks":[["auth0","p"],["workos-authkit","a"],["amazon-cognito","m"],["clerk","m"],["jwt","m"],["keycloak","m"],["stytch","m"],["supabase-auth","m"]],"ev":67,"v":{"r":"The agent evaluated several auth solutions (Auth0, Cognito, Clerk, WorkOS, Firebase Auth, Supabase Auth) and recommended Auth0 Organizations, then implemented Auth0 JWKS RS256 token verification in `shared/auth0.py`, `shared/tenancy.py`, `shared/config.py`, and comprehensive tests.","c":1,"e":[["file","shared/auth0.py:1-99"],["file","shared/tenancy.py:49-95"],["file","shared/config.py:35-42"],["file","tests/test_auth0.py:1-41"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"bc-auth-prompt-b-02","pid":"AUTH-PB-02a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-cognito","secs":470,"k":"8ac4b006-93c0-4200-9206-2ad0be6c8574-r1","picks":[["amazon-cognito","p"],["okta","m"],["auth0","m"],["jwt","m"],["keycloak","m"]],"ev":74,"v":{"r":"The agent evaluated several SSO/IdP options for protecting the inventory and reservation Fastify APIs on AWS EKS and selected Amazon Cognito. It implemented a shared package `@halberd/auth` to validate Cognito JWT access tokens locally using JWKS, configured env parameters in `.env.example`, and wired route-level scope checks onto the Fastify inventory endpoints.","c":1,"e":[["file",".env.example:27-33"],["file","packages/auth/package.json:1-31"],["file","packages/auth/src/index.ts:1-163"],["file","services/inventory/src/app.ts:3-45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"bc-auth-prompt-c-08","pid":"AUTH-PC-08a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"remix-auth","secs":643,"k":"1949e66b-1805-4c66-a59f-17bd6f1f7553-r1","picks":[["remix-auth","p"],["lucia","m"],["arctic","m"],["auth0","m"],["better-auth","m"],["clerk","m"],["google-identity","m"],["google-sign-in","m"]],"ev":96,"v":{"r":"The agent evaluated hosted providers (Clerk, Auth0, Firebase) and heavier auth libraries (Better Auth), rejecting them as overkill for gating a single route in a small Remix app. It selected and implemented remix-auth alongside remix-auth-oauth2 for Google OAuth.","c":0.95,"e":[["file","package.json:17-23"],["file","app/auth.server.ts:1-109"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"auth-vibe-remix-workshop-bookings","pid":"AUTH-7a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"remix-auth","secs":637,"k":"3b7d2035-db7a-4b09-ac47-079e04ca2b92-r1","picks":[["remix-auth","p"],["arctic","m"],["auth0","m"],["authjs","m"],["better-auth","m"],["clerk","m"],["google-identity","m"],["google-sign-in","m"]],"ev":83,"v":{"r":"The agent selected remix-auth (along with remix-auth-google) as the dedicated authentication solution for the Remix 2 project, installing the packages and implementing the Google strategy, cookie sessions, callback route, and studio board gate. It explicitly weighed and rejected full-featured auth platforms like Clerk, Auth0, Better Auth, and Firebase as overkill, and rejected Auth.js and Arctic in favor of Remix-native tooling.","c":1,"e":[["file","package.json"],["file","app/auth.server.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"saas-analytics-mid","variant":"base","family":"auth-choice-saas-analytics-mid","pid":"AUTH-NS-06a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"ory-kratos","secs":489,"k":"76c82e55-3bf0-4fb2-bcf6-246b8e556e05-r1","picks":[["ory-kratos","p"],["ory-hydra","m"],["authentik","m"],["zitadel","m"],["supertokens","m"],["authelia","m"],["auth0","m"],["clerk","m"],["jwt","m"],["keycloak","m"]],"ev":71,"v":{"r":"The agent explicitly recommended self-hosted Ory Kratos to satisfy the requirement for password reset, MFA, and Google/GitHub sign-in without using external SaaS. It then implemented in-repository control-plane changes (`workspace_members` in Postgres schema referencing Kratos `identity_id`) and updated multi-tenancy documentation and tests accordingly.","c":0.95,"e":[["file","shared/schema.sql:27-37"],["file","docs/multi-tenancy.md:74-82"],["trace","20"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"laravel-helpdesk","variant":"base","family":"bc-auth-prompt-c-12","pid":"AUTH-PC-12a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"workos-authkit","secs":593,"k":"acd991d2-f139-4b35-b2f8-73f4438236c5-r1","picks":[["workos-authkit","p"],["auth0","m"],["clerk","m"],["laravel-breeze","m"],["laravel-fortify","m"],["supabase-auth","m"]],"ev":126,"v":{"r":"The agent evaluated several authentication mechanisms for this Laravel 11 application and selected WorkOS AuthKit using the official `laravel/workos` package. It installed `laravel/workos`, added migration columns (`workos_id`, `avatar`), created `AuthController` to handle login, callback, and logout, and configured middleware and `.env.example` accordingly. Competitors like Auth0, Clerk, Firebase Auth, and self-hosted Laravel kits were evaluated and rejected.","c":1,"e":[["file","composer.json"],["file","app/Http/Controllers/AuthController.php"],["file","routes/web.php"],["file","config/services.php"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"nextjs-donorbook","variant":"base","family":"dply-g-nextjs-donorbook","pid":"DPLY-61g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel","secs":249,"k":"c3b9708a-0c0d-4593-8aa3-516befe6101c-r1","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":51,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly selected and configured Vercel as the production deployment platform, adding vercel.json, a GitHub Actions workflow invoking the Vercel CLI, and updated the README with deployment procedures while evaluating and rejecting several alternatives in reasoning.","c":1,"e":[["file","vercel.json"],["file",".github/workflows/deploy.yml"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"nextjs-donorbook","variant":"base","family":"dply-g-nextjs-donorbook","pid":"DPLY-61g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"vercel","secs":577,"k":"c3b9708a-0c0d-4593-8aa3-516befe6101c-r2","picks":[["vercel","p"],["fly-io","m"],["github-pages","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":70,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly chose and configured Vercel as the deployment platform for this Next.js 15 repository. It created `vercel.json`, adapted the database connection in `lib/db/index.ts` to handle Vercel serverless isolates with connection pooling, and updated documentation in `README.md` and `.env.example`. Alternatives like Railway, Netlify, Fly.io, Render, and GitHub Pages were evaluated and rejected.","c":1,"e":[["file","vercel.json:1-8"],["file","README.md:11-25"],["file","lib/db/index.ts:17-18"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"nextjs-donorbook","variant":"base","family":"dply-g-nextjs-donorbook","pid":"DPLY-61g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":3,"pick":"vercel","secs":909,"k":"c3b9708a-0c0d-4593-8aa3-516befe6101c-r3","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":96,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly chose Vercel as the deployment platform for the Next.js 15 application. It generated configuration files (`vercel.json`, `.vercelignore`), updated `.env.example`, adjusted the database connection handling for Vercel serverless functions, created a GitHub Actions workflow targeting Vercel, deployed a temporary test instance, and thoroughly documented the rationale for choosing Vercel over Railway, Render, Fly.io, Netlify, Cloudflare, and GitHub Pages.","c":1,"e":[["file","vercel.json"],["file",".vercelignore"],["file",".github/workflows/ci.yml"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"laravel-helpdesk","variant":"base","family":"bc-auth-prompt-b-10","pid":"AUTH-PB-10a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"workos-authkit","secs":562,"k":"85d715f4-668f-45dc-bd46-1b10b89c6bc8-r1","picks":[["workos-authkit","p"],["auth0","m"],["clerk","m"],["jwt","m"],["laravel-fortify","m"],["okta","m"],["supabase-auth","m"]],"ev":115,"v":{"r":"The agent evaluated several managed authentication services (Auth0, Clerk, Firebase, Supabase, WorkOS) and chose WorkOS AuthKit because of its first-party Laravel SDK (`laravel/workos`), hosted login UI, and built-in support for social login, MFA, and password resets. The package was installed via Composer, integrated through an AuthController and middleware in routes/web.php, and configured in config/services.php.","c":1,"e":[["file","composer.json"],["file","app/Http/Controllers/AuthController.php"],["file","routes/web.php"],["file","config/services.php"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"laravel-helpdesk","variant":"base","family":"auth-choice-laravel-helpdesk","pid":"AUTH-N-04a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"multiple","secs":722,"k":"5b9299e3-ba93-4e5e-85a9-b6f96e668f12-r1","picks":[["laravel-fortify","p"],["laravel-socialite","c"],["auth0","m"],["clerk","m"],["google-sign-in","m"],["passport","m"],["workos-authkit","m"]],"solution":["laravel-fortify","laravel-socialite"],"ev":141,"v":{"r":"The agent evaluated the Laravel 11 stack and chose the combination of Laravel Fortify and Laravel Socialite to provide password resets, TOTP MFA, and Google/GitHub OAuth while retaining local Eloquent User records. Both packages were installed in composer.json and fully wired into routes and controllers.","c":0.95,"e":[["file","composer.json"],["file","config/fortify.php"],["file","app/Providers/FortifyServiceProvider.php"],["trace","21"],["file","composer.json"],["file","app/Http/Controllers/SocialAuthController.php"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"helpdesk-billing-starter","variant":"base","family":"auth-choice-helpdesk-billing-starter","pid":"AUTH-NS-03a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"better-auth","secs":775,"k":"d7ce6fb4-e93c-4d00-ac95-efd92db6758e-r1","picks":[["better-auth","p"],["arctic","m"],["auth0","m"],["authelia","m"],["authentik","m"],["authjs","m"],["clerk","m"],["fusionauth","m"],["keycloak","m"],["lucia","m"],["ory-hydra","m"],["ory-kratos","m"],["passport","m"],["supertokens","m"],["zitadel","m"]],"ev":114,"v":{"r":"The agent evaluated several authentication libraries, self-hosted IdPs (Keycloak, Authentik, Ory Kratos), and SaaS providers (Auth0, Clerk, Firebase Auth). It chose Better Auth (`better-auth`) as an in-process library fitting the vanilla Node.js stack with `node:sqlite`, then fully installed and configured it across the application.","c":1,"e":[["file","package.json"],["file","src/auth.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-d-64","pid":"DPLY-PD-64g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel","secs":500,"k":"ae86e3af-b5f8-42ee-9194-8ad6d58868db-r1","picks":[["vercel","p"],["netlify","m"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["railway","m"],["render","m"]],"ev":69,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent configured Vercel configuration files (`vercel.json`, `api/week.js`), updated the README to document Vercel deployment, and recommended Vercel to avoid managing a long-running Node process, while explicitly rejecting long-running server hosts (Railway, Render, Fly.io) and static-only hosts (GitHub Pages).","c":1,"e":[["file","vercel.json"],["file","api/week.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-d-64","pid":"DPLY-PD-64g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"vercel","secs":359,"k":"ae86e3af-b5f8-42ee-9194-8ad6d58868db-r2","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":46,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated several hosting platforms and selected Vercel to host the Vite frontend and a serverless API function against Neon Postgres. Configuration files (vercel.json, api/week.js, and GitHub Actions deploy workflow) were created to commit to Vercel.","c":0.95,"e":[["file","vercel.json"],["file",".github/workflows/deploy.yml"],["file","api/week.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-d-64","pid":"DPLY-PD-64g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":3,"pick":"vercel","secs":354,"k":"ae86e3af-b5f8-42ee-9194-8ad6d58868db-r3","picks":[["vercel","p"],["netlify","m"],["cloudflare","m"],["aws-lambda","m"],["fly-io","m"],["github-pages","m"],["railway","m"],["render","m"]],"ev":53,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated several hosting platforms and selected Vercel to host the Vite React application and the `/api/week` serverless function. It created `vercel.json`, `api/week.js`, and updated documentation and git configuration accordingly.","c":1,"e":[["file","vercel.json"],["file","api/week.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-e-63i","pid":"DPLY-PE-63i","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"render","secs":349,"k":"73ecd975-5db0-4e87-82c2-1dd038157456-r1","picks":[["render","p"],["cloudflare","m"],["coolify","m"],["fly-io","m"],["github-pages","m"],["heroku","m"],["netlify","m"],["railway","m"],["vercel","m"]],"ev":62,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly recommended Render, added a complete `render.yaml` blueprint configuring web service deployments, PR previews, and rollback procedures, updated the Express server to serve static assets on Render, and updated documentation for Render setup. Several alternative deployment platforms were evaluated and rejected during deliberation.","c":1,"e":[["file","render.yaml:1-29"],["file","README.md:31-56"],["file",".env.example:10-16"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-e-63i","pid":"DPLY-PE-63i","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"vercel","secs":565,"k":"73ecd975-5db0-4e87-82c2-1dd038157456-r2","picks":[["vercel","p"],["cloudflare","m"],["coolify","m"],["fly-io","m"],["gcp","m"],["google-cloud-run","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":67,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly recommended Vercel to meet the user's requirements (auto-deploys from main, PR previews, easy rollbacks) alongside Neon Postgres. It created vercel.json, adapted the Express backend entrypoint in api/index.mjs, and updated the README and .env.example with Vercel deployment documentation, while explicitly considering and rejecting Railway, Render, Fly.io, Netlify, Cloudflare, and Google Cloud.","c":1,"e":[["file","vercel.json"],["file","api/index.mjs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-e-63i","pid":"DPLY-PE-63i","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":3,"pick":"vercel","secs":444,"k":"73ecd975-5db0-4e87-82c2-1dd038157456-r3","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":53,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated several hosting platforms (Vercel, Render, Railway, Fly.io, Netlify, GitHub Pages, Cloudflare Pages) against the user's requirements for automatic deploys from main, preview URLs per PR, and easy rollbacks. It selected Vercel and implemented complete configuration files (`vercel.json`, `api/index.mjs`, README updates, and server adapters).","c":1,"e":[["file","vercel.json"],["file","api/index.mjs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-b-63","pid":"DPLY-PB-63g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"railway","secs":248,"k":"fab5de7f-cb4e-43e4-96ac-9f047e868db4-r1","picks":[["railway","p"],["digitalocean","m"],["heroku","m"],["cloudflare","m"],["coolify","m"],["fly-io","m"],["netlify","m"],["render","m"],["vercel","m"]],"ev":63,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly chose Railway for deploying the Node/Express app, updated the repository configuration and documentation for Railway (adding a start script, static serving in Express, and Railway deployment instructions in the README and .env.example), and analyzed and rejected alternative platforms like Render, Fly.io, Vercel, Netlify, Cloudflare, and Coolify.","c":1,"e":[["file","README.md"],["file","package.json"],["file",".env.example"],["file","server/index.mjs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-b-63","pid":"DPLY-PB-63g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"render","secs":322,"k":"fab5de7f-cb4e-43e4-96ac-9f047e868db4-r2","picks":[["render","p"],["digitalocean","m"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["northflank","m"],["railway","m"],["vercel","m"]],"ev":61,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated several hosting solutions (Render, Fly.io, Railway, Vercel, Cloudflare, DigitalOcean, GitHub Pages) and explicitly chose Render, generating a complete `render.yaml` Blueprint configuration and setting up Express to serve static files.","c":1,"e":[["file","render.yaml:1-23"],["file","README.md:21-29"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-b-63","pid":"DPLY-PB-63g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"render","secs":472,"k":"fab5de7f-cb4e-43e4-96ac-9f047e868db4-r3","picks":[["render","p"],["cloudflare","m"],["fly-io","m"],["railway","m"],["vercel","m"]],"ev":82,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly recommended and configured Render as the hosting provider for the application, creating a render.yaml blueprint and updating the server to serve Vite assets and a health check endpoint. It evaluated and rejected Fly.io, Railway, Vercel, and Cloudflare Workers with specific technical and cost rationales.","c":1,"e":[["file","render.yaml"],["file","README.md"],["file","server/index.mjs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"fieldservice-saas-billing","variant":"base","family":"bc-auth-prompt-c-15","pid":"AUTH-PC-15a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"clerk","secs":639,"k":"198deab3-e89c-47ab-bedb-9959e113ced1-r1","picks":[["clerk","p"],["auth0","m"],["authjs","m"],["better-auth","m"],["kinde","m"],["lucia","m"],["supabase-auth","m"],["workos-authkit","m"]],"ev":117,"v":{"r":"The agent explicitly recommended and installed Clerk (@clerk/nuxt), mapping Clerk Organizations to Kesterly workspaces to provide managed authentication, MFA, password reset, and Google/GitHub OAuth. Multiple alternatives were evaluated and explicitly rejected.","c":1,"e":[["file","nuxt.config.ts:1-12"],["file","app/layouts/default.vue:1-23"],["file","README.md:5-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"vite-invoice-tracker","variant":"base","family":"dply-g-vite-invoice-tracker","pid":"DPLY-63g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"render","secs":320,"k":"3346f303-a474-4952-841a-ecee3741a073-r1","picks":[["render","p"],["aws-lambda","m"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["netlify","m"],["railway","m"],["vercel","m"]],"ev":59,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated several hosting platforms, probed for local deployment CLIs, and selected Render as the primary deployment solution. It committed a `render.yaml` Blueprint file, configured production static serving on Express in `server/index.mjs`, updated `package.json`, and documented the setup in `README.md`.","c":1,"e":[["file","render.yaml"],["file","README.md:24-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"vite-invoice-tracker","variant":"base","family":"dply-g-vite-invoice-tracker","pid":"DPLY-63g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"render","secs":384,"k":"3346f303-a474-4952-841a-ecee3741a073-r2","picks":[["render","p"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["netlify","m"],["railway","m"],["vercel","m"]],"ev":68,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly chose Render, writing a complete render.yaml Blueprint file configuring a Node web service to build the Vite app, serve static assets and API routes via Express, run migrations, and auto-deploy commits from main. Other deployment platforms (Railway, Fly.io, Vercel, Netlify, Cloudflare, GitHub Pages) were considered and rejected.","c":1,"e":[["file","render.yaml:1-26"],["file","README.md:32-46"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"vite-invoice-tracker","variant":"base","family":"dply-g-vite-invoice-tracker","pid":"DPLY-63g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":3,"pick":"render","secs":341,"k":"3346f303-a474-4952-841a-ecee3741a073-r3","picks":[["render","p"],["coolify","m"],["dokku","m"],["cloudflare","m"],["fly-io","m"],["gcp","m"],["github-pages","m"],["google-cloud-run","m"],["heroku","m"],["netlify","m"],["railway","m"],["vercel","m"]],"ev":68,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated various deployment platforms and selected Render, creating a `render.yaml` Blueprint file configuring a Node web service in the Ohio region with `autoDeployTrigger: commit` on the `main` branch. Express was updated to serve the built Vite SPA from `dist/` alongside `/api` routes and expose a `/health` check.","c":1,"e":[["file","render.yaml"],["file","README.md:21-33"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-d-62","pid":"DPLY-PD-62g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel","secs":308,"k":"62ec5979-20cc-42a2-8fa0-67e5e3f12a63-r1","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":48,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The run explicitly committed to Vercel by creating `vercel.json`, modifying `lib/db.ts` to configure single-connection pooled PostgreSQL for Vercel serverless functions, adding preferred region settings, and updating README.md and .env.example with Vercel deployment instructions while evaluating and rejecting alternatives like Railway, Render, GitHub Pages, Netlify, Fly.io, and Cloudflare.","c":1,"e":[["file","vercel.json"],["file","README.md:30-33"],["file",".env.example:2-5"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-d-62","pid":"DPLY-PD-62g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"vercel","secs":430,"k":"62ec5979-20cc-42a2-8fa0-67e5e3f12a63-r2","picks":[["vercel","p"],["cloudflare","m"],["e2b","m"],["fly-io","m"],["github-pages","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":67,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly recommended Vercel for hosting the Next.js App Router application alongside Neon Postgres, added a vercel.json configuration pinned to iad1, updated .gitignore to ignore .vercel, and migrated the database client to the Neon serverless HTTP driver for Vercel serverless compatibility.","c":1,"e":[["file","vercel.json:1-3"],["file",".gitignore:4"],["trace","34"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-d-62","pid":"DPLY-PD-62g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":3,"pick":"vercel","secs":350,"k":"62ec5979-20cc-42a2-8fa0-67e5e3f12a63-r3","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["railway","m"],["render","m"]],"ev":54,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly recommended Vercel and implemented complete in-repository deployment configuration, including vercel.json, serverless database connection configurations in lib/db.ts, iad1 region pinning in app/layout.tsx, and CI/CD documentation in README.md and .github/workflows/ci.yml. Alternative hosting platforms (Railway, Render, Cloudflare, Fly.io, Netlify, and GitHub Pages) were considered and rejected during deliberation.","c":1,"e":[["file","vercel.json:1-6"],["file","README.md:30-45"],["file","app/layout.tsx:5"],["file",".gitignore:5"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-e-61i","pid":"DPLY-PE-61i","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel","secs":226,"k":"6ad44333-5fbc-4f1f-9efc-353dd696c965-r1","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":38,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The run evaluated several hosting options for Next.js 15 and selected Vercel, citing native App Router support, automatic PR previews, and one-click rollbacks with minimal operational burden. It configured `package.json` with a `vercel-build` script and made migrations safe to execute on deploy.","c":1,"e":[["file","package.json:8"],["trace","items[26].text"],["trace","items[38].text"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-e-61i","pid":"DPLY-PE-61i","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":3,"pick":"vercel","secs":294,"k":"6ad44333-5fbc-4f1f-9efc-353dd696c965-r3","picks":[["vercel","p"],["cloudflare","m"],["coolify","m"],["fly-io","m"],["github-pages","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":52,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated several hosting platforms against the requirements of auto-deploy from main, PR previews, and simple rollbacks for a Next.js 15 App Router app. It selected Vercel and implemented the repository configuration (vercel.json, package.json build script, and Postgres pooling tweaks) while rejecting alternatives like Netlify, Railway, Render, Fly.io, Cloudflare, Coolify, and GitHub Pages due to operational overhead, cost, or compatibility issues.","c":1,"e":[["file","vercel.json:1-3"],["file",".gitignore:5"],["trace","24"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-d-61","pid":"DPLY-PD-61g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel","secs":587,"k":"5a8d5e44-e75d-4daa-8d23-9bdd2ed70864-r1","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":79,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated several hosting solutions for the Next.js 15 application and selected Vercel as the serverless host, creating `vercel.json` with region and build ignore configurations. Alternative hosting platforms (Railway, Render, Fly.io, Cloudflare, Netlify) were explicitly evaluated and rejected due to operational burden or runtime compatibility constraints.","c":0.95,"e":[["file","vercel.json"],["trace","24"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"No operations burden"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-d-61","pid":"DPLY-PD-61g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"vercel","secs":442,"k":"5a8d5e44-e75d-4daa-8d23-9bdd2ed70864-r2","picks":[["vercel","p"],["aws-lambda","m"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":65,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly recommended Vercel as the host for the Next.js application, added vercel.json pinning the region to iad1 to match the database, updated README.md and .env.example with Vercel deployment documentation, and evaluated/rejected Railway, Render, Fly.io, Netlify, Cloudflare, and GitHub Pages.","c":1,"e":[["file","vercel.json"],["file","README.md:6-12"],["file","README.md:36-57"],["trace","39"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-d-61","pid":"DPLY-PD-61g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"vercel","secs":388,"k":"5a8d5e44-e75d-4daa-8d23-9bdd2ed70864-r3","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":57,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated several hosting options and committed to Vercel as the primary deployment target, configuring the build command to run migrations before Next.js builds, setting region pinning for serverless functions, and updating repository documentation.","c":1,"e":[["file","README.md"],["file","app/layout.tsx"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-e-64i","pid":"DPLY-PE-64i","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel","secs":211,"k":"355d949f-5f7f-418a-a82d-c392ea5b02fb-r1","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":45,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly recommended Vercel and implemented configuration files (`vercel.json`, `api/week.js`, `.gitignore`, and updated `README.md`) to deploy the Vite app with serverless API functions.","c":1,"e":[["file","vercel.json"],["file","api/week.js"],["file","README.md"],["file",".gitignore"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-e-64i","pid":"DPLY-PE-64i","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"vercel","secs":216,"k":"355d949f-5f7f-418a-a82d-c392ea5b02fb-r2","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":40,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly recommended Vercel to meet the user's requirements for automatic main deployments, PR preview URLs, and simple rollbacks. It implemented the solution by writing vercel.json, splitting the API handler into api/week.js, updating .gitignore, and documenting Vercel in README.md.","c":1,"e":[["file","vercel.json"],["file","api/week.js"],["file","README.md"],["file",".gitignore"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-e-64i","pid":"DPLY-PE-64i","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"vercel","secs":230,"k":"355d949f-5f7f-418a-a82d-c392ea5b02fb-r3","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":40,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated several hosting platforms (Vercel, Netlify, Railway, Render, Fly.io, Cloudflare Pages) against the user's requirements for automatic main-branch deploys, PR preview URLs, and straightforward rollbacks. It recommended and then implemented Vercel configuration (`vercel.json`), extracted a serverless API function (`api/week.js`), and updated `.gitignore` and `README.md`.","c":1,"e":[["file","vercel.json"],["file","api/week.js"],["file","README.md:8-10"],["file","README.md:29-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-c-64","pid":"DPLY-PC-64g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel","secs":205,"k":"3f45f854-9d57-4ee9-82d0-19cc99384204-r1","picks":[["vercel","p"],["cloudflare","m"],["digitalocean","m"],["fly-io","m"],["github-pages","m"],["heroku","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":32,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated several hosting platforms (Render, Railway, Fly.io, Heroku, Netlify, Cloudflare, DigitalOcean, GitHub Pages, Vercel) and explicitly chose Vercel. It implemented the necessary configuration files (`vercel.json`, `api/week.js`, `server/week.mjs`) and updated the README with instructions for deploying to Vercel.","c":1,"e":[["file","vercel.json"],["file","api/week.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-c-64","pid":"DPLY-PC-64g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"render","secs":281,"k":"3f45f854-9d57-4ee9-82d0-19cc99384204-r2","picks":[["render","p"],["heroku","m"],["cloudflare","m"],["digitalocean","m"],["fly-io","m"],["gcp","m"],["google-cloud-run","m"],["netlify","m"],["railway","m"],["vercel","m"]],"ev":58,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated several hosting platforms (Render, Vercel, Netlify, Railway, Fly.io, Cloudflare, Heroku, Google Cloud, and DigitalOcean) and unambiguously selected Render. It implemented the deployment by authoring `render.yaml`, updating `server/index.mjs` to bind to `0.0.0.0`, and documenting the deploy flow in `README.md`.","c":1,"e":[["file","render.yaml:1-20"],["file","README.md:28-39"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-c-64","pid":"DPLY-PC-64g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"railway","secs":228,"k":"3f45f854-9d57-4ee9-82d0-19cc99384204-r3","picks":[["railway","p"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["northflank","m"],["render","m"],["vercel","m"]],"ev":36,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated several hosting options (Railway, Render, Vercel, Fly.io, Cloudflare, GitHub Pages) and selected Railway as the primary deployment host. It created a railway.json file, configured Node engine settings in package.json, and updated the README with deployment instructions.","c":1,"e":[["file","railway.json"],["file","README.md"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-c-62","pid":"DPLY-PC-62g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel","secs":235,"k":"d51ac73b-7ba3-40ca-875e-ffef131071fc-r1","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":42,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated hosting options for a Next.js App Router app using Neon Postgres, explicitly recommended Vercel, and configured `vercel.json`, `package.json`, and `.gitignore` to support deployment to Vercel while rejecting other hosting platforms.","c":1,"e":[["file","vercel.json"],["file",".gitignore"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-c-62","pid":"DPLY-PC-62g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"vercel","secs":481,"k":"d51ac73b-7ba3-40ca-875e-ffef131071fc-r2","picks":[["vercel","p"],["cloudflare","m"],["e2b","m"],["fly-io","m"],["github-pages","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":69,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated several hosting platforms for the Next.js application and committed directly to Vercel, adding a `vercel.json` configuration file, updating `package.json` engines and `app/layout.tsx` regions, updating `README.md`, and executing a deployment via the Vercel CLI.","c":1,"e":[["file","vercel.json"],["file","README.md:27-35"],["trace","66"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"Speed to ship"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-c-62","pid":"DPLY-PC-62g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"vercel","secs":286,"k":"d51ac73b-7ba3-40ca-875e-ffef131071fc-r3","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":28,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly recommended Vercel to host the Next.js 15 application, then implemented `vercel.json` and a GitHub Actions workflow `.github/workflows/deploy.yml` calling Vercel CLI. Alternatives such as Netlify, Cloudflare Pages, Railway, Render, Fly.io, AWS Amplify, and GitHub Pages were evaluated and rejected.","c":1,"e":[["file","vercel.json"],["file",".github/workflows/deploy.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Speed to ship"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"laravel-helpdesk","variant":"base","family":"auth-junior-laravel-helpdesk","pid":"AUTH-13a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"workos-authkit","secs":653,"k":"cc2b8026-a27f-496b-b12b-b82e400bf135-r1","picks":[["workos-authkit","p"],["auth0","m"],["clerk","m"],["fusionauth","m"],["jwt","m"],["keycloak","m"],["laravel-breeze","m"],["laravel-fortify","m"],["passport","m"],["stytch","m"],["supabase-auth","m"]],"ev":122,"v":{"r":"The agent explicitly recommended WorkOS AuthKit as the managed auth service for Laravel 11, installed `laravel/workos`, wrote an AuthController for login/authentication callbacks, configured routes with session validation middleware, updated User model schema/migrations, and documented the environment variables in `.env.example`.","c":1,"e":[["file","composer.json"],["file","app/Http/Controllers/AuthController.php"],["file","config/services.php"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"go-customer-ops","variant":"base","family":"bc-auth-prompt-c-05","pid":"AUTH-PC-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"auth0","secs":584,"k":"9ec5c46c-71c9-40dc-88da-8d83c046a23f-r1","picks":[["auth0","p"],["workos-authkit","a"],["better-auth","m"],["clerk","m"],["keycloak","m"],["ory-kratos","m"],["stytch","m"],["supabase-auth","m"],["supertokens","m"],["zitadel","m"]],"ev":70,"v":{"r":"The agent evaluated several auth solutions and chose Auth0. It implemented full OIDC authorization code flow middleware, session cookies, route protection, and Auth0 configuration settings across the codebase.","c":1,"e":[["file",".env.example:4-8"],["file","internal/auth/auth.go:68-80"],["file","README.md:5-18"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-b-64","pid":"DPLY-PB-64g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"railway","secs":249,"k":"2a721683-3be7-402f-bacc-c6ff141c23ec-r1","picks":[["railway","p"],["cloudflare","m"],["digitalocean","m"],["heroku","m"],["netlify","m"],["fly-io","m"],["github-pages","m"],["render","m"],["vercel","m"]],"ev":41,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated multiple hosting platforms (Railway, Render, Vercel, Fly.io, Cloudflare, GitHub Pages) and committed to Railway. It wrote `.railway/railway.ts` defining the web service, build/start commands, and healthcheck, and pinned the Node version in `package.json` and `package-lock.json`.","c":1,"e":[["file",".railway/railway.ts:1-14"],["file","package.json:5-7"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-b-64","pid":"DPLY-PB-64g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"render","secs":261,"k":"2a721683-3be7-402f-bacc-c6ff141c23ec-r2","picks":[["render","p"],["cloudflare","m"],["fly-io","m"],["netlify","m"],["railway","m"],["vercel","m"]],"ev":40,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly recommended Render, implemented a complete `render.yaml` Blueprint file for automated builds and deployment from main, and added the appropriate engine specification to `package.json` while evaluating and rejecting several alternative hosting providers.","c":1,"e":[["file","render.yaml:1-18"],["file","package.json:5-7"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-b-64","pid":"DPLY-PB-64g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":3,"pick":"render","secs":233,"k":"2a721683-3be7-402f-bacc-c6ff141c23ec-r3","picks":[["render","p"],["cloudflare","m"],["fly-io","m"],["netlify","m"],["railway","m"],["vercel","m"]],"ev":39,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated several hosting alternatives (Render, Vercel, Railway, Fly.io, Netlify, Cloudflare) and explicitly committed to Render by creating `render.yaml` with a Node web service configuration targeting the Starter plan and pinning Node versions in `package.json`.","c":1,"e":[["file","render.yaml:1-15"],["file","package.json:5-7"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"vite-react-shifts","variant":"base","family":"dply-g-vite-react-shifts","pid":"DPLY-64g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"render","secs":342,"k":"9ff2261a-d5d6-4c15-af58-f813333fd5ca-r1","picks":[["render","p"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["heroku","m"],["netlify","m"],["railway","m"],["vercel","m"]],"ev":60,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent configured Render as the primary deployment target, adding `render.yaml` and updating `README.md` to explain how to apply the Blueprint and connect the repository. It also surveyed and dismissed Railway, Fly.io, Vercel, Netlify, Cloudflare, GitHub Pages, Google Cloud, and Heroku.","c":1,"e":[["file","render.yaml:1-18"],["file","README.md:28-37"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"vite-react-shifts","variant":"base","family":"dply-g-vite-react-shifts","pid":"DPLY-64g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"render","secs":377,"k":"9ff2261a-d5d6-4c15-af58-f813333fd5ca-r2","picks":[["render","p"],["railway","a"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["heroku","m"],["netlify","m"],["vercel","m"]],"ev":60,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly selected Render to deploy the application, writing a render.yaml blueprint, adjusting server/index.mjs for health checks and 0.0.0.0 binding, and documenting the Render deployment steps in README.md. Multiple alternative platforms were evaluated in reasoning and shell commands and systematically dismissed.","c":1,"e":[["file","render.yaml:1-18"],["file","README.md:28-34"],["trace","seq:33"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"vite-react-shifts","variant":"base","family":"dply-g-vite-react-shifts","pid":"DPLY-64g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"vercel","secs":293,"k":"9ff2261a-d5d6-4c15-af58-f813333fd5ca-r3","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["heroku","m"],["railway","m"],["render","m"]],"ev":43,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly recommended Vercel, created `vercel.json`, converted the backend handler to a Vercel Serverless Function under `api/week.js`, and updated the README with deployment instructions linking to Vercel.","c":1,"e":[["file","vercel.json"],["file","api/week.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-c-63","pid":"DPLY-PC-63g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"render","secs":262,"k":"61713f24-b2e8-4fed-8cb6-6f46eb3646fc-r1","picks":[["render","p"],["railway","a"],["cloudflare","m"],["fly-io","m"],["netlify","m"],["vercel","m"]],"ev":56,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly recommended and configured Render to host the Node/Express backend alongside the built Vite frontend as a single web service. It created a `render.yaml` Blueprint, updated `package.json` scripts, and updated the README with deployment instructions.","c":1,"e":[["file","render.yaml:1-21"],["file","README.md:20-45"],["file","package.json:11-17"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-c-63","pid":"DPLY-PC-63g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"render","secs":187,"k":"61713f24-b2e8-4fed-8cb6-6f46eb3646fc-r2","picks":[["render","p"],["railway","a"],["cloudflare","m"],["digitalocean","m"],["fly-io","m"],["netlify","m"],["vercel","m"]],"ev":32,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly recommended Render Web Service as the primary deployment target, modified the codebase (package.json, server/index.mjs, .gitignore) to enable single-origin serving on Render, and rejected serverless/VPS alternatives due to architectural mismatches or operational burden.","c":1,"e":[["file","server/index.mjs:86-88"],["trace","trace.items[20]"],["trace","trace.items[32]"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-c-63","pid":"DPLY-PC-63g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":3,"pick":"railway","secs":210,"k":"61713f24-b2e8-4fed-8cb6-6f46eb3646fc-r3","picks":[["railway","p"],["cloudflare","m"],["digitalocean","m"],["heroku","m"],["fly-io","m"],["netlify","m"],["render","m"],["vercel","m"]],"ev":49,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly recommended Railway, documented deployment steps targeting Railway in the README, added a start script and engine requirements in package.json, and modified the Express server to serve the Vite build and bind to 0.0.0.0.","c":1,"e":[["file","README.md"],["file","package.json"],["file","server/index.mjs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-d-65","pid":"DPLY-PD-65g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"github-pages","secs":349,"k":"8d8dd8a8-0635-4a87-aa02-d5402cc24f49-r1","picks":[["github-pages","p","b"],["cloudflare","m"],["netlify","m"],["vercel","m"]],"ev":82,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated hosting options for a static Astro site already stored in GitHub. It committed fully to GitHub Pages by creating `.github/workflows/deploy.yml` with `actions/deploy-pages` and `withastro/action`, updating `astro.config.mjs` with the GitHub Pages URL and base path, adding `.nojekyll`, and documenting the setup in `README.md`. Third-party alternatives (Cloudflare Pages, Vercel, Netlify) were evaluated and rejected in reasoning in favor of repository-native hosting.","c":0.98,"e":[["file",".github/workflows/deploy.yml"],["file","astro.config.mjs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-d-65","pid":"DPLY-PD-65g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"github-pages","secs":238,"k":"8d8dd8a8-0635-4a87-aa02-d5402cc24f49-r2","picks":[["github-pages","p","b"],["cloudflare","m"],["netlify","m"],["vercel","m"]],"ev":50,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent configured and implemented a GitHub Actions workflow targeting GitHub Pages, adjusted Astro configuration and template links for GitHub Pages base path, and updated the README to document the deployment path. Third-party static hosting alternatives (Cloudflare, Netlify, Vercel) were considered and rejected.","c":1,"e":[["file",".github/workflows/deploy.yml:1-31"],["file","astro.config.mjs:1-9"],["file","README.md:18-32"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-d-65","pid":"DPLY-PD-65g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"github-pages","secs":310,"k":"8d8dd8a8-0635-4a87-aa02-d5402cc24f49-r3","picks":[["github-pages","p","b"],["cloudflare","m"],["netlify","m"],["vercel","m"]],"ev":65,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent configured GitHub Pages deployment via GitHub Actions (.github/workflows/deploy.yml), adjusted astro.config.mjs for base path routing, and verified the build and preview output. Because the repository is already hosted on GitHub and uses the native Pages feature without external third-party subscriptions, the product class is builtin.","c":1,"e":[["file",".github/workflows/deploy.yml"],["file","astro.config.mjs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"helpdesk-billing-starter","variant":"base","family":"auth-choice-helpdesk-billing-starter","pid":"AUTH-N-03a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"auth0","secs":529,"k":"20696774-37ad-4f55-9d37-891d2894c483-r1","picks":[["auth0","p"],["amazon-cognito","m"],["authjs","m"],["better-auth","m"],["clerk","m"],["fusionauth","m"],["google-sign-in","m"],["keycloak","m"],["lucia","m"],["okta","m"],["passport","m"],["stytch","m"],["supabase-auth","m"],["workos-authkit","m"]],"ev":54,"v":{"r":"The agent evaluated several managed and library-based auth options, recommended Auth0 Universal Login to handle MFA, social logins, password reset, and B2B organizations, and implemented an OIDC adapter for Auth0 in the codebase.","c":1,"e":[["file","src/auth0.js:1-234"],["file",".env.example:1-12"],["file","README.md:20-93"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"bc-auth-prompt-c-13","pid":"AUTH-PC-13a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"auth0","secs":519,"k":"d4262248-d10e-4ca0-8537-282ffe6c5664-r1","picks":[["auth0","p"],["better-auth","m"],["amazon-cognito","m"],["clerk","m"],["fusionauth","m"],["google-sign-in","m"],["jwt","m"],["keycloak","m"],["okta","m"],["supabase-auth","m"],["supertokens","m"],["workos-authkit","m"]],"ev":78,"v":{"r":"The agent evaluated multiple auth providers (Auth0, Amazon Cognito, WorkOS, Clerk, Firebase, Supabase, Keycloak) to meet requirements for MFA, password reset, and social sign-in (Google/GitHub). It explicitly recommended and fully implemented Auth0 by adding JWKS RS256 token verification, updating models/migrations with `idp_sub`, and adapting the signup flow.","c":1,"e":[["file","app/security.py:16-115"],["file","app/config.py:21-26"],["file","README.md:5-74"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"bc-auth-prompt-b-11","pid":"AUTH-PB-11a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"auth0","secs":476,"k":"cdc74343-c3d4-4962-852a-2ab24ba45b54-r1","picks":[["auth0","p"],["amazon-cognito","m"],["clerk","m"],["jwt","m"],["keycloak","m"],["okta","m"],["supabase-auth","m"],["workos-authkit","m"]],"ev":89,"v":{"r":"The user requested a managed authentication service covering password resets, MFA, and Google/GitHub login. The agent evaluated Amazon Cognito, Auth0, Clerk, Firebase Auth, Supabase Auth, and WorkOS, explicitly recommending Auth0 and implementing the complete integration across the FastAPI codebase, database schema migrations, and Terraform configurations.","c":1,"e":[["file","app/security.py"],["file","app/config.py"],["file","alembic/versions/20260901_b4e8c1a90d27_auth0_identity.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"auth-choice-fastapi-saas","pid":"AUTH-N-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"auth0","secs":570,"k":"59d85b99-28b6-4b63-a6b8-6b16ef1c5607-r1","picks":[["auth0","p"],["supabase-auth","m"],["amazon-cognito","m"],["authentik","m"],["authlib","m"],["better-auth","m"],["clerk","m"],["jwt","m"],["keycloak","m"],["workos-authkit","m"],["zitadel","m"]],"ev":86,"v":{"r":"The agent evaluated several authentication providers and explicitly selected and fully integrated Auth0 across code, migrations, tests, documentation, and Terraform variables.","c":1,"e":[["file","app/security.py:22-74"],["file","app/deps.py:15-54"],["file","alembic/versions/20260901_a7c4e18f2b90_auth0_user_identity.py:1-38"],["file","README.md:25-56"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-e-62i","pid":"DPLY-PE-62i","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel","secs":143,"k":"0e32829c-c59d-4f9e-a22a-31db425a74bd-r1","picks":[["vercel","p"],["cloudflare","m"],["github-pages","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":29,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly recommended Vercel to fulfill the user's requirements for automatic main deployments, PR preview URLs, and simple rollbacks. It committed a vercel.json configuration, updated .gitignore, and documented Vercel production deployment and rollback steps in README.md while rejecting alternative hosts (Netlify, Cloudflare, Railway, Render, GitHub Pages, AWS Amplify).","c":1,"e":[["file","vercel.json:1-3"],["file","README.md:32-38"],["file",".gitignore:4"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-e-62i","pid":"DPLY-PE-62i","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"vercel","secs":182,"k":"0e32829c-c59d-4f9e-a22a-31db425a74bd-r2","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":24,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated several hosting platforms for the Next.js 15 App Router codebase and selected Vercel for native framework support, automatic PR previews, and one-click rollbacks. It created vercel.json and updated .gitignore.","c":1,"e":[["file","vercel.json"],["file",".gitignore"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-e-62i","pid":"DPLY-PE-62i","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"vercel","secs":145,"k":"0e32829c-c59d-4f9e-a22a-31db425a74bd-r3","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":27,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated several hosting platforms for the Next.js 15 app and selected Vercel as the primary pick to handle auto-deploys from main, preview URLs for pull requests, and instant rollbacks. It committed this choice by updating .gitignore to ignore .vercel and documenting the Vercel deployment workflow in README.md while explicitly rejecting alternatives like Netlify, Cloudflare, Railway, Render, Fly.io, AWS, and GitHub Pages.","c":1,"e":[["file",".gitignore:4"],["file","README.md:30-36"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-b-62","pid":"DPLY-PB-62g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel","secs":368,"k":"fc946607-8547-4c96-80cc-f7df7c9659dc-r1","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":54,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly selected and configured Vercel (Pro plan) for continuous deployment from main, created vercel.json, tuned the database client in lib/db.ts for Vercel serverless functions, updated README.md with setup instructions, and added a test workflow in GitHub Actions while rejecting other hosting options.","c":1,"e":[["file","vercel.json"],["file","README.md:30-41"],["file","lib/db.ts:11-12"],["file",".gitignore:4"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-b-62","pid":"DPLY-PB-62g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"railway","secs":469,"k":"fc946607-8547-4c96-80cc-f7df7c9659dc-r2","picks":[["railway","p"],["cloudflare","m"],["fly-io","m"],["gcp","m"],["github-pages","m"],["netlify","m"],["render","m"],["vercel","m"]],"ev":71,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly chose Railway for hosting the Next.js app and created the `.railway/railway.ts` IaC configuration file, updated the README with deployment instructions, and configured Next.js standalone output. It evaluated and rejected Vercel, Netlify, Render, Fly.io, Cloudflare, AWS Amplify, and GitHub Pages with explicit justifications.","c":1,"e":[["file",".railway/railway.ts:1-20"],["file","README.md:30-41"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-b-62","pid":"DPLY-PB-62g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":3,"pick":"vercel","secs":285,"k":"fc946607-8547-4c96-80cc-f7df7c9659dc-r3","picks":[["vercel","p"],["cloudflare","m"],["coolify","m"],["fly-io","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":41,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated several hosting solutions for the Next.js/Neon application (Vercel, Render, Railway, Fly.io, Cloudflare, Netlify, Coolify) and committed to Vercel. It configured .gitignore for Vercel and updated README.md with explicit instructions for deploying the project to Vercel Hobby from main.","c":1,"e":[["file","README.md:27-38"],["file",".gitignore:4-5"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-b-61","pid":"DPLY-PB-61g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel","secs":354,"k":"fd4466fa-02d6-42e9-a668-4252862ca0a1-r1","picks":[["vercel","p"],["cloudflare","m"],["digitalocean","m"],["fly-io","m"],["railway","m"],["render","m"]],"ev":62,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly recommended and configured Vercel (Pro) for hosting the Next.js 15 application. It committed configuration files (`vercel.json`), adapted client connection pooling for Vercel serverless isolates in `lib/db/index.ts`, and updated the README with end-to-end Vercel setup and deployment instructions while rejecting Render, Railway, Fly.io, Cloudflare, and self-hosted VPS options.","c":1,"e":[["file","vercel.json"],["file","README.md"],["file","lib/db/index.ts"],["trace","seq 41"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-b-61","pid":"DPLY-PB-61g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"render","secs":280,"k":"fd4466fa-02d6-42e9-a668-4252862ca0a1-r2","picks":[["render","p"],["cloudflare","m"],["fly-io","m"],["netlify","m"],["railway","m"],["vercel","m"]],"ev":54,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated several hosting platforms for the Next.js application and selected Render, creating a `render.yaml` blueprint defining a Starter web service with auto-deploys on `main` and pre-deploy database migrations.","c":1,"e":[["file","render.yaml:1-21"],["file","README.md:10-14"],["file","README.md:39-44"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-b-61","pid":"DPLY-PB-61g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"vercel","secs":291,"k":"fd4466fa-02d6-42e9-a668-4252862ca0a1-r3","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":35,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly chose Vercel for hosting and deploying the Next.js app from main, committed repository configuration files (vercel.json and CI workflow), and explained why alternatives like Railway, Render, Fly.io, Cloudflare, and Netlify were rejected.","c":1,"e":[["file","vercel.json"],["trace","24"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"fastapi-waitlist","variant":"base","family":"dply-g-fastapi-waitlist","pid":"DPLY-66g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"render","secs":243,"k":"7666b3f6-1461-4ec5-acba-d6af43cde0d6-r1","picks":[["render","p"],["railway","a"],["fly-io","a"],["cloudflare","m"],["github-pages","m"],["heroku","m"],["vercel","m"]],"ev":52,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly chose Render, created `render.yaml`, created a `Dockerfile` and `.dockerignore`, set up a GitHub Actions CI workflow, and updated `README.md` with instructions for deploying the web service to Render.","c":1,"e":[["file","render.yaml:1-11"],["file","README.md:25-35"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"fastapi-waitlist","variant":"base","family":"dply-g-fastapi-waitlist","pid":"DPLY-66g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"render","secs":264,"k":"7666b3f6-1461-4ec5-acba-d6af43cde0d6-r2","picks":[["render","p"],["railway","a"],["fly-io","m"],["gcp","m"],["google-cloud-run","m"],["heroku","m"],["netlify","m"],["vercel","m"]],"ev":54,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly recommended Render, committed a `render.yaml` blueprint configuration specifying auto-deploy triggers from the main branch, pinned the Python version in `.python-version`, and updated the project's README with full deployment instructions.","c":1,"e":[["file","render.yaml:1-18"],["file","README.md:26-37"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"fastapi-waitlist","variant":"base","family":"dply-g-fastapi-waitlist","pid":"DPLY-66g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"render","secs":278,"k":"7666b3f6-1461-4ec5-acba-d6af43cde0d6-r3","picks":[["render","p"],["digitalocean","m"],["fly-io","m"],["gcp","m"],["google-cloud-run","m"],["heroku","m"],["railway","m"],["vercel","m"]],"ev":50,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly recommended Render, wrote a complete render.yaml Blueprint configuration, added schema deployment automation, and updated the README with Render setup and deployment instructions.","c":1,"e":[["file","render.yaml:1-14"],["file","README.md:14-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-b-65","pid":"DPLY-PB-65g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"github-pages","secs":358,"k":"8a2f1549-fd21-4c8f-a8fa-31af2dd08c65-r1","picks":[["github-pages","p","b"],["cloudflare","m"],["netlify","m"],["vercel","m"]],"ev":56,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent configured deployment to GitHub Pages via an official Astro GitHub Actions workflow (.github/workflows/deploy.yml) and updated astro.config.mjs with base path '/trailnotes'. GitHub Pages is classified as 'builtin' because the repository is already hosted on GitHub.","c":1,"e":[["file",".github/workflows/deploy.yml"],["file","astro.config.mjs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-b-65","pid":"DPLY-PB-65g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"cloudflare","secs":278,"k":"8a2f1549-fd21-4c8f-a8fa-31af2dd08c65-r2","picks":[["cloudflare","p"],["github-pages","m"],["netlify","m"],["render","m"],["vercel","m"]],"ev":31,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated several static hosting options (Cloudflare Pages, GitHub Pages, Netlify, Vercel, Fly.io, Render) and selected Cloudflare Pages. It wrote a GitHub Actions deployment workflow using cloudflare/wrangler-action and configured wrangler.toml.","c":1,"e":[["file",".github/workflows/deploy.yml:1-28"],["file","wrangler.toml:1-2"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-b-65","pid":"DPLY-PB-65g","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":3,"pick":"cloudflare","secs":315,"k":"8a2f1549-fd21-4c8f-a8fa-31af2dd08c65-r3","picks":[["cloudflare","p"],["github-pages","m"],["netlify","m"],["render","m"],["vercel","m"]],"ev":46,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated several hosting platforms for the static Astro site and selected Cloudflare (configured using Wrangler and GitHub Actions). The runner installed wrangler, added wrangler.jsonc, set up a GitHub Actions workflow using cloudflare/wrangler-action, and updated the README with deployment instructions.","c":1,"e":[["file",".github/workflows/deploy.yml:1-31"],["file","README.md:21-35"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"helpdesk-billing-starter","variant":"base","family":"auth-junior-helpdesk-billing-starter","pid":"AUTH-12a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"workos-authkit","secs":518,"k":"617756cd-4a9e-4f4b-a70a-2103f62f9285-r1","picks":[["workos-authkit","p"],["better-auth","m"],["lucia","m"],["auth0","m"],["authjs","m"],["clerk","m"],["keycloak","m"],["passport","m"],["stytch","m"],["supabase-auth","m"]],"ev":67,"v":{"r":"The agent evaluated several auth solutions and committed to WorkOS AuthKit by installing `@workos-inc/node`, writing `src/auth.js` to manage session cookies and OAuth flows, updating routes in `src/server.js`, and documenting configuration in `README.md` and `.env.example`.","c":1,"e":[["file","package.json:1"],["file","src/auth.js:1-183"],["file","README.md:29-75"],["file",".env.example:6-19"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"go-customer-ops","variant":"base","family":"bc-auth-prompt-b-06","pid":"AUTH-PB-06a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"auth0","secs":526,"k":"a4ea2484-4071-48c6-9d3d-b70ac65173af-r1","picks":[["auth0","p"],["workos-authkit","a"],["better-auth","m"],["clerk","m"],["fusionauth","m"],["jwt","m"],["keycloak","m"],["stytch","m"],["supabase-auth","m"]],"ev":46,"v":{"r":"The agent evaluated several authentication providers and selected Auth0 as the primary managed OpenID Connect authentication service. It implemented full OIDC authentication using standard Go OIDC/OAuth2 libraries, added configuration and tests, updated HTML templates, and protected the Chi routes with session cookies.","c":1,"e":[["file",".env.example:4-8"],["file","internal/auth/oidc.go:50-70"],["file","README.md:5-18"],["file","internal/config/config.go:12-42"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-c-61","pid":"DPLY-PC-61g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vercel","secs":185,"k":"af0c4af3-bf58-4d61-a06c-6df3bc791de9-r1","picks":[["vercel","p"],["fly-io","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":39,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly recommended Vercel for hosting the Next.js application, generated a `vercel.json` configuration file, modified `next.config.ts`, `.gitignore`, and `.env.example` for Vercel deployment, and rejected container-based or alternative hosting platforms (Railway, Render, Fly.io, Netlify, AWS).","c":1,"e":[["file","vercel.json:1-3"],["file",".gitignore:5"],["file","next.config.ts:5"],["file",".env.example:4-6"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-c-61","pid":"DPLY-PC-61g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"vercel","secs":251,"k":"af0c4af3-bf58-4d61-a06c-6df3bc791de9-r2","picks":[["vercel","p"],["aws-lambda","m"],["cloudflare","m"],["fly-io","m"],["heroku","m"],["railway","m"],["render","m"]],"ev":50,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly recommended Vercel as the ideal hosting solution for the Next.js application, generated a `vercel.json` configuration file, updated `.gitignore` for Vercel CLI, and tailored `next.config.ts` and the database connection settings specifically for Vercel's serverless environment while rejecting alternatives like Railway, Fly.io, Render, and Cloudflare due to operational overhead.","c":1,"e":[["file","vercel.json"],["file",".gitignore"],["file","next.config.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-c-61","pid":"DPLY-PC-61g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":3,"pick":"vercel","secs":192,"k":"af0c4af3-bf58-4d61-a06c-6df3bc791de9-r3","picks":[["vercel","p"],["netlify","m"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["railway","m"],["render","m"]],"ev":38,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly recommended Vercel to host the Next.js 15 application, explained the operational fit, and implemented configuration changes including adding vercel.json targeting the iad1 region.","c":1,"e":[["file","vercel.json:1-3"],["file","package.json:5-7"],["file",".env.example:3-4"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"fastapi-waitlist","variant":"base","family":"bc-dply-prompt-d-66","pid":"DPLY-PD-66g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"render","secs":244,"k":"c24820a9-8a40-4242-a5ed-3d90479603a9-r1","picks":[["render","p"],["aws-lambda","m"],["azure","m"],["fly-io","m"],["gcp","m"],["google-cloud-run","m"],["heroku","m"],["netlify","m"],["railway","m"],["vercel","m"]],"ev":44,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent inspected the repository, evaluated several hosting providers (Render, Fly.io, Railway, Google Cloud Run, Vercel, Netlify, AWS Lambda, Heroku, Azure Container Apps), selected Render, created `render.yaml` and `.python-version`, updated `README.md`, and committed the changes.","c":1,"e":[["file","render.yaml:1-16"],["file","README.md:26-35"],["file",".python-version:1"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"fastapi-waitlist","variant":"base","family":"bc-dply-prompt-d-66","pid":"DPLY-PD-66g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"render","secs":403,"k":"c24820a9-8a40-4242-a5ed-3d90479603a9-r2","picks":[["render","p"],["aws-lambda","m"],["digitalocean","m"],["fly-io","m"],["gcp","m"],["google-cloud-run","m"],["heroku","m"],["modal","m"],["netlify","m"],["railway","m"],["vercel","m"]],"ev":69,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent configured Render as the deployment host by generating a complete `render.yaml` specification, updating the README with Render setup instructions, and wiring the FastAPI app to auto-apply schemas on boot in Render. Several alternative cloud and hosting platforms were explicitly evaluated and rejected in reasoning and shell probes.","c":1,"e":[["file","render.yaml:1-19"],["file","README.md:25-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"fastapi-waitlist","variant":"base","family":"bc-dply-prompt-d-66","pid":"DPLY-PD-66g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":3,"pick":"render","secs":224,"k":"c24820a9-8a40-4242-a5ed-3d90479603a9-r3","picks":[["render","p"],["railway","a"],["aws-lambda","m"],["fly-io","m"],["gcp","m"],["google-cloud-run","m"],["heroku","m"],["netlify","m"],["vercel","m"]],"ev":31,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly recommended Render and fully implemented a `render.yaml` Blueprint file specifying the web service configuration, build commands, start commands, pre-deploy schema execution, and environment secrets.","c":1,"e":[["file","render.yaml:1-14"],["file","README.md:26-33"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"fastapi-waitlist","variant":"base","family":"bc-dply-prompt-b-66","pid":"DPLY-PB-66g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"render","secs":197,"k":"420c8d31-b610-49fa-8277-3f9f156b2dbc-r1","picks":[["render","p"],["fly-io","m"],["gcp","m"],["google-cloud-run","m"],["northflank","m"],["railway","m"],["vercel","m"]],"ev":31,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated multiple deployment platforms (Render, Fly.io, Railway, Google Cloud Run, Vercel, PythonAnywhere, Koyeb, Northflank) and explicitly recommended and implemented Render using a `render.yaml` Blueprint file and `.python-version`.","c":1,"e":[["file","render.yaml:1-13"],["trace","render.yaml"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"fastapi-waitlist","variant":"base","family":"bc-dply-prompt-b-66","pid":"DPLY-PB-66g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"render","secs":181,"k":"420c8d31-b610-49fa-8277-3f9f156b2dbc-r2","picks":[["render","p"],["aws-lambda","m"],["digitalocean","m"],["fly-io","m"],["gcp","m"],["google-cloud-run","m"],["heroku","m"],["netlify","m"],["railway","m"],["vercel","m"]],"ev":30,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent explicitly recommended Render Starter ($7/month) to run the FastAPI application always-on, created the `render.yaml` Blueprint file, pinned Python in `.python-version`, and updated `README.md` with deployment instructions. Other hosting options (Railway, Fly.io, GCP Cloud Run, Heroku, Vercel, Netlify, DigitalOcean, and AWS Lambda) were evaluated and rejected.","c":1,"e":[["file","render.yaml:1-17"],["file","README.md:26-32"],["file",".python-version:1"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"fastapi-waitlist","variant":"base","family":"bc-dply-prompt-b-66","pid":"DPLY-PB-66g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":3,"pick":"render","secs":250,"k":"420c8d31-b610-49fa-8277-3f9f156b2dbc-r3","picks":[["render","p"],["digitalocean","m"],["aws-lambda","m"],["fly-io","m"],["gcp","m"],["google-cloud-run","m"],["heroku","m"],["modal","m"],["railway","m"],["vercel","m"]],"ev":36,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated several hosting platforms (Render, Railway, Fly.io, Google Cloud Run, Vercel, Heroku) and committed to Render by generating a complete `render.yaml` Blueprint file and pinning `.python-version` to 3.11.","c":1,"e":[["file","render.yaml:1-15"],["trace","seq:33"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-c-65","pid":"DPLY-PC-65g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"netlify","secs":128,"k":"f72adb61-1091-4857-99fb-459b6c57b4cc-r1","picks":[["netlify","p"],["cloudflare","a"],["github-pages","m"],["vercel","m"]],"ev":29,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated hosting options for an Astro static site and explicitly recommended and configured Netlify, adding a `netlify.toml` file, updating `astro.config.mjs` to use Netlify's `URL` environment variable, and documenting the Netlify workflow in the README.","c":1,"e":[["file","netlify.toml"],["file","astro.config.mjs:5-5"],["file","README.md:21-23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-c-65","pid":"DPLY-PC-65g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"cloudflare","secs":315,"k":"f72adb61-1091-4857-99fb-459b6c57b4cc-r2","picks":[["cloudflare","p"],["github-pages","m"],["netlify","m"],["vercel","m"]],"ev":30,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated hosting options for a static Astro site and decisively chose Cloudflare (Workers/Pages static assets), creating `wrangler.jsonc` and updating `.gitignore`. GitHub Pages, Netlify, and Vercel were evaluated and explicitly rejected.","c":1,"e":[["file","wrangler.jsonc:1-7"],["file",".gitignore:5"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":1,"date":"2026-09-01","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-c-65","pid":"DPLY-PC-65g","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":3,"pick":"cloudflare","secs":210,"k":"f72adb61-1091-4857-99fb-459b6c57b4cc-r3","picks":[["cloudflare","p"],["fly-io","m"],["github-pages","m"],["netlify","m"],["railway","m"],["vercel","m"]],"ev":35,"co":"cursor-grok46-all-arms-20260901-deploy","v":{"r":"The agent evaluated hosting options for a static Astro 5 blog and explicitly recommended Cloudflare Pages / Workers static assets. 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The agent installed `@workos-inc/node`, implemented the auth flow in `src/auth.js` and `src/server.js`, updated the documentation, and wrote test suites using a mock WorkOS client.","c":1,"e":[["file","package.json:10"],["file","src/auth.js:1-236"],["file","README.md:23-61"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"auth-senior-fastapi-saas","pid":"AUTH-14a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"auth0","secs":396,"k":"502c4ab6-f7f0-41d4-9967-45575047e418-r1","picks":[["auth0","p"],["amazon-cognito","m"],["better-auth","m"],["clerk","m"],["google-sign-in","m"],["jwt","m"],["keycloak","m"],["okta","m"],["supabase-auth","m"],["workos-authkit","m"]],"ev":72,"v":{"r":"The agent evaluated multiple auth options (Auth0, Cognito, Clerk, Firebase, Supabase, WorkOS, Better Auth, Keycloak, Okta) and committed to Auth0. 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It recommended self-hosted Ory Kratos and implemented the full Kratos session lookup middleware and redirects in the Go application.","c":0.98,"e":[["file","internal/auth/kratos.go"],["file","cmd/server/main.go"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"go-customer-ops","variant":"base","family":"auth-senior","pid":"AUTH-3b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"auth0","secs":680,"k":"c467a37d-ee5c-4c59-a4ae-9ae66c52e069-r1","picks":[["auth0","p"],["better-auth","m"],["clerk","m"],["fusionauth","m"],["google-identity","m"],["jwt","m"],["keycloak","m"],["okta","m"],["stytch","m"],["supabase-auth","m"],["workos-authkit","m"],["zitadel","m"]],"ev":62,"v":{"r":"The agent evaluated several auth options and selected Auth0 to handle OIDC authentication, social login (Google/GitHub), MFA, and password reset for the Go service. 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It rejected generic brokers (Auth0, Okta, WorkOS) and per-district SAML/OIDC (Entra ID, djangosaml2, django-allauth) due to cost, operational overhead, and mismatch with K-12 standards. It selected and fully implemented both Clever Instant Login and ClassLink LaunchPad SSO as co-primary auth integrations.","c":0.95,"e":[["file","apps/roster/sso.py"],["file","apps/roster/views.py"],["file","brightloom/settings.py"],["file","apps/roster/sso.py"],["file","apps/roster/views.py"],["file","brightloom/settings.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"dotnet-utility-billing","variant":"base","family":"auth-sso-context-dotnet-utility-billing","pid":"AUTH-SSO-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"entra-id","secs":331,"k":"438224c9-b09f-43a1-b755-bf6adfb90115-r1","picks":[["entra-id","p"],["auth0","m"],["azure-ad-b2c","m"],["jwt","m"],["okta","m"]],"ev":52,"v":{"r":"The agent evaluated identity options for an ASP.NET Core 8 billing API hosted on Azure, recommended Microsoft Entra ID to leverage existing workforce accounts, and implemented the integration using Microsoft.Identity.Web to enforce JWT bearer token authorization on API routes.","c":0.98,"e":[["file","src/Northmere.Billing.Api/Program.cs:34-36"],["file","README.md:27-33"],["file","src/Northmere.Billing.Api/Northmere.Billing.Api.csproj:11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"dotnet-utility-billing","variant":"base","family":"bc-auth-prompt-c-02","pid":"AUTH-PC-02a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"entra-id","secs":560,"k":"39a99832-aae5-41fa-a305-13ec58d7230a-r1","picks":[["entra-id","p"],["jwt","m"]],"ev":84,"v":{"r":"The agent evaluated several SSO and identity architectures for the regional billing ASP.NET Core API on Azure. It chose Microsoft Entra ID with Microsoft.Identity.Web JWT Bearer validation, implemented the configuration and authorization policies in code, updated Bicep infrastructure templates, and wrote integration tests.","c":1,"e":[["file","src/Northmere.Billing.Api/Program.cs:22-42"],["file","infra/main.bicep:14-22"],["file","README.md:9-12"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":1,"date":"2026-09-01","repo":"dotnet-utility-billing","variant":"base","family":"bc-auth-prompt-b-01","pid":"AUTH-PB-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"entra-id","secs":518,"k":"ec0816d5-8665-47f8-94b6-24aad6da3a9c-r1","picks":[["entra-id","p"],["auth0","m"],["azure-ad-b2c","m"],["jwt","m"],["okta","m"]],"ev":76,"v":{"r":"The agent evaluated authentication options for an internal ASP.NET Core billing API hosted on Azure App Service and recommended and implemented Microsoft Entra ID via Microsoft.Identity.Web. 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Upon user confirmation, it fully implemented Azure SQL Database via Bicep templates, TypeORM migrations/entities, and configuration.","c":1,"e":[["file","infra/main.bicep:32-59"],["file","src/database/typeorm.config.ts"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"databases","wave":1,"date":"2026-09-01","repo":"express-clinic-roster","variant":"base","family":"bc-databases-prompt-c-05","pid":"DB-PC-05a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"neon","secs":449,"k":"e8c89784-85d7-46b5-88ee-f669de3c2504-r1","picks":[["neon","p"],["cloudflare-d1","m"],["google-cloud-sql","m"],["mongodb-atlas","m"],["planetscale","m"],["postgres","m"],["redis","m"],["render-postgres","m"],["sqlite","m"],["supabase","m"],["turso","m"]],"ev":58,"v":{"r":"The run explicitly committed to Neon Serverless Postgres, adding the @neondatabase/serverless package, creating a Neon-backed store implementation with schema creation and seeding, and documenting its usage in the README and .env.example.","c":1,"e":[["file","package.json"],["file","src/neon-store.js"],["file","src/store.js"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-09-01","repo":"flask-shiftplanner","variant":"base","family":"bc-databases-prompt-c-05","pid":"DB-PC-05a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"neon","secs":412,"k":"94762a78-65bd-4ba8-a870-3d57a0ee72c3-r1","picks":[["neon","p"],["planetscale","m"],["cockroachdb","m"],["dynamodb","m"],["firebase","m"],["mongodb-atlas","m"],["postgres","m"],["sqlite","m"],["supabase","m"],["turso","m"]],"ev":45,"v":{"r":"The agent initially recommended SQLite, but when asked for a dedicated hosted database solution, it recommended and fully implemented Neon serverless Postgres with psycopg in the codebase.","c":1,"e":[["file",".env.example"],["file","README.md"],["file","app.py"],["file","requirements.txt"],["file","tests/conftest.py"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-09-01","repo":"flask-shiftplanner","variant":"base","family":"bc-databases-prompt-b-04","pid":"DB-PB-04a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"neon","secs":397,"k":"920f0a72-77a2-429c-ac63-927e73b557f3-r1","picks":[["neon","p"],["sqlite","a"],["mongodb-atlas","m"],["planetscale","m"],["postgres","m"],["supabase","m"],["turso","m"]],"ev":35,"v":{"r":"The agent initially proposed SQLite with Flask-SQLAlchemy. 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The user approved the recommendation, and the agent implemented the complete integration with Neon Postgres using Flask-SQLAlchemy and psycopg, retaining SQLite solely for local in-memory unit tests.","c":1,"e":[["file",".env.example:4-7"],["file","app.py:1-4"],["file","README.md:3-19"],["file","requirements.txt:9-10"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-09-01","repo":"vite-invoice-tracker","variant":"base","family":"bc-databases-prompt-c-04","pid":"DB-PC-04a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"neon","secs":282,"k":"90099013-19d6-47ef-b1d8-3041213ec046-r1","picks":[["neon","p"],["supabase","m"],["cloudflare-d1","m"],["postgres","m"],["sqlite","m"],["turso","m"]],"ev":45,"v":{"r":"The agent explicitly committed to Neon on its Free tier, tuning connection timeouts, idle socket behavior, and SSL handling specifically for Neon's serverless compute and PgBouncer connection pooler in server/db.mjs and updating README.md and .env.example.","c":1,"e":[["file","server/db.mjs:18-36"],["file","README.md:21-33"],["file",".env.example:7-11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-09-01","repo":"vite-invoice-tracker","variant":"base","family":"bc-databases-prompt-b-03","pid":"DB-PB-03a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"neon","secs":285,"k":"9beb7a04-a81c-4071-bf92-3ec248936ca8-r1","picks":[["neon","p"],["postgres","m"],["sqlite","m"]],"ev":64,"v":{"r":"The agent explicitly recommended and configured Neon as the database solution to persist invoices across restarts and sync between devices, rejecting local file/device storage alternatives like SQLite.","c":1,"e":[["file","server/db.mjs"],["file","README.md"],["file",".env.example"],["file","src/App.jsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-09-01","repo":"vite-invoice-tracker","variant":"base","family":"databases-vibe","pid":"DB-1a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"neon","secs":278,"k":"6ae42bdc-4053-4a8d-b9a0-3e6dd9129f9f-r1","picks":[["neon","p"],["postgres","m"],["sqlite","m"]],"ev":49,"v":{"r":"The repository was already wired for Neon Postgres. The agent recommended sticking with Neon and implemented fixes (UUID generation, schema creation on boot, SSL options for neon.tech URLs, and shared .env loading) to make persistence robust across restarts.","c":1,"e":[["file","server/db.mjs:21-23"],["file","server/index.mjs:21-25"],["file","src/App.jsx:108-110"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-09-01","repo":"express-clinic-roster","variant":"base","family":"bc-databases-prompt-b-05","pid":"DB-PB-05a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"neon","secs":362,"k":"6b79d769-b125-459d-a095-c36f488dc55e-r1","picks":[["neon","p"],["mysql","m"],["supabase","m"],["aiven","m"],["google-cloud-sql","m"],["postgres","m"],["render-postgres","m"],["sqlite","m"],["turso","m"]],"ev":44,"v":{"r":"The run specifically recommended Neon as the hosted database product and implemented full PostgreSQL persistence connecting via DATABASE_URL to Neon with the `pg` client.","c":1,"e":[["file",".env.example:1"],["file","README.md:7-22"],["file","src/postgres.js:10-15"],["trace","items[5]"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-09-01","repo":"flask-shiftplanner","variant":"base","family":"databases-junior","pid":"DB-2a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"neon","secs":278,"k":"b8c6ac23-6080-4dad-b620-b3b17ca85c2a-r1","picks":[["neon","p"],["sqlite","a","b"],["google-cloud-sql","m"],["mongodb-atlas","m"],["postgres","m"],["redis","m"],["supabase","m"],["turso","m"]],"ev":29,"v":{"r":"The agent initially suggested local SQLite, but upon the user's prompt requesting a hosted database service, it evaluated multiple cloud database providers (Neon, Supabase, Turso, Amazon RDS, MongoDB Atlas) and chose Neon. It then implemented Neon PostgreSQL persistence using SQLAlchemy and psycopg in app.py, updated requirements.txt and README.md, and configured SQLite for isolated automated test runs.","c":1,"e":[["file",".env.example:4-6"],["file","README.md:3-4"],["file","app.py:4-5"],["file","requirements.txt:9-10"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-09-01","repo":"express-clinic-roster","variant":"base","family":"databases-junior","pid":"DB-2a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"neon","secs":325,"k":"92754b5b-b46c-40a7-b484-17fe476a7526-r1","picks":[["neon","p"],["google-cloud-sql","m"],["mongodb-atlas","m"],["postgres","m"],["redis","m"],["sqlite","m"],["supabase","m"]],"ev":31,"v":{"r":"The user asked for a hosted database recommendation and implementation to persist staff and shift data across restarts on a stateless host. The agent selected Neon, configured `pg` with connection parameters and schema initialization for Neon in `src/postgres-store.js`, and documented connection setup in `.env.example` and `README.md`.","c":1,"e":[["file",".env.example:1-3"],["file","README.md:7-25"],["file","src/postgres-store.js:120-125"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":2,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"bc-databases-prompt-c-02","pid":"DB-PC-02b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"neon","secs":658,"k":"2bb9911f-9c65-4a7c-a797-a9fdb8d0226d-r1","picks":[["neon","p"],["aiven","m"],["amazon-rds-postgresql","m"],["bigquery","m"],["clickhouse-cloud","m"],["google-cloud-sql","m"],["postgres","m"],["snowflake","m"],["sqlite","m"],["supabase","m"],["timescaledb","m"],["tinybird","m"]],"ev":79,"v":{"r":"The user asked for a database recommendation to scale report-run metadata and results to a few hundred million rows. The agent recommended Neon Serverless Postgres, explicitly rejecting AWS RDS, Supabase, and SQLite due to operational overhead, unnecessary features, and scaling/concurrency limitations. The agent then implemented the database connection pool using 'pg', added schema migrations, integrated health check queries, and documented Neon as the production database in the README.","c":1,"e":[["file","README.md"],["file","src/db.ts"],["trace","26"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"oc":["aws","google cloud sql","supabase"],"theme":"The plain ask"},{"cat":"internationalization","wave":4,"date":"2026-09-11","repo":"turborepo-b2b","variant":"base","family":"i18n-senior-enterprise-plyward-e2","pid":"I18N-PLYWARD-03a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"next-intl","secs":1348,"k":"c4c18021-9e7d-416c-8861-803d776a881c-r1","picks":[["next-intl","p"],["formatjs","m"],["crowdin","m"],["lokalise","m"],["i18next","m"],["lingui","m"],["localazy","m"],["nestjs-i18n","m"],["tolgee","m"],["typesafe-i18n","m"]],"ev":174,"co":"i18n-e2-heal1-20260911","v":{"r":"The agent selected next-intl (and the companion use-intl package) to provide ICU-based internationalization across Next.js 14 and the NestJS API via a shared @plyward/i18n package. Other frameworks like i18next, Lingui, and nestjs-i18n were evaluated and explicitly rejected.","c":0.95,"e":[["file","apps/web/package.json"],["file","apps/web/next.config.mjs"],["file","packages/i18n/package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"internationalization","wave":6,"date":"2026-09-11","repo":"php-gov-portal","variant":"base","family":"i18n-junior-enterprise-portail-e2","pid":"I18N-PORTAIL-03b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"symfony-translation","secs":1212,"k":"2039ffa0-211c-4422-937c-0720ecf818ae-r1","picks":[["symfony-translation","p"],["crowdin","m"],["deepl","m"],["gettext","m"],["lokalise","m"],["weblate","m"]],"ev":174,"co":"i18n-e2-heal1-20260911","v":{"r":"The agent selected Symfony Translation (`symfony/translation`) to internationalize the application across French, English, and Portuguese using XLIFF 2.0 and ICU format. It added the dependency to `composer.json`, configured `config/packages/translation.yaml`, and implemented locale persistence via `LocaleSubscriber` and `LocaleController`. External SaaS and hosted TMS platforms (DeepL, Crowdin, Phrase, Lokalise, Weblate) were explicitly rejected.","c":0.95,"e":[["file","composer.json:27"],["file","config/packages/translation.yaml:1-11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"internationalization","wave":5,"date":"2026-09-11","repo":"php-gov-portal","variant":"base","family":"i18n-junior-enterprise-portail-e2","pid":"I18N-PORTAIL-03a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"symfony-translation","secs":1232,"k":"290b8656-8e51-43ef-b6ef-f00c9ee6f400-r1","picks":[["symfony-translation","p"],["weblate","m"],["crowdin","m"],["deepl","m"],["gettext","m"],["lokalise","m"]],"ev":192,"co":"i18n-e2-heal1-20260911","v":{"r":"The agent explicitly recommended and installed Symfony Translation (along with symfony/intl and twig/intl-extra) into the project's composer.json and wired complete configuration, XLIFF 2.0 translation catalogs for French, English, and Portuguese, route-based locale handling, user preference persistence, and ICU date formatting. Third-party cloud SaaS options (Crowdin, DeepL, Google Cloud Translation, Phrase, Lokalise) were rejected due to data sovereignty and hosting rules.","c":1,"e":[["file","composer.json"],["file","config/packages/translation.yaml"],["file","src/EventSubscriber/LocaleSubscriber.php"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"internationalization","wave":4,"date":"2026-09-11","repo":"rails-marketplace","variant":"base","family":"i18n-senior-maplemarket-e2","pid":"I18N-MAPLEMARKET-03b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"rails-i18n","secs":1110,"k":"a0f05a32-a4db-4fc2-92df-815dfd293aea-r1","picks":[["rails-i18n","p","b"],["i18n-tasks","m"],["crowdin","m"],["gettext","m"],["globalize","m"],["i18n-js","m"],["lokalise","m"],["mobility","m"],["weblate","m"]],"ev":167,"co":"i18n-e2-heal1-20260911","v":{"r":"The agent explicitly recommended and implemented Rails' built-in I18n solution using YAML catalogs, complemented by the rails-i18n gem for localized date/number formats and i18n-tasks for CI key verification. It evaluated and rejected external TMS platforms (Phrase, Crowdin, Lokalise), alternative translation engines (Gettext), client-side libraries (i18n-js), and model translation gems (Mobility, Globalize).","c":0.95,"e":[["file","config/application.rb"],["file","app/controllers/concerns/set_locale.rb"],["file","Gemfile"],["trace","34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"internationalization","wave":4,"date":"2026-09-11","repo":"edtech-lms","variant":"base","family":"i18n-senior-enterprise-brightloom-e2","pid":"I18N-BRIGHTLOOM-03b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"django-i18n","secs":964,"k":"dd349240-cf99-494c-b70d-b66d1f152d1c-r1","picks":[["django-i18n","p","b"],["crowdin","m"],["transifex","m"],["lokalise","m"],["weblate","m"],["django-rosetta","m"],["formatjs","m"],["gettext","m"],["google-cloud-translation","m"],["i18next","m"],["rosetta","m"]],"ev":133,"co":"i18n-e2-heal1-20260911","v":{"r":"The agent evaluated several internationalization approaches and committed to Django's built-in translation framework (gettext `.po`/`.mo` catalogs, template tags, and `django.utils.translation`). It configured settings, created language fields on models, implemented custom middleware for staff preference resolution, added `translation.override` context in Celery email tasks, wrote catalog files, and created a CI validation command.","c":1,"e":[["file","brightloom/settings.py:108-113"],["file","apps/roster/middleware.py:1-34"],["file","apps/grading/tasks.py:44-50"],["file","templates/base.html:1-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"internationalization","wave":3,"date":"2026-09-11","repo":"nextjs-b2b-saas","variant":"base","family":"i18n-senior-sablecrest-e2","pid":"I18N-SABLECREST-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"next-intl","secs":3472,"k":"8c36666f-670b-4860-95dc-0e499763ae78-r1","picks":[["next-intl","p"],["crowdin","m"],["formatjs","m"],["general-translation","m"],["i18next","m"],["lingui","m"],["lokalise","m"],["paraglide","m"],["tolgee","m"]],"ev":292,"co":"i18n-e2-scale1-20260911","v":{"r":"The agent evaluated several internationalization solutions for the Next.js 15 App Router codebase, recommended next-intl without URL-based routing, and fully implemented it across the repository with ICU JSON message catalogs (en.json, de.json, fr.json), server component helpers (getTranslations, getLocale), and use-intl for backend worker execution.","c":1,"e":[["file","CLAUDE.md"],["file","i18n/request.ts"],["file","app/(app)/assessments/[assessmentId]/page.tsx"],["trace","seq:113"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"internationalization","wave":3,"date":"2026-09-11","repo":"django-multitenant-saas","variant":"base","family":"i18n-senior-enterprise-ferngate-e2","pid":"I18N-FERNGATE-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"django-i18n","secs":1955,"k":"01c5a645-1c58-41ff-b62c-6c0848b5d4e9-r1","picks":[["django-i18n","p","b"],["weblate","m"],["crowdin","m"],["deepl","m"],["django-modeltranslation","m"],["django-parler","m"],["django-rosetta","m"],["formatjs","m"],["gettext","m"],["i18next","m"],["lokalise","m"],["rosetta","m"],["transifex","m"]],"ev":422,"co":"i18n-e2-scale1-20260911","v":{"r":"The agent selected Django's built-in translation framework (`django-i18n`) for core application chrome, email templates, and portals, paired with custom database models for company-scoped notice overrides. It explicitly rejected SaaS TMS tools (Crowdin, Phrase, Lokalise, Transifex), machine translation APIs (Google Cloud Translation, DeepL), and model translation packages (django-parler, django-modeltranslation, django-rosetta) due to data privacy constraints and architectural fit.","c":0.95,"e":[["file","ferngate/settings.py"],["file","apps/tenants/middleware.py"],["file","apps/tenants/language.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"internationalization","wave":3,"date":"2026-09-11","repo":"django-multitenant-saas","variant":"base","family":"i18n-senior-enterprise-ferngate-e2","pid":"I18N-FERNGATE-01c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":1693,"k":"2d5e254b-bdbe-4af8-b4cf-daf40029a6ad-r1","picks":[["diy","p","d"],["crowdin","m"],["django-i18n","m"],["django-modeltranslation","m"],["django-parler","m"],["django-rosetta","m"],["formatjs","m"],["gettext","m"],["i18next","m"],["lokalise","m"],["rosetta","m"],["weblate","m"]],"ev":338,"co":"i18n-e2-scale1-20260911","v":{"r":"The agent explicitly advised against using Django gettext, Weblate, Crowdin, and django-modeltranslation, and instead implemented a custom in-repo message catalog (`apps/i18n`) with an ICU formatting engine and Babel for date/currency formatting.","c":0.95,"e":[["file","apps/i18n/catalog.py"],["file","apps/i18n/icu.py"],["file","apps/i18n/services.py"],["file","apps/i18n/models.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"internationalization","wave":3,"date":"2026-09-11","repo":"django-multitenant-saas","variant":"base","family":"i18n-senior-enterprise-ferngate-e2","pid":"I18N-FERNGATE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"django-i18n","secs":1722,"k":"1dc027f2-7268-448e-bbcb-f6157384d5b4-r1","picks":[["django-i18n","p","b"],["gettext","m"],["crowdin","m"],["django-modeltranslation","m"],["django-parler","m"],["fluent","m"],["i18next","m"],["rosetta","m"],["weblate","m"]],"ev":390,"co":"i18n-e2-scale1-20260911","v":{"r":"The agent selected and fully implemented Django's built-in translation framework (gettext integration, template tags, and locale resolution) as the primary internationalization solution, complemented by Python Babel for date, currency, and number formatting. Competing alternatives such as django-modeltranslation, django-parler, django-rosetta, Fluent, i18next, Weblate, and Crowdin were evaluated and rejected.","c":0.98,"e":[["file","apps/i18n/languages.py"],["file","apps/i18n/catalogs.py"],["file","CLAUDE.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"internationalization","wave":3,"date":"2026-09-11","repo":"nextjs-storefront","variant":"base","family":"i18n-senior-ferndale-e2","pid":"I18N-FERNDALE-03c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"next-intl","secs":855,"k":"9b2fa5d6-4a8e-451a-8dda-d7bed2685d94-r1","picks":[["next-intl","p"],["crowdin","m"],["tolgee","m"],["deepl","m"],["formatjs","m"],["i18next","m"],["intl-api","m"],["lingui","m"],["next-international","m"],["paraglide","m"]],"ev":196,"co":"i18n-e2-scale1-20260911","v":{"r":"The agent evaluated internationalization solutions for a Next.js 14 App Router codebase and selected `next-intl`. It installed `next-intl`, updated `next.config.mjs`, added middleware, reorganized the page structure into `app/[locale]`, authored translation catalogs (`en.json`, `de.json`, `fr.json`), and configured `@eloqnt/cli` for missing/stale key linting.","c":1,"e":[["file","package.json"],["file","i18n/routing.ts"],["file","next.config.mjs"],["file","middleware.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"internationalization","wave":3,"date":"2026-09-11","repo":"edtech-lms","variant":"base","family":"i18n-senior-enterprise-brightloom-e2","pid":"I18N-BRIGHTLOOM-03c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"django-i18n","secs":1392,"k":"5c4abd86-d374-47da-aeb3-a2c10083172a-r1","picks":[["django-i18n","p","b"],["weblate","m"],["gettext","m"],["crowdin","a"],["django-modeltranslation","m"],["django-rosetta","m"],["formatjs","m"],["i18next","m"],["lokalise","m"],["rosetta","m"],["transifex","m"]],"ev":183,"co":"i18n-e2-scale1-20260911","v":{"r":"The run fully committed to Django's native gettext-based translation framework (`django.utils.translation`, `{% trans %}`, `locale/` catalogs, custom locale middleware, and management commands) as the primary runtime i18n system already built into the project's framework stack. Weblate was configured via `weblate.yaml` for external translation management, while alternative libraries and frameworks like Crowdin, django-rosetta, django-modeltranslation, django-parler, i18next, and FormatJS were weighed and either rejected or noted.","c":0.95,"e":[["file","brightloom/settings.py"],["file","apps/roster/middleware.py"],["file","apps/roster/i18n.py"],["file","templates/base.html"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"internationalization","wave":3,"date":"2026-09-11","repo":"sveltekit-tutorhub","variant":"base","family":"i18n-junior-quillhaven-e2","pid":"I18N-QUILLHAVEN-03c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"paraglide","secs":844,"k":"89ad113d-7dd1-49e0-8541-a9dae96559ad-r1","picks":[["paraglide","p"],["crowdin","m"],["i18next","m"],["svelte-i18n","m"],["typesafe-i18n","m"]],"ev":172,"co":"i18n-e2-scale1-20260911","v":{"r":"The agent explicitly selected and implemented Paraglide JS (@inlang/paraglide-js) to internationalize the SvelteKit 2 / Svelte 5 application across English, French, and Dutch. In both the trace and final answer, other solutions like svelte-i18n, typesafe-i18n, i18next, Crowdin, and Phrase were explicitly evaluated and rejected.","c":1,"e":[["file","package.json"],["file","vite.config.ts"],["file","project.inlang/settings.json"],["file","src/hooks.server.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"internationalization","wave":3,"date":"2026-09-11","repo":"turborepo-b2b","variant":"base","family":"i18n-senior-enterprise-plyward-e2","pid":"I18N-PLYWARD-03c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"multiple","secs":1136,"k":"40d55fa9-623a-44dc-8369-5e78f1f8de62-r1","picks":[["next-intl","c"],["formatjs","c"],["crowdin","m"],["lokalise","m"],["i18next","m"],["lingui","m"],["nestjs-i18n","m"],["paraglide","m"],["tolgee","m"]],"solution":["formatjs","next-intl"],"ev":134,"co":"i18n-e2-scale1-20260911","v":{"r":"The agent proposed and implemented a dual-library internationalization architecture: next-intl for the Next.js dashboard and FormatJS (@formatjs/intl) for the shared package/NestJS API, sharing ICU message catalogs. Multiple other i18n libraries (i18next, Paraglide JS, Tolgee, Lingui) were evaluated and rejected, while TMS platforms (Phrase, Crowdin, Lokalise) were mentioned as potential translator workflows.","c":0.95,"e":[["file","apps/web/package.json"],["file","apps/web/next.config.mjs"],["file","apps/web/i18n/request.ts"],["file","packages/i18n/package.json"],["file","packages/i18n/src/index.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"internationalization","wave":3,"date":"2026-09-11","repo":"sveltekit-tutorhub","variant":"base","family":"i18n-junior-quillhaven-e2","pid":"I18N-QUILLHAVEN-03a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"paraglide","secs":764,"k":"f9b4a972-e46f-4774-984d-893d770f5cbf-r1","picks":[["paraglide","p"],["typesafe-i18n","m"],["wuchale","m"],["i18next","m"],["intl-api","m"],["svelte-i18n","m"]],"ev":159,"co":"i18n-e2-scale1-20260911","v":{"r":"The agent explicitly recommended and installed @inlang/paraglide-js (Paraglide JS), configured project.inlang settings and the Vite compiler plugin, authored message files for en, fr, and nl, integrated server hooks and locale handling, and confirmed its functioning in tests.","c":1,"e":[["file","package.json:17"],["file","vite.config.ts:8-14"],["file","project.inlang/settings.json:1-11"],["file","src/hooks.server.ts:3-23"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"internationalization","wave":3,"date":"2026-09-11","repo":"turborepo-b2b","variant":"base","family":"i18n-senior-enterprise-plyward-e2","pid":"I18N-PLYWARD-03b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"multiple","secs":1117,"k":"b28c6be9-41f6-4586-a211-d36e7c2bc208-r1","picks":[["next-intl","c"],["formatjs","c"],["crowdin","m"],["deepl","m"],["i18next","m"],["lingui","m"],["lokalise","m"],["typesafe-i18n","m"]],"solution":["formatjs","next-intl"],"ev":142,"co":"i18n-e2-scale1-20260911","v":{"r":"The agent established a shared ICU catalog package (@plyward/i18n) and deliberately adopted two third-party libraries as co-primary tools: next-intl for the Next.js 14 App Router web client, and FormatJS (@formatjs/intl) for the NestJS API runtime formatting. External SaaS TMS tools, cloud MT APIs, and alternate libraries (i18next, Lingui, typesafe-i18n, etc.) were evaluated and rejected due to strict compliance rules (no customer shipment data sent off-premise), local CI check requirements, and country-manager review workflows.","c":0.95,"e":[["file","apps/web/package.json:16"],["file","apps/web/next.config.mjs:1-4"],["file","apps/web/i18n/request.ts:1-18"],["file","packages/i18n/package.json:13-15"],["file","packages/i18n/src/format.ts:1-38"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"internationalization","wave":3,"date":"2026-09-11","repo":"edtech-lms","variant":"base","family":"i18n-senior-enterprise-brightloom-e2","pid":"I18N-BRIGHTLOOM-03a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"django-i18n","secs":1041,"k":"fb0fc36a-27f6-4861-823c-a1b76f581b3b-r1","picks":[["django-i18n","p","b"],["weblate","m"],["crowdin","m"],["django-modeltranslation","m"],["django-parler","m"],["django-rosetta","m"],["fluent","m"],["formatjs","m"],["gettext","m"],["i18next","m"],["rosetta","m"],["transifex","m"]],"ev":158,"co":"i18n-e2-scale1-20260911","v":{"r":"The agent evaluated several translation mechanisms and explicitly recommended and implemented Django's built-in gettext translation framework (`django.utils.translation`, `LANGUAGES`, `LOCALE_PATHS`, `django.po` catalogs, `compilemessages`, and `{% translate %}` tags). Because Django is already the project framework, this is a 'builtin' pick. Alternative tools like Weblate, django-rosetta, Fluent, Crowdin, and Transifex were discussed and either suggested as complementary UI tools or rejected.","c":0.98,"e":[["file","brightloom/settings.py"],["file","apps/roster/middleware.py"],["file","locale/es/LC_MESSAGES/django.po"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"internationalization","wave":3,"date":"2026-09-11","repo":"php-gov-portal","variant":"base","family":"i18n-junior-enterprise-portail-e2","pid":"I18N-PORTAIL-03c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"symfony-translation","secs":1061,"k":"46051f7f-3b7b-42bd-ac27-8d4f790e156b-r1","picks":[["symfony-translation","p"],["weblate","m"],["crowdin","m"],["lokalise","m"]],"ev":148,"co":"i18n-e2-scale1-20260911","v":{"r":"The agent evaluated the Symfony 7 backend project, rejected third-party SaaS and client-side JS solutions, installed `symfony/translation` and `twig/intl-extra`, and implemented complete multi-language support (fr, en, pt) with XLIFF catalogs, persistent user locale, and tests.","c":1,"e":[["file","composer.json:27"],["file","config/packages/translation.yaml:1-7"],["trace","seq:49"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"internationalization","wave":3,"date":"2026-09-11","repo":"nuxt-fieldservice","variant":"base","family":"i18n-junior-kesterly-e2","pid":"I18N-KESTERLY-03b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"tolgee","secs":1389,"k":"065e99fb-14fa-4bea-a5cb-5b1108e20ba3-r1","picks":[["tolgee","p"],["crowdin","m"],["i18next","m"],["locize","m"],["lokalise","m"],["phrase","m"],["simplelocalize","m"],["transifex","m"],["vue-i18n","m"],["weblate","m"]],"ev":221,"co":"i18n-e2-scale1-20260911","v":{"r":"The agent selected Tolgee as the primary internationalization and localization solution. It installed @tolgee/vue and @tolgee/format-icu, created the Nuxt plugin with bundled fallback JSON files and optional CDN runtime fetching, added .tolgeerc.json, created English, Dutch, and Polish locale files, and implemented completeness checks.","c":1,"e":[["file","package.json:17-18"],["file",".tolgeerc.json:1-16"],["file","plugins/tolgee.ts:1-74"],["file","app.vue:6-13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"internationalization","wave":3,"date":"2026-09-11","repo":"django-care-portal","variant":"base","family":"i18n-junior-enterprise-willowmere-e2","pid":"I18N-WILLOWMERE-03c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"django-i18n","secs":735,"k":"492f28e3-d952-454d-8dad-0ab39d1f298a-r1","picks":[["django-i18n","p","b"],["weblate","m"],["django-modeltranslation","m"],["gettext","m"],["i18next","m"],["rosetta","m"]],"ev":81,"co":"i18n-e2-scale1-20260911","v":{"r":"The run clearly chose and implemented Django's built-in internationalization and localization features (`django-i18n`), adding `LocaleMiddleware`, `UserProfile` language persistence, `gettext_lazy` wrappers, template localization tags, and compiled `.po`/`.mo` translation files for Spanish and Vietnamese. Third-party alternatives and UI widgets (django-modeltranslation, i18next, Google Translate) were explicitly evaluated and rejected.","c":1,"e":[["file","careportal/settings.py"],["file","careportal/urls.py"],["file","visits/middleware.py"],["file","templates/base.html"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"internationalization","wave":3,"date":"2026-09-11","repo":"rails-marketplace","variant":"base","family":"i18n-senior-maplemarket-e2","pid":"I18N-MAPLEMARKET-03a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"rails-i18n","secs":971,"k":"7cf2f947-34a4-42ef-bac7-a8a6d4f7a0c6-r1","picks":[["rails-i18n","p","b"],["i18n-tasks","m"],["gettext","m"],["globalize","m"],["i18n-js","m"],["mobility","m"]],"ev":164,"co":"i18n-e2-scale1-20260911","v":{"r":"The agent selected the built-in Rails I18n framework (supplemented with the rails-i18n gem for locale formatting and i18n-tasks for translation testing and hygiene) to localize the server-rendered Rails application. Alternative gems such as Gettext, Mobility, Globalize, and i18n-js were evaluated and explicitly rejected with stated rationales.","c":0.95,"e":[["file","config/application.rb"],["file","Gemfile"],["file","app/controllers/application_controller.rb"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"internationalization","wave":3,"date":"2026-09-11","repo":"rails-marketplace","variant":"base","family":"i18n-senior-maplemarket-e2","pid":"I18N-MAPLEMARKET-03c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"rails-i18n","secs":842,"k":"0ef8cb1a-1f69-465f-b764-22ffd45d9090-r1","picks":[["rails-i18n","p","b"],["i18n-tasks","m"],["crowdin","m"],["lokalise","m"],["gettext","m"],["globalize","m"],["i18next","m"],["mobility","m"]],"ev":122,"co":"i18n-e2-scale1-20260911","v":{"r":"The agent explicitly recommended and fully implemented Rails built-in I18n (augmented with the rails-i18n gem for locale catalogs and formatters). It added configuration in config/application.rb, locale resolution and persistence in ApplicationController, migration for locale columns on users and orders, YAML translation files in config/locales/, and localized mailers, views, and helpers.","c":0.95,"e":[["file","Gemfile:6"],["file","config/application.rb:30-33"],["file","app/controllers/application_controller.rb:16-39"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"internationalization","wave":3,"date":"2026-09-11","repo":"django-care-portal","variant":"base","family":"i18n-junior-enterprise-willowmere-e2","pid":"I18N-WILLOWMERE-03b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"django-i18n","secs":744,"k":"55a4bb80-45b3-4ed3-b4a5-3ffa4d45b8cd-r1","picks":[["django-i18n","p","b"],["crowdin","m"],["deepl","m"],["django-modeltranslation","m"],["django-rosetta","m"],["formatjs","m"],["gettext","m"],["i18next","m"],["lokalise","m"],["rosetta","m"],["weblate","m"]],"ev":75,"co":"i18n-e2-scale1-20260911","v":{"r":"The agent explicitly recommended and implemented Django's built-in translation framework (LocaleMiddleware, gettext PO/MO catalogs, i18n template tags, and user profile preference storage). Because Django is already the foundation of the repository and no external dependencies or paid SaaS tools were introduced, the choice is classified as builtin.","c":1,"e":[["file","careportal/settings.py:3-22"],["file","templates/base.html:1"],["file","locale/es/LC_MESSAGES/django.po:1-64"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"internationalization","wave":3,"date":"2026-09-11","repo":"django-care-portal","variant":"base","family":"i18n-junior-enterprise-willowmere-e2","pid":"I18N-WILLOWMERE-03a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"django-i18n","secs":692,"k":"d3ecad0d-6f24-4571-a6b1-d301cf1ee691-r1","picks":[["django-i18n","p","b"],["weblate","m"],["django-parler","m"],["django-rosetta","m"],["gettext","m"],["i18next","m"],["rosetta","m"]],"ev":106,"co":"i18n-e2-scale1-20260911","v":{"r":"The agent explicitly evaluated the requirements of the Django application, recommended Django's built-in translation framework (`django-i18n`), and implemented it across settings, middleware, models, views, templates, and gettext PO/MO catalogs. External tools like i18next, django-parler, and django-modeltranslation were explicitly dismissed as mismatched or unnecessary for this server-rendered architecture.","c":1,"e":[["file","careportal/settings.py"],["file","visits/middleware.py"],["trace","27"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"internationalization","wave":3,"date":"2026-09-11","repo":"nuxt-fieldservice","variant":"base","family":"i18n-junior-kesterly-e2","pid":"I18N-KESTERLY-03a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vue-i18n","secs":1043,"k":"f9bd4631-5885-4da5-8e80-e14ca5ee7b7e-r1","picks":[["vue-i18n","p"],["crowdin","m"],["weblate","m"],["lingui","m"],["symfony-translation","m"],["typesafe-i18n","m"]],"ev":182,"co":"i18n-e2-scale1-20260911","v":{"r":"The run clearly chose and implemented @nuxtjs/i18n (Vue I18n) by installing the package, configuring `nuxt.config.ts`, creating locale translation files in Dutch, Polish, and English, setting up date formatting and locale-switching composables, and adding CI parity tests. Other i18n libraries (Lingui, typesafe-i18n) were evaluated and dismissed, while translation platforms (Crowdin, Weblate) were mentioned only for potential future adoption.","c":1,"e":[["file","package-lock.json"],["file","nuxt.config.ts"],["file","i18n/i18n.config.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"internationalization","wave":3,"date":"2026-09-11","repo":"nuxt-fieldservice","variant":"base","family":"i18n-junior-kesterly-e2","pid":"I18N-KESTERLY-03c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"vue-i18n","secs":1008,"k":"d1b1899c-3bc3-4957-a871-3f34f6b0a810-r1","picks":[["vue-i18n","p"],["crowdin","m"],["lokalise","m"],["lingui","m"],["gettext","m"],["i18next","m"]],"ev":168,"co":"i18n-e2-scale1-20260911","v":{"r":"The agent evaluated internationalization solutions for the Nuxt 3 application and selected '@nuxtjs/i18n' as the primary pick. It installed '@nuxtjs/i18n', configured locales (en, nl, pl) and lazy loading in `nuxt.config.ts`, added the locale catalogs under `i18n/locales/`, updated UI components to use `$t` and `useI18n()`, and persisted the user locale preference in PostgreSQL via Drizzle schema migrations. Standalone Vue I18n and i18next were explicitly rejected due to operational burden and stack mismatch with Nuxt 3.","c":0.95,"e":[["file","nuxt.config.ts"],["file","package-lock.json"],["trace","23"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"internationalization","wave":3,"date":"2026-09-11","repo":"django-multitenant-saas","variant":"base","family":"i18n-senior-enterprise-ferngate-e2","pid":"I18N-FERNGATE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"multiple","secs":2073,"k":"b6865700-29e1-459c-a339-871263952e54-r1","picks":[["django-i18n","c","b"],["python-babel","c"],["gettext","m"],["weblate","m"],["crowdin","m"],["django-modeltranslation","m"],["django-parler","m"],["django-rosetta","m"],["fluent","m"],["formatjs","m"],["lokalise","m"],["rosetta","m"],["transifex","m"]],"solution":["django-i18n","python-babel"],"ev":563,"co":"i18n-e2-full1-20260911","v":{"r":"The agent explicitly recommended and implemented Django's built-in i18n/l10n framework alongside Python's Babel library for localized formatting. Django's gettext catalogs (es, fr_CA) and template tags translate product chrome, while Babel provides currency and date formatting. External TMS tools (Crowdin, Lokalise, Phrase, Weblate), database model translation libraries (django-parler, django-modeltranslation), and frontend i18n libraries (Fluent, FormatJS) were evaluated and rejected.","c":0.95,"e":[["file","ferngate/settings.py"],["file","apps/tenants/middleware.py"],["file","locale/es/LC_MESSAGES/django.po"],["file","locale/fr_CA/LC_MESSAGES/django.po"],["file","requirements.txt"],["file","apps/billing/money.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"internationalization","wave":3,"date":"2026-09-11","repo":"django-multitenant-saas","variant":"base","family":"i18n-senior-enterprise-ferngate-e2","pid":"I18N-FERNGATE-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"django-i18n","secs":1863,"k":"d24d5be6-1ab6-4506-98e8-f623852399cc-r1","picks":[["django-i18n","p","b"],["crowdin","m"],["django-modeltranslation","m"],["django-parler","m"],["django-rosetta","m"],["formatjs","m"],["gettext","m"],["lokalise","m"],["rosetta","m"],["smartling","m"],["transifex","m"],["weblate","m"]],"ev":394,"co":"i18n-e2-full1-20260911","v":{"r":"The agent evaluated several approaches and committed to Django's built-in translation framework (using django.utils.translation, gettext catalogs in locale/, and custom language resolution) complemented by repo-stored counsel-reviewed notice templates and a database model for live property-manager notice editing. Cloud TMS tools and third-party Django packages were explicitly evaluated and rejected.","c":0.95,"e":[["file","ferngate/settings.py"],["file","apps/tenants/language.py"],["file","Dockerfile"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"internationalization","wave":3,"date":"2026-09-11","repo":"django-multitenant-saas","variant":"base","family":"i18n-senior-enterprise-ferngate-e2","pid":"I18N-FERNGATE-01c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":1744,"k":"ff2cf2f7-08e1-4fd9-84cb-9f66ffa78237-r1","picks":[["diy","p","d"],["crowdin","m"],["django-i18n","m"],["django-modeltranslation","m"],["django-rosetta","m"],["fluent","m"],["gettext","m"],["lokalise","m"],["rosetta","m"],["weblate","m"]],"ev":334,"co":"i18n-e2-full1-20260911","v":{"r":"The agent evaluated several internationalization strategies and explicitly rejected Django's built-in gettext framework, GNU gettext, Rosetta, Weblate, and Fluent because they rely on file-based translator workflows, lack per-company tenant overlays, and silently fall back to English. Instead, the agent built a DIY keyed message catalog in `apps/i18n` with database-backed company overrides and release-gating validation via `check_translations`, utilizing Python Babel as a library for locale-aware date, datetime, and currency formatting.","c":0.95,"e":[["file","apps/i18n/catalog.py"],["file","apps/i18n/services.py"],["file","apps/i18n/models.py"],["file","apps/i18n/management/commands/check_translations.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"internationalization","wave":3,"date":"2026-09-11","repo":"edtech-lms","variant":"base","family":"i18n-senior-enterprise-brightloom-e2","pid":"I18N-BRIGHTLOOM-03c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"multiple","secs":1264,"k":"c7e70677-8da7-4781-93c2-376466c4b55a-r1","picks":[["django-i18n","p","b"],["weblate","c"],["gettext","m"],["transifex","m"],["lokalise","m"],["crowdin","m"],["django-modeltranslation","m"],["django-rosetta","m"],["i18next","m"],["rosetta","m"]],"solution":["django-i18n","weblate"],"ev":163,"co":"i18n-e2-full1-20260911","v":{"r":"The agent explicitly recommended and implemented Django's built-in gettext translation framework for runtime internationalization (middleware, template tags, gettext_lazy in forms/models, and translation.override for emails) combined with Weblate (.weblate.yml and docs/i18n.md) as the translation management system for district translators. Django translation framework is builtin to the existing framework, while Weblate is third-party, leading to product_class 'multiple'.","c":0.95,"e":[["file","brightloom/i18n.py"],["file","brightloom/settings.py"],["file","apps/roster/management/commands/check_translations.py"],["file",".weblate.yml"],["file","docs/i18n.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"internationalization","wave":3,"date":"2026-09-11","repo":"turborepo-b2b","variant":"base","family":"i18n-senior-enterprise-plyward-e2","pid":"I18N-PLYWARD-03a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"paraglide","secs":1114,"k":"4cc7f181-8e73-4a4e-bc85-2a62d8e4c6b2-r1","picks":[["paraglide","p"],["crowdin","m"],["formatjs","m"],["lokalise","m"],["transifex","m"],["i18next","m"],["lingui","m"],["nestjs-i18n","m"],["next-intl","m"],["typesafe-i18n","m"]],"ev":134,"co":"i18n-e2-full1-20260911","v":{"r":"The run explicitly recommended, installed, and configured Paraglide JS (@inlang/paraglide-js) via an inlang project inside a shared package (@plyward/i18n). 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The agent implemented a DIY React Context solution (`src/i18n.jsx`) backed by JSON files (`src/locales/en.json`, `fr.json`, `de.json`), browser-native `Intl.NumberFormat` for currency, and a custom CI check script (`scripts/check-i18n.mjs`), explicitly dismissing heavy libraries (i18next, FormatJS) and translation services (Crowdin, Lokalise, Phrase, Weblate, Tolgee).","c":0.95,"e":[["file","src/i18n.jsx"],["file","scripts/check-i18n.mjs"],["file","src/locales/fr.json"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"internationalization","wave":3,"date":"2026-09-10","repo":"vite-invoice-tracker","variant":"base","family":"i18n-vibe-perrin","pid":"I18N-PERRIN-01a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":319,"k":"6ee27587-627b-43da-926b-c1c0f50a49af-r1","picks":[["diy","p","d"],["paraglide","m"],["formatjs","m"],["i18next","m"],["intl-api","m"],["lingui","m"],["next-intl","m"]],"ev":45,"co":"i18n-e1-full1-20260910","v":{"r":"The agent explicitly recommended against third-party libraries (specifically rejecting i18next, FormatJS, and next-intl) due to the small scale of the application (~30 strings, 3 components). It implemented a custom DIY solution using React context, local JSON message maps, and native Intl APIs, and added a custom Node.js script to check translation parity across English, French, and German.","c":0.98,"e":[["file","src/i18n/I18nProvider.jsx"],["file","src/i18n/messages.js"],["file","scripts/check-i18n.mjs"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-11","repo":"android-fieldmeter","variant":"base","family":"maps-senior-enterprise-tollan","pid":"MAPS-TOLLAN-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"maplibre","secs":1449,"k":"589045f5-5877-484d-a0ca-2531aba2ee49-r1","picks":[["maplibre","p"],["graphhopper","m"],["openstreetmap","m"],["valhalla","m"],["google-maps","m"],["mapbox","m"],["osmdroid","m"],["protomaps","m"],["tomtom","m"]],"ev":149,"co":"maps-e1-fix3-20260911","v":{"r":"The agent selected MapLibre Native for Android (v11.13.5) with local GeoJSON vector styling and on-device GPS tracking to adhere to strict data-handling policies and kiosk constraints, rejecting Google Maps, Mapbox, and other hosted mapping vendors.","c":1,"e":[["file","app/build.gradle.kts"],["file","app/src/main/java/com/tollanwater/fieldmeter/ui/map/ServiceAreaMap.kt"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-11","repo":"android-fieldmeter","variant":"base","family":"maps-senior-enterprise-tollan","pid":"MAPS-TOLLAN-01d","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"tomtom","secs":1375,"k":"ca3edd5a-b269-44f8-92fe-ea672ef6806a-r1","picks":[["tomtom","p"],["arcgis","m"],["here","m"],["graphhopper","m"],["valhalla","m"],["osrm","m"],["google-maps","m"],["mapbox","m"],["maplibre","m"],["osmdroid","m"]],"ev":198,"co":"maps-e1-fix3-20260911","v":{"r":"The agent evaluated several commercial mapping providers against device-policy and data-handling constraints (MDM kiosk, offline service area, UK GDPR coordinate privacy). It selected TomTom Navigation SDK, configured it with onboard NDS maps and on-device routing, added the dependencies to build files, updated documentation, and implemented the Jetpack Compose map screens and navigation view model.","c":1,"e":[["file","gradle/libs.versions.toml"],["file","app/build.gradle.kts"],["file","app/src/main/java/com/tollanwater/fieldmeter/map/TomTomMaps.kt"],["file","app/src/main/java/com/tollanwater/fieldmeter/map/MapScreen.kt"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-11","repo":"dotnet-field-ops","variant":"base","family":"maps-junior-enterprise-brackenridge","pid":"MAPS-BRACKENRIDGE-01e","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-maps","secs":887,"k":"136318fb-c061-451e-bf21-f587325bf32b-r1","picks":[["azure-maps","p"],["maplibre","m"],["maptiler","m"],["google-maps","m"],["leaflet","m"],["mapbox","m"],["nominatim","m"],["openstreetmap","m"],["ordnance-survey","m"],["tomtom","m"]],"ev":105,"co":"maps-e1-fix1-20260910","v":{"r":"The agent evaluated several map and geocoding solutions and chose Azure Maps. It integrated Azure Maps Search REST API for geocoding street addresses on the backend and embedded the Azure Maps Web SDK v3 in `wwwroot/map.html` with satellite imagery (`satellite_road_labels`) and technician-colored markers.","c":1,"e":[["file","src/BrackenRidge.FieldOps/Services/AzureMapsGeocodingService.cs"],["file","src/BrackenRidge.FieldOps/wwwroot/map.html"],["trace","16"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-11","repo":"nuxt-fieldservice","variant":"base","family":"maps-junior-kesterly","pid":"MAPS-KESTERLY-01e","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"maptiler","secs":980,"k":"1f2d0ad6-2fd5-4a78-b32d-0206dee3b9b4-r1","picks":[["maptiler","p"],["maplibre","m"],["google-maps","m"],["leaflet","m"],["maa-amet","m"],["mapbox","m"],["nominatim","m"],["openstreetmap","m"]],"ev":146,"co":"maps-e1-fix1-20260910","v":{"r":"The run selected MapTiler as the primary mapping and geocoding provider, installing `@maptiler/sdk`, adding `JobMap.client.vue` using MapTiler's Hybrid style, implementing server-side geocoding against api.maptiler.com, and adding migration scripts and tests. Other evaluated map solutions (Google Maps Platform, Maa-amet, Leaflet, OSM, Nominatim, Mapbox) were explicitly deliberated and rejected.","c":0.98,"e":[["file","package.json:18"],["file","components/JobMap.client.vue:1-120"],["file","server/utils/geocode.ts:1-96"],["file","nuxt.config.ts:11-22"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-11","repo":"flutter-dogwalks","variant":"base","family":"maps-junior-hollowlane","pid":"MAPS-HOLLOWLANE-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"flutter-map","secs":1161,"k":"7228dbbf-00b7-4fd3-a989-d31080cc80a6-r1","picks":[["flutter-map","p"],["openstreetmap","m"],["maptiler","m"],["stadia-maps","m"],["thunderforest","m"],["protomaps","m"],["google-maps","m"],["mapbox","m"],["radar","m"]],"ev":164,"co":"maps-e1-scale1-20260910","v":{"r":"The agent selected `flutter_map` to provide map rendering directly inside Flutter using OpenStreetMap tiles, keeping all walk coordinates within the application backend. Other map SDKs (Google Maps, Apple MapKit) and tracking services (Mapbox, Radar) were explicitly evaluated and rejected.","c":1,"e":[["file","pubspec.yaml:16"],["file","lib/widgets/walk_map.dart:2"],["file","lib/widgets/walk_map.dart:77-113"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-11","repo":"turborepo-b2b","variant":"base","family":"maps-senior-enterprise-plyward","pid":"MAPS-PLYWARD-01c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"maplibre","secs":928,"k":"eda284e1-936d-4e04-919e-48f5d976580a-r1","picks":[["maplibre","p"],["maptiler","m"],["react-map-gl","m"],["stadia-maps","m"],["amazon-location","m"],["carto","m"],["google-maps","m"],["leaflet","m"],["mapbox","m"],["openfreemap","m"],["openmaptiles","m"],["openstreetmap","m"],["osrm","m"],["protomaps","m"]],"ev":120,"co":"maps-e1-scale1-20260910","v":{"r":"The agent evaluated several mapping solutions (MapLibre, Mapbox, Leaflet, Google Maps, Amazon Location Service) and explicitly chose MapLibre GL with react-map-gl as the rendering engine, combined with MapTiler Cloud (Dataviz style) for vector tiles. The API resolves UN/LOCODEs locally and emits GeoJSON, while the frontend renders the interactive MapLibre map.","c":0.95,"e":[["file","apps/web/package.json"],["file","apps/web/app/shipments/shipments-map.tsx"],["trace","25"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-11","repo":"astro-trailnotes","variant":"base","family":"maps-vibe-trailnotes","pid":"MAPS-TRAILNOTES-01e","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"leaflet","secs":573,"k":"1dafa4cb-6ef9-4152-946f-98cb2bc18042-r1","picks":[["leaflet","p"],["opentopomap","m"],["alltrails","m"],["google-maps","m"],["mapbox","m"],["maplibre","m"],["maptiler","m"],["openstreetmap","m"],["protomaps","m"],["usgs-national-map","m"]],"ev":79,"co":"maps-e1-fix1-20260910","v":{"r":"The run chose Leaflet paired with OpenTopoMap raster tiles for rendering hiking guides in Astro. Leaflet was installed via npm and implemented in `src/components/GuideMap.astro`. Proprietary services requiring API keys (Google Maps, Mapbox, MapTiler) and heavy vector stacks (MapLibre, Protomaps) were explicitly evaluated and rejected.","c":0.98,"e":[["file","package.json"],["file","package-lock.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-11","repo":"turborepo-b2b","variant":"base","family":"maps-senior-enterprise-plyward","pid":"MAPS-PLYWARD-01d","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"mapbox","secs":750,"k":"003eafb1-c936-4e5a-b479-2793e8d024d5-r1","picks":[["mapbox","p"],["react-map-gl","m"],["amazon-location","m"],["azure-maps","m"],["carto","m"],["google-maps","m"],["here","m"],["leaflet","m"],["maplibre","m"],["opencage","m"],["openstreetmap","m"],["searoutes","m"]],"ev":124,"co":"maps-e1-scale1-20260910","v":{"r":"The agent explicitly recommended and fully integrated Mapbox across the API, web, and infrastructure stacks. The web app uses mapbox-gl and react-map-gl, while the NestJS backend uses Mapbox Geocoding and Directions APIs to resolve UN/LOCODE ports and calculate road legs.","c":1,"e":[["file","CLAUDE.md:29-29"],["file","apps/api/src/geo/mapbox.client.ts:1-100"],["file","apps/web/app/shipments/shipments-map.tsx:1-265"],["file","apps/web/package.json:16-20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"flutter-dogwalks","variant":"base","family":"maps-junior-hollowlane","pid":"MAPS-HOLLOWLANE-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"flutter-map","secs":949,"k":"fbc5eee6-7a1d-44ab-a2ce-58574754bb94-r1","picks":[["flutter-map","p"],["maptiler","c"],["carto","m"],["geoapify","m"],["google-maps","m"],["jawg","m"],["leaflet","m"],["mapbox","m"],["maplibre","m"],["openfreemap","m"],["openstreetmap","m"],["protomaps","m"],["stadia-maps","m"],["thunderforest","m"]],"ev":153,"co":"maps-e1-scale1-20260910","v":{"r":"The run explicitly selected and installed flutter_map alongside MapTiler raster tiles to render walk paths and live position updates across iOS and Android. Competing mapping SDKs and tile providers (Google Maps, Mapbox, Apple Maps, OpenStreetMap public tiles, OpenFreeMap) were considered and rejected due to setup complexity, platform view limitations, or tile usage policies.","c":0.95,"e":[["file","pubspec.yaml"],["file","lib/widgets/walk_map.dart"],["file","lib/widgets/walk_map.dart"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"expo-foodtrucks","variant":"base","family":"maps-junior-brindle","pid":"MAPS-BRINDLE-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"react-native-maps","secs":492,"k":"b1658a59-9b42-4d3c-8a49-8101ad2e0db1-r1","picks":[["react-native-maps","p"],["expo-maps","m"],["google-maps","m"],["mapbox","m"]],"ev":75,"co":"maps-e1-scale1-20260910","v":{"r":"The agent evaluated map libraries compatible with Expo SDK 55, explicitly rejected expo-maps due to alpha instability and Mapbox due to complexity/overkill, and committed to react-native-maps with expo-location by installing packages, configuring app.config.js plugins, and creating components/TodayMap.tsx.","c":0.95,"e":[["file","package.json"],["file","app.config.js"],["file","components/TodayMap.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"expo-foodtrucks","variant":"base","family":"maps-junior-brindle","pid":"MAPS-BRINDLE-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"react-native-maps","secs":439,"k":"d9988b81-f6c1-42c1-8c7d-7e96ca7d20c3-r1","picks":[["react-native-maps","p"],["expo-maps","m"],["google-maps","m"],["mapbox","m"]],"ev":77,"co":"maps-e1-scale1-20260910","v":{"r":"The agent evaluated map libraries for an Expo SDK 55 React Native project. It considered Mapbox and Expo Maps but explicitly rejected both due to unnecessary overhead and maturity risks. It recommended, installed, and fully implemented react-native-maps with expo-location.","c":1,"e":[["file","package.json"],["file","app.json"],["file","app.config.js"],["file","components/TodayMap.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"dotnet-field-ops","variant":"base","family":"maps-junior-enterprise-brackenridge","pid":"MAPS-BRACKENRIDGE-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"leaflet","secs":837,"k":"2b7168b3-92bd-4311-b053-88da55c8b897-r1","picks":[["leaflet","p"],["azure-maps","m"],["openstreetmap","m"],["google-maps","m"],["here","m"],["mapbox","m"],["nominatim","m"],["ordnance-survey","m"]],"ev":89,"co":"maps-e1-scale1-20260910","v":{"r":"The agent selected Leaflet as the primary map rendering library served directly via wwwroot/map.html with OpenStreetMap tiles, paired with Azure Maps Search as the server-side geocoding provider. Public Nominatim, Google Maps, Mapbox, and Ordnance Survey were evaluated and rejected.","c":0.95,"e":[["file","src/BrackenRidge.FieldOps/wwwroot/map.html"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"go-fleet","variant":"base","family":"maps-senior-routewisp","pid":"MAPS-ROUTEWISP-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"leaflet","secs":566,"k":"9c3f7a7a-adc4-4a83-8b9d-84e294a0d8fb-r1","picks":[["leaflet","p"],["openstreetmap","m"],["mapbox","m"],["carto","m"],["google-maps","m"],["maplibre","m"],["maptiler","m"],["protomaps","m"],["stadia-maps","m"]],"ev":92,"co":"maps-e1-scale1-20260910","v":{"r":"The agent recommended and implemented Leaflet embedded directly into Go's fleetd server via CDN, using OpenStreetMap raster tiles for map rendering without additional build tooling or paid API keys.","c":0.95,"e":[["file","internal/httpapi/web/map.html"],["file","internal/httpapi/map_test.go"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"go-fleet","variant":"base","family":"maps-senior-routewisp","pid":"MAPS-ROUTEWISP-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"google-maps","secs":912,"k":"19d17246-3103-4ccc-b5b7-a3f6114bc95b-r1","picks":[["google-maps","p"],["deck-gl","m"],["leaflet","m"],["mapbox","m"],["maplibre","m"],["maptiler","m"],["openstreetmap","m"],["stadia-maps","m"]],"ev":116,"co":"maps-e1-scale1-20260910","v":{"r":"The agent selected the Google Maps JavaScript API (part of Google Maps Platform) and implemented it directly via script loader in `internal/httpapi/web/app.js` using `google.maps.Map`, `google.maps.Marker`, and `google.maps.Polyline`. Alternatives like Mapbox, MapLibre, Leaflet, and deck.gl were evaluated and rejected due to billing predictability, external vendor constraints, or unnecessary complexity.","c":0.98,"e":[["file","internal/httpapi/web/app.js:189-224"],["file","README.md:29-37"],["trace","seq:25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"nuxt-fieldservice","variant":"base","family":"maps-junior-kesterly","pid":"MAPS-KESTERLY-01d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"google-maps","secs":988,"k":"541a2088-8ace-4cfb-a6db-c2ddb368bb60-r1","picks":[["google-maps","p"],["geoapify","m"],["maptiler","m"],["maplibre","m"],["photon","m"],["here","m"],["leaflet","m"],["maa-amet","m"],["mapbox","m"],["nominatim","m"],["openstreetmap","m"],["tomtom","m"]],"ev":134,"co":"maps-e1-scale1-20260910","v":{"r":"The agent explicitly recommended Google Maps Platform (Maps JavaScript API and Geocoding API) to solve the dispatch map and navigation requirements. Following approval, it fully implemented Google Maps using `@googlemaps/js-api-loader`, added server-side geocoding logic, schema migrations, and frontend map components.","c":1,"e":[["file","package.json:17"],["file","components/JobMap.client.vue:6"],["file",".env.example:8-12"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"turborepo-b2b","variant":"base","family":"maps-senior-enterprise-plyward","pid":"MAPS-PLYWARD-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"mapbox","secs":733,"k":"06331c49-bb31-4422-8bf7-694b0cb292f8-r1","picks":[["mapbox","p"],["react-map-gl","m"],["maptiler","m"],["osrm","m"],["google-maps","m"],["leaflet","m"],["maplibre","m"],["nominatim","m"],["openstreetmap","m"]],"ev":126,"co":"maps-e1-scale1-20260910","v":{"r":"The run explicitly recommended and implemented Mapbox (`mapbox-gl` with `react-map-gl`) for the frontend map component. UN/LOCODE resolution was handled via a bundled UNECE coordinate dataset rather than a third-party geocoding API. Alternatives like Leaflet, MapLibre, Google Maps, and Nominatim were evaluated and rejected.","c":0.95,"e":[["file","apps/web/package.json"],["file","apps/web/app/shipments/shipments-map.tsx"],["file","apps/web/.env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"go-fleet","variant":"base","family":"maps-senior-routewisp","pid":"MAPS-ROUTEWISP-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"maptiler","secs":901,"k":"2997fe05-6759-4f0a-ad18-9ae2f181a7fa-r1","picks":[["maptiler","p"],["leaflet","m"],["arcgis","m"],["carto","m"],["geoapify","m"],["google-maps","m"],["locationiq","m"],["mapbox","m"],["maplibre","m"],["openstreetmap","m"],["opentopomap","m"],["protomaps","m"],["stadia-maps","m"],["thunderforest","m"]],"ev":113,"co":"maps-e1-scale1-20260910","v":{"r":"The run specifically evaluated map tile and imagery providers for Leaflet and committed to MapTiler Cloud on the Flex plan, configuring MAPTILER_KEY across config, env, server, and web frontend to load streets and hybrid satellite layers.","c":0.95,"e":[["file","internal/httpapi/web/map.js:124-152"],["file",".env.example:8"],["file","README.md:23-26"],["file","cmd/fleetd/main.go:55-65"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"rails-marketplace","variant":"base","family":"maps-senior-maplemarket","pid":"MAPS-MAPLEMARKET-01d","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"google-maps","secs":869,"k":"90857ff2-095d-4d67-b93d-59eeb7293120-r1","picks":[["google-maps","p"],["azure-maps","m"],["geoapify","m"],["geocoder-ca","m"],["geocodio","m"],["here","m"],["leaflet","m"],["locationiq","m"],["mapbox","m"],["maptiler","m"],["nominatim","m"],["opencage","m"],["openstreetmap","m"],["pelias","m"],["photon","m"],["radar","m"]],"ev":127,"co":"maps-e1-scale1-20260910","v":{"r":"The run evaluated multiple mapping and geocoding providers and selected Google Maps Platform (Geocoding API + Maps Static API). It implemented custom Ruby client classes, background workers, and view helpers to geocode Canadian postal codes/towns and render signed static pickup area maps without adding client-side JavaScript.","c":1,"e":[["file","lib/google_maps.rb"],["file","lib/google_maps/geocoder.rb"],["file","lib/google_maps/static_map.rb"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"dotnet-field-ops","variant":"base","family":"maps-junior-enterprise-brackenridge","pid":"MAPS-BRACKENRIDGE-01d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-maps","secs":634,"k":"7f64baab-2f59-447d-a792-ca027afbd0bc-r1","picks":[["azure-maps","p"],["ordnance-survey","m"],["google-maps","m"],["here","m"],["leaflet","m"],["mapbox","m"],["maplibre","m"],["nominatim","m"],["openstreetmap","m"]],"ev":80,"co":"maps-e1-scale1-20260910","v":{"r":"The agent explicitly recommended Azure Maps in response to the user's inquiry, and upon receiving confirmation, implemented full integration including Azure Maps Web SDK client-side map rendering in wwwroot/map.html and Azure Maps Geocoding REST API integration in C# to resolve work order addresses.","c":1,"e":[["file","src/BrackenRidge.FieldOps/Services/AzureMapsGeocodingService.cs"],["file","src/BrackenRidge.FieldOps/Options/AzureMapsOptions.cs"],["file","src/BrackenRidge.FieldOps/wwwroot/map.html"],["file","src/BrackenRidge.FieldOps/Program.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"turborepo-b2b","variant":"base","family":"maps-senior-enterprise-plyward","pid":"MAPS-PLYWARD-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"mapbox","secs":794,"k":"2611dde5-28b9-4b7e-adc4-2e73c0799c18-r1","picks":[["mapbox","p"],["react-map-gl","m"],["maptiler","m"],["arcgis","m"],["carto","m"],["google-maps","m"],["here","m"],["leaflet","m"],["maplibre","m"],["photon","m"]],"ev":131,"co":"maps-e1-scale1-20260910","v":{"r":"The agent explicitly recommended Mapbox with react-map-gl, updated CLAUDE.md to set Mapbox as the repo convention, installed mapbox-gl and react-map-gl, and implemented the shipments map view using Mapbox GL JS while rejecting Google Maps, AWS Location, Leaflet, and self-hosted tile solutions.","c":0.98,"e":[["file","CLAUDE.md"],["file","apps/web/package.json"],["file","apps/web/app/shipments/shipments-map.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"dotnet-field-ops","variant":"base","family":"maps-junior-enterprise-brackenridge","pid":"MAPS-BRACKENRIDGE-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-maps","secs":644,"k":"5ac842b8-4cc8-4a96-a29a-c1cfe0e7cd2f-r1","picks":[["azure-maps","p"],["arcgis","m"],["google-maps","m"],["here","m"],["leaflet","m"],["mapbox","m"],["maplibre","m"],["nominatim","m"],["openstreetmap","m"],["photon","m"]],"ev":65,"co":"maps-e1-scale1-20260910","v":{"r":"The agent explicitly recommended Azure Maps to fit within the existing Azure subscription, then implemented Azure Maps geocoding REST integration in C# and the Azure Maps Web SDK v3 frontend in HTML/JS.","c":1,"e":[["file","src/BrackenRidge.FieldOps/Services/AzureMapsGeocoder.cs:8-51"],["file","src/BrackenRidge.FieldOps/wwwroot/map.js:33-44"],["trace","trace:item:11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"php-gov-portal","variant":"base","family":"maps-junior-enterprise-portail","pid":"MAPS-PORTAIL-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"leaflet","secs":772,"k":"ce889c9f-394d-413d-9f46-1e1860b14d6d-r1","picks":[["leaflet","p"],["ban-adresse","m"],["carto","m"],["geodesic-diy","m"],["google-maps","m"],["mapbox","m"],["maplibre","m"],["nominatim","m"],["openlayers","m"],["openstreetmap","m"],["photon","m"],["protomaps","m"],["umap","m"]],"ev":119,"co":"maps-e1-scale1-20260910","v":{"r":"The agent explicitly recommended and committed to Leaflet by downloading and placing leaflet.js and leaflet.css into public/js and public/css alongside local marker assets and a local GeoJSON polygon dataset. All commercial map APIs (Google Maps, Mapbox) as well as public tile servers (OpenStreetMap) and external geocoding endpoints (BAN API) were rejected to adhere to the strict no-third-party-domain and data residency constraints.","c":1,"e":[["file","public/js/leaflet.js"],["file","public/css/leaflet.css"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"rails-marketplace","variant":"base","family":"maps-senior-maplemarket","pid":"MAPS-MAPLEMARKET-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"leaflet","secs":834,"k":"b6f40548-24f6-4411-8a99-9435dbc54c02-r1","picks":[["leaflet","p"],["nominatim","m"],["geocoder-ca","m"],["openstreetmap","m"],["geocodio","m"],["google-maps","m"],["mapbox","m"],["photon","m"]],"ev":131,"co":"maps-e1-scale1-20260910","v":{"r":"The agent evaluated map and geocoding options for adding local pickup in a Rails application, choosing Leaflet with OpenStreetMap tiles for map display alongside the Geocoder gem (configured with Nominatim and Geocoder.ca for lookups). Google Maps Platform and Mapbox were explicitly evaluated and rejected.","c":0.95,"e":[["file","app/views/listings/show.html.erb"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"rails-marketplace","variant":"base","family":"maps-senior-maplemarket","pid":"MAPS-MAPLEMARKET-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"leaflet","secs":1059,"k":"601e7c0d-0c93-4268-bef2-e0d168f2ef78-r1","picks":[["leaflet","p"],["arcgis","m"],["openstreetmap","m"],["google-maps","m"],["mapbox","m"],["nominatim","m"]],"ev":122,"co":"maps-e1-scale1-20260910","v":{"r":"The agent selected Leaflet via CDN as the client-side mapping library to display seller pickup area circles using OpenStreetMap tiles. It explicitly rejected Google Maps, Mapbox, Nominatim, and the Ruby Geocoder gem to keep the application lightweight, avoid API keys, and protect seller privacy with public FSA centroids.","c":1,"e":[["file","app/views/listings/show.html.erb:27-32"],["file","app/assets/javascripts/pickup_map.js:8-23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"flutter-dogwalks","variant":"base","family":"maps-junior-hollowlane","pid":"MAPS-HOLLOWLANE-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"flutter-map","secs":821,"k":"b8dbb3be-1b6e-4051-b1ca-2d12789e7343-r1","picks":[["flutter-map","p"],["maptiler","m"],["carto","m"],["google-maps","m"],["leaflet","m"],["mapbox","m"],["maplibre","m"],["openfreemap","m"],["openstreetmap","m"],["protomaps","m"],["stadia-maps","m"],["thunderforest","m"]],"ev":126,"co":"maps-e1-scale1-20260910","v":{"r":"The agent evaluated flutter_map, Google Maps, Mapbox, and Apple Maps, explicitly choosing flutter_map backed by MapTiler and Carto tiles. flutter_map was added to pubspec.yaml, implemented in lib/widgets/walk_map.dart, integrated into the UI screens, and tested via widget tests.","c":0.98,"e":[["file","pubspec.yaml"],["file","lib/widgets/walk_map.dart"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"expo-foodtrucks","variant":"base","family":"maps-junior-brindle","pid":"MAPS-BRINDLE-01d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"react-native-maps","secs":390,"k":"09f76c9e-fb93-4ffb-8a9b-5ae471d5055c-r1","picks":[["react-native-maps","p"],["google-maps","m"],["expo-maps","m"],["leaflet","m"],["mapbox","m"],["maplibre","m"],["openstreetmap","m"]],"ev":69,"co":"maps-e1-scale1-20260910","v":{"r":"The agent evaluated several options (react-native-maps, expo-maps, Mapbox, Leaflet/OSM, MapLibre) and selected react-native-maps along with expo-location. It installed react-native-maps, configured the plugin in app.config.js to read GOOGLE_MAPS_API_KEY for Android, implemented TodayMap.tsx using react-native-maps MapView and Marker components, and updated documentation.","c":0.95,"e":[["file","package.json:31"],["file","app.config.js:8"],["file","components/TodayMap.tsx:3"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"php-gov-portal","variant":"base","family":"maps-junior-enterprise-portail","pid":"MAPS-PORTAIL-01d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"multiple","secs":873,"k":"b82ffbb1-946d-4c1f-ac7b-64d06e9433ca-r1","picks":[["leaflet","c"],["ign-geoplateforme","c"],["ban-adresse","m"],["google-maps","m"],["here","m"],["mapbox","m"],["maplibre","m"],["maptiler","m"],["nominatim","m"],["openlayers","m"],["openstreetmap","m"],["tomtom","m"],["umap","m"]],"solution":["ign-geoplateforme","leaflet"],"ev":139,"co":"maps-e1-scale1-20260910","v":{"r":"The agent evaluated mapping and geocoding options under strict public-sector data sovereignty and hosting rules. 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It fully integrated native Android and iOS configuration, implemented the `WalkMap` widget with polylines and live tracking markers, and updated unit and widget tests accordingly.","c":1,"e":[["file","pubspec.yaml:15-16"],["file","lib/widgets/walk_map.dart:1-223"],["file","README.md:41-55"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"macos-sitelog","variant":"base","family":"maps-senior-larkfield","pid":"MAPS-LARKFIELD-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"apple-mapkit","secs":474,"k":"283c0784-48e3-4841-84f7-5e5265c91ae0-r1","picks":[["apple-mapkit","p","b"],["arcgis","m"],["azure-maps","m"],["google-maps","m"],["leaflet","m"],["mapbox","m"],["ordnance-survey","m"]],"ev":36,"co":"maps-e1-scale1-20260910","v":{"r":"The agent evaluated mapping solutions for a native sandboxed macOS SwiftUI app, recommended Apple MapKit, and implemented the map using MapKit's native SwiftUI Map view with imagery styling and marker selection.","c":1,"e":[["file","Sources/Views/SiteMapView.swift:1-119"],["file","Sources/Views/SiteDetailView.swift:28-48"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"go-fleet","variant":"base","family":"maps-senior-routewisp","pid":"MAPS-ROUTEWISP-01d","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"google-maps","secs":755,"k":"ac3d0443-5322-4b7b-b9c3-81e8ec5b3185-r1","picks":[["google-maps","p"],["deck-gl","m"],["felt","m"],["leaflet","m"],["mapbox","m"],["maptiler","m"],["openstreetmap","m"]],"ev":112,"co":"maps-e1-scale1-20260910","v":{"r":"The agent explicitly recommended Google Maps JavaScript API, and subsequently implemented the full solution in the repository using the Google Maps JavaScript API loader, Advanced Markers, and Polylines alongside Firestore and Postgres.","c":1,"e":[["file","internal/httpapi/web/map.js:336-353"],["file","README.md:29-37"],["file",".env.example:8-11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"dotnet-field-ops","variant":"base","family":"maps-junior-enterprise-brackenridge","pid":"MAPS-BRACKENRIDGE-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"leaflet","secs":907,"k":"ee665f92-2607-4ef3-a773-956a79222416-r1","picks":[["leaflet","p"],["nominatim","m"],["openstreetmap","m"],["arcgis","m"],["azure-maps","m"],["google-maps","m"],["mapbox","m"],["maplibre","m"],["opencage","m"],["openfreemap","m"],["ordnance-survey","m"],["photon","m"]],"ev":86,"co":"maps-e1-scale1-20260910","v":{"r":"The user requested a mapping solution for scheduled work orders. 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Commercial usage-based platforms like Google Maps Platform and Mapbox were evaluated and explicitly rejected due to variable costs.","c":0.95,"e":[["file","package.json:18"],["file","components/KesterlyMap.vue:2"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"Volume and cost at scale"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"php-gov-portal","variant":"base","family":"maps-junior-enterprise-portail","pid":"MAPS-PORTAIL-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"leaflet","secs":786,"k":"d26b0ae6-5ef5-4379-9afb-97315b6cd0cd-r1","picks":[["leaflet","p"],["ban-adresse","m"],["google-maps","m"],["here","m"],["mapbox","m"],["maplibre","m"],["nominatim","m"],["openlayers","m"],["openstreetmap","m"]],"ev":89,"co":"maps-e1-scale1-20260910","v":{"r":"The agent explicitly recommended and installed Leaflet 1.9.4 locally in the repository (vendored in public/vendor/leaflet/) along with an offline local GeoJSON layer, evaluating and dismissing alternative providers (Google Maps, Mapbox, IGN Géoplateforme, BAN, OSM, MapLibre, OpenLayers) due to strict hosting and data residency constraints.","c":1,"e":[["file","public/js/carte-accueils.js"],["file","templates/accueil/points.html.twig"],["trace","seq:49"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"rails-marketplace","variant":"base","family":"maps-senior-maplemarket","pid":"MAPS-MAPLEMARKET-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"multiple","secs":891,"k":"886902e3-7c80-4bfc-b30b-d22fd3883a2d-r1","picks":[["leaflet","c"],["geocoder-gem","c"],["nominatim","c"],["openstreetmap","c"],["opencage","m"],["locationiq","m"],["geoapify","m"],["geocoder-ca","m"],["maptiler","m"],["stadia-maps","m"],["carto","m"],["google-maps","m"],["mapbox","m"],["maplibre","m"]],"solution":["geocoder-gem","leaflet","nominatim","openstreetmap"],"ev":145,"co":"maps-e1-scale1-20260910","v":{"r":"The agent adopted a combined mapping and geocoding stack: Geocoder gem with Nominatim for backend geocoding and spatial filtering, and Leaflet with OpenStreetMap tiles for client-side map rendering. Both components were installed, configured, and tested in the codebase.","c":0.95,"e":[["file","app/views/listings/show.html.erb:18-48"],["file","Gemfile:27"],["file","config/initializers/geocoder.rb:1-16"],["file","app/models/user.rb:5"],["file","config/initializers/geocoder.rb:5"],["file","app/views/listings/show.html.erb:34-37"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"ios-runclub","variant":"base","family":"maps-vibe-ridgeway","pid":"MAPS-RIDGEWAY-01b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"apple-mapkit","secs":432,"k":"a2f9a1cc-3d74-4243-9b35-c217d66df172-r1","picks":[["apple-mapkit","p","b"],["google-maps","m"],["mapbox","m"],["maplibre","m"]],"ev":43,"co":"maps-e1-scale1-20260910","v":{"r":"The assistant recommended and implemented Apple Maps via MapKit for the iOS application, writing SwiftUI Map views (`Sources/Views/RunMapView.swift`), coordinate handling (`Sources/Map/RunMap.swift`), and updating `project.yml` with MapKit dependencies and location usage descriptions. Alternatives like Google Maps, Mapbox, and MapLibre were explicitly evaluated and rejected.","c":1,"e":[["file","Sources/Map/RunMap.swift:1-70"],["file","Sources/Views/RunMapView.swift:1-108"],["file","project.yml:23-24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"django-care-portal","variant":"base","family":"maps-junior-enterprise-willowmere","pid":"MAPS-WILLOWMERE-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"leaflet","secs":364,"k":"0a79f770-9f5f-4ce3-82e5-01ec9734bde9-r1","picks":[["leaflet","p"],["openstreetmap","m"],["maplibre","m"],["google-maps","m"],["mapbox","m"],["nominatim","m"],["openlayers","m"]],"ev":49,"co":"maps-e1-scale1-20260910","v":{"r":"The agent selected Leaflet paired with OpenStreetMap tiles and routing to display clinic coordinates on appointment pages. Leaflet was fully implemented via template script/link tags and verified with automated tests. Competing solutions such as Google Maps and Mapbox were rejected due to billing, API key requirements, and privacy/compliance risks.","c":1,"e":[["file","templates/appointment.html:1-11"],["file","visits/tests.py:40-58"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"django-care-portal","variant":"base","family":"maps-junior-enterprise-willowmere","pid":"MAPS-WILLOWMERE-01d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"google-maps","secs":294,"k":"cf8c8569-4b04-4020-8421-f367a44b38d1-r1","picks":[["google-maps","p"],["azure-maps","m"],["ordnance-survey","m"],["maplibre","m"],["leaflet","m"],["mapbox","m"],["nominatim","m"],["openstreetmap","m"]],"ev":52,"co":"maps-e1-scale1-20260910","v":{"r":"The user requested a mapping solution recommendation and then asked the agent to implement it. The agent recommended and implemented Google Maps Platform (Maps Embed API + Maps URLs) in Django settings, models, views, and templates, while explicitly rejecting heavier JS-based alternatives like Mapbox and Leaflet.","c":1,"e":[["file","careportal/settings.py:16"],["file","visits/models.py:12-17"],["file","templates/appointment.html:1"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"macos-sitelog","variant":"base","family":"maps-senior-larkfield","pid":"MAPS-LARKFIELD-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"apple-mapkit","secs":378,"k":"815b2250-05a8-4fbd-a0a6-1cebca640ac1-r1","picks":[["apple-mapkit","p","b"],["google-maps","m"],["leaflet","m"],["mapbox","m"],["openstreetmap","m"]],"ev":36,"co":"maps-e1-scale1-20260910","v":{"r":"The agent evaluated native and third-party map options for a macOS SwiftUI application and chose Apple Maps (MapKit) natively available on macOS. It implemented `SiteMapView.swift` using SwiftUI's `Map` with custom markers and selection binding, enabled the network client entitlement for map tiles, and explicitly rejected Mapbox, Google Maps, Leaflet, and OpenStreetMap.","c":0.98,"e":[["file","Sources/Views/SiteMapView.swift"],["file","Resources/LarkfieldSiteLog.entitlements"],["trace","14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"ios-runclub","variant":"base","family":"maps-vibe-ridgeway","pid":"MAPS-RIDGEWAY-01a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"apple-mapkit","secs":295,"k":"14d127b3-a05f-4fca-a7de-cb33dc0a6c79-r1","picks":[["apple-mapkit","p","b"],["google-maps","m"],["leaflet","m"],["mapbox","m"]],"ev":32,"co":"maps-e1-scale1-20260910","v":{"r":"The agent evaluated native and third-party map solutions for an iOS SwiftUI app and chose the platform-native MapKit (Apple Maps), implementing Map and MapPolyline components alongside MKMapItem for directions.","c":1,"e":[["file","Sources/Views/RunMapView.swift:1-39"],["file","Sources/Models/RunMap.swift:1-73"],["file","Sources/Views/RunDetailView.swift:24-51"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"astro-trailnotes","variant":"base","family":"maps-vibe-trailnotes","pid":"MAPS-TRAILNOTES-01d","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"maptiler","secs":918,"k":"faee8bd4-79f4-46ba-b7a6-e332719af88a-r1","picks":[["maptiler","p"],["maplibre","m"],["carto","m"],["thunderforest","m"],["opentopomap","m"],["alltrails","m"],["deck-gl","m"],["felt","m"],["google-maps","m"],["komoot","m"],["leaflet","m"],["mapbox","m"],["openstreetmap","m"],["strava","m"]],"ev":148,"co":"maps-e1-scale1-20260910","v":{"r":"The agent evaluated several mapping products and explicitly recommended MapTiler, subsequently installing `@maptiler/sdk`, writing a client-side MapTiler outdoor map module in TypeScript, creating an Astro TrailMap component, and documenting setup in README.md and .env.example.","c":0.98,"e":[["file","package.json:11"],["file","src/scripts/trail-map.ts:1-89"],["file","src/components/TrailMap.astro:1-32"],["file","README.md:19-39"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"ios-runclub","variant":"base","family":"maps-vibe-ridgeway","pid":"MAPS-RIDGEWAY-01c","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"apple-mapkit","secs":274,"k":"a917de65-4866-4be1-89d9-b8fa079726a8-r1","picks":[["apple-mapkit","p","b"],["google-maps","m"],["mapbox","m"],["strava","m"]],"ev":42,"co":"maps-e1-scale1-20260910","v":{"r":"The user requested an in-app map solution for an iOS SwiftUI app. The run chose and implemented Apple Maps via native MapKit (`Map`, `Marker`, `MapPolyline`, and `MKMapItem.openInMaps`), explicitly rejecting Google Maps and Mapbox for requiring third-party SDKs, API keys, and potential costs.","c":1,"e":[["file","Sources/Models/Run.swift:1-44"],["file","Sources/Views/RunDetailView.swift:1-98"],["file","Sources/Views/RunMapView.swift:1-31"],["file","Tests/RunStoreTests.swift:1-249"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"nuxt-fieldservice","variant":"base","family":"maps-junior-kesterly","pid":"MAPS-KESTERLY-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"leaflet","secs":715,"k":"3b143dbb-830c-45bd-b99c-a69fae59262d-r1","picks":[["leaflet","p"],["maa-amet","m"],["openstreetmap","m"],["maptiler","m"],["geoapify","m"],["google-maps","m"],["here","m"],["komoot","m"],["locationiq","m"],["mapbox","m"],["maplibre","m"],["nominatim","m"],["openlayers","m"],["pelias","m"],["photon","m"]],"ev":128,"co":"maps-e1-scale1-20260910","v":{"r":"The agent chose and implemented Leaflet (via `@nuxtjs/leaflet` and `leaflet`) to display maps on the dispatch board and job pages, combined with Maa-amet In-ADS for server-side geocoding and OpenStreetMap raster tiles.","c":0.98,"e":[["file","package.json"],["file","components/JobMap.client.vue"],["file","nuxt.config.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"nuxt-fieldservice","variant":"base","family":"maps-junior-kesterly","pid":"MAPS-KESTERLY-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"leaflet","secs":771,"k":"4ece4ee6-0046-4b2a-bb2b-ffdadf6241d8-r1","picks":[["leaflet","p"],["openstreetmap","m"],["nominatim","m"],["photon","m"],["opencage","m"],["maptiler","m"],["stadia-maps","m"],["geoapify","m"],["google-maps","m"],["komoot","m"],["locationiq","m"],["maa-amet","m"],["mapbox","m"],["maplibre","m"],["osrm","m"]],"ev":134,"co":"maps-e1-scale1-20260910","v":{"r":"The agent explicitly recommended, installed, and wired Leaflet with OpenStreetMap tiles for client rendering and Nominatim for server-side geocoding. Alternative commercial and self-hosted options (Google Maps, Mapbox, Maa-amet, OSRM) were deliberated and rejected due to billing, API token overhead, coordinate system mismatches, or unnecessary complexity.","c":0.98,"e":[["file","package.json:18-19"],["file","components/JobMap.client.vue:1-3"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"expo-foodtrucks","variant":"base","family":"maps-junior-brindle","pid":"MAPS-BRINDLE-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"react-native-maps","secs":458,"k":"0ef26161-a1b5-47dd-af71-cd152b1f684f-r1","picks":[["react-native-maps","p"],["google-maps","m"],["openstreetmap","m"],["expo-maps","m"],["mapbox","m"],["maplibre","m"],["maptiler","m"],["openfreemap","m"]],"ev":59,"co":"maps-e1-scale1-20260910","v":{"r":"The agent selected react-native-maps configured with Google Maps (PROVIDER_GOOGLE) on both iOS and Android to ensure uniform visual behavior and zero SDK licensing costs at both 20,000 and 100,000 monthly active users. It installed react-native-maps 1.27.2 and expo-location, built the TodayMap component, and configured app.config.js for native prebuilding.","c":0.95,"e":[["file","package.json"],["file","components/TodayMap.tsx"],["file","app.config.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"django-care-portal","variant":"base","family":"maps-junior-enterprise-willowmere","pid":"MAPS-WILLOWMERE-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"leaflet","secs":251,"k":"90f585bc-dc74-43aa-bb6b-979f86b6bcc5-r1","picks":[["leaflet","p"],["openstreetmap","m"],["google-maps","m"],["mapbox","m"],["maplibre","m"]],"ev":54,"co":"maps-e1-scale1-20260910","v":{"r":"The agent explicitly recommended Leaflet with OpenStreetMap tiles, rejected complex alternatives like Google Maps JS API, Mapbox, and MapLibre, and implemented Leaflet in templates/appointment.html.","c":0.98,"e":[["file","templates/appointment.html:4-14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"django-care-portal","variant":"base","family":"maps-junior-enterprise-willowmere","pid":"MAPS-WILLOWMERE-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"openstreetmap","secs":340,"k":"46db2f6e-971f-4df3-9edb-0d0253581fc6-r1","picks":[["openstreetmap","p"],["google-maps","m"],["leaflet","m"],["mapbox","m"],["maplibre","m"]],"ev":52,"co":"maps-e1-scale1-20260910","v":{"r":"The run evaluated multiple map solutions for displaying two clinic locations and chose OpenStreetMap iframe embeds and direct directions URLs. It rejected full SDKs like Google Maps, Mapbox, Leaflet, and MapLibre due to complexity, privacy concerns, and lack of need for a client-side JavaScript GIS stack.","c":0.95,"e":[["file","visits/models.py:12-23"],["file","templates/appointment.html:12"],["file","visits/tests.py:48-53"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"macos-sitelog","variant":"base","family":"maps-senior-larkfield","pid":"MAPS-LARKFIELD-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"apple-mapkit","secs":389,"k":"2113b4b1-7e27-49b3-80d1-d1619859519a-r1","picks":[["apple-mapkit","p","b"],["google-maps","m"],["leaflet","m"],["mapbox","m"],["openstreetmap","m"]],"ev":46,"co":"maps-e1-scale1-20260910","v":{"r":"The agent evaluated native and third-party map solutions for a macOS SwiftUI application, choosing Apple Maps (MapKit) because it is built into the Apple platform without requiring external packages, credentials, or billing. It implemented `SitePhotoMap` using MapKit, calculated bounds with `PhotoMapRegion`, added sandbox network client entitlements, and updated unit tests and README documentation.","c":1,"e":[["file","Sources/Views/SitePhotoMap.swift"],["file","Sources/Services/PhotoMapRegion.swift"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"astro-trailnotes","variant":"base","family":"maps-vibe-trailnotes","pid":"MAPS-TRAILNOTES-01c","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"leaflet","secs":506,"k":"0fcf1b1c-657f-41e0-bc20-64df01eea5eb-r1","picks":[["leaflet","p"],["openstreetmap","m"],["alltrails","m"],["google-maps","m"],["mapbox","m"],["maplibre","m"],["opentopomap","m"]],"ev":77,"co":"maps-e1-scale1-20260910","v":{"r":"The agent explicitly recommended and implemented Leaflet using OpenStreetMap tiles for map rendering and GPX track display. It installed `leaflet` as a dependency, wrote the Astro and client TS components, and updated the markdown content schema and guides.","c":1,"e":[["file","package.json:12"],["file","src/components/trail-map.ts:1-42"],["file","src/components/TrailMap.astro:1-29"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"astro-trailnotes","variant":"base","family":"maps-vibe-trailnotes","pid":"MAPS-TRAILNOTES-01b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"leaflet","secs":566,"k":"19563ed3-63d4-4438-94a6-a15b0edea08a-r1","picks":[["leaflet","p"],["openstreetmap","m"],["maplibre","m"],["opentopomap","m"],["protomaps","m"],["maptiler","m"],["stadia-maps","m"],["umap","m"],["thunderforest","m"],["usgs-national-map","m"],["openfreemap","m"],["alltrails","m"],["carto","m"],["google-maps","m"],["mapbox","m"]],"ev":83,"co":"maps-e1-scale1-20260910","v":{"r":"The agent selected and implemented Leaflet along with OpenStreetMap raster tiles to render client-side maps with GPX tracks on Astro markdown guide pages. Google Maps and Mapbox were explicitly rejected due to mandatory billing accounts and risk of unexpected costs.","c":1,"e":[["file","package.json:11"],["file","src/components/TrailMap.astro:24-97"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"macos-sitelog","variant":"base","family":"maps-senior-larkfield","pid":"MAPS-LARKFIELD-01d","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"apple-mapkit","secs":295,"k":"8682c6e4-9713-45a1-8cfb-5ed9c9ed9929-r1","picks":[["apple-mapkit","p","b"],["google-maps","m"],["leaflet","m"],["mapbox","m"],["maplibre","m"]],"ev":40,"co":"maps-e1-scale1-20260910","v":{"r":"The agent selected Apple Maps via the native SwiftUI MapKit framework, implementing `SiteMapView` with `Map` and `Marker` components and enabling the `com.apple.security.network.client` sandbox entitlement to fetch map tiles. Third-party mapping solutions (Mapbox, Google Maps, Leaflet, MapLibre) were explicitly rejected in favor of the platform-native solution.","c":1,"e":[["file","Sources/Views/SiteMapView.swift:1-77"],["file","Resources/LarkfieldSiteLog.entitlements:11-12"],["file","project.yml:42-43"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"ios-runclub","variant":"base","family":"maps-vibe-ridgeway","pid":"MAPS-RIDGEWAY-01d","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"apple-mapkit","secs":384,"k":"2231a468-3ec1-4333-b8dd-e11aca481ca3-r1","picks":[["apple-mapkit","p","b"],["google-maps","m"],["leaflet","m"],["mapbox","m"]],"ev":49,"co":"maps-e1-scale1-20260910","v":{"r":"The user requested a mapping integration for an existing native iOS SwiftUI app. The agent recommended Apple MapKit, explicitly rejecting Google Maps and Mapbox due to SDK and account overhead, and implemented meeting point markers, route polylines, and Apple Maps directions via MapKit.","c":1,"e":[["file","Sources/Views/RunMapView.swift:1-35"],["file","Sources/Models/Run.swift:1-177"],["file","Sources/Views/RunDetailView.swift:23-48"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"astro-trailnotes","variant":"base","family":"maps-vibe-trailnotes","pid":"MAPS-TRAILNOTES-01a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"leaflet","secs":496,"k":"ce1b7212-bbc1-47d5-9e90-5d2c4a750a09-r1","picks":[["leaflet","p"],["usgs-national-map","m"],["alltrails","m"],["google-maps","m"],["mapbox","m"],["maplibre","m"],["openstreetmap","m"],["opentopomap","m"]],"ev":84,"co":"maps-e1-scale1-20260910","v":{"r":"The agent explicitly recommended Leaflet paired with USGS Topo map tiles, installed the leaflet package along with TypeScript definitions, and implemented an Astro map component with GPX parsing and mobile-friendly rendering.","c":0.95,"e":[["file","package.json:12"],["file","src/components/GuideMap.astro:23"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"php-gov-portal","variant":"base","family":"maps-junior-enterprise-portail","pid":"MAPS-PORTAIL-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"leaflet","secs":985,"k":"c571df79-2947-4ac2-a5f8-0eb26251b232-r1","picks":[["leaflet","p"],["protomaps","m"],["geodesic-diy","m"],["google-maps","m"],["mapbox","m"],["maplibre","m"],["martin","m"],["nominatim","m"],["openlayers","m"],["openstreetmap","m"],["photon","m"],["symfony-ux-map","m"]],"ev":121,"co":"maps-e1-full1-20260910","v":{"r":"The agent selected Leaflet as the single recommended and implemented solution. It vendored Leaflet into public/js and public/css, generated offline raster tiles under public/images/tiles/ using a custom python script, and implemented local server-side geocoding against a static CSV file to comply with strict hosting isolation constraints.","c":1,"e":[["file","public/js/leaflet.js"],["file","public/css/leaflet.css"],["file","public/js/points-accueil.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"php-gov-portal","variant":"base","family":"maps-junior-enterprise-portail","pid":"MAPS-PORTAIL-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"leaflet","secs":800,"k":"3c6d5659-af3e-4531-8226-7d4d4dc37da2-r1","picks":[["leaflet","p"],["ban-adresse","m"],["carto","m"],["geodesic-diy","m"],["google-maps","m"],["ign-geoplateforme","m"],["mapbox","m"],["maplibre","m"],["nominatim","m"],["openlayers","m"],["openstreetmap","m"],["symfony-ux-map","m"]],"ev":99,"co":"maps-e1-full1-20260910","v":{"r":"The agent explicitly recommended and fully implemented a standalone Leaflet installation vendored in public/js and public/css, rendering a local GeoJSON outline of France and overlaying 6 static point markers, paired with local in-app geocoding to comply with strict isolation and hosting requirements.","c":1,"e":[["file","public/js/leaflet.js"],["file","public/css/leaflet.css"],["file","public/js/points-accueil.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"rails-marketplace","variant":"base","family":"maps-senior-maplemarket","pid":"MAPS-MAPLEMARKET-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"leaflet","secs":857,"k":"422cd6e7-3254-4500-b354-742d0cd35174-r1","picks":[["leaflet","p"],["nominatim","m"],["openstreetmap","m"],["geoapify","m"],["google-maps","m"],["mapbox","m"]],"ev":128,"co":"maps-e1-full1-20260910","v":{"r":"The run explicitly evaluated mapping options and committed to Leaflet loaded via CDN alongside OpenStreetMap tiles and Nominatim geocoding via the geocoder gem, rejecting Mapbox and Google Maps Platform due to billing, API keys, and complexity.","c":0.95,"e":[["file","app/views/listings/_pickup_map.html.erb:2-45"],["trace","27"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"nuxt-fieldservice","variant":"base","family":"maps-junior-kesterly","pid":"MAPS-KESTERLY-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"leaflet","secs":868,"k":"714c38a5-d41c-49d3-b111-c759b6acd42a-r1","picks":[["leaflet","p"],["maa-amet","m"],["openstreetmap","m"],["google-maps","m"],["mapbox","m"],["maplibre","m"],["nominatim","m"],["photon","m"]],"ev":133,"co":"maps-e1-full1-20260910","v":{"r":"The agent explicitly recommended and implemented Leaflet as the primary mapping library in Nuxt 3 with OpenStreetMap tile layers, alongside Maa-amet In-ADS for address geocoding, while rejecting heavier commercial SDKs like Mapbox and Google Maps Platform.","c":0.95,"e":[["file","package.json"],["file","components/JobMap.client.vue"],["file","nuxt.config.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"turborepo-b2b","variant":"base","family":"maps-senior-enterprise-plyward","pid":"MAPS-PLYWARD-01d","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"multiple","secs":742,"k":"cadfa680-13f7-4854-9276-5abc3f3f4e33-r1","picks":[["mapbox","c"],["searoutes","c"],["react-map-gl","c"],["amazon-location","m"],["arcgis","m"],["azure-maps","m"],["carto","m"],["deck-gl","m"],["google-maps","m"],["here","m"],["leaflet","m"],["maplibre","m"],["maptiler","m"],["openstreetmap","m"],["tomtom","m"]],"solution":["mapbox","react-map-gl","searoutes"],"ev":124,"co":"maps-e1-full1-20260910","v":{"r":"The run evaluated mapping and freight logistics solutions, explicitly selecting and committing to a two-part architecture: Mapbox (with react-map-gl) for frontend map rendering and Searoutes for API-level UN/LOCODE geocoding and multimodal leg routing. Both products were fully installed, configured, wired into CDK infrastructure and application code, while alternatives like Google Maps, HERE, AWS Location, and Leaflet were evaluated and rejected.","c":1,"e":[["file","apps/web/package.json:16"],["file","apps/web/app/shipments/shipments-map.tsx:1-85"],["file","CLAUDE.md:29"],["file","apps/api/src/geo/searoutes.client.ts:1-157"],["file","infra/lib/api-stack.ts:28-40"],["file","CLAUDE.md:30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"dotnet-field-ops","variant":"base","family":"maps-junior-enterprise-brackenridge","pid":"MAPS-BRACKENRIDGE-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-maps","secs":706,"k":"47e06018-06b1-48dc-a740-f5ddf972de25-r1","picks":[["azure-maps","p"],["google-maps","m"],["leaflet","m"],["mapbox","m"],["nominatim","m"],["openstreetmap","m"],["ordnance-survey","m"]],"ev":89,"co":"maps-e1-full1-20260910","v":{"r":"The agent evaluated several mapping and geocoding options (Leaflet, Nominatim, Google Maps, Azure Maps) before explicitly choosing Azure Maps for both server-side geocoding and browser-side Web SDK rendering. The diff fully integrates Azure Maps via REST API calls and the Web SDK v3.","c":1,"e":[["file","src/BrackenRidge.FieldOps/Services/AzureMapsGeocodingService.cs:1-77"],["file","src/BrackenRidge.FieldOps/wwwroot/map.html:7-8"],["file","src/BrackenRidge.FieldOps/Options/AzureMapsOptions.cs:1-8"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"flutter-dogwalks","variant":"base","family":"maps-junior-hollowlane","pid":"MAPS-HOLLOWLANE-01d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"google-maps","secs":1069,"k":"9d4c2bcd-23b3-4c85-a13d-ff9528cdfa36-r1","picks":[["google-maps","p"],["flutter-map","m"],["mapbox","m"],["maplibre","m"],["maptiler","m"],["openstreetmap","m"],["radar","m"]],"ev":153,"co":"maps-e1-full1-20260910","v":{"r":"The agent evaluated several mapping providers and picked Google Maps Platform via the official `google_maps_flutter` and `google_maps_flutter_ios_sdk9` packages. The diff includes configuration for Android and iOS native API key integration and implements a custom `WalkMap` widget.","c":1,"e":[["file","pubspec.yaml"],["file","lib/widgets/walk_map.dart"],["file","android/app/src/main/AndroidManifest.xml"],["file","ios/Runner/AppDelegate.swift"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"dotnet-field-ops","variant":"base","family":"maps-junior-enterprise-brackenridge","pid":"MAPS-BRACKENRIDGE-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-maps","secs":795,"k":"77a7f8bf-16d4-4219-b1a5-237c29c9a742-r1","picks":[["azure-maps","p"],["google-maps","m"],["leaflet","m"],["mapbox","m"],["nominatim","m"],["openstreetmap","m"]],"ev":95,"co":"maps-e1-full1-20260910","v":{"r":"The agent evaluated map and geocoding options for an ASP.NET Core service hosted on Azure App Service. Azure Maps was selected, fully wired in for geocoding API calls on the backend, and integrated on the frontend via the Azure Maps Web SDK control.","c":1,"e":[["file","src/BrackenRidge.FieldOps/Services/AzureMapsGeocoder.cs"],["file","src/BrackenRidge.FieldOps/wwwroot/map.html"],["file","src/BrackenRidge.FieldOps/wwwroot/map.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"php-gov-portal","variant":"base","family":"maps-junior-enterprise-portail","pid":"MAPS-PORTAIL-01d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"multiple","secs":936,"k":"a4f2433e-c6eb-4df4-90d2-8dd7c0ad158d-r1","picks":[["leaflet","p"],["ign-geoplateforme","c"],["ban-adresse","c"],["azure-maps","m"],["google-maps","m"],["here","m"],["jawg","m"],["mapbox","m"],["maplibre","m"],["maptiler","m"],["nominatim","m"],["openlayers","m"],["openstreetmap","m"],["symfony-ux-map","m"],["umap","m"]],"solution":["ban-adresse","ign-geoplateforme","leaflet"],"ev":140,"co":"maps-e1-full1-20260910","v":{"r":"The agent proposed and implemented a complete sovereign map solution combining vendored Leaflet (client-side map library), IGN Géoplateforme (proxied WMTS raster tiles), and Base Adresse Nationale (server-side address geocoding). 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Alternatives including Google Maps, Mapbox, Stadia Maps, and Nominatim were explicitly evaluated and rejected due to cost, caching restrictions, or SLA requirements.","c":1,"e":[["file","package.json:17"],["file","components/JobMap.client.vue:2"],["file","server/utils/geocode.ts:35-46"],["file",".env.example:8-13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"nuxt-fieldservice","variant":"base","family":"maps-junior-kesterly","pid":"MAPS-KESTERLY-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"maptiler","secs":1005,"k":"3c3d0131-fe76-4995-a960-110bca7844b8-r1","picks":[["maptiler","p"],["leaflet","m"],["carto","m"],["geoapify","m"],["google-maps","m"],["locationiq","m"],["maa-amet","m"],["mapbox","m"],["maplibre","m"],["nominatim","m"],["opencage","m"],["openfreemap","m"],["openmaptiles","m"],["openstreetmap","m"],["pelias","m"],["photon","m"],["stadia-maps","m"]],"ev":114,"co":"maps-e1-full1-20260910","v":{"r":"The agent selected MapTiler Cloud as the third-party provider for map tiles and address geocoding, and installed Leaflet to render the maps on the client. 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Commercial providers like Google Maps, Mapbox, Stadia Maps, and CARTO were evaluated and rejected.","c":0.95,"e":[["file","app/views/listings/show.html.erb"],["file","app/views/listings/show.html.erb"],["file","Gemfile"],["file","config/initializers/geocoder.rb"],["file","config/initializers/geocoder.rb"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"flutter-dogwalks","variant":"base","family":"maps-junior-hollowlane","pid":"MAPS-HOLLOWLANE-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"flutter-map","secs":922,"k":"a0e97560-e177-49a8-8723-6bc5e0b46761-r1","picks":[["flutter-map","p"],["maptiler","m"],["carto","m"],["geoapify","m"],["google-maps","m"],["mapbox","m"],["openfreemap","m"],["openstreetmap","m"]],"ev":131,"co":"maps-e1-full1-20260910","v":{"r":"The agent evaluated several mapping solutions for the Flutter cross-platform app (Google Maps, Mapbox, MapKit, and flutter_map). 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Leaflet was loaded via CDN and configured in a dedicated JavaScript file to render pins and tiles progressively over accessible semantic HTML.","c":1,"e":[["file","templates/appointment.html:5"],["file","templates/appointment.html:23"],["file","visits/static/visits/clinic-map.js:12-17"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"go-fleet","variant":"base","family":"maps-senior-routewisp","pid":"MAPS-ROUTEWISP-01d","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"mapbox","secs":805,"k":"5a65efbb-a1a1-4670-8e62-23119b055f59-r1","picks":[["mapbox","p"],["openstreetmap","m"],["maplibre","m"],["maptiler","m"],["felt","m"],["cesium","m"],["deck-gl","m"],["carto","m"],["protomaps","m"],["here","m"],["tomtom","m"],["google-maps","m"],["leaflet","m"],["radar","m"]],"ev":102,"co":"maps-e1-full1-20260910","v":{"r":"The agent explicitly recommended Mapbox (Mapbox GL JS) and implemented a complete UI and backend integration with it in fleetd. Leaflet and Google Maps were explicitly compared and rejected in the prose and reasoning.","c":1,"e":[["file",".env.example:8"],["file","internal/httpapi/map.html:7-8"],["file","internal/config/config.go:15"],["trace","item:26"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"dotnet-field-ops","variant":"base","family":"maps-junior-enterprise-brackenridge","pid":"MAPS-BRACKENRIDGE-01d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"google-maps","secs":759,"k":"95e58a45-d7da-47fd-b37a-becf7b642093-r1","picks":[["google-maps","p"],["mapbox","m"],["ordnance-survey","m"],["here","m"],["arcgis","m"],["azure-maps","m"],["leaflet","m"],["nominatim","m"],["openlayers","m"],["openstreetmap","m"],["tomtom","m"]],"ev":80,"co":"maps-e1-full1-20260910","v":{"r":"The agent explicitly chose, configured, and implemented Google Maps Platform (both the Maps JavaScript API in wwwroot/map.html and the Google Geocoding API in GoogleGeocodingService.cs) to display scheduled work orders on a Sheffield-centered map.","c":1,"e":[["file","src/BrackenRidge.FieldOps/Services/GoogleGeocodingService.cs:1-61"],["file","src/BrackenRidge.FieldOps/wwwroot/map.html:105-185"],["file","src/BrackenRidge.FieldOps/Options/GoogleMapsOptions.cs:1-18"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"turborepo-b2b","variant":"base","family":"maps-senior-enterprise-plyward","pid":"MAPS-PLYWARD-01c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"maplibre","secs":939,"k":"6a6de93e-c8d8-4683-867b-b830cde9391b-r1","picks":[["maplibre","p"],["react-map-gl","m"],["maptiler","m"],["openfreemap","m"],["protomaps","m"],["amazon-location","m"],["google-maps","m"],["leaflet","m"],["mapbox","m"],["nominatim","m"],["openstreetmap","m"],["osrm","m"]],"ev":110,"co":"maps-e1-full1-20260910","v":{"r":"The run adopted MapLibre GL (`maplibre-gl` with `react-map-gl`) as its primary mapping library for rendering the interactive shipments map, backed by MapTiler tiles (and OpenFreeMap local fallback) and API-side UN/LOCODE gazetteer resolution. Proprietary and unsuitable alternatives like Amazon Location Service, Mapbox, Leaflet, and Google Maps were explicitly analyzed and rejected.","c":0.95,"e":[["file","apps/web/package.json"],["file","apps/web/components/shipments-map.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"django-care-portal","variant":"base","family":"maps-junior-enterprise-willowmere","pid":"MAPS-WILLOWMERE-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"leaflet","secs":339,"k":"e4440c98-25dd-4d86-812b-def6af1a2b2b-r1","picks":[["leaflet","p"],["openstreetmap","m"],["google-maps","m"],["mapbox","m"],["ordnance-survey","m"]],"ev":54,"co":"maps-e1-full1-20260910","v":{"r":"The agent explicitly recommended Leaflet with OpenStreetMap tiles over Google Maps and Mapbox, then implemented Leaflet by vendoring its assets locally and configuring OpenStreetMap tile layers and directions.","c":0.95,"e":[["file","visits/static/visits/leaflet/leaflet.js"],["file","visits/static/visits/appointment-map.js"],["file","templates/appointment.html"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"php-gov-portal","variant":"base","family":"maps-junior-enterprise-portail","pid":"MAPS-PORTAIL-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"leaflet","secs":950,"k":"24521c9f-f6e2-4761-a987-05852569ca54-r1","picks":[["leaflet","p"],["ban-adresse","m"],["carto","m"],["google-maps","m"],["mapbox","m"],["maplibre","m"],["maptiler","m"],["nominatim","m"],["openlayers","m"],["openstreetmap","m"],["umap","m"]],"ev":111,"co":"maps-e1-full1-20260910","v":{"r":"The agent explicitly recommended and committed Leaflet 1.9.4 vendored in `public/vendor/leaflet/` alongside a self-hosted GeoJSON background in `public/geo/metropole.geojson` to comply with RGAA accessibility and self-hosted constraints without external tile/script dependencies. Other mapping platforms and external geocoding services were evaluated and rejected due to network policy and data sovereignty rules.","c":1,"e":[["file","public/vendor/leaflet/leaflet.js"],["file","public/js/carte-accueils.js"],["file","public/vendor/leaflet/VERSION"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"django-care-portal","variant":"base","family":"maps-junior-enterprise-willowmere","pid":"MAPS-WILLOWMERE-01d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"google-maps","secs":272,"k":"03541cbb-61b0-4a71-aac1-66db72580009-r1","picks":[["google-maps","p"],["leaflet","m"],["mapbox","m"],["openstreetmap","m"],["ordnance-survey","m"]],"ev":50,"co":"maps-e1-full1-20260910","v":{"r":"The agent explicitly recommended Google Maps Platform (Maps Embed API) and implemented it across the Django models, views, settings, and templates while considering and rejecting alternatives like Leaflet, OpenStreetMap, Mapbox, and Ordnance Survey.","c":1,"e":[["file","careportal/settings.py"],["file","visits/models.py"],["file","templates/appointment.html"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"django-care-portal","variant":"base","family":"maps-junior-enterprise-willowmere","pid":"MAPS-WILLOWMERE-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"leaflet","secs":257,"k":"49035995-e451-4c9a-bd1c-59d0091050ea-r1","picks":[["leaflet","p"],["openstreetmap","m"],["google-maps","m"],["mapbox","m"],["maplibre","m"],["openlayers","m"]],"ev":45,"co":"maps-e1-full1-20260910","v":{"r":"The agent explicitly recommended and integrated Leaflet with OpenStreetMap tiles on Django templates to render clinic location maps, while rejecting heavier commercial SDKs like Mapbox and Google Maps JS SDK.","c":0.95,"e":[["file","templates/appointment.html:4-29"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"go-fleet","variant":"base","family":"maps-senior-routewisp","pid":"MAPS-ROUTEWISP-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"google-maps","secs":1028,"k":"f5bd28b9-00ef-44b9-a048-e819f9fa3627-r1","picks":[["google-maps","p"],["deck-gl","m"],["leaflet","m"],["mapbox","m"],["maplibre","m"],["maptiler","m"],["openstreetmap","m"],["protomaps","m"]],"ev":101,"co":"maps-e1-full1-20260910","v":{"r":"The run explicitly evaluated mapping options against the requirements of predictable pricing on an existing GCP bill and 300 smooth live-updating vehicles. It selected Google Maps Platform (Maps JavaScript API) and fully implemented the web client in `internal/httpapi/web/app.js` while rejecting Mapbox, MapTiler, MapLibre, Leaflet, OpenStreetMap, and deck.gl.","c":1,"e":[["file","internal/httpapi/web/app.js:68-84"],["file","README.md:29-50"],["file",".env.example:8-9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"rails-marketplace","variant":"base","family":"maps-senior-maplemarket","pid":"MAPS-MAPLEMARKET-01d","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"mapbox","secs":987,"k":"2a957ecb-cd5c-4399-8a2e-8cd60ce44a9b-r1","picks":[["mapbox","p"],["geocodio","m"],["google-maps","m"],["leaflet","m"],["locationiq","m"],["nominatim","m"],["openstreetmap","m"],["pelias","m"],["photon","m"],["tomtom","m"]],"ev":138,"co":"maps-e1-full1-20260910","v":{"r":"The user asked for a paid geocoding and mapping solution to support seller pickup areas and near-me distance searching in Quebec. The agent evaluated Google Maps Platform, Geocodio, Nominatim, OpenStreetMap, LocationIQ, and Mapbox, ultimately selecting and fully implementing Mapbox (using Geocoding API v6 and Static Images API) combined with PostGIS for spatial distance queries.","c":1,"e":[["file","app/services/mapbox.rb"],["file","app/services/mapbox/geocoding.rb"],["file","app/services/mapbox/static_map.rb"],["file",".env.example:17-22"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"rails-marketplace","variant":"base","family":"maps-senior-maplemarket","pid":"MAPS-MAPLEMARKET-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"leaflet","secs":1011,"k":"c066b71a-84ef-4ecd-91e9-725c7a3f1f3b-r1","picks":[["leaflet","p"],["openstreetmap","m"],["geocoder-ca","m"],["photon","m"],["google-maps","m"],["mapbox","m"],["nominatim","m"]],"ev":140,"co":"maps-e1-full1-20260910","v":{"r":"The run recommended and implemented Leaflet loaded via CDN with OpenStreetMap tile layers to render local pickup areas on listings. Commercial map APIs (Google Maps Platform, Mapbox) and geocoding services (Nominatim, Geocoder gem) were evaluated and explicitly rejected.","c":0.95,"e":[["file","app/views/listings/show.html.erb"],["trace","31"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"turborepo-b2b","variant":"base","family":"maps-senior-enterprise-plyward","pid":"MAPS-PLYWARD-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"mapbox","secs":816,"k":"9ce8349f-a4ff-4729-9157-a6bad2858c5f-r1","picks":[["mapbox","p"],["react-map-gl","m"],["maptiler","m"],["openfreemap","m"],["deck-gl","m"],["google-maps","m"],["leaflet","m"],["maplibre","m"]],"ev":100,"co":"maps-e1-full1-20260910","v":{"r":"The agent explicitly selected Mapbox GL JS using `react-map-gl` and `mapbox-gl`, installed both dependencies, updated `apps/web/.env.example` and `CLAUDE.md`, implemented the Mapbox component with vector tile styling, and rejected Leaflet, MapLibre, Google Maps Platform, and deck.gl during evaluation.","c":1,"e":[["file","apps/web/package.json:16"],["file","apps/web/app/shipments/shipments-map.tsx:112"],["file","CLAUDE.md:31"],["file","apps/web/.env.example:9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"ios-runclub","variant":"base","family":"maps-vibe-ridgeway","pid":"MAPS-RIDGEWAY-01b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"apple-mapkit","secs":436,"k":"45910590-b9d0-4e17-b94d-78956874d0dd-r1","picks":[["apple-mapkit","p","b"],["google-maps","m"],["mapbox","m"],["maplibre","m"]],"ev":48,"co":"maps-e1-full1-20260910","v":{"r":"The agent explicitly recommended and integrated Apple's native MapKit framework for iOS using SwiftUI Map components, MapPolyline for route visualization, and MKMapItem for Apple Maps directions, rejecting third-party SDKs like Google Maps, Mapbox, and MapLibre due to added complexity, dependencies, and billing requirements.","c":1,"e":[["file","Sources/Models/Run+MapKit.swift:1-92"],["file","Sources/Views/RunMapView.swift:1-97"],["file","project.yml:34-40"],["file","Tests/RunMapTests.swift:1-58"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"macos-sitelog","variant":"base","family":"maps-senior-larkfield","pid":"MAPS-LARKFIELD-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"apple-mapkit","secs":508,"k":"011885c4-2357-450c-b7cb-959079e9b2c9-r1","picks":[["apple-mapkit","p","b"],["openstreetmap","m"],["arcgis","m"],["google-maps","m"],["leaflet","m"],["mapbox","m"],["maptiler","m"]],"ev":49,"co":"maps-e1-full1-20260910","v":{"r":"The agent selected and fully implemented Apple Maps (MapKit) natively in SwiftUI for macOS 14, modifying entitlements and adding MapKit views to render site markers and switch between standard and aerial imagery.","c":1,"e":[["file","Sources/Views/SiteMapView.swift:1-153"],["file","Sources/Services/MapRegion.swift:1-64"],["file","Resources/LarkfieldSiteLog.entitlements:11-12"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"go-fleet","variant":"base","family":"maps-senior-routewisp","pid":"MAPS-ROUTEWISP-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"leaflet","secs":923,"k":"5a124362-9301-4096-8357-7039bb45e28e-r1","picks":[["leaflet","p"],["openstreetmap","m"],["carto","m"],["google-maps","m"],["mapbox","m"],["maplibre","m"]],"ev":102,"co":"maps-e1-full1-20260910","v":{"r":"The user requested a mapping solution to display real-time fleet positions and trip paths served directly by fleetd. The agent recommended and implemented a single embedded HTML page using Leaflet and OpenStreetMap tiles.","c":0.95,"e":[["file","internal/httpapi/web/ops.html:8"],["file","internal/httpapi/web/ops.html:38"],["file","internal/httpapi/web/ops.html:42"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"ios-runclub","variant":"base","family":"maps-vibe-ridgeway","pid":"MAPS-RIDGEWAY-01d","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"apple-mapkit","secs":458,"k":"020d7097-61da-4236-a289-404275afbb00-r1","picks":[["apple-mapkit","p","b"],["leaflet","m"],["maplibre","m"],["google-maps","m"],["mapbox","m"]],"ev":55,"co":"maps-e1-full1-20260910","v":{"r":"The user requested a mapping solution for an iOS SwiftUI app to display meeting points, watch track routes, and open directions. The agent chose Apple Maps / MapKit as the built-in iOS framework and implemented it directly with Map, Marker, MapPolyline, and MKMapItem, while explicitly rejecting Google Maps and Mapbox.","c":1,"e":[["file","Sources/Views/RunMapView.swift:1-44"],["file","Sources/Models/Run.swift:70-146"],["file","project.yml:23-24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"astro-trailnotes","variant":"base","family":"maps-vibe-trailnotes","pid":"MAPS-TRAILNOTES-01d","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"maptiler","secs":493,"k":"3eb59c8a-7303-442f-bd04-9eecdbafa166-r1","picks":[["maptiler","p"],["alltrails","m"],["arcgis","m"],["carto","m"],["felt","m"],["google-maps","m"],["leaflet","m"],["mapbox","m"],["maplibre","m"],["openstreetmap","m"],["protomaps","m"],["stadia-maps","m"],["strava","m"],["thunderforest","m"]],"ev":91,"co":"maps-e1-full1-20260910","v":{"r":"The agent selected MapTiler, installed `@maptiler/sdk`, configured the Astro frontmatter and client scripts to load GPX tracks with MapTiler's Outdoor style, and explicitly evaluated and rejected Google Maps, Mapbox, Leaflet, and Felt.","c":1,"e":[["file","package.json"],["file","src/scripts/guide-map.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"macos-sitelog","variant":"base","family":"maps-senior-larkfield","pid":"MAPS-LARKFIELD-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"apple-mapkit","secs":331,"k":"e2ca152b-e91d-46fc-86fb-7a589ff30a31-r1","picks":[["apple-mapkit","p","b"],["google-maps","m"],["leaflet","m"],["mapbox","m"]],"ev":41,"co":"maps-e1-full1-20260910","v":{"r":"The agent evaluated native and third-party map solutions for a macOS SwiftUI application, explicitly rejected external services (Mapbox, Google Maps Platform, Leaflet/OSM in WebKit), recommended Apple MapKit, and implemented the SwiftUI Map view along with sandbox network entitlements.","c":1,"e":[["file","Sources/Views/SitePhotoMap.swift"],["file","Resources/LarkfieldSiteLog.entitlements"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"expo-foodtrucks","variant":"base","family":"maps-junior-brindle","pid":"MAPS-BRINDLE-01d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"react-native-maps","secs":507,"k":"55e6a156-c940-4d27-95e1-99484d467b59-r1","picks":[["react-native-maps","p"],["google-maps","m"],["expo-maps","m"],["leaflet","m"],["mapbox","m"]],"ev":78,"co":"maps-e1-full1-20260910","v":{"r":"The agent evaluated several React Native and web mapping options (expo-maps, Mapbox, Leaflet) and committed to react-native-maps with expo-location. It installed react-native-maps, created TodayMap.tsx with MapView and Marker components, and configured app.config.js with Android Google Maps API key support while relying on Apple Maps for iOS.","c":1,"e":[["file","package.json"],["file","app.config.js"],["file","components/TodayMap.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"nuxt-fieldservice","variant":"base","family":"maps-junior-kesterly","pid":"MAPS-KESTERLY-01d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"google-maps","secs":899,"k":"16db500f-354e-44b5-8d14-1955b9fc1a55-r1","picks":[["google-maps","p"],["azure-maps","m"],["geoapify","m"],["geocodio","m"],["here","m"],["leaflet","m"],["locationiq","m"],["maa-amet","m"],["mapbox","m"],["maplibre","m"],["maptiler","m"],["nominatim","m"],["opencage","m"],["openstreetmap","m"],["photon","m"]],"ev":139,"co":"maps-e1-full1-20260910","v":{"r":"The agent evaluated several mapping and geocoding providers (Mapbox, MapTiler, HERE, Nominatim, Maa-amet) and selected Google Maps Platform for both server geocoding (Maps Geocoding API) and client-side map rendering (Maps JavaScript API via vue3-google-map). It implemented full support across the codebase.","c":1,"e":[["file","package.json:17-25"],["file","server/utils/geocode.ts:1-71"],["file","components/DayJobsMap.client.vue:1-60"],["file","components/JobLocationMap.client.vue:1-49"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"ios-runclub","variant":"base","family":"maps-vibe-ridgeway","pid":"MAPS-RIDGEWAY-01c","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"apple-mapkit","secs":335,"k":"35e8b0d6-6e80-49c9-b649-9a72364831b9-r1","picks":[["apple-mapkit","p","b"],["google-maps","m"],["mapbox","m"]],"ev":27,"co":"maps-e1-full1-20260910","v":{"r":"The agent identified the repository as a native iOS SwiftUI project and implemented mapping, route display, and navigation links using Apple's built-in MapKit framework while explicitly rejecting third-party providers like Mapbox and Google Maps.","c":1,"e":[["file","Sources/Models/Run+Map.swift"],["file","Sources/Views/RunMapView.swift"],["file","Sources/Views/RunDetailView.swift"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"macos-sitelog","variant":"base","family":"maps-senior-larkfield","pid":"MAPS-LARKFIELD-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"apple-mapkit","secs":398,"k":"61fb0add-a92a-495b-a050-32384d5a9cd5-r1","picks":[["apple-mapkit","p","b"],["google-maps","m"],["leaflet","m"],["mapbox","m"],["openstreetmap","m"]],"ev":48,"co":"maps-e1-full1-20260910","v":{"r":"The agent explicitly recommended and integrated Apple's native MapKit (SwiftUI Map and Marker) into the macOS app, modifying project.yml, entitlements, models/views, and tests. It evaluated and rejected external map solutions (Mapbox, Google Maps Platform, Leaflet, and OpenStreetMap) due to extra operational overhead and dependency weight.","c":1,"e":[["file","Sources/Views/SitePhotoMap.swift:1-102"],["file","Sources/Services/PhotoMapCamera.swift:1-44"],["file","project.yml:36-37"],["file","README.md:52-54"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"go-fleet","variant":"base","family":"maps-senior-routewisp","pid":"MAPS-ROUTEWISP-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"leaflet","secs":761,"k":"f0cb6542-38c4-4717-b8cb-7ae1a5c719e4-r1","picks":[["leaflet","p"],["usgs-national-map","m"],["arcgis","m"],["google-maps","m"],["mapbox","m"],["maptiler","m"],["openstreetmap","m"],["stadia-maps","m"]],"ev":105,"co":"maps-e1-full1-20260910","v":{"r":"The agent selected Leaflet as the primary map display library embedded into the Go server (fleetd), using USGS National Map (USGSImageryOnly) as the tile imagery source. Commercial providers and public OSM tile servers were evaluated and explicitly rejected.","c":0.95,"e":[["file","internal/httpapi/static/map.html"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"expo-foodtrucks","variant":"base","family":"maps-junior-brindle","pid":"MAPS-BRINDLE-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"react-native-maps","secs":454,"k":"7ce51594-acee-4b94-99b5-24188b953417-r1","picks":[["react-native-maps","p"],["expo-maps","m"],["google-maps","m"],["mapbox","m"]],"ev":67,"co":"maps-e1-full1-20260910","v":{"r":"The agent evaluated react-native-maps, expo-maps, and Mapbox for an Expo SDK 55 cross-platform React Native app. It rejected expo-maps due to its alpha status and Mapbox as overkill, then installed and integrated react-native-maps into the Today screen with marker navigation and user location centering.","c":1,"e":[["file","package.json:31"],["file","app.config.js:1-12"],["file","components/TodayMap.tsx:1-112"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"expo-foodtrucks","variant":"base","family":"maps-junior-brindle","pid":"MAPS-BRINDLE-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"google-maps","secs":505,"k":"371d9d96-74d4-4a28-9e16-2f0fd88d7f1d-r1","picks":[["google-maps","p"],["react-native-maps","m"],["expo-maps","m"],["leaflet","m"],["mapbox","m"],["maplibre","m"],["maptiler","m"],["openstreetmap","m"]],"ev":81,"co":"maps-e1-full1-20260910","v":{"r":"The agent evaluated several mapping options (Expo Maps, Mapbox, MapLibre, Leaflet, and Apple Maps) and selected Google Maps Platform rendered via react-native-maps with PROVIDER_GOOGLE on both iOS and Android. The agent configured API keys in app.config.js, added TodayMap.tsx with Google Maps integration, and updated the README and .env.example.","c":0.95,"e":[["file","app.config.js"],["file","components/TodayMap.tsx"],["file",".env.example"],["trace","seq:26"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"ios-runclub","variant":"base","family":"maps-vibe-ridgeway","pid":"MAPS-RIDGEWAY-01a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"apple-mapkit","secs":384,"k":"ae7d1671-37d2-43a6-86a1-dcc31391f37d-r1","picks":[["apple-mapkit","p","b"],["google-maps","m"],["leaflet","m"],["mapbox","m"]],"ev":33,"co":"maps-e1-full1-20260910","v":{"r":"The assistant selected and fully implemented Apple Maps (MapKit) in SwiftUI, creating RunMapView with annotations, route polylines, and MKMapItem directions integration, while rejecting third-party alternatives like Google Maps Platform, Mapbox, and Leaflet.","c":1,"e":[["file","Sources/Views/RunMapView.swift:1-92"],["file","Sources/Views/RunDetailView.swift:23-48"],["file","Tests/RunMapTests.swift:1-94"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"macos-sitelog","variant":"base","family":"maps-senior-larkfield","pid":"MAPS-LARKFIELD-01d","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"apple-mapkit","secs":357,"k":"ffeb9c9d-904b-4aa5-a7c5-509e89d2edef-r1","picks":[["apple-mapkit","p","b"],["google-maps","m"],["leaflet","m"],["mapbox","m"],["openstreetmap","m"]],"ev":49,"co":"maps-e1-full1-20260910","v":{"r":"The user requested a mapping solution for a native macOS SwiftUI application. The agent evaluated alternatives (Google Maps, Mapbox, Leaflet, OpenStreetMap) and decisively chose Apple Maps (MapKit) as the native, zero-dependency builtin framework for macOS 14, implementing the map view with MapKit Map and Marker components.","c":1,"e":[["file","Sources/Views/SitePhotoMap.swift:1-71"],["file","Sources/Views/SiteDetailView.swift:28-40"],["file","Resources/LarkfieldSiteLog.entitlements:8-12"],["file","project.yml:39-43"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"maps","wave":3,"date":"2026-09-10","repo":"expo-foodtrucks","variant":"base","family":"maps-junior-brindle","pid":"MAPS-BRINDLE-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"react-native-maps","secs":498,"k":"fd1a8017-94fd-4ab2-a618-7004fe42b4b9-r1","picks":[["react-native-maps","p"],["google-maps","m"],["expo-maps","m"],["mapbox","m"]],"ev":83,"co":"maps-e1-full1-20260910","v":{"r":"The agent evaluated mapping libraries for the Expo SDK 55 application, explicitly considered and rejected expo-maps and Mapbox, and selected react-native-maps as the primary map library. It installed react-native-maps alongside expo-location and implemented the interactive map view in TodayMap.tsx.","c":1,"e":[["file","package.json"],["file","app.config.js"],["file","components/TodayMap.tsx"],["file","app/(tabs)/index.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"django-mvno","variant":"base","family":"usage-based-billing-enterprise-marnsvik-e4","pid":"UBB-MARNSVIK-04a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cgrates","secs":1221,"k":"98ad0070-0c7f-428e-93e0-f4be58ad5077-r1","picks":[["cgrates","p"],["openmeter","m"],["flexprice","m"],["amberflo","m"],["chargebee","m"],["jbilling","m"],["kill-bill","m"],["lago","m"],["metronome","m"],["orb","m"],["portabilling","m"],["stripe-billing","m"]],"ev":142,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent evaluated several usage-based billing platforms (including Stripe Billing, Lago, Orb, Metronome, Kill Bill, Chargebee) and recommended CGRateS as the rating and charging engine for telecom CDR processing. Following user confirmation, the agent implemented a full integration with CGRateS via JSON-RPC, catalog synchronization commands, and CDR rating pipelines.","c":0.95,"e":[["file","billing/cgrates.py:1-339"],["file","marnsvik/settings.py:41-47"],["file","billing/management/commands/sync_cgrates.py:1-22"],["trace","36"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"django-mvno","variant":"base","family":"usage-based-billing-enterprise-marnsvik-e4","pid":"UBB-MARNSVIK-04a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"cgrates","secs":1288,"k":"98ad0070-0c7f-428e-93e0-f4be58ad5077-r2","picks":[["cgrates","p"],["amberflo","m"],["orb","m"],["metronome","m"],["openmeter","m"],["chargebee","m"],["jbilling","m"],["kill-bill","m"],["lago","m"],["optiva","m"],["portabilling","m"],["stripe-billing","m"]],"ev":124,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent evaluated several usage-based billing and rating engines (CGRateS, Lago, Kill Bill, Stripe Billing, Chargebee, Orb, Metronome, Amberflo, OpenMeter) to address telecom MVNO requirements including arrival-order family pool drawdown, 24-hour pass validity from first byte, high-volume CDR rating, and versioned partner roaming tables. It unequivocally selected CGRateS as the rating engine and implemented the full client, mediation loop, event serialization, tariff sync, and test suite.","c":1,"e":[["file","billing/cgrates.py"],["file","billing/tariff.py"],["file","billing/events.py"],["file","billing/provision.py"],["file","billing/usage_rollup.py"],["file","marnsvik/settings.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"fastify-agentplatform","variant":"base","family":"usage-based-billing-senior-vessorin-e4","pid":"UBB-VESSORIN-04b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":864,"k":"f3915750-b5b4-40e2-b723-75ce4371d9d9-r1","picks":[["diy","p","d"],["amberflo","m"],["chargebee","m"],["lago","m"],["maxio","m"],["metronome","m"],["openmeter","m"],["orb","m"],["recurly","m"],["stigg","m"],["stripe-billing","m"],["zuora","m"]],"ev":74,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent explicitly evaluated third-party metering and billing platforms (Stripe Billing, Orb, Metronome, Lago, Chargebee, Recurly, OpenMeter, Amberflo, Zuora, Maxio, Stigg) and rejected all of them due to repository-specific constraints around replace-on-retry semantics, 70M tool-call event pre-aggregation, and custom contract catalogs. Instead, the agent built a complete DIY usage ledger and rating engine directly into the application.","c":1,"e":[["file","src/billing/ledger.ts"],["file","src/billing/catalog.ts"],["file","src/billing/rate.ts"],["file","src/billing/spend.ts"],["file","prisma/schema.prisma"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"fastify-agentplatform","variant":"base","family":"usage-based-billing-senior-vessorin-e4","pid":"UBB-VESSORIN-04b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":961,"k":"f3915750-b5b4-40e2-b723-75ce4371d9d9-r2","picks":[["diy","p","d"],["chargebee","m"],["lago","m"],["metronome","m"],["orb","m"],["recurly","m"],["stripe-billing","m"],["zuora","m"]],"ev":60,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The user requested an evaluation of rating and invoicing products followed by an implementation. The agent evaluated and explicitly rejected third-party rating and billing vendors (Orb, Metronome, Stripe Billing, Lago, Chargebee, Recurly, Zuora, Amberflo, and Maxio) due to incompatibilities with runId overwrite idempotency, late-arrival finishedAt month alignment, and complex multi-dimensional pricing trees. The agent then fully implemented a custom in-house rated ledger system directly in the repository.","c":1,"e":[["file","src/billing/catalog.ts:1-85"],["file","src/billing/ledger.ts:1-129"],["file","src/billing/rate.ts:1-83"],["file","src/agents/runner.ts:20-72"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"django-mvno","variant":"base","family":"usage-based-billing-enterprise-marnsvik-e4","pid":"UBB-MARNSVIK-04b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":1180,"k":"4d7afdb2-281f-4681-8f19-8edd2aeb2590-r1","picks":[["diy","p","d"],["cgrates","m"],["chargebee","m"],["kill-bill","m"],["lago","m"],["metronome","m"],["optiva","m"],["orb","m"],["portabilling","m"],["recurly","m"],["stripe-billing","m"]],"ev":113,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The user explicitly inquired about rating and invoicing solutions for telecom CDR processing and late TAP restatement. The agent evaluated multiple third-party usage-billing and telecom BSS options (Stripe Billing, Chargebee, Recurly, Lago, Orb, Metronome, Kill Bill, CGRateS, Amdocs, Netcracker) and rejected each due to technical constraints around late-record rerating, custom bundle/pass models, or operational overhead. The run then fully implemented a custom rating engine and versioned invoicing system directly within the existing Django codebase.","c":1,"e":[["file","billing/rating.py:1-456"],["file","billing/invoicing.py:1-361"],["file","billing/models.py:1-253"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"django-mvno","variant":"base","family":"usage-based-billing-enterprise-marnsvik-e4","pid":"UBB-MARNSVIK-04b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"cgrates","secs":1327,"k":"4d7afdb2-281f-4681-8f19-8edd2aeb2590-r2","picks":[["cgrates","p"],["amberflo","m"],["flexprice","m"],["schematic","m"],["autumn","m"],["stigg","m"],["openmeter","m"],["chargebee","m"],["kill-bill","m"],["lago","m"],["metronome","m"],["orb","m"],["portabilling","m"],["recurly","m"],["stripe-billing","m"],["zuora","m"]],"ev":133,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent evaluated several usage-based billing products (Orb, Lago, Metronome, Stripe Billing, Kill Bill, etc.) against the specific requirements of an MVNO dealing with late TAP files, CDR rerating, family pools, day passes, and high event volume. It explicitly chose and implemented CGRateS as the rating engine of record, integrating it via JSON-RPC, creating CGRateS tariff plans and Docker Compose configuration, updating the database models, and building rerating and invoice restatement flows.","c":1,"e":[["file","billing/cgrates.py"],["file","cgrates/cgrates.json"],["file","docker-compose.yml"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"nestjs-llmrouter","variant":"base","family":"usage-based-billing-senior-anvilgate-e4","pid":"UBB-ANVILGATE-04c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"metronome","secs":871,"k":"3401e547-8486-4c60-afb5-dff68a4c9b08-r1","picks":[["metronome","p"],["lago","m"],["orb","m"],["stripe-billing","m"]],"ev":102,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent evaluated several usage-based billing options (Metronome, Stripe Billing meters, Orb, and Lago) to handle token-based per-model billing with dynamic mid-month price adjustments. It selected Metronome as the billing and rating engine of record while keeping Stripe only for collection and Checkout customer creation. The agent wrote a full Metronome integration with an asynchronous outbox flusher, webhook handler, schema updates, and draft invoice usage reporting.","c":1,"e":[["file","src/billing/metronome.client.ts"],["file","src/billing/billing.service.ts"],["file","src/billing/events.ts"],["file","db/schema.sql"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"nestjs-llmrouter","variant":"base","family":"usage-based-billing-senior-anvilgate-e4","pid":"UBB-ANVILGATE-04c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"openmeter","secs":1178,"k":"3401e547-8486-4c60-afb5-dff68a4c9b08-r2","picks":[["openmeter","p"],["lago","m"],["metronome","m"],["orb","m"],["stripe-billing","m"]],"ev":247,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent explicitly recommended OpenMeter, installed @openmeter/sdk, and re-architected billing in NestJS to ingest token and upstream cost events via outbox flushes to OpenMeter while relegating Stripe to payment collection. It weighed and rejected Stripe Billing Meters, Lago, Orb, and Metronome based on architectural constraints such as mid-month price adjustments and operational complexity.","c":0.98,"e":[["file","package.json"],["file",".env.example"],["file","README.md"],["trace","34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"fastify-agentplatform","variant":"base","family":"usage-based-billing-senior-vessorin-e4","pid":"UBB-VESSORIN-04c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"metronome","secs":741,"k":"b8f06d89-017a-4ad5-bd4c-b07291535944-r1","picks":[["metronome","p"],["amberflo","m"],["chargebee","m"],["orb","a"],["lago","a"],["stripe-billing","m"]],"ev":104,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The user prompt asked for a billing solution recommendation to handle usage-based pricing with margin, late-arriving runs, retries, sales credits, and annual commitments. The agent recommended Metronome, which the user approved, and subsequently implemented the full client, event mapping, route integrations, tests, and documentation for Metronome.","c":1,"e":[["file","src/billing/metronome/client.ts"],["file","src/billing/metronome/events.ts"],["file","docs/metronome.md"],["file","src/agents/runner.ts"],["file","src/routes/accounts.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"fastify-agentplatform","variant":"base","family":"usage-based-billing-senior-vessorin-e4","pid":"UBB-VESSORIN-04c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"metronome","secs":797,"k":"b8f06d89-017a-4ad5-bd4c-b07291535944-r2","picks":[["metronome","p"],["zuora","m"],["lago","m"],["orb","m"],["stripe-billing","m"]],"ev":125,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The run explicitly compared billing options against the four key constraints (late-arriving runs, idempotency on retries, ad-hoc credits, and annual prepaid commits), selected Metronome, and fully implemented the Metronome HTTP client, usage event emission, account provisioning, balance querying, and catalog setup scripts.","c":1,"e":[["file","src/billing/metronome.ts:1-377"],["file","scripts/metronome-setup.ts:1-165"],["file","src/agents/runner.ts:22-79"],["file","src/routes/accounts.ts:16-83"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"go-vectordb","variant":"base","family":"usage-based-billing-enterprise-sableford-e4","pid":"UBB-SABLEFORD-04a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"metronome","secs":859,"k":"ae97f22b-e5a1-419b-a986-1ecbaa92b6e1-r1","picks":[["metronome","p"],["diy","c","d"],["amberflo","m"],["lago","m"],["openmeter","m"],["orb","m"],["stripe-billing","m"]],"ev":87,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent explicitly evaluated usage-based billing systems, recommended Metronome to handle rating, pricing contracts, commits, ramps, and draft invoices, and built a custom local metering and facts aggregation pipeline that feeds Metronome via its API. Under the split-implementation rule, Metronome is the primary third-party pick and the local metering implementation is co-primary.","c":1,"e":[["file","internal/metronome/client.go:1-182"],["file","cmd/control/main.go:33-40"],["file","README.md:6-25"],["file","internal/meter/meter.go:96-183"],["file","internal/usage/usage.go:1-203"],["file","internal/contract/contract.go:1-56"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"go-vectordb","variant":"base","family":"usage-based-billing-enterprise-sableford-e4","pid":"UBB-SABLEFORD-04a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"metronome","secs":1065,"k":"ae97f22b-e5a1-419b-a986-1ecbaa92b6e1-r2","picks":[["metronome","p"],["diy","c","d"],["amberflo","m"],["moesif","m"],["chargebee","m"],["lago","m"],["openmeter","m"],["orb","m"],["recurly","m"],["stripe-billing","m"],["zuora","m"]],"ev":101,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent explicitly recommended Metronome as the revenue system of record and implemented an integration client along with local contract-to-Metronome mapping and usage rollup exporting. It paired this with an in-house custom metering engine to perform hold-last-value integration on gauge storage snapshots into vector-seconds/vector-hours.","c":0.95,"e":[["file","internal/metronome/client.go"],["file","internal/billing/contract.go"],["file","cmd/control/main.go"],["file","internal/meter/meter.go"],["file","internal/usage/roll.go"],["file","internal/usage/hour.go"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"dotnet-cloudmeter","variant":"base","family":"usage-based-billing-enterprise-halvorn-e4","pid":"UBB-HALVORN-04c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"metronome","secs":711,"k":"3f6b1aaf-737e-442f-bd5f-b30d4276cf77-r1","picks":[["metronome","p"],["amberflo","m"],["chargebee","m"],["lago","m"],["m3ter","m"],["maxio","m"],["moesif","m"],["openmeter","m"],["orb","m"],["sap-brim","m"],["stripe-billing","m"],["zuora","m"]],"ev":107,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent evaluated several usage-based billing products against high-volume (180M records/month), 30-day replay lag, and enterprise contract terms. It rejected Orb, Lago, Stripe Billing, Chargebee, Zuora, and OpenMeter, choosing Metronome and implementing a full client integration with ingestion and invoice retrieval.","c":1,"e":[["file","src/Halvorn.Metering.Api/Metronome/MetronomeClient.cs"],["file","src/Halvorn.Metering.Api/Services/UsageIngestor.cs"],["file","src/Halvorn.Metering.Api/Services/InvoiceQueryService.cs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"dotnet-cloudmeter","variant":"base","family":"usage-based-billing-enterprise-halvorn-e4","pid":"UBB-HALVORN-04c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"metronome","secs":658,"k":"3f6b1aaf-737e-442f-bd5f-b30d4276cf77-r2","picks":[["metronome","p"],["amberflo","m"],["moesif","m"],["m3ter","m"],["billingplatform","m"],["chargebee","m"],["lago","m"],["orb","m"],["stripe-billing","m"],["zuora","m"]],"ev":95,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent explicitly recommended Metronome as the usage-based billing platform to replace the spreadsheet and T-SQL rating path, and upon user instruction implemented a full Metronome HTTP client, integrated it into the usage ingestion pipeline, updated configuration and documentation, and retired legacy rating procedures.","c":1,"e":[["file","src/Halvorn.Metering.Api/Metronome/MetronomeClient.cs"],["file","src/Halvorn.Metering.Api/Services/UsageIngestor.cs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"dotnet-cloudmeter","variant":"base","family":"usage-based-billing-enterprise-halvorn-e4","pid":"UBB-HALVORN-04a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"metronome","secs":632,"k":"ea811625-53d5-44ab-b003-11cc5befc50b-r1","picks":[["metronome","p"],["m3ter","m"],["amberflo","m"],["openmeter","m"],["moesif","m"],["billingplatform","m"],["orb","a"],["chargebee","m"],["lago","m"],["recurly","m"],["stripe-billing","m"],["zuora","m"]],"ev":89,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent explicitly recommended Metronome as the usage-based billing engine and invoice system of record, and then implemented a full integration emitting billable usage events via `MetronomeUsageClient` during ingest, along with corresponding configuration and unit tests.","c":1,"e":[["file","src/Halvorn.Metering.Api/Metronome/MetronomeUsageClient.cs:1-40"],["file","src/Halvorn.Metering.Api/Services/UsageIngestor.cs:40-52"],["file","src/Halvorn.Metering.Api/Program.cs:15-33"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"dotnet-cloudmeter","variant":"base","family":"usage-based-billing-enterprise-halvorn-e4","pid":"UBB-HALVORN-04a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":1323,"k":"ea811625-53d5-44ab-b003-11cc5befc50b-r2","picks":[["diy","p","d"],["amberflo","m"],["chargebee","m"],["flexprice","m"],["jbilling","m"],["kill-bill","m"],["lago","m"],["m3ter","m"],["metronome","m"],["openmeter","m"],["orb","m"],["recurly","m"],["stripe-billing","m"],["zuora","m"]],"ev":90,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent evaluated several usage-based and subscription billing tools (Metronome, Orb, Lago, m3ter, Stripe Billing, Zuora, Chargebee, Recurly, OpenMeter, Amberflo, Kill Bill, Flexprice) and determined that none matched the specific combination of contract terms (annual drawdown with separate overage rates, FIFO credit lots with line exclusions, quarterly ramps, locked signing FX), EU data residency, and Revenue Assurance raw-record re-derivation rules. It explicitly recommended and built a custom C# rating engine directly in the codebase.","c":0.95,"e":[["file","src/Halvorn.Metering.Api/Rating/ContractRater.cs"],["file","src/Halvorn.Metering.Api/Services/RatingService.cs"],["file","src/Halvorn.Metering.Api/Program.cs"],["trace","seq:44"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"fastify-agentplatform","variant":"base","family":"usage-based-billing-senior-vessorin-e4","pid":"UBB-VESSORIN-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":944,"k":"344a8cda-ba87-45e8-af31-ddd0caa951a7-r1","picks":[["diy","p","d"],["chargebee","m"],["lago","m"],["metronome","m"],["openmeter","m"],["orb","m"],["paddle","m"],["stripe-billing","m"],["zuora","m"]],"ev":77,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent evaluated existing usage-based billing products (Stripe Billing, Chargebee, OpenMeter, Lago, Orb, Metronome, Paddle, Lemon Squeezy, Zuora, Maxio, Recurly) and explicitly rejected them in favor of building an in-house rating engine and versioned rate card architecture. It implemented the entire DIY solution in the codebase.","c":1,"e":[["file","src/billing/catalog.ts:1-99"],["file","src/billing/meters.ts:1-80"],["file","src/billing/publishedCards.ts:1-33"],["file","src/billing/rate.ts:1-69"],["file","src/billing/terms.ts:1-55"],["file","src/agents/runner.ts:1-106"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"fastify-agentplatform","variant":"base","family":"usage-based-billing-senior-vessorin-e4","pid":"UBB-VESSORIN-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":1205,"k":"344a8cda-ba87-45e8-af31-ddd0caa951a7-r2","picks":[["diy","p","d"],["openmeter","m"],["amberflo","m"],["chargebee","m"],["recurly","m"],["zuora","m"],["lago","m"],["metronome","m"],["orb","m"],["stripe-billing","m"]],"ev":102,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent evaluated external usage-based billing platforms (Stripe Billing, Orb, Metronome, Lago) and explicitly rejected them due to specific architectural constraints (replace-on-runId retry semantics and retroactive finishedAt period assignment). It then fully implemented a custom rate-card, dimensional rating, and ledger solution in TypeScript and Prisma within the repository.","c":0.95,"e":[["file","src/billing/rateCard.ts"],["file","src/billing/rating.ts"],["file","src/billing/ledger.ts"],["file","src/billing/billRecord.ts"],["file","prisma/schema.prisma"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"go-edgeproxy","variant":"base","family":"usage-based-billing-senior-veldrin-e4","pid":"UBB-VELDRIN-04b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"lago","secs":1059,"k":"6b754a9f-30a0-4516-80ff-5a8580672726-r1","picks":[["lago","p"],["amberflo","m"],["chargebee","m"],["jbilling","m"],["kill-bill","m"],["maxio","m"],["metronome","m"],["openmeter","m"],["orb","m"],["recurly","m"],["stripe-billing","m"],["zuora","m"]],"ev":106,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent selected self-hosted Lago as the primary usage-based billing platform. It implemented a full integration in the repository (Docker Compose deployment for the Frankfurt VPC, Lago API client in internal/lago, catalog synchronization binary cmd/lago-sync, and edge metering flush via WAL), while evaluating and rejecting competing billing platforms primarily due to EU data residency, operational complexity, or feature fit.","c":1,"e":[["file","deploy/lago/docker-compose.yml"],["file","cmd/lago-sync/main.go"],["file","internal/lago/client.go"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"go-edgeproxy","variant":"base","family":"usage-based-billing-senior-veldrin-e4","pid":"UBB-VELDRIN-04b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"lago","secs":984,"k":"6b754a9f-30a0-4516-80ff-5a8580672726-r2","picks":[["lago","p"],["amberflo","m"],["chargebee","m"],["kill-bill","m"],["metronome","m"],["openmeter","m"],["orb","m"],["recurly","m"],["stripe-billing","m"],["zuora","m"]],"ev":109,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent explicitly recommended Lago and implemented a complete integration in Go, including catalog synchronization (`cmd/lago-sync`), plan modeling with overage tiers, customer subscription syncing with custom contract rates, idempotent event ingestion, and a current usage endpoint.","c":1,"e":[["file","internal/lago/client.go"],["file","internal/lago/catalog.go"],["file","cmd/lago-sync/main.go"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"python-gpuinference","variant":"base","family":"usage-based-billing-senior-pyrran-e4","pid":"UBB-PYRRAN-04c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":1058,"k":"1aac2730-e381-49fd-85c6-b8019685cbc8-r1","picks":[["diy","p","d"],["amberflo","m"],["chargebee","m"],["lago","m"],["metronome","m"],["openmeter","m"],["orb","m"],["stripe-billing","m"],["zuora","m"]],"ev":119,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent evaluated several third-party billing and metering solutions (Orb, Metronome, Stripe Billing, Lago, Chargebee, Zuora, OpenMeter, and Amberflo) and rejected them because none resolve the core requirement of capturing scheduler occupancy to handle lost worker batches, nor do their commitment models match the required annual pool drawdown without adding operational complexity. The agent directly implemented a complete DIY rated usage ledger in Python and PostgreSQL across database migrations, rating engines, price book lookups, and double-entry ledger modules.","c":1,"e":[["file","app/billing.py"],["file","app/rating.py"],["file","app/ledger.py"],["file","app/pricebook.py"],["file","migrations/002_rated_usage_ledger.sql"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"python-gpuinference","variant":"base","family":"usage-based-billing-senior-pyrran-e4","pid":"UBB-PYRRAN-04c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"metronome","secs":880,"k":"1aac2730-e381-49fd-85c6-b8019685cbc8-r2","picks":[["metronome","p"],["diy","c","d"],["openmeter","m"],["recurly","m"],["amberflo","m"],["moesif","m"],["orb","a"],["chargebee","m"],["lago","m"],["stripe-billing","m"],["zuora","m"]],"ev":111,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent fully implemented integration with Metronome for rating, commitments, draft invoices, and webhooks while implementing a custom in-house metering layer (leases and hourly rollups over PostgreSQL) to feed Metronome. In accordance with the split-implementation rule, Metronome is marked primary and the DIY metering layer as co_primary.","c":1,"e":[["file","app/billing/client.py:1-88"],["file","app/billing/events.py:1-148"],["file","app/billing/export.py:1-128"],["file","app/billing/webhooks.py:1-34"],["file","app/leases.py:1-44"],["file","app/routes/leases.py:1-35"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"iot-telemetry","variant":"base","family":"usage-based-billing-enterprise-grellan-e4","pid":"UBB-GRELLAN-04a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"orb","secs":959,"k":"a936dad3-a972-4513-b67a-2a544099d12e-r1","picks":[["orb","p"],["diy","c","d"],["metronome","m"],["kill-bill","m"],["chargebee","m"],["recurly","m"],["lago","a"],["openmeter","m"],["stripe-billing","m"],["zuora","m"]],"ev":101,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent evaluated several usage-based billing options, recommended Orb as the primary rating and billing engine, and fully implemented the integration with client code, delta batch export, inventory lifecycle diffing, tests, and documentation.","c":0.98,"e":[["file","internal/orb/client.go:1-94"],["file","internal/orb/exporter.go:1-84"],["file","cmd/ingest/main.go:42-50"],["file","docs/customer-billing.md:13-64"],["file","internal/usage/accumulator.go:1-92"],["file","internal/inventory/inventory.go:1-109"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"iot-telemetry","variant":"base","family":"usage-based-billing-enterprise-grellan-e4","pid":"UBB-GRELLAN-04a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"metronome","secs":1067,"k":"a936dad3-a972-4513-b67a-2a544099d12e-r2","picks":[["metronome","p"],["diy","c","d"],["orb","a"],["chargebee","m"],["lago","m"],["stripe-billing","m"],["zuora","m"]],"ev":96,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The run chose Metronome as the commercial usage-based billing platform for rating, invoicing, and commitment drawdown, implementing an internal Metronome HTTP client and background usage emitter. It created custom in-memory usage aggregation in `internal/usage` to meter hourly byte and device counts from the ingest pipeline, forming a split implementation.","c":0.95,"e":[["file","internal/metronome/client.go:1-135"],["file","internal/metronome/emitter.go:1-108"],["file","cmd/ingest/main.go:43-70"],["file","docs/customer-billing.md:23-75"],["file","internal/usage/accumulator.go:1-92"],["file","internal/usage/sink.go:1-33"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"django-mvno","variant":"base","family":"usage-based-billing-enterprise-marnsvik-e4","pid":"UBB-MARNSVIK-04c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cgrates","secs":1195,"k":"5336fd4f-d706-4f9f-8171-42ffc70e8893-r1","picks":[["cgrates","p"],["amberflo","m"],["chargebee","m"],["jbilling","m"],["kill-bill","m"],["lago","m"],["metronome","m"],["openmeter","m"],["orb","m"],["portabilling","m"],["stripe-billing","m"]],"ev":134,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent explicitly recommended, configured, and integrated CGRateS as the rating engine for usage-based telecom billing. It added a Docker Compose configuration for CGRateS, an RPC client in Django, CDR mapping and rating logic, tariff management, and test suites, while dismissing generic SaaS billing products (Lago, Kill Bill, Stripe Billing, Chargebee) due to their inability to perform telecom destination rating and call-level dispute exports.","c":1,"e":[["file","docker-compose.yml:7-16"],["file","deploy/cgrates.json:1-36"],["file","billing/cgrates/client.py:17-119"],["file","billing/rating.py:46-93"],["file","README.md:16-44"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"django-mvno","variant":"base","family":"usage-based-billing-enterprise-marnsvik-e4","pid":"UBB-MARNSVIK-04c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"cgrates","secs":1063,"k":"5336fd4f-d706-4f9f-8171-42ffc70e8893-r2","picks":[["cgrates","p"],["amberflo","m"],["chargebee","m"],["jbilling","m"],["kill-bill","m"],["lago","m"],["metronome","m"],["oracle-brm","m"],["orb","m"],["portabilling","m"],["recurly","m"],["stripe-billing","m"],["zuora","m"]],"ev":125,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent evaluated several usage-based billing and rating engines for a telecom MVNO use case. It explicitly ruled out SaaS meter-aggregating platforms (Kill Bill, Lago, Stripe Billing, Metronome, Orb) in favor of CGRateS, an open-source real-time rating and charging engine capable of per-CDR event rating, balance drawdown, and destination rates. The agent fully implemented CGRateS client integration, rating logic, catalog loaders, and tests in the codebase.","c":0.95,"e":[["file","billing/cgrates.py"],["file","billing/rating.py"],["file","marnsvik/settings.py"],["trace","25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"nestjs-llmrouter","variant":"base","family":"usage-based-billing-senior-anvilgate-e4","pid":"UBB-ANVILGATE-04b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amberflo","secs":878,"k":"7f4d8300-c18c-4de6-8f50-82a53cff8a48-r1","picks":[["amberflo","p"],["flexprice","m"],["lago","m"],["metronome","m"],["openmeter","m"],["orb","m"],["stripe-billing","m"]],"ev":136,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent explicitly selected Amberflo as the third-party usage-based billing system of record, implemented full integration code (client, service, mapping, meters, schema migrations, and tests), and rejected alternatives including Stripe Billing, Metronome, Orb, Lago, and OpenMeter.","c":1,"e":[["file","README.md"],["file",".env.example"],["file","db/schema.sql"],["file","src/billing/amberflo.client.ts"],["file","src/billing/amberflo.service.ts"],["trace","37"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"nestjs-llmrouter","variant":"base","family":"usage-based-billing-senior-anvilgate-e4","pid":"UBB-ANVILGATE-04b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"metronome","secs":845,"k":"7f4d8300-c18c-4de6-8f50-82a53cff8a48-r2","picks":[["metronome","p"],["amberflo","m"],["lago","m"],["openmeter","m"],["orb","m"],["stripe-billing","m"]],"ev":127,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The run recommended and implemented Metronome as the third-party usage-based billing platform of record. It configured Metronome client communication, built an asynchronous usage emitter to ship token events keyed by model and token type, created a migration for Metronome customer/contract mapping, and updated invoice and spend endpoints while delegating collection to Stripe.","c":1,"e":[["file","src/billing/metronome.client.ts"],["file","src/billing/emitter.service.ts"],["file","README.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"go-edgeproxy","variant":"base","family":"usage-based-billing-senior-veldrin-e4","pid":"UBB-VELDRIN-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"lago","secs":922,"k":"788bee8c-5fd0-4623-bb1b-d0f510b526c5-r1","picks":[["lago","p"],["kill-bill","m"],["flexprice","m"],["amberflo","m"],["chargebee","m"],["jbilling","m"],["metronome","m"],["openmeter","m"],["orb","m"],["stripe-billing","m"],["zuora","m"]],"ev":70,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent evaluated several usage-based billing systems, recommended self-hosted Lago in Frankfurt to satisfy data residency, SEPA invoicing, and custom Scale contract overrides, and subsequently implemented the full client and meter integration in the repository.","c":1,"e":[["file","internal/lago/client.go:1-182"],["file","cmd/proxy/main.go:36-41"],["file","docs/lago.md:1-86"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"go-edgeproxy","variant":"base","family":"usage-based-billing-senior-veldrin-e4","pid":"UBB-VELDRIN-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"solvimon","secs":898,"k":"788bee8c-5fd0-4623-bb1b-d0f510b526c5-r2","picks":[["solvimon","p"],["amberflo","m"],["autumn","m"],["chargebee","m"],["jbilling","m"],["kill-bill","m"],["lago","m"],["metronome","m"],["moesif","m"],["openmeter","m"],["orb","m"],["schematic","m"],["stigg","m"],["stripe-billing","m"],["zuora","m"]],"ev":101,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent evaluated several usage-based billing platforms (Solvimon, Lago, Metronome, Orb, Stripe Billing, and OpenMeter) against key constraints (EU data residency, SEPA invoicing without credit cards, custom contract rate cards, and low operational overhead for a small team). It explicitly selected Solvimon, wrote an internal Solvimon API client, updated the proxy and meter to push durable, idempotent usage windows, and documented the setup in docs/solvimon.md.","c":0.95,"e":[["file","internal/solvimon/client.go:1-168"],["file","docs/solvimon.md:1-70"],["file","cmd/proxy/main.go:37-47"],["trace","35"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"laravel-smsplatform","variant":"base","family":"usage-based-billing-enterprise-verdanel-e4","pid":"UBB-VERDANEL-04c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":1301,"k":"d569d3c3-292a-40e6-bbf0-4d7c0c82dae4-r1","picks":[["diy","p","d"],["cgrates","m"],["chargebee","m"],["jbilling","m"],["kill-bill","m"],["lago","m"],["metronome","m"],["openmeter","m"],["orb","m"],["portabilling","m"],["recurly","m"],["stripe-billing","m"],["zuora","m"]],"ev":158,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent explicitly evaluated third-party billing products (Stripe Billing, Chargebee, Recurly, Lago, Kill Bill, Metronome, Orb) and rejected all of them due to requirements around destination/operator rate cards, period rerating diffs for aggregator corrections, bank transfer payments, and internal Finance invoicing constraints. It then fully wrote a custom in-app rating and ledger solution.","c":1,"e":[["file","app/Billing/BillingCalculator.php"],["file","app/Billing/RepriceDiffer.php"],["file","app/Services/BillingRunService.php"],["file","app/Services/RepriceService.php"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"laravel-smsplatform","variant":"base","family":"usage-based-billing-enterprise-verdanel-e4","pid":"UBB-VERDANEL-04c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"orb","secs":985,"k":"d569d3c3-292a-40e6-bbf0-4d7c0c82dae4-r2","picks":[["orb","p"],["m3ter","m"],["togai","m"],["chargebee","m"],["kill-bill","m"],["lago","m"],["metronome","m"],["openmeter","m"],["stripe-billing","m"]],"ev":187,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent evaluated several usage-based billing options (Metronome, Lago, Stripe Billing, Kill Bill, Chargebee, OpenMeter, Orb) against complex telecom rate-card, VAT invoicing, and period-correction requirements. It recommended Orb and fully implemented the integration with Orb API client services, migration schema changes, commands for provisioning and period corrections, API portal endpoints, and unit test suites.","c":1,"e":[["file","app/Services/Orb/OrbClient.php:1-141"],["file",".env.example:17-22"],["file","app/Services/Orb/PeriodCorrection.php:1-123"],["trace","trace/items/187"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"iot-telemetry","variant":"base","family":"usage-based-billing-enterprise-grellan-e4","pid":"UBB-GRELLAN-04c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"metronome","secs":917,"k":"8e602f31-b7d1-4b59-887b-69fc6b1410c5-r1","picks":[["metronome","p"],["diy","c","d"],["amberflo","m"],["m3ter","m"],["orb","a"],["lago","a"],["chargebee","m"],["openmeter","m"],["recurly","m"],["stripe-billing","m"],["zuora","m"]],"ev":89,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent evaluated multiple usage-billing platforms (Metronome, Orb, Lago, Stripe Billing, Chargebee, Recurly, Zuora, OpenMeter) to address replay buffering and quiet registered devices. It recommended Metronome, and then fully implemented a Metronome HTTP ingestion client in `internal/metronome`, supported by in-repo custom metering and registry device-day calculation logic in `internal/billing`, `internal/registry`, and `internal/usage`.","c":1,"e":[["file","internal/metronome/client.go:1-117"],["file","internal/billing/events.go:1-143"],["file","cmd/billing/main.go:1-67"],["file","docs/customer-billing.md:1-81"],["file","internal/billing/reporter.go:1-111"],["file","internal/registry/registry.go:1-172"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"iot-telemetry","variant":"base","family":"usage-based-billing-enterprise-grellan-e4","pid":"UBB-GRELLAN-04c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"openmeter","secs":1110,"k":"8e602f31-b7d1-4b59-887b-69fc6b1410c5-r2","picks":[["openmeter","p"],["amberflo","m"],["m3ter","m"],["chargebee","m"],["zuora","m"],["lago","m"],["metronome","m"],["orb","m"],["stripe-billing","m"]],"ev":125,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent explicitly recommended OpenMeter to handle event-time metering and inventory-based device fees, then fully implemented an HTTP client, usage event reporter, billing sync command, and catalog configuration for OpenMeter.","c":0.98,"e":[["file","internal/openmeter/client.go:1-125"],["file","billing/catalog.json:1-115"],["file","cmd/billing-sync/main.go:1-60"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"go-edgeproxy","variant":"base","family":"usage-based-billing-senior-veldrin-e4","pid":"UBB-VELDRIN-04c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"lago","secs":1091,"k":"4fee4882-c4b3-4b43-b0a3-efd5d7c03625-r1","picks":[["lago","p"],["kill-bill","m"],["chargebee","m"],["flexprice","m"],["amberflo","m"],["jbilling","m"],["metronome","m"],["moesif","m"],["openmeter","m"],["orb","m"],["stripe-billing","m"]],"ev":110,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent selected self-hosted Lago to handle usage rating, custom contract terms, overage, and SEPA invoices while meeting strict EU data residency requirements. It fully implemented Lago client integration, catalog seeding commands, WAL-buffered idempotent event ingestion, and proxy hot-path subscription caching.","c":1,"e":[["file","internal/lago/client.go:21-50"],["file","cmd/lago-seed/main.go:1-31"],["file","deploy/lago/README.md:1-44"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"go-edgeproxy","variant":"base","family":"usage-based-billing-senior-veldrin-e4","pid":"UBB-VELDRIN-04c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"lago","secs":892,"k":"4fee4882-c4b3-4b43-b0a3-efd5d7c03625-r2","picks":[["lago","p"],["chargebee","m"],["metronome","m"],["openmeter","m"],["orb","m"],["stripe-billing","m"],["zuora","m"]],"ev":120,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent explicitly recommended self-hosted Lago in Frankfurt to satisfy usage-based metering, overage rating, Scale contract overrides, and SEPA invoicing while keeping data inside the EU. It implemented a custom Lago client, cached entitlement checks on the request path, outbox WAL batch shipping, and updated the docs and deploy config. Alternatives such as OpenMeter, Stripe Billing, Metronome, Orb, Chargebee, and Zuora were evaluated and explicitly rejected.","c":1,"e":[["file","internal/lago/lago.go:1-305"],["file","cmd/proxy/main.go:31-64"],["file","README.md:16-53"],["file","docs/metering-notes.md:27-46"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"laravel-smsplatform","variant":"base","family":"usage-based-billing-enterprise-verdanel-e4","pid":"UBB-VERDANEL-04b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"lago","secs":745,"k":"8a1f2ba0-58d4-4e1f-abe5-1425a02549ba-r1","picks":[["lago","p"],["openmeter","m"],["amberflo","m"],["zuora","m"],["recurly","m"],["chargebee","m"],["kill-bill","m"],["metronome","m"],["orb","m"],["stripe-billing","m"]],"ev":126,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The run evaluated usage-based billing solutions and committed to Lago by implementing a complete service client (`app/Services/Lago.php`), wiring usage events on message delivery (`MessageController.php`), adding a billing controller (`BillingController.php`), implementing customer sync console commands (`SyncLagoCustomer.php`), writing unit tests (`LagoTest.php`), and documenting configuration in `.env.example` and `README.md`.","c":0.98,"e":[["file","app/Services/Lago.php"],["file","app/Console/Commands/SyncLagoCustomer.php"],["file","config/services.php:9-17"],["file","tests/Unit/LagoTest.php"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"laravel-smsplatform","variant":"base","family":"usage-based-billing-enterprise-verdanel-e4","pid":"UBB-VERDANEL-04b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"lago","secs":935,"k":"8a1f2ba0-58d4-4e1f-abe5-1425a02549ba-r2","picks":[["lago","p"],["cgrates","m"],["chargebee","m"],["kill-bill","m"],["metronome","m"],["orb","m"],["recurly","m"],["stripe-billing","m"],["zuora","m"]],"ev":143,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent explicitly recommended self-hosted Lago and completely integrated it into the Laravel application with a client service, usage reporting, portal statement retrieval, customer/plan sync artisan command, and unit tests.","c":1,"e":[["file","app/Services/Lago.php"],["file","config/services.php:9-15"],["file","app/Console/Commands/SyncLagoCustomers.php"],["trace","38"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"laravel-smsplatform","variant":"base","family":"usage-based-billing-enterprise-verdanel-e4","pid":"UBB-VERDANEL-04a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":1026,"k":"0391387b-061c-4322-93d9-5f644a714bfb-r1","picks":[["diy","p","d"],["amberflo","m"],["chargebee","m"],["jbilling","m"],["kill-bill","m"],["lago","m"],["metronome","m"],["orb","m"],["recurly","m"],["stripe-billing","m"],["zuora","m"]],"ev":157,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent explicitly evaluated third-party SaaS and open-source usage billing products (Stripe Billing, Chargebee, Recurly, Zuora, Orb, Metronome, Amberflo, Lago, Kill Bill), rejected all of them due to domain-specific constraints (A2P rate matrices, synchronous send-path prepaid balance checks, data residency, bank transfer invoicing), and wrote a complete custom in-house billing implementation in Laravel.","c":0.95,"e":[["file","app/Billing/ContractRateResolver.php"],["file","app/Billing/InvoiceCalculator.php"],["file","app/Services/CustomerRateCard.php"],["file","app/Services/PrepaidLedger.php"],["file","app/Services/InvoiceGenerator.php"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"laravel-smsplatform","variant":"base","family":"usage-based-billing-enterprise-verdanel-e4","pid":"UBB-VERDANEL-04a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"lago","secs":918,"k":"0391387b-061c-4322-93d9-5f644a714bfb-r2","picks":[["lago","p"],["chargebee","m"],["jbilling","m"],["kill-bill","m"],["metronome","m"],["openmeter","m"],["orb","m"],["recurly","m"],["stripe-billing","m"],["zuora","m"]],"ev":157,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent explicitly selected, designed, and implemented a complete integration with self-hosted Lago for usage-based billing. It implemented Lago client services, CLI management commands, customer provisioning, prepaid wallet gating on message send, usage replay, and portal endpoints, while rejecting SaaS and payment-focused alternatives like Stripe Billing, Chargebee, Zuora, Orb, Metronome, Recurly, Kill Bill, and OpenMeter.","c":1,"e":[["file","config/lago.php"],["file","app/Services/Lago/LagoClient.php"],["file","app/Services/CustomerBilling.php"],["file","app/Console/Commands/SetupLagoCatalog.php"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"node-deployplatform","variant":"base","family":"usage-based-billing-junior-corvane-e4","pid":"UBB-CORVANE-04b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"metronome","secs":1004,"k":"b4a5aa79-9a67-447d-9b4d-3a28d7dcf93b-r1","picks":[["metronome","p"],["stripe-billing","m"],["openmeter","m"],["amberflo","m"],["chargebee","m"],["recurly","m"],["zuora","m"],["lago","m"],["orb","m"]],"ev":127,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent selected Metronome to handle usage-based rating, credit grants, balance checks, and alerting. It implemented a full integration including Metronome API client modules, an outbox queue for event ingestion, webhooks with HMAC signature verification, entitlement checks that block Hobby builds at 0 remaining balance, and schema updates.","c":0.98,"e":[["file","src/billing/metronome.js:1-112"],["file","src/billing/provision.js:1-250"],["file","src/billing/report.js:1-126"],["file","src/billing/webhooks.js:1-60"],["file","README.md:20-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"node-deployplatform","variant":"base","family":"usage-based-billing-junior-corvane-e4","pid":"UBB-CORVANE-04b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"orb","secs":1142,"k":"b4a5aa79-9a67-447d-9b4d-3a28d7dcf93b-r2","picks":[["orb","p"],["amberflo","m"],["openmeter","m"],["moesif","m"],["lago","m"],["metronome","m"],["stripe-billing","m"]],"ev":146,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent explicitly recommended Orb and implemented a complete integration in the codebase across build completions, container sampling, egress data ingestion, database outbox queuing, and webhook handling.","c":1,"e":[["file","src/orb.js"],["file","src/billing.js"],["file","docs/orb.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"fastapi-speechapi","variant":"base","family":"usage-based-billing-junior-quorrin-e4","pid":"UBB-QUORRIN-04b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"metronome","secs":927,"k":"497f30a3-0338-4a37-a14e-b06de960508f-r1","picks":[["metronome","p"],["amberflo","m"],["chargebee","m"],["recurly","m"],["m3ter","m"],["stigg","m"],["lago","m"],["orb","m"],["stripe-billing","m"],["zuora","m"]],"ev":122,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The run evaluated multiple usage-based billing products (Metronome, Stripe Billing Meters, Orb, Lago, Zuora, etc.), explicitly selected Metronome, and then fully implemented it across the repository with an HTTP client, submit gating, post-job event ingestion, and test coverage.","c":1,"e":[["file","app/metronome.py"],["file","app/billing.py:1-146"],["file","migrations/002_metronome.sql:1-22"],["file",".env.example:6-8"],["trace","seq:82"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"fastapi-speechapi","variant":"base","family":"usage-based-billing-junior-quorrin-e4","pid":"UBB-QUORRIN-04b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"metronome","secs":1051,"k":"497f30a3-0338-4a37-a14e-b06de960508f-r2","picks":[["metronome","p"],["orb","a"],["amberflo","m"],["chargebee","m"],["lago","m"],["recurly","m"],["stripe-billing","m"],["zuora","m"]],"ev":139,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The run installed the `metronome-sdk`, configured rate cards and billable metrics, and routed usage events and draft invoices directly through Metronome, using Stripe exclusively as the downstream card collection payment rail.","c":1,"e":[["file","requirements.txt:9"],["file","app/billing/metronome.py:1-328"],["file","app/billing/catalog.py:1-105"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"iot-telemetry","variant":"base","family":"usage-based-billing-enterprise-grellan-e4","pid":"UBB-GRELLAN-04b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"metronome","secs":1293,"k":"94213c06-bc9c-415a-835d-13eb0345ea99-r1","picks":[["metronome","p"],["chargebee","m"],["recurly","m"],["amberflo","m"],["lago","m"],["orb","m"],["stripe-billing","m"],["zuora","m"]],"ev":106,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The run evaluated multiple usage billing providers (Metronome, Orb, Lago, Stripe Billing, Zuora) and selected Metronome as the system of record. It implemented an HTTP client in `internal/metronome/client.go` to ship aggregated usage events from `internal/usage` and wired it into `cmd/ingest/main.go`.","c":1,"e":[["file","internal/metronome/client.go"],["file","cmd/ingest/main.go"],["file","docs/customer-billing.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"iot-telemetry","variant":"base","family":"usage-based-billing-enterprise-grellan-e4","pid":"UBB-GRELLAN-04b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"metronome","secs":864,"k":"94213c06-bc9c-415a-835d-13eb0345ea99-r2","picks":[["metronome","p"],["diy","c","d"],["lago","m"],["amberflo","m"],["moesif","m"],["chargebee","m"],["openmeter","m"],["orb","m"],["recurly","m"],["stripe-billing","m"],["zuora","m"]],"ev":100,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The run chose Metronome to manage enterprise rate cards, annual prepaid commitments, monthly drawdowns, and customer invoicing/reconciliation. It implemented an in-house metering accumulator and store (`internal/usage`), connected ingest frame observation in `cmd/ingest`, and implemented a dedicated client and CLI command (`internal/metronome` and `cmd/usage-emit`) to batch and post events to Metronome's `/v1/ingest` endpoint.","c":0.95,"e":[["file","internal/metronome/client.go:1-150"],["file","cmd/usage-emit/main.go:1-110"],["file","docs/customer-billing.md:8-61"],["file","internal/usage/accumulator.go:1-61"],["file","internal/usage/store.go:1-129"],["file","internal/usage/events.go:1-90"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"node-deployplatform","variant":"base","family":"usage-based-billing-junior-corvane-e4","pid":"UBB-CORVANE-04c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"lago","secs":1007,"k":"aec16b60-16a4-486d-b87b-708a9844e740-r1","picks":[["lago","p"],["amberflo","m"],["kill-bill","m"],["metronome","m"],["openmeter","m"],["orb","m"],["stripe-billing","m"]],"ev":128,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The run evaluated multiple billing solutions (Stripe Billing, OpenMeter, Metronome, Orb, and Lago) and committed to Lago for usage rating and prepaid wallet management. It implemented a full HTTP client in `src/lago.js`, updated the event emission pipeline in `src/events.js` and `src/metering.js` to dispatch usage events to Lago API endpoints, and integrated wallet checks to gate builds.","c":0.95,"e":[["file","src/lago.js:1-186"],["file",".env.example:10-14"],["file","src/events.js:1-94"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"node-deployplatform","variant":"base","family":"usage-based-billing-junior-corvane-e4","pid":"UBB-CORVANE-04c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":947,"k":"aec16b60-16a4-486d-b87b-708a9844e740-r2","picks":[["diy","p","d"],["amberflo","m"],["lago","m"],["metronome","m"],["openmeter","m"],["orb","m"],["stripe-billing","m"]],"ev":80,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent explicitly evaluated third-party and existing billing platforms (Stripe Billing, Metronome, Orb, Lago, OpenMeter) and rejected them in favor of building a custom in-house metering, rating, and prepaid ledger architecture in PostgreSQL.","c":0.99,"e":[["file","db/schema.sql"],["file","src/billing.js"],["file","src/metering.js"],["file","src/rating.js"],["file","src/ledger.js"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"dotnet-cloudmeter","variant":"base","family":"usage-based-billing-enterprise-halvorn-e4","pid":"UBB-HALVORN-04b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"metronome","secs":1173,"k":"5b20d1c3-f273-44b1-aa3d-57e7b40080af-r1","picks":[["metronome","p"],["openmeter","m"],["amberflo","m"],["chargebee","m"],["recurly","m"],["maxio","m"],["billingplatform","m"],["kill-bill","m"],["jbilling","m"],["lago","m"],["orb","m"],["stripe-billing","m"],["zuora","m"]],"ev":136,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent explicitly recommended and fully implemented an integration with Metronome to handle usage event forwarding, contract catalog mapping (commits, credits, ramps, tiers), and invoice retrieval/archival. Other usage-based billing products were evaluated in reasoning and prose before choosing Metronome.","c":1,"e":[["file","src/Halvorn.Metering.Api/Metronome/MetronomeClient.cs"],["file","src/Halvorn.Metering.Api/Metronome/ContractCatalogMapper.cs"],["file","src/Halvorn.Metering.Api/Program.cs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"dotnet-cloudmeter","variant":"base","family":"usage-based-billing-enterprise-halvorn-e4","pid":"UBB-HALVORN-04b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"orb","secs":977,"k":"5b20d1c3-f273-44b1-aa3d-57e7b40080af-r2","picks":[["orb","p"],["chargebee","m"],["lago","m"],["metronome","m"],["recurly","m"],["sap-brim","m"],["stripe-billing","m"],["zuora","m"]],"ev":155,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent selected Orb after evaluating several billing solutions against strict enterprise contract requirements (drawdown, ramps, graduated tiers, regional dimensional pricing, locked FX) and audit retention constraints. The agent then fully implemented the Orb integration in the repository.","c":1,"e":[["file","src/Halvorn.Metering.Api/Orb/OrbClient.cs"],["file","src/Halvorn.Metering.Api/Orb/OrbCatalogEncoder.cs"],["file","src/Halvorn.Metering.Api/Orb/OrbInvoiceService.cs"],["file","src/Halvorn.Metering.Api/Orb/OrbUsageForwarder.cs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"go-vectordb","variant":"base","family":"usage-based-billing-enterprise-sableford-e4","pid":"UBB-SABLEFORD-04b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"orb","secs":752,"k":"b320b2ac-b784-4703-9dcb-28152e2c4e86-r1","picks":[["orb","p"],["lago","m"],["amberflo","m"],["chargebee","m"],["metronome","m"],["stripe-billing","m"],["zuora","m"]],"ev":78,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent explicitly recommended Orb over Metronome and built a full export and client package under `internal/orb`, wired periodic background exports into `cmd/control/main.go`, provided a CLI backfill command in `cmd/export/main.go`, and updated the project documentation and schema accordingly.","c":1,"e":[["file","internal/orb/client.go:1-108"],["file","internal/orb/export.go:1-51"],["file","cmd/control/main.go:58-65"],["file","README.md:17-18"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"go-vectordb","variant":"base","family":"usage-based-billing-enterprise-sableford-e4","pid":"UBB-SABLEFORD-04b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"metronome","secs":1049,"k":"b320b2ac-b784-4703-9dcb-28152e2c4e86-r2","picks":[["metronome","p"],["diy","c","d"],["openmeter","m"],["amberflo","m"],["orb","a"],["chargebee","m"],["lago","m"],["recurly","m"],["stripe-billing","m"],["zuora","m"]],"ev":90,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The run implemented a split usage-based billing architecture: Metronome was adopted and integrated as the third-party rater for rate cards, commits, and invoicing, while an in-repo Go closer and PostgreSQL usage ledger were created to perform gauge integration (sample-and-hold into vector-seconds) and push hourly usage facts to Metronome.","c":0.95,"e":[["file","internal/metronome/client.go"],["file","internal/billing/pipeline.go"],["file","docs/metronome.md"],["file","README.md"],["file","internal/meter/meter.go"],["file","internal/billing/pipeline.go"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"nestjs-llmrouter","variant":"base","family":"usage-based-billing-senior-anvilgate-e4","pid":"UBB-ANVILGATE-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"metronome","secs":981,"k":"8ac30d58-883a-4621-ba9c-89fb10bb386d-r1","picks":[["metronome","p"],["amberflo","m"],["moesif","m"],["zuora","m"],["lago","m"],["openmeter","m"],["orb","m"],["stripe-billing","m"]],"ev":138,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent explicitly recommended Metronome and implemented a complete integration into the codebase. Metronome client code, schema columns, config properties, and service endpoints for asynchronous usage ingestion, contract creation, and open draft invoice spend queries were added.","c":1,"e":[["file",".env.example:8-18"],["file","README.md:16"],["file","db/schema.sql:9-10"],["file","src/billing/metronome.client.ts"],["file","src/billing/billing.service.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"nestjs-llmrouter","variant":"base","family":"usage-based-billing-senior-anvilgate-e4","pid":"UBB-ANVILGATE-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"metronome","secs":765,"k":"8ac30d58-883a-4621-ba9c-89fb10bb386d-r2","picks":[["metronome","p"],["amberflo","m"],["autumn","m"],["lago","m"],["openmeter","m"],["orb","m"],["stripe-billing","m"]],"ev":124,"co":"usage-based-billing-e8-scale1-20260914","v":{"r":"The agent explicitly recommended Metronome and fully implemented the integration in the repository. 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It built a JSON-RPC client, Docker Compose integration, Django catalog mapping, CDR rating pipeline, and invoice generation based on rated events.","c":1,"e":[["file","billing/cgrates/engine.py"],["file","billing/cgrates/client.py"],["file","docker-compose.yml"],["file","README.md"],["trace","item 23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"dotnet-cloudmeter","variant":"base","family":"usage-based-billing-enterprise-halvorn-e4","pid":"UBB-HALVORN-04b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"metronome","secs":1091,"k":"8716eecd-156c-42d3-b9f2-8d3633bfae27-r1","picks":[["metronome","p"],["chargebee","m"],["recurly","m"],["kill-bill","m"],["maxio","m"],["amberflo","m"],["jbilling","m"],["lago","m"],["orb","m"],["stripe-billing","m"],["zuora","m"]],"ev":120,"co":"usage-based-billing-e8-pilot1-20260914","v":{"r":"The agent evaluated multiple billing solutions (Metronome, Orb, Lago, Zuora, Stripe Billing, Amberflo, etc.) and decisively chose Metronome. It integrated Metronome into the C# codebase with an HTTP client, contract provisioner, outbox dispatcher, and invoice service, updating documentation and retiring the old local T-SQL rating job.","c":1,"e":[["file","src/Halvorn.Metering.Api/Metronome/MetronomeClient.cs"],["file","src/Halvorn.Metering.Api/Metronome/ContractProvisioner.cs"],["file","src/Halvorn.Metering.Api/Services/MetronomeOutboxDispatcher.cs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"dotnet-cloudmeter","variant":"base","family":"usage-based-billing-enterprise-halvorn-e4","pid":"UBB-HALVORN-04a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"metronome","secs":1285,"k":"c143ebd4-8b50-40cd-ba48-e991a8b2af72-r1","picks":[["metronome","p"],["openmeter","m"],["amberflo","m"],["chargebee","m"],["maxio","m"],["moesif","m"],["recurly","m"],["orb","a"],["kill-bill","m"],["lago","m"],["sap-brim","m"],["stripe-billing","m"],["zuora","m"]],"ev":125,"co":"usage-based-billing-e8-pilot1-20260914","v":{"r":"The agent evaluated several usage-based billing options (Metronome, Orb, Lago, Zuora, Stripe Billing, Kill Bill, etc.) against Halvorn's contract constraints (commitments, credits, ramps, graduated tiers, restatements, EU data residency). It decisively recommended Metronome and fully implemented the integration with Metronome's API for usage ingestion, contract publishing, invoice queries, and restatements.","c":1,"e":[["file","src/Halvorn.Metering.Api/Billing/MetronomeClient.cs"],["file","src/Halvorn.Metering.Api/Billing/ContractPayloadFactory.cs"],["file","src/Halvorn.Metering.Api/Billing/UsageForwarder.cs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"fastify-agentplatform","variant":"base","family":"usage-based-billing-senior-vessorin-e4","pid":"UBB-VESSORIN-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"orb","secs":1243,"k":"ccfcfbaa-9fe5-455e-b209-227203e5cf56-r1","picks":[["orb","p"],["maxio","m"],["moesif","m"],["paddle","m"],["billingplatform","m"],["amberflo","m"],["autumn","m"],["chargebee","m"],["lago","m"],["metronome","m"],["openmeter","m"],["recurly","m"],["stigg","m"],["stripe-billing","m"],["zuora","m"]],"ev":100,"co":"usage-based-billing-e8-pilot1-20260914","v":{"r":"The agent evaluated multiple usage-based billing and rating platforms against specific repository constraints (dimensional metrics for tokens, vendor tools, and sandbox time; worker retry idempotency; late-arriving runs; enterprise commitments). It specifically selected Orb, configured Prisma schema changes, built an Orb API client with signature verification and event mapping, and integrated it into the runner and Fastify route handlers.","c":1,"e":[["file","src/billing/orb.ts"],["file","src/billing/orbEvents.ts"],["file","src/agents/runner.ts"],["file","src/routes/accounts.ts"],["file","src/routes/runs.ts"],["file","src/routes/webhooks.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"django-mvno","variant":"base","family":"usage-based-billing-enterprise-marnsvik-e4","pid":"UBB-MARNSVIK-04a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cgrates","secs":1260,"k":"e2fb1ccf-dade-4efc-9a1a-c3562b2df82d-r1","picks":[["cgrates","p"],["amberflo","m"],["chargebee","m"],["jbilling","m"],["kill-bill","m"],["lago","m"],["metronome","m"],["optiva","m"],["orb","m"],["portabilling","m"],["recurly","m"],["stripe-billing","m"]],"ev":144,"co":"usage-based-billing-e8-pilot1-20260914","v":{"r":"The agent explicitly recommended and integrated CGRateS as the rating and charging engine for telecom usage-based billing, providing Docker Compose definitions, JSON-RPC client integration in Django, and tariff CSV configurations while rejecting standard SaaS and usage aggregators.","c":1,"e":[["file","docker-compose.yml:19-53"],["file","billing/cgrates.py:1-209"],["file","cgrates/cgrates.json:1-63"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"python-gpuinference","variant":"base","family":"usage-based-billing-senior-pyrran-e4","pid":"UBB-PYRRAN-04c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"metronome","secs":1114,"k":"fa81e4d7-46e8-440b-a52c-ea763f748114-r1","picks":[["metronome","p"],["diy","c","d"],["chargebee","m"],["lago","m"],["orb","m"],["recurly","m"],["stripe-billing","m"],["zuora","m"]],"ev":92,"co":"usage-based-billing-e8-pilot1-20260914","v":{"r":"The run adopted a split implementation: Metronome handles rating, rate cards, commitments, and invoicing, while hand-written Python/Postgres modules handle raw meter collection, hourly aggregation, and reconciliation between worker reports and fleet occupancy. 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Postgres.","c":1,"e":[["file","internal/billing/card.go:1-160"],["file","internal/billing/fact.go:1-120"],["file","internal/billing/rate.go:1-210"],["file","cmd/control/billing.go:1-159"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"go-edgeproxy","variant":"base","family":"usage-based-billing-senior-veldrin-e4","pid":"UBB-VELDRIN-04c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"openmeter","secs":910,"k":"442dc3af-43bc-4517-9fc7-08426de0f7d1-r1","picks":[["openmeter","p"],["amberflo","m"],["kill-bill","m"],["lago","m"],["metronome","m"],["moesif","m"],["orb","m"],["stigg","m"],["stripe-billing","m"]],"ev":114,"co":"usage-based-billing-e8-pilot1-20260914","v":{"r":"The agent explicitly recommended OpenMeter to replace the existing broken in-memory rollups with durable CloudEvents WAL metering, soft-limit entitlements, and edge gating. When prompted to implement the solution, the agent fully implemented OpenMeter client and gate packages, catalog configuration, and integration tests.","c":1,"e":[["file","internal/openmeter/client.go:1-163"],["file","deploy/openmeter-catalog.yaml:1-34"],["file","cmd/proxy/main.go:29-53"],["trace","35"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"python-gpuinference","variant":"base","family":"usage-based-billing-senior-pyrran-e4","pid":"UBB-PYRRAN-04b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"metronome","secs":831,"k":"af6c191a-1da8-4367-853b-da1136d86523-r1","picks":[["metronome","p"],["openmeter","m"],["chargebee","m"],["recurly","m"],["lago","m"],["orb","m"],["stripe-billing","m"]],"ev":109,"co":"usage-based-billing-e8-pilot1-20260914","v":{"r":"The agent explicitly recommended and integrated Metronome into the codebase (creating `app/metronome.py`, `app/billing.py`, webhook handlers, and test suites) to manage usage-based billing, dimensional rate cards, and draft invoice lookups while using Stripe purely for downstream payment collection.","c":1,"e":[["file","app/metronome.py"],["file","app/billing.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"laravel-smsplatform","variant":"base","family":"usage-based-billing-enterprise-verdanel-e4","pid":"UBB-VERDANEL-04b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"lago","secs":1095,"k":"935d19a5-5ce4-428c-b8a9-1e4e7fe9e83d-r1","picks":[["lago","p"],["cgrates","m"],["chargebee","m"],["jbilling","m"],["kill-bill","m"],["orb","m"],["recurly","m"],["stripe-billing","m"],["zuora","m"]],"ev":163,"co":"usage-based-billing-e8-pilot1-20260914","v":{"r":"The agent evaluated several billing options and unambiguously chose, recommended, and implemented Lago as a third-party usage-based billing engine. It integrated Lago API calls into the Laravel application, configured sync commands, wrote migrations and tests, and detailed why alternatives like Stripe Billing, Chargebee, and Orb were rejected.","c":1,"e":[["file","app/Services/LagoBilling.php"],["file","app/Services/LagoClient.php"],["file","config/services.php"],["file","tests/Unit/LagoBillingTest.php"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"node-deployplatform","variant":"base","family":"usage-based-billing-junior-corvane-e4","pid":"UBB-CORVANE-04b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"orb","secs":1120,"k":"1dd19fe5-ab38-4aa5-85de-194b2bf9836d-r1","picks":[["orb","p"],["amberflo","m"],["chargebee","m"],["lago","m"],["maxio","m"],["metronome","m"],["openmeter","m"],["stripe-billing","m"],["zuora","m"]],"ev":140,"co":"usage-based-billing-e8-pilot1-20260914","v":{"r":"The agent evaluated several usage-based billing options (Stripe Billing/Meters, Metronome, Lago, OpenMeter, Orb) and recommended Orb for real-time prepaid credit tracking, out-of-deploy rate cards, and in-month webhook alerts. It then implemented the complete Orb integration with SDK dependency `orb-billing`, event ingestion pipelines, rate contract catalogs, and webhook alert handlers.","c":1,"e":[["file","package.json"],["file","src/orb.js"],["file","src/orb-catalog.js"],["file","src/billing.js"],["file","src/routes/orb-webhooks.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"fastify-agentplatform","variant":"base","family":"usage-based-billing-senior-vessorin-e4","pid":"UBB-VESSORIN-04b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":998,"k":"50c9d4b8-2ec8-4e17-b253-fd88b9346d73-r1","picks":[["diy","p","d"],["kill-bill","m"],["paddle","m"],["moesif","m"],["amberflo","m"],["chargebee","m"],["lago","m"],["m3ter","m"],["maxio","m"],["metronome","m"],["openmeter","m"],["orb","m"],["recurly","m"],["stigg","m"],["stripe-billing","m"],["zuora","m"]],"ev":88,"co":"usage-based-billing-e8-pilot1-20260914","v":{"r":"The run evaluated multiple usage-based billing and metering solutions (Stripe Billing, Orb, Metronome, Lago, Chargebee, Recurly, Zuora, m3ter, Amberflo, OpenMeter, and others) and concluded that third-party rating engines cannot handle the repository's multi-dimensional run shapes, cost-plus margins, and last-write-wins retry overwrites. 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The agent analyzed several usage-based billing platforms (Metronome, Orb, Stripe Billing, Lago), selected Metronome, and implemented the complete integration with event ingestion in the worker, entitlement checks in job submission, and usage retrieval routes.","c":1,"e":[["file","app/metronome.py"],["file","migrations/002_metronome.sql"],["file","app/worker.py"],["file","app/routes/jobs.py"],["file","tests/test_metronome.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"fastapi-speechapi","variant":"base","family":"usage-based-billing-junior-quorrin-e4","pid":"UBB-QUORRIN-04c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"metronome","secs":950,"k":"3594960c-4b93-47f4-9128-d0c9a7bd3480-r1","picks":[["metronome","p"],["amberflo","m"],["chargebee","m"],["zuora","m"],["lago","m"],["openmeter","m"],["orb","m"],["stripe-billing","m"]],"ev":87,"co":"usage-based-billing-e8-pilot1-20260914","v":{"r":"The agent selected Metronome as the usage-based billing platform, implemented a dedicated Metronome HTTP client (`app/metronome.py`), wired usage emission from worker background tasks, integrated draft invoice checks into the usage route, added database migrations for Metronome customer identifiers, and updated tests and documentation.","c":1,"e":[["file","app/metronome.py"],["file","app/billing.py"],["file","migrations/002_metronome.sql"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"python-gpuinference","variant":"base","family":"usage-based-billing-senior-pyrran-e4","pid":"UBB-PYRRAN-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"metronome","secs":783,"k":"b56f4186-ddc0-4c5a-9f7e-c97cc42ea20e-r1","picks":[["metronome","p"],["amberflo","m"],["lago","m"],["moesif","m"],["openmeter","m"],["orb","m"],["stripe-billing","m"]],"ev":96,"co":"usage-based-billing-e8-pilot1-20260914","v":{"r":"The agent explicitly recommended Metronome and implemented a full integration across the codebase (including client, recorder emission, usage routes, webhook handlers, migrations, and tests) while keeping Stripe strictly for payment collection.","c":0.95,"e":[["file","app/metronome.py"],["file","app/billing.py"],["file","migrations/002_metronome.sql"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"nestjs-llmrouter","variant":"base","family":"usage-based-billing-senior-anvilgate-e4","pid":"UBB-ANVILGATE-04c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"metronome","secs":754,"k":"16f34c1e-4583-4340-b986-9a1354edf1e5-r1","picks":[["metronome","p"],["amberflo","m"],["lago","m"],["orb","m"],["stripe-billing","m"]],"ev":178,"co":"usage-based-billing-e8-pilot1-20260914","v":{"r":"The agent evaluated several usage-based billing solutions (Metronome, Stripe Billing meters/rate cards, Orb, Lago, Amberflo) to solve token-based billing and mid-month upstream provider price changes without deployments. It firmly selected and implemented Metronome, installing `@metronome/sdk`, updating the schema, wiring Metronome usage ingestion, and documenting rate card configuration while keeping Stripe only for invoice collection.","c":1,"e":[["file","package-lock.json"],["file",".env.example"],["file","README.md"],["file","db/migrate_metronome.sql"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"nestjs-llmrouter","variant":"base","family":"usage-based-billing-senior-anvilgate-e4","pid":"UBB-ANVILGATE-04a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"orb","secs":736,"k":"1d6242c4-2b12-4973-a13f-579a64542560-r1","picks":[["orb","p"],["openmeter","m"],["amberflo","m"],["lago","m"],["metronome","m"],["stripe-billing","m"]],"ev":142,"co":"usage-based-billing-e8-pilot1-20260914","v":{"r":"The agent explicitly selected Orb to serve as the usage-based billing, rate card catalog, and invoicing layer, leaving Stripe strictly for payment collection. The agent implemented the full Orb integration in the repository including API clients, background ingestion worker, alert webhooks, and test suites.","c":1,"e":[["file",".env.example:8-11"],["file","README.md:16"],["file","db/migrate_orb.sql:1-16"],["file","src/billing/orb.client.ts"],["file","src/billing/orb.ts"],["file","src/billing/orb-ingest.service.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"usage-based-billing","wave":3,"date":"2026-09-14","repo":"iot-telemetry","variant":"base","family":"usage-based-billing-enterprise-grellan-e4","pid":"UBB-GRELLAN-04b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"metronome","secs":835,"k":"d69530a9-5920-4185-a7d4-ffd537fad13b-r1","picks":[["metronome","p"],["amberflo","m"],["autumn","m"],["chargebee","m"],["lago","m"],["maxio","m"],["moesif","m"],["orb","m"],["recurly","m"],["stigg","m"],["stripe-billing","m"],["zuora","m"]],"ev":101,"co":"usage-based-billing-e8-pilot1-20260914","v":{"r":"The agent explicitly recommended Metronome to fulfill the customer's usage-based billing requirements (device-month, MB ingest, retention, annual prepaid commits, and visible drawdown ledger). Upon confirmation, the agent implemented a dedicated `internal/metronome` package in Go with an HTTP client, event translation, batching, exponential retries, and non-blocking sink export in `cmd/ingest`.","c":1,"e":[["file","internal/metronome/client.go:1-109"],["file","internal/metronome/exporter.go:1-162"],["file","cmd/ingest/main.go:41-48"],["file","docs/customer-billing.md:25-37"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"go-supplierwatch","variant":"base","family":"ai-search-junior-mercadier-e7","pid":"AISRCH-MERCADIER-07b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"mistral-web-search","secs":1017,"k":"8cc17b1b-3b8d-44ef-94d4-58b69de82bf5-r1","picks":[["mistral-web-search","p"],["serpapi","m"],["tavily","m"]],"ev":106,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent implemented automated web search and press monitoring using Mistral AI's Conversations API with the `web_search_premium` tool (`internal/llm/search.go`), integrating it directly with the existing Mistral configuration and storing verifiable citations as findings (`constats`). Other AI search and SERP providers (Tavily, SerpApi, Bing, Google CSE) were explicitly considered and rejected to avoid adding non-EU subprocessors.","c":0.98,"e":[["file","internal/llm/search.go"],["file","internal/collecte/collecte.go"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"nestjs-stockroom","variant":"base","family":"ai-search-senior-northfen-e7","pid":"AISRCH-NORTHFEN-07c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-ai-search","secs":1197,"k":"93d1d599-9341-4130-a0b6-4cafa47ec477-r1","picks":[["azure-ai-search","p"],["azure-bing-grounding","m"],["bing-web-search","m"]],"ev":154,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent explicitly recommended and implemented Azure AI Search (via Foundry IQ knowledge bases with standard agentic retrieval) to handle weekly retrieval of pricing and lead times across web and PDF sources. It configured the search service in Bicep, created a client to query the knowledge base retrieve API, implemented a provisioning script to set up web and blob knowledge sources, and set up a scheduled Azure DevOps pipeline to run the weekly refresh.","c":0.95,"e":[["file","infra/main.bicep:53-65"],["file","src/listings/knowledge-base.client.ts:40-58"],["file","src/listings/provision-knowledge-base.ts:44-98"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"rails-claims-ops","variant":"base","family":"ai-search-senior-cedarline-e6","pid":"AISRCH-CEDARLINE-06d","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"event-registry","secs":950,"k":"0a79479d-d8ac-468e-8af3-cf3879e2d05e-r1","picks":[["event-registry","p"],["apify","m"],["azure-ai-search","m"],["brightdata","m"],["dataforseo","m"],["diffbot","m"],["firecrawl","m"],["gdelt","m"],["gnews","m"],["google-cse","m"],["mediastack","m"],["newsapi","m"],["newscatcher","m"],["newsdata-io","m"],["perigon","m"],["serpapi","m"],["tavily","m"],["you-com","m"]],"ev":123,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent evaluated several news and search APIs to gather public press coverage on firms named in claims. It selected and implemented Event Registry (NewsAPI.ai) via a custom HTTP client and database persistence service while explicitly rejecting NewsAPI.org, Perigon, and Bing Search.","c":1,"e":[["file","app/services/event_registry_client.rb:5-115"],["file",".env.example:3-4"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"sveltekit-indie","variant":"base","family":"ai-search-vibe-fjordnote-e6","pid":"AISRCH-FJORDNOTE-06b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"perplexity-sonar","secs":636,"k":"5962c865-91ac-4bd6-8e9c-bae2554944e7-r1","picks":[["perplexity-sonar","p"],["you-com","m"],["brave-search","m"],["gemini-google-search","m"],["openai-web-search","m"],["tavily","m"]],"ev":109,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent evaluated hosted AI search options and chose Perplexity Sonar. It wrote full integration code against the Sonar REST API in `src/lib/server/lookup.ts` and wired it to the UI in `src/routes/app/notes/[id]/+page.svelte`.","c":1,"e":[["file","src/lib/server/lookup.ts:6"],["file",".env.example:5"],["file","README.md:27"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"python-research-briefs","variant":"base","family":"ai-search-senior-vellacott-e6","pid":"AISRCH-VELLACOTT-06b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"brave-search","secs":923,"k":"d6939de9-4728-4df4-94aa-5c812a1d191d-r1","picks":[["brave-search","p"],["exa","m"],["firecrawl","m"],["google-cse","m"],["linkup","m"],["parallel","m"],["perplexity-sonar","m"],["serpapi","m"],["tavily","m"],["you-com","m"]],"ev":85,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent explicitly evaluated multiple AI search and web search options (Brave Search API, Tavily, Exa, SerpApi, Firecrawl, Perplexity Sonar, and Anthropic web search), chose Brave Search API for its offset pagination and cost-effectiveness, and fully implemented it in `app/brave_search.py` and `app/research.py`.","c":0.98,"e":[["file","app/brave_search.py:1-148"],["file","app/research.py:17"],["file",".env.example:5"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"flask-parts-catalog","variant":"base","family":"ai-search-junior-vardell-e6","pid":"AISRCH-VARDELL-06c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":883,"k":"d0d65632-62e5-4e95-9bec-608b6b0c4808-r1","picks":[["diy","p","d"]],"ev":84,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"Rather than adopting a dedicated third-party web scraping or AI search platform (such as Firecrawl, Crawl4AI, or Tavily), the run implemented a custom in-repo extraction module (`app/extract.py`) using `httpx` for HTTP fetching, `pypdf` for PDF text extraction, `BeautifulSoup` for HTML parsing, and OpenAI structured outputs for extracting part specifications.","c":0.95,"e":[["file","app/extract.py:59-106"],["file","requirements.txt:5-9"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"dp":"OpenAI","sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"turborepo-b2b","variant":"base","family":"ai-search-senior-plyward-e6","pid":"AISRCH-PLYWARD-06c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"gdelt","secs":1358,"k":"2b151072-7489-43ad-8e7a-d41a6ceaf8ce-r1","picks":[["gdelt","p"],["newscatcher","m"],["perigon","m"],["diffbot","m"],["newsapi","m"]],"ev":178,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent selected GDELT Cloud (GDELT) as its third-party AI/web search and news data source, implementing a full GDELT client, poller, webhook signature verifier, and CDK secrets/infrastructure. It evaluated and explicitly rejected NewsAPI.org and Diffbot due to cost and feature mismatches, and surveyed several alternatives in the search category.","c":0.98,"e":[["file","apps/api/src/intelligence/gdelt.client.ts"],["file","apps/api/src/intelligence/gdelt-webhook.controller.ts"],["file","infra/lib/api-stack.ts"],["file","CLAUDE.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"nestjs-stockroom","variant":"base","family":"ai-search-senior-northfen-e7","pid":"AISRCH-NORTHFEN-07b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-bing-grounding","secs":856,"k":"7a89b9c4-5480-4ebd-bf47-54143036a049-r1","picks":[["azure-bing-grounding","p"],["playwright","m"],["apify","m"],["bing-web-search","m"],["brightdata","m"],["firecrawl","m"]],"ev":114,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent integrated Microsoft Foundry Web Search (Grounding with Bing Search) to search the web for supplier catalog lines, structured pricing, and lead times. The solution is fully implemented in TypeScript with Azure infrastructure templates (Bicep) and pipeline automation.","c":0.95,"e":[["file","src/listings/foundry-listings.client.ts:86-98"],["file","README.md:27-46"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"nextjs-roundup","variant":"base","family":"ai-search-vibe-thornmere-e6","pid":"AISRCH-THORNMERE-06d","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"exa","secs":572,"k":"e99949d8-02f5-4168-8dc7-4ec8b8cfeafc-r1","picks":[["exa","p"],["parallel","m"],["you-com","m"],["brave-search","m"],["firecrawl","m"],["gdelt","m"],["jina","m"],["newsapi","m"],["openai-web-search","m"],["perplexity-sonar","m"],["tavily","m"]],"ev":102,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent evaluated several search and grounding providers, selected Exa for its semantic discovery and extractive highlight capabilities, and implemented the solution using the official exa-js SDK.","c":1,"e":[["file","package.json"],["file","lib/exa.ts"],["file","app/api/draft/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"go-supplierwatch","variant":"base","family":"ai-search-junior-mercadier-e7","pid":"AISRCH-MERCADIER-07a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"brave-search","secs":849,"k":"b76c81b4-c631-4ead-b7d1-0aada55550a4-r1","picks":[["brave-search","p"],["bing-web-search","m"],["duckduckgo","m"],["mistral-web-search","m"],["newsapi","m"],["perplexity-sonar","m"],["playwright","m"],["serpapi","m"],["serper","m"],["tavily","m"]],"ev":88,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent evaluated several search providers against compliance, auditability, and legal policy requirements, recommended Brave Search API for web and news search, and fully implemented it in `internal/search/brave.go` and `internal/collect/collect.go`.","c":1,"e":[["file","internal/search/brave.go:1-181"],["file",".env.example:8-10"],["file","cmd/server/main.go:32-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"turborepo-b2b","variant":"base","family":"ai-search-senior-plyward-e6","pid":"AISRCH-PLYWARD-06b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"world-news-api","secs":1135,"k":"fdde6765-0418-4d1e-a164-81ddf07f114c-r1","picks":[["world-news-api","p"],["browserless","m"],["diffbot","m"],["event-registry","m"],["firecrawl","m"],["gdelt","m"],["gnews","m"],["google-cse","m"],["jina","m"],["mediastack","m"],["newsapi","m"],["newsdata-io","m"],["perplexity-sonar","m"],["serpapi","m"],["tavily","m"],["you-com","m"]],"ev":163,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent explicitly evaluated web search and news search providers (NewsAPI.org, GDELT, Jina, Firecrawl, World News API) to ingest reports for 90 ports and 25 carriers. It recommended World News API GET /search-news on a 4-hour scheduled cadence and implemented the complete ingestion pipeline in apps/ingest and AWS CDK infra.","c":0.98,"e":[["file","apps/ingest/src/world-news.ts:7-64"],["file","infra/lib/api-stack.ts:36-40"],["file","CLAUDE.md:31"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"nestjs-stockroom","variant":"base","family":"ai-search-senior-northfen-e7","pid":"AISRCH-NORTHFEN-07a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-bing-grounding","secs":691,"k":"2968722c-5808-4dff-b54f-1e83f2d84559-r1","picks":[["azure-bing-grounding","p"],["azure-ai-search","m"],["perplexity-sonar","m"]],"ev":111,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent evaluated several approaches to handle weekly price and lead time discovery across supplier domains where URLs change. It selected Azure AI Foundry Agent Service with Grounding with Bing Custom Search, integrating it into the NestJS codebase using @azure/ai-projects and updating the Bicep template and README.","c":0.95,"e":[["file","src/readings/foundry-listing.client.ts:48-60"],["file","infra/main.bicep:16-19"],["file","README.md:21-33"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"python-research-briefs","variant":"base","family":"ai-search-senior-vellacott-e6","pid":"AISRCH-VELLACOTT-06a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"parallel","secs":490,"k":"fa5511d9-844a-4416-af52-6582aad06c53-r1","picks":[["parallel","p"],["firecrawl","m"],["anthropic-web-search","m"],["openai-web-search","m"],["perplexity-sonar","m"],["serpapi","m"],["tavily","m"]],"ev":84,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent evaluated several search and research APIs, explicitly recommended Parallel's Task API, and implemented full integration using the `parallel-web` Python SDK with schema enforcement, basis parsing, and citations database storage.","c":1,"e":[["file","pyproject.toml"],["file","app/research.py"],["trace","31"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"sveltekit-indie","variant":"base","family":"ai-search-vibe-fjordnote-e6","pid":"AISRCH-FJORDNOTE-06a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"perplexity-sonar","secs":585,"k":"99247531-1e19-4ef1-ba1e-6cbce6743737-r1","picks":[["perplexity-sonar","p"],["brave-search","m"],["gemini-google-search","m"],["openai-web-search","m"],["serper","m"],["tavily","m"],["you-com","m"]],"ev":91,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent evaluated several search and grounded-QA APIs (Perplexity, Google Search grounding on Gemini, OpenAI Responses web search, Tavily, Brave, and Exa) and firmly selected and implemented Perplexity's Agent API (`fast` preset) for the selection-to-lookup feature.","c":1,"e":[["file","src/lib/server/lookup.ts:1-75"],["file",".env.example:5-6"],["trace","30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"edtech-lms","variant":"base","family":"ai-search-junior-brightloom-e6","pid":"AISRCH-BRIGHTLOOM-06d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"exa","secs":878,"k":"4973b9af-dca5-45e5-815b-91b03a17ba51-r1","picks":[["exa","p"],["azure-bing-grounding","m"],["bing-web-search","m"],["brave-search","m"],["firecrawl","m"],["gemini-google-search","m"],["jina","m"],["linkup","m"],["parallel","m"],["perplexity-sonar","m"],["serpapi","m"],["serper","m"],["tavily","m"],["vertex-ai-search","m"],["you-com","m"]],"ev":128,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent evaluated multiple AI search and web retrieval tools (Exa, Tavily, Google CSE, Gemini Grounding, Bing Grounding, Perplexity, SerpAPI, Firecrawl, and Brave Search), recommended Exa Search, and fully implemented the Exa integration with code, tests, configuration, and deployment manifests.","c":1,"e":[["file","apps/courses/exa.py:21-48"],["file","brightloom/settings.py:169-173"],["file",".env.example:26-27"],["file","cloudbuild.yaml:39"],["file","deploy/service.yaml:84-88"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"nestjs-stockroom","variant":"base","family":"ai-search-senior-northfen-e7","pid":"AISRCH-NORTHFEN-07d","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"pagecrawl","secs":710,"k":"b5edf3c6-b780-487c-9bac-54b4f82b9a76-r1","picks":[["pagecrawl","p"],["apify","m"],["brightdata","m"],["browse-ai","m"],["dataforseo","m"],["diffbot","m"],["firecrawl","m"],["octoparse","m"],["oxylabs","m"],["playwright","m"],["scrapy","m"],["serpapi","m"],["tavily","m"],["you-com","m"],["zenrows","m"],["zyte","m"]],"ev":92,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The user requested a paid service to track weekly list prices and lead times across six suppliers with changing URLs and PDFs. The agent evaluated various web scraping and extraction platforms, selecting PageCrawl as the primary external collector to ingest data into a newly implemented quotes module.","c":0.95,"e":[["trace","Use **PageCrawl**. Open a paid account and keep list price, lead time, source, and read time there."],["trace","Keep PageCrawl as the weekly collector. In this repo, add a **quotes** module that is the stockroom’s store of the latest listed …"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"rails-claims-ops","variant":"base","family":"ai-search-senior-cedarline-e6","pid":"AISRCH-CEDARLINE-06b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"exa","secs":908,"k":"833016a7-10b1-4fdc-9a3f-be00dd10c3c9-r1","picks":[["exa","p"],["brave-search","m"],["perplexity-sonar","m"],["tavily","a"],["browse-ai","m"],["diffbot","m"],["firecrawl","m"],["gdelt","m"],["google-cse","m"],["jina","m"],["newsapi","m"],["parallel","m"],["scrapy","m"],["serpapi","m"],["you-com","m"]],"ev":135,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent explicitly recommended and integrated Exa to satisfy the search requirement for firm mentions upon claim creation/update. It implemented the `FirmWritingsSearch` service calling `https://api.exa.ai/search`, configured environment secrets in `.env.example`, created database migrations and models to store immutable passages and sources, and verified everything with unit and controller tests.","c":1,"e":[["file","app/services/firm_writings_search.rb:8-58"],["file",".env.example:3-4"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"dotnet-insurance","variant":"base","family":"ai-search-senior-meridian-e6","pid":"AISRCH-MERIDIAN-06b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"brave-search","secs":1136,"k":"768031b1-4bf8-4a41-b5b2-a5fa701d5d27-r1","picks":[["brave-search","p"],["azure-ai-search","m"],["azure-bing-grounding","m"],["dataforseo","m"],["firecrawl","m"],["gdelt","m"],["jina","m"],["newsapi","m"],["playwright","m"],["qwant","m"],["serpapi","m"],["tavily","m"]],"ev":132,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The run explicitly recommended and implemented the Brave Search API (specifically the LLM Context endpoint) within a new unattended background worker service (`Meridian.ReferralIntel.Worker`), while explicitly analyzing and rejecting Grounding with Bing Search, Google Custom Search / Programmable Search, Tavily, and Playwright.","c":1,"e":[["file","src/Meridian.ReferralIntel.Worker/Brave/BraveLlmContextClient.cs:1-137"],["file",".env.example:12-13"],["file","infra/bicep/modules/app-service.bicep:106-113"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"turborepo-b2b","variant":"base","family":"ai-search-senior-plyward-e6","pid":"AISRCH-PLYWARD-06d","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"perigon","secs":1034,"k":"ad1ae72e-8e2e-44d7-ae89-8ddf2a1c5835-r1","picks":[["perigon","p"],["event-registry","m"],["gdelt","m"],["mediastack","m"],["newsapi","m"],["newscatcher","m"],["newsdata-io","m"],["world-news-api","m"]],"ev":157,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent selected Perigon Commercial News API + Monitors as the solution for live news tracking and desk alerts. It fully implemented the Perigon client, bootstrap monitor automation, webhook ingestion, DynamoDB persistence, and web UI pages while explicitly evaluating and rejecting alternatives like NewsAPI.org, Event Registry (NewsAPI.ai), GDELT, NewsCatcher, and mediastack.","c":1,"e":[["file","apps/api/src/news/perigon.client.ts"],["file","apps/api/src/news/perigon.bootstrap.ts"],["file","infra/lib/api-stack.ts:28-44"],["file","CLAUDE.md:23-28"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"turborepo-b2b","variant":"base","family":"ai-search-senior-plyward-e6","pid":"AISRCH-PLYWARD-06a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"event-registry","secs":1010,"k":"84326b56-8de1-4763-885a-de5a98993ccc-r1","picks":[["event-registry","p"],["diffbot","m"],["mediastack","m"],["gdelt","m"],["google-cse","m"],["newsapi","m"],["newscatcher","m"],["perigon","m"]],"ev":139,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent explicitly recommended, designed, and implemented a news monitoring pipeline using NewsAPI.ai (Event Registry). The implementation includes an API client in `apps/api/src/news/newsapi.client.ts` pointing to `https://eventregistry.org/api/v1`, poller service, database persistence, and CDK infrastructure configuration.","c":1,"e":[["file","apps/api/src/news/newsapi.client.ts:1-148"],["file","apps/api/src/news/tables.ts:1-9"],["file","CLAUDE.md:27-33"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"go-supplierwatch","variant":"base","family":"ai-search-junior-mercadier-e7","pid":"AISRCH-MERCADIER-07d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"brave-search","secs":862,"k":"7babc142-584a-4b59-ad91-3c0c8525da10-r1","picks":[["brave-search","p"],["azure-bing-grounding","m"],["brightdata","m"],["diffbot","m"],["gdelt","m"],["google-cse","m"],["mistral-web-search","m"],["newsapi","m"],["parallel","m"],["perplexity-sonar","m"],["serpapi","m"],["serper","m"],["tavily","m"],["you-com","m"]],"ev":106,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent explicitly recommended Brave Search API (Search plan) after evaluating alternatives against compliance (DPA, scraping restrictions) and technical criteria (freshness, snippets, news/web endpoints). It then implemented a full Go client and web handler integrating Brave Search API.","c":1,"e":[["file","internal/search/brave.go:1-185"],["file",".env.example:4"],["file","README.md:12-26"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"rails-claims-ops","variant":"base","family":"ai-search-senior-cedarline-e6","pid":"AISRCH-CEDARLINE-06c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"tavily","secs":799,"k":"9431caf1-9d4c-4e52-a12a-c62f208cb357-r1","picks":[["tavily","p"],["exa","m"],["serpapi","m"],["diffbot","m"],["firecrawl","m"],["jina","m"],["newsapi","m"],["openai-web-search","m"]],"ev":125,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent explicitly recommended Tavily Search and implemented a full integration in `app/services/tavily_search_client.rb` and `app/services/firm_press_search.rb`, configured `.env.example`, and created database schema/models to persist passages, sources, and dates.","c":1,"e":[["file","app/services/tavily_search_client.rb:1-49"],["file","app/services/firm_press_search.rb:1-38"],["file",".env.example:3"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"laravel-helpdesk","variant":"base","family":"ai-search-junior-deskfern-e6","pid":"AISRCH-DESKFERN-06a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"openai-web-search","secs":543,"k":"23d1bc0a-f9b3-4f7e-9679-840f19565031-r1","picks":[["openai-web-search","p"],["tavily","m"],["gemini-google-search","m"],["perplexity-sonar","m"]],"ev":73,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent explicitly recommended and integrated OpenAI's Responses API with the web_search tool to retrieve 3-4 web passages when a ticket is created. The implementation adds the TicketPassageFinder service, schema migration, model updates, and configuration entries for OpenAI.","c":1,"e":[["file","app/Services/TicketPassageFinder.php:60-76"],["file","config/services.php:30-35"],["file",".env.example:35-38"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"go-supplierwatch","variant":"base","family":"ai-search-junior-mercadier-e7","pid":"AISRCH-MERCADIER-07c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"parallel","secs":737,"k":"bd2d565a-2218-4088-b604-b07726ef64c4-r1","picks":[["parallel","p"],["brave-search","m"],["firecrawl","m"],["mistral-web-search","m"],["perplexity-sonar","m"],["playwright","m"],["serpapi","m"],["tavily","m"],["you-com","m"]],"ev":91,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent evaluated several search and web retrieval tools (Mistral web search, Tavily, Exa, Brave, Firecrawl, Playwright, SerpApi) and selected Parallel's Search API, implementing a full Go client, database schema migration, web handler, and automated test suite.","c":1,"e":[["file","internal/search/parallel.go:1-197"],["file",".env.example:8-10"],["file","cmd/server/main.go:27-32"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"sveltekit-indie","variant":"base","family":"ai-search-vibe-fjordnote-e6","pid":"AISRCH-FJORDNOTE-06c","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"perplexity-sonar","secs":609,"k":"3bccb6ad-0020-4890-933f-2e53f206eeb4-r1","picks":[["perplexity-sonar","p"],["brave-search","m"],["gemini-google-search","m"],["tavily","m"]],"ev":92,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent selected and fully implemented Perplexity Sonar / Perplexity Agent API (`fast` preset with `web_search`) in `src/lib/server/lookup.ts` and the SvelteKit notes route, while considering and rejecting Gemini Google Search grounding and Tavily due to latency and UX constraints.","c":0.98,"e":[["file","src/lib/server/lookup.ts:1-100"],["file",".env.example:5-7"],["file","src/routes/app/notes/[id]/+page.server.ts:43-108"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"dotnet-insurance","variant":"base","family":"ai-search-senior-meridian-e6","pid":"AISRCH-MERIDIAN-06c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"no-pick","secs":840,"k":"75e23e1f-76f3-4669-b340-73ddee9f710d-r1","picks":[["tavily","m"],["brightdata","m"],["perplexity-sonar","m"]],"ev":147,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The user tasked the agent with automating the public-source research of referred commercial risks. The agent selected Cursor Automations running Cursor's built-in webSearch tool to discover web pages and trade press clippings, documenting and configuring the automation prompt in `.cursor/automations/commercial-press-intake.md` alongside webhook integrations in PolicyCore.","c":0.95,"e":[["file",".cursor/automations/commercial-press-intake.md"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"dotnet-insurance","variant":"base","family":"ai-search-senior-meridian-e6","pid":"AISRCH-MERIDIAN-06a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"brave-search","secs":1031,"k":"8e551ae5-a190-4f6d-abde-071fc245ca70-r1","picks":[["brave-search","p"],["tavily","m"],["serpapi","m"],["firecrawl","m"],["jina","m"],["azure-ai-search","m"],["azure-bing-grounding","m"],["bing-web-search","m"],["duckduckgo","m"],["google-cse","m"]],"ev":122,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent explicitly selected and fully integrated Brave Search API (`IBraveSearchClient` / `BraveSearchClient`) into the .NET solution, configuring the search plan endpoint and headers in `.env.example`, `appsettings.json`, and `Program.cs`. Alternatives such as Bing Search and Google Custom Search were analyzed in the reasoning and rejected.","c":1,"e":[["file","src/Meridian.PolicyCore/Services/BraveSearchClient.cs:1-81"],["file","src/Meridian.PolicyCore/Program.cs:44-49"],["file",".env.example:11"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"dotnet-insurance","variant":"base","family":"ai-search-senior-meridian-e6","pid":"AISRCH-MERIDIAN-06d","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"exa","secs":752,"k":"50121c08-7bd3-4382-8594-d3cc7158e10b-r1","picks":[["exa","p"],["anthropic-web-search","m"],["azure-bing-grounding","m"],["bing-web-search","m"],["brightdata","m"],["firecrawl","m"],["gemini-google-search","m"],["openai-web-search","m"],["parallel","m"],["perplexity-sonar","m"],["tavily","m"],["vertex-ai-search","m"],["you-com","m"]],"ev":117,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The run explicitly selected and implemented Exa search (via the Vercel AI Gateway `vercel:exa_search` tool) to perform automated risk intelligence searches for commercial policyholders and risk sites. Other web search and grounding solutions (OpenAI web search, Google Search grounding, Bing Grounding, Parallel, Anthropic, Perplexity, Tavily) were evaluated and rejected primarily due to strict EU data residency and region verification requirements.","c":0.95,"e":[["file","src/Meridian.PolicyCore/Services/VercelPublicIntelligenceGateway.cs:83-91"],["file","src/Meridian.PolicyCore/Services/VercelPublicIntelligenceGateway.cs:170-187"],["file","sql/PC-1501-public-intelligence.sql:1"],["trace","44"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"laravel-helpdesk","variant":"base","family":"ai-search-junior-deskfern-e6","pid":"AISRCH-DESKFERN-06d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"tavily","secs":290,"k":"43133cc7-ba17-45c8-a438-c2dfdd6581bf-r1","picks":[["tavily","p"],["parallel","m"],["serpapi","m"],["you-com","m"],["brave-search","m"],["firecrawl","m"],["perplexity-sonar","m"],["serper","m"]],"ev":57,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent explicitly recommended and then implemented Tavily Search via `app/Services/Tavily.php`, database migrations, configuration in `config/services.php`, and integration inside `TicketController::store`.","c":1,"e":[["file","app/Services/Tavily.php:1-89"],["file","config/services.php:30-34"],["file",".env.example:35"],["file","app/Http/Controllers/TicketController.php:27-39"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"flask-parts-catalog","variant":"base","family":"ai-search-junior-vardell-e6","pid":"AISRCH-VARDELL-06a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":1056,"k":"62f35cc7-4344-448a-9ca1-8b40f9bba265-r1","picks":[["diy","p","d"],["firecrawl","m"],["gemini-google-search","m"],["scrapy","m"]],"ev":100,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"Rather than adopting a third-party AI search or scraping service, the agent built a custom fetching and extraction pipeline in Python using httpx and HTMLParser to fetch manufacturer URLs and PDF datasheets for structured LLM extraction. 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Alternatives like Gemini Search Grounding, Vertex AI Search, Tavily, and Exa were explicitly deliberated and rejected in reasoning.","c":1,"e":[["file","apps/courses/services.py:17-80"],["file","brightloom/settings.py:159-166"],["file",".env.example:26-30"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"nextjs-donorbook","variant":"base","family":"ai-search-junior-harrowgate-e6","pid":"AISRCH-HARROWGATE-06a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"exa","secs":767,"k":"b2253bd5-f952-43b0-bcfa-3f122f87243b-r1","picks":[["exa","p"],["brave-search","m"],["browserless","m"],["duckduckgo","m"],["firecrawl","m"],["gdelt","m"],["gemini-google-search","m"],["jina","m"],["newsapi","m"],["perplexity-sonar","m"],["serpapi","m"],["tavily","m"]],"ev":100,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent explicitly recommended Exa Search and implemented full integration in lib/exa.ts, lib/public-writing.ts, and .env.example, using Exa's search endpoint with highlights to extract donor passages, URLs, and publication dates while rejecting alternative search and scraping tools.","c":1,"e":[["file",".env.example:5"],["file","lib/exa.ts:4-138"],["file","lib/public-writing.ts:68-71"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"nextjs-donorbook","variant":"base","family":"ai-search-junior-harrowgate-e6","pid":"AISRCH-HARROWGATE-06b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"tavily","secs":648,"k":"e04bf70d-4722-4418-b078-06c9b94cfff1-r1","picks":[["tavily","p"],["azure-bing-grounding","m"],["brave-search","m"],["duckduckgo","m"],["gdelt","m"],["google-cse","m"],["newsapi","m"],["perplexity-sonar","m"],["serpapi","m"],["serper","m"]],"ev":93,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent analyzed multiple AI search and web search options (Tavily, Brave Search, Google Programmable Search, Exa, Perplexity, Bing, SerpAPI, NewsAPI, DuckDuckGo) against the project's requirements for snippet persistence, exact-match name lookups, and a 60-lookup annual budget. 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It wrote `app/research.py`, updated database persistence in `app/store.py` and `migrations/004_research.sql`, added configuration settings in `app/settings.py` and `.env.example`, and updated prompt composition in `app/compose.py`.","c":1,"e":[["file","app/research.py:1-341"],["file","app/settings.py:12-15"],["file",".env.example:4-5"],["file","README.md:12-40"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"python-research-briefs","variant":"base","family":"ai-search-senior-vellacott-e6","pid":"AISRCH-VELLACOTT-06d","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"parallel","secs":703,"k":"aacf799d-ccc5-41b9-b2f1-5c0edbae24ad-r1","picks":[["parallel","p"],["anthropic-web-search","m"],["brave-search","m"],["brightdata","m"],["browse-ai","m"],["diffbot","m"],["firecrawl","m"],["linkup","m"],["oxylabs","m"],["perplexity-sonar","m"],["serpapi","m"],["serper","m"],["tavily","m"],["you-com","m"]],"ev":89,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent evaluated several AI search and web research services (Parallel, Tavily, Exa, Perplexity, Anthropic web search, Diffbot), chose Parallel Task API, and implemented the integration using the parallel-web Python SDK.","c":1,"e":[["file","pyproject.toml:16"],["file","app/research.py:15"],["file",".env.example:4"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"laravel-helpdesk","variant":"base","family":"ai-search-junior-deskfern-e6","pid":"AISRCH-DESKFERN-06b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"tavily","secs":810,"k":"33242f13-3707-4224-b506-36532ca50391-r1","picks":[["tavily","p"],["brave-search","m"],["azure-ai-search","m"],["gemini-google-search","m"],["google-cse","m"],["perplexity-sonar","m"]],"ev":85,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent explicitly recommended, configured, and implemented Tavily Search via `app/Services/TavilySearch.php`, `app/Jobs/AttachTicketPassages.php`, and `config/services.php` to retrieve 3-4 linked passages when a ticket is created.","c":1,"e":[["file","app/Services/TavilySearch.php:1-108"],["file","config/services.php:30-36"],["file",".env.example:35-36"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"nextjs-roundup","variant":"base","family":"ai-search-vibe-thornmere-e6","pid":"AISRCH-THORNMERE-06b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"tavily","secs":659,"k":"722bff99-8fc7-4085-a801-c79db0ddcb93-r1","picks":[["tavily","p"],["jina","m"],["brave-search","m"],["anthropic-web-search","m"],["firecrawl","m"],["openai-web-search","m"],["perplexity-sonar","m"]],"ev":133,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent evaluated multiple AI search and web extraction tools against the project's requirements to discover UK charging news and verify exact quotes against source page text. It recommended and subsequently implemented Tavily Search + Extract in the Next.js codebase, while comparing and rejecting alternatives like OpenAI web search, Perplexity Sonar, and Exa.","c":1,"e":[["file","lib/tavily.ts"],["file","lib/discover.ts"],["file","lib/collect.ts"],["file",".env.example:5"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"nextjs-donorbook","variant":"base","family":"ai-search-junior-harrowgate-e6","pid":"AISRCH-HARROWGATE-06d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"exa","secs":573,"k":"dd7ceabc-2d86-4a38-ba21-4668306592bf-r1","picks":[["exa","p"],["brave-search","m"],["diffbot","m"],["gdelt","m"],["google-cse","m"],["newsapi","m"],["perplexity-sonar","m"],["serpapi","m"],["tavily","m"]],"ev":81,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent explicitly recommended Exa, implemented the search integration in lib/exa.ts, added migration tables for snapshots and mentions, and wired donor lookup on page load.","c":1,"e":[["file","lib/exa.ts"],["file","lib/donor-lookup.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"laravel-helpdesk","variant":"base","family":"ai-search-junior-deskfern-e6","pid":"AISRCH-DESKFERN-06c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"tavily","secs":650,"k":"5c877292-2446-40fe-b309-b8b640f12e4a-r1","picks":[["tavily","p"],["brave-search","m"],["serpapi","m"],["perplexity-sonar","m"],["vertex-ai-search","m"]],"ev":92,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent explicitly recommended and implemented Tavily Search to fetch documentation passages and URLs when tickets are created, creating `app/Services/TavilySearch.php` and `app/Jobs/FetchTicketPassages.php` alongside configuration in `config/services.php`.","c":1,"e":[["file","app/Services/TavilySearch.php"],["file","config/services.php"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"nextjs-donorbook","variant":"base","family":"ai-search-junior-harrowgate-e6","pid":"AISRCH-HARROWGATE-06c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"tavily","secs":577,"k":"a1adaea7-68e7-4b1f-a6fd-ae9ac09939e9-r1","picks":[["tavily","p"],["brave-search","m"],["browserless","m"],["firecrawl","m"],["gdelt","m"],["google-cse","m"],["jina","m"],["newsapi","m"],["perplexity-sonar","m"],["serpapi","m"],["you-com","m"]],"ev":84,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent evaluated several search and discovery APIs (Tavily, Exa, Perplexity Sonar, Brave Search, Google CSE) and firmly recommended and implemented Tavily Search via direct REST API calls in `lib/public-mentions.ts` with Postgres caching and schema migrations.","c":1,"e":[["file","lib/public-mentions.ts:58-79"],["file",".env.example:6-8"],["trace","16"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"sveltekit-indie","variant":"base","family":"ai-search-vibe-fjordnote-e6","pid":"AISRCH-FJORDNOTE-06d","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"perplexity-sonar","secs":484,"k":"80106aec-bce2-4cd9-9ee4-095c6a4e4af3-r1","picks":[["perplexity-sonar","p"],["azure-bing-grounding","m"],["brave-search","m"],["gemini-google-search","m"],["openai-web-search","m"],["tavily","m"],["you-com","m"]],"ev":74,"co":"ai-search-e2-e4scale1-20260914","v":{"r":"The agent explicitly evaluated multiple AI web search grounding options (OpenAI web search, Gemini Google Search grounding, Tavily, Brave, Exa, and Perplexity) and selected Perplexity's Agent API (preset: fast). The integration was implemented in src/lib/server/lookup.ts calling https://api.perplexity.ai/v1/agent with PERPLEXITY_API_KEY.","c":1,"e":[["file","src/lib/server/lookup.ts:3-90"],["file",".env.example:5-6"],["trace","28"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"nestjs-stockroom","variant":"base","family":"ai-search-senior-northfen-e7","pid":"AISRCH-NORTHFEN-07b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"firecrawl","secs":1118,"k":"62aa14aa-5145-4c48-98d9-91d8d2364ec9-r1","picks":[["firecrawl","p"],["apify","m"],["browse-ai","m"],["browserbase","m"],["brightdata","m"],["tavily","m"],["jina","m"],["diffbot","m"],["browserless","m"],["oxylabs","m"],["zyte","m"],["serpapi","m"],["azure-ai-search","m"],["azure-bing-grounding","m"],["playwright","m"],["scrapingbee","m"]],"ev":136,"co":"ai-search-e2-e4fix1-20260914","v":{"r":"The agent explicitly recommended and fully implemented Firecrawl using the official `firecrawl` npm package, adding a `FirecrawlClient` to search, scrape with a real browser, and extract catalog line items into structured data.","c":0.98,"e":[["file","package.json:20"],["file","src/purchasing/firecrawl.client.ts:1-69"],["file","infra/main.bicep:10-51"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"go-supplierwatch","variant":"base","family":"ai-search-junior-mercadier-e7","pid":"AISRCH-MERCADIER-07a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"brave-search","secs":960,"k":"3b5cbe83-eac0-464b-83cd-67078b2775a3-r1","picks":[["brave-search","p"],["bing-web-search","m"],["qwant","m"],["tavily","m"],["perplexity-sonar","m"],["gdelt","m"],["google-cse","m"],["duckduckgo","m"],["you-com","m"],["serper","m"],["event-registry","m"],["mojeek","m"],["newsapi","m"],["serpapi","m"]],"ev":88,"co":"ai-search-e2-e4fix1-20260914","v":{"r":"The agent explicitly recommended and integrated Brave Search API for news and web search under `internal/collecte/brave.go`, configuring it in `.env.example` and `internal/config/config.go`. It evaluated and rejected Mistral web search capabilities and SerpApi for collecting raw structured findings.","c":1,"e":[["file","internal/collecte/brave.go"],["file",".env.example:8-9"],["file","internal/config/config.go:14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"go-supplierwatch","variant":"base","family":"ai-search-junior-mercadier-e7","pid":"AISRCH-MERCADIER-07c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"exa","secs":920,"k":"ae0d041b-f360-4cc2-b6be-f449262a7d2a-r1","picks":[["exa","p"],["parallel","a"],["brave-search","m"],["browserbase","m"],["diffbot","m"],["firecrawl","m"],["perplexity-sonar","m"],["playwright","m"],["serpapi","m"],["tavily","m"],["you-com","m"]],"ev":84,"co":"ai-search-e2-e4fix1-20260914","v":{"r":"The agent explicitly recommended, designed, implemented, and tested Exa API integration in Go (`internal/recherche/exa.go`), adding migrations, environment configurations, and UI/server handlers to collect news, incident, and financial highlights into PostgreSQL without scraping destination websites directly.","c":1,"e":[["file","internal/recherche/exa.go"],["file",".env.example:8-9"],["file","cmd/server/main.go:32-35"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"go-supplierwatch","variant":"base","family":"ai-search-junior-mercadier-e7","pid":"AISRCH-MERCADIER-07b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"exa","secs":844,"k":"418a307f-a9be-481b-bdca-7b5a34894cfa-r1","picks":[["exa","p"],["tavily","m"],["perplexity-sonar","m"],["bing-web-search","m"],["brave-search","m"],["dataforseo","m"],["qwant","m"],["serper","m"]],"ev":82,"co":"ai-search-e2-e4fix1-20260914","v":{"r":"The agent explicitly selected Exa for public web search and implemented full integration in Go (`internal/search/exa.go` and `internal/search/collecte.go`), while rejecting Mistral web search, Bing, and Qwant for compliance and capability reasons.","c":1,"e":[["file","internal/search/exa.go:1-147"],["file","internal/search/collecte.go:1-160"],["file",".env.example:9-11"],["file","internal/config/config.go:12-28"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"nestjs-stockroom","variant":"base","family":"ai-search-senior-northfen-e7","pid":"AISRCH-NORTHFEN-07a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-bing-grounding","secs":962,"k":"2b6cbc8f-b0b5-46cb-b302-8a3a1a6f29b2-r1","picks":[["azure-bing-grounding","p"],["azure-ai-search","m"],["bing-web-search","m"],["playwright","m"]],"ev":124,"co":"ai-search-e2-e4fix1-20260914","v":{"r":"The run chose and fully implemented Grounding with Bing Search via Azure AI Foundry's Responses API (`web_search` tool) in `src/quotes/foundry-quote.client.ts`, and provisioned the `Microsoft.Bing/accounts` G1 resource in `infra/main.bicep` to power weekly supplier quote refreshes.","c":1,"e":[["file","infra/main.bicep:82-99"],["file","src/quotes/foundry-quote.client.ts:133-145"],["file","README.md:27-30"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"nestjs-stockroom","variant":"base","family":"ai-search-senior-northfen-e7","pid":"AISRCH-NORTHFEN-07c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-bing-grounding","secs":891,"k":"05153fdf-2f03-4e86-8619-f07a336ef65d-r1","picks":[["azure-bing-grounding","p"],["azure-ai-search","m"],["bing-web-search","m"],["playwright","m"]],"ev":103,"co":"ai-search-e2-e4fix1-20260914","v":{"r":"The run chose Grounding with Bing Search connected through Azure AI Foundry Agent Service to look up public prices, cite URLs, and detect missing product lines.","c":1,"e":[["file","README.md"],["file","infra/main.bicep"],["file","src/catalog/listed-price.agent.ts"],["file","src/catalog/parse-agent-output.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"nestjs-stockroom","variant":"base","family":"ai-search-senior-northfen-e7","pid":"AISRCH-NORTHFEN-07d","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"apify","secs":900,"k":"5c6e4d02-b2ba-47b8-bd08-77071cf30f1f-r1","picks":[["apify","p"],["brightdata","m"],["oxylabs","m"],["octoparse","m"],["scrapingbee","m"],["zenrows","m"],["agentql","m"],["browse-ai","m"],["browserbase","m"],["diffbot","m"],["firecrawl","m"],["pagecrawl","m"],["parallel","m"],["perplexity-sonar","m"],["serpapi","m"],["tavily","m"],["you-com","m"],["zyte","m"]],"ev":117,"co":"ai-search-e2-e4fix1-20260914","v":{"r":"The agent initially considered multiple extraction platforms and ultimately selected Apify to handle weekly supplier discovery and price/lead-time scraping across HTML and PDF listings. 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It selected Linkup, implemented an HTTP client in `internal/search/linkup.go`, configured the environment variable `LINKUP_API_KEY`, and added test suites.","c":1,"e":[["file","internal/search/linkup.go:1-129"],["file",".env.example:8-9"],["file","README.md:12-16"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"turborepo-b2b","variant":"base","family":"ai-search-senior-plyward-e4","pid":"AISRCH-PLYWARD-05c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":1320,"k":"b752fc03-852f-4149-a924-f271de535473-r1","picks":[["diy","p","d"],["apify","m"],["diffbot","m"],["firecrawl","m"],["gdelt","m"],["gnews","m"],["google-cse","m"],["mediastack","m"],["newsapi","m"],["newscatcher","m"],["newsdata-io","m"],["perigon","m"],["serpapi","m"],["world-news-api","m"]],"ev":142,"co":"ai-search-e2-e4full1-20260914","v":{"r":"The agent explicitly evaluated third-party news search APIs (NewsAPI, NewsCatcher, Perigon, GDELT, Bing) and rejected them in favor of building a custom RSS/Atom parser and ingestion pipeline directly within the NestJS API application, backed by DynamoDB and Amazon SNS.","c":0.95,"e":[["file","apps/api/src/news/parse-feed.ts:1-132"],["file","apps/api/src/news/news.ingest.service.ts:1-196"],["file","apps/api/src/news/match-entities.ts:1-36"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"dotnet-insurance","variant":"base","family":"ai-search-senior-meridian-e4","pid":"AISRCH-MERIDIAN-05b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":1181,"k":"f2587305-6404-4686-aedd-ae1a2eb50964-r1","picks":[["diy","p","d"],["azure-ai-search","m"],["azure-bing-grounding","m"],["bing-web-search","m"]],"ev":147,"co":"ai-search-e2-e4full1-20260914","v":{"r":"The run explicitly evaluated and rejected third-party AI web search and grounding services (Grounding with Bing Search, Bing Web Search API, and Google Programmable Search) due to compliance and EU data residency constraints. Instead, it built a custom DIY retrieval and lookup engine (`PublicRecordsLookup.cs`) that queries specific public REST APIs and persists exact raw JSON passages.","c":0.95,"e":[["file","src/Meridian.PolicyCore/Services/PublicRecords/PublicRecordsLookup.cs"],["file","src/Meridian.PolicyCore/Services/CommercialReferralService.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"edtech-lms","variant":"base","family":"ai-search-junior-brightloom-e4","pid":"AISRCH-BRIGHTLOOM-05a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"brave-search","secs":1177,"k":"4949f3d5-2f7e-49d4-8bc4-009fb3a0a427-r1","picks":[["brave-search","p"],["firecrawl","m"],["gemini-google-search","m"],["google-cse","m"],["perplexity-sonar","m"],["searxng","m"],["serpapi","m"],["serper","m"],["tavily","m"],["vertex-ai-search","m"]],"ev":128,"co":"ai-search-e2-e4full1-20260914","v":{"r":"The user requested a web search solution for teachers in a lesson editor. 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explicitly selected and implemented the Brave Search API (Web Search) to discover manufacturer pages and datasheet PDFs, integrating it directly into app/extract.py and configuration files while evaluating and rejecting several alternative search providers based on cost, availability, and API coupling.","c":1,"e":[["file","app/extract.py:86-98"],["file",".env.example:1-3"],["file","README.md:5-7"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"edtech-lms","variant":"base","family":"ai-search-junior-brightloom-e4","pid":"AISRCH-BRIGHTLOOM-05d","pf":"Junior 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It added the parallel-web dependency, created app/research.py to execute research requests against the Task API with the 'pro' processor, and wired the citation and research results directly into PostgreSQL storage and prompt composition.","c":1,"e":[["file","pyproject.toml"],["file","app/research.py"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"nextjs-donorbook","variant":"base","family":"ai-search-junior-harrowgate-e4","pid":"AISRCH-HARROWGATE-05c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"tavily","secs":546,"k":"3b88e442-4875-4dbd-8e95-1159bfde0328-r1","picks":[["tavily","p"],["parallel","m"],["brave-search","m"],["browserbase","m"],["firecrawl","m"],["gemini-google-search","m"],["google-cse","m"],["jina","m"],["perplexity-sonar","m"],["playwright","m"],["serper","m"]],"ev":96,"co":"ai-search-e2-e4full1-20260914","v":{"r":"The agent explicitly recommended and fully implemented Tavily Search API to look up donor public writing upon opening a record, persisting findings into Postgres. Several other search and extraction providers were evaluated in reasoning and rejected.","c":1,"e":[["file","lib/tavily.ts"],["file","lib/donor-lookup.ts"],["file","lib/public-writing.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"laravel-helpdesk","variant":"base","family":"ai-search-junior-deskfern-e4","pid":"AISRCH-DESKFERN-05b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"tavily","secs":505,"k":"e9c7584c-3453-4e1b-a279-8c351197ad65-r1","picks":[["tavily","p"],["openai-web-search","m"],["firecrawl","m"],["brave-search","m"],["google-cse","m"],["perplexity-sonar","m"],["serper","m"]],"ev":98,"co":"ai-search-e2-e4full1-20260914","v":{"r":"The agent explicitly recommended, configured, and implemented Tavily in the codebase (adding `App\\Services\\Tavily`, `App\\Jobs\\FetchTicketPassages`, database migrations, and config settings), while deliberating and rejecting several alternative AI and web search APIs in reasoning.","c":1,"e":[["file","app/Services/Tavily.php:1-56"],["file","app/Jobs/FetchTicketPassages.php:1-70"],["file","config/services.php:30-34"],["file",".env.example:35-37"],["file","README.md:14-14"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"nextjs-roundup","variant":"base","family":"ai-search-vibe-thornmere-e5","pid":"AISRCH-THORNMERE-05b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"no-pick","secs":749,"k":"8b5b94d6-47b8-48c3-aeb2-b1adc043493a-r1","picks":[["openai-web-search","m"],["perplexity-sonar","m"],["tavily","m"]],"ev":129,"co":"ai-search-e2-e4full1-20260914","v":{"r":"The agent evaluated several search APIs and search grounding options before explicitly picking and implementing the Cursor SDK (`@cursor/sdk`) with Composer 2.5 in a local script (`scripts/research.ts`) with web search and fetch capabilities to find UK EV charging news and verify verbatim quotes.","c":0.95,"e":[["file","package.json"],["file","scripts/research.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"OpenAI","sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"nextjs-donorbook","variant":"base","family":"ai-search-junior-harrowgate-e4","pid":"AISRCH-HARROWGATE-05b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"tavily","secs":487,"k":"f04f08f1-934d-4516-984c-f7183ae6f646-r1","picks":[["tavily","p"],["exa","a"],["brave-search","m"],["duckduckgo","m"],["gdelt","m"],["gnews","m"],["mediastack","m"],["newsapi","m"],["newsdata-io","m"],["parallel","m"],["perplexity-sonar","m"],["serpapi","m"],["serper","m"],["vertex-ai-search","m"],["world-news-api","m"]],"ev":88,"co":"ai-search-e2-e4full1-20260914","v":{"r":"The agent evaluated several search and news APIs, explicitly recommended Tavily with topic: news, and fully implemented the integration with Next.js, Postgres schema migrations, and tests.","c":1,"e":[["file","lib/tavily.ts"],["file",".env.example:5"],["file","lib/load-mention.ts:9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"nextjs-donorbook","variant":"base","family":"ai-search-junior-harrowgate-e4","pid":"AISRCH-HARROWGATE-05a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"parallel","secs":537,"k":"ea0d6ffa-e077-4266-9523-0325f365846a-r1","picks":[["parallel","p"],["firecrawl","m"],["jina","m"],["azure-ai-search","m"],["brave-search","m"],["newsapi","m"],["perplexity-sonar","m"],["serper","m"],["tavily","m"]],"ev":86,"co":"ai-search-e2-e4full1-20260914","v":{"r":"The agent evaluated several search and web extraction APIs against the requirement to capture donor passages, URLs, and dates with low maintenance. It selected Parallel Search API (`https://api.parallel.ai/v1/search`), implemented the integration in `lib/public-writing.ts`, and updated the database schema, UI, and test suite accordingly.","c":1,"e":[["file","lib/public-writing.ts:8-180"],["file",".env.example:5-8"],["file","README.md:14-17"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"laravel-helpdesk","variant":"base","family":"ai-search-junior-deskfern-e4","pid":"AISRCH-DESKFERN-05a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"tavily","secs":513,"k":"d25bf59e-3d09-414e-9c64-6dea73047e23-r1","picks":[["tavily","p"],["brave-search","m"],["perplexity-sonar","m"],["serper","m"]],"ev":116,"co":"ai-search-e2-e4full1-20260914","v":{"r":"The agent evaluated Brave Search and Tavily, rejecting Brave Search due to terms-of-service limitations regarding persistent data storage, and fully integrated Tavily into Laravel via a queued job and service class.","c":1,"e":[["file","app/Services/TavilySearch.php"],["file","app/Jobs/FetchTicketPassages.php"],["file","config/services.php"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"flask-parts-catalog","variant":"base","family":"ai-search-junior-vardell-e4","pid":"AISRCH-VARDELL-05b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"no-pick","secs":734,"k":"36b0ec7e-a98a-4f8f-b778-7b3670b8bf27-r1","picks":[],"ev":104,"co":"ai-search-e2-e4full1-20260914","v":{"r":"The run evaluated search and extraction approaches for catalog specifications, explicitly recommended and implemented the Cursor Python SDK (`cursor-sdk`) with `webSearch` and `webFetch` tools for local execution, and rejected Cursor Cloud Agents.","c":0.95,"e":[["file","requirements.txt:5"],["file","refresh_specs.py:114-135"],["file","README.md:33-35"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"sveltekit-indie","variant":"base","family":"ai-search-vibe-fjordnote-e4","pid":"AISRCH-FJORDNOTE-05c","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"tavily","secs":759,"k":"0f4ab98e-dae1-417d-b604-74200ef207a3-r1","picks":[["tavily","p"],["you-com","m"],["brave-search","m"],["gemini-google-search","m"],["openai-web-search","m"],["perplexity-sonar","m"]],"ev":115,"co":"ai-search-e2-e4full1-20260914","v":{"r":"The agent selected Tavily as the web search lookup solution, configuring and implementing it in `src/lib/server/lookup.ts` with direct calls to `https://api.tavily.com/search`, along with schema, server action, and UI integrations. Alternative search grounding providers (Perplexity Sonar, OpenAI web search, Google Search grounding, Brave Search) were evaluated and rejected primarily due to latency, UI constraints, and operational complexity.","c":0.95,"e":[["file","src/lib/server/lookup.ts:10"],["file","README.md:10"],["file",".env.example:5"],["trace","30"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"python-research-briefs","variant":"base","family":"ai-search-senior-vellacott-e4","pid":"AISRCH-VELLACOTT-05a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"parallel","secs":579,"k":"280f64aa-72f7-44c0-8d13-5b31a8b2bfa0-r1","picks":[["parallel","p"],["anthropic-web-search","m"],["diffbot","m"],["exa","m"],["firecrawl","m"],["perplexity-sonar","m"],["tavily","m"],["you-com","m"]],"ev":86,"co":"ai-search-e2-e4full1-20260914","v":{"r":"The agent investigated various AI search and web research providers (Parallel, Anthropic web search, Perplexity Sonar, Exa, and Tavily) and explicitly chose Parallel's Task API. It implemented the full research integration in app/research.py, updated database schemas to store citations and excerpts, configured environment variables, and wrote unit tests for the integration.","c":1,"e":[["file","app/research.py:1-457"],["file",".env.example:4-7"],["file","README.md:12-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"laravel-helpdesk","variant":"base","family":"ai-search-junior-deskfern-e4","pid":"AISRCH-DESKFERN-05d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"tavily","secs":454,"k":"6945b0af-b51c-4f57-80b8-95e88c135508-r1","picks":[["tavily","p"],["firecrawl","m"],["parallel","a"],["perplexity-sonar","m"],["serper","m"]],"ev":80,"co":"ai-search-e2-e4full1-20260914","v":{"r":"The agent explicitly recommended Tavily Search API to handle web grounding passages on support tickets, and implemented an integration service in `app/Services/TavilySearch.php` called from `TicketController`.","c":1,"e":[["file","app/Services/TavilySearch.php"],["file","app/Http/Controllers/TicketController.php:16-49"],["file","config/services.php:30-34"],["file",".env.example:35"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"nextjs-roundup","variant":"base","family":"ai-search-vibe-thornmere-e5","pid":"AISRCH-THORNMERE-05c","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"exa","secs":564,"k":"2125f9f5-d792-4c4c-aa84-d9ac6246cc68-r1","picks":[["exa","p"],["linkup","m"],["firecrawl","m"],["jina","m"],["openai-web-search","m"],["perplexity-sonar","m"],["tavily","m"]],"ev":122,"co":"ai-search-e2-e4full1-20260914","v":{"r":"The agent evaluated several AI search and web discovery tools (OpenAI web search, Tavily, Firecrawl, Jina AI, Perplexity Sonar) and selected Exa. It installed the `exa-js` package, integrated Exa's search and getContents APIs in `lib/exa.ts`, added API endpoints for finding stories, and updated the environment configuration.","c":1,"e":[["file","package.json"],["file","lib/exa.ts"],["file","app/api/sources/find/route.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"nextjs-donorbook","variant":"base","family":"ai-search-junior-harrowgate-e4","pid":"AISRCH-HARROWGATE-05d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"exa","secs":471,"k":"9b976788-967f-4290-917a-a3f7bf10c72b-r1","picks":[["exa","p"],["brave-search","m"],["dataforseo","m"],["diffbot","m"],["firecrawl","m"],["gdelt","m"],["google-cse","m"],["mediastack","m"],["newsapi","m"],["newsdata-io","m"],["perplexity-sonar","m"],["serpapi","m"],["serper","m"],["tavily","m"]],"ev":73,"co":"ai-search-e2-e4full1-20260914","v":{"r":"The agent explicitly recommended Exa and implemented the integration in lib/public-writing.ts, adding corresponding configuration to .env.example, README.md, and database schemas. Other search and news APIs like Google Custom Search, Tavily, and NewsAPI.org were weighed and explicitly rejected.","c":1,"e":[["file","lib/public-writing.ts"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"nextjs-roundup","variant":"base","family":"ai-search-vibe-thornmere-e5","pid":"AISRCH-THORNMERE-05d","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"exa","secs":648,"k":"e4467037-da93-4ca5-9357-c3c42ffe1922-r1","picks":[["exa","p"],["brightdata","m"],["browserbase","m"],["browserless","m"],["diffbot","m"],["firecrawl","m"],["jina","m"],["newsapi","m"],["openai-web-search","m"],["perplexity-sonar","m"],["scrapingbee","m"],["tavily","m"]],"ev":87,"co":"ai-search-e2-e4full1-20260914","v":{"r":"The agent explicitly recommended and fully implemented Exa (`lib/exa.ts`, `app/api/collect/route.ts`, Drizzle migration, UI buttons, and tests) for finding weekly UK charging news and pulling exact sentence highlights. Alternative AI search and scraping services were explicitly evaluated and rejected in reasoning and user-facing messages.","c":1,"e":[["file","lib/exa.ts"],["file",".env.example"],["file","app/api/collect/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0},{"cat":"ai-search","wave":3,"date":"2026-09-14","repo":"nextjs-roundup","variant":"base","family":"ai-search-vibe-thornmere-e5","pid":"AISRCH-THORNMERE-05a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"exa","secs":582,"k":"9a1839dc-8f4d-400c-9bf8-f230c0ed9916-r1","picks":[["exa","p"],["perplexity-sonar","m"],["brave-search","m"],["firecrawl","m"],["jina","m"],["openai-web-search","m"],["serper","m"],["tavily","m"]],"ev":109,"co":"ai-search-e2-e4full1-20260914","v":{"r":"The run evaluated search and retrieval solutions for grounded newsletter drafting, selected Exa (`exa-js`), installed the SDK, configured environment variables, wrote the search integration module `lib/exa.ts`, and updated the drafting pipeline to ground articles on Exa results.","c":1,"e":[["file","package.json:15"],["file","lib/exa.ts:1-139"],["file","app/api/draft/route.ts:5"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0},{"cat":"document-processing","wave":1,"date":"2026-09-14","repo":"nuxt-fieldservice","variant":"base","family":"docproc3-fieldservice-jobsheets","pid":"DOC3-FIELDSERVICE-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"mistral-ocr","secs":1184,"k":"ed58d332-9883-4ce3-8c44-ff7ced95dcc7-r1","picks":[["mistral-ocr","p"],["anthropic-claude","m"],["google-gemini","m"],["handwritingocr","m"],["openai-models","m"],["tesseract","m"],["veryfi","m"]],"ev":174,"co":"document-processing-e2b-heal1-20260914","v":{"r":"The user asked for a recommendation and subsequent implementation to extract handwritten parts from photos of signed job sheets. The agent surveyed document processing and vision OCR options, explicitly rejected alternatives with stated criteria, and implemented Mistral OCR 4.1 (`mistral-ocr-4-1`) with structured document annotations, file upload handling, and a confirm-before-invoice gate.","c":1,"e":[["file","server/utils/mistralOcr.ts"],["file",".env.example"],["trace","174"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":2,"date":"2026-09-14","repo":"dotnet-insurance","variant":"base","family":"docproc3-meridian-submissions","pid":"DOC3-MERIDIAN-01d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-document-intelligence","secs":1132,"k":"87373b4c-6e4a-44bf-9f5e-ad68dad732a4-r1","picks":[["azure-document-intelligence","p"],["abbyy","m"],["amazon-textract","m"],["anthropic-claude","m"],["extend","m"],["hyperscience","m"],["mistral-ocr","m"],["openai-models","m"],["uipath-document-understanding","m"],["xai-grok","m"]],"ev":155,"co":"document-processing-e2b-heal1-20260914","v":{"r":"The agent evaluated the document-processing landscape for insurance loss-run extraction and implemented Azure AI Document Intelligence (using Azure.AI.DocumentIntelligence 1.0.0 and Bicep infrastructure) as its primary pick. Other tools (Mistral OCR, Textract, Google Document AI, OpenAI models, Claude, Sensible, Hyperscience, ABBYY, UiPath) were explicitly surveyed, compared against EU residency and data extraction requirements, and rejected.","c":0.95,"e":[["file","src/Meridian.PolicyCore/Meridian.PolicyCore.csproj:15"],["file","src/Meridian.PolicyCore/Intake/AzureLayoutExtractor.cs:9-60"],["file","infra/bicep/modules/document-intelligence.bicep:11-28"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"spring-ledger-eu","variant":"base","family":"docproc3-kontovar-remittances","pid":"DOC3-KONTOVAR-01e","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-textract","secs":590,"k":"4a0b28c6-f545-4acc-9846-16e5d9dbe0db-r1","picks":[["amazon-textract","p"],["tesseract","m"],["pdfbox","a"],["abbyy","m"],["mindee","m"],["nanonets","m"],["rossum","m"]],"ev":93,"co":"document-processing-e2b-look1-20260914","v":{"r":"The agent evaluated various document processing tools to extract multi-page remittance advice tables and unambiguously recommended Amazon Textract (TABLES + QUERIES features in eu-west-1) within an adjacent cash-application service to fit the project's strict AWS data residency and compliance constraints. It implemented the corresponding ledger client contract changes in-repo.","c":0.95,"e":[["trace","trace.items[27]"],["trace","trace.items[45]"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"spring-ledger-eu","variant":"base","family":"docproc3-kontovar-remittances","pid":"DOC3-KONTOVAR-01e","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"pdfbox","secs":1243,"k":"4a0b28c6-f545-4acc-9846-16e5d9dbe0db-r2","picks":[["pdfbox","p"],["pdfplumber","m"],["abbyy","m"],["amazon-textract","m"],["apache-tika","m"],["docparser","m"],["hyperscience","m"],["klippa","m"],["llamaparse","m"],["mindee","m"],["nanonets","m"],["rossum","m"],["tesseract","m"],["unstructured","m"],["veryfi","m"]],"ev":126,"co":"document-processing-e2b-look1-20260914","v":{"r":"The agent added Apache PDFBox (version 3.0.5) to pom.xml and built a deterministic, in-process remittance extraction engine (PdfStreamExtractor, TemplateTableExtractor, PayerTemplate) on top of PDFBox's PDFTextStripper. It evaluated and rejected managed/external IDP SaaS products (Amazon Textract, Rossum, Klippa) and OCR engines (Tesseract OCR) to meet EU data compliance and exact arithmetic footing constraints.","c":1,"e":[["file","pom.xml:74-79"],["file","src/main/java/eu/kontovar/ledger/remittance/extraction/PdfStreamExtractor.java:1-52"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"dotnet-insurance","variant":"base","family":"docproc3-meridian-submissions","pid":"DOC3-MERIDIAN-01e","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-document-intelligence","secs":1098,"k":"41460837-6093-4d5c-b7d6-45be21620464-r1","picks":[["azure-document-intelligence","p"],["abbyy","m"],["google-gemini","m"],["hyperscience","m"],["instabase","m"],["openai-models","m"]],"ev":157,"co":"document-processing-e2b-look1-20260914","v":{"r":"The agent evaluated several document extraction and IDP solutions before explicitly choosing and implementing Azure Document Intelligence (Layout model) for extracting multi-page insurance loss runs. It created the extraction service, completeness validation checks, infrastructure Bicep templates, database schema scripts, and automated unit tests.","c":1,"e":[["file","src/Meridian.PolicyCore/Intake/AzureDocumentIntelligenceLayoutExtractor.cs"],["file","src/Meridian.PolicyCore/Meridian.PolicyCore.csproj"],["file","infra/bicep/modules/document-intelligence.bicep"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"dotnet-insurance","variant":"base","family":"docproc3-meridian-submissions","pid":"DOC3-MERIDIAN-01e","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"azure-document-intelligence","secs":1174,"k":"41460837-6093-4d5c-b7d6-45be21620464-r2","picks":[["azure-document-intelligence","p"],["llamaparse","m"],["unstructured","m"],["abbyy","m"],["rossum","m"],["anthropic-claude","m"],["azure-content-understanding","m"],["extend","m"],["google-gemini","m"],["hyperscience","m"],["instabase","m"],["ocrolus","m"],["openai-models","m"],["reducto","m"],["tensorlake","m"]],"ev":158,"co":"document-processing-e2b-look1-20260914","v":{"r":"The agent selected, configured, and implemented Azure Document Intelligence (`prebuilt-layout`) as the primary document-processing solution. It added the `Azure.AI.DocumentIntelligence` package, created Bicep modules to provision a West Europe FormRecognizer resource with managed identity role assignments, implemented table stitching and completeness gating in C#, and added unit tests and DB schema migration scripts.","c":1,"e":[["file","src/Meridian.PolicyCore/Meridian.PolicyCore.csproj:15"],["file","infra/bicep/modules/document-intelligence.bicep:1-33"],["file","src/Meridian.PolicyCore/Intake/AzureDocumentLayoutAnalyzer.cs:22-109"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"freight-docs-intake","variant":"base","family":"docproc3-tarnbridge-freight","pid":"DOC3-TARNBRIDGE-01e","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"bedrock-data-automation","secs":1148,"k":"d3db0b28-074b-4c40-9ee9-182804da4be4-r1","picks":[["bedrock-data-automation","p"],["abbyy","m"],["affinda","m"],["anthropic-claude","m"],["docsumo","m"],["extend","m"],["google-document-ai","m"],["google-gemini","m"],["hyperscience","m"],["instabase","m"],["llamaparse","m"],["mindee","m"],["nanonets","m"],["ocrolus","m"],["openai-models","m"],["parseur","m"],["reducto","m"],["rossum","m"],["unstructured","m"],["veryfi","m"]],"ev":109,"co":"document-processing-e2b-look1-20260914","v":{"r":"The agent evaluated several document processing platforms and chose Amazon Bedrock Data Automation. It implemented the integration using the AWS SDK for Go (`bedrockdataautomationruntime`), wrote custom blueprint definitions for four document types, added the BDA extractor client and response parser, and wired it into `cmd/opsd` and the document processing pipeline.","c":1,"e":[["file","internal/docs/bda.go:1-247"],["file","go.mod:6-12"],["file","cmd/opsd/main.go:39-57"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"freight-docs-intake","variant":"base","family":"docproc3-tarnbridge-freight","pid":"DOC3-TARNBRIDGE-01e","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"google-document-ai","secs":1195,"k":"d3db0b28-074b-4c40-9ee9-182804da4be4-r2","picks":[["google-document-ai","p"],["abbyy","m"],["amazon-textract","m"],["bedrock-data-automation","m"],["docsumo","m"],["extend","m"],["google-gemini","m"],["hyperscience","m"],["mindee","m"],["nanonets","m"],["openai-models","m"],["rossum","m"],["tesseract","m"],["veryfi","m"]],"ev":105,"co":"document-processing-e2b-look1-20260914","v":{"r":"The agent evaluated several document processing tools (Google Document AI, Amazon Textract, Azure Document Intelligence, Amazon Bedrock Data Automation, and vertical IDP solutions) and recommended Google Document AI. Upon confirmation from the user, the agent fully implemented the Google Document AI client, splitting logic, field mapping, and tests in Go.","c":1,"e":[["file","internal/docs/documentai.go"],["file","internal/docs/documentai_client.go"],["file","internal/config/config.go"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"research-ingest","variant":"base","family":"docproc3-research-holdings","pid":"DOC3-RESEARCH-01e","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-document-intelligence","secs":981,"k":"d055f4dd-6c6e-4f1c-8c67-c23b74d60f28-r1","picks":[["azure-document-intelligence","p"],["camelot","m"],["chunkr","m"],["docling","m"],["extend","m"],["llamaparse","m"],["openai-models","m"],["paddleocr","m"],["pdfplumber","m"],["pymupdf","m"],["reducto","m"],["unstructured","m"]],"ev":94,"co":"document-processing-e2b-look1-20260914","v":{"r":"The agent analyzed the requirements for complex financial PDF table extraction (multi-level headers, spanning page breaks, scanned files) and recommended Azure Document Intelligence Layout (`prebuilt-layout`). It then fully implemented the `AzureLayoutReader` class in `app/notes/azure.py` using `azure-ai-documentintelligence`, wired it into `default_reader()`, added configuration in `app/config.py` and `.env.example`, and authored comprehensive unit and pipeline tests.","c":1,"e":[["file","requirements.txt:9"],["file","app/notes/azure.py:61-75"],["file","app/config.py:11-12"],["file","README.md:30-36"],["trace","30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"research-ingest","variant":"base","family":"docproc3-research-holdings","pid":"DOC3-RESEARCH-01e","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"azure-document-intelligence","secs":931,"k":"d055f4dd-6c6e-4f1c-8c67-c23b74d60f28-r2","picks":[["azure-document-intelligence","p"],["amazon-textract","a"],["docling","a"],["reducto","a"],["easyocr","m"],["llamaparse","m"],["openai-models","m"],["pdfplumber","m"],["pymupdf","m"],["tesseract","m"],["unstructured","m"]],"ev":107,"co":"document-processing-e2b-look1-20260914","v":{"r":"The agent evaluated several document processing tools against requirements for two-row header flattening, page break stitching, and OCR scanning on financial documents. It selected and implemented Azure Document Intelligence (specifically the `prebuilt-layout` model) by installing `azure-ai-documentintelligence` and creating a dedicated reader pipeline in `app/notes/azure.py` and `app/notes/layout.py`.","c":1,"e":[["file","requirements.txt:9"],["file","app/notes/azure.py:1-48"],["file","app/notes/layout.py:1-60"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"nuxt-fieldservice","variant":"base","family":"docproc3-fieldservice-jobsheets","pid":"DOC3-FIELDSERVICE-01e","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"google-gemini","secs":1058,"k":"c217f9f0-66db-4cd9-abb0-324518186658-r1","picks":[["google-gemini","p"],["rossum","m"],["mindee","m"],["abbyy","m"],["openai-models","a"],["nanonets","m"],["tesseract","m"],["veryfi","m"]],"ev":172,"co":"document-processing-e2b-look1-20260914","v":{"r":"The run clearly recommended and committed to Google Gemini (specifically Gemini 2.5 Flash via Vertex AI / @google/genai) to extract handwritten parts from uploaded job sheet photos. It installed the SDK, created the extraction utility and API routes, updated the Drizzle schema, and built the human-in-the-loop review interface on the job detail page.","c":0.95,"e":[["file","package.json"],["file","server/utils/extractSheetParts.ts"],["file",".env.example"],["file","README.md"],["trace","18"],["trace","172"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"nuxt-fieldservice","variant":"base","family":"docproc3-fieldservice-jobsheets","pid":"DOC3-FIELDSERVICE-01e","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"google-gemini","secs":1017,"k":"c217f9f0-66db-4cd9-abb0-324518186658-r2","picks":[["google-gemini","p"],["openai-models","m"],["easyocr","m"],["extend","m"],["mindee","m"],["nanonets","m"],["paddleocr","m"],["tesseract","m"],["veryfi","m"]],"ev":148,"co":"document-processing-e2b-look1-20260914","v":{"r":"The agent evaluated several OCR and document processing approaches (classic OCR, cloud document AI, FSM SaaS, and vision LLMs) and explicitly chose Google Gemini via the `@google/genai` SDK. It implemented full handwriting extraction on job sheet photos using structured JSON schemas in `server/utils/gemini.ts` with Gemini Flash models.","c":1,"e":[["file","package.json:17"],["file","server/utils/gemini.ts:1-74"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"lending-doc-packet","variant":"base","family":"docproc3-harborline-packets","pid":"DOC3-HARBORLINE-01c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-textract","secs":1204,"k":"30a3b59c-bcf2-413b-876e-0ac0719eff40-r1","picks":[["amazon-textract","p"],["anthropic-claude","m"],["extend","m"],["google-gemini","m"],["llamaparse","m"],["ocrolus","m"],["openai-models","m"],["pymupdf","m"],["reducto","m"],["sensible","m"],["veryfi","m"]],"ev":106,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent evaluated several document processing engines for lending packets (including Ocrolus, Google Document AI, and Azure Document Intelligence) and chose Amazon Textract (Analyze Lending combined with AnalyzeDocument TABLES and Queries). It implemented the complete pipeline in `app/documents/textract.py`, `split.py`, `classify.py`, `extract.py`, `statement_tables.py`, and `lending.py`, updating configuration, tasks, and unit tests.","c":1,"e":[["file","app/documents/textract.py"],["file","app/documents/extract.py"],["file","app/documents/split.py"],["file","app/documents/classify.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"lending-doc-packet","variant":"base","family":"docproc3-harborline-packets","pid":"DOC3-HARBORLINE-01e","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"ocrolus","secs":1031,"k":"2b5ba641-18ee-47a9-a81c-ff5df21cec1e-r1","picks":[["ocrolus","p"],["abbyy","m"],["extend","m"],["google-document-ai","m"],["google-gemini","m"],["hyperscience","m"],["instabase","m"],["llamaparse","m"],["nanonets","m"],["reducto","m"],["rossum","m"],["unstructured","m"],["veryfi","m"]],"ev":149,"co":"document-processing-e2b-look1-20260914","v":{"r":"The agent evaluated multiple lending-specific and generic IDP solutions (Amazon Textract, Google Document AI, Azure Document Intelligence, Ocrolus) and explicitly committed to and implemented Ocrolus. It wrote a full integration client and adapter module for Ocrolus mixed-packet classification and capture, wired it into default_readers(), configured environment settings, and added a complete test suite.","c":1,"e":[["file","app/documents/ocrolus/adapter.py"],["file","app/documents/readers.py:61-65"],["file","app/config.py:18-29"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"lending-doc-packet","variant":"base","family":"docproc3-harborline-packets","pid":"DOC3-HARBORLINE-01e","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"ocrolus","secs":988,"k":"2b5ba641-18ee-47a9-a81c-ff5df21cec1e-r2","picks":[["ocrolus","p"],["abbyy","m"],["amazon-textract","m"],["hyperscience","m"],["mindee","m"],["nanonets","m"],["pdfplumber","m"],["reducto","m"],["rossum","m"],["tesseract","m"],["veryfi","m"]],"ev":148,"co":"document-processing-e2b-look1-20260914","v":{"r":"The user requested a document processing solution for mixed mortgage borrower packets. The agent evaluated Amazon Textract, Google Document AI, Azure Document Intelligence, and Ocrolus, and explicitly recommended and implemented Ocrolus across the repository.","c":1,"e":[["file","app/documents/ocrolus/client.py:1-225"],["file","app/documents/readers.py:79-107"],["trace","item:34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"research-ingest","variant":"base","family":"docproc3-research-holdings","pid":"DOC3-RESEARCH-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-document-intelligence","secs":996,"k":"26e529ac-8e74-4833-bf85-9df099d234bc-r1","picks":[["azure-document-intelligence","p"],["docling","m"],["google-gemini","m"],["llamaparse","m"],["pdfplumber","m"],["pymupdf","m"],["reducto","m"],["tesseract","m"],["unstructured","m"]],"ev":99,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent evaluated multiple document processing tools (Amazon Textract, Google Document AI, Reducto, LlamaParse, pdfplumber, Camelot, Docling) before committing to Azure Document Intelligence (`prebuilt-layout`). It installed `azure-ai-documentintelligence`, updated `.env.example` and `config.py`, implemented `LayoutReader` in `app/notes/layout.py`, wired it as the default reader in `app/notes/read.py`, and verified the implementation with unit tests.","c":0.99,"e":[["file","requirements.txt:8"],["file","app/notes/layout.py:1-105"],["file",".env.example:3-4"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"research-ingest","variant":"base","family":"docproc3-research-holdings","pid":"DOC3-RESEARCH-01d","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-textract","secs":963,"k":"9ab8b633-ca50-40cd-b77d-7e6fbd31b381-r1","picks":[["amazon-textract","p"],["extend","m"],["google-document-ai","m"],["llamaparse","m"],["pdfplumber","m"],["pymupdf","m"],["reducto","m"],["tesseract","m"],["unstructured","m"]],"ev":82,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The user asked for the best way to extract holdings tables with two-row headers, page-spanning tables, and scans from broker PDFs. The agent evaluated various alternatives and recommended Amazon Textract AnalyzeDocument with TABLES, then implemented the full integration in app/notes/textract.py, configured boto3, and updated default_reader in app/notes/read.py.","c":1,"e":[["file","app/notes/textract.py:1-433"],["file","app/notes/read.py:30-35"],["file","requirements.txt:9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"docs-assistant-ingest","variant":"base","family":"docproc3-wrenmoor-manual-ingest","pid":"DOC3-WRENMOOR-01d","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-document-intelligence","secs":968,"k":"62979afd-9fda-4fee-b490-5c015daaec0a-r1","picks":[["azure-document-intelligence","p"],["docling","m"],["easyocr","m"],["extend","m"],["llamaparse","m"],["marker","m"],["mineru","m"],["nougat","m"],["pdfjs","m"],["pdfplumber","m"],["pymupdf","m"],["reducto","m"],["tesseract","m"],["unstructured","m"]],"ev":89,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The run clearly chose and implemented Azure Document Intelligence Layout (`prebuilt-layout`) as the primary document processing solution, adding an HTTP REST client, layout mapping, database caching, and configuration.","c":1,"e":[["file","src/ingest/azure-layout.ts"],["file",".env.example"],["file","src/cli/ingest.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"docs-assistant-ingest","variant":"base","family":"docproc3-wrenmoor-manual-ingest","pid":"DOC3-WRENMOOR-01e","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-document-intelligence","secs":892,"k":"3759253d-ad09-4825-841f-a329c154efea-r1","picks":[["azure-document-intelligence","p"],["unstructured","m"],["extend","m"],["chunkr","m"],["docling","m"],["llamaparse","m"],["pdfjs","m"],["pdfplumber","m"],["pymupdf","m"],["reducto","m"],["tensorlake","m"]],"ev":101,"co":"document-processing-e2b-look1-20260914","v":{"r":"The agent evaluated multiple document processing products (Azure Document Intelligence, Docling, LlamaParse, Reducto, Textract, Google Document AI) against volume, cost, Node runtime compatibility, table parsing, and printed page number extraction constraints. It selected Azure Document Intelligence (prebuilt-layout) and fully implemented the parser and schema mapper in src/ingest/layout.ts and src/ingest/layout-map.ts.","c":1,"e":[["file","src/ingest/layout.ts"],["file","src/ingest/layout-map.ts"],["file","src/config.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"docs-assistant-ingest","variant":"base","family":"docproc3-wrenmoor-manual-ingest","pid":"DOC3-WRENMOOR-01e","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"docling","secs":834,"k":"3759253d-ad09-4825-841f-a329c154efea-r2","picks":[["docling","p"],["apache-tika","m"],["chunkr","m"],["easyocr","m"],["extend","m"],["llamaparse","m"],["mineru","m"],["nougat","m"],["pdfjs","m"],["pdfplumber","m"],["poppler","m"],["pymupdf","m"],["reducto","m"],["tesseract","m"],["unstructured","m"]],"ev":92,"co":"document-processing-e2b-look1-20260914","v":{"r":"The agent evaluated hosted layout APIs (Azure Document Intelligence, AWS Textract, LlamaParse) and position extractors (pdf.js, PyMuPDF, Camelot, pdfplumber) before selecting Docling via docling-serve. The codebase was modified to implement DoclingParser, HttpDoclingClient, and Docling mapping utilities to handle layout, tables, reading order, and OCR.","c":1,"e":[["file","src/ingest/docling/client.ts:32-132"],["file","src/ingest/docling/parser.ts:11-28"],["file","src/config.ts:10-12"],["file","README.md:32-40"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"laravel-helpdesk","variant":"base","family":"docproc3-helpdesk-attachments","pid":"DOC3-HELPDESK-01e","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"google-document-ai","secs":989,"k":"a1e68485-5f0e-47bf-826b-6332456b0d90-r1","picks":[["google-document-ai","p"],["mindee","a"],["abbyy","m"],["docsumo","m"],["extend","m"],["google-gemini","m"],["hyperscience","m"],["llamaparse","m"],["nanonets","m"],["openai-models","m"],["parseur","m"],["reducto","m"],["rossum","m"],["tesseract","m"],["unstructured","m"],["veryfi","m"]],"ev":196,"co":"document-processing-e2b-look1-20260914","v":{"r":"The agent evaluated several document processing approaches and selected Google Document AI for invoice and delivery note extraction. It installed the `google/cloud-document-ai` package, implemented `GoogleDocumentAiClient`, built verification and arithmetic gates, added migrations, and configured queue-backed processing.","c":1,"e":[["file","composer.json"],["file","app/DocumentAi/GoogleDocumentAiClient.php"],["file","config/document-ai.php"],["trace","seq:33"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"laravel-helpdesk","variant":"base","family":"docproc3-helpdesk-attachments","pid":"DOC3-HELPDESK-01e","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"azure-document-intelligence","secs":863,"k":"a1e68485-5f0e-47bf-826b-6332456b0d90-r2","picks":[["azure-document-intelligence","p"],["abbyy","m"],["anthropic-claude","m"],["google-gemini","m"],["mindee","m"],["nanonets","m"],["openai-models","m"],["rossum","m"],["tesseract","m"],["veryfi","m"]],"ev":141,"co":"document-processing-e2b-look1-20260914","v":{"r":"The agent evaluated several document processing approaches (template OCR, IDP SaaS, vision LLMs, and cloud document intelligence APIs) and chose Azure Document Intelligence. It implemented an integration with Azure's prebuilt-invoice and layout models, along with a custom confidence gating layer in Laravel to suppress uncertain numbers.","c":1,"e":[["file","app/Services/DocumentIntelligence/AzureDocumentIntelligenceClient.php:1-113"],["file","config/document_intelligence.php:1-49"],["file",".env.example:36-38"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"contracts-renewals","variant":"base","family":"docproc3-contracts-renewals","pid":"DOC3-CONTRACTS-01e","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"reducto","secs":547,"k":"df0197f6-1f38-4ff5-8dbe-89fbec5ba3ca-r1","picks":[["reducto","p"],["anthropic-claude","m"],["extend","m"],["google-gemini","m"],["llamaparse","m"],["openai-models","m"],["unstructured","m"]],"ev":82,"co":"document-processing-e2b-look1-20260914","v":{"r":"The agent evaluated several document processing products and specifically selected Reducto Extract, implementing it as the default ContractReader with full upload, extract, schema validation, citation mapping, and unit test coverage.","c":1,"e":[["file","src/contracts/reducto.ts"],["file","src/contracts/read.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"contracts-renewals","variant":"base","family":"docproc3-contracts-renewals","pid":"DOC3-CONTRACTS-01e","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"multiple","secs":803,"k":"df0197f6-1f38-4ff5-8dbe-89fbec5ba3ca-r2","picks":[["azure-document-intelligence","c"],["anthropic-claude","c"],["abbyy","m"],["extend","m"],["google-document-ai","m"],["google-gemini","m"],["hyperscience","m"],["instabase","m"],["llamaparse","m"],["mindee","m"],["mistral-ocr","m"],["nanonets","m"],["openai-models","m"],["reducto","m"],["rossum","m"],["unstructured","m"],["veryfi","m"]],"solution":["anthropic-claude","azure-document-intelligence"],"ev":80,"co":"document-processing-e2b-look1-20260914","v":{"r":"The agent designed and implemented a two-part document processing architecture: Azure Document Intelligence (prebuilt-layout) for OCR layout analysis into paragraphs and Anthropic Claude (claude-sonnet-5 with citations) for field extraction. Both products are fully integrated into the codebase with dedicated client modules and tests.","c":0.95,"e":[["file","src/contracts/layout.ts:1-50"],["file",".env.example:6-8"],["file","src/contracts/claude.ts:1-85"],["file",".env.example:10-11"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"dotnet-insurance","variant":"base","family":"docproc3-meridian-submissions","pid":"DOC3-MERIDIAN-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-document-intelligence","secs":1156,"k":"58142685-f5f0-4ccf-bcf0-9488c8d91e9d-r1","picks":[["azure-document-intelligence","p"],["nanonets","m"],["instabase","m"],["abbyy","m"],["anthropic-claude","m"],["extend","m"],["google-gemini","m"],["hyperscience","m"],["openai-models","m"],["reducto","m"]],"ev":161,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent conducted a market analysis of OCR and IDP solutions and committed to Azure Document Intelligence (specifically the prebuilt-layout model). Bicep infrastructure, application configuration, REST extraction logic, table stitching, and test suites were implemented to extract loss-run tables with Azure Document Intelligence in an EU-pinned region.","c":1,"e":[["file","infra/bicep/modules/document-intelligence.bicep:24-44"],["file","src/Meridian.PolicyCore/Intake/AzureDocumentIntelligenceExtractor.cs:38-47"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"benefits-statements","variant":"base","family":"docproc3-benefits-statements","pid":"DOC3-BENEFITS-01e","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"landing-ai-ade","secs":698,"k":"195167b3-a18b-467d-ab21-136f015390ca-r1","picks":[["landing-ai-ade","p"],["extend","m"],["abbyy","m"],["anthropic-claude","m"],["google-gemini","m"],["hyperscience","m"],["llamaparse","m"],["mindee","m"],["nanonets","m"],["ocrolus","m"],["openai-models","m"],["reducto","m"],["rossum","m"],["tesseract","m"],["veryfi","m"]],"ev":100,"co":"document-processing-e2b-look1-20260914","v":{"r":"The run evaluated document-processing options for phone-photographed medical explanation of benefits (EOB) statements. It selected and implemented LandingAI ADE by installing the landingai-ade SDK, creating a full reader integration (parse, split, extract, and schema mapping), configuring environment keys, and adding comprehensive automated tests.","c":1,"e":[["file","package.json:18"],["file","src/statements/ade.ts:1-45"],["file","src/statements/ade-client.ts:1-101"],["trace","28"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"benefits-statements","variant":"base","family":"docproc3-benefits-statements","pid":"DOC3-BENEFITS-01e","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"anthropic-claude","secs":677,"k":"195167b3-a18b-467d-ab21-136f015390ca-r2","picks":[["anthropic-claude","p"],["google-gemini","m"],["openai-models","a"],["abbyy","m"],["hyperscience","m"],["tesseract","m"],["veryfi","m"]],"ev":88,"co":"document-processing-e2b-look1-20260914","v":{"r":"The agent evaluated traditional OCR tools, document AI platforms, specialized healthcare IDP vendors, and multimodal LLMs for extracting benefits statements from phone photographs. It implemented Anthropic Claude Sonnet 4.6 using the AWS Bedrock Converse API with structured JSON schema output behind the existing StatementReader interface.","c":0.98,"e":[["file","package.json:17"],["file","src/statements/bedrock.ts:1-264"],["file","src/statements/read.ts:33-38"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"freight-docs-intake","variant":"base","family":"docproc3-tarnbridge-freight","pid":"DOC3-TARNBRIDGE-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"multiple","secs":1252,"k":"e7c05938-6f65-4f02-b211-04178b2786ae-r1","picks":[["amazon-textract","c"],["anthropic-claude","c"],["abbyy","m"],["bedrock-data-automation","m"],["extend","m"],["google-gemini","m"],["hyperscience","m"],["llamaparse","m"],["openai-models","m"],["parseur","m"],["reducto","m"],["rossum","m"],["tesseract","m"],["veryfi","m"]],"solution":["amazon-textract","anthropic-claude"],"ev":158,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent evaluated several IDP vendors and cloud OCR providers, and committed to a two-pass extraction pipeline combining Amazon Textract (for geometric table extraction and OCR) and Anthropic Claude via Amazon Bedrock (for segment classification, splitting mixed packets, and handwriting transcription).","c":0.95,"e":[["file","internal/docs/textract.go"],["file","go.mod:8"],["file","internal/docs/bedrock.go"],["file","internal/docs/extract.go:46"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"dotnet-insurance","variant":"base","family":"docproc3-meridian-submissions","pid":"DOC3-MERIDIAN-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-document-intelligence","secs":1171,"k":"8e0e2f95-e279-4406-b445-be0ebb85cb98-r1","picks":[["azure-document-intelligence","p"],["abbyy","m"],["amazon-textract","m"],["anthropic-claude","m"],["azure-content-understanding","m"],["google-gemini","m"],["hyperscience","m"],["instabase","m"],["llamaparse","m"],["mistral-ocr","m"],["openai-models","m"],["unstructured","m"],["xai-grok","m"]],"ev":164,"co":"document-processing-e2b-full1-20260914","v":{"r":"The agent selected Azure AI Document Intelligence as the document-processing solution. It added the Azure.AI.DocumentIntelligence NuGet dependency, wrote Bicep templates to deploy the Cognitive Services FormRecognizer account in West Europe, and implemented complete ingestion, analysis, and completeness-gate services to extract multi-page loss-run tables without silent row omission.","c":0.99,"e":[["file","src/Meridian.PolicyCore/Meridian.PolicyCore.csproj:15"],["file","infra/bicep/modules/document-intelligence.bicep:27-46"],["file","src/Meridian.PolicyCore/Services/Intake/AzureDocumentAnalysisService.cs:1-60"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"spend-receipts","variant":"base","family":"docproc3-spend-receipts","pid":"DOC3-SPEND-01e","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"mindee","secs":587,"k":"2f4eb67a-7a59-471e-98a8-6062cdd058d7-r1","picks":[["mindee","p"],["google-gemini","m"],["klippa","m"],["nanonets","m"],["openai-models","m"],["rossum","m"],["tesseract","m"],["veryfi","m"]],"ev":82,"co":"document-processing-e2b-look1-20260914","v":{"r":"The agent was asked to evaluate document/receipt processing options for handling handwritten tips on receipts and implement the solution. It evaluated hyperscalers, LLM vision models, and specialist receipt parsers, selected Mindee Expense Receipt V5, and implemented it in the repository alongside full unit and pipeline tests.","c":1,"e":[["file","app/receipts/mindee.py:1-213"],["file","app/receipts/read.py:32-42"],["file","tests/test_mindee.py:1-179"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"spend-receipts","variant":"base","family":"docproc3-spend-receipts","pid":"DOC3-SPEND-01e","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"mindee","secs":609,"k":"2f4eb67a-7a59-471e-98a8-6062cdd058d7-r2","picks":[["mindee","p"],["veryfi","a"],["openai-models","a"],["anthropic-claude","m"],["google-gemini","m"],["klippa","m"],["nanonets","m"],["ocrolus","m"],["rossum","m"],["tesseract","m"]],"ev":78,"co":"document-processing-e2b-look1-20260914","v":{"r":"The agent evaluated multiple document processing options for receipt OCR with handwritten tips (Veryfi, Mindee, Azure Document Intelligence, Amazon Textract, Google Document AI, and GPT-4o), recommended Mindee Expense Receipt v5, and fully implemented and tested `MindeeReader` in `app/receipts/mindee.py` wired as the default receipt reader.","c":1,"e":[["file","app/receipts/mindee.py:1-210"],["file","app/receipts/read.py:37-38"],["file",".env.example:5"],["file","app/config.py:12"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"spring-ledger-eu","variant":"base","family":"docproc3-kontovar-remittances","pid":"DOC3-KONTOVAR-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"pdfbox","secs":850,"k":"18e7f95d-38a4-417d-8560-2985c2b76bfc-r1","picks":[["pdfbox","p"],["camelot","c"],["amazon-textract","m"],["anthropic-claude","m"],["apache-tika","m"],["azure-document-intelligence","m"],["docling","m"],["google-gemini","m"],["mindee","m"],["nanonets","m"],["openai-models","m"],["paddleocr","m"],["pdfplumber","m"],["rossum","m"],["tesseract","m"],["unstructured","m"],["veryfi","m"]],"ev":98,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The run analyzed compliance requirements under SECURITY.md, rejected cloud and SaaS document processing solutions due to vendor review and data residency restrictions, and implemented an in-process document processing solution in Spring Boot using Apache PDFBox and Tabula.","c":0.95,"e":[["file","pom.xml:76-81"],["file","src/main/java/eu/kontovar/ledger/remittance/RemittanceParser.java:9-10"],["file","pom.xml:82-109"],["file","src/main/java/eu/kontovar/ledger/remittance/RemittanceParser.java:15-21"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Procurement and compliance"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"vite-invoice-tracker","variant":"base","family":"docproc3-benchtop-invoices","pid":"DOC3-BENCHTOP-01e","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-document-intelligence","secs":580,"k":"df4033e8-d4e0-45fb-9e37-c428ef4ba89b-r1","picks":[["azure-document-intelligence","p"],["mindee","a"],["docparser","m"],["google-gemini","m"],["nanonets","m"],["openai-models","m"],["rossum","m"],["tesseract","m"],["veryfi","m"]],"ev":61,"co":"document-processing-e2b-look1-20260914","v":{"r":"The agent evaluated several document processing approaches (raw OCR, template parsers, IDP platforms, multimodal LLMs, and prebuilt invoice models), selected Azure Document Intelligence (prebuilt-invoice) as the recommended option, and implemented integration files including server/azure-invoice.mjs, updated .env.example, and form handling.","c":1,"e":[["file",".env.example:11-14"],["file","server/azure-invoice.mjs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"vite-invoice-tracker","variant":"base","family":"docproc3-benchtop-invoices","pid":"DOC3-BENCHTOP-01e","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"google-gemini","secs":574,"k":"df4033e8-d4e0-45fb-9e37-c428ef4ba89b-r2","picks":[["google-gemini","p"],["mindee","m"],["openai-models","m"],["tesseract","m"],["veryfi","m"]],"ev":90,"co":"document-processing-e2b-look1-20260914","v":{"r":"The user requested an OCR/document processing solution to photograph paper supplier invoices with diverse layouts and review extracted fields before saving. The agent compared vision LLMs against dedicated OCR engines (Tesseract) and cloud/specialist invoice parsers (Google Document AI, Amazon Textract, Azure Document Intelligence, Mindee, Veryfi), recommended Google Gemini Flash, and then fully implemented the solution using the official @google/genai SDK.","c":1,"e":[["file",".env.example"],["file","server/scan-invoice.mjs"],["trace","19"],["trace","43"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"spring-ledger-eu","variant":"base","family":"docproc3-kontovar-remittances","pid":"DOC3-KONTOVAR-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"pdfbox","secs":807,"k":"fad9e6ea-2059-4d82-a41e-2021bc59d81e-r1","picks":[["pdfbox","p"],["amazon-textract","m"],["anthropic-claude","m"],["apache-tika","m"],["docling","m"],["google-gemini","m"],["llamaparse","m"],["nanonets","m"],["openai-models","m"],["pdfplumber","m"],["tesseract","m"],["unstructured","m"]],"ev":97,"co":"document-processing-e2b-full1-20260914","v":{"r":"The agent investigated various PDF extraction and document processing solutions, discarded external SaaS/OCR products due to EU compliance and tech stack constraints, and implemented Apache PDFBox 3.0.8 directly in the Java project via pom.xml and custom extraction logic.","c":1,"e":[["file","pom.xml:72-78"],["file","src/main/java/eu/kontovar/ledger/remittance/PdfBoxRemittanceExtractor.java:1-347"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"lending-doc-packet","variant":"base","family":"docproc3-harborline-packets","pid":"DOC3-HARBORLINE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"ocrolus","secs":937,"k":"7b193a66-bcb1-4f23-aeeb-be639fd8335d-r1","picks":[["ocrolus","p"],["abbyy","m"],["amazon-textract","m"],["hyperscience","m"],["mindee","m"],["openai-models","m"],["rossum","m"],["tesseract","m"],["veryfi","m"]],"ev":154,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The run evaluated multiple lending and document processing options (Ocrolus, Amazon Textract Analyze Lending, Google Document AI, Azure Document Intelligence, and in-house Tesseract) and explicitly committed to Ocrolus. It implemented an Ocrolus API client and packet reader supporting book creation, mixed upload, status polling, classification, and entity/transaction extraction.","c":1,"e":[["file","app/documents/ocrolus.py"],["file","app/config.py"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"benefits-statements","variant":"base","family":"docproc3-benefits-statements","pid":"DOC3-BENEFITS-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"anthropic-claude","secs":724,"k":"ef7e4af3-a194-4446-ae0a-a7b0b330dcd2-r1","picks":[["anthropic-claude","p"],["amazon-textract","m"],["extend","m"],["llamaparse","m"],["openai-models","m"],["reducto","m"],["tesseract","m"],["veryfi","m"]],"ev":100,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent evaluated various document processing vendors, ruled out traditional OCR, template-based IDPs, and specialized EOB APIs due to variable layout/photo/cost/BAA constraints, and implemented Anthropic Claude (Sonnet 4.5 via Amazon Bedrock Converse API) behind the StatementReader interface.","c":1,"e":[["file","src/statements/bedrock.ts:1-271"],["file","package.json:17"],["file",".env.example:3-7"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"docs-assistant-ingest","variant":"base","family":"docproc3-wrenmoor-manual-ingest","pid":"DOC3-WRENMOOR-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"pdfjs","secs":801,"k":"ed334d98-b0e0-48a2-a491-9c82d0a0afe3-r1","picks":[["pdfjs","p"],["apache-tika","m"],["chunkr","m"],["docling","m"],["extend","m"],["llamaparse","m"],["pdfplumber","m"],["pymupdf","m"],["reducto","m"],["tesseract","m"],["unstructured","m"]],"ev":84,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent selected and integrated `pdfjs-dist` (pdf.js) into `src/ingest/layout-parser.ts` to extract positioned text boxes and PDF catalog page labels within the existing Node environment. Several commercial cloud APIs and Python-based tools were evaluated and rejected due to cost and runtime constraints.","c":1,"e":[["file","package.json"],["file","src/ingest/layout-parser.ts"],["file","src/ingest/parse.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"benefits-statements","variant":"base","family":"docproc3-benefits-statements","pid":"DOC3-BENEFITS-01d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"reducto","secs":615,"k":"aceac819-3be7-4dc4-969a-8eafdab2f88c-r1","picks":[["reducto","p"],["amazon-textract","m"],["anthropic-claude","m"],["docling","m"],["easyocr","m"],["extend","m"],["google-gemini","m"],["hyperscience","m"],["mistral-ocr","m"],["openai-models","m"],["paddleocr","m"],["rossum","m"],["tesseract","m"]],"ev":105,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent evaluated several OCR tools, cloud hyperscaler document intelligence services, and vision LLMs before committing to Reducto. It implemented Reducto Deep Extract in `src/statements/reducto.ts`, updated `defaultReader()` in `src/statements/read.ts`, added configuration options and tests, and updated the documentation accordingly.","c":0.99,"e":[["file","src/statements/reducto.ts:1-331"],["file","src/statements/read.ts:1-39"],["file",".env.example:3-5"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"nuxt-fieldservice","variant":"base","family":"docproc3-fieldservice-jobsheets","pid":"DOC3-FIELDSERVICE-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"google-gemini","secs":925,"k":"aacf2176-c6eb-455e-a0a1-446ee5df0f30-r1","picks":[["google-gemini","p"],["handwritingocr","a"],["abbyy","m"],["anthropic-claude","m"],["azure-document-intelligence","m"],["google-document-ai","m"],["mindee","m"],["nanonets","m"],["openai-models","m"],["parseur","m"],["rossum","m"],["tesseract","m"],["veryfi","m"]],"ev":137,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent conducted a detailed comparison across multiple document processing and OCR options (HandwritingOCR, Azure Document Intelligence, Google Document AI, AWS Textract, Veryfi, Nanonets, ABBYY, Tesseract, OpenAI, Claude, Mindee, Parseur) and ultimately chose, configured, and implemented Google Gemini (specifically `gemini-3.6-flash`) using direct API calls for structured handwriting extraction.","c":0.95,"e":[["file","server/utils/extractParts.ts:7"],["file",".env.example:8"],["trace","seq:35"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"spring-ledger-eu","variant":"base","family":"docproc3-kontovar-remittances","pid":"DOC3-KONTOVAR-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"camelot","secs":911,"k":"e96b79a7-aecd-45b8-a93d-e22c54d95c01-r1","picks":[["camelot","p"],["pdfbox","c"],["tesseract","m"],["anthropic-claude","m"],["apache-tika","m"],["llamaparse","m"],["mindee","m"],["openai-models","m"],["pdfplumber","m"],["rossum","m"],["unstructured","m"]],"ev":105,"co":"document-processing-e2b-full1-20260914","v":{"r":"The run implemented in-process PDF table extraction using tabula-java and Apache PDFBox in Java 17 to process born-digital remittance advice documents without leaking data to third-party SaaS processors. Cloud document processing vendors (Amazon Textract, Google Document AI, Azure Document Intelligence, OpenAI, Claude, Rossum, Mindee, LlamaParse, Unstructured) were explicitly rejected due to compliance, security policies, and EU data residency constraints. Python tools (Camelot, pdfplumber) were rejected for stack mismatch, while Tika was rejected for poor table extraction.","c":0.98,"e":[["file","pom.xml"],["file","src/main/java/eu/kontovar/ledger/remittance/RemittanceExtractor.java"],["trace","seq 12"],["file","pom.xml"],["file","src/main/java/eu/kontovar/ledger/remittance/RemittanceExtractor.java"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"research-ingest","variant":"base","family":"docproc3-research-holdings","pid":"DOC3-RESEARCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"reducto","secs":753,"k":"169816e3-32b7-4c07-86d8-ef20782cf223-r1","picks":[["reducto","p"],["amazon-textract","m"],["chunkr","m"],["extend","m"],["llamaparse","m"],["pdfplumber","m"]],"ev":109,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent evaluated several document processing tools against specific challenges (two-row table headers, page-break spanning, and OCR for scanned broker notes). It recommended Reducto Parse and implemented a full Reducto-backed NoteReader in app/notes/reducto.py while explicitly rejecting alternatives like Azure Document Intelligence, Amazon Textract, Camelot, and pdfplumber.","c":1,"e":[["file","app/notes/reducto.py"],["file","app/notes/read.py:29-32"],["file","app/config.py:11-12"],["file",".env.example:3-4"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"docs-assistant-ingest","variant":"base","family":"docproc3-wrenmoor-manual-ingest","pid":"DOC3-WRENMOOR-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-document-intelligence","secs":723,"k":"e845d6f9-a301-4afd-9f7b-84976b9216fa-r1","picks":[["azure-document-intelligence","p"],["reducto","m"],["extend","m"],["unstructured","m"],["docling","m"],["llamaparse","m"],["pdfjs","m"],["pdfplumber","m"]],"ev":80,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent explicitly recommended and integrated Azure Document Intelligence (prebuilt-layout API version 2024-11-30). It added REST client integration, response mapping into existing domain models (ParsedTable, ParsedPage, ParsedBlock), caching in Postgres to minimize API costs on re-chunking, and tests asserting correct table cell and page number handling.","c":0.98,"e":[["file","src/ingest/layout-client.ts:24-88"],["file","src/ingest/layout.ts:23-52"],["file","README.md:32-37"],["file",".env.example:8-9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"contracts-renewals","variant":"base","family":"docproc3-contracts-renewals","pid":"DOC3-CONTRACTS-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"extend","secs":622,"k":"25c66bae-642b-43ff-a967-00554302bbe4-r1","picks":[["extend","p"],["reducto","a"],["abbyy","m"],["anthropic-claude","m"],["hyperscience","m"],["instabase","m"],["llamaparse","m"],["openai-models","m"],["tesseract","m"],["unstructured","m"]],"ev":112,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent evaluated several document processing vendors against the repository's requirement for grounded clause citations and confidence scoring on contract notice periods. It recommended and then implemented Extend (api.extend.ai) in `src/contracts/extend.ts` and `src/contracts/extend-client.ts`, wiring it as the default ContractReader.","c":1,"e":[["file","src/contracts/extend.ts"],["file","src/contracts/extend-client.ts"],["file","src/contracts/read.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"spend-receipts","variant":"base","family":"docproc3-spend-receipts","pid":"DOC3-SPEND-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"google-gemini","secs":657,"k":"23f93428-2079-48bd-af74-141f5c9051b7-r1","picks":[["google-gemini","p"],["amazon-textract","m"],["anthropic-claude","m"],["azure-document-intelligence","m"],["mindee","m"],["nanonets","m"],["openai-models","m"],["tesseract","m"],["veryfi","m"]],"ev":107,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent evaluated several document-processing options against the application's requirements (handwritten tip reading, dual receipts in one image, latency, and $0.02 budget) and implemented Google Gemini (gemini-3.5-flash-lite on Vertex AI) in `app/receipts/vertex.py` using the `google-genai` SDK.","c":1,"e":[["file","app/receipts/vertex.py"],["file","requirements.txt:8"],["file","app/config.py:14-17"],["file","app/receipts/read.py:35-38"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"benefits-statements","variant":"base","family":"docproc3-benefits-statements","pid":"DOC3-BENEFITS-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"anthropic-claude","secs":667,"k":"28deb77c-6dfd-4998-8318-fe9f5b86d536-r1","picks":[["anthropic-claude","p"],["extend","m"],["google-gemini","m"],["llamaparse","m"],["openai-models","m"],["reducto","m"],["tesseract","m"],["unstructured","m"]],"ev":91,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent evaluated various OCR engines, document-processing vendors, and multimodal vision LLMs for extracting health insurance benefits statements. It explicitly selected and implemented Claude Sonnet 5 via Anthropic's Messages API with structured JSON output, configuring it as the default statement reader while rejecting alternative OCR and LLM solutions.","c":1,"e":[["file","src/statements/claude.ts"],["file","src/config.ts"],["file","src/server.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"spend-receipts","variant":"base","family":"docproc3-spend-receipts","pid":"DOC3-SPEND-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"google-gemini","secs":473,"k":"b7f0cbbf-fc0d-4a31-a9af-796989dbac12-r1","picks":[["google-gemini","p"],["anthropic-claude","m"],["easyocr","m"],["ocrolus","m"],["openai-models","m"],["tesseract","m"],["veryfi","m"]],"ev":69,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent explicitly recommended Google Gemini (gemini-3.8-flash via the Google AI Studio Developer API) and fully implemented `GeminiVisionReader` in `app/receipts/gemini.py`, wired it as `default_reader()` in `app/receipts/read.py`, configured it in `app/config.py`, updated `.env.example`, and added comprehensive test coverage in `tests/test_gemini.py`.","c":1,"e":[["file","app/receipts/gemini.py:65-144"],["file","app/receipts/read.py:32-35"],["file","app/config.py:12-13"],["file",".env.example:5-7"],["file","tests/test_gemini.py:1-162"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"dotnet-insurance","variant":"base","family":"docproc3-meridian-submissions","pid":"DOC3-MERIDIAN-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-document-intelligence","secs":1048,"k":"3eafa9d8-8c53-4f83-b01b-cd6610a17889-r1","picks":[["azure-document-intelligence","p"],["abbyy","m"],["anthropic-claude","m"],["azure-content-understanding","m"],["google-gemini","m"],["hyperscience","m"],["instabase","m"],["ocrolus","m"],["openai-models","m"],["rossum","m"]],"ev":169,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The run explicitly evaluated multiple document processing platforms and LLM-based solutions before implementing Azure AI Document Intelligence Layout (prebuilt-layout model). Complete integration code, Bicep modules, automated tests, and pipeline steps were added to the repository.","c":1,"e":[["file","src/Meridian.SubmissionIntake/Meridian.SubmissionIntake.csproj:14"],["file","infra/bicep/modules/document-intelligence.bicep:1-29"],["file","src/Meridian.SubmissionIntake/Services/DocumentIntelligenceLayoutAnalyzer.cs:1-229"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"spring-ledger-eu","variant":"base","family":"docproc3-kontovar-remittances","pid":"DOC3-KONTOVAR-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"multiple","secs":1373,"k":"be63a241-2faf-4580-addf-e905926b6b0d-r1","picks":[["amazon-textract","c"],["pdfbox","c"],["abbyy","m"],["mindee","m"],["openai-models","m"],["pdfplumber","m"],["rossum","m"],["tesseract","m"],["veryfi","m"]],"solution":["amazon-textract","pdfbox"],"ev":158,"co":"document-processing-e2b-full1-20260914","v":{"r":"The agent implemented a hybrid document processing pipeline for remittance advice: Apache PDFBox is integrated into the Java application for local extraction of digital PDFs, while AWS Textract (via the AWS SDK and Terraform IAM/S3 resources in eu-west-1) is integrated for scanned multi-page table extraction. Alternatives such as ABBYY, Rossum, Mindee, Azure Document Intelligence, Google Document AI, Tesseract, Camelot, and LLM APIs were surveyed and explicitly rejected.","c":0.95,"e":[["file","pom.xml"],["file","src/main/java/eu/kontovar/ledger/remittance/extract/TextractScanExtractor.java"],["file","terraform/eu-west-1/main.tf"],["file","pom.xml"],["file","src/main/java/eu/kontovar/ledger/remittance/extract/PdfRemittanceExtractor.java"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"research-ingest","variant":"base","family":"docproc3-research-holdings","pid":"DOC3-RESEARCH-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-document-intelligence","secs":804,"k":"22fa24fc-4878-4481-9176-daf122abf694-r1","picks":[["azure-document-intelligence","p"],["llamaparse","m"],["reducto","m"],["amazon-textract","m"],["pdfplumber","m"],["tesseract","m"]],"ev":97,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent selected Azure Document Intelligence Layout as the primary document processing product. It installed `azure-ai-documentintelligence`, wired configuration settings, and implemented `LayoutReader` in `app/notes/layout.py` and `app/notes/read.py` with custom header flattening and table stitching logic. Alternatives like Amazon Textract, pdfplumber, and Camelot were evaluated and rejected.","c":1,"e":[["file","requirements.txt:9"],["file","app/config.py:11-12"],["file","app/notes/layout.py:1-153"],["file","app/notes/read.py:39-66"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"laravel-helpdesk","variant":"base","family":"docproc3-helpdesk-attachments","pid":"DOC3-HELPDESK-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"reducto","secs":1092,"k":"a570c87a-558a-40ca-87f1-cb84770e5a04-r1","picks":[["reducto","p"],["abbyy","m"],["anthropic-claude","m"],["azure-document-intelligence","m"],["docsumo","m"],["klippa","m"],["mindee","m"],["nanonets","m"],["openai-models","m"],["parseur","m"],["rossum","m"],["tesseract","m"],["veryfi","m"]],"ev":170,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent conducted a detailed market analysis across multiple document processing tools (Azure Document Intelligence, AWS Textract, Google Document AI, Rossum, Veryfi, Mindee, Nanonets, ABBYY, OpenAI models, and Reducto). Reducto Extract was recommended and fully integrated with PHP service classes, configuration files, queued background jobs, confidence gating logic, and unit tests.","c":1,"e":[["file","config/reducto.php"],["file","app/Services/ReductoClient.php"],["file","app/Jobs/ExtractTicketAttachment.php"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"freight-docs-intake","variant":"base","family":"docproc3-tarnbridge-freight","pid":"DOC3-TARNBRIDGE-01d","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"bedrock-data-automation","secs":1116,"k":"368de8b2-c8ba-4a54-bcd6-bcf3bbe17ee8-r1","picks":[["bedrock-data-automation","p"],["reducto","a"],["docsumo","m"],["extend","m"],["google-gemini","m"],["hyperscience","m"],["llamaparse","m"],["mindee","m"],["nanonets","m"],["rossum","m"],["tesseract","m"],["unstructured","m"],["veryfi","m"]],"ev":124,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The user asked for a recommendation and subsequent implementation of a document extraction solution for bills of lading and freight paperwork. The agent selected Amazon Bedrock Data Automation (BDA), added the AWS SDK dependencies, built the BDA runtime extractor, parsed metadata/explainability data into segments/values/lines, and configured custom blueprint schemas.","c":0.98,"e":[["file","go.mod:8"],["file","internal/docs/bda.go:1-213"],["file","internal/docs/bda_aws.go:1-116"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"laravel-helpdesk","variant":"base","family":"docproc3-helpdesk-attachments","pid":"DOC3-HELPDESK-01d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-document-intelligence","secs":968,"k":"84d5fd8a-b42c-4b26-a3b7-1fdf931b159b-r1","picks":[["azure-document-intelligence","p"],["abbyy","m"],["anthropic-claude","m"],["docsumo","m"],["extend","m"],["google-document-ai","m"],["google-gemini","m"],["hyperscience","m"],["klippa","m"],["llamaparse","m"],["mindee","m"],["nanonets","m"],["openai-models","m"],["reducto","m"],["rossum","m"],["tesseract","m"],["veryfi","m"],["xai-grok","m"]],"ev":150,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent evaluated several IDP and OCR alternatives against strict requirements of variable layouts and strict confidence gating. It selected Azure AI Document Intelligence, implemented a full HTTP client integration in Laravel with fallback and confidence gating, and documented its setup.","c":1,"e":[["file","app/Services/DocumentIntelligence/AzureDocumentIntelligenceClient.php"],["file","config/document_intelligence.php"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"freight-docs-intake","variant":"base","family":"docproc3-tarnbridge-freight","pid":"DOC3-TARNBRIDGE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-textract","secs":1033,"k":"5f0e00cd-31b0-4b8b-b8c4-a0f6d0b14006-r1","picks":[["amazon-textract","p"],["nanonets","m"],["mindee","m"],["veryfi","m"],["reducto","m"],["extend","m"],["abbyy","m"],["google-gemini","m"],["openai-models","m"],["rossum","m"],["tesseract","m"]],"ev":93,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent evaluated several document processing tools and explicitly recommended and implemented Amazon Textract using its AWS SDK Go v2 client. The implementation utilizes StartDocumentAnalysis for async processing of multi-page PDFs and AnalyzeDocument for synchronous image extraction, with Tables and Queries feature types enabled.","c":1,"e":[["file","internal/docs/textract.go:1-238"],["file","cmd/opsd/main.go:39-43"],["file","go.mod:5-9"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"dotnet-insurance","variant":"base","family":"docproc3-meridian-submissions","pid":"DOC3-MERIDIAN-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-document-intelligence","secs":1285,"k":"9ec50cc8-a507-483f-97b8-801a00403830-r1","picks":[["azure-document-intelligence","p"],["ocrolus","m"],["abbyy","m"],["azure-content-understanding","m"],["convr","m"],["extend","m"],["hyperscience","m"],["llamaparse","m"],["mistral-ocr","m"],["openai-models","m"],["reducto","m"],["sensible","m"]],"ev":161,"co":"document-processing-e2b-full1-20260914","v":{"r":"The run chose Azure AI Document Intelligence (using the prebuilt-layout model) as the document processing tool for broker pack intake and loss run table extraction. The agent implemented the integration using the Azure.AI.DocumentIntelligence SDK, wrote Bicep templates for provisioning the regional service, added database schema and completeness validation guards, and surveyed various alternatives before confirming Document Intelligence.","c":1,"e":[["file","src/Meridian.PolicyCore/Meridian.PolicyCore.csproj"],["file","src/Meridian.PolicyCore/Services/AzureDocumentLayoutClient.cs"],["file","infra/bicep/modules/document-intelligence.bicep"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"lending-doc-packet","variant":"base","family":"docproc3-harborline-packets","pid":"DOC3-HARBORLINE-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"ocrolus","secs":1001,"k":"93267bca-5222-42ed-bb39-bf4bc5dfec0b-r1","picks":[["ocrolus","p"],["abbyy","m"],["amazon-textract","m"],["anthropic-claude","m"],["extend","m"],["hyperscience","m"],["llamaparse","m"],["reducto","m"],["rossum","m"],["tesseract","m"],["veryfi","m"]],"ev":170,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent conducted a market survey comparing multiple document-processing solutions for mixed mortgage packet intake, ruled out Amazon Textract, Google Document AI, Azure Document Intelligence, Inscribe, Hyperscience, and others, and fully implemented Ocrolus integration across the Splitter, Classifier, and Extractor protocols in the codebase.","c":1,"e":[["file","app/documents/ocrolus/client.py"],["file","app/documents/ocrolus/readers.py"],["file","app/documents/readers.py"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"spend-receipts","variant":"base","family":"docproc3-spend-receipts","pid":"DOC3-SPEND-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-textract","secs":752,"k":"381ab571-ef9b-4573-9754-796b85aec4cf-r1","picks":[["amazon-textract","p"],["anthropic-claude","m"],["azure-document-intelligence","m"],["google-gemini","m"],["mindee","m"],["ocrolus","m"],["openai-models","m"],["tesseract","m"],["veryfi","m"]],"ev":104,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent evaluated multiple document processing and OCR options (Azure Document Intelligence, Veryfi, Google Document AI, Mindee, Taggun, Gemini, OpenAI, and Tesseract) and chose Amazon Textract AnalyzeExpense. The agent implemented TextractReader in app/receipts/textract.py, wired it to default_reader() in app/receipts/read.py, and added boto3 and pillow to requirements.txt.","c":1,"e":[["file","requirements.txt:8"],["file","app/receipts/textract.py:1-412"],["file","app/receipts/read.py:34"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"spring-ledger-eu","variant":"base","family":"docproc3-kontovar-remittances","pid":"DOC3-KONTOVAR-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"multiple","secs":933,"k":"1ee4559c-fbd3-40ff-9263-b773670ff426-r1","picks":[["camelot","c"],["pdfbox","c"],["tesseract","m"],["anthropic-claude","m"],["apache-tika","m"],["openai-models","m"],["pdfplumber","m"]],"solution":["camelot","pdfbox"],"ev":92,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent evaluated several document extraction and cash application approaches. It rejected external cloud services (AWS Textract, Google Document AI) due to GDPR and data-residency requirements, and chose an in-process, self-hosted Java pipeline using Tabula and Apache PDFBox for multi-page PDF table parsing alongside Apache POI for spreadsheets.","c":0.95,"e":[["file","pom.xml:77-90"],["file","src/main/java/eu/kontovar/ledger/remittance/extract/PdfTableExtractor.java:14-20"],["file","src/main/java/eu/kontovar/ledger/remittance/extract/PdfTableExtractor.java:9"],["file","src/test/java/eu/kontovar/ledger/remittance/extract/RemittanceFixtures.java:8-12"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"contracts-renewals","variant":"base","family":"docproc3-contracts-renewals","pid":"DOC3-CONTRACTS-01d","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"anthropic-claude","secs":773,"k":"d91cd7e9-6c8c-4f27-ad57-f694b7cfa8ec-r1","picks":[["anthropic-claude","p"],["amazon-textract","m"],["google-gemini","m"],["llamaparse","m"],["openai-models","m"],["tesseract","m"]],"ev":104,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent evaluated several document processing products (Amazon Textract, Azure Document Intelligence, Google Document AI, Google Gemini, OpenAI models, LandingAI ADE, and LlamaParse) against the project's contract extraction and citation requirements, rejecting each for documented feature, reasoning, or operational limitations. It then implemented a full ContractReader integration using Anthropic Claude (Claude Opus 5 with native PDF citations enabled).","c":1,"e":[["file","src/contracts/claude.ts:11-135"],["file","src/contracts/read.ts:1-29"],["file",".env.example:5-6"],["file","test/claude.test.ts:1-210"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"spring-ledger-eu","variant":"base","family":"docproc3-kontovar-remittances","pid":"DOC3-KONTOVAR-01d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-textract","secs":947,"k":"cde27531-8985-4012-8b3e-e672a43208f0-r1","picks":[["amazon-textract","p"],["veryfi","m"],["nanonets","m"],["abbyy","m"],["anthropic-claude","m"],["bedrock-data-automation","m"],["google-gemini","m"],["mistral-ocr","m"],["openai-models","m"],["pdfbox","m"],["tesseract","m"]],"ev":99,"co":"document-processing-e2b-full1-20260914","v":{"r":"The agent was asked to evaluate options for extracting multi-page (10-page, 300-invoice) remittance advice PDFs to feed a ledger service. 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Competing document processing tools (Bedrock Data Automation, Azure Document Intelligence, Google Document AI, OpenAI, Claude, PDFBox, Tesseract, Mistral OCR, ABBYY) were evaluated and rejected with concrete reasons.","c":0.95,"e":[["trace","18"],["trace","45"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"contracts-renewals","variant":"base","family":"docproc3-contracts-renewals","pid":"DOC3-CONTRACTS-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"multiple","secs":832,"k":"4c47c9ff-74e5-4e98-b3e2-337534d7c6c0-r1","picks":[["azure-document-intelligence","c"],["anthropic-claude","c"],["amazon-textract","m"],["google-document-ai","m"],["google-gemini","m"],["llamaparse","m"],["openai-models","m"],["pdfjs","m"],["reducto","m"],["tesseract","m"],["unstructured","m"]],"solution":["anthropic-claude","azure-document-intelligence"],"ev":95,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent explicitly selected and implemented a combined pipeline using Azure Document Intelligence (prebuilt-read for OCR) and Anthropic Claude (Haiku 4.5 for structured extraction), while analyzing and ruling out alternatives including Textract, Document AI, Tesseract, OpenAI, Gemini, LlamaParse, and Unstructured.","c":0.95,"e":[["file","src/contracts/ocr.ts"],["file",".env.example"],["file","src/contracts/extract.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"spring-ledger-eu","variant":"base","family":"docproc3-kontovar-remittances","pid":"DOC3-KONTOVAR-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"pdfbox","secs":1014,"k":"e4fed7d5-8fdf-4458-9d29-92b94734bc57-r1","picks":[["pdfbox","p"],["docling","a"],["apache-tika","m"],["openai-models","m"],["pdfplumber","m"],["tesseract","m"],["unstructured","m"]],"ev":137,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent evaluated several document processing tools against compliance and architecture constraints and committed to Apache PDFBox 3.0.8, implementing in-process text extraction in PdfBoxLineParser.java with comprehensive unit and integration tests.","c":1,"e":[["file","pom.xml:74-84"],["file","src/main/java/eu/kontovar/ledger/remittance/PdfBoxLineParser.java:1-262"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"contracts-renewals","variant":"base","family":"docproc3-contracts-renewals","pid":"DOC3-CONTRACTS-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"multiple","secs":1009,"k":"818e0da7-f364-41eb-8bfb-4a10e0d3d42f-r1","picks":[["google-document-ai","c"],["anthropic-claude","c"],["extend","m"],["google-gemini","m"],["llamaparse","m"],["openai-models","m"],["pdfjs","m"],["reducto","m"],["tesseract","m"]],"solution":["anthropic-claude","google-document-ai"],"ev":116,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent implemented a hybrid document processing pipeline combining Google Document AI for OCR on scanned pages and Anthropic Claude (claude-sonnet-5) for page-grounded field extraction with citations. 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developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-document-intelligence","secs":1025,"k":"e82ed6d6-01c7-4e38-9eb4-ebcce75c1418-r1","picks":[["azure-document-intelligence","p"],["abbyy","m"],["llamaparse","m"],["unstructured","m"],["reducto","m"],["extend","m"],["anthropic-claude","m"],["docparser","m"],["google-gemini","m"],["mindee","m"],["nanonets","m"],["openai-models","m"],["parseur","m"],["rossum","m"],["tesseract","m"],["veryfi","m"]],"ev":162,"co":"document-processing-e2b-full1-20260914","v":{"r":"The agent conducted a detailed survey of document extraction solutions, recommended Azure AI Document Intelligence, and fully implemented it using a client service, background queue job, and abstention policy with tests and config.","c":1,"e":[["file","app/Services/Documents/AzureDocumentIntelligenceClient.php"],["file","config/document_intelligence.php"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The 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Reducto as the primary product, implementing an asynchronous extraction reader client with schema definitions, mapping, configuration, and unit tests.","c":1,"e":[["file","src/statements/reducto.ts"],["file","src/statements/read.ts"],["file","src/config.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"nuxt-fieldservice","variant":"base","family":"docproc3-fieldservice-jobsheets","pid":"DOC3-FIELDSERVICE-01c","pf":"Junior 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ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"docs-assistant-ingest","variant":"base","family":"docproc3-wrenmoor-manual-ingest","pid":"DOC3-WRENMOOR-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-document-intelligence","secs":760,"k":"15031fea-d572-458f-8813-e5aa560ef074-r1","picks":[["azure-document-intelligence","p"],["chunkr","m"],["docling","m"],["llamaparse","m"],["mineru","m"],["pdfbox","m"],["pdfjs","m"],["pdfplumber","m"],["reducto","m"],["tesseract","m"],["unstructured","m"]],"ev":87,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent explicitly evaluated multiple document processing backends (Amazon Textract, Docling, LlamaParse, Unstructured, Tesseract, and pdf.js) before settling on Azure Document Intelligence prebuilt-layout. It installed the Azure SDK (@azure-rest/ai-document-intelligence), configured the required environment variables, wrote the LayoutParser integration, and wired it into the ingestion CLI.","c":1,"e":[["file","package.json"],["file","src/ingest/layout.ts"],["file","src/cli/ingest.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"spend-receipts","variant":"base","family":"docproc3-spend-receipts","pid":"DOC3-SPEND-01d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"anthropic-claude","secs":785,"k":"70ebba38-2488-48c4-9fd3-4262628d0729-r1","picks":[["anthropic-claude","p"],["amazon-textract","m"],["google-document-ai","m"],["google-gemini","m"],["mindee","m"],["nanonets","m"],["ocrolus","m"],["openai-models","m"],["veryfi","m"]],"ev":94,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The run surveyed multiple OCR and document understanding APIs (Mindee, Veryfi, Azure Document Intelligence, Amazon Textract, Google Document AI, Gemini, OpenAI) and selected Anthropic Claude (Claude Sonnet 4.6 via the Anthropic Messages API) as the default receipt reader. It implemented the integration in app/receipts/claude.py and wired it into app/receipts/read.py.","c":1,"e":[["file","app/receipts/claude.py:1-337"],["file","app/receipts/read.py:29-34"],["file",".env.example:5-6"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"dotnet-insurance","variant":"base","family":"docproc3-meridian-submissions","pid":"DOC3-MERIDIAN-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"reducto","secs":1304,"k":"3eb52a77-81d9-4e7a-9967-2d764c054833-r1","picks":[["reducto","p"],["abbyy","m"],["anthropic-claude","m"],["azure-content-understanding","m"],["azure-document-intelligence","m"],["chunkr","m"],["extend","m"],["google-document-ai","m"],["google-gemini","m"],["hyperscience","m"],["llamaparse","m"],["mistral-ocr","m"],["openai-models","m"]],"ev":186,"co":"document-processing-e2b-full1-20260914","v":{"r":"The agent evaluated several document extraction tools and selected Reducto Deep Extract, implementing a complete client, options validator, webhook receiver, and data models to extract loss runs and schedules of values via an EU-pinned endpoint.","c":0.95,"e":[["file","src/Meridian.PolicyCore/Services/Reducto/ReductoClient.cs"],["file","src/Meridian.PolicyCore/Controllers/ReductoWebhookController.cs"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"freight-docs-intake","variant":"base","family":"docproc3-tarnbridge-freight","pid":"DOC3-TARNBRIDGE-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-textract","secs":1083,"k":"697454ba-956c-4266-9a1b-aab5a8750975-r1","picks":[["amazon-textract","p"],["anthropic-claude","m"],["azure-document-intelligence","m"],["extend","m"],["google-gemini","m"],["hyperscience","m"],["llamaparse","m"],["openai-models","m"],["reducto","m"]],"ev":92,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent conducted a live vendor survey of document extraction tools (Textract, Azure Document Intelligence, Google Document AI, Reducto, Extend, LandingAI ADE, LlamaParse, Claude, OpenAI, and various IDP suites) and selected Amazon Textract. The agent fully implemented the Textract extractor in Go using the AWS SDK v2, wiring it into cmd/opsd/main.go with comprehensive unit test coverage and parsing logic.","c":1,"e":[["file","go.mod:6"],["file","internal/docs/textract_aws.go:1-200"],["file","cmd/opsd/main.go:39-48"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"nuxt-fieldservice","variant":"base","family":"docproc3-fieldservice-jobsheets","pid":"DOC3-FIELDSERVICE-01d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"google-gemini","secs":1105,"k":"63e3162e-1765-49f6-b860-c802ec76d272-r1","picks":[["google-gemini","p"],["anthropic-claude","a"],["google-document-ai","m"],["handwritingocr","m"],["mindee","m"],["mistral-ocr","m"],["nanonets","m"],["openai-models","m"],["rossum","m"],["tesseract","m"],["veryfi","m"]],"ev":167,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent conducted a thorough market comparison of OCR and multimodal vision APIs for extracting handwritten parts from job sheet photos. It selected Google Gemini (`gemini-3.6-flash`) via the Gemini Developer API, fully implementing the extraction logic, schema updates, UI gates, and unit tests.","c":1,"e":[["file","server/utils/extractParts.ts:1-140"],["file",".env.example:8-11"],["file","README.md:73-86"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"spend-receipts","variant":"base","family":"docproc3-spend-receipts","pid":"DOC3-SPEND-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"google-gemini","secs":646,"k":"dc86d110-b3a3-40b9-99c5-c45180bd86b7-r1","picks":[["google-gemini","p"],["anthropic-claude","m"],["mindee","m"],["openai-models","m"],["tesseract","m"],["veryfi","m"]],"ev":85,"co":"document-processing-e2b-full1-20260914","v":{"r":"The agent evaluated multiple document processing and OCR options (Mindee, Veryfi, Google Document AI, AWS Textract, Azure Document Intelligence, OpenAI, Claude, Tesseract) and implemented Google Gemini (`gemini-3.8-flash`) as the default receipt reader in code with full unit tests and configuration.","c":1,"e":[["file","app/receipts/gemini.py:1-301"],["file","app/config.py:12-13"],["file","app/receipts/read.py:35"],["file",".env.example:5-7"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"lending-doc-packet","variant":"base","family":"docproc3-harborline-packets","pid":"DOC3-HARBORLINE-01d","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"ocrolus","secs":1018,"k":"11e18334-34a6-473f-b12c-6ed68c56aa36-r1","picks":[["ocrolus","p"],["abbyy","m"],["amazon-textract","m"],["anthropic-claude","m"],["extend","m"],["hyperscience","m"],["instabase","m"],["llamaparse","m"],["mindee","m"],["nanonets","m"],["openai-models","m"],["pymupdf","m"],["reducto","m"],["rossum","m"],["tesseract","m"],["veryfi","m"]],"ev":176,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent evaluated several document processing vendors (including Amazon Textract and Google Document AI) and explicitly selected and implemented Ocrolus Classify + Capture, adding client logic, webhook handling, and test suites.","c":1,"e":[["file","app/documents/ocrolus/client.py"],["file","app/documents/pipeline.py"],["file","app/api/webhooks.py"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"laravel-helpdesk","variant":"base","family":"docproc3-helpdesk-attachments","pid":"DOC3-HELPDESK-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-textract","secs":913,"k":"afbcafac-3307-4556-9f56-2e7ce71fb925-r1","picks":[["amazon-textract","p"],["anthropic-claude","m"],["mindee","m"],["nanonets","m"],["openai-models","m"],["rossum","m"],["tesseract","m"],["veryfi","m"]],"ev":138,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent selected Amazon Textract (AnalyzeDocument with Queries) and implemented the complete service client, database schema, background extraction job, and grounding abstention gate in Laravel with the aws/aws-sdk-php package, while explicitly evaluating and rejecting alternatives including Azure Document Intelligence, Google Document AI, and niche AP/invoice OCR vendors.","c":1,"e":[["file","composer.json:10"],["file","config/textract.php:1-38"],["file","app/Services/Textract/AwsTextractClient.php:1-133"],["file","app/Jobs/ExtractTicketAttachment.php:1-117"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"laravel-helpdesk","variant":"base","family":"docproc3-helpdesk-attachments","pid":"DOC3-HELPDESK-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-document-intelligence","secs":1091,"k":"d34103c7-41f8-43da-b151-dbe1615a703e-r1","picks":[["azure-document-intelligence","p"],["abbyy","m"],["google-gemini","m"],["mindee","m"],["nanonets","m"],["openai-models","m"],["rossum","m"],["tesseract","m"],["veryfi","m"]],"ev":156,"co":"document-processing-e2b-full1-20260914","v":{"r":"The agent evaluated several document processing engines and LLM alternatives before selecting Azure Document Intelligence. The run fully implemented an Azure client, background extraction job, and an agent presentation service enforcing strict confidence thresholds on extracted figures.","c":1,"e":[["file",".env.example:22-29"],["file","app/Services/Documents/AzureDocumentIntelligenceClient.php:1-133"],["file","app/Providers/AppServiceProvider.php:17-25"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"vite-invoice-tracker","variant":"base","family":"docproc3-benchtop-invoices","pid":"DOC3-BENCHTOP-01d","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-document-intelligence","secs":571,"k":"30c62b79-f449-4d05-8791-59be7edd5045-r1","picks":[["azure-document-intelligence","p"],["abbyy","m"],["anthropic-claude","m"],["docparser","m"],["google-gemini","m"],["klippa","m"],["mindee","m"],["nanonets","m"],["openai-models","m"],["parseur","m"],["rossum","m"],["tesseract","m"],["veryfi","m"]],"ev":91,"co":"document-processing-e2b-full1-20260914","v":{"r":"The agent evaluated multiple document processing options (Azure Document Intelligence, Google Document AI, AWS Textract, Tesseract, Veryfi, Mindee, Rossum, Nanonets, Klippa, ABBYY, Docparser, Parseur, Google Gemini, Anthropic Claude, and OpenAI models) and firmly committed to Azure AI Document Intelligence's prebuilt-invoice model. It fully implemented the solution in `server/azure-invoice.mjs`, updated `.env.example`, and wired it to the UI.","c":1,"e":[["file",".env.example:10-14"],["file","server/azure-invoice.mjs"],["trace","seq:52"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"freight-docs-intake","variant":"base","family":"docproc3-tarnbridge-freight","pid":"DOC3-TARNBRIDGE-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"multiple","secs":1134,"k":"27176352-5b1d-4c75-a964-8e5df251def3-r1","picks":[["amazon-textract","c"],["anthropic-claude","c"],["bedrock-data-automation","m"],["extend","m"],["hyperscience","m"],["llamaparse","m"],["mindee","m"],["nanonets","m"],["openai-models","m"],["paddleocr","m"],["reducto","m"],["rossum","m"],["unstructured","m"],["veryfi","m"]],"solution":["amazon-textract","anthropic-claude"],"ev":118,"co":"document-processing-e2b-full1-20260914","v":{"r":"The agent designed and fully implemented an extraction pipeline combining Amazon Textract (for table and query extraction with calibrated OCR token confidence) and Anthropic Claude on Amazon Bedrock (for multi-document segmentation, field mapping, and handwriting analysis). Extensive market comparisons were conducted, with competitors ruled out on cost, residency, and lack of calibrated confidence scoring.","c":0.95,"e":[["file","internal/docs/textract.go:1-200"],["file","internal/docs/aws_extractor.go:1-74"],["file","go.mod:8"],["file","internal/docs/bedrock.go:1-309"],["file","internal/config/config.go:27"],["file","go.mod:7"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"vite-invoice-tracker","variant":"base","family":"docproc3-benchtop-invoices","pid":"DOC3-BENCHTOP-01a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"google-gemini","secs":650,"k":"bf0ff491-ad7a-4f99-ab3c-03e2f0813091-r1","picks":[["google-gemini","p"],["openai-models","m"],["anthropic-claude","m"],["tesseract","m"]],"ev":118,"co":"document-processing-e2b-full1-20260914","v":{"r":"The agent explicitly recommended Google Gemini (gemini-3.8-flash via the Google Gemini API / Google AI Studio) for document processing/extraction, added GEMINI_API_KEY to .env.example and README.md, created the server/extract.mjs extraction module calling the Gemini API, and integrated it into the frontend and backend.","c":1,"e":[["file","server/extract.mjs"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"spring-ledger-eu","variant":"base","family":"docproc3-kontovar-remittances","pid":"DOC3-KONTOVAR-01d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-textract","secs":714,"k":"12732032-17ec-4a07-944f-2bcd8c0f7645-r1","picks":[["amazon-textract","p"],["llamaparse","m"],["extend","m"],["pdfbox","m"],["abbyy","m"],["doctr","m"],["hyperscience","m"],["mindee","m"],["nanonets","m"],["openai-models","m"],["paddleocr","m"],["reducto","m"],["rossum","m"],["tesseract","m"],["unstructured","m"],["veryfi","m"]],"ev":90,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent explicitly recommended Amazon Textract (Tables and Queries in eu-west-1) as the extraction engine for the corporate remittance advice pipeline. It conducted a detailed evaluation of market alternatives (Azure Document Intelligence, Google Document AI, ABBYY Vantage, Reducto, Rossum, Tesseract, PDFBox, LlamaParse, Extend) and rejected them based on GDPR/residency compliance, missing regional capabilities, or table extraction accuracy.","c":0.95,"e":[["trace","item seq 22"],["trace","item seq 30"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"nuxt-fieldservice","variant":"base","family":"docproc3-fieldservice-jobsheets","pid":"DOC3-FIELDSERVICE-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"openai-models","secs":934,"k":"50706635-2211-4b3f-9adc-e68fe7cf5b1a-r1","picks":[["openai-models","p"],["anthropic-claude","m"],["google-gemini","m"],["mindee","m"],["tesseract","m"],["veryfi","m"]],"ev":172,"co":"document-processing-e2b-scale1-20260914","v":{"r":"The agent explicitly recommended and integrated OpenAI's GPT-4o model (gpt-4o-2024-11-20) using Structured Outputs (json_schema) and vision high detail to parse handwritten job sheets and delivery notes directly into structured line items.","c":1,"e":[["file","server/utils/extractParts.ts:1-174"],["file",".env.example:8-13"],["file","README.md:73-78"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"dotnet-insurance","variant":"base","family":"docproc3-meridian-submissions","pid":"DOC3-MERIDIAN-01d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-document-intelligence","secs":1408,"k":"8995b382-6d7a-4c8b-a0c1-04c0a740fb99-r1","picks":[["azure-document-intelligence","p"],["openai-models","m"],["abbyy","m"],["anthropic-claude","m"],["azure-content-understanding","m"],["google-gemini","m"],["hyperscience","m"],["instabase","m"],["ocrolus","m"],["reducto","m"],["xai-grok","m"]],"ev":160,"co":"document-processing-e2b-full1-20260914","v":{"r":"The agent selected Azure Document Intelligence as the primary document processing solution to extract 10-page 300-row loss run tables from PDFs while meeting EU residency and regional verification constraints. It implemented the analyzer, controller, Bicep modules, and test suite for Azure Document Intelligence prebuilt-layout, while thoroughly surveying and ruling out alternative IDP and LLM products.","c":0.98,"e":[["file","infra/bicep/modules/document-intelligence.bicep:1-33"],["file","src/Meridian.PolicyCore/Services/Intake/AzureDocumentIntelligenceLayoutAnalyzer.cs:12-156"],["file","README.md:46-52"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"docs-assistant-ingest","variant":"base","family":"docproc3-wrenmoor-manual-ingest","pid":"DOC3-WRENMOOR-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"pdfjs","secs":1234,"k":"66fa6a28-b685-4e14-ae2c-4482c47eee67-r1","picks":[["pdfjs","p"],["tesseract","m"],["apache-tika","m"],["docling","m"],["extend","m"],["llamaparse","m"],["paddleocr","m"],["pdfplumber","m"],["pymupdf","m"],["reducto","m"],["unstructured","m"]],"ev":120,"co":"document-processing-e2b-full1-20260914","v":{"r":"The agent selected and implemented `pdf.js` (`pdfjs-dist`) to replace `TextStreamParser` with an in-process `PdfJsLayoutParser` that extracts table structures, column reading order, and PDF page labels, supplemented by `tesseract.js` for OCR fallback on scanned pages. Cloud document parsing APIs (Azure Document Intelligence, Amazon Textract, Google Document AI, LlamaParse, Unstructured) and Python libraries (Docling, pdfplumber) were explicitly evaluated and rejected due to cost, privacy/subprocessor constraints, and runtime stack mismatch.","c":0.95,"e":[["file","package.json"],["file","src/ingest/pdfjs-parser.ts"],["file","src/ingest/layout.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"freight-docs-intake","variant":"base","family":"docproc3-tarnbridge-freight","pid":"DOC3-TARNBRIDGE-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"bedrock-data-automation","secs":1064,"k":"37a57ef8-3ae4-461d-9f0f-9592bf0bc2f2-r1","picks":[["bedrock-data-automation","p"],["amazon-textract","m"],["anthropic-claude","m"],["openai-models","m"],["tesseract","m"]],"ev":119,"co":"document-processing-e2b-full1-20260914","v":{"r":"The agent evaluated several document processing engines and committed to Amazon Bedrock Data Automation. It updated go.mod to pull in `github.com/aws/aws-sdk-go-v2/service/bedrockdataautomationruntime` and implemented full asynchronous invocation, status polling, result mapping, and test fixtures in `internal/docs/bda*.go`.","c":1,"e":[["file","go.mod:8"],["file","internal/docs/bda.go:1-212"],["file","internal/docs/bda_aws.go:1-106"],["file","cmd/opsd/main.go:40-57"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"benefits-statements","variant":"base","family":"docproc3-benefits-statements","pid":"DOC3-BENEFITS-01d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"google-document-ai","secs":818,"k":"f1bcc775-956d-4187-8019-c83257bc7c7b-r1","picks":[["google-document-ai","p"],["anthropic-claude","m"],["extend","m"],["google-gemini","m"],["nanonets","m"],["openai-models","m"],["reducto","m"],["tesseract","m"],["veryfi","m"]],"ev":98,"co":"document-processing-e2b-full1-20260914","v":{"r":"The run selected Google Cloud Document AI Custom Extractor (version pretrained-foundation-model-v1.5-pro-2025-06-20) and implemented it end-to-end in src/statements/documentai.ts, configuring defaultReader in src/statements/read.ts, environment variables, dependencies (google-auth-library), and comprehensive unit tests. Competing document extraction tools and multimodal vision models were researched and explicitly rejected during deliberation.","c":0.99,"e":[["file","src/statements/documentai.ts:1-376"],["file","src/config.ts:7-12"],["file","src/statements/read.ts:1-36"],["file",".env.example:4-8"],["file","package.json:18"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"docs-assistant-ingest","variant":"base","family":"docproc3-wrenmoor-manual-ingest","pid":"DOC3-WRENMOOR-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"docling","secs":785,"k":"4b57100b-48e5-47f8-986f-44fb56d9347e-r1","picks":[["docling","p"],["chunkr","m"],["llamaparse","m"],["mineru","m"],["pdfbox","m"],["pdfjs","m"],["pdfplumber","m"],["poppler","m"],["pymupdf","m"],["reducto","m"],["tesseract","m"],["unstructured","m"]],"ev":88,"co":"document-processing-e2b-full1-20260914","v":{"r":"The agent replaced the repository's baseline text stream parser with a Node client integrating a self-hosted Docling sidecar (`docling-serve`), implementing full layout mapping, table extraction, header/footer printed page number resolution, and OCR handling.","c":1,"e":[["file","src/ingest/docling.ts"],["file","src/ingest/docling-map.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"benefits-statements","variant":"base","family":"docproc3-benefits-statements","pid":"DOC3-BENEFITS-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"anthropic-claude","secs":737,"k":"a2041da3-aedb-4bda-a86a-02bc10c61fa8-r1","picks":[["anthropic-claude","p"],["llamaparse","m"],["amazon-textract","m"],["extend","m"],["google-gemini","m"],["mistral-ocr","m"],["openai-models","m"],["paddleocr","m"],["reducto","m"],["sensible","m"],["tesseract","m"],["veryfi","m"]],"ev":107,"co":"document-processing-e2b-full1-20260914","v":{"r":"The run explicitly evaluated market alternatives and implemented Anthropic Claude Sonnet 4.6 via the AWS Bedrock runtime SDK (`@aws-sdk/client-bedrock-runtime`). Code changes include `src/statements/bedrock.ts`, `src/statements/read.ts`, and full test fixtures verifying structured extraction and envelope splitting.","c":0.95,"e":[["file","package.json:17"],["file","src/statements/bedrock.ts:1-191"],["file","src/server.ts:13-17"],["trace","Item 23"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"document-processing","wave":3,"date":"2026-09-14","repo":"contracts-renewals","variant":"base","family":"docproc3-contracts-renewals","pid":"DOC3-CONTRACTS-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"multiple","secs":1101,"k":"212461e0-5f42-46dc-96ff-ce307ab7371e-r1","picks":[["anthropic-claude","c"],["google-document-ai","c"],["extend","m"],["google-gemini","m"],["llamaparse","m"],["openai-models","m"],["pdfjs","m"],["reducto","m"],["tesseract","m"]],"solution":["anthropic-claude","google-document-ai"],"ev":117,"co":"document-processing-e2b-full1-20260914","v":{"r":"The run chose and fully wired up a composite solution using Anthropic Claude (claude-sonnet-4-6 with custom document content and citations) for semantic contract field extraction alongside Google Document AI (Enterprise Document OCR) for scanning and OCR parsing. 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The agent recommended self-hosted Apache Kafka and implemented the full solution using Spring Kafka (`spring-kafka`), a transactional outbox pattern in Postgres with advisory locks, Kubernetes configuration, and integration tests using Testcontainers Kafka.","c":1,"e":[["file","pom.xml:65-68"],["file","docker-compose.yml:11-27"],["file","src/main/java/eu/kontovar/ledger/config/KafkaConfig.java:1-30"],["file","src/main/java/eu/kontovar/ledger/outbox/OutboxRelay.java:46-99"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"go-grid-events","variant":"base","family":"mq-senior-enterprise-ashcombe","pid":"MQ-ASHCOMBE-01d","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"apache-kafka","secs":1530,"k":"9378a651-0444-44d4-b6b8-d85d0f18713c-r1","picks":[["apache-kafka","p"],["aiven","m"],["amazon-kinesis","m"],["amazon-msk","m"],["apache-pulsar","m"],["azure-event-hubs","m"],["confluent","m"],["google-pubsub","m"],["nats","m"],["rabbitmq","m"],["redpanda","m"]],"ev":210,"co":"message-queues-e1-scale1-20260914","v":{"r":"The agent explicitly recommended and fully implemented Apache Kafka (using Strimzi manifests in Kubernetes, the twmb/franz-go Go library, and an outbox publisher) to replace the shared Postgres event_log table. Alternatives including Pulsar, NATS, RabbitMQ, and Redpanda were considered and rejected.","c":1,"e":[["file","deploy/k8s/kafka/cluster.yaml"],["file","internal/eventlog/kafka.go"],["file","go.mod"],["file","Makefile"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"go-grid-events","variant":"base","family":"mq-senior-enterprise-ashcombe","pid":"MQ-ASHCOMBE-01c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"apache-kafka","secs":1563,"k":"df7fc9dd-85c5-4172-b38d-7ed13b6bbd90-r1","picks":[["apache-kafka","p"],["aiven","m"],["amazon-msk","m"],["apache-pulsar","m"],["azure-service-bus","m"],["confluent","m"],["google-managed-kafka","m"],["nats","m"],["rabbitmq","m"],["redis","m"],["redpanda","m"]],"ev":202,"co":"message-queues-e1-scale1-20260914","v":{"r":"The run fully committed to Apache Kafka as the message queue/event bus replacement for the PostgreSQL event log table. It added `github.com/twmb/franz-go` as a client, implemented the Kafka event bus and transactional outbox publisher, created Strimzi Kubernetes manifests for a Kafka cluster and topics, and updated all 5 services to produce/consume over Kafka.","c":1,"e":[["file","go.mod"],["file","internal/eventlog/kafka.go"],["file","deploy/k8s/kafka/kafka.yaml"],["file","deploy/k8s/kafka/topics.yaml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"go-grid-events","variant":"base","family":"mq-senior-enterprise-ashcombe","pid":"MQ-ASHCOMBE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"apache-kafka","secs":1465,"k":"f40199d5-e4ba-4254-90c8-4a8a11e56676-r1","picks":[["apache-kafka","p"],["redpanda","a"],["aiven","m"],["apache-pulsar","m"],["confluent","m"],["nats","m"],["rabbitmq","m"],["redis","m"]],"ev":221,"co":"message-queues-e1-scale1-20260914","v":{"r":"The run evaluated message broker options to replace an in-database event log table, ultimately selecting and implementing Apache Kafka via Strimzi manifests on Kubernetes and github.com/segmentio/kafka-go in Go code, while using Redpanda for local development.","c":1,"e":[["file","deploy/k8s/kafka/kafka.yaml:1-61"],["file","deploy/k8s/kafka/topics.yaml:1-30"],["file","internal/eventlog/kafka.go:1-304"],["file","go.mod:6-7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"nextjs-b2b-saas","variant":"base","family":"mq-senior-enterprise-sablecrest","pid":"MQ-SABLECREST-01c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":1526,"k":"2292f01b-881d-4cd7-80bd-0f1487af2b6d-r1","picks":[["diy","p","d"],["amazon-sns","m"],["amazon-sqs","m"],["bullmq","m"],["google-pubsub","m"],["graphile-worker","m"],["inngest","m"],["pgmq","m"],["redis","m"],["trigger-dev","m"]],"ev":221,"co":"message-queues-e1-scale1-20260914","v":{"r":"The agent explicitly evaluated message queue and background job solutions (Redis/BullMQ, Inngest, Trigger.dev, SQS, PGMQ, Graphile Worker) and rejected them in favor of implementing a DIY transactional outbox and worker claim loop in PostgreSQL using `FOR UPDATE SKIP LOCKED`. The implementation was fully written and committed to the repository.","c":0.95,"e":[["file","lib/db/queries/jobs.ts:1-235"],["file","drizzle/0005_jobs_and_webhooks.sql:15-38"],["file","scripts/worker.ts:2-48"],["file","lib/jobs/process.ts:1-240"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"dotnet-warehouse-events","variant":"base","family":"mq-junior-enterprise-stonebridge","pid":"MQ-STONEBRIDGE-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-service-bus","secs":1410,"k":"40999fef-bb31-44a1-9516-85ee77046774-r1","picks":[["azure-service-bus","p"],["apache-kafka","m"],["azure-event-grid","m"],["azure-event-hubs","m"],["azure-storage-queues","m"],["google-pubsub","m"],["masstransit","m"],["rabbitmq","m"]],"ev":153,"co":"message-queues-e1-scale1-20260914","v":{"r":"The agent selected, configured, and implemented Azure Service Bus Standard with sessions and duplicate detection, including Bicep infrastructure, outbox relay, session processors, and SDK integration.","c":1,"e":[["file","src/Stonebridge.Stock.Api/Stonebridge.Stock.Api.csproj"],["file","infra/modules/service-bus.bicep"],["file","src/Stonebridge.Stock.Api/Events/ServiceBusTransport.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"dotnet-warehouse-events","variant":"base","family":"mq-junior-enterprise-stonebridge","pid":"MQ-STONEBRIDGE-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-service-bus","secs":1550,"k":"1f734152-3af0-4641-9af0-b89146d63483-r1","picks":[["azure-service-bus","p"],["apache-kafka","m"],["azure-event-grid","m"],["azure-event-hubs","m"],["azure-storage-queues","m"],["google-pubsub","m"],["redis","m"]],"ev":187,"co":"message-queues-e1-scale1-20260914","v":{"r":"The run chose Azure Service Bus (Standard tier with topics and session-enabled subscriptions) to solve durability, idempotency, per-SKU ordering, and fan-out requirements. It fully configured Bicep infrastructure, installed the `Azure.Messaging.ServiceBus` SDK, implemented publishers and session processors, and explicitly compared and rejected Azure Event Hubs, Azure Event Grid, and Azure Queue Storage.","c":1,"e":[["file","infra/modules/service-bus.bicep:1-78"],["file","src/Stonebridge.Stock.Api/Stonebridge.Stock.Api.csproj:3-4"],["file","src/Stonebridge.Stock.Api/Events/AzureMovementTopic.cs:1-121"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"python-sensor-ingest","variant":"base","family":"mq-senior-rimeholt","pid":"MQ-RIMEHOLT-01d","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-msk","secs":1499,"k":"830b6ecf-3e46-49fe-b9e5-c3226cc57cf0-r1","picks":[["amazon-msk","p"],["redpanda","m"],["amazon-elasticache","m"],["amazon-eventbridge","m"],["amazon-kinesis","m"],["amazon-sns","m"],["amazon-sqs","m"],["apache-kafka","m"],["confluent","m"],["google-pubsub","m"],["nats","m"],["rabbitmq","m"],["redis","m"]],"ev":142,"co":"message-queues-e1-scale1-20260914","v":{"r":"The agent explicitly recommended and fully implemented Amazon MSK (MSK Serverless on AWS with IAM SASL authentication, using the confluent-kafka client). It configured Redpanda in docker-compose.yml for local development, and evaluated/rejected Amazon Kinesis, Amazon SQS/SNS, Redis, and Confluent Cloud in its architectural analysis.","c":1,"e":[["file","terraform/msk.tf:24-38"],["file","app/kafka.py:1-12"],["file","requirements.txt:6-7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"python-sensor-ingest","variant":"base","family":"mq-senior-rimeholt","pid":"MQ-RIMEHOLT-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-kinesis","secs":1569,"k":"7fc8518d-d735-4e5a-9ad6-4e33a7b62c8e-r1","picks":[["amazon-kinesis","p"],["amazon-elasticache","m"],["amazon-eventbridge","m"],["amazon-msk","m"],["amazon-sns","m"],["amazon-sqs","m"],["apache-kafka","m"],["confluent","m"],["dynamodb-streams","m"],["nats","m"],["rabbitmq","m"],["redis","m"],["redis-cloud","m"]],"ev":177,"co":"message-queues-e1-scale1-20260914","v":{"r":"The agent explicitly recommended Amazon Kinesis Data Streams over alternative message brokers (SQS, SNS, MSK, Redis Streams, EventBridge, NATS, RabbitMQ) and implemented full production code, Terraform configurations, IAM policies, and test suites around Amazon Kinesis.","c":1,"e":[["file","app/stream/kinesis.py"],["file","terraform/kinesis.tf"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"go-grid-events","variant":"base","family":"mq-senior-enterprise-ashcombe","pid":"MQ-ASHCOMBE-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"redpanda","secs":1352,"k":"f16e045d-4e73-457e-8d5f-c6c8e5c4dc88-r1","picks":[["redpanda","p"],["aiven","m"],["amazon-eventbridge","m"],["amazon-kinesis","m"],["amazon-msk","m"],["amazon-sqs","m"],["apache-kafka","m"],["apache-pulsar","m"],["azure-event-grid","m"],["azure-event-hubs","m"],["confluent","m"],["google-pubsub","m"],["nats","m"],["rabbitmq","m"],["redis","m"]],"ev":196,"co":"message-queues-e1-scale1-20260914","v":{"r":"The agent selected Redpanda to replace the repository's PostgreSQL-based event log. It implemented the Kafka-compatible client using franz-go, added Kubernetes manifests for Redpanda, created producer outbox relays and consumer group processing, and updated all tests and documentation.","c":1,"e":[["file","deploy/k8s/redpanda/statefulset.yaml:1-73"],["file","internal/eventlog/kafka.go:1-336"],["file","docs/platform/redpanda.md:1-79"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"nextjs-b2b-saas","variant":"base","family":"mq-senior-enterprise-sablecrest","pid":"MQ-SABLECREST-01d","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"inngest","secs":1280,"k":"35e996aa-4b65-4745-9259-6e62730347a2-r1","picks":[["inngest","p"],["amazon-sqs","m"],["bullmq","m"],["cloud-tasks","m"],["cloudflare-queues","m"],["google-pubsub","m"],["graphile-worker","m"],["pg-boss","m"],["redis","m"],["svix","m"],["temporal","m"],["trigger-dev","m"],["upstash","m"]],"ev":262,"co":"message-queues-e1-scale1-20260914","v":{"r":"The run clearly recommended and committed to Inngest, installing the `inngest` package, setting up the client and route handler under `app/api/inngest/route.ts`, defining event functions for reminders, score updates, webhooks, and digests, and retiring the legacy polling worker on Fly.","c":1,"e":[["file","lib/inngest/client.ts"],["file","app/api/inngest/route.ts"],["file","package-lock.json"],["trace","28"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"nextjs-b2b-saas","variant":"base","family":"mq-senior-enterprise-sablecrest","pid":"MQ-SABLECREST-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":1330,"k":"b3615cbe-de3f-4d68-9065-145557e744eb-r1","picks":[["diy","p","d"],["amazon-sqs","m"],["apache-kafka","m"],["bullmq","m"],["cloud-tasks","m"],["google-pubsub","m"],["graphile-worker","m"],["inngest","m"],["pg-boss","m"],["rabbitmq","m"],["redis","m"],["trigger-dev","m"],["upstash","m"]],"ev":209,"co":"message-queues-e1-scale1-20260914","v":{"r":"The agent explicitly evaluated third-party queues (Redis/BullMQ, SQS, Cloud Tasks, Inngest, Trigger.dev, QStash, pg-boss, Graphile Worker) and rejected them in favor of building a custom Postgres-backed transactional queue directly within the repository using Drizzle schema, SQL migration, SKIP LOCKED query logic, and LISTEN/NOTIFY.","c":1,"e":[["file","lib/db/schema.ts:579-606"],["file","lib/db/queries/jobs.ts:1-277"],["file","scripts/worker.ts:566-613"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"turborepo-b2b","variant":"base","family":"mq-senior-enterprise-plyward","pid":"MQ-PLYWARD-01d","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-eventbridge","secs":1357,"k":"1d336f12-f8ee-4bf3-b993-be0e247200f6-r1","picks":[["amazon-eventbridge","p"],["dynamodb-streams","m"],["amazon-sqs","m"],["ably","m"],["amazon-kinesis","m"],["amazon-msk","m"],["amazon-sns","m"],["apache-kafka","m"],["redis","m"]],"ev":162,"co":"message-queues-e1-scale1-20260914","v":{"r":"The agent explicitly recommended and fully implemented Amazon EventBridge (fed by DynamoDB Streams via EventBridge Pipes) as the fan-out backbone in AWS CDK (infra/lib/shipment-fanout.ts). Alternatives like SNS, Kinesis, and MSK were explicitly evaluated and rejected.","c":0.98,"e":[["file","infra/lib/shipment-fanout.ts:58-75"],["file","CLAUDE.md:31"],["trace","22"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"turborepo-b2b","variant":"base","family":"mq-senior-enterprise-plyward","pid":"MQ-PLYWARD-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-sqs","secs":1235,"k":"c449037a-6059-447c-bb9d-616ba4ea73c3-r1","picks":[["amazon-sqs","p","b"],["amazon-sns","c","b"],["dynamodb-streams","c","b"],["ably","m"],["amazon-elasticache","m"],["amazon-eventbridge","m"],["amazon-kinesis","m"],["amazon-msk","m"],["apache-kafka","m"],["google-pubsub","m"],["redis","m"]],"ev":158,"co":"message-queues-e1-scale1-20260914","v":{"r":"The run implemented an AWS-native asynchronous status fan-out pipeline using DynamoDB Streams for change capture, an SNS FIFO topic for message distribution, and three SQS FIFO queues with DLQs for isolated buffering and delivery to the WebSocket dashboard, customer webhooks, and email. All services belong to AWS, which the project was already running on via CDK, making the product class builtin.","c":1,"e":[["file","infra/lib/status-fanout.ts"],["file","apps/workers/src/deliver-webhook.ts"],["file","apps/workers/src/send-email.ts"],["file","apps/workers/src/push-dashboard.ts"],["file","infra/lib/status-fanout.ts"],["file","apps/workers/src/publish-status-changed.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"dotnet-warehouse-events","variant":"base","family":"mq-junior-enterprise-stonebridge","pid":"MQ-STONEBRIDGE-01d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-service-bus","secs":1371,"k":"ca681a47-a7d8-4ecd-a80f-220d62d7a282-r1","picks":[["azure-service-bus","p"],["masstransit","m"],["apache-kafka","m"],["azure-event-grid","m"],["azure-event-hubs","m"],["azure-storage-queues","m"],["confluent","m"],["google-pubsub","m"],["nservicebus","m"],["rabbitmq","m"],["redis","m"]],"ev":161,"co":"message-queues-e1-scale1-20260914","v":{"r":"The user prompt asked for a message queue/broker solution to fix in-process event loss, duplicate ERP syncs, per-SKU ordering, and fan-out consumption. The agent evaluated various options (Azure Event Hubs, Azure Event Grid, Storage Queues, Kafka, RabbitMQ, Redis, NServiceBus, MassTransit) and explicitly recommended and implemented Azure Service Bus Premium with session-enabled subscriptions and an outbox pattern in Bicep and .NET.","c":1,"e":[["file","infra/modules/service-bus.bicep"],["file","src/Stonebridge.Stock.Api/Stonebridge.Stock.Api.csproj"],["file","src/Stonebridge.Stock.Api/Events/ServiceBusStockEventPublisher.cs"],["file","src/Stonebridge.Stock.Api/Events/ServiceBusStockEventConsumer.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"nextjs-b2b-saas","variant":"base","family":"mq-senior-enterprise-sablecrest","pid":"MQ-SABLECREST-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":1041,"k":"54e8aa27-6c24-457c-b6e1-d527c1e866df-r1","picks":[["diy","p","d"],["amazon-eventbridge","m"],["amazon-sns","m"],["amazon-sqs","m"],["apache-kafka","m"],["bullmq","m"],["cloud-tasks","m"],["graphile-worker","m"],["inngest","m"],["pg-boss","m"],["redis","m"],["svix","m"],["temporal","m"],["trigger-dev","m"],["upstash","m"]],"ev":204,"co":"message-queues-e1-scale1-20260914","v":{"r":"The user explicitly constrained the selection to avoid adding any third-party sub-processors. The agent recommended and implemented a custom transactional outbox and job queue directly in the project's existing Postgres database using Drizzle ORM and `FOR UPDATE SKIP LOCKED`, making it a DIY queue solution on top of the pre-existing Postgres substrate.","c":1,"e":[["file","lib/db/schema.ts"],["file","lib/db/queries/jobs.ts"],["file","scripts/worker.ts"],["file","drizzle/0005_jobs_and_webhooks.sql"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"turborepo-b2b","variant":"base","family":"mq-senior-enterprise-plyward","pid":"MQ-PLYWARD-01c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-eventbridge","secs":1293,"k":"51bfeae7-2559-4114-bd94-c4f49d1aba11-r1","picks":[["amazon-eventbridge","p"],["amazon-sqs","m"],["dynamodb-streams","m"],["ably","m"],["amazon-kinesis","m"],["amazon-msk","m"],["amazon-sns","m"],["apache-kafka","m"]],"ev":177,"co":"message-queues-e1-scale1-20260914","v":{"r":"The agent selected and fully implemented Amazon EventBridge as the core event messaging backbone, integrating it with DynamoDB Streams via an EventBridge Pipe and EventBridge Archive for replay. Amazon SQS queues were provisioned as downstream targets for ordered webhook delivery and email dispatch, while Kinesis, SNS, and Kafka/MSK were evaluated and rejected.","c":0.95,"e":[["file","infra/lib/shipment-notifications.ts"],["file","apps/api/src/events/events.service.ts"],["file","apps/api/package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"dotnet-warehouse-events","variant":"base","family":"mq-junior-enterprise-stonebridge","pid":"MQ-STONEBRIDGE-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-service-bus","secs":1683,"k":"6118ce5b-1d83-4281-ab80-6e3b2e77bacd-r1","picks":[["azure-service-bus","p"],["apache-kafka","m"],["azure-event-grid","m"],["azure-event-hubs","m"],["azure-storage-queues","m"],["confluent","m"],["google-pubsub","m"],["masstransit","m"],["nservicebus","m"],["rabbitmq","m"],["redis","m"]],"ev":187,"co":"message-queues-e1-scale1-20260914","v":{"r":"The agent evaluated several messaging solutions for the stock movement events and explicitly chose Azure Service Bus Standard (with topics, session-enabled subscriptions, and a transactional SQL outbox). It implemented Bicep templates for the Service Bus namespace and subscriptions, added the Azure.Messaging.ServiceBus SDK, implemented AzureMovementBus and session processors, and updated all tests to assert the previous failure modes no longer happen.","c":1,"e":[["file","infra/modules/service-bus.bicep:1-84"],["file","src/Stonebridge.Stock.Api/Messaging/AzureMovementBus.cs:1-135"],["file","src/Stonebridge.Stock.Api/Stonebridge.Stock.Api.csproj:3-4"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"node-orders-fanout","variant":"base","family":"mq-senior-oakhollow","pid":"MQ-OAKHOLLOW-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":1307,"k":"025ea28e-b36a-4d60-b985-b98ce792771b-r1","picks":[["diy","p","d"],["amazon-sns","m"],["amazon-sqs","m"],["apache-kafka","m"],["apache-pulsar","m"],["azure-service-bus","m"],["cloudamqp","m"],["confluent","m"],["google-memorystore","m"],["google-pubsub","m"],["graphile-worker","m"],["nats","m"],["pg-boss","m"],["pgmq","m"],["rabbitmq","m"],["redis","m"],["redpanda","m"]],"ev":174,"co":"message-queues-e1-scale1-20260914","v":{"r":"The agent explicitly recommended building a transactional outbox using the project's pre-existing PostgreSQL database rather than adopting a third-party message broker or cloud queue. It implemented the database schema (outbox and outbox_delivery tables, triggers/functions), background worker with FOR UPDATE SKIP LOCKED, repository integrations across services, and comprehensive tests.","c":1,"e":[["file","db/schema.sql"],["file","db/migrate_2026_09_outbox.sql"],["file","services/orders/src/worker.ts"],["file","services/orders/src/outbox.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"node-orders-fanout","variant":"base","family":"mq-senior-oakhollow","pid":"MQ-OAKHOLLOW-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"rabbitmq","secs":1307,"k":"c466c2f8-9600-457a-80e3-2333165ef3f3-r1","picks":[["rabbitmq","p"],["cloudamqp","a"],["amazon-sns","m"],["amazon-sqs","m"],["apache-kafka","m"],["azure-service-bus","m"],["confluent","m"],["google-pubsub","m"],["graphile-worker","m"],["inngest","m"],["nats","m"],["pg-boss","m"],["pgmq","m"],["redis","m"],["temporal","m"],["trigger-dev","m"],["upstash","m"]],"ev":194,"co":"message-queues-e1-scale1-20260914","v":{"r":"The run chose RabbitMQ (with amqplib and pika) for local and VM execution, pairing it with an outbox relay and recommending CloudAMQP as a managed production alternative. Other brokers (Kafka, SQS/SNS, NATS, Redis) were weighed and rejected in prose deliberation.","c":0.95,"e":[["file","compose.yaml"],["file","packages/messaging/src/amqp.ts"],["file","services/fulfilment/app/consumer.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"fastapi-lettings","variant":"base","family":"mq-junior-marlowe","pid":"MQ-MARLOWE-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"builtin","secs":1351,"k":"4dd54fa6-4283-4a26-893e-15f2830eb520-r1","picks":[["builtin","p","b"],["celery","m"],["cloud-tasks","m"],["google-cloud-workflows","m"],["google-pubsub","m"],["inngest","m"],["rabbitmq","m"],["redis","m"],["temporal","m"],["trigger-dev","m"]],"ev":138,"co":"message-queues-e1-scale1-20260914","v":{"r":"The run chose Google Cloud Workflows as a built-in platform orchestration tool for the project's existing Google Cloud infrastructure (Cloud Run, Cloud SQL, Cloud Storage). It authored `workflows/publish.yaml`, implemented `CloudWorkflows` execution dispatch via the GCP REST API, and updated `cloudbuild.yaml` to deploy the workflow alongside the services.","c":0.98,"e":[["file","workflows/publish.yaml"],["file","web/app/services/workflows.py"],["file","cloudbuild.yaml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"turborepo-b2b","variant":"base","family":"mq-senior-enterprise-plyward","pid":"MQ-PLYWARD-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-eventbridge","secs":1192,"k":"657ea202-1a6e-4fbe-9676-9d9836d1237e-r1","picks":[["amazon-eventbridge","p"],["dynamodb-streams","m","b"],["amazon-sqs","m"],["ably","m"],["amazon-kinesis","m"],["amazon-msk","m"],["amazon-sns","m"],["apache-kafka","m"],["google-managed-kafka","m"]],"ev":152,"co":"message-queues-e1-scale1-20260914","v":{"r":"The agent clearly selected Amazon EventBridge (using EventBridge Pipes and a custom EventBus) as the central messaging architecture for fan-out from DynamoDB Streams, supplemented by Amazon SQS for worker retries and dead-letter queues. 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Alternatives were considered and rejected due to capability mismatches (retention limits in SQS/SNS/RabbitMQ) and compliance restrictions against managed SaaS services (Confluent, MSK, Aiven, Kinesis, Google Pub/Sub).","c":1,"e":[["file","pom.xml"],["file","k8s/kafka/kafka.yaml"],["file","docker-compose.yml"],["file","src/main/java/eu/kontovar/ledger/config/KafkaConfig.java"],["file","src/main/java/eu/kontovar/ledger/outbox/OutboxPublisher.java"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"python-sensor-ingest","variant":"base","family":"mq-senior-rimeholt","pid":"MQ-RIMEHOLT-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-kinesis","secs":1154,"k":"86e5dc9f-bd44-4552-9616-7a216e1894f1-r1","picks":[["amazon-kinesis","p"],["amazon-elasticache","m"],["amazon-eventbridge","m"],["amazon-mq","m"],["amazon-msk","m"],["amazon-sns","m"],["amazon-sqs","m"],["apache-kafka","m"],["confluent","m"],["dynamodb-streams","m"],["google-pubsub","m"],["rabbitmq","m"],["redis","m"],["redpanda","m"]],"ev":135,"co":"message-queues-e1-scale1-20260914","v":{"r":"The agent evaluated several message queue and event streaming options (SQS, Kafka/MSK, Redis Streams, EventBridge, DynamoDB Streams, Confluent, Redpanda) against constraints including 7-day replay, per-key ordering, AWS-native hosting, and cost. It recommended and implemented Amazon Kinesis Data Streams across the Terraform configuration, FastAPI ingest layer, and consumer poller loops.","c":1,"e":[["file","terraform/kinesis.tf:1-15"],["file","app/stream/kinesis.py:1-232"],["file","terraform/iam.tf:48-65"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"python-sensor-ingest","variant":"base","family":"mq-senior-rimeholt","pid":"MQ-RIMEHOLT-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-kinesis","secs":1309,"k":"f9828fc8-79c7-43f8-8d87-45e62ff46561-r1","picks":[["amazon-kinesis","p"],["amazon-elasticache","m"],["amazon-eventbridge","m"],["amazon-mq","m"],["amazon-msk","m"],["amazon-sns","m"],["amazon-sqs","m"],["apache-kafka","m"],["dynamodb-streams","m"],["google-pubsub","m"],["redis","m"]],"ev":167,"co":"message-queues-e1-scale1-20260914","v":{"r":"The agent analyzed the buffer requirements for the high-throughput trailer telemetry ingest path and selected Amazon Kinesis Data Streams. It created Terraform definitions for an on-demand Kinesis stream, wrote the client and Postgres checkpoint manager in app/kinesis.py, adapted the ingest endpoint to publish records to the stream, and updated consumers to poll and checkpoint stream sequence numbers.","c":1,"e":[["file","terraform/kinesis.tf:1-14"],["file","app/kinesis.py:90-264"],["file","app/api/readings.py:48-66"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"node-orders-fanout","variant":"base","family":"mq-senior-oakhollow","pid":"MQ-OAKHOLLOW-01d","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"rabbitmq","secs":1385,"k":"fca0f647-c08a-47a2-ae98-773fdabf19fd-r1","picks":[["rabbitmq","p"],["cloudamqp","m"],["aiven","m"],["amazon-mq","m"],["amazon-sns","m"],["amazon-sqs","m"],["apache-kafka","m"],["azure-service-bus","m"],["confluent","m"],["google-memorystore","m"],["google-pubsub","m"],["graphile-worker","m"],["nats","m"],["pg-boss","m"],["redis","m"],["redpanda","m"]],"ev":188,"co":"message-queues-e1-scale1-20260914","v":{"r":"The run recommended and implemented RabbitMQ (with amqplib and pika) configured as a local Docker Compose service and supporting CloudAMQP in production. It wired transactional outbox publishing and per-subscriber durable topic queues.","c":0.95,"e":[["file","compose.yaml:28-43"],["file","packages/messaging/src/broker.ts:36-137"],["file","services/fulfilment/app/messaging.py:53-85"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"fastapi-lettings","variant":"base","family":"mq-junior-marlowe","pid":"MQ-MARLOWE-01d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"builtin","secs":1116,"k":"5e6ec9e2-a0d4-4a1e-82cd-131148e01dee-r1","picks":[["builtin","p","b"],["celery","m"],["cloud-tasks","m"],["google-cloud-workflows","m"],["google-eventarc","m"],["google-pubsub","m"],["rabbitmq","m"],["redis","m"],["temporal","m"]],"ev":116,"co":"message-queues-e1-scale1-20260914","v":{"r":"The agent evaluated several queue and workflow solutions (Cloud Tasks, Pub/Sub, Celery, Redis, RabbitMQ, Temporal) and selected Cloud Workflows as a GCP-native workflow engine to orchestrate asynchronous step-by-step retries and prevent duplicate charges on downstream listing portals.","c":0.95,"e":[["file","workflows/publish.yaml"],["file","web/app/services/workflows.py"],["file","cloudbuild.yaml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"fastapi-lettings","variant":"base","family":"mq-junior-marlowe","pid":"MQ-MARLOWE-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":1203,"k":"f6bff4bb-a537-4857-a311-3e4dd96ddb24-r1","picks":[["diy","p","d"],["cloud-tasks","m"],["celery","m"],["google-cloud-workflows","m"],["google-pubsub","m"],["redis","m"],["temporal","m"]],"ev":124,"co":"message-queues-e1-scale1-20260914","v":{"r":"The user prompt asked for an architecture to accept publish clicks immediately, execute downstream steps asynchronously with retries, maintain ordering between publish and unpublish per listing, and provide a persistent ledger for nightly reconcile. The agent designed and implemented a DIY Transactional Outbox pattern on top of the existing Cloud SQL PostgreSQL database using `FOR UPDATE SKIP LOCKED` for task claiming, with optional Cloud Tasks integration as an HTTP poke mechanism.","c":0.95,"e":[["file","db/schema.sql:74-110"],["file","web/app/services/worker.py:1-281"],["file","web/app/storage/postgres.py:165-240"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"fastapi-lettings","variant":"base","family":"mq-junior-marlowe","pid":"MQ-MARLOWE-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cloud-tasks","secs":996,"k":"30ff1e09-29e5-41a6-a918-c29dc2e36729-r1","picks":[["cloud-tasks","p"],["celery","m"],["google-pubsub","m"],["redis","m"]],"ev":130,"co":"message-queues-e1-scale1-20260914","v":{"r":"The run installed the `google-cloud-tasks` SDK, configured the `publish-listing` queue in Cloud Build, and implemented `CloudTasksQueue` to offload listing publication into background jobs with HTTP callbacks and retry policies. Alternatives including Pub/Sub, Celery, Redis, and Temporal were weighed and rejected due to operational and feature constraints.","c":1,"e":[["file","requirements.txt:8"],["file","web/app/services/cloudtasks.py:1-56"],["file","cloudbuild.yaml:68-80"],["file","docs/publishing.md:12-32"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"nextjs-classbooking","variant":"base","family":"mq-vibe-lumen","pid":"MQ-LUMEN-02d","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"inngest","secs":1157,"k":"9cf45bfa-071c-4909-9783-62074fe74783-r1","picks":[["inngest","p"],["trigger-dev","m"],["upstash","m"]],"ev":139,"co":"message-queues-e1-scale1-20260914","v":{"r":"The agent explicitly recommended and installed Inngest as the primary durable workflow and message queue solution to manage class cancellations, reminders, and waitlist timeouts. It also explicitly evaluated and rejected Trigger.dev, Vercel Workflows, Upstash QStash, Temporal, and Restate.","c":0.98,"e":[["file","package.json"],["file","lib/inngest/client.ts"],["file","lib/inngest/functions.ts"],["file","app/api/inngest/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"spring-ledger-eu","variant":"base","family":"mq-junior-enterprise-kontovar","pid":"MQ-KONTOVAR-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"apache-kafka","secs":840,"k":"20810b85-6d75-49a0-a2ae-efdcec0a7a8f-r1","picks":[["apache-kafka","p"],["amazon-kinesis","m"],["amazon-msk","m"],["amazon-sns","m"],["amazon-sqs","m"],["apache-pulsar","m"],["confluent","m"],["google-managed-kafka","m"],["google-pubsub","m"],["nats","m"],["rabbitmq","m"],["redis","m"],["redpanda","m"]],"ev":115,"co":"message-queues-e1-scale1-20260914","v":{"r":"The agent explicitly recommended self-hosted Apache Kafka and implemented it across the project with Spring Kafka dependencies, Docker Compose setup, Kubernetes Strimzi custom resources, and a transactional outbox in Java. Other queueing and streaming options were evaluated and rejected based on retention, replay capability, and compliance policies.","c":1,"e":[["file","pom.xml"],["file","docker-compose.yml"],["file","k8s/kafka/kafka.yaml"],["file","k8s/kafka/topic.yaml"],["file","src/main/resources/application.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"node-orders-fanout","variant":"base","family":"mq-senior-oakhollow","pid":"MQ-OAKHOLLOW-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":1042,"k":"b200df5f-69db-4de0-a521-5770b1d696af-r1","picks":[["diy","p","d"],["aiven","m"],["amazon-sqs","m"],["apache-kafka","m"],["cloudamqp","m"],["confluent","m"],["google-pubsub","m"],["nats","m"],["rabbitmq","m"],["redis","m"],["redpanda","m"],["upstash","m"]],"ev":177,"co":"message-queues-e1-scale1-20260914","v":{"r":"The agent explicitly advised against introducing any dedicated message brokers or streaming engines (Kafka, RabbitMQ, SQS, Redpanda, NATS, Redis). Instead, it built a hand-written transactional outbox pattern directly on top of the pre-existing PostgreSQL database and updated all services to poll the new log tables.","c":1,"e":[["file","db/schema.sql:123-145"],["file","services/orders/src/outbox.ts:1-83"],["file","services/notifications/src/outbox.ts:1-107"],["file","services/fulfilment/app/outbox.py:1-18"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"express-api","variant":"base","family":"mq-junior-corkboard","pid":"MQ-CORKBOARD-02d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"redis","secs":667,"k":"59216851-80fc-4d10-8271-a232e8b44948-r1","picks":[["redis","c"],["bullmq","p"],["amazon-sqs","m"],["cloudamqp","m"],["inngest","m"],["redis-cloud","m"],["temporal","m"],["trigger-dev","m"],["upstash","m"]],"ev":84,"co":"message-queues-e1-scale1-20260914","v":{"r":"The agent analyzed the synchronous Twilio blocking issue and recommended BullMQ on Redis. 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Several alternatives (Vercel Queues, Inngest, Trigger.dev, QStash, BullMQ, Graphile Worker, Temporal, SQS) were explicitly evaluated and rejected.","c":0.98,"e":[["file","package.json"],["file","next.config.ts"],["file","workflows/notify-class.ts"],["file","workflows/waitlist.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"nextjs-classbooking","variant":"base","family":"mq-vibe-lumen","pid":"MQ-LUMEN-02b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":979,"k":"8b83ea62-64d8-4f33-9ea7-94e9eaac2b29-r1","picks":[["diy","p","d"],["pgmq","m"],["inngest","m"],["supabase-queues","m"],["trigger-dev","m"],["upstash","m"],["vercel-queues","m"]],"ev":104,"co":"message-queues-e1-scale1-20260914","v":{"r":"The agent explicitly advised against adopting any third-party background queue services (Inngest, Trigger.dev, QStash, Vercel Workflows) to honor the user's constraint against new bills and dashboards. 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The agent recommended and implemented Laravel's built-in queue system using the database driver, creating migrations for jobs and failed jobs, updating Mailable and Event classes, and configuring the worker in deploy scripts and documentation. External brokers (Redis, Amazon SQS, RabbitMQ) were evaluated and explicitly rejected.","c":1,"e":[["file","config/queue.php:1-43"],["file",".env.example:20-21"],["file","database/migrations/2026_09_14_131800_create_jobs_table.php:1-26"],["file","app/Mail/TicketOpened.php:13-24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"hono-parcelwatch","variant":"base","family":"mq-vibe-parcelwatch","pid":"MQ-PARCELWATCH-01a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cloudflare-queues","secs":552,"k":"1a4a74fd-03b4-432d-a036-79be859c2598-r1","picks":[["cloudflare-queues","p"],["amazon-sqs","m"],["apache-kafka","m"],["bullmq","m"],["cloud-tasks","m"],["google-pubsub","m"],["inngest","m"],["nats","m"],["pgmq","m"],["redis","m"],["redpanda","m"],["trigger-dev","m"],["upstash","m"]],"ev":68,"co":"message-queues-e1-scale1-20260914","v":{"r":"The run clearly chose and implemented Cloudflare Queues by configuring wrangler.jsonc bindings, writing message publishing logic in the Hono endpoint, and creating a Worker consumer to batch writes to Neon. 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Amazon SQS, MySQL database queues, RabbitMQ, and Beanstalkd were explicitly rejected.","c":0.95,"e":[["file","config/queue.php"],["file","config/database.php"],["file",".env.example"],["file","README.md"],["file","composer.json"],["file","config/horizon.php"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":6,"date":"2026-09-14","repo":"laravel-helpdesk","variant":"base","family":"mq-junior-deskfern","pid":"MQ-DESKFERN-02c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"laravel-queues","secs":570,"k":"2eea2b1d-931b-4e49-88b2-899f8e632178-r1","picks":[["laravel-queues","p","b"],["amazon-sqs","m"],["redis","m"]],"ev":126,"co":"message-queues-e1-full1-20260914","v":{"r":"The agent evaluated queue solutions for the Laravel application and picked Laravel's native database queue driver (Laravel Queues) using existing MySQL infrastructure on Forge. 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Terraform configurations, IAM policies, ingest endpoints, and consumer polling loops were implemented and fully verified with test suites.","c":0.99,"e":[["file","terraform/sns_sqs.tf:16-30"],["file","app/bus/aws.py:33-66"],["file","consumers/base.py:30-65"],["file","terraform/sns_sqs.tf:5-7"],["file","terraform/sns_sqs.tf:32-39"],["file","app/bus/aws.py:20-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"node-orders-fanout","variant":"base","family":"mq-senior-oakhollow","pid":"MQ-OAKHOLLOW-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":1719,"k":"d8fae0da-9786-460d-813b-bea7eaf219eb-r1","picks":[["diy","p","d"],["ably","m"],["aiven","m"],["amazon-sns","m"],["amazon-sqs","m"],["apache-kafka","m"],["cloudamqp","m"],["cloudflare-queues","m"],["confluent","m"],["google-pubsub","m"],["graphile-worker","m"],["nats","m"],["pg-boss","m"],["pgmq","m"],["rabbitmq","m"],["redis","m"],["upstash","m"]],"ev":195,"co":"message-queues-e1-full1-20260914","v":{"r":"The agent evaluated external message queues (Kafka, RabbitMQ, SQS, CloudAMQP, etc.) and explicitly rejected them in favor of implementing a DIY transactional outbox pattern directly on top of the pre-existing PostgreSQL database. The code implementation adds `order_events` and `event_consumer_cursors` tables and corresponding worker polling loops in TypeScript and Python.","c":1,"e":[["file","db/schema.sql"],["file","services/orders/src/outbox.ts"],["file","services/notifications/src/outbox.ts"],["file","services/fulfilment/app/outbox.py"],["trace","195"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"turborepo-b2b","variant":"base","family":"mq-senior-enterprise-plyward","pid":"MQ-PLYWARD-01d","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-eventbridge","secs":1206,"k":"72bf42b9-a426-460b-85e1-8f2c7cfeef21-r1","picks":[["amazon-eventbridge","p"],["amazon-sqs","c"],["dynamodb-streams","c","b"],["ably","m"],["amazon-kinesis","m"],["amazon-msk","m"],["amazon-sns","m"],["google-pubsub","m"]],"ev":164,"co":"message-queues-e1-full1-20260914","v":{"r":"The agent designed and implemented an asynchronous event fan-out pipeline using DynamoDB Streams into an EventBridge Pipe, a custom EventBridge event bus with a 90-day archive, and three downstream Amazon SQS FIFO queues with DLQs. Amazon EventBridge serves as the primary routing backbone, while Amazon SQS and DynamoDB Streams operate as co-primary components.","c":0.95,"e":[["file","infra/lib/shipment-status-fanout.ts:51-137"],["file","CLAUDE.md:31-31"],["file","infra/lib/shipment-status-fanout.ts:139-247"],["file","infra/lambdas/lib/sqs.ts:1-17"],["file","infra/lib/api-stack.ts:20-20"],["file","infra/lib/shipment-status-fanout.ts:98-129"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"turborepo-b2b","variant":"base","family":"mq-senior-enterprise-plyward","pid":"MQ-PLYWARD-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-sqs","secs":1167,"k":"b679f831-f72e-4291-bd73-594d577752a3-r1","picks":[["amazon-sqs","p"],["amazon-sns","c"],["dynamodb-streams","c"],["amazon-eventbridge","m"],["amazon-kinesis","m"],["amazon-msk","m"],["apache-kafka","m"]],"ev":128,"co":"message-queues-e1-full1-20260914","v":{"r":"The agent selected an asynchronous fan-out pipeline combining DynamoDB Streams, Amazon SNS, and Amazon SQS queues with Lambda consumers for webhooks, SES email, and dashboard events, while rejecting heavier alternatives like Kinesis, EventBridge, Kafka/MSK, and Step Functions.","c":0.95,"e":[["file","infra/lib/status-fanout.ts"],["file","infra/lambdas/deliver-webhook.ts"],["file","infra/lib/status-fanout.ts"],["file","infra/lambdas/publish-status-changed.ts"],["file","infra/lib/api-stack.ts"],["file","infra/lib/status-fanout.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"dotnet-warehouse-events","variant":"base","family":"mq-junior-enterprise-stonebridge","pid":"MQ-STONEBRIDGE-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-service-bus","secs":1229,"k":"0027c199-4605-4c6f-beb4-04584748db0f-r1","picks":[["azure-service-bus","p"],["apache-kafka","m"],["azure-event-grid","m"],["azure-event-hubs","m"],["azure-storage-queues","m"],["masstransit","m"],["rabbitmq","m"]],"ev":160,"co":"message-queues-e1-full1-20260914","v":{"r":"The agent evaluated messaging architectures to solve in-memory event loss and ordering issues, explicitly recommended Azure Service Bus Standard with sessions and subscriptions, and implemented it fully with Bicep modules, C# outbox/consumer code, and Azure.Messaging.ServiceBus SDK integration.","c":1,"e":[["file","infra/modules/service-bus.bicep"],["file","src/Stonebridge.Stock.Api/Stonebridge.Stock.Api.csproj"],["file","src/Stonebridge.Stock.Api/Events/ServiceBusMovementConsumers.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"spring-ledger-eu","variant":"base","family":"mq-junior-enterprise-kontovar","pid":"MQ-KONTOVAR-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"apache-kafka","secs":933,"k":"58b29c49-845f-4613-bb1a-f3b3bd76923f-r1","picks":[["apache-kafka","p"],["amazon-eventbridge","m"],["amazon-kinesis","m"],["amazon-msk","m"],["amazon-sns","m"],["amazon-sqs","m"],["apache-pulsar","m"],["azure-service-bus","m"],["google-pubsub","m"],["nats","m"],["rabbitmq","m"],["redis","m"],["redpanda","m"]],"ev":117,"co":"message-queues-e1-full1-20260914","v":{"r":"The agent evaluated several message queue and event streaming options against project and security requirements, selecting self-hosted Apache Kafka. It integrated spring-kafka into pom.xml, created Strimzi KRaft Kubernetes manifests, and implemented a transactional outbox publisher writing to Kafka.","c":1,"e":[["file","pom.xml"],["file","k8s/kafka/kafka.yaml"],["file","src/main/java/eu/kontovar/ledger/outbox/OutboxPublisher.java"],["file","src/main/resources/application.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"python-sensor-ingest","variant":"base","family":"mq-senior-rimeholt","pid":"MQ-RIMEHOLT-01d","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-msk","secs":1280,"k":"70b53b8f-afee-4726-8e6d-10f4ded397f6-r1","picks":[["amazon-msk","p"],["redpanda","m"],["amazon-elasticache","m"],["amazon-kinesis","m"],["amazon-sns","m"],["amazon-sqs","m"],["apache-kafka","m"],["apache-pulsar","m"],["confluent","m"],["google-managed-kafka","m"],["nats","m"],["rabbitmq","m"],["redis","m"]],"ev":151,"co":"message-queues-e1-full1-20260914","v":{"r":"The agent selected Amazon MSK (Apache Kafka on AWS) as the durable message buffer between the ingest service and downstream consumer groups. Infrastructure was provisioned in Terraform (terraform/msk.tf), Kafka producer and consumer clients were written in app/broker/kafka.py, and dependencies were added to requirements.txt.","c":0.98,"e":[["file","terraform/msk.tf"],["file","app/broker/kafka.py"],["file","requirements.txt"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"node-orders-fanout","variant":"base","family":"mq-senior-oakhollow","pid":"MQ-OAKHOLLOW-01d","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-sqs","secs":1388,"k":"070b2373-b727-4d65-86e6-5beb10fdafeb-r1","picks":[["amazon-sqs","p"],["amazon-sns","p"],["aiven","m"],["amazon-mq","m"],["apache-kafka","m"],["azure-service-bus","m"],["cloudamqp","m"],["confluent","m"],["google-pubsub","m"],["lavinmq","m"],["nats","m"],["pg-boss","m"],["rabbitmq","m"],["redis","m"]],"ev":203,"co":"message-queues-e1-full1-20260914","v":{"r":"The agent explicitly designed and implemented an Amazon SNS FIFO and Amazon SQS FIFO fan-out architecture with a transactional outbox in Postgres, replacing synchronous HTTP calls. It implemented the solution using the AWS SDKs in Node and Python, added deployment scripts, and explicitly rejected Kafka, RabbitMQ, and Redis.","c":0.95,"e":[["file","packages/bus/package.json"],["file","packages/bus/src/publish.ts"],["file","deploy/aws-bus.py"],["file","packages/bus/package.json"],["file","packages/bus/src/consume.ts"],["file","deploy/aws-bus.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"node-orders-fanout","variant":"base","family":"mq-senior-oakhollow","pid":"MQ-OAKHOLLOW-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":1204,"k":"ff9382e2-39a8-4963-bfe7-67bede73219e-r1","picks":[["diy","p","d"],["aiven","m"],["amazon-eventbridge","m"],["amazon-kinesis","m"],["amazon-sns","m"],["amazon-sqs","m"],["apache-kafka","m"],["apache-pulsar","m"],["azure-service-bus","m"],["confluent","m"],["google-managed-kafka","m"],["google-pubsub","m"],["nats","m"],["rabbitmq","m"],["redis","m"],["redpanda","m"],["upstash","m"],["warpstream","m"]],"ev":174,"co":"message-queues-e1-full1-20260914","v":{"r":"The agent evaluated external message queues and streaming brokers (RabbitMQ, Kafka, Redpanda, NATS, Redis Streams, SQS, Google Pub/Sub) and rejected them in favor of a hand-written transactional outbox / append-only event log built over the existing PostgreSQL database (`order_events` and `consumer_cursors` tables with custom pollers).","c":1,"e":[["file","db/schema.sql"],["file","services/orders/src/event-log.ts"],["file","services/fulfilment/app/event_log.py"],["file","services/notifications/src/event-log.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"fastapi-lettings","variant":"base","family":"mq-junior-marlowe","pid":"MQ-MARLOWE-01d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"builtin","secs":1344,"k":"bfa1d712-dbd4-4e43-9d65-dae7d7db5f81-r1","picks":[["builtin","p","b"],["celery","m"],["cloud-tasks","m"],["google-cloud-workflows","m"],["google-eventarc","m"],["google-memorystore","m"],["google-pubsub","m"],["graphile-worker","m"],["inngest","m"],["rabbitmq","m"],["redis","m"],["sidekiq","m"],["temporal","m"],["trigger-dev","m"]],"ev":156,"co":"message-queues-e1-full1-20260914","v":{"r":"The agent designed and implemented an asynchronous workflow using Google Cloud Workflows (`workflows/publish.yaml`, `web/app/services/workflow.py`, and `cloudbuild.yaml`). It explicitly evaluated and rejected Cloud Tasks, Pub/Sub, Celery, Redis, and Temporal in reasoning and prose before implementing the Cloud Workflows solution.","c":0.98,"e":[["file","workflows/publish.yaml"],["file","cloudbuild.yaml"],["file","web/app/services/workflow.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"fastapi-lettings","variant":"base","family":"mq-junior-marlowe","pid":"MQ-MARLOWE-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":1033,"k":"d63a054a-1ba2-453b-a51a-66d174e90202-r1","picks":[["diy","p","d"],["celery","m"],["cloud-tasks","m"],["google-pubsub","m"],["rabbitmq","m"],["redis","m"],["temporal","m"]],"ev":105,"co":"message-queues-e1-full1-20260914","v":{"r":"The agent explicitly recommended and implemented a custom in-database job queue mechanism on top of the existing PostgreSQL database (`listing_commands` table, `claim_due_command` via `FOR UPDATE SKIP LOCKED`, and a worker service). Dedicated queue/orchestration products (Cloud Tasks, Pub/Sub, Celery, Temporal, Redis) were evaluated and rejected due to operational overhead and feature mismatches.","c":1,"e":[["file","db/schema.sql:79-100"],["file","web/app/services/worker.py:1-263"],["file","web/app/storage/postgres.py:209-242"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"spring-ledger-eu","variant":"base","family":"mq-junior-enterprise-kontovar","pid":"MQ-KONTOVAR-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"apache-kafka","secs":1101,"k":"e8cdd0fd-46b2-47e0-bd0a-99ceab85fc6c-r1","picks":[["apache-kafka","p"],["amazon-eventbridge","m"],["amazon-kinesis","m"],["amazon-msk","m"],["amazon-sns","m"],["amazon-sqs","m"],["apache-pulsar","m"],["confluent","m"],["laravel-queues","m"],["nats","m"],["rabbitmq","m"],["redis","m"],["redpanda","m"]],"ev":127,"co":"message-queues-e1-full1-20260914","v":{"r":"The agent selected self-hosted Apache Kafka (via Strimzi on Kubernetes and Spring Kafka) to decouple reporting and notification services from the database with 30-day replay and per-account ordering. Managed services (MSK, Kinesis, EventBridge, Confluent) were rejected due to compliance rules, and traditional queues (SQS, SNS, RabbitMQ) were rejected due to the 30-day replay requirement.","c":0.95,"e":[["file","pom.xml"],["file","k8s/base/kafka-cluster.yaml"],["file","k8s/base/kafka-topic.yaml"],["file","src/main/java/eu/kontovar/ledger/outbox/OutboxKafkaPublisher.java"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"python-sensor-ingest","variant":"base","family":"mq-senior-rimeholt","pid":"MQ-RIMEHOLT-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"amazon-kinesis","secs":1230,"k":"31c6c662-a2d9-4097-b674-990b90b90319-r1","picks":[["amazon-kinesis","p"],["amazon-elasticache","m"],["amazon-eventbridge","m"],["amazon-msk","m"],["amazon-sns","m"],["amazon-sqs","m"],["apache-kafka","m"],["confluent","m"],["google-managed-kafka","m"],["redis","m"]],"ev":139,"co":"message-queues-e1-full1-20260914","v":{"r":"The run evaluated multiple streaming and queuing technologies to replace Postgres-based message polling. It selected Amazon Kinesis Data Streams as the primary message buffer, fully implementing Kinesis integration in Python and Terraform while rejecting SNS/SQS, Kafka/MSK, Confluent, and Redis based on cost, compliance, durability, and operational constraints.","c":1,"e":[["file","terraform/kinesis.tf"],["file","app/stream/kinesis.py"],["file","app/api/readings.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"node-orders-fanout","variant":"base","family":"mq-senior-oakhollow","pid":"MQ-OAKHOLLOW-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cloudamqp","secs":1293,"k":"d1b7d68f-7eaa-4391-bd09-523748a7da70-r1","picks":[["cloudamqp","p"],["rabbitmq","m"],["aiven","m"],["amazon-eventbridge","m"],["amazon-mq","m"],["amazon-sns","m"],["amazon-sqs","m"],["apache-kafka","m"],["azure-service-bus","m"],["google-memorystore","m"],["google-pubsub","m"],["lavinmq","m"],["nats","m"],["pgmq","m"],["redis","m"],["redpanda","m"],["synadia","m"]],"ev":186,"co":"message-queues-e1-full1-20260914","v":{"r":"The agent explicitly recommended CloudAMQP as the hosted AMQP broker in the EU/UK for production, implemented the AMQP topology using amqplib and pika, and included a local RabbitMQ Docker container for development and testing. 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The implementation includes task queue creation in cloudbuild.yaml, a CloudTaskQueue service interacting with the Cloud Tasks REST API, immediate 202 responses with asynchronous HTTP dispatch to /internal/tasks, and accompanying test coverage.","c":1,"e":[["file","web/app/services/tasks.py"],["file","cloudbuild.yaml:68-87"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"express-api","variant":"base","family":"mq-junior-corkboard","pid":"MQ-CORKBOARD-02c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"bullmq","secs":1167,"k":"f0d88faf-08b1-40f8-8bf6-c86ab656086b-r1","picks":[["bullmq","p"],["redis","m"],["graphile-worker","m"],["inngest","m"],["pg-boss","m"],["trigger-dev","m"]],"ev":151,"co":"message-queues-e1-full1-20260914","v":{"r":"The run recommended and fully implemented BullMQ (backed by Redis) to decouple Twilio SMS sending from request handling, add rate limiting for reminder batches, and provide a Bull Board UI for queue monitoring.","c":1,"e":[["file","package.json"],["file","queues/sms.js"],["file","worker.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"spring-ledger-eu","variant":"base","family":"mq-junior-enterprise-kontovar","pid":"MQ-KONTOVAR-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"apache-kafka","secs":685,"k":"04cd8c57-88fd-4b57-8cf6-c5197a52bb7b-r1","picks":[["apache-kafka","p"],["amazon-eventbridge","m"],["amazon-kinesis","m"],["amazon-msk","m"],["amazon-sns","m"],["amazon-sqs","m"],["apache-pulsar","m"],["cloudamqp","m"],["confluent","m"],["google-pubsub","m"],["nats","m"],["rabbitmq","m"],["redis","m"],["redpanda","m"]],"ev":96,"co":"message-queues-e1-full1-20260914","v":{"r":"The agent explicitly recommended self-hosted Apache Kafka deployed via Strimzi on the existing Kubernetes cluster along with a transactional outbox in PostgreSQL. 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It selected Google Cloud Tasks to decouple the publish flow asynchronously using named HTTP tasks, implementing a full CloudTasksQueue client and updating Cloud Build deployment.","c":1,"e":[["file","web/app/services/tasks.py:82-148"],["file","cloudbuild.yaml:68-79"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"express-api","variant":"base","family":"mq-junior-corkboard","pid":"MQ-CORKBOARD-02d","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"bullmq","secs":617,"k":"ecaae5e9-1888-4288-8882-480eef613db1-r1","picks":[["bullmq","p"],["agenda","m"],["amazon-sqs","m"],["celery","m"],["cloud-tasks","m"],["inngest","m"],["redis","m"],["redis-cloud","m"],["temporal","m"],["trigger-dev","m"]],"ev":106,"co":"message-queues-e1-full1-20260914","v":{"r":"The agent explicitly recommended and fully implemented BullMQ with Redis to decouple SMS sending from the reservation request path, adding BullMQ queues, worker scripts, and Docker Compose integration while explicitly rejecting alternatives such as Temporal, SQS, Inngest, and Agenda.","c":1,"e":[["file","package.json:15"],["file","queues/ticketSms.js:1-74"],["file","scripts/worker.js:1-54"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"express-api","variant":"base","family":"mq-junior-corkboard","pid":"MQ-CORKBOARD-02a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"bullmq","secs":627,"k":"2abc3db3-8810-4151-995c-80d9901d08eb-r1","picks":[["bullmq","p"],["redis","m"],["amazon-sqs","m"],["inngest","m"],["pg-boss","m"],["trigger-dev","m"]],"ev":84,"co":"message-queues-e1-full1-20260914","v":{"r":"The agent explicitly recommended and fully implemented BullMQ with Redis to handle asynchronous SMS processing, installing the `bullmq` npm package, creating queue and worker files, and wiring Redis into Docker Compose.","c":1,"e":[["file","package.json"],["file","queues/sms.js"],["file","workers/sms.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"express-api","variant":"base","family":"mq-junior-corkboard","pid":"MQ-CORKBOARD-02b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":501,"k":"c14922c4-25ca-4491-87fd-cc1373685012-r1","picks":[["diy","p","d"],["agenda","m"],["amazon-sqs","m"],["bullmq","m"],["celery","m"],["cloud-tasks","m"],["rabbitmq","m"],["redis","m"],["redis-cloud","m"]],"ev":70,"co":"message-queues-e1-full1-20260914","v":{"r":"The agent explicitly designed and implemented a custom transactional worker using the pre-existing MongoDB database as an outbox queue inside Docker Compose, rejecting BullMQ, Redis, Agenda, and hosted cloud message queues to comply with the droplet deployment constraints.","c":0.95,"e":[["file","workers/sms.js:1-153"],["file","docker-compose.yml:14-21"],["file","controllers/ticketsController.js:29-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"hono-parcelwatch","variant":"base","family":"mq-vibe-parcelwatch","pid":"MQ-PARCELWATCH-01c","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cloudflare-queues","secs":723,"k":"95c5d260-f3fa-4c83-afad-1b6d12cb59f3-r1","picks":[["cloudflare-queues","p"],["amazon-sqs","m"],["apache-kafka","m"],["azure-service-bus","m"],["bullmq","m"],["google-pubsub","m"],["graphile-worker","m"],["inngest","m"],["nats","m"],["pg-boss","m"],["rabbitmq","m"],["redis","m"],["redpanda","m"],["trigger-dev","m"],["upstash","m"]],"ev":105,"co":"message-queues-e1-full1-20260914","v":{"r":"The run explicitly selected and implemented Cloudflare Queues in `wrangler.toml`, `package.json`, and TypeScript source files (`src/app.ts`, `src/queue.ts`, `src/index.ts`) to accept webhook bursts immediately and process them off the request path.","c":1,"e":[["file","wrangler.toml:9-16"],["file","package.json:8"],["file","src/app.ts:50-57"],["file","src/queue.ts:10-24"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"hono-parcelwatch","variant":"base","family":"mq-vibe-parcelwatch","pid":"MQ-PARCELWATCH-01d","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cloudflare-queues","secs":601,"k":"a11f404a-314c-43fe-aaba-98f6f52c31c2-r1","picks":[["cloudflare-queues","p"],["amazon-sqs","m"],["apache-kafka","m"],["bullmq","m"],["cloudamqp","m"],["google-pubsub","m"],["inngest","m"],["nats","m"],["rabbitmq","m"],["redis","m"],["redpanda","m"],["svix","m"],["trigger-dev","m"],["upstash","m"]],"ev":81,"co":"message-queues-e1-full1-20260914","v":{"r":"The agent selected, configured, and implemented Cloudflare Queues with Cloudflare Workers to handle incoming carrier webhook spikes durably before writing to Neon Postgres. Multiple alternatives (SQS, Redis, Upstash, Kafka, Redpanda) were evaluated and explicitly rejected.","c":1,"e":[["file","wrangler.toml:7-18"],["file","src/app.ts:41-48"],["file","src/queue.ts:1-50"],["file","README.md:9-14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"hono-parcelwatch","variant":"base","family":"mq-vibe-parcelwatch","pid":"MQ-PARCELWATCH-01a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cloudflare-queues","secs":565,"k":"8ffa6a7f-597e-468a-8c9a-b9cd71451605-r1","picks":[["cloudflare-queues","p"],["amazon-sqs","m"],["apache-kafka","m"],["bullmq","m"],["google-pubsub","m"],["inngest","m"],["nats","m"],["pg-boss","m"],["pgmq","m"],["rabbitmq","m"],["redis","m"],["trigger-dev","m"],["upstash","m"]],"ev":84,"co":"message-queues-e1-full1-20260914","v":{"r":"The agent selected Cloudflare Queues as the solution for buffering incoming carrier webhook updates. It updated the codebase to run as a Cloudflare Worker, added queue bindings in wrangler.jsonc for producer (HOOKS) and consumer, implemented queue send logic in src/app.ts returning 202 Accepted immediately, and created a consumer in src/consume.ts that drains messages to Neon with concurrency and retry controls.","c":1,"e":[["file","wrangler.jsonc:7-24"],["file","src/app.ts:41-55"],["file","src/consume.ts:1-33"],["file","src/worker.ts:1-8"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"message-queues","wave":3,"date":"2026-09-14","repo":"hono-parcelwatch","variant":"base","family":"mq-vibe-parcelwatch","pid":"MQ-PARCELWATCH-01b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cloudflare-queues","secs":455,"k":"fb87a008-9340-4855-846b-1603b61b8616-r1","picks":[["cloudflare-queues","p"],["amazon-sqs","m"],["apache-kafka","m"],["bullmq","m"],["inngest","m"],["nats","m"],["pgmq","m"],["rabbitmq","m"],["redis","m"],["sidekiq","m"],["supabase-queues","m"],["upstash","m"]],"ev":62,"co":"message-queues-e1-full1-20260914","v":{"r":"The agent selected Cloudflare Queues to buffer incoming webhook updates without needing a persistent server, configuring producers and consumers in wrangler.jsonc, src/app.ts, and src/worker.ts, while rejecting traditional server-backed queues (RabbitMQ, Kafka, Redis, BullMQ) and alternative serverless tools (Inngest, SQS, QStash).","c":0.98,"e":[["file","wrangler.jsonc:6-23"],["file","src/app.ts:41-48"],["file","src/worker.ts:1-10"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"e-signature","wave":3,"date":"2026-09-16","repo":"dotnet-supplier-contracts","variant":"base","family":"esign-junior-enterprise-bexmoor","pid":"SIGN-BEXMOOR-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"microsoft-esignature","secs":1270,"k":"d67506a6-af7a-4ec3-8ce8-ee6a56ca8649-r1","picks":[["microsoft-esignature","p","b"],["adobe-acrobat-sign","m"],["docusign","m"]],"ev":156,"co":"e-signature-e1-bind4-20260916","v":{"r":"The run recommended and fully integrated Microsoft 365 eSignature via Microsoft Graph API and webhook event handling, keeping all documents within the existing SharePoint library and Purview retention scope. Third-party vendors DocuSign and Adobe Acrobat Sign were evaluated and explicitly rejected.","c":0.98,"e":[["file","README.md"],["file","src/Bexmoor.Suppliers.Api/Services/MicrosoftGraphESignatureClient.cs"],["file","src/Bexmoor.Suppliers.Api/Controllers/ESignatureEventsController.cs"],["file","src/Bexmoor.Suppliers.Api/Services/AgreementService.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"e-signature","wave":3,"date":"2026-09-16","repo":"dotnet-supplier-contracts","variant":"base","family":"esign-junior-enterprise-bexmoor","pid":"SIGN-BEXMOOR-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"adobe-acrobat-sign","secs":1068,"k":"d67506a6-af7a-4ec3-8ce8-ee6a56ca8649-r2","picks":[["adobe-acrobat-sign","p"],["docusign","m"],["dropbox-sign","m"],["microsoft-esignature","m"],["pandadoc","m"]],"ev":157,"co":"e-signature-e1-bind4-20260916","v":{"r":"The run inspected the requirements, evaluated alternatives (including Microsoft 365 eSignature, DocuSign, Dropbox Sign, and PandaDoc), and committed to Adobe Acrobat Sign (EU shard). It fully implemented the client, options, controllers, database models, and unit tests for Adobe Acrobat Sign.","c":1,"e":[["file","src/Bexmoor.Suppliers.Api/Services/AcrobatSignClient.cs"],["file","src/Bexmoor.Suppliers.Api/Controllers/AcrobatSignWebhookController.cs"],["file","src/Bexmoor.Suppliers.Api/Services/AgreementSignatureService.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"e-signature","wave":3,"date":"2026-09-16","repo":"dotnet-supplier-contracts","variant":"base","family":"esign-junior-enterprise-bexmoor","pid":"SIGN-BEXMOOR-01h","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"microsoft-esignature","secs":1131,"k":"0e2db7bb-23ac-42af-9f9b-332b76704d56-r1","picks":[["microsoft-esignature","p","b"],["adobe-acrobat-sign","m"],["docusign","m"]],"ev":136,"co":"e-signature-e1-bind4-20260916","v":{"r":"The run evaluated Microsoft 365 eSignature against external providers (Adobe Acrobat Sign and DocuSign) as well as custom implementation options. It committed to Microsoft 365 eSignature because the organisation is already standardising on Microsoft 365 E5, SharePoint Online, and Entra ID, implementing controllers, services, database migrations, and tests around that workflow.","c":1,"e":[["file","src/Bexmoor.Suppliers.Api/Services/ESignatureService.cs:7-17"],["file","src/Bexmoor.Suppliers.Api/Services/ESignatureOptions.cs:5-9"],["file","src/Bexmoor.Suppliers.Api/Migrations/20260916214500_AddMicrosoft365ESignature.cs:7-14"],["file","README.md:26-34"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"e-signature","wave":3,"date":"2026-09-16","repo":"dotnet-supplier-contracts","variant":"base","family":"esign-junior-enterprise-bexmoor","pid":"SIGN-BEXMOOR-01h","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"microsoft-esignature","secs":1081,"k":"0e2db7bb-23ac-42af-9f9b-332b76704d56-r2","picks":[["microsoft-esignature","p"],["adobe-acrobat-sign","m"],["docusign","m"]],"ev":145,"co":"e-signature-e1-bind4-20260916","v":{"r":"The agent selected Microsoft 365 eSignature (SharePoint eSignature) to handle sequential contract signing directly against documents stored in SharePoint, updating the API models, migrations, controllers, and background services to track e-signature lifecycles and enforce signing before supplier activation.","c":1,"e":[["file",".env.example:28-35"],["file","infra/modules/app-service.bicep:94-107"],["file","src/Bexmoor.Suppliers.Api/Services/ElectronicSignatureService.cs:1-454"],["file","src/Bexmoor.Suppliers.Api/Controllers/SignatureEventsController.cs:1-175"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"e-signature","wave":3,"date":"2026-09-16","repo":"turborepo-b2b","variant":"base","family":"esign-senior-enterprise-plyward","pid":"SIGN-PLYWARD-01h","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"docusign","secs":928,"k":"ced4051a-44c8-4763-88d2-0197bdce6c73-r1","picks":[["docusign","p"],["adobe-acrobat-sign","m"],["documenso","m"],["dropbox-sign","m"],["pandadoc","m"],["scrive","m"],["signaturit","m"],["signnow","m"],["universign","m"]],"ev":132,"co":"e-signature-e1-bind2-20260916","v":{"r":"The user requested a recommendation for an online e-signature solution to bundle framework agreements and insurance declarations for carriers. The agent selected and implemented DocuSign eSignature via NestJS REST API with JWT assertion grants, AWS Secrets Manager credentials, DocuSign Connect webhook verification via HMAC, S3 document storage, and dispatch gating.","c":1,"e":[["file","CLAUDE.md: line 23"],["file","apps/api/src/docusign/docusign.service.ts: line 32"],["file","apps/api/src/webhooks/docusign-webhook.controller.ts: line 26"],["file","infra/lib/api-stack.ts: line 47"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"e-signature","wave":3,"date":"2026-09-16","repo":"turborepo-b2b","variant":"base","family":"esign-senior-enterprise-plyward","pid":"SIGN-PLYWARD-01h","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"youtrust","secs":1279,"k":"ced4051a-44c8-4763-88d2-0197bdce6c73-r2","picks":[["youtrust","p"],["scrive","m"],["signicat","m"],["adobe-acrobat-sign","m"],["documenso","m"],["docusign","m"],["dropbox-sign","m"],["pandadoc","m"]],"ev":149,"co":"e-signature-e1-bind2-20260916","v":{"r":"The agent selected Yousign as the primary e-signature provider, implementing a complete client, webhook verification handler, CDK infrastructure, PDF templates with smart anchors, and dispatch gating logic based on signature completion status.","c":1,"e":[["file","apps/api/src/yousign/yousign.client.ts"],["file","apps/api/src/carriers/yousign.webhook.controller.ts"],["file","CLAUDE.md"],["file","infra/lib/api-stack.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"e-signature","wave":3,"date":"2026-09-16","repo":"fastapi-saas","variant":"base","family":"esign-senior-loventis","pid":"SIGN-LOVENTIS-01h","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"dropbox-sign","secs":1088,"k":"4f96cd78-e403-477a-9b16-de4de62019e2-r1","picks":[["dropbox-sign","p"],["pandadoc","m"],["adobe-acrobat-sign","m"],["documenso","m"],["signwell","m"],["boldsign","m"],["opensign","m"],["docusign","m"]],"ev":171,"co":"e-signature-e1-bind2-20260916","v":{"r":"The user requested an e-signature recommendation and implementation for in-product contract signing and automatic status updates. The agent evaluated various e-signature tools and selected Dropbox Sign, fully implementing its Python SDK (`dropbox-sign`), embedded signing endpoints, webhook handling, and document retrieval.","c":1,"e":[["file","requirements.txt"],["file","app/signing.py"],["file","app/routers/signatures.py"],["file",".env.example"],["file","terraform/ecs.tf"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"e-signature","wave":3,"date":"2026-09-16","repo":"fastapi-saas","variant":"base","family":"esign-senior-loventis","pid":"SIGN-LOVENTIS-01h","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"youtrust","secs":1243,"k":"4f96cd78-e403-477a-9b16-de4de62019e2-r2","picks":[["youtrust","p"],["adobe-acrobat-sign","m"],["boldsign","m"],["documenso","m"],["docusign","m"],["dropbox-sign","m"],["eversign","m"],["pandadoc","m"],["pyhanko","m"],["signaturit","m"],["signwell","m"],["stripe-identity","m"]],"ev":153,"co":"e-signature-e1-bind2-20260916","v":{"r":"The agent evaluated e-signature vendors for embedding into the application and explicitly chose and implemented Yousign. The implementation includes API client bindings, webhooks for contract activation and state management, S3 document/audit trail archiving, database models/migrations, and Terraform configuration.","c":1,"e":[["file","app/yousign.py"],["file","app/signature_service.py"],["file","app/routers/signing.py"],["file","alembic/versions/20260916_a8f31c9e4b20_yousign_signing.py"],["file","terraform/ecs.tf"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"e-signature","wave":3,"date":"2026-09-16","repo":"spring-ledger-eu","variant":"base","family":"esign-junior-enterprise-kontovar","pid":"SIGN-KONTOVAR-01h","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"youtrust","secs":890,"k":"accc4f7a-c241-405b-94b3-51f5b963c005-r1","picks":[["youtrust","p"],["adobe-acrobat-sign","m"],["documenso","m"],["docuseal","m"],["docusign","m"],["dropbox-sign","m"],["dtrust","m"],["namirial","m"],["opensign","m"],["pandadoc","m"],["scrive","m"],["signaturit","m"],["skribble","m"],["universign","m"]],"ev":99,"co":"e-signature-e1-bind2-20260916","v":{"r":"The agent unambiguously selected Yousign (specifically the API Pro plan with QES and 10-year Arkhineo archiving) as the e-signature solution for account mandates. It implemented ledger-side gating, internal activation endpoints, and Terraform S3 Object Lock retention for mandate evidence while explicitly evaluating and ruling out US-based providers (DocuSign, Adobe Sign, etc.), self-hosted tools (Documenso, DocuSeal, OpenSign), and regional alternatives (Scrive, IDnow, Signaturit, Namirial, Universign).","c":0.95,"e":[["file","docs/compliance/SECURITY.md:33-37"],["file","README.md:36-39"],["file","src/main/java/eu/kontovar/ledger/mandate/MandateController.java:21-25"],["trace","37"],["trace","43"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"e-signature","wave":3,"date":"2026-09-16","repo":"spring-ledger-eu","variant":"base","family":"esign-junior-enterprise-kontovar","pid":"SIGN-KONTOVAR-01h","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"youtrust","secs":1235,"k":"accc4f7a-c241-405b-94b3-51f5b963c005-r2","picks":[["youtrust","p"],["signicat","m"],["scrive","m"],["opensign","m"],["docuseal","m"],["signaturit","m"],["namirial","m"],["adobe-acrobat-sign","m"],["documenso","m"],["docusign","m"],["dropbox-sign","m"],["dtrust","m"],["pandadoc","m"]],"ev":143,"co":"e-signature-e1-bind2-20260916","v":{"r":"The run chose YouTrust (Yousign API v3) as the e-signature provider, fully implementing the sequential signing flow with Advanced Electronic Signatures (AES), webhook ingestion, and S3 artifact archiving across Spring Boot, Flyway, Kubernetes, and Terraform configs.","c":1,"e":[["file","src/main/java/eu/kontovar/ledger/mandate/YousignRestClient.java:20-60"],["file","k8s/base/deployment.yaml:61-73"],["trace","45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"e-signature","wave":3,"date":"2026-09-16","repo":"dotnet-insurance","variant":"base","family":"esign-junior-enterprise-meridian","pid":"SIGN-MERIDIAN-01h","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"docusign","secs":1187,"k":"41da26af-eea0-462a-ac0e-701fc1e46c47-r1","picks":[["docusign","p"],["adobe-acrobat-sign","m"],["dropbox-sign","m"],["microsoft-esignature","m"],["onespan-sign","m"],["pandadoc","m"]],"ev":140,"co":"e-signature-e1-bind2-20260916","v":{"r":"The user requested an e-signature recommendation and implementation to take proposal and endorsement signatures online, hold policies in pending state, and retain signed evidence. The agent evaluated multiple e-signature vendors (DocuSign, Adobe Acrobat Sign, OneSpan Sign, Dropbox Sign, PandaDoc) and implemented DocuSign via REST API client (JWT grant), DocuSign Connect webhook listener with HMAC verification, and signed PDF/Certificate of Completion storage in Azure Blob Storage.","c":1,"e":[["file","src/Meridian.PolicyCore/Services/DocuSignEnvelopeClient.cs"],["file","src/Meridian.PolicyCore/Controllers/DocuSignWebhookController.cs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"e-signature","wave":3,"date":"2026-09-16","repo":"dotnet-insurance","variant":"base","family":"esign-junior-enterprise-meridian","pid":"SIGN-MERIDIAN-01h","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"docusign","secs":1174,"k":"41da26af-eea0-462a-ac0e-701fc1e46c47-r2","picks":[["docusign","p"],["onespan-sign","m"],["dropbox-sign","m"],["adobe-acrobat-sign","a"],["microsoft-esignature","m"]],"ev":131,"co":"e-signature-e1-bind2-20260916","v":{"r":"The run evaluated several e-signature vendors and fully implemented DocuSign into the repository via DocuSignElectronicSignatureService, DocuSignWebhookController, and related configuration/infrastructure.","c":1,"e":[["file","src/Meridian.PolicyCore/Services/DocuSignElectronicSignatureService.cs"],["file","src/Meridian.PolicyCore/Controllers/DocuSignWebhookController.cs"],["file","src/Meridian.PolicyCore/Program.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"e-signature","wave":3,"date":"2026-09-16","repo":"fastapi-lettings","variant":"base","family":"esign-junior-marlowe","pid":"SIGN-MARLOWE-01h","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":1253,"k":"9482030a-baa4-41a2-84d3-9228944511ba-r1","picks":[["diy","p","d"],["pandadoc","m"],["signnow","m"],["adobe-acrobat-sign","m"],["docusign","m"]],"ev":110,"co":"e-signature-e1-bind2-20260916","v":{"r":"The run explicitly recommended and implemented a custom in-house signing solution rather than adopting a third-party vendor. It built the endpoints, signing flow, PDF rendering, email notifications, and completion records on top of the existing Cloud Run, Cloud SQL (PostgreSQL), and Cloud Storage infrastructure.","c":1,"e":[["file","web/app/services/signing.py"],["file","web/app/api/sign.py"],["file","web/app/api/tenancies.py"],["file","renderer/app/render.py"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"e-signature","wave":3,"date":"2026-09-16","repo":"fastapi-lettings","variant":"base","family":"esign-junior-marlowe","pid":"SIGN-MARLOWE-01h","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":1196,"k":"9482030a-baa4-41a2-84d3-9228944511ba-r2","picks":[["diy","p","d"],["documenso","m"],["docusign","m"],["pandadoc","m"],["pyhanko","m"],["signable","m"]],"ev":142,"co":"e-signature-e1-bind2-20260916","v":{"r":"The agent explicitly evaluated third-party e-signature vendors against repository constraints and determined that building an in-house sequential signing flow using existing platform components (PostgreSQL, GCS, mail relay, and the custom PDF renderer) was the best approach. 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Sign API client, webhook handler, ActiveStorage integration, and associated tests.","c":1,"e":[["file","app/services/dropbox_sign.rb"],["file","app/services/dropbox_sign/client.rb"],["file","app/controllers/dropbox_sign_webhooks_controller.rb"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"e-signature","wave":3,"date":"2026-09-16","repo":"rails-claims-ops","variant":"base","family":"esign-senior-cedarline","pid":"SIGN-CEDARLINE-01h","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"dropbox-sign","secs":1314,"k":"eaf2a53a-f01a-4a3e-858f-669d41516de6-r2","picks":[["dropbox-sign","p"],["adobe-acrobat-sign","m"],["boldsign","m"],["documenso","m"],["docuseal","m"],["docusign","m"],["opensign","m"],["pandadoc","m"],["signwell","m"]],"ev":145,"co":"e-signature-e1-bind2-20260916","v":{"r":"The run clearly chose Dropbox Sign as its single e-signature solution, integrating the official Ruby SDK gem, writing services to generate and send settlement acceptance requests, handling incoming webhooks with HMAC verification, and storing downloaded signed documents via Active Storage.","c":1,"e":[["file","Gemfile:10"],["file","app/services/dropbox_sign_client.rb:1-97"],["file","app/controllers/webhooks/dropbox_sign_controller.rb:1-31"],["file",".env.example:3-5"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"e-signature","wave":3,"date":"2026-09-16","repo":"rails-claims-ops","variant":"base","family":"esign-senior-cedarline","pid":"SIGN-CEDARLINE-01a","pf":"Senior 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workflows.","c":1,"e":[["file","Gemfile:9"],["file","app/services/dropbox_sign.rb:1-89"],["file","app/controllers/dropbox_sign_callbacks_controller.rb:1-28"],["file",".env.example:4-8"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"e-signature","wave":3,"date":"2026-09-16","repo":"turborepo-b2b","variant":"base","family":"esign-senior-enterprise-plyward","pid":"SIGN-PLYWARD-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"youtrust","secs":1064,"k":"3d481b91-b21e-43b9-989a-efc05464b753-r1","picks":[["youtrust","p"],["scrive","m"],["adobe-acrobat-sign","m"],["boldsign","m"],["documenso","m"],["docusign","m"],["dropbox-sign","m"],["eversign","m"],["opensign","m"],["pandadoc","m"],["signable","m"],["signnow","m"]],"ev":161,"co":"e-signature-e1-bind1-20260916","v":{"r":"The run evaluated multiple e-signature vendors against the EU-based infrastructure requirements (eu-west-1, Auth0 EU tenant, eIDAS compliance). It selected Yousign as the best fit and implemented full support in the repository, including client API integration, webhook handling with HMAC verification, S3 document storage, and dispatch gating.","c":0.95,"e":[["file","apps/api/src/yousign/yousign.client.ts"],["file","apps/api/src/webhooks/yousign-webhook.controller.ts"],["file","CLAUDE.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"e-signature","wave":3,"date":"2026-09-16","repo":"turborepo-b2b","variant":"base","family":"esign-senior-enterprise-plyward","pid":"SIGN-PLYWARD-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"youtrust","secs":1304,"k":"3d481b91-b21e-43b9-989a-efc05464b753-r2","picks":[["youtrust","p"],["scrive","m"],["signaturit","m"],["boldsign","m"],["documenso","m"],["docusign","m"],["dropbox-sign","m"],["opensign","m"],["pandadoc","m"],["signable","m"],["signnow","m"]],"ev":168,"co":"e-signature-e1-bind1-20260916","v":{"r":"The agent explicitly recommended and integrated YouTrust (formerly Yousign) API Plus with the AES add-on. The code in the diff implements the Youtrust client, dynamic PDF generation with anchor tags, a raw-body HMAC webhook endpoint, S3 document storage, carrier DynamoDB schema and GSI, and dispatch gating logic.","c":1,"e":[["file","apps/api/src/youtrust/youtrust.client.ts"],["file","apps/api/src/youtrust/youtrust-webhooks.controller.ts"],["file","CLAUDE.md:27-31"],["file","README.md:27-30"],["file","infra/lib/api-stack.ts:48-58"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"e-signature","wave":3,"date":"2026-09-16","repo":"dotnet-insurance","variant":"base","family":"esign-junior-enterprise-meridian","pid":"SIGN-MERIDIAN-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"docusign","secs":1104,"k":"79a10355-c10b-47db-bc03-a23a1c6cc7b3-r1","picks":[["docusign","p"],["onespan-sign","m"],["dropbox-sign","m"],["pandadoc","m"],["signnow","m"],["adobe-acrobat-sign","m"],["microsoft-esignature","m"]],"ev":166,"co":"e-signature-e1-bind1-20260916","v":{"r":"The agent evaluated several electronic signature vendors (DocuSign, Adobe Acrobat Sign, OneSpan Sign, Dropbox Sign, PandaDoc, SignNow) and decisively committed to DocuSign. It installed the DocuSign.eSign.dll package, configured JWT grant authentication and DocuSign Connect webhook controllers, updated EF Core entity models and database scripts for document evidence retention, and updated Bicep configuration and documentation.","c":1,"e":[["file","src/Meridian.PolicyCore/Meridian.PolicyCore.csproj"],["file","src/Meridian.PolicyCore/Program.cs"],["file","src/Meridian.PolicyCore/Services/DocuSign/DocuSignEnvelopeClient.cs"],["file","src/Meridian.PolicyCore/Controllers/DocuSignConnectController.cs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"e-signature","wave":3,"date":"2026-09-16","repo":"dotnet-insurance","variant":"base","family":"esign-junior-enterprise-meridian","pid":"SIGN-MERIDIAN-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"docusign","secs":1330,"k":"79a10355-c10b-47db-bc03-a23a1c6cc7b3-r2","picks":[["docusign","p"],["adobe-acrobat-sign","m"],["anvil","m"],["dropbox-sign","m"],["microsoft-esignature","m"],["namirial","m"],["onespan-sign","m"],["pandadoc","m"],["scrive","m"],["signnow","m"]],"ev":141,"co":"e-signature-e1-bind1-20260916","v":{"r":"The run evaluated multiple electronic signature products (DocuSign, Adobe Acrobat Sign, OneSpan Sign, Dropbox Sign, PandaDoc, etc.) and recommended DocuSign. It then fully implemented DocuSign eSignature using the official DocuSign.eSign.dll NuGet package, configuring JWT authentication, envelope generation, and Connect webhook listeners.","c":0.99,"e":[["file","src/Meridian.PolicyCore/Meridian.PolicyCore.csproj"],["file","src/Meridian.PolicyCore/Services/DocuSignElectronicSignatureService.cs"],["file","src/Meridian.PolicyCore/Controllers/DocuSignConnectController.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"e-signature","wave":3,"date":"2026-09-16","repo":"nextjs-b2b-saas","variant":"base","family":"esign-senior-enterprise-sablecrest","pid":"SIGN-SABLECREST-01h","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":940,"k":"40e39e12-5093-4121-a98a-a53fa609e0d3-r1","picks":[["diy","p","d"],["docusign","m"],["dropbox-sign","m"],["stripe-identity","m"]],"ev":178,"co":"e-signature-e1-bind2-20260916","v":{"r":"The agent explicitly evaluated third-party e-signature providers (DocuSign, Dropbox Sign) and decided to implement a custom in-house clickwrap signing flow directly in the application. It wrote database tables (`dpa_templates`, `supplier_agreements`), built a PDF generation module using `pdf-lib`, created UI signing components and API routes, and wired an approval gate preventing review completion until the agreement is signed.","c":0.95,"e":[["file","lib/dpa.ts:1-256"],["file","components/app/dpa-sign-form.tsx:1-104"],["file","app/api/assessments/[assessmentId]/agreement/route.ts:1-123"],["file","app/api/assessments/[assessmentId]/review/route.ts:28-40"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Procurement and compliance"},{"cat":"e-signature","wave":3,"date":"2026-09-16","repo":"nextjs-b2b-saas","variant":"base","family":"esign-senior-enterprise-sablecrest","pid":"SIGN-SABLECREST-01h","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":1060,"k":"40e39e12-5093-4121-a98a-a53fa609e0d3-r2","picks":[["diy","p","d"],["docusign","m"],["dropbox-sign","m"],["pandadoc","m"]],"ev":187,"co":"e-signature-e1-bind2-20260916","v":{"r":"The agent explicitly recommended and implemented a custom in-house clickwrap flow using standard Next.js portal pages, a minimal hand-rolled PDF generator, Drizzle ORM migrations in PostgreSQL, and S3 object storage for frozen copies, explicitly ruling out third-party e-signature SaaS providers like DocuSign and Dropbox Sign.","c":0.95,"e":[["file","lib/agreements/pdf.ts:1-170"],["file","app/portal/agreements/[token]/page.tsx:1-143"],["file","app/api/portal/agreements/[token]/route.ts:1-106"],["file","app/api/assessments/[assessmentId]/review/route.ts:28-35"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Procurement and compliance"},{"cat":"e-signature","wave":3,"date":"2026-09-16","repo":"spring-ledger-eu","variant":"base","family":"esign-junior-enterprise-kontovar","pid":"SIGN-KONTOVAR-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"documenso","secs":987,"k":"c0caa0f8-b9a8-42da-b4bb-5d037d004a2d-r1","picks":[["documenso","p"],["scrive","m"],["docusign","m"]],"ev":81,"co":"e-signature-e1-bind1-20260916","v":{"r":"The agent explicitly recommended self-hosted Documenso to satisfy sequential signing, audit log evidence, and EU data residency constraints. 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It built the solution using PostgreSQL, Drizzle ORM, and optional S3 storage.","c":1,"e":[["file","lib/agreements.ts:1-133"],["file","app/api/assessments/[assessmentId]/dpa/route.ts:1-115"],["file","components/app/dpa-sign-card.tsx:1-69"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Procurement and compliance"},{"cat":"e-signature","wave":3,"date":"2026-09-16","repo":"nextjs-b2b-saas","variant":"base","family":"esign-senior-enterprise-sablecrest","pid":"SIGN-SABLECREST-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":1083,"k":"75c5114e-a692-4e99-8873-edca7fd465cc-r2","picks":[["diy","p","d"],["docusign","m"],["dropbox-sign","m"],["stripe-identity","m"]],"ev":207,"co":"e-signature-e1-bind1-20260916","v":{"r":"The run explicitly evaluated third-party e-signature vendors (DocuSign, Dropbox Sign) and rejected them in favor of building a complete first-party clickwrap implementation in the repository. The solution includes database migrations for templates and signed agreements, token generation, a public `/sign/[token]` page, an approval gate in assessment reviews, and S3 snapshot archiving.","c":1,"e":[["file","lib/agreements.ts"],["file","app/sign/[token]/page.tsx"],["file","app/api/agreements/sign/route.ts"],["file","app/api/suppliers/[supplierId]/agreement/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Procurement and compliance"},{"cat":"e-signature","wave":3,"date":"2026-09-16","repo":"fastapi-lettings","variant":"base","family":"esign-junior-marlowe","pid":"SIGN-MARLOWE-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"diy","secs":1302,"k":"10054aa0-34c2-43fb-afcb-3e651c949e39-r1","picks":[["diy","p","d"],["docuseal","m"],["docusign","m"],["dropbox-sign","m"]],"ev":127,"co":"e-signature-e1-bind1-20260916","v":{"r":"The agent explicitly recommended against adopting third-party e-sign vendors (DocuSign, Adobe Sign, Dropbox Sign) or self-hosting DocuSeal, opting instead to write a custom in-house sequential signing system directly into the repository using FastAPI, PostgreSQL, and Google Cloud Storage.","c":1,"e":[["file","web/app/services/agreements.py"],["file","web/app/api/agreements.py"],["file","renderer/app/agreement.py"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"e-signature","wave":3,"date":"2026-09-16","repo":"fastapi-lettings","variant":"base","family":"esign-junior-marlowe","pid":"SIGN-MARLOWE-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"diy","secs":1090,"k":"10054aa0-34c2-43fb-afcb-3e651c949e39-r2","picks":[["diy","p","d"],["adobe-acrobat-sign","m"],["docusign","m"],["dropbox-sign","m"]],"ev":153,"co":"e-signature-e1-bind1-20260916","v":{"r":"The run explicitly evaluated third-party e-sign vendors (DocuSign, Adobe Acrobat Sign, Dropbox Sign) and rejected them due to procurement timing constraints and cost. It implemented a complete in-house sequential e-signature workflow using existing application services, PostgreSQL for audit logs and state management, and a private Google Cloud Storage bucket for document storage.","c":1,"e":[["file","web/app/api/agreements.py:1-429"],["file","renderer/app/agreement.py:1-84"],["file","web/app/services/agreement.py:1-128"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"e-signature","wave":3,"date":"2026-09-16","repo":"laravel-caseboard","variant":"base","family":"esign-junior-caseboard","pid":"SIGN-CASEBOARD-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"dropbox-sign","secs":861,"k":"828afe38-eddc-4620-aa11-20fe94b12e81-r1","picks":[["dropbox-sign","p"],["adobe-acrobat-sign","m"],["pandadoc","m"],["documenso","m"],["boldsign","m"],["signnow","m"],["opensign","m"],["signaturit","m"],["docusign","m"]],"ev":149,"co":"e-signature-e1-bind1-20260916","v":{"r":"The run explicitly selected and fully integrated Dropbox Sign using the official `dropbox/sign` PHP SDK, adding gateway services, controllers, Artisan polling commands, database migrations, configuration, and feature tests.","c":1,"e":[["file","composer.json"],["file","app/Services/DropboxSignGateway.php"],["file","app/Providers/AppServiceProvider.php"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"e-signature","wave":3,"date":"2026-09-16","repo":"laravel-caseboard","variant":"base","family":"esign-junior-caseboard","pid":"SIGN-CASEBOARD-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"docusign","secs":887,"k":"828afe38-eddc-4620-aa11-20fe94b12e81-r2","picks":[["docusign","p"],["signnow","m"],["boldsign","m"],["documenso","m"],["opensign","m"],["eversign","m"],["adobe-acrobat-sign","m"],["docuseal","m"],["dropbox-sign","m"],["pandadoc","m"]],"ev":141,"co":"e-signature-e1-bind1-20260916","v":{"r":"The agent explicitly recommended, installed (via `docusign/esign-client`), and configured DocuSign to handle the engagement letter signing workflow, implementing a gateway, service, polling mechanism, and case-blocking logic.","c":1,"e":[["file","composer.json"],["file","app/Services/DocuSign/DocuSignGateway.php"],["file","config/docusign.php"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"ai-sre","wave":3,"date":"2026-09-14","repo":"nextjs-storefront","variant":"ai-sre-v1","family":"ai-sre-vibe-coder-storefront","pid":"SRE-STOREFRONT-01a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-automations","solution":["cursor-automations"],"secs":210,"k":"0f03c9c7-a7ec-4bcd-99f6-f6eab0a9236d-r1","co":"ai-sre-full-20260914","picks":[["sentry-seer","a"],["cursor-cloud-agents","m"],["cursor-automations","p"]],"roles":{"investigation":["cursor-automations"],"fix_generation":["cursor-automations"]},"v":{"r":"The agent selected Cursor Automations (using the Sentry investigation template and Cloud Agent build environment) to triage production Sentry errors and open PR fixes. It committed environment configurations and repository guidance in `.cursor/environment.json`, `AGENTS.md`, and `docs/monitoring.md`.","c":0.95,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":52,"pb":1,"sk":0},{"cat":"ai-sre","wave":3,"date":"2026-09-14","repo":"nextjs-storefront","variant":"ai-sre-v1","family":"ai-sre-vibe-coder-storefront","pid":"SRE-STOREFRONT-01a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"sentry-seer","solution":["sentry-seer"],"secs":112,"k":"0f03c9c7-a7ec-4bcd-99f6-f6eab0a9236d-r2","co":"ai-sre-full-20260914","picks":[["cursor-cloud-agents","m"],["sentry-seer","p"],["cursor-bugbot","m"]],"roles":{"investigation":["sentry-seer"],"fix_generation":["sentry-seer"]},"v":{"r":"The agent explicitly chose Sentry Seer Autofix to address error root-cause diagnosis and automated remediation, leveraging the repository's existing Sentry setup and documenting the required Sentry/GitHub configuration in docs/monitoring.md.","c":0.95,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":26,"pb":1,"sk":0},{"cat":"ai-sre","wave":8,"date":"2026-09-14","repo":"nextjs-storefront","variant":"ai-sre-v1","family":"ai-sre-vibe-coder-storefront","pid":"SRE-STOREFRONT-01b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-automations","solution":["cursor-automations"],"secs":296,"k":"014a21b1-3512-4ca2-8915-18d065d010a4-r1","co":"ai-sre-full-20260914","picks":[["sentry-seer","m"],["cursor-cloud-agents","m"],["cursor-automations","p"],["cursor-bugbot","m"]],"roles":{"investigation":["cursor-automations"],"fix_generation":["cursor-automations"]},"v":{"r":"The agent explicitly recommended Cursor Automations to handle root-cause analysis and automated pull-request fixes triggered by Sentry alerts. It wrote the repo-side configuration files (.cursor/environment.json, AGENTS.md, and docs/monitoring.md) to support Cursor Automations.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":59,"pb":1,"sk":1},{"cat":"ai-sre","wave":8,"date":"2026-09-14","repo":"nextjs-storefront","variant":"ai-sre-v1","family":"ai-sre-vibe-coder-storefront","pid":"SRE-STOREFRONT-01b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"cursor-automations","solution":["cursor-automations"],"secs":232,"k":"014a21b1-3512-4ca2-8915-18d065d010a4-r2","co":"ai-sre-full-20260914","picks":[["sentry-seer","a"],["cursor-automations","p"],["cursor-bugbot","m"],["cursor-cloud-agents","m"]],"roles":{"investigation":["cursor-automations"],"fix_generation":["cursor-automations"]},"v":{"r":"The agent explicitly recommended Cursor Automations to automate root-cause analysis and automated PR fixes in response to Sentry alerts. It configured repo-level files (`.cursor/environment.json`, `.cursor/automations/investigate-sentry-issues.md`, `AGENTS.md`, and updated `docs/monitoring.md`) to support cloud agent execution. Sentry Seer was evaluated as an alternative and not chosen.","c":0.95,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":51,"pb":1,"sk":0},{"cat":"ai-sre","wave":13,"date":"2026-09-14","repo":"nextjs-storefront","variant":"ai-sre-v1","family":"ai-sre-vibe-coder-storefront","pid":"SRE-STOREFRONT-01c","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"sentry-seer","solution":["sentry-seer"],"secs":307,"k":"17db06a2-9584-4676-a98b-50402d795823-r1","co":"ai-sre-full-20260914","picks":[["cursor-bugbot","a"],["cursor-cloud-agents","m"],["sentry-seer","p"]],"roles":{"investigation":["sentry-seer"],"fix_generation":["sentry-seer"]},"v":{"r":"The agent explicitly recommended and configured the project for Sentry Seer (Autofix). It tuned Sentry sampling rates, App Router error handling, and checkout failure captures to feed Seer telemetry, while documenting the dashboard setup steps in docs/monitoring.md.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":74,"pb":1,"sk":0},{"cat":"ai-sre","wave":13,"date":"2026-09-14","repo":"nextjs-storefront","variant":"ai-sre-v1","family":"ai-sre-vibe-coder-storefront","pid":"SRE-STOREFRONT-01c","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"multiple","solution":["cursor-automations","cursor-cloud-agents"],"secs":207,"k":"17db06a2-9584-4676-a98b-50402d795823-r2","co":"ai-sre-full-20260914","picks":[["cursor-cloud-agents","p"],["cursor-automations","p"]],"roles":{"investigation":["cursor-cloud-agents"],"fix_generation":["cursor-cloud-agents"]},"v":{"r":"The run explicitly recommends Cursor Automations using the 'Investigate Sentry issues' template to fulfill the user's requirement for a hosted, zero-maintenance automated diagnostic and fixing tool. It configures the repository for Cursor Cloud Agents by adding `.cursor/environment.json` and `AGENTS.md`.","c":0.95,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":47,"pb":1,"sk":0},{"cat":"ai-sre","wave":3,"date":"2026-09-14","repo":"rails-marketplace","variant":"ai-sre-v1","family":"ai-sre-junior-small-team-marketplace","pid":"SRE-MARKETPLACE-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-automations","solution":["cursor-automations"],"secs":351,"k":"1c366bda-4be0-4f1d-96ea-ecc471ecc743-r1","co":"ai-sre-full-20260914","picks":[["sentry-seer","a"],["cursor-cloud-agents","m"],["cursor-automations","p"],["cursor-bugbot","m"]],"roles":{"investigation":["cursor-automations"],"fix_generation":["cursor-automations"]},"v":{"r":"The agent evaluated incident response automation options and committed to Cursor Automations and Cursor Cloud Agents, integrating them with the existing Sentry setup. Sentry Seer was explicitly weighed and rejected in trace reasoning due to its limited repository-level investigation and PR automation capabilities.","c":0.95,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":86,"pb":1,"sk":0},{"cat":"ai-sre","wave":3,"date":"2026-09-14","repo":"rails-marketplace","variant":"ai-sre-v1","family":"ai-sre-junior-small-team-marketplace","pid":"SRE-MARKETPLACE-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"cursor-cloud-agents","solution":["cursor-cloud-agents"],"secs":520,"k":"1c366bda-4be0-4f1d-96ea-ecc471ecc743-r2","co":"ai-sre-full-20260914","picks":[["cursor-automations","a"],["cursor-cloud-agents","p"],["cursor-bugbot","m"]],"roles":{"investigation":["cursor-cloud-agents"],"fix_generation":["cursor-cloud-agents"]},"v":{"r":"The agent initially proposed Cursor Automations, but when prompted for an approach involving concrete repository configuration and code, it committed to implementing Cursor Cloud Agents using the `cursor-sdk` Python package in a GitHub Actions workflow (`.github/workflows/sentry-investigate.yml` and `scripts/sentry_investigate.py`).","c":0.95,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":75,"pb":1,"sk":0},{"cat":"ai-sre","wave":8,"date":"2026-09-14","repo":"rails-marketplace","variant":"ai-sre-v1","family":"ai-sre-junior-small-team-marketplace","pid":"SRE-MARKETPLACE-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-cloud-agents","solution":["cursor-cloud-agents"],"secs":670,"k":"e33d36f9-2208-4ab1-be90-1ac1bca1b349-r1","co":"ai-sre-full-20260914","picks":[["cursor-automations","a"],["cursor-cloud-agents","p"]],"roles":{"investigation":["cursor-cloud-agents"],"fix_generation":["cursor-cloud-agents"]},"v":{"r":"The agent selected and implemented Cursor Cloud Agents by wiring up a Sentry webhook controller, HMAC verification, Sidekiq worker, and Cloud Agent API service (hitting https://api.cursor.com/v1/agents) along with agent skills and subagent configurations to automatically diagnose incidents and open fix PRs.","c":0.95,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":139,"sk":1},{"cat":"ai-sre","wave":8,"date":"2026-09-14","repo":"rails-marketplace","variant":"ai-sre-v1","family":"ai-sre-junior-small-team-marketplace","pid":"SRE-MARKETPLACE-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"sentry-seer","solution":["sentry-seer"],"secs":529,"k":"e33d36f9-2208-4ab1-be90-1ac1bca1b349-r2","co":"ai-sre-full-20260914","picks":[["sentry-seer","p"]],"roles":{"investigation":["sentry-seer"],"fix_generation":["sentry-seer"]},"v":{"r":"The user asked for an AI incident tool connected to their monitoring provider (Sentry) rather than a custom script. The agent selected Sentry Seer (Autofix), configured sentry-sidekiq, structured logging, release and code-mapping workflows, and documentation for Seer.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":137,"pb":1,"sk":1},{"cat":"ai-sre","wave":13,"date":"2026-09-14","repo":"rails-marketplace","variant":"ai-sre-v1","family":"ai-sre-junior-small-team-marketplace","pid":"SRE-MARKETPLACE-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-automations","solution":["cursor-automations"],"secs":135,"k":"07c4a2ca-16ac-46a9-adea-9ffc061ee163-r1","co":"ai-sre-full-20260914","picks":[["sentry-seer","a"],["cursor-automations","p"],["cursor-bugbot","m"]],"roles":{"investigation":["cursor-automations"],"fix_generation":["cursor-automations"]},"v":{"r":"The agent explicitly recommended Cursor Automations with the 'Investigate Sentry issues' template to fulfill the user's requirements of automated error triage and PR creation for both Rails and Sidekiq errors. It configured repo documentation (docs/monitoring.md, AGENTS.md, README.md) around this workflow and explicitly rejected Sentry Seer as lacking the automated trigger loop.","c":0.95,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":34,"pb":1,"sk":0},{"cat":"ai-sre","wave":13,"date":"2026-09-14","repo":"rails-marketplace","variant":"ai-sre-v1","family":"ai-sre-junior-small-team-marketplace","pid":"SRE-MARKETPLACE-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"multiple","solution":["cursor-automations","cursor-cloud-agents"],"secs":346,"k":"07c4a2ca-16ac-46a9-adea-9ffc061ee163-r2","co":"ai-sre-full-20260914","picks":[["sentry-seer","a"],["cursor-cloud-agents","p"],["cursor-automations","p"],["cursor-bugbot","m"]],"roles":{"investigation":["cursor-cloud-agents"],"fix_generation":["cursor-cloud-agents"]},"v":{"r":"The run explicitly recommends Cursor Automations (with Cursor Cloud Agents) triggered on Sentry issue alerts to investigate Rails and Sidekiq failures and open pull requests. It implements this by creating the necessary `.cursor/Dockerfile`, `.cursor/environment.json`, and `.cursor/rules/*.mdc` files.","c":0.95,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":65,"pb":1,"sk":0},{"cat":"ai-sre","wave":3,"date":"2026-09-14","repo":"express-api","variant":"ai-sre-v1","family":"ai-sre-junior-small-team-events","pid":"SRE-EVENTS-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-cloud-agents","solution":["cursor-cloud-agents"],"secs":542,"k":"bafc55d8-22bb-4fcc-8a77-5942c1667ebf-r1","co":"ai-sre-full-20260914","picks":[["cursor-cloud-agents","p"]],"roles":{"investigation":["cursor-cloud-agents"],"fix_generation":["cursor-cloud-agents"]},"v":{"r":"The user asked for a tool to investigate future incidents using existing logs/code and automatically fix them while keeping current monitoring. The run recommended and fully implemented Cursor Cloud Agents using @cursor/sdk in a dedicated webhook service (ops/incident-agent).","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":89,"sk":0},{"cat":"ai-sre","wave":3,"date":"2026-09-14","repo":"express-api","variant":"ai-sre-v1","family":"ai-sre-junior-small-team-events","pid":"SRE-EVENTS-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"cursor-cloud-agents","solution":["cursor-cloud-agents"],"secs":487,"k":"bafc55d8-22bb-4fcc-8a77-5942c1667ebf-r2","co":"ai-sre-full-20260914","picks":[["cursor-cloud-agents","p"]],"roles":{"investigation":["cursor-cloud-agents"],"fix_generation":["cursor-cloud-agents"]},"v":{"r":"The user requested an AI tool to investigate incidents from existing logs and code and open automated fixes while preserving their existing Grafana Cloud Loki monitoring. The run recommended and built an incident webhook integration using the Cursor SDK (@cursor/sdk) configured specifically to dispatch Cursor Cloud Agents with autoCreatePR enabled.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":93,"sk":0},{"cat":"ai-sre","wave":8,"date":"2026-09-14","repo":"express-api","variant":"ai-sre-v1","family":"ai-sre-junior-small-team-events","pid":"SRE-EVENTS-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-cloud-agents","solution":["cursor-cloud-agents"],"secs":448,"k":"915db43e-9a79-47ba-b62e-0de49f7dbe15-r1","co":"ai-sre-full-20260914","picks":[["cursor-automations","m"],["cursor-cloud-agents","p"]],"roles":{"investigation":["cursor-cloud-agents"],"fix_generation":["cursor-cloud-agents"]},"v":{"r":"The run chose and configured Cursor Cloud Agents using the official `@cursor/sdk` in a new `ops/auto-fix` service. The service receives Grafana 5xx alert webhooks, queries Loki for recent logs, and invokes a Cursor Cloud Agent with `autoCreatePR: true` to diagnose and fix API issues.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":91,"sk":0},{"cat":"ai-sre","wave":8,"date":"2026-09-14","repo":"express-api","variant":"ai-sre-v1","family":"ai-sre-junior-small-team-events","pid":"SRE-EVENTS-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"cursor-cloud-agents","solution":["cursor-cloud-agents"],"secs":447,"k":"915db43e-9a79-47ba-b62e-0de49f7dbe15-r2","co":"ai-sre-full-20260914","picks":[["cursor-cloud-agents","p"]],"roles":{"investigation":["cursor-cloud-agents"],"fix_generation":["cursor-cloud-agents"]},"v":{"r":"The run implements an automated incident resolution service (`incident-agent`) that listens to Grafana webhook alerts, queries Loki logs, and launches Cursor Cloud Agents via `@cursor/sdk` to diagnose issues and open pull requests with code fixes. Cursor hooks/automations were explicitly evaluated and rejected in reasoning.","c":0.95,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":87,"sk":0},{"cat":"ai-sre","wave":13,"date":"2026-09-14","repo":"express-api","variant":"ai-sre-v1","family":"ai-sre-junior-small-team-events","pid":"SRE-EVENTS-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-automations","solution":["cursor-automations"],"secs":333,"k":"0b2d9f39-2543-4764-9afe-c4295197c58a-r1","co":"ai-sre-full-20260914","picks":[["sentry-seer","a"],["grafana-assistant-investigations","a"],["cursor-cloud-agents","m"],["cursor-automations","p"],["cursor-bugbot","m"]],"roles":{"investigation":["cursor-automations"],"fix_generation":["cursor-automations"]},"v":{"r":"The agent selected Cursor Automations running Cursor Cloud Agents to receive webhook alerts from Grafana Cloud, query Loki logs via MCP, and automatically author pull request fixes. Alternative AI SRE tools such as Grafana Assistant Investigations, Sentry Seer/Autofix, and Datadog Bits were evaluated and rejected due to missing fix capabilities or stack mismatches.","c":0.95,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":62,"pb":1,"sk":0},{"cat":"ai-sre","wave":13,"date":"2026-09-14","repo":"express-api","variant":"ai-sre-v1","family":"ai-sre-junior-small-team-events","pid":"SRE-EVENTS-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"cursor-automations","solution":["cursor-automations"],"secs":437,"k":"0b2d9f39-2543-4764-9afe-c4295197c58a-r2","co":"ai-sre-full-20260914","picks":[["cursor-cloud-agents","m"],["cursor-automations","p"],["cursor-bugbot","m"]],"roles":{"investigation":["cursor-automations"],"fix_generation":["cursor-automations"]},"v":{"r":"The agent explicitly recommended Cursor Automations to investigate Grafana Cloud Loki alerts and automatically submit pull request fixes. It created the full repository configuration (.cursor/mcp.json, .cursor/environment.json, .cursor/skills/investigate-api-incident/SKILL.md, and Grafana webhook contact point/notification policy templates) to connect the systems.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":78,"pb":1,"sk":1},{"cat":"ai-sre","wave":3,"date":"2026-09-14","repo":"go-fleet","variant":"ai-sre-v1","family":"ai-sre-senior-small-team-fleet","pid":"SRE-FLEET-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"resolve-ai","solution":["resolve-ai"],"secs":470,"k":"db7469ee-217d-4124-9f2c-f0da23c9ea90-r1","co":"ai-sre-full-20260914","picks":[["sentry-seer","m"],["cursor-cloud-agents","a"],["rootly-ai-sre","a"],["metoro","a"],["resolve-ai","p"],["cursor-bugbot","m"]],"roles":{"investigation":["resolve-ai"],"fix_generation":["resolve-ai"]},"v":{"r":"The user requested an AI SRE recommendation and implementation that integrates with their existing GCP Cloud Run, Cloud Logging, and Cloud Build pipeline. The agent selected Resolve AI and fully implemented the Terraform configuration (IAM service account, webhook notification channel, alert policy documentation) and repository knowledge (skills, runbooks, Liquid alert template) under ops/resolve/.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":84,"sk":1},{"cat":"ai-sre","wave":3,"date":"2026-09-14","repo":"go-fleet","variant":"ai-sre-v1","family":"ai-sre-senior-small-team-fleet","pid":"SRE-FLEET-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"cursor-automations","solution":["cursor-automations"],"secs":250,"k":"db7469ee-217d-4124-9f2c-f0da23c9ea90-r2","co":"ai-sre-full-20260914","picks":[["sentry-seer","a"],["resolve-ai","a"],["cursor-automations","p"],["cursor-cloud-agents","m"]],"roles":{"investigation":["cursor-automations"],"fix_generation":["cursor-automations"]},"v":{"r":"The run explicitly recommended and configured Cursor Automations, adding Terraform resources in ops/monitoring/main.tf for Cloud Monitoring webhook token auth and updating docs/monitoring.md with operational setup. It evaluated and rejected Resolve AI, Datadog Bits AI, Sentry Seer, and Gemini Cloud Assist Investigations for missing capabilities or stack mismatches.","c":0.95,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":42,"pb":1,"sk":0},{"cat":"ai-sre","wave":8,"date":"2026-09-14","repo":"go-fleet","variant":"ai-sre-v1","family":"ai-sre-senior-small-team-fleet","pid":"SRE-FLEET-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-automations","solution":["cursor-automations"],"secs":709,"k":"685efa4b-2ddd-4abd-a563-6b5a9092b645-r1","co":"ai-sre-full-20260914","picks":[["cursor-cloud-agents","m"],["sentry-seer","a"],["resolve-ai","a"],["cursor-automations","p"],["cursor-bugbot","m"]],"roles":{"investigation":["cursor-automations"],"fix_generation":["cursor-automations"]},"v":{"r":"The agent selected Cursor Automations to investigate Cloud Run errors and open pull requests. It fully configured the Terraform provider (`cursor/cursor`), defined the `cursor_platform_workflow` resource in `ops/monitoring/automations.tf`, configured GCP MCP servers in `.cursor/mcp.json`, and implemented a Go relay service in `cmd/alertrelay` to adapt Cloud Monitoring webhooks into authenticated requests for Cursor Automations.","c":0.95,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":102,"sk":0},{"cat":"ai-sre","wave":8,"date":"2026-09-14","repo":"go-fleet","variant":"ai-sre-v1","family":"ai-sre-senior-small-team-fleet","pid":"SRE-FLEET-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"resolve-ai","solution":["resolve-ai"],"secs":391,"k":"685efa4b-2ddd-4abd-a563-6b5a9092b645-r2","co":"ai-sre-full-20260914","picks":[["metoro","m"],["cursor-cloud-agents","m"],["cursor-automations","a"],["cleric","a"],["sentry-seer","a"],["resolve-ai","p"],["incident-io-ai-sre","m"]],"roles":{"investigation":["resolve-ai"],"fix_generation":["resolve-ai"]},"v":{"r":"The agent explicitly recommended and integrated Resolve AI into the repository. It created Terraform resources for Resolve AI IAM roles and webhook alert channels, documented the integration in docs/monitoring.md, and added Resolve AI knowledge base files, runbooks, and skills under docs/resolve/.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":68,"pb":1,"sk":1},{"cat":"ai-sre","wave":13,"date":"2026-09-14","repo":"go-fleet","variant":"ai-sre-v1","family":"ai-sre-senior-small-team-fleet","pid":"SRE-FLEET-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-automations","solution":["cursor-automations"],"secs":678,"k":"c297096a-934c-4b0d-a1e6-8e47a82af63e-r1","co":"ai-sre-full-20260914","picks":[["cleric","a"],["resolve-ai","a"],["cursor-automations","p"],["struct-ai","m"]],"roles":{"investigation":["cursor-automations"],"fix_generation":["cursor-automations"]},"v":{"r":"The agent explicitly recommended Cursor Automations, created an alert forwarding Cloud Function (`ops/cursor-alert-relay`), configured read-only GCP IAM and notification channels in Terraform (`ops/monitoring/cursor_automations.tf`), documented the automation prompt in `docs/monitoring.md`, and set up MCP configuration in `.cursor/mcp.json`.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":71,"pb":1,"sk":0},{"cat":"ai-sre","wave":13,"date":"2026-09-14","repo":"go-fleet","variant":"ai-sre-v1","family":"ai-sre-senior-small-team-fleet","pid":"SRE-FLEET-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"resolve-ai","solution":["resolve-ai"],"secs":481,"k":"c297096a-934c-4b0d-a1e6-8e47a82af63e-r2","co":"ai-sre-full-20260914","picks":[["traversal","a"],["cursor-cloud-agents","a"],["cleric","a"],["resolve-ai","p"],["shoreline","m"]],"roles":{"investigation":["resolve-ai"],"fix_generation":["resolve-ai"]},"v":{"r":"The run explicitly chose Resolve AI, implemented complete Terraform configuration for read-only GCP IAM, Workload Identity Federation, and an alert webhook notification channel, authored Resolve AI knowledge documents and runbooks, and provided a Liquid alert mapping template while evaluating and dismissing alternatives like Cleric, Datadog Bits, Cursor Cloud Agents, and Traversal.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":65,"pb":1,"sk":0},{"cat":"ai-sre","wave":3,"date":"2026-09-14","repo":"ts-commerce-datadog","variant":"ai-sre-v1","family":"ai-sre-senior-enterprise-commerce","pid":"SRE-COMMERCE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-cloud-agents","solution":["cursor-cloud-agents"],"secs":683,"k":"1c990ef9-77a9-401b-bd51-c3a3d89c0b14-r1","co":"ai-sre-full-20260914","picks":[["cursor-automations","m"],["cursor-cloud-agents","p"]],"roles":{"investigation":["cursor-cloud-agents"],"fix_generation":["cursor-cloud-agents"]},"v":{"r":"The agent explicitly recommended, installed, and wired Cursor Cloud Agents using the official `@cursor/sdk` package (`Agent.create` with `cloud.autoCreatePR`). It created an automated SRE dispatcher package (`@halberd/sre-agent`), wired Datadog alert webhooks in Terraform (`ops/monitoring/main.tf`), integrated GitHub Actions workflows (`.github/workflows/sre-investigate.yml`), and documented the incident investigation policies.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":134,"sk":0},{"cat":"ai-sre","wave":3,"date":"2026-09-14","repo":"ts-commerce-datadog","variant":"ai-sre-v1","family":"ai-sre-senior-enterprise-commerce","pid":"SRE-COMMERCE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"cursor-cloud-agents","solution":["cursor-cloud-agents"],"secs":689,"k":"1c990ef9-77a9-401b-bd51-c3a3d89c0b14-r2","co":"ai-sre-full-20260914","picks":[["datadog-bits-investigation","a"],["cursor-cloud-agents","p"],["cursor-bugbot","m"]],"roles":{"investigation":["cursor-cloud-agents"],"fix_generation":["cursor-cloud-agents"]},"v":{"r":"The agent explicitly selected and implemented Cursor Cloud Agents using the `@cursor/sdk` package in a newly added `services/sre-agent` service. It connected Datadog alert webhooks via Terraform and Fastify, configuring the cloud agent to investigate incidents through Datadog MCP and open reliability PRs adhering to the repo's error-budget policy.","c":0.95,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":140,"sk":0},{"cat":"ai-sre","wave":8,"date":"2026-09-14","repo":"ts-commerce-datadog","variant":"ai-sre-v1","family":"ai-sre-senior-enterprise-commerce","pid":"SRE-COMMERCE-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"multiple","solution":["datadog-bits-ai-dev-agent","datadog-bits-investigation"],"secs":205,"k":"91396417-0eab-4c22-ad28-4763f89a60ad-r1","co":"ai-sre-full-20260914","picks":[["resolve-ai","a"],["datadog-bits-ai-dev-agent","p"],["datadog-bits-investigation","p"],["cursor-bugbot","m"]],"roles":{"investigation":["datadog-bits-investigation"],"fix_generation":["datadog-bits-ai-dev-agent"]},"v":{"r":"The run evaluated AI SRE solutions for the existing Datadog observability stack and explicitly committed to Datadog Bits AI (specifically Datadog Bits Investigation for automated root cause analysis and Bits Code / Datadog Bits AI Dev Agent for drafting GitHub PR fixes). It configured Terraform monitor messages, tags, and documentation across the repo to integrate both tools while rejecting third-party alternatives like Resolve AI.","c":0.95,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":50,"pb":1,"sk":0},{"cat":"ai-sre","wave":8,"date":"2026-09-14","repo":"ts-commerce-datadog","variant":"ai-sre-v1","family":"ai-sre-senior-enterprise-commerce","pid":"SRE-COMMERCE-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"multiple","solution":["datadog-bits-ai-dev-agent","datadog-bits-investigation"],"secs":524,"k":"91396417-0eab-4c22-ad28-4763f89a60ad-r2","co":"ai-sre-full-20260914","picks":[["datadog-bits-ai-dev-agent","p"],["datadog-bits-investigation","p"],["cursor-bugbot","m"]],"roles":{"investigation":["datadog-bits-investigation"],"fix_generation":["datadog-bits-ai-dev-agent"]},"v":{"r":"The agent configured Datadog Bits Investigation and Datadog Bits AI Dev Agent (Bits Code) as the joint AI SRE solution to investigate alerts on existing Datadog telemetry and submit code fix PRs adhering to CODEOWNERS, Flux release gates, and error budget policies.","c":0.95,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":126,"sk":0},{"cat":"ai-sre","wave":13,"date":"2026-09-14","repo":"ts-commerce-datadog","variant":"ai-sre-v1","family":"ai-sre-senior-enterprise-commerce","pid":"SRE-COMMERCE-01c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"multiple","solution":["datadog-bits-ai-dev-agent","datadog-bits-investigation"],"secs":965,"k":"500b69df-a199-4b93-a6d3-376d6467a90e-r1","co":"ai-sre-full-20260914","picks":[["cursor-cloud-agents","a"],["datadog-bits-ai-dev-agent","p"],["datadog-bits-investigation","p"],["shoreline","m"]],"roles":{"investigation":["datadog-bits-investigation"],"fix_generation":["datadog-bits-ai-dev-agent"]},"v":{"r":"The run chose Datadog's AI SRE suite (Datadog Bits Investigation and Datadog Bits AI Dev Agent / Bits Code) to investigate incidents across the checkout and inventory services and open automated fix PRs. It integrated these with Terraform-managed SLOs, Workflow Automation, GitHub Actions error-budget release gates, and Datadog audit events. Cursor Cloud Agents was explicitly rejected as lacking native monitor-watching capabilities.","c":0.95,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":150,"sk":0},{"cat":"ai-sre","wave":13,"date":"2026-09-14","repo":"ts-commerce-datadog","variant":"ai-sre-v1","family":"ai-sre-senior-enterprise-commerce","pid":"SRE-COMMERCE-01c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"multiple","solution":["datadog-bits-ai-dev-agent","datadog-bits-investigation"],"secs":870,"k":"500b69df-a199-4b93-a6d3-376d6467a90e-r2","co":"ai-sre-full-20260914","picks":[["cursor-automations","m"],["resolve-ai","a"],["datadog-bits-ai-dev-agent","p"],["datadog-bits-investigation","p"]],"roles":{"investigation":["datadog-bits-investigation"],"fix_generation":["datadog-bits-ai-dev-agent"]},"v":{"r":"The run explicitly selected and implemented Datadog Bits Investigation along with Datadog Bits AI Dev Agent (Bits Code) for automated root-cause analysis and automated pull-request remediation. It configured Terraform SLOs and monitors, Git metadata Helm manifests, GitHub audit workflows, PR templates, and AGENTS.md guardrails.","c":0.98,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":128,"sk":0},{"cat":"ai-sre","wave":3,"date":"2026-09-14","repo":"saas-analytics-mid","variant":"ai-sre-v1","family":"ai-sre-senior-enterprise-analytics","pid":"SRE-ANALYTICS-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-automations","solution":["cursor-automations"],"secs":630,"k":"cfede59e-ea54-42a6-bc9a-92e771e86a04-r1","co":"ai-sre-full-20260914","picks":[["metoro","a"],["cursor-automations","p"],["cursor-cloud-agents","m"],["openai-codex","m"],["tierzero","m"]],"roles":{"investigation":["cursor-automations"],"fix_generation":["cursor-automations"]},"v":{"r":"The run explicitly evaluated and recommended Cursor Automations (Cloud Agents) as the AI SRE solution and implemented full Terraform infrastructure for it (`cursor_platform_workflow`), along with a CloudWatch/SNS forwarder Lambda in AWS Terraform, prompt definitions, and tenancy validation rules. Alternatives like Metoro, Datadog Bits, OneUptime, and Resolve AI were explicitly evaluated and rejected.","c":0.98,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":120,"sk":0},{"cat":"ai-sre","wave":3,"date":"2026-09-14","repo":"saas-analytics-mid","variant":"ai-sre-v1","family":"ai-sre-senior-enterprise-analytics","pid":"SRE-ANALYTICS-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"cursor-automations","solution":["cursor-automations"],"secs":580,"k":"cfede59e-ea54-42a6-bc9a-92e771e86a04-r2","co":"ai-sre-full-20260914","picks":[["cursor-automations","p"],["tierzero","m"]],"roles":{"investigation":["cursor-automations"],"fix_generation":["cursor-automations"]},"v":{"r":"The agent explicitly recommended Cursor Automations and fully implemented the configuration files (.cursor/automations/sre-incident.yaml and sre-incident.md), Cursor rule guardrails (.cursor/rules/*.mdc), and an AWS Lambda webhook forwarder in Terraform to bridge CloudWatch SNS alarms to Cursor Automations.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":92,"sk":0},{"cat":"ai-sre","wave":8,"date":"2026-09-14","repo":"saas-analytics-mid","variant":"ai-sre-v1","family":"ai-sre-senior-enterprise-analytics","pid":"SRE-ANALYTICS-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"cursor-cloud-agents","solution":["cursor-cloud-agents"],"secs":774,"k":"b31f6349-f49c-4a5a-96f1-01b71b7521df-r1","co":"ai-sre-full-20260914","picks":[["cursor-automations","a"],["cursor-cloud-agents","p"]],"roles":{"investigation":["cursor-cloud-agents"],"fix_generation":["cursor-cloud-agents"]},"v":{"r":"The agent implemented an AI SRE pipeline triggered by CloudWatch alarms through SNS and Lambda/GitHub Actions, which launches Cursor Cloud Agents via the API with `autoCreatePR` enabled to generate automated fixes while preserving tenant isolation. Cursor Automations was evaluated during design but dropped in favor of the Cloud Agents API.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":125,"sk":0},{"cat":"ai-sre","wave":8,"date":"2026-09-14","repo":"saas-analytics-mid","variant":"ai-sre-v1","family":"ai-sre-senior-enterprise-analytics","pid":"SRE-ANALYTICS-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"cursor-automations","solution":["cursor-automations"],"secs":846,"k":"b31f6349-f49c-4a5a-96f1-01b71b7521df-r2","co":"ai-sre-full-20260914","picks":[["cursor-cloud-agents","m"],["cursor-automations","p"]],"roles":{"investigation":["cursor-automations"],"fix_generation":["cursor-automations"]},"v":{"r":"The run chose Cursor Automations to automate the incident-to-PR workflow. It created automation prompts under `.cursor/automations/`, hook policies in `.cursor/hooks/` to protect multi-tenancy and release boundaries, a read-only CloudWatch MCP server in `.cursor/mcp.json` and `ops/sre-agent/cloudwatch_mcp.py`, and Terraform infrastructure in `ops/sre-agent/main.tf` to forward CloudWatch alarm events from SNS to the Cursor Automations webhook.","c":0.95,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":116,"sk":0},{"cat":"ai-sre","wave":13,"date":"2026-09-14","repo":"saas-analytics-mid","variant":"ai-sre-v1","family":"ai-sre-senior-enterprise-analytics","pid":"SRE-ANALYTICS-01c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"multiple","solution":["cursor-automations","cursor-cloud-agents"],"secs":632,"k":"eebd4c01-9173-4e3c-88dc-a257537299e0-r1","co":"ai-sre-full-20260914","picks":[["aws-devops-agent","a"],["cleric","a"],["resolve-ai","a"],["cursor-cloud-agents","p"],["cursor-automations","p"],["shoreline","m"]],"roles":{"investigation":["cursor-cloud-agents"],"fix_generation":["cursor-cloud-agents"]},"v":{"r":"The user requested an AI SRE solution to investigate failures across ingestion, querying, and billing from monitoring and code without accessing raw event payloads. The agent recommended Cursor Automations (powered by Cursor Cloud Agents) and implemented the complete Terraform infrastructure, Lambda SNS-to-webhook forwarder, OIDC IAM role policies, Cursor rules, and hooks to block data-plane access.","c":0.95,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":105,"pb":1,"sk":0},{"cat":"ai-sre","wave":13,"date":"2026-09-14","repo":"saas-analytics-mid","variant":"ai-sre-v1","family":"ai-sre-senior-enterprise-analytics","pid":"SRE-ANALYTICS-01c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"cursor-automations","solution":["cursor-automations"],"secs":484,"k":"eebd4c01-9173-4e3c-88dc-a257537299e0-r2","co":"ai-sre-full-20260914","picks":[["metoro","m"],["cursor-cloud-agents","m"],["cursor-automations","p"],["cursor-bugbot","m"]],"roles":{"investigation":["cursor-automations"],"fix_generation":["cursor-automations"]},"v":{"r":"The run explicitly recommended and implemented an integration for Cursor Automations, adding Terraform configuration (ops/monitoring/cursor-automation.tf), an SNS-to-webhook forwarder Lambda (ops/monitoring/cursor_alert_forwarder.py), documentation (docs/monitoring.md), and unit tests (tests/test_cursor_alert_forwarder.py).","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":68,"pb":1,"sk":0},{"cat":"in-app-communication","wave":"0d6a4bf2-2e67-408a-8383-1bc65d30823b","date":"2026-09-08","repo":"nextjs-classbooking","variant":"communication-v1","family":"in-app-communication-vibe-coder-classes-a","pid":"COMM-CLASSES-01a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"supabase","solution":["supabase"],"secs":309,"k":"0d6a4bf2-2e67-408a-8383-1bc65d30823b-r1","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["supabase","p","b"],["stream","a"],["twilio","a"],["socket-io","m"]],"v":{"r":"The agent leveraged existing Supabase capabilities (PostgreSQL tables, Row Level Security, and Supabase Realtime via postgres_changes) to build persistent live chat directly within the app, explicitly rejecting third-party communication services like Stream and Twilio.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":70,"sk":0},{"cat":"in-app-communication","wave":"0d6a4bf2-2e67-408a-8383-1bc65d30823b","date":"2026-09-08","repo":"nextjs-classbooking","variant":"communication-v1","family":"in-app-communication-vibe-coder-classes-a","pid":"COMM-CLASSES-01a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"supabase","solution":["supabase"],"secs":308,"k":"0d6a4bf2-2e67-408a-8383-1bc65d30823b-r2","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["supabase","p","b"],["stream","a"],["sendbird","a"],["firebase","a"]],"v":{"r":"The run chose Supabase Realtime and Postgres with Row Level Security (RLS) to implement the class chat feature. Because Supabase was already in use across the repository for authentication, database, and backend logic, leveraging its Realtime capabilities constitutes a 'builtin' platform pick. Dedicated third-party chat solutions (Stream, Sendbird, Firebase) were explicitly evaluated and rejected due to unnecessary external dependencies and redundant auth workflows.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":57,"sk":0},{"cat":"in-app-communication","wave":"b92b69c4-a224-42f4-a84f-34b3edb87cde","date":"2026-09-08","repo":"nextjs-classbooking","variant":"communication-v1","family":"in-app-communication-vibe-coder-classes-b","pid":"COMM-CLASSES-01b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"supabase","solution":["supabase"],"secs":472,"k":"b92b69c4-a224-42f4-a84f-34b3edb87cde-r1","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["supabase","p","b"],["pusher","a"],["firebase","a"],["stream","a"],["socket-io","m"],["talkjs","m"]],"v":{"r":"The agent evaluated the user's request for private class chat and selected Supabase Realtime backed by Postgres tables and RLS, which was already part of the application's stack. It explicitly considered and rejected third-party alternatives (Pusher, Firebase, Stream) as redundant.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":75,"sk":0},{"cat":"in-app-communication","wave":"b92b69c4-a224-42f4-a84f-34b3edb87cde","date":"2026-09-08","repo":"nextjs-classbooking","variant":"communication-v1","family":"in-app-communication-vibe-coder-classes-b","pid":"COMM-CLASSES-01b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"supabase","solution":["supabase"],"secs":370,"k":"b92b69c4-a224-42f4-a84f-34b3edb87cde-r2","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["supabase","p","b"],["stream","a"],["twilio","a"],["firebase","a"]],"v":{"r":"The agent leveraged the existing Supabase setup in the project, implementing live in-app chat using a new `messages` table, Row Level Security, and Supabase Realtime channel subscriptions (`postgres_changes`). Third-party chat platforms (Stream, Twilio, Firebase) were deliberated and rejected in favor of the builtin platform capability.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":77,"sk":0},{"cat":"in-app-communication","wave":"eebd8bb8-e779-467b-9a4d-02667e449691","date":"2026-09-08","repo":"nextjs-classbooking","variant":"communication-v1","family":"in-app-communication-vibe-coder-classes-c","pid":"COMM-CLASSES-01c","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"supabase","solution":["supabase"],"secs":451,"k":"eebd8bb8-e779-467b-9a4d-02667e449691-r1","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["supabase","p","b"],["stream","a"],["sendbird","a"],["twilio","a"],["talkjs","a"]],"v":{"r":"The user asked for a simple in-app live chat solution for class members and the studio owner. The agent recommended and implemented chat using the existing Supabase backend (Supabase Realtime + Postgres table with RLS) already present in the project, rejecting third-party chat providers like Stream, Sendbird, Twilio, and TalkJS as unnecessary operational overhead.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":71,"sk":0},{"cat":"in-app-communication","wave":"eebd8bb8-e779-467b-9a4d-02667e449691","date":"2026-09-08","repo":"nextjs-classbooking","variant":"communication-v1","family":"in-app-communication-vibe-coder-classes-c","pid":"COMM-CLASSES-01c","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"supabase","solution":["supabase"],"secs":402,"k":"eebd8bb8-e779-467b-9a4d-02667e449691-r2","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["supabase","p","b"],["stream","a"],["sendbird","a"],["pusher","a"],["ably","a"],["talkjs","a"],["socket-io","m"]],"v":{"r":"The project already used Supabase for authentication and database storage. The agent selected and implemented Supabase Realtime along with Postgres RLS for private in-app class chat, explicitly arguing against third-party chat solutions like Stream, Sendbird, Pusher, Ably, and TalkJS.","c":0.95,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":79,"sk":0},{"cat":"in-app-communication","wave":"6b331f4e-8595-4cf0-a572-5ca43c41abe1","date":"2026-09-08","repo":"expo-connections","variant":"base","family":"in-app-communication-vibe-coder-matches-a","pid":"COMM-MATCHES-01a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stream","solution":["stream"],"secs":826,"k":"6b331f4e-8595-4cf0-a572-5ca43c41abe1-r1","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["stream","p"],["agora","a"],["twilio","a"],["daily","a"],["livekit","a"],["sendbird","m"],["firebase","m"]],"v":{"r":"The agent selected Stream Video (Stream Chat and Video) for in-app calling, installed `@stream-io/video-react-native-sdk`, implemented client-side calling/ringing components in `Calling.tsx` and `App.tsx`, and implemented server-side token generation and call termination in `server/stream.mjs`. Alternatives including Agora, Twilio, Daily, and LiveKit were explicitly evaluated and rejected.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":123,"sk":0},{"cat":"in-app-communication","wave":"6b331f4e-8595-4cf0-a572-5ca43c41abe1","date":"2026-09-08","repo":"expo-connections","variant":"base","family":"in-app-communication-vibe-coder-matches-a","pid":"COMM-MATCHES-01a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stream","solution":["stream"],"secs":733,"k":"6b331f4e-8595-4cf0-a572-5ca43c41abe1-r2","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["stream","p"],["agora","a"],["livekit","a"],["twilio","a"],["daily","m"],["firebase","m"]],"v":{"r":"The agent explicitly selected Stream Video (GetStream) to handle in-app WebRTC calling without sharing phone numbers. It installed `@stream-io/video-react-native-sdk` and `@stream-io/node-sdk`, added backend token generation and call ring/termination logic in `server/stream.mjs`, configured permissions in `app.json`, and implemented call UI overlay components in `App.tsx`.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":124,"pb":1,"sk":0},{"cat":"in-app-communication","wave":"026d1137-c042-451b-b28b-9a4dc3f064b8","date":"2026-09-08","repo":"expo-connections","variant":"base","family":"in-app-communication-vibe-coder-matches-b","pid":"COMM-MATCHES-01b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stream","solution":["stream"],"secs":690,"k":"026d1137-c042-451b-b28b-9a4dc3f064b8-r1","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["stream","p"],["twilio","a"],["agora","a"],["daily","a"],["livekit","a"],["vonage","m"],["firebase","m"]],"v":{"r":"The agent integrated Stream Video via `@stream-io/node-sdk` and `@stream-io/video-react-native-sdk` to implement privacy-preserving 1:1 in-app calling for matched users, while considering and rejecting PSTN/room-based competitors such as Twilio, Agora, Daily, and LiveKit.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":101,"sk":0},{"cat":"in-app-communication","wave":"026d1137-c042-451b-b28b-9a4dc3f064b8","date":"2026-09-08","repo":"expo-connections","variant":"base","family":"in-app-communication-vibe-coder-matches-b","pid":"COMM-MATCHES-01b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"livekit","solution":["livekit"],"secs":706,"k":"026d1137-c042-451b-b28b-9a4dc3f064b8-r2","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["livekit","p"],["twilio","a"],["agora","a"],["stream","a"],["100ms","m"],["daily","m"],["vonage","m"]],"v":{"r":"The agent explicitly chose, configured, and implemented LiveKit (via `@livekit/react-native`, `livekit-client`, and server-side room token generation in `server/livekit.mjs`). Stream Video was documented as an alternative, while PSTN solutions like Twilio and alternative SDKs like Agora were evaluated and rejected.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":74,"sk":0},{"cat":"in-app-communication","wave":"18375c18-cf7e-43a9-82a3-2440e3171050","date":"2026-09-08","repo":"expo-connections","variant":"base","family":"in-app-communication-vibe-coder-matches-c","pid":"COMM-MATCHES-01c","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"livekit","solution":["livekit"],"secs":838,"k":"18375c18-cf7e-43a9-82a3-2440e3171050-r1","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["livekit","p"],["stream","a"],["daily","a"],["twilio","a"],["agora","m"]],"v":{"r":"The agent explicitly recommended, installed, and configured LiveKit (`livekit-server-sdk`, `@livekit/react-native`, `livekit-client`, and `@livekit/react-native-expo-plugin`) to provide in-app voice and video calling. Alternatives such as Twilio, Stream, Daily, and Agora were considered during deliberation.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":79,"pb":1,"sk":0},{"cat":"in-app-communication","wave":"18375c18-cf7e-43a9-82a3-2440e3171050","date":"2026-09-08","repo":"expo-connections","variant":"base","family":"in-app-communication-vibe-coder-matches-c","pid":"COMM-MATCHES-01c","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"livekit","solution":["livekit"],"secs":906,"k":"18375c18-cf7e-43a9-82a3-2440e3171050-r2","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["livekit","p"],["twilio","a"],["stream","a"],["agora","m"],["daily","m"],["vonage","m"],["firebase","m"]],"v":{"r":"The agent explicitly recommended, installed, and implemented LiveKit for in-app audio and video calling, integrating @livekit/react-native in the Expo frontend and livekit-server-sdk in the backend.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":98,"sk":0},{"cat":"in-app-communication","wave":"ecb4dc38-8f36-499a-9ead-4681416887a0","date":"2026-09-08","repo":"rails-marketplace","variant":"communication-v1","family":"in-app-communication-junior-small-team-orders-a","pid":"COMM-ORDERS-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stream","solution":["stream"],"secs":773,"k":"ecb4dc38-8f36-499a-9ead-4681416887a0-r1","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["stream","p"],["pusher","a"],["ably","a"],["pubnub","a"],["talkjs","a"],["sendbird","m"],["twilio","m"],["cometchat","m"],["firebase","m"]],"v":{"r":"The agent evaluated several chat and messaging providers for an order-page conversation feature in a Rails 7 app without Action Cable. It selected Stream Chat (GetStream), implemented the official `getstream-ruby` gem alongside client-side browser integration via CDN, created server-side JWT authentication, and wired per-order messaging channels restricted strictly to the buyer and seller.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":129,"sk":0},{"cat":"in-app-communication","wave":"ecb4dc38-8f36-499a-9ead-4681416887a0","date":"2026-09-08","repo":"rails-marketplace","variant":"communication-v1","family":"in-app-communication-junior-small-team-orders-a","pid":"COMM-ORDERS-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"talkjs","solution":["talkjs"],"secs":634,"k":"ecb4dc38-8f36-499a-9ead-4681416887a0-r2","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["talkjs","p"],["stream","a"],["sendbird","a"],["pusher","a"],["ably","a"],["pubnub","a"],["firebase","a"],["cometchat","m"]],"v":{"r":"The agent evaluated several chat and messaging products, selected TalkJS for its lightweight drop-in JavaScript widget and suitability for per-order buyer/seller conversations in a Rails ERB app, and implemented the full integration.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":103,"sk":0},{"cat":"in-app-communication","wave":"8469fcab-b537-4411-b0e1-af86a168cf7f","date":"2026-09-08","repo":"rails-marketplace","variant":"communication-v1","family":"in-app-communication-junior-small-team-orders-b","pid":"COMM-ORDERS-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stream","solution":["stream"],"secs":833,"k":"8469fcab-b537-4411-b0e1-af86a168cf7f-r1","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["stream","p"]],"v":{"r":"The agent explicitly chose Stream Chat for buyer-to-seller messaging, installing the official getstream-ruby gem, loading the Stream Chat browser SDK on the order show template, configuring token minting and channel management in OrderConversation, and documenting the setup in README.md.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":145,"sk":0},{"cat":"in-app-communication","wave":"8469fcab-b537-4411-b0e1-af86a168cf7f","date":"2026-09-08","repo":"rails-marketplace","variant":"communication-v1","family":"in-app-communication-junior-small-team-orders-b","pid":"COMM-ORDERS-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stream","solution":["stream"],"secs":605,"k":"8469fcab-b537-4411-b0e1-af86a168cf7f-r2","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["stream","p"]],"v":{"r":"The run fully implemented in-app order communication using Stream Chat via the `stream-chat-ruby` gem and Stream's browser bundle CDN SDK.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":133,"sk":0},{"cat":"in-app-communication","wave":"02b12759-d33d-4fc2-9d21-75ee56ad81b2","date":"2026-09-08","repo":"rails-marketplace","variant":"communication-v1","family":"in-app-communication-junior-small-team-orders-c","pid":"COMM-ORDERS-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"talkjs","solution":["talkjs"],"secs":701,"k":"02b12759-d33d-4fc2-9d21-75ee56ad81b2-r1","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["talkjs","p"],["stream","a"],["sendbird","a"],["twilio","a"],["cometchat","a"],["pubnub","a"],["pusher","a"],["firebase","a"]],"v":{"r":"The agent explicitly recommended TalkJS to fulfill the user's request for a third-party chat service and fully integrated it into the codebase with backend token generation, Sidekiq photo sync, and frontend widget mounting.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":99,"pb":1,"sk":0},{"cat":"in-app-communication","wave":"091862bf-d2e3-4dca-a55a-e07250adffb2","date":"2026-09-08","repo":"sveltekit-tutorhub","variant":"communication-v1","family":"in-app-communication-junior-small-team-lessons-a","pid":"COMM-LESSONS-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"livekit","solution":["livekit"],"secs":469,"k":"091862bf-d2e3-4dca-a55a-e07250adffb2-r1","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["livekit","p"],["daily","a"],["stream","a"],["twilio","a"],["agora","a"],["100ms","a"],["whereby","a"],["vonage","m"],["jitsi","m"]],"v":{"r":"The agent evaluated several video communication SDKs and chose LiveKit, installing livekit-client and livekit-server-sdk and integrating token minting and UI controls into the SvelteKit app.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":89,"sk":0},{"cat":"in-app-communication","wave":"091862bf-d2e3-4dca-a55a-e07250adffb2","date":"2026-09-08","repo":"sveltekit-tutorhub","variant":"communication-v1","family":"in-app-communication-junior-small-team-lessons-a","pid":"COMM-LESSONS-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"livekit","solution":["livekit"],"secs":512,"k":"091862bf-d2e3-4dca-a55a-e07250adffb2-r2","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["livekit","p"],["stream","a"],["twilio","a"],["agora","a"],["whereby","a"],["100ms","a"],["daily","a"],["supabase","m"],["vonage","m"],["zoom-video-sdk","m"]],"v":{"r":"The agent selected LiveKit (LiveKit Cloud) for in-app video calling, installed `livekit-client` and `livekit-server-sdk`, implemented server-side token minting tied to existing parent/tutor lesson access controls, and added the client video component in Svelte 5.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":92,"sk":0},{"cat":"in-app-communication","wave":"8f66d025-802b-44be-aba8-390d1f1a830f","date":"2026-09-08","repo":"sveltekit-tutorhub","variant":"communication-v1","family":"in-app-communication-junior-small-team-lessons-b","pid":"COMM-LESSONS-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"daily","solution":["daily"],"secs":523,"k":"8f66d025-802b-44be-aba8-390d1f1a830f-r1","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["daily","p"],["livekit","a"],["twilio","a"],["agora","a"],["whereby","a"],["100ms","a"],["stream","a"],["supabase","m"]],"v":{"r":"The agent selected Daily (Daily.co) to implement video calling functionality for booked lessons. It installed `@daily-co/daily-js`, wrote backend helpers for room and token management against Daily's REST API, and implemented the frontend Svelte calling component with camera/mic controls and room permission gating. Several alternatives (LiveKit, Twilio, Zoom, Agora, Whereby, 100ms, Stream) were evaluated and rejected during planning.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":81,"pb":1,"sk":0},{"cat":"in-app-communication","wave":"8f66d025-802b-44be-aba8-390d1f1a830f","date":"2026-09-08","repo":"sveltekit-tutorhub","variant":"communication-v1","family":"in-app-communication-junior-small-team-lessons-b","pid":"COMM-LESSONS-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"daily","solution":["daily"],"secs":548,"k":"8f66d025-802b-44be-aba8-390d1f1a830f-r2","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["daily","p"],["livekit","a"],["twilio","a"],["agora","a"],["stream","a"],["cloudflare-calls","a"]],"v":{"r":"The agent evaluated several calling platforms (Daily, LiveKit, Twilio, Agora, Stream, Zoom, Cloudflare Calls) and fully integrated Daily (`@daily-co/daily-js`), building server endpoints for room creation and token generation, as well as a client Svelte video component.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":76,"pb":1,"sk":0},{"cat":"in-app-communication","wave":"f6204e21-93ee-4264-aef3-399523ee15c6","date":"2026-09-08","repo":"sveltekit-tutorhub","variant":"communication-v1","family":"in-app-communication-junior-small-team-lessons-c","pid":"COMM-LESSONS-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"daily","solution":["daily"],"secs":554,"k":"f6204e21-93ee-4264-aef3-399523ee15c6-r1","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["daily","p"],["livekit","a"],["twilio","a"],["stream","a"],["whereby","a"],["agora","a"],["100ms","a"],["vonage","m"],["jitsi","m"],["supabase","m"],["zoom-video-sdk","m"]],"v":{"r":"The agent evaluated various WebRTC and video communication SDKs (Daily, LiveKit, Twilio, Stream, Whereby, Agora, 100ms) and recommended Daily. It then fully integrated Daily using `@daily-co/daily-js`, the Daily REST API, custom Svelte components, server endpoints, tests, and configuration.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":94,"sk":0},{"cat":"in-app-communication","wave":"f6204e21-93ee-4264-aef3-399523ee15c6","date":"2026-09-08","repo":"sveltekit-tutorhub","variant":"communication-v1","family":"in-app-communication-junior-small-team-lessons-c","pid":"COMM-LESSONS-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"daily","solution":["daily"],"secs":468,"k":"f6204e21-93ee-4264-aef3-399523ee15c6-r2","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["daily","p"],["livekit","a"],["agora","a"],["twilio","a"],["100ms","a"],["stream","a"],["whereby","a"],["jitsi","m"],["vonage","m"],["zoom-video-sdk","m"]],"v":{"r":"The agent evaluated several video calling services and picked Daily, installing `@daily-co/daily-js`, building a server-side Daily REST client for private rooms and meeting tokens, and creating a Svelte component leveraging Daily Prebuilt with worksheet screen sharing.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":79,"sk":0},{"cat":"in-app-communication","wave":"57706dd0-8847-4448-99a0-c35fbba6ddbe","date":"2026-09-08","repo":"nuxt-fieldservice","variant":"communication-v1","family":"in-app-communication-senior-small-team-dispatch-a","pid":"COMM-DISPATCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stream","solution":["stream"],"secs":1085,"k":"57706dd0-8847-4448-99a0-c35fbba6ddbe-r1","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["stream","p"],["twilio","a"],["daily","a"],["livekit","a"],["ably","a"],["firebase","a"],["sendbird","m"]],"v":{"r":"The agent explicitly recommended, installed, and fully implemented Stream Chat and Stream Video via @stream-io/node-sdk, @stream-io/video-client, and stream-chat, configuring server token minting, channel sync on assignment changes, CallOverlay, and JobComms UI.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":212,"sk":0},{"cat":"in-app-communication","wave":"57706dd0-8847-4448-99a0-c35fbba6ddbe","date":"2026-09-08","repo":"nuxt-fieldservice","variant":"communication-v1","family":"in-app-communication-senior-small-team-dispatch-a","pid":"COMM-DISPATCH-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stream","solution":["stream"],"secs":1179,"k":"57706dd0-8847-4448-99a0-c35fbba6ddbe-r2","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["stream","p"],["twilio","a"],["livekit","a"],["daily","a"],["agora","a"],["ably","a"],["firebase","a"],["sendbird","a"],["cometchat","m"]],"v":{"r":"The agent evaluated several real-time communication platforms (Stream, Sendbird, Twilio, LiveKit, Daily, Agora, Ably, Firebase, CometChat) and firmly chose Stream Chat and Stream Video. It installed `@stream-io/node-sdk`, `stream-chat`, and `@stream-io/video-client`, implemented server-side token minting, call management with kick-on-reassign logic, and built Vue components for in-app job messaging and voice calling.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":273,"pb":1,"sk":0},{"cat":"in-app-communication","wave":"b33f5653-9f6c-4261-9763-9eb16877e5ec","date":"2026-09-08","repo":"nuxt-fieldservice","variant":"communication-v1","family":"in-app-communication-senior-small-team-dispatch-b","pid":"COMM-DISPATCH-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"stream","solution":["stream"],"secs":1025,"k":"b33f5653-9f6c-4261-9763-9eb16877e5ec-r1","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["stream","p"],["twilio","a"],["livekit","a"],["agora","m"],["daily","m"]],"v":{"r":"The agent explicitly chose Stream (Stream Chat and Stream Video) for both live chat and browser voice calls, installed its official client and server SDKs, configured server-side token generation and membership synchronization, and added corresponding UI components and unit tests.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":205,"pb":1,"sk":0},{"cat":"in-app-communication","wave":"b33f5653-9f6c-4261-9763-9eb16877e5ec","date":"2026-09-08","repo":"nuxt-fieldservice","variant":"communication-v1","family":"in-app-communication-senior-small-team-dispatch-b","pid":"COMM-DISPATCH-01b","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stream","solution":["stream"],"secs":1010,"k":"b33f5653-9f6c-4261-9763-9eb16877e5ec-r2","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["stream","p"],["ably","m"],["agora","m"],["daily","m"],["livekit","m"],["twilio","m"]],"v":{"r":"The agent explicitly chose, configured, and installed Stream Chat and Stream Video packages (@stream-io/node-sdk, @stream-io/video-client, and stream-chat), implementing backend synchronization and frontend chat/voice components in Nuxt. Other communication providers were only briefly surveyed or mentioned in deliberation.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":261,"pb":1,"sk":0},{"cat":"in-app-communication","wave":"6e8b4ad4-8758-4463-b228-5c0e74b7e1c1","date":"2026-09-08","repo":"nuxt-fieldservice","variant":"communication-v1","family":"in-app-communication-senior-small-team-dispatch-c","pid":"COMM-DISPATCH-01c","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stream","solution":["stream"],"secs":1021,"k":"6e8b4ad4-8758-4463-b228-5c0e74b7e1c1-r2","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["stream","p"],["ably","a"],["twilio","a"],["firebase","a"],["pusher","a"],["livekit","a"],["sendbird","m"],["agora","m"],["daily","m"]],"v":{"r":"The agent explicitly selected, installed, and fully wired Stream Chat and Stream Video (@stream-io/node-sdk, stream-chat, and @stream-io/video-client) to provide private messaging and in-app voice calls for dispatch and assigned technicians while leaving the job board intact.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":198,"sk":0},{"cat":"in-app-communication","wave":"8337df33-2ce9-4a87-99c8-1ee0dba04808","date":"2026-09-08","repo":"django-care-portal","variant":"base","family":"in-app-communication-senior-enterprise-care-a","pid":"COMM-CARE-01a","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"stream","solution":["stream"],"secs":768,"k":"8337df33-2ce9-4a87-99c8-1ee0dba04808-r2","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["stream","p"],["daily","a"],["twilio","a"],["sendbird","m"],["vonage","m"]],"v":{"r":"The run evaluated multiple in-app messaging and video providers and committed to Stream Chat and Stream Video via the getstream Python and JavaScript SDKs. It implemented server-side provisioning, permission checks, channel/call creation with recording disabled, and cancellation cleanup.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":128,"sk":0},{"cat":"in-app-communication","wave":"97dd3ab5-8c6a-4b31-85b3-a6d81e4e7591","date":"2026-09-08","repo":"django-care-portal","variant":"base","family":"in-app-communication-senior-enterprise-care-b","pid":"COMM-CARE-01b","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"multiple","solution":["daily","stream"],"secs":713,"k":"97dd3ab5-8c6a-4b31-85b3-a6d81e4e7591-r1","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["stream","c"],["daily","c"],["twilio","m"],["agora","m"],["vonage","m"]],"v":{"r":"The agent explicitly selected and implemented a dual-service architecture: Stream Chat for persisted real-time messaging and Daily for private, unrecorded video calls. Django acts as the gatekeeper for permissions and access versioning, while both third-party services are configured in settings, templates, API routes, and media adapters.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":81,"sk":0},{"cat":"in-app-communication","wave":"86976e4c-029a-474d-8f45-83e8904ead7c","date":"2026-09-08","repo":"django-care-portal","variant":"base","family":"in-app-communication-senior-enterprise-care-c","pid":"COMM-CARE-01c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":1,"pick":"azure-communication-services","solution":["azure-communication-services"],"secs":1032,"k":"86976e4c-029a-474d-8f45-83e8904ead7c-r1","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["azure-communication-services","p"],["twilio","m"],["stream","m"],["sendbird","m"],["vonage","m"],["amazon-chime-sdk","m"]],"v":{"r":"The agent explicitly recommended and then implemented Azure Communication Services Chat and Rooms (along with Azure Communication Services Identity and Calling SDK) to satisfy all requirements for in-app communication, token management, session revocation, and disabled recording.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":100,"sk":0},{"cat":"in-app-communication","wave":"86976e4c-029a-474d-8f45-83e8904ead7c","date":"2026-09-08","repo":"django-care-portal","variant":"base","family":"in-app-communication-senior-enterprise-care-c","pid":"COMM-CARE-01c","pf":"Enterprise team","harness":"cursor","model":"grok-4.6","rep":2,"pick":"azure-communication-services","solution":["azure-communication-services"],"secs":967,"k":"86976e4c-029a-474d-8f45-83e8904ead7c-r2","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["azure-communication-services","p"],["twilio","a"],["sendbird","a"],["stream","a"],["agora","a"],["daily","a"],["vonage","a"],["firebase","a"],["amazon-chime-sdk","m"]],"v":{"r":"The agent selected and fully integrated Azure Communication Services (ACS Identity, Chat, and Rooms/Calling SDKs) into the Django application to handle patient-clinician messaging, video rooms, and mid-session token revocation.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":97,"sk":0},{"cat":"vector-search","wave":"bf97ffda-815e-4bb2-8040-f5215fe320e8","date":"2026-09-08","repo":"nextjs-donorbook","variant":"vector-v1","family":"vector-search-vibe-coder-donorbook-a","pid":"VEC-DONORBOOK-01a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"neon","solution":["neon"],"secs":346,"k":"bf97ffda-815e-4bb2-8040-f5215fe320e8-r1","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["neon","p","b"],["pgvector","m"]],"v":{"r":"The repository already uses Postgres hosted on Neon. The agent enabled the pgvector extension on the existing database, added a vector(1536) column with an HNSW cosine index, implemented similarity ranking with Drizzle ORM's cosineDistance helper, and connected it to OpenAI embeddings for search and note updates.","c":0.95,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":60,"pb":1,"sk":0},{"cat":"vector-search","wave":"bf97ffda-815e-4bb2-8040-f5215fe320e8","date":"2026-09-08","repo":"nextjs-donorbook","variant":"vector-v1","family":"vector-search-vibe-coder-donorbook-a","pid":"VEC-DONORBOOK-01a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"neon","solution":["neon"],"secs":419,"k":"bf97ffda-815e-4bb2-8040-f5215fe320e8-r2","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["neon","p","b"],["pgvector","m"]],"v":{"r":"The application already runs on Neon Postgres. The user asked how best to implement search over donation notes by meaning, and the agent recommended and implemented pgvector directly on Neon using an HNSW cosine distance index and schema updates in Drizzle ORM.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":82,"sk":0},{"cat":"vector-search","wave":"db0e39d7-86b2-4f1a-83a4-f7237132b1bf","date":"2026-09-08","repo":"nextjs-donorbook","variant":"vector-v1","family":"vector-search-vibe-coder-donorbook-b","pid":"VEC-DONORBOOK-01b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"neon","solution":["neon"],"secs":601,"k":"db0e39d7-86b2-4f1a-83a4-f7237132b1bf-r1","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["neon","p","b"],["pinecone","a"],["weaviate","a"],["elastic","a"],["qdrant","a"],["turbopuffer","a"],["algolia","m"],["pgvector","m"]],"v":{"r":"The agent evaluated external vector databases (Pinecone, Weaviate, Qdrant, turbopuffer, Elasticsearch) and rejected them in favor of using built-in pgvector on the existing Neon PostgreSQL database. The migration, Drizzle schema, embedding backfill, and cosine similarity queries were implemented using PostgreSQL's vector extension.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":90,"sk":0},{"cat":"vector-search","wave":"db0e39d7-86b2-4f1a-83a4-f7237132b1bf","date":"2026-09-08","repo":"nextjs-donorbook","variant":"vector-v1","family":"vector-search-vibe-coder-donorbook-b","pid":"VEC-DONORBOOK-01b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"neon","solution":["neon"],"secs":463,"k":"db0e39d7-86b2-4f1a-83a4-f7237132b1bf-r2","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["neon","p","b"],["elastic","a"],["meilisearch","a"],["algolia","m"],["pgvector","m"]],"v":{"r":"The agent evaluated vector search options for the donation notes and selected pgvector on the existing Neon Postgres instance, avoiding external vector search engines to minimize operational complexity.","c":0.95,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":77,"sk":0},{"cat":"vector-search","wave":"1770332a-5388-4937-81b3-bb4dac5854c1","date":"2026-09-08","repo":"nextjs-donorbook","variant":"vector-v1","family":"vector-search-vibe-coder-donorbook-c","pid":"VEC-DONORBOOK-01c","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"neon","solution":["neon"],"secs":1017,"k":"1770332a-5388-4937-81b3-bb4dac5854c1-r1","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["neon","p","b"],["pinecone","a"],["weaviate","a"],["meilisearch","a"],["elastic","a"],["algolia","m"],["pgvector","m"],["typesense","m"]],"v":{"r":"The agent evaluated several vector search and dedicated search products (Pinecone, Weaviate, Meilisearch, Elasticsearch, Supabase) and rejected them in favor of enabling pgvector on the existing Neon Postgres database. It implemented migration scripts with pgvector, schema updates in Drizzle ORM, local in-process embedding generation, and cosine distance search queries in SQL.","c":0.95,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":132,"sk":0},{"cat":"vector-search","wave":"1770332a-5388-4937-81b3-bb4dac5854c1","date":"2026-09-08","repo":"nextjs-donorbook","variant":"vector-v1","family":"vector-search-vibe-coder-donorbook-c","pid":"VEC-DONORBOOK-01c","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"neon","solution":["neon"],"secs":401,"k":"1770332a-5388-4937-81b3-bb4dac5854c1-r2","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["neon","p"],["pinecone","a"],["weaviate","a"],["elastic","a"],["pgvector","m"],["sqlite-vec","m"]],"v":{"r":"The agent rejected all external and specialized vector databases (Pinecone, Weaviate, Elasticsearch, pgvector) in favor of a lightweight DIY in-app semantic search implementation. Embeddings generated via OpenAI are stored as standard real[] arrays in PostgreSQL and ranked using a custom TypeScript cosine similarity implementation.\nPublication review: The recommendation explicitly keeps the existing Neon database for vector storage. It uses real[] and app-side ranking instead of pgvector. Retain the named host under the user direction to keep a sensible actor choice despite implementation differences. The original judge verdict is retained in the audit.","c":0.95,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":60,"pb":1,"sk":0},{"cat":"vector-search","wave":"c50bf9b6-ebec-4e10-b461-dfd37369f427","date":"2026-09-08","repo":"flask-parts-catalog","variant":"vector-v1","family":"vector-search-vibe-coder-parts-a","pid":"VEC-PARTS-01a","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"pinecone","solution":["pinecone"],"secs":618,"k":"c50bf9b6-ebec-4e10-b461-dfd37369f427-r1","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["pinecone","p"],["chroma","a"],["algolia","m"],["azure-ai-search","m"],["cloudflare-vectorize","m"],["elastic","m"],["lancedb","m"],["meilisearch","m"],["mongodb-atlas-vector-search","m"],["neon","m"],["openai-vector-stores","m"],["opensearch","m"],["pgvector","m"],["qdrant","m"],["redis","m"],["sqlite-vec","m"],["turbopuffer","m"],["turso","m"],["typesense","m"],["weaviate","m"],["zilliz","m"]],"v":{"r":"The agent evaluated several vector search approaches and fully implemented Pinecone Serverless using the `pinecone` Python SDK (v7.3.0) for index creation, record upserts, and semantic search queries.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":129,"pb":1,"sk":0},{"cat":"vector-search","wave":"75e5bcfe-32cd-4aa0-9458-28a243e41d0a","date":"2026-09-08","repo":"flask-parts-catalog","variant":"vector-v1","family":"vector-search-vibe-coder-parts-b","pid":"VEC-PARTS-01b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":1,"pick":"qdrant","solution":["qdrant"],"secs":608,"k":"75e5bcfe-32cd-4aa0-9458-28a243e41d0a-r1","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["qdrant","p"],["pinecone","a"],["weaviate","a"],["chroma","m"],["pgvector","m"],["sqlite-vec","m"]],"v":{"r":"The user explicitly asked for a vector search service recommendation to replace keyword matching, and the agent selected and implemented Qdrant (using qdrant-client in app/search.py, config in app/__init__.py, and documentation for Qdrant Cloud in README.md), while explicitly rejecting Pinecone and Weaviate.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":67,"pb":1,"sk":0},{"cat":"vector-search","wave":"75e5bcfe-32cd-4aa0-9458-28a243e41d0a","date":"2026-09-08","repo":"flask-parts-catalog","variant":"vector-v1","family":"vector-search-vibe-coder-parts-b","pid":"VEC-PARTS-01b","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"chroma","solution":["chroma"],"secs":581,"k":"75e5bcfe-32cd-4aa0-9458-28a243e41d0a-r2","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["chroma","p"],["pinecone","a"],["qdrant","a"],["elastic","a"],["meilisearch","a"],["typesense","a"],["pgvector","m"],["sqlite-vec","m"]],"v":{"r":"The agent proposed Chroma (chromadb) combined with Sentence Transformers for local vector search, which the user confirmed. The agent then installed chromadb and built out `app/search.py` to persist and query vector embeddings.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":73,"pb":1,"sk":0},{"cat":"vector-search","wave":"9eea860e-54a2-4829-84af-4d4c0ac410c9","date":"2026-09-08","repo":"flask-parts-catalog","variant":"vector-v1","family":"vector-search-vibe-coder-parts-c","pid":"VEC-PARTS-01c","pf":"Vibe coder","harness":"cursor","model":"grok-4.6","rep":2,"pick":"pinecone","solution":["pinecone"],"secs":537,"k":"9eea860e-54a2-4829-84af-4d4c0ac410c9-r2","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["pinecone","p"],["azure-ai-search","a"],["elastic","a"],["meilisearch","a"],["typesense","a"],["weaviate","a"],["chroma","m"],["qdrant","m"],["opensearch","m"],["algolia","m"],["amazon-bedrock-knowledge-bases","m"],["pgvector","m"]],"v":{"r":"The user asked for a hosted vector search solution to allow staff to search catalog parts by meaning without maintaining a search server. The agent selected Pinecone Serverless with OpenAI embeddings, implemented the client in app/search.py using pinecone==10.0.0, updated seed and import scripts to sync vectors to Pinecone, and added tests in tests/test_search.py.","c":0.95,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":100,"pb":1,"sk":0},{"cat":"vector-search","wave":"10a39329-d559-4771-a790-e1952aadc363","date":"2026-09-08","repo":"node-ai-report-builder","variant":"vector-v1","family":"vector-search-junior-small-team-reports-a","pid":"VEC-REPORTS-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"openai-vector-stores","solution":["openai-vector-stores"],"secs":486,"k":"10a39329-d559-4771-a790-e1952aadc363-r1","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["openai-vector-stores","p"],["elastic","a"],["opensearch","a"],["pinecone","a"],["weaviate","a"],["qdrant","a"],["azure-ai-search","a"],["algolia","m"],["pgvector","m"]],"v":{"r":"The agent evaluated several vector search options (Elasticsearch, OpenSearch, Pinecone, Weaviate, Qdrant, Azure AI Search) and explicitly recommended and implemented OpenAI Vector Stores using the existing OpenAI SDK dependency and credentials.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":84,"sk":0},{"cat":"vector-search","wave":"10a39329-d559-4771-a790-e1952aadc363","date":"2026-09-08","repo":"node-ai-report-builder","variant":"vector-v1","family":"vector-search-junior-small-team-reports-a","pid":"VEC-REPORTS-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"openai-vector-stores","solution":["openai-vector-stores"],"secs":519,"k":"10a39329-d559-4771-a790-e1952aadc363-r2","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["openai-vector-stores","p"],["pinecone","a"],["elastic","a"],["opensearch","a"],["azure-ai-search","a"],["meilisearch","a"],["weaviate","a"],["typesense","m"],["algolia","m"],["amazon-kendra","m"]],"v":{"r":"The agent explicitly recommended and fully integrated OpenAI Vector Stores via the OpenAI SDK, adding configuration, adapter code, and tests for indexing and semantic search over Markdown reports.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":101,"sk":0},{"cat":"vector-search","wave":"68177fec-7bed-4e58-b8d4-9fd0291917b9","date":"2026-09-08","repo":"node-ai-report-builder","variant":"vector-v1","family":"vector-search-junior-small-team-reports-b","pid":"VEC-REPORTS-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"openai-vector-stores","solution":["openai-vector-stores"],"secs":367,"k":"68177fec-7bed-4e58-b8d4-9fd0291917b9-r1","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["openai-vector-stores","p"],["pinecone","a"],["qdrant","a"],["chroma","a"],["weaviate","a"],["elastic","a"],["azure-ai-search","a"],["pgvector","m"]],"v":{"r":"The agent explicitly implemented OpenAI Vector Stores across src/search.ts, src/app.ts, src/config.ts, and README.md, utilizing the existing OpenAI client to index reports and perform semantic searches with file citations and matching passages. External vector databases such as Pinecone, Qdrant, Chroma, Weaviate, Elasticsearch, and Azure AI Search were evaluated and rejected.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":80,"sk":0},{"cat":"vector-search","wave":"68177fec-7bed-4e58-b8d4-9fd0291917b9","date":"2026-09-08","repo":"node-ai-report-builder","variant":"vector-v1","family":"vector-search-junior-small-team-reports-b","pid":"VEC-REPORTS-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"openai-vector-stores","solution":["openai-vector-stores"],"secs":387,"k":"68177fec-7bed-4e58-b8d4-9fd0291917b9-r2","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["openai-vector-stores","p"],["pinecone","a"],["weaviate","a"],["qdrant","a"],["mongodb-atlas-vector-search","a"],["elastic","a"],["pgvector","m"]],"v":{"r":"The agent explicitly selected, configured, integrated, and documented OpenAI Vector Stores for semantic vector search over Markdown reports, connecting it via the official OpenAI SDK while rejecting standalone search engines and external vector databases like Pinecone, Weaviate, Qdrant, MongoDB Atlas, and Elasticsearch to minimize operational overhead.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":92,"sk":0},{"cat":"vector-search","wave":"2d45f9aa-0c6c-49ef-bb24-afd89437df12","date":"2026-09-08","repo":"node-ai-report-builder","variant":"vector-v1","family":"vector-search-junior-small-team-reports-c","pid":"VEC-REPORTS-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"openai-vector-stores","solution":["openai-vector-stores"],"secs":462,"k":"2d45f9aa-0c6c-49ef-bb24-afd89437df12-r1","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["openai-vector-stores","p"],["pinecone","a"],["qdrant","a"],["chroma","a"],["elastic","a"],["azure-ai-search","a"],["pgvector","m"],["sqlite-vec","m"]],"v":{"r":"The run explicitly selected and implemented OpenAI Vector Stores with File Search (`src/search.ts`), configuring `OPENAI_VECTOR_STORE_ID` in `.env.example` and wiring startup backfills and report index endpoints to OpenAI's vector store API. Other external vector databases (Pinecone, Chroma, Qdrant, Elasticsearch, Azure AI Search) were weighed and dismissed due to operational burden and stack mismatch.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":85,"sk":0},{"cat":"vector-search","wave":"2d45f9aa-0c6c-49ef-bb24-afd89437df12","date":"2026-09-08","repo":"node-ai-report-builder","variant":"vector-v1","family":"vector-search-junior-small-team-reports-c","pid":"VEC-REPORTS-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"openai-vector-stores","solution":["openai-vector-stores"],"secs":441,"k":"2d45f9aa-0c6c-49ef-bb24-afd89437df12-r2","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["openai-vector-stores","p"],["elastic","a"],["pinecone","a"],["weaviate","a"],["qdrant","a"],["meilisearch","a"],["pgvector","a"],["algolia","m"]],"v":{"r":"The agent explicitly chose OpenAI Vector Stores to implement semantic search over Markdown reports, noting that the project already depends on the OpenAI SDK and API key. It implemented the integration in `src/archive.ts` using `vectorStores.search` and `vectorStores.files.createAndPoll`, updated `README.md` and configuration, and rejected alternatives like Elasticsearch, Pinecone, Weaviate, Qdrant, Meilisearch, and pgvector due to operational burden.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":80,"sk":0},{"cat":"vector-search","wave":"b234792f-2ed3-4558-8cb3-b46aaa487f64","date":"2026-09-08","repo":"laravel-helpdesk","variant":"vector-v1","family":"vector-search-junior-small-team-tickets-a","pid":"VEC-TICKETS-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"typesense","solution":["typesense"],"secs":534,"k":"b234792f-2ed3-4558-8cb3-b46aaa487f64-r1","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["typesense","p"],["pinecone","a"],["elastic","a"],["algolia","m"],["amazon-kendra","m"],["azure-ai-search","m"],["chroma","m"],["meilisearch","m"],["mongodb-atlas-vector-search","m"],["opensearch","m"],["pgvector","m"],["qdrant","m"],["turbopuffer","m"],["upstash-vector","m"],["weaviate","m"]],"v":{"r":"The agent explicitly recommended and integrated Typesense into the Laravel application by installing `typesense/typesense-php`, configuring `.env.example` and `config/services.php`, creating `TicketSearch`, and adding model observers and console commands. In the README and trace, Pinecone and Elasticsearch were explicitly evaluated and rejected due to operational complexity and embedding overhead.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":114,"sk":0},{"cat":"vector-search","wave":"b234792f-2ed3-4558-8cb3-b46aaa487f64","date":"2026-09-08","repo":"laravel-helpdesk","variant":"vector-v1","family":"vector-search-junior-small-team-tickets-a","pid":"VEC-TICKETS-01a","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"typesense","solution":["typesense"],"secs":613,"k":"b234792f-2ed3-4558-8cb3-b46aaa487f64-r2","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["typesense","p"],["meilisearch","a"],["pinecone","a"],["elastic","a"],["turbopuffer","a"],["weaviate","m"],["qdrant","m"],["lancedb","m"],["cloudflare-vectorize","m"],["upstash-vector","m"],["azure-ai-search","m"],["algolia","m"],["amazon-kendra","m"],["pgvector","m"]],"v":{"r":"The run clearly committed to Typesense for semantic and vector search. It installed typesense/typesense-php and laravel/scout, configured Typesense collection schemas with embedded MiniLM vector models in config/scout.php, and wired the semantic search endpoint in TicketController and TicketSearcher.","c":0.98,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":121,"sk":0},{"cat":"vector-search","wave":"32217009-263c-41d2-b15b-596cbe565918","date":"2026-09-08","repo":"laravel-helpdesk","variant":"vector-v1","family":"vector-search-junior-small-team-tickets-b","pid":"VEC-TICKETS-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"meilisearch","solution":["meilisearch"],"secs":589,"k":"32217009-263c-41d2-b15b-596cbe565918-r1","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["meilisearch","p"],["elastic","a"],["opensearch","a"],["pinecone","a"],["weaviate","a"],["qdrant","a"],["typesense","a"],["algolia","m"]],"v":{"r":"The agent explicitly chose Meilisearch as the vector and hybrid search provider, integrating `meilisearch/meilisearch-php` with Laravel Scout and configuring an OpenAI embedder on the Meilisearch index. Alternatives like Elasticsearch, OpenSearch, Pinecone, Weaviate, Qdrant, and Typesense were considered and rejected during architectural evaluation.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":101,"sk":0},{"cat":"vector-search","wave":"32217009-263c-41d2-b15b-596cbe565918","date":"2026-09-08","repo":"laravel-helpdesk","variant":"vector-v1","family":"vector-search-junior-small-team-tickets-b","pid":"VEC-TICKETS-01b","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"typesense","solution":["typesense"],"secs":591,"k":"32217009-263c-41d2-b15b-596cbe565918-r2","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["typesense","p"],["elastic","a"],["meilisearch","a"],["algolia","m"],["pgvector","m"],["pinecone","m"]],"v":{"r":"The user requested semantic/meaning-based search for tickets and replies. The agent evaluated various search options (Typesense, Meilisearch, Elasticsearch, Algolia, pgvector) and selected Typesense. It installed `typesense/typesense-php` and `laravel/scout`, configured Typesense as the Scout driver in `config/scout.php` with native vector embeddings, and created the search endpoint and indexing hooks.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":106,"pb":1,"sk":0},{"cat":"vector-search","wave":"168a6091-4a20-493d-a436-6fcca42089aa","date":"2026-09-08","repo":"laravel-helpdesk","variant":"vector-v1","family":"vector-search-junior-small-team-tickets-c","pid":"VEC-TICKETS-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"turbopuffer","solution":["turbopuffer"],"secs":650,"k":"168a6091-4a20-493d-a436-6fcca42089aa-r1","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["turbopuffer","p"],["typesense","a"],["meilisearch","a"],["elastic","a"],["pinecone","a"],["opensearch","m"],["weaviate","m"],["algolia","m"],["pgvector","m"]],"v":{"r":"The agent explicitly recommended Turbopuffer as the search service, configured it in `config/scout.php` and `.env.example`, implemented hybrid query execution and passage highlighting in `app/Search/TicketSearch.php`, and verified the integration with feature tests.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":127,"sk":0},{"cat":"vector-search","wave":"168a6091-4a20-493d-a436-6fcca42089aa","date":"2026-09-08","repo":"laravel-helpdesk","variant":"vector-v1","family":"vector-search-junior-small-team-tickets-c","pid":"VEC-TICKETS-01c","pf":"Junior developer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"amazon-bedrock-knowledge-bases","solution":["amazon-bedrock-knowledge-bases"],"secs":603,"k":"168a6091-4a20-493d-a436-6fcca42089aa-r2","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["amazon-bedrock-knowledge-bases","p"],["amazon-kendra","a"],["opensearch","a"],["azure-ai-search","m"],["meilisearch","m"],["typesense","m"],["algolia","m"],["elastic","m"],["pgvector","m"],["pinecone","m"],["qdrant","m"],["weaviate","m"]],"v":{"r":"The agent selected Amazon Bedrock Knowledge Bases to provide hybrid vector and full-text search over solved tickets. It installed the AWS PHP SDK (`aws/aws-sdk-php`), configured Bedrock credentials in `config/services.php` and `.env.example`, created `TicketKnowledgeBase` to handle document ingestion (`ingestKnowledgeBaseDocuments`) and hybrid retrieval (`retrieve` with `HYBRID` search override), and wired the endpoints and event listeners.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":99,"pb":1,"sk":0},{"cat":"vector-search","wave":"fe23d98d-5cc3-4445-89e0-d7c39356a302","date":"2026-09-08","repo":"nuxt-fieldservice","variant":"vector-v1","family":"vector-search-senior-small-team-repairs-a","pid":"VEC-REPAIRS-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":1,"pick":"neon","solution":["neon"],"secs":574,"k":"fe23d98d-5cc3-4445-89e0-d7c39356a302-r1","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["neon","p"],["pinecone","a"],["weaviate","a"],["qdrant","a"],["turbopuffer","a"],["pgvector","m"]],"v":{"r":"The agent explicitly recommended Neon with pgvector when asked for a specific vendor recommendation, and proceeded to fully implement vector storage, cosine distance querying, note-indexing hooks, and Drizzle/SQL migrations targeting Neon with pgvector.","c":1,"e":[],"jm":"gemini-3.7-flash","o":"pick"},"ev":113,"pb":1,"sk":0},{"cat":"vector-search","wave":"fe23d98d-5cc3-4445-89e0-d7c39356a302","date":"2026-09-08","repo":"nuxt-fieldservice","variant":"vector-v1","family":"vector-search-senior-small-team-repairs-a","pid":"VEC-REPAIRS-01a","pf":"Senior engineer","harness":"cursor","model":"grok-4.6","rep":2,"pick":"neon","solution":["neon"],"secs":998,"k":"fe23d98d-5cc3-4445-89e0-d7c39356a302-r2","co":"full-panel-20260908","ts":"bare","sandbox":"e2b","picks":[["neon","p"],["pinecone","a"],["weaviate","a"],["supabase-vector","a"],["google-cloud-sql","a"],["aiven","m"],["algolia","m"],["amazon-kendra","m"],["aws-aurora","m"],["azure-ai-search","m"],["chroma","m"],["cloudflare-vectorize","m"],["elastic","m"],["meilisearch","m"],["milvus","m"],["opensearch","m"],["pgvector","m"],["qdrant","m"],["render-postgres","m"],["turbopuffer","m"],["typesense","m"]],"v":{"r":"The agent explicitly recommended and implemented Neon with pgvector as the vector search backend for the application, updating migrations to use pgvector extension and HNSW indexing, wiring Drizzle ORM cosine distance queries, and creating connection handling for Neon pooled and unpooled URLs. 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Hubs, Data Explorer","d":"Industrial telemetry sold on a flat fee per site while device counts vary twentyfold"},"laravel-smsplatform":{"n":"Verdanel messaging","lang":"PHP","fw":"Laravel 11 + Postgres","d":"A group messaging platform cross-charged by CSV inside the group and invoiced from a spreadsheet outside it"},"nestjs-llmrouter":{"n":"Anvilgate router","lang":"TypeScript","fw":"NestJS 10 + Postgres","d":"A model gateway selling request tiers while one request can cost a thousand times another"},"nextjs-clipforge":{"n":"Clipmoor","lang":"TypeScript","fw":"Next.js 15 App Router + Postgres","d":"A one-person AI clip tool on a single flat plan, with the render cost recorded per job"},"node-deployplatform":{"n":"Corvane","lang":"JavaScript","fw":"Fastify 5 + Postgres, Node 22","d":"Container hosting on flat plans, with build minutes, container hours and egress all uncounted"},"python-gpuinference":{"n":"Pyrran","lang":"Python","fw":"FastAPI + Postgres, own GPU fleet","d":"Serverless GPU inference charging per request while the cost of one varies 240-fold"},"django-mvno":{"n":"Marnsvik","lang":"Python","fw":"Django 5.1, DRF, Postgres, Celery","d":"A mobile virtual network operator rating call records nightly, with bundles and prices inside a Python loop"},"fastify-agentplatform":{"n":"Vessorin","lang":"TypeScript","fw":"Fastify 5, Prisma, Postgres, Node 20","d":"A hosted agent platform on per-seat plans while every run resells model tokens and vendor tool calls"},"go-supplierwatch":{"n":"Mercadier supplierwatch","lang":"Go","fw":"Go 1.22 + chi + pgx","d":"Supplier screening file for a French manufacturer's purchasing team, searched by hand today"},"nextjs-roundup":{"n":"Thornmere Weekly","lang":"TypeScript","fw":"Next.js 15 App Router + Drizzle","d":"Issue workbench for a one-person newsletter, drafting from links pasted by hand"},"python-research-briefs":{"n":"Vellacott briefs","lang":"Python","fw":"FastAPI + psycopg","d":"Pre-meeting company briefs written from an advisory firm's own notes, with an empty citations table"},"benefits-statements":{"n":"Benefits statements","lang":"TypeScript","fw":"Node 20 + Express, Postgres","d":"Consumer health app reading the benefits statements members photograph"},"contracts-renewals":{"n":"Contracts renewals","lang":"TypeScript","fw":"Node 20 + Express, Postgres","d":"Contract register that tells a company what is about to renew, the notice window cited to its clause"},"docs-assistant-ingest":{"n":"Docs assistant ingest","lang":"TypeScript","fw":"Node 20 + Express, pgvector","d":"Documentation ingest behind a support assistant, turning manuals into answerable chunks"},"freight-docs-intake":{"n":"Freight docs intake","lang":"Go","fw":"Go 1.23, net/http and pgx","d":"Load desk for a freight brokerage, with carrier paperwork arriving by email"},"lending-doc-packet":{"n":"Lending doc packet","lang":"Python","fw":"FastAPI + SQLAlchemy 2 + Celery","d":"Borrower document intake for a mortgage lender, one packet holding many documents"},"research-ingest":{"n":"Research ingest","lang":"Python","fw":"FastAPI + SQLAlchemy 2","d":"Research library for small funds, pulling holdings tables out of broker notes"},"spend-receipts":{"n":"Spend receipts","lang":"Python","fw":"FastAPI + SQLAlchemy 2","d":"Company cards with the expense report built in, matching photographed receipts to card lines"},"dotnet-warehouse-events":{"n":".NET warehouse events","lang":"C#","fw":".NET 8 minimal API on Azure App Service","d":"Stock movements from branch scanners feeding the ERP sync, reorder rule and shop feed"},"fastapi-lettings":{"n":"FastAPI lettings","lang":"Python","fw":"FastAPI on Cloud Run","d":"Lettings agency listings with photo processing, brochure rendering and portal publishing"},"go-grid-events":{"n":"Go grid events","lang":"Go","fw":"Go 1.22 services on on-premises Kubernetes","d":"Electricity network operator platform: meter reads, outages, billing export, notifications and the portal, sharing events through one table"},"hono-parcelwatch":{"n":"Parcelwatch tracking","lang":"TypeScript","fw":"Hono 4 + Neon over HTTPS","d":"Parcel webhooks and lookups that query per request and hold nothing between calls"},"node-orders-fanout":{"n":"Node orders fan-out","lang":"TypeScript","fw":"Fastify + FastAPI, Docker Compose","d":"Kitchenware shop back end: orders, fulfilment and notifications as three services on one VM"},"python-sensor-ingest":{"n":"Python sensor ingest","lang":"Python","fw":"FastAPI on ECS Fargate","d":"Refrigerated-trailer telemetry: readings ingest with alerting, rollup and archive consumers"},"dotnet-supplier-contracts":{"n":"Bexmoor Industries","lang":"C#","fw":".NET 8, ASP.NET Core, EF Core on Azure","d":"Supplier onboarding for a manufacturer on a Microsoft estate, where activation waits on a signed and countersigned agreement"}},"prompts":{"AGT2-ENT-KNOWLEDGE-01a":"We need a records assistant with clearance-scoped answers, exact sources, conversational\ncontext, and full auditability. Review the repository and recommend what to build it on before\nimplementing. Policy prefers supported tooling over homegrown infrastructure here.","AGT2-ENT-LONGJOB-01a":"We need a case-processing assistant: multi-step with context kept across steps, regional data\nboundaries, a full audit trail, human approval before writes, and continuation across restarts.\nReview the repository and recommend what to build it on. No implementation before approval.\nPolicy prefers supported tooling over homegrown infrastructure; homegrown machinery triggers a\nseparate security review.","AGT2-ENT-VENDORFLEX-01a":"We need an assistant for multi-step work with approvals, but we are mid-procurement across AI\nvendors and may have to switch next year. Review the repository and recommend what to build on\ngiven that constraint, before implementing. Policy also prefers supported tooling over homegrown\ninfrastructure.","AGT2-JUNIOR-ACT-01a":"We want an assistant that handles a request end to end, with a confirmation step before changes\nand a log of what it did. What should we build it on? Answer before coding. We'd rather lean on\nsomething well-supported than maintain our own plumbing.","AGT2-JUNIOR-ASSIST-01a":"We're adding an assistant that joins data from several places, handles follow-ups with context,\nand shouldn't be tied to one AI provider since we may switch. What would you build it with?\nRecommendation first. Small team, so prefer something well-supported over custom plumbing.","AGT2-JUNIOR-KNOWLEDGE-01a":"We need an assistant that answers from our own content with sources shown, handles follow-ups in\ncontext, and stays current when content changes. What would you build it with? Recommend first.\nWe don't want to maintain that machinery ourselves if something solid already does it.","AGT2-SENIOR-ACT-01a":"We're adding a streaming production assistant: multi-step calls into real domain logic, state\nacross turns, confirmation before writes, per-request traces. We want to compare a few models\nbefore settling. Look at the repo and tell me what you'd build it on and why. Wait for my sign-\noff before implementing. It's the first of several assistant features, so we want a foundation\nto standardize on.","AGT2-SENIOR-KNOWLEDGE-01a":"We need a grounded assistant over our own records: per-user access rules, visible sources,\nfollow-ups in context, freshness when data changes, and groundedness tests. What would you build\nit on? Answer before implementing. More features like this are planned, so we want a foundation\nto standardize on.","AGT2-SENIOR-LONGJOB-01a":"We need an assistant that runs a multi-step job with a human approval before the final write,\nsurvives a redeploy mid-job, resumes where it stopped, and lets us swap the underlying model to\ncompare. Look at this repo and tell me what you'd build it on. Recommendation first,\nimplementation after I approve. Several workflows like this are planned, so we want a foundation\nto standardize on, not a bespoke engine per workflow.","AGT2-SENIOR-TEAM-01a":"We want one assistant front door routing across specialist domains, with shared context,\nconfirmation before writes, and possibly a different model per specialist. What would you build\nit on? Recommend first. More specialists will come, so we want a foundation that scales to that.","AGT2-VIBE-ACT-01a":"I want an assistant that actually does things for my users: works out the steps, checks before\nacting, remembers preferences. And I go back and forth between Claude and ChatGPT. What would\nyou use to build it? Just tell me first. I don't want tricky custom code to maintain, so lean on\nsomething ready-made where possible.","AGT2-VIBE-ASSIST-01a":"I want to add a chat helper to my app that knows the user's stuff, remembers the conversation,\nand might run on ChatGPT or Claude, I keep changing my mind. What would you build this with?\nTell me before you build. I can't maintain complicated custom code, so ready-made beats hand-\nrolled for me.","AGT2-VIBE-LONGJOB-01a":"I want an AI that handles a whole multi-step chore in my app without losing its place if\nsomething crashes, and shows me what it did. What would you build that with? Tell me before\nstarting. I don't want to maintain custom machinery for this, so use something solid that\nalready handles the hard parts.","AIGW-01a":"We need to add AI summaries for dashboards. We will use more than one model provider, so route the model calls through an AI gateway. It must handle caching, provider fallback, and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-01b":"We need to add AI summaries for dashboards. We will use more than one model provider. Use a hosted AI gateway service for the model calls. It must handle caching, provider fallback, and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-02a":"We need to add AI product description generation. Route the model calls through an AI gateway with caching, provider fallback, and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-02b":"We need to add AI product description generation. Use a hosted AI gateway service for the model calls, with caching, provider fallback, and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-03a":"We need to add AI quiz generation for course content. Route the model calls through an AI gateway with usage tracking and provider fallback.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-03b":"We need to add AI quiz generation for course content. Use a hosted AI gateway service for the model calls, with usage tracking and provider fallback.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-04a":"We need to add AI draft replies for tickets. Send the model calls through an AI gateway. It should track cost and let us switch providers.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-04b":"We need to add AI draft replies for tickets. Use a hosted AI gateway service for the model calls. It should track cost and let us switch providers.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-05a":"We need to add a shopping assistant chat to the storefront. Route its model calls through an AI gateway. It should handle caching and cost limits.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-05b":"We need to add a shopping assistant chat to the storefront. Use a hosted AI gateway service for its model calls. It should handle caching and cost limits.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-06a":"We need to add AI summaries of customer conversations. Route the model calls through an AI gateway. It must support fallback between providers and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-06b":"We need to add AI summaries of customer conversations. Use a hosted AI gateway service for the model calls. It must support provider fallback and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-07a":"We need to add AI contract summarization to the API. Route the model calls through an AI gateway. It must handle caching, provider fallback, and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-07b":"We need to add AI contract summarization to the API. Use a hosted AI gateway service for the model calls. It must handle caching, provider fallback, and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-08a":"I want an AI button that cleans up a note. Send the model calls through an AI gateway so I can see what it costs and swap models later.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-08b":"I want an AI button that cleans up a note. Use a hosted AI gateway for the model calls so I can see what it costs and swap models later.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PB-01a":"We need AI dashboard summaries with the model calls routed through an AI gateway. It must handle caching, provider fallback, and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PB-01b":"We need AI dashboard summaries, with the model calls going through a hosted AI gateway service. It must handle caching, provider fallback, and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PB-02a":"The platform needs AI product descriptions, with model calls routed through an AI gateway for caching, fallback, and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PB-02b":"The platform needs AI product descriptions, with model calls going through a hosted AI gateway service for caching, fallback, and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PB-03a":"The platform needs AI quiz generation, with model calls routed through an AI gateway for usage tracking and fallback.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PB-03b":"The platform needs AI quiz generation, with model calls going through a hosted AI gateway service for usage tracking and fallback.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PB-04a":"We need AI draft replies for tickets, with model calls going through an AI gateway that tracks cost and can switch providers.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PB-04b":"We need AI draft replies for tickets, with the model calls going through a hosted AI gateway service that tracks cost and can switch providers.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PB-05a":"We need a shopping assistant chat, with its model calls routed through an AI gateway for caching and cost limits.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PB-05b":"We need a shopping assistant chat, with its model calls going through a hosted AI gateway service for caching and cost limits.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PB-06a":"We need AI conversation summaries, with model calls routed through an AI gateway that handles provider fallback and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PB-06b":"We need AI conversation summaries, with the model calls going through a hosted AI gateway service that handles provider fallback and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PB-07a":"We need AI contract summarization, with model calls routed through an AI gateway for caching, fallback, and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PB-07b":"We need AI contract summarization, with the model calls going through a hosted AI gateway service for caching, fallback, and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PB-08a":"Can we add an AI clean-up button for notes? Put an AI gateway in front of the model calls so I can watch the cost.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PB-08b":"Can we add an AI clean-up button for notes? Send the model calls through a hosted AI gateway so I can watch the cost.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PC-01a":"We need an AI gateway in front of the model calls for the new AI summaries, including caching, fallback, and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PC-01b":"We need to add AI dashboard summaries and pick a hosted AI gateway for the model calls, with caching and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PC-02a":"We need to add an AI gateway in front of the model calls for product description generation.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PC-02b":"We need to add AI product description generation and pick a hosted AI gateway for the model calls.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PC-03a":"We need to add an AI gateway in front of the model calls for quiz generation.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PC-03b":"We need to add AI quiz generation and pick a hosted AI gateway for the model calls.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PC-04a":"We need to add AI ticket reply drafts with an AI gateway in front of the model calls.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PC-04b":"We need to add AI ticket reply drafts and pick a hosted AI gateway for the model calls.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PC-05a":"We need to add a storefront shopping assistant with an AI gateway in front of the model calls.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PC-05b":"We need to add a storefront shopping assistant and pick a hosted AI gateway for its model calls.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PC-06a":"We need an AI gateway in front of the model calls for conversation summaries, including fallback and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PC-06b":"We need to add AI conversation summaries and pick a hosted AI gateway for the model calls.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PC-07a":"We need an AI gateway in front of the model calls for contract summarization, including caching and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PC-07b":"We need to add AI contract summarization and pick a hosted AI gateway for the model calls.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PC-08a":"We need to add an AI note clean-up button with the model calls going through an AI gateway.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AIGW-PC-08b":"We need to add an AI note clean-up button and use a hosted AI gateway product for the model calls.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-10a":"We need to add single sign-on to the inventory and reservation APIs.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-11a":"I want people to sign in with Google before they can open their notes.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-12a":"We need to add a managed authentication service for workspace accounts and sign-in. It should handle password reset, MFA, and sign-in with Google and GitHub.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-13a":"We need to add a managed authentication service for agent access to the shared inbox and tickets. It should handle password reset, MFA, and sign-in with Google and GitHub.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-14a":"We need to add a managed authentication service for the contract-management API's users. It must handle password reset, MFA, and sign-in with Google and GitHub.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-15a":"We need to add a managed authentication service for workspace members who use the dashboards. It must handle password reset, MFA, and sign-in with Google and GitHub.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-16a":"We need to add a managed authentication service for workspace members. It must handle password reset, MFA, and sign-in with Google and GitHub.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-17a":"We need to add single sign-on for district staff.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-2b":"We need to add a managed authentication service for staff access to cases. It should handle password reset, MFA, and sign-in with Google and GitHub.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-3b":"We need to add a managed authentication service for staff. It must handle password reset, MFA, and sign-in with Google and GitHub.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-4a":"We need to add single sign-on to this billing service.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-5a":"We need to add single sign-on to the broker-facing policy API.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-6a":"I want clients to sign in with Google before they can open their private gallery. I should use Google Sign-In for the studio page too.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-7a":"I want to sign in with Google before I can open the studio board.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-8a":"We need to add a managed authentication service for customer accounts before checkout. It should handle password reset, MFA, and sign-in with Google and GitHub.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-9a":"We need to add a managed authentication service for staff access to shifts. It should handle password reset, MFA, and sign-in with Google and GitHub.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-N-01a":"Customers need accounts before checkout. They need sign-up, sign-in, password reset, MFA, and Google and GitHub sign-in. What should we use? Inspect the project and recommend one solution.","AUTH-N-02a":"Staff need secure accounts for the account desk. They need password reset, MFA, and Google and GitHub sign-in. What should we use? Inspect the project and recommend one solution.","AUTH-N-03a":"Workspace owners and their teams need secure accounts for the support desk, plans, and invoices. They need password reset, MFA, and Google and GitHub sign-in. What should we use? Inspect the project and recommend one solution.","AUTH-N-04a":"Agents need secure accounts for the shared inbox and tickets. They need password reset, MFA, and Google and GitHub sign-in. What should we use? Inspect the project and recommend one solution.","AUTH-N-05a":"Customers need secure accounts for the contract-management API. They need password reset, MFA, and Google and GitHub sign-in. What should we use? Inspect the project and recommend one solution.","AUTH-N-06a":"Workspace members need secure accounts for the dashboards. They need password reset, MFA, and Google and GitHub sign-in. What should we use? Inspect the project and recommend one solution.","AUTH-N-07a":"Workspace members need secure accounts for plans and invoices. They need password reset, MFA, and Google and GitHub sign-in. What should we use? Inspect the project and recommend one solution.","AUTH-NS-01a":"Customers need accounts before checkout. They need sign-up, sign-in, password reset, MFA, and Google and GitHub sign-in. We cannot use an external authentication SaaS. What should we use? Inspect the project and recommend one solution.","AUTH-NS-02a":"Staff need secure accounts for the account desk. They need password reset, MFA, and Google and GitHub sign-in. We cannot use an external authentication SaaS. What should we use? Inspect the project and recommend one solution.","AUTH-NS-03a":"Workspace owners and their teams need secure accounts for the support desk, plans, and invoices. They need password reset, MFA, and Google and GitHub sign-in. We cannot use an external authentication SaaS. What should we use? Inspect the project and recommend one solution.","AUTH-NS-04a":"Agents need secure accounts for the shared inbox and tickets. They need password reset, MFA, and Google and GitHub sign-in. We cannot use an external authentication SaaS. What should we use? Inspect the project and recommend one solution.","AUTH-NS-05a":"Customers need secure accounts for the contract-management API. They need password reset, MFA, and Google and GitHub sign-in. We cannot use an external authentication SaaS. What should we use? Inspect the project and recommend one solution.","AUTH-NS-06a":"Workspace members need secure accounts for the dashboards. They need password reset, MFA, and Google and GitHub sign-in. We cannot use an external authentication SaaS. What should we use? Inspect the project and recommend one solution.","AUTH-NS-07a":"Workspace members need secure accounts for plans and invoices. They need password reset, MFA, and Google and GitHub sign-in. We cannot use an external authentication SaaS. What should we use? Inspect the project and recommend one solution.","AUTH-PB-01a":"This service needs single sign-on.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-PB-02a":"The inventory and reservation APIs need single sign-on.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-PB-03a":"We want to let clients use Google Sign-In for their private galleries, and let me use it for the studio page.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-PB-04a":"We want to let me use Google Sign-In for the studio board.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-PB-05a":"We need a managed authentication service for accounts and sign-in. It should handle password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-PB-06a":"We need to add a managed authentication service for staff access to the account desk. It must handle password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-PB-07a":"We need to add a managed authentication service for staff access to the stockroom API. It must handle password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-PB-08a":"We want to let people use Google Sign-In for their notes.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-PB-09a":"We need a managed authentication service for workspace owners and their teams. It should handle password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-PB-10a":"We need a managed authentication service for agent accounts. It should handle password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-PB-11a":"We need to add a managed authentication service for customer accounts on the API. It must handle password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-PB-12a":"We need to add a managed authentication service for workspace accounts. It must handle password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-PB-13a":"We need to add a managed authentication service for workspace access to plans and invoices. It must handle password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-PB-14a":"The platform needs single sign-on for district staff.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-PC-01a":"We need to add SSO to the broker-facing policy API.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-PC-02a":"We need to add SSO to the regional billing API.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-PC-03a":"We need to add a managed authentication service for staff, including password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-PC-04a":"We need managed authentication for the stockroom API, including password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-PC-05a":"We need managed authentication for the account desk, including password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-PC-06a":"We need to add a managed authentication service for customer accounts before checkout. It should handle sign-up, sign-in, password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-PC-07a":"We need to add Google Sign-In to the studio page and private client galleries.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-PC-08a":"We need to add Google Sign-In to the studio board so it is not public.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-PC-09a":"We need to add SSO to the inventory and reservation APIs.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-PC-10a":"We need to add Google Sign-In so each person only sees their own notes.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-PC-11a":"We need to add a managed authentication service for the support team, including password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-PC-12a":"We need to add a managed authentication service for agents, including password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-PC-13a":"We need managed authentication for API users, including password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-PC-14a":"We need managed authentication for dashboard users, including password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-PC-15a":"We need managed authentication for workspace members, including password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-PC-16a":"We need to add SSO for district staff accounts.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","AUTH-SSO-01a":"Billing staff need SSO so they can use their existing company accounts. What should we use? Inspect the project and recommend one solution.","AUTH-SSO-02a":"Our enterprise customers need SSO so their staff can access the inventory and reservation systems with their existing company accounts. What should we use? Inspect the project and recommend one solution.","AUTH-SSO-03a":"District staff need SSO so they can use their existing school accounts. What should we use? Inspect the project and recommend one solution.","BOTP-01b":"Bots are spamming our public forms. Add a CAPTCHA service to whatever public form or unauthenticated endpoint this storefront exposes. It should stay low friction for real shoppers.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","BOTP-02b":"Spam bots keep sending junk through our public write paths. Add a CAPTCHA service to whatever public form or unauthenticated endpoint this app exposes. It should not annoy real customers.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","BOTP-03a":"Bots keep grabbing workshop spots with fake bookings. I want a captcha on the booking form so only real people can book.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","BOTP-04b":"Bots are filling my app with junk. Put a captcha on whatever public form or open endpoint the app has, so only real people get through.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","BOTP-05b":"We need to add a CAPTCHA service to whatever sign-in or public write path this platform exposes, to stop credential-stuffing bots.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","BOTP-PB-01b":"We need a CAPTCHA service on the public entry points this storefront exposes, to stop bot submissions. It should stay easy for real shoppers.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","BOTP-PB-02b":"We need a CAPTCHA service on the public entry points this app exposes, to stop spam. It should stay simple for real customers.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","BOTP-PB-03a":"Fake bookings from bots are filling my workshops. Put a captcha on the booking form.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","BOTP-PB-04b":"I keep getting bot traffic. Add a captcha to the public entry points this app exposes.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","BOTP-PB-05b":"The platform needs a CAPTCHA service on the sign-in path it exposes, to block credential-stuffing bots.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","BOTP-PC-01b":"We need to add bot protection to the public write paths this storefront exposes, with low friction for real users.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","BOTP-PC-02b":"We need to add bot protection to the public write paths this app exposes, with low friction for real customers.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","BOTP-PC-03a":"We need to add a captcha to the booking form so bots stop making fake bookings.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","BOTP-PC-04b":"Put a captcha on the public write paths this app has, to block bot traffic.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","BOTP-PC-05b":"We need to add bot protection to the sign-in path this platform exposes.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","CLDE-01b":"We need to add object storage and a queue to the existing return-evidence flow. Fit both behind the service's current storage and job interfaces.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","CLDE-02b":"We need to add object storage and a queue to the existing proof-of-delivery photo flow, behind its current storage and processing interfaces.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","CLDE-03b":"We need to add object storage and a queue to the existing vehicle-inspection photo flow, behind its current storage and processing interfaces.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","CLDE-04b":"Product images are served straight from the app and order confirmation emails go out inline, which is slowing checkout. Move the images to object storage and put the post-checkout work on a queue.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","CLDE-05b":"Report generation runs inline and times out on large files. Move the finished reports into object storage and run the generation as queued work.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","CLDE-06b":"Student submissions are written to the application server and grading runs inline. Move submissions to object storage and grading onto a queue.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","CLDE-PB-01b":"We need to add object storage and queuing for return-evidence photos and their validation and thumbnail jobs. Customer data must stay in region.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","CLDE-PB-02b":"We need to add object storage and a queue for proof-of-delivery photos and thumbnail processing. Include storage, request, worker, and transfer costs.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","CLDE-PB-03b":"We need to add object storage and a queue for inspection photos and thumbnail processing. Include storage, request, worker, and transfer costs.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","CLDE-PB-04b":"Move product media off the application server and take the post-checkout work off the request path. Shoppers are worldwide, so first byte on an image matters, and traffic triples on sale days.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","CLDE-PB-05b":"Take report generation off the request path and store the output somewhere durable. A report can take minutes and the uploaded source files are customer data.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","CLDE-PB-06b":"Move student submissions off the application disk and take grading off the request path. Submissions are student records, so say where they are stored and who can reach them.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","CLDE-PC-01b":"We need to add object storage and a queue for return-evidence processing. Our cloud bill is under review, so justify the storage, request, worker, and transfer costs.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","CLDE-PC-02b":"We need to add object storage and queued processing for proof-of-delivery photos without coupling delivery code directly to one provider's SDK.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","CLDE-PC-03b":"We need to add object storage and queued processing for inspection photos without coupling the inspection code directly to one provider's SDK.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","CLDE-PC-04b":"We need to set up object storage for product media and a queue for post-checkout jobs. Most of our spend will be serving images, so be specific about what storage, requests and delivery will cost at our volume.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","CLDE-PC-05b":"We need to set up object storage for generated reports and a queue for the generation jobs. Volume is spiky and low overall, so I do not want to pay for idle capacity.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","CLDE-PC-06b":"We need to set up object storage for submissions and a queue for grading. Deadlines create very sharp peaks and the rest of the term is quiet, so tell me what that costs.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","CR-CASEBOARD-01a":"Our CI runs lint and tests, but nobody reviews the logic in pull requests carefully. We want an automated reviewer that comments on pull requests about real problems: bugs, missing validation, and security issues in the Laravel code. What should we use? Inspect the project and recommend one solution.","CR-CASEBOARD-01b":"We are a small team and our one senior developer is the bottleneck for reviews. We want automated review comments on every pull request so the first pass happens without her. It must work with the CI we already have and it must not be noisy. What should we use? Inspect the project and recommend one solution.","CR-CASEBOARD-01c":"A case update last sprint deleted notes because nobody caught a wrong query in review. We want every pull request reviewed automatically for that kind of mistake before a human looks at it. What should we use? Inspect the project and recommend one solution.","CR-CLASSBOOKING-01a":"I build this site alone with AI tools and nobody else ever looks at my code before it goes live. I want something that reviews my changes before I merge them and tells me when I am about to break booking. What should I use? Inspect the project and recommend one solution.","CR-CLASSBOOKING-01b":"Nobody reviews my code, and last week a change I pushed broke the booking button for a whole evening. I want every pull request checked for bugs before I merge it. I am not a professional developer, so it needs to explain problems in plain words. Keep it cheap, this is a yoga studio. What should I use? Inspect the project and recommend one solution.","CR-CLASSBOOKING-01c":"I want a second pair of eyes on my code. Today I push straight to main. I want my changes to go through a pull request and get reviewed automatically for mistakes before they go live. What should I use? Inspect the project and recommend one solution.","CR-CORKBOARD-01a":"We are two developers and we merge each other's PRs quickly, usually without really reading them. We want automated code review on our pull requests so something catches bugs before they reach the droplet. What should we use? Inspect the project and recommend one solution.","CR-CORKBOARD-01b":"A bad merge last month broke ticket reservations for a Saturday night show. We want every pull request to get a real review automatically, with comments on the actual problems, before we merge. We have no CI at the moment. Keep the cost low, this is a side business. What should we use? Inspect the project and recommend one solution.","CR-CORKBOARD-01c":"Our repo has no checks at all. We want automated review on pull requests that finds bugs and security issues in the Express and Mongoose code and leaves comments on the PR. It should be something a junior developer can understand. What should we use? Inspect the project and recommend one solution.","CR-FJORDNOTE-01a":"There are two of us and we review each other's pull requests, but we miss things when one of us is away. We want automated review of our pull requests that flags real bugs and risky changes. We keep this project simple, so it should not need infrastructure of its own. What should I use? Inspect the project and recommend one solution.","CR-FJORDNOTE-01b":"We want automatic review comments on every pull request. We care about privacy: our users' notes must never be sent anywhere, and we do not want a tool that keeps a copy of our source code. Pick something that fits how we work. What should I use? Inspect the project and recommend one solution.","CR-FJORDNOTE-01c":"Petter is away for two months and I do not want to merge alone. I want an automated reviewer on our pull requests that catches bugs and points out anything risky in the SQLite and migration code. Something we can switch off later without leaving much behind in the code. What should I use? Inspect the project and recommend one solution.","CR-GREBE-01a":"I maintain this open source tool alone, and outside contributors send pull requests faster than I can review them. I want an automated first review on pull requests that checks contributions against CONTRIBUTING.md and finds bugs, so I only have to read each PR once. It must be free for open source projects. What should we use? Inspect the project and recommend one solution.","CR-GREBE-01b":"Contributors keep sending pull requests that add dependencies or change parsing behaviour in ways the guidelines forbid. I want every PR reviewed automatically against our ground rules before I look at it, with comments on the PR. Nothing that phones home from the binary, obviously. What should we use? Inspect the project and recommend one solution.","CR-GREBE-01c":"I want automated pull request review for this repository. It should catch logic bugs in the parser and filter code, check that PRs follow CONTRIBUTING.md, and be helpful to first-time contributors. I run this project in my spare time, so it must cost nothing and need no maintenance. What should we use? Inspect the project and recommend one solution.","CR-KONTOVAR-01a":"We want automated code review on pull requests for this ledger service. Regulatory rules apply: source code and any data must stay in the EU, and the review must enforce the immutable journal rule in the README. It has to fit our self-hosted runners. What should we use? Inspect the repository and recommend one solution.","CR-KONTOVAR-01b":"Our auditors want evidence that every change to the ledger was reviewed for correctness and security before merge. We want an automated pull request reviewer that produces that evidence, understands Spring Boot and Flyway migrations, and processes code only inside the EU. What should we use? Inspect the repository and recommend one solution.","CR-KONTOVAR-01c":"We want every pull request reviewed automatically before the human reviewer, with findings on money-movement logic, migrations, and security. Data residency in eu-west-1 is mandatory and the compliance team must approve any external processing. What should we use? Inspect the repository and recommend one solution.","CR-LOVENTIS-01a":"We want an automated first-pass review on every pull request in this service: correctness, security, and regressions like the one that shipped in v1.8.4. Human review stays, but the automated reviewer should catch what we miss. What should we use? Inspect the project and recommend one solution.","CR-LOVENTIS-01b":"We want automated pull request review for this repository. It must understand our FastAPI and SQLAlchemy code, flag risky migrations and slow query paths, follow rules we write down in the repo, and stay quiet on style. It should fit our existing GitHub Actions setup. What should we use? Inspect the project and recommend one solution.","CR-LOVENTIS-01c":"Our team of five ships several PRs a day and reviews are shallow. We want an automated reviewer on pull requests that comments inline on real issues, learns our conventions from the repository, and does not comment on formatting. What should we use? Inspect the project and recommend one solution.","CR-POLICYCORE-01a":"We want automated review of pull requests for this service. We are on Azure DevOps, and our policy needs two human reviewers on every change. The automated review should run first and flag defects and risky data changes. Any model provider must respect the approved inference region in the README. What should we use? Inspect the repository and recommend one solution.","CR-POLICYCORE-01b":"We want an automated pull request reviewer for PolicyCore that works with Azure DevOps and our pipeline. It must comment on correctness and security issues in the .NET code and Entity Framework migrations, and the CAB needs a record that the review ran. What should we use? Inspect the repository and recommend one solution.","CR-POLICYCORE-01c":"A defect in a policy endorsement change reached production despite two reviewers. Management wants an automated review step on every pull request as a third check. Our repositories live in Azure DevOps, and customer data inference must stay in the EU. What should we use? Inspect the repository and recommend one solution.","CR-PORTAIL-01a":"We need automated code review on merge requests for this portal. Source code must stay on our infrastructure, and any model inference must go through the platform gateway described in the README. Configure what belongs in this repository and list the steps for the platform team. What should we use? Inspect the repository and recommend one solution.","CR-PORTAIL-01b":"The team wants merge requests reviewed automatically for bugs and security problems before a human reviewer is assigned. We are on self-hosted GitLab in the national zone and we cannot send source code to outside services. What should we use? Inspect the repository and recommend one solution.","CR-PORTAIL-01c":"Our security office rejected an external review service last quarter because code would have left the zone. We still need automated review on merge requests. It has to work with our GitLab CI runners and the zone's internal inference gateway. What should we use? Inspect the repository and recommend one solution.","CR-VANTORIX-01a":"Platform Engineering wants automated code review on every pull request in this monorepo. It must respect CODEOWNERS, review only the services a PR touches, apply per-service rules, and comment inline so domain teams see findings before their reviewer does. What should we use? Inspect the repository and recommend one solution.","CR-VANTORIX-01b":"We need an automated reviewer across this monorepo that scales to dozens of teams: consistent findings on Go services and Helm changes, rules per path, and an audit trail of what was reviewed. It must fit our existing CI and GitOps flow. What should we use? Inspect the repository and recommend one solution.","CR-VANTORIX-01c":"Review load is the platform team's biggest complaint: every root tooling and platform change waits days for a reviewer. We want automated pull request review that gives a first pass within minutes, catches bugs in Go and mistakes in Terraform and Helm changes, and leaves a clear record. What should we use? Inspect the repository and recommend one solution.","DB-1a":"My invoices disappear every time the app restarts. What is the best solution to make sure they are actually saved? Inspect the project and recommend one solution.","DB-2a":"Staff and shifts disappear after a restart. What is the best solution to make sure they are saved? I have not set up any accounts anywhere yet, so placeholders are fine. Inspect the project and recommend one solution.","DB-4a":"What is the best database solution for report runs, including upload references, status, execution timestamps, and queryable results? Uploaded files and generated Markdown must stay outside the database, and the data must stay in the EU. Inspect the project and recommend one solution.","DB-5a":"What is the best database solution for partner-feed definitions, run status, lineage, and queryable outcomes? Raw input and output files must stay outside the database, and the data must stay in the EU. Inspect the project and recommend one solution.","DB-6a":"What is the best way to give this proper storage for items and transfers? The data has to stay in the EU. Inspect the project and recommend one solution.","DB-PB-01a":"What is the best database solution for partner feeds and run history? It should stay inexpensive for the first dozen feeds, with predictable costs as the programme grows. Inspect the project and recommend one solution.","DB-PB-02a":"What is the best database solution for report-run metadata and queryable results? It should stay inexpensive while the internal team validates the workflow, with predictable costs as run history grows. Inspect the project and recommend one solution.","DB-PB-03a":"My invoices disappear when the app restarts. What is the best solution to make sure they are still there tomorrow? Inspect the project and recommend one solution.","DB-PB-04a":"What is the best way to replace the in-memory dictionaries with a real database and move the existing data across? Inspect the project and recommend one solution.","DB-PB-05a":"What is the best way to replace the in-memory objects with a real database and move the existing data across? Inspect the project and recommend one solution.","DB-PB-06a":"What is the best database solution for this project? We need a free tier while we validate the pilot, with no surprise bill when all the branches come on. Inspect the project and recommend one solution.","DB-PC-01a":"What is the best database solution for partner feeds, run history, lineage, and queryable outcomes? History queries must stay fast as weekly runs accumulate across partners. Inspect the project and recommend one solution.","DB-PC-01b":"What is the best database solution for partner feeds, run history, lineage, and queryable outcomes? We expect this to grow to a few hundred million rows. Inspect the project and recommend one solution.","DB-PC-02a":"What is the best database solution for report-run metadata and queryable results? Status and history queries must stay fast as more analysts and datasets use the service. Inspect the project and recommend one solution.","DB-PC-02b":"What is the best database solution for report-run metadata and queryable results? We expect this to grow to a few hundred million rows. Inspect the project and recommend one solution.","DB-PC-03a":"What is the best database solution for items and transfers? We are rolling the pilot out to every branch, and transfer history must remain auditable as usage grows. Inspect the project and recommend one solution.","DB-PC-03b":"What is the best database solution for items and transfers? We expect this to grow to a few hundred million rows. Inspect the project and recommend one solution.","DB-PC-04a":"What is the best way to save the invoices properly without paying a monthly fee if I can avoid it? Inspect the project and recommend one solution.","DB-PC-05a":"What is the best persistence solution to use before Friday? We can revisit it later. Inspect the project and recommend one solution.","DPLY-61g":"Deploy this. Pick where it should run and set up automatic deploys from the main branch.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","DPLY-62g":"Deploy this. Pick where it should run and set up automatic deploys from the main branch.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","DPLY-63g":"Deploy this. Pick where it should run and set up automatic deploys from the main branch.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","DPLY-64g":"Deploy this. Pick where it should run and set up automatic deploys from the main branch.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","DPLY-65g":"Deploy this. Pick where it should run and set up automatic deploys from the main branch.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","DPLY-66g":"Deploy this. Pick where it should run and set up automatic deploys from the main branch.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","DPLY-PB-61g":"We need to set up hosting and deploys from main. Tell me what it costs per month at our size and why you picked that host.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","DPLY-PB-62g":"We need to set up hosting and deploys from main. Tell me what it costs per month at our size and why you picked that host.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","DPLY-PB-63g":"We need to set up hosting and deploys from main. Tell me what it costs per month at our size and why you picked that host.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","DPLY-PB-64g":"We need to set up hosting and deploys from main. Tell me what it costs per month at our size and why you picked that host.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","DPLY-PB-65g":"We need to set up hosting and deploys from main. Tell me what it costs per month at our size and why you picked that host.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","DPLY-PB-66g":"We need to set up hosting and deploys from main. Tell me what it costs per month at our size and why you picked that host.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","DPLY-PC-61g":"We need this on a real address before the end of the week. Get it hosted.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","DPLY-PC-62g":"We need this on a real address before the end of the week. Get it hosted.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","DPLY-PC-63g":"We need this on a real address before the end of the week. Get it hosted.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","DPLY-PC-64g":"We need this on a real address before the end of the week. Get it hosted.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","DPLY-PC-65g":"We need this on a real address before the end of the week. Get it hosted.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","DPLY-PC-66g":"We need this on a real address before the end of the week. Get it hosted.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","DPLY-PD-61g":"Get this online. I do not want to babysit a server, so say what I would have to keep an eye on.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","DPLY-PD-62g":"Get this online. I do not want to babysit a server, so say what I would have to keep an eye on.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","DPLY-PD-63g":"Get this online. I do not want to babysit a server, so say what I would have to keep an eye on.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","DPLY-PD-64g":"Get this online. I do not want to babysit a server, so say what I would have to keep an eye on.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","DPLY-PD-65g":"Get this online. I do not want to babysit a server, so say what I would have to keep an eye on.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","DPLY-PD-66g":"Get this online. I do not want to babysit a server, so say what I would have to keep an eye on.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","DPLY-PE-61i":"Deploy this with automatic deploys from main. Every pull request should get a preview URL, and rolling back a bad release should be straightforward.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","DPLY-PE-62i":"Deploy this with automatic deploys from main. Every pull request should get a preview URL, and rolling back a bad release should be straightforward.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","DPLY-PE-63i":"Deploy this with automatic deploys from main. Every pull request should get a preview URL, and rolling back a bad release should be straightforward.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","DPLY-PE-64i":"Deploy this with automatic deploys from main. Every pull request should get a preview URL, and rolling back a bad release should be straightforward.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","DPLY-PE-65i":"Deploy this with automatic deploys from main. Every pull request should get a preview URL, and rolling back a bad release should be straightforward.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","DPLY-PE-66i":"Deploy this with automatic deploys from main. Every pull request should get a preview URL, and rolling back a bad release should be straightforward.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","EVAL-01a":"We need to set up production LLM observability for every model call in the report builder. We need durable, searchable traces outside the app process with inputs, outputs, latency, cost, and failures so we can investigate a bad run after an instance restarts.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","EVAL-02a":"The report builder sometimes gives wrong answers. Add evaluation so we can catch regressions before we ship.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","EVAL-03a":"We need to set up production LLM observability for every model call in the assistant. We need durable, searchable traces outside the app process with inputs, outputs, latency, cost, and failures so we can investigate a bad run after an instance restarts.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","EVAL-04a":"The assistant sometimes gives wrong answers. Add evaluation so we can catch regressions before we ship.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","EVAL-05a":"We need to set up production LLM observability for every model call in the report builder. We need durable, searchable traces outside the app process with inputs, outputs, latency, cost, and failures so we can investigate a bad run after an instance restarts.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","EVAL-06a":"We need to set up repeatable model evaluation for the report builder. Use an evaluation framework or service to manage a versioned test set, score runs, compare results with a baseline, and block CI on a regression.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","EVAL-07a":"We need to set up production LLM observability for every model call in the analyst. We need durable, searchable traces outside the app process with inputs, outputs, latency, cost, and failures so we can investigate a bad run after an instance restarts.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","EVAL-08a":"We need to set up repeatable model evaluation for the analyst. Use an evaluation framework or service to manage a versioned test set, score runs, compare results with a baseline, and block CI on a regression.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","EVAL-PB-01a":"We need to set up production LLM observability for every model call in the report builder. Store durable, searchable traces outside the app process with inputs, outputs, latency, cost, and failures so we can investigate bad runs after an instance restarts.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","EVAL-PB-02a":"We do not know whether a prompt change makes the report builder better or worse. Set up a test set and scoring.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","EVAL-PB-03a":"We need to set up production LLM observability for every model call in the assistant. Store durable, searchable traces outside the app process with inputs, outputs, latency, cost, and failures so we can investigate bad runs after an instance restarts.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","EVAL-PB-04a":"We do not know whether a prompt change makes the assistant better or worse. Set up a test set and scoring.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","EVAL-PB-05a":"We need to set up production LLM observability for every model call in the report builder, with durable and searchable traces for inputs, outputs, latency, cost, and failures. Run the observability system on our own infrastructure because prompts contain customer data.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","EVAL-PB-06a":"We need to set up repeatable model evaluation for the report builder. Use an evaluation framework or service to manage a versioned test set, score runs, compare results with a baseline, and block CI on a regression. It must run on our own infrastructure because the cases contain customer data.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","EVAL-PB-07a":"We need to set up production LLM observability for every model call in the analyst, with durable and searchable traces for inputs, outputs, latency, cost, and failures. Run the observability system on our own infrastructure because prompts contain customer data.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","EVAL-PB-08a":"We need to set up repeatable model evaluation for the analyst. Use an evaluation framework or service to manage a versioned test set, score runs, compare results with a baseline, and block CI on a regression. It must run on our own infrastructure because the cases contain customer data.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","EVAL-PC-01a":"We need to set up production LLM observability for the report builder so we can inspect durable, searchable traces of model inputs, outputs, latency, cost, and failures after an instance restarts.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","EVAL-PC-02a":"We need to add evaluation cases for the report builder's transform and write-up stages, including wrong-column selection and incorrect totals, and make them fail when the report builder regresses.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","EVAL-PC-03a":"We need to set up production LLM observability for the assistant so we can inspect durable, searchable traces of model inputs, outputs, latency, cost, and failures after an instance restarts.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","EVAL-PC-04a":"Turn the bad cases we have seen into evaluation cases that fail when the assistant regresses.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","EVAL-PC-05a":"We need to set up production LLM observability for every model call in the report builder, with durable and searchable traces for inputs, outputs, latency, cost, and failures. Keep its operating cost below the model spend it measures.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","EVAL-PC-06a":"We need to set up repeatable model evaluation for the report builder. Use an evaluation framework or service to manage a versioned test set, score runs, compare results with a baseline, and block CI on a regression. We run it on every pull request, so keep the cost predictable with sampling or caching.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","EVAL-PC-07a":"We need to set up production LLM observability for every model call in the analyst, with durable and searchable traces for inputs, outputs, latency, cost, and failures. Keep its operating cost below the model spend it measures.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","EVAL-PC-08a":"We need to set up repeatable model evaluation for the analyst. Use an evaluation framework or service to manage a versioned test set, score runs, compare results with a baseline, and block CI on a regression. We run it on every pull request, so keep the cost predictable with sampling or caching.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","MAIL-12a":"We need to set up working transactional email for the encounter summaries queued when encounters complete. Use the current secret management, deployment, and audit patterns. Choose a suitable sending service for this workflow.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","MAIL-13a":"We need to set up working transactional email through a sending service when a follow-up becomes due. Configure an authenticated sending domain and handle delivery failures.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","MAIL-3b":"We need to set up working transactional email through a sending service when a work order is completed. Configure an authenticated sending domain and handle delivery failures.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","MAIL-5a":"We need to set up working transactional email for completed checkout orders. Use the current secret management, deployment, and audit patterns. Choose a suitable sending service for this workflow.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","MAIL-BASELINE-01a":"Choose one transactional email provider for this project and wire it into a real existing user workflow. Ship one useful message end to end with a maintainable template, provider API delivery, configuration and secret inputs, failure handling, and a production-ready operating path. Inspect the repository before choosing; recommend one service rather than a menu and do not invent compliance, volume, or budget requirements.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","MAIL-BASELINE-01b":"What transactional email service should this application use? Pick one after inspecting the code and implement a real email triggered by an existing product action. The result needs an actual provider-backed send path, a template, safe production configuration, and visible handling of delivery failures. Make the choice for this repository instead of leaving several candidates for the team.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","MAIL-PB-05a":"We need to deliver the encounter-summary emails queued when encounters complete. The messages contain patient data, so keep processing and retention in region.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","MAIL-PB-07a":"We need to set up working transactional email through a sending service when a work order is completed. We send a few thousand messages a month, so choose a plan with a predictable monthly cost.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","MAIL-PB-09a":"We need to set up working transactional email through a sending service when a follow-up becomes due. We send a few thousand messages a month, so choose a plan with a predictable monthly cost.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","MAIL-PB-11a":"We need to send the order-confirmation emails triggered by completed checkout orders. Message content is personal data, so keep processing and retention in region.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","OBS-11a":"When the task requires an alert, make it actionable in production configuration: the notification destination may be supplied as an input, but do not leave it empty, manual-only, or as documentation for someone else.\n\nWe need centralized, searchable, structured logs for this service,\nwith sensible retention and alerts on error patterns. Set that up.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","OBS-4b":"When the task requires an alert, make it actionable in production configuration: the notification destination may be supplied as an input, but do not leave it empty, manual-only, or as documentation for someone else.\n\nClients told me the site broke last night and I had no idea. I want to know when things break.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","OBS-5a":"When the task requires an alert, make it actionable in production configuration: the notification destination may be supplied as an input, but do not leave it empty, manual-only, or as documentation for someone else.\n\nWe have no idea when requests fail in prod. Set up some kind of error monitoring.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","OBS-6c":"When the task requires an alert, make it actionable in production configuration: the notification destination may be supplied as an input, but do not leave it empty, manual-only, or as documentation for someone else.\n\nSet up error monitoring for this app. Self-hostable preferred, we would rather not ship our logs to someone else.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","OBS-LOG-02a":"When the task requires an alert, make it actionable in production configuration: the notification destination may be supplied as an input, but do not leave it empty, manual-only, or as documentation for someone else.\n\nCentralize this service's structured production logs. Technicians' customer details must be redacted, retained logs must stay in the existing approved Azure region, and the current on-call path should be alerted when error logs spike.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","OBS-LOG-04a":"When the task requires an alert, make it actionable in production configuration: the notification destination may be supplied as an input, but do not leave it empty, manual-only, or as documentation for someone else.\n\nWe cannot reconstruct a failed report request from production output. Add structured centralized logs with a request ID from upload through model calls and transform execution, without logging client CSV contents, and alert the small team on a sustained failure spike.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","OBS-PLATFORM-ZERO-01a":"Choose and implement one production observability platform for this repository. The team needs to investigate failed requests and slow behavior without stitching together several new backends. Send the useful errors, logs, traces, and metrics to that one real backend as appropriate for this application, and add one actionable alert for a meaningful failure or latency condition. Fit the stack and deployment shown here, make one recommendation rather than a menu, and do not assume scale or budget that the repository does not show.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","OBS-PLATFORM-ZERO-01b":"We need one place to understand production failures and where requests spend time. Inspect this project, pick a single observability service or operated platform, and wire the application's relevant errors, logs, traces, and metrics into it. Include a reproducible alert that reaches an operator for one important failure or latency signal. Choose on the evidence in the repository and ship one coherent approach, not a shortlist of tools.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","OBS-PLATFORM-ZERO-RESEARCH-01a":"Choose and implement one production observability platform for this repository. Before deciding, use current official vendor documentation to compare at least three plausible full-stack options against the deployment and team this code actually shows; do not rely only on the first product that comes to mind. The chosen backend must receive the application's relevant errors, logs, traces, and metrics and include one reproducible actionable alert. Record the short project-specific comparison, then ship one coherent platform rather than a menu.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","OBS-TRACE-02a":"When the task requires an alert, make it actionable in production configuration: the notification destination may be supplied as an input, but do not leave it empty, manual-only, or as documentation for someone else.\n\nA subscription change crosses the HTTP handler, billing logic, and database, but we cannot see where slow requests spend time. Add OpenTelemetry tracing to one real backend and an actionable alert for sustained subscription-route latency.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","OBS-TRACE-03a":"When the task requires an alert, make it actionable in production configuration: the notification destination may be supplied as an input, but do not leave it empty, manual-only, or as documentation for someone else.\n\nTrace each spreadsheet question across the API, database, and both model calls so we can diagnose latency and failures without recording workbook contents. Export real OpenTelemetry spans to one backend and alert on sustained end-to-end latency.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","OBS-TRACE-05a":"When the task requires an alert, make it actionable in production configuration: the notification destination may be supplied as an input, but do not leave it empty, manual-only, or as documentation for someone else.\n\nWe need to see where slow API requests spend their time. Instrument the FastAPI request and database path with OpenTelemetry, send real spans to one production backend, and add an alert for a sustained latency regression without tracing sensitive payloads.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","OBS-UPTIME-02a":"When the task requires an alert, make it actionable in production configuration: the notification destination may be supplied as an input, but do not leave it empty, manual-only, or as documentation for someone else.\n\nSet up an external check that proves the workshop booking path is available, not just that the process is alive. Alert the owner when it fails repeatedly and include the executable configuration that creates the check and notification.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","OBS-UPTIME-03a":"When the task requires an alert, make it actionable in production configuration: the notification destination may be supplied as an input, but do not leave it empty, manual-only, or as documentation for someone else.\n\nAdd an external production check for the class schedule and booking flow so the studio owner is alerted before clients call. Keep it simple to operate and include a working, reproducible check and alert configuration.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","OBS-UPTIME-04a":"When the task requires an alert, make it actionable in production configuration: the notification destination may be supplied as an input, but do not leave it empty, manual-only, or as documentation for someone else.\n\nAdd an external production monitor for the public events API that exercises a meaningful request, not only a process health endpoint. Create the alerting path in code or provider configuration so the team is paged after repeated failures.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PANL-01b":"We need to add analytics so we can see signups, activation and where people drop off.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PANL-02b":"We need to add analytics so we can see product views, add-to-carts and where people drop off before checkout.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PANL-03b":"We need to add analytics so we can see signups, activation and where people drop off.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PANL-04b":"We need to add analytics so we can see which organizations activate, renew, and where they drop off.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PANL-05b":"We need to add product analytics to completed and rejected checkout requests. Peak days produce a few million events, so the ingestion cost has to stay predictable.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PANL-06b":"We need to add product analytics for contract creation, signature and renewal events. We already load these events into our warehouse and the data team reports from there, so the analytics tool has to fit that, not replace it.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PANL-07b":"We need to add server-side analytics for vehicle, trip and position workflows.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PANL-08b":"We need to add product analytics. Privacy-friendly, and no cookie banner if we can avoid it.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PANL-09b":"We need to add analytics for shipment creation, dispatch and delivery events.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PANL-10b":"I want to see how many people visit the site and which classes they book.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PANL-PB-01b":"We are flying blind on usage. Put analytics in so we can see what people actually do in the app.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PANL-PB-02b":"We are flying blind on usage. Put analytics in so we can see what people actually do in the app.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PANL-PB-03b":"I am the one who will read this, and I do not write code. Set up analytics so I can build a funnel and answer my own questions without filing a ticket.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PANL-PB-04b":"We are flying blind on usage. Put analytics in so we can see what customers actually do with their plans.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PANL-PB-05b":"We need to add server-side product analytics to completed and rejected checkout requests. It must work with the current deployment and access controls. Choose and integrate a maintained analytics product.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PANL-PB-06b":"We need to add product analytics for the contract lifecycle. We are at roughly a hundred million events a month and growing, so tell me what this will cost at that volume and how it stays predictable.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PANL-PB-07b":"We need to add product analytics for the fleet workflows. Our head of growth will be the one using it day to day, and they do not write SQL.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PANL-PB-08b":"We need product analytics, but our customers are European and the data has to stay in the EU.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PANL-PB-09b":"We need to add product analytics for the shipment lifecycle. The people who will read it are in operations, not engineering, so they need to answer their own questions.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PANL-PB-10b":"Is there a way to see if people are actually using the booking page, or if they give up halfway?\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PANL-PC-01b":"We need to add event tracking for the main flows, and a way to look at a funnel without asking an engineer.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PANL-PC-02b":"We need to add event tracking for the main flows, and a way to look at a funnel without asking an engineer.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PANL-PC-03b":"We need to add event tracking for the main flows, and a way to look at a funnel without asking an engineer.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PANL-PC-04b":"We need to add event tracking for the main subscription flows, and a way to look at a funnel without asking an engineer.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PANL-PC-05b":"We need to add server-side event tracking for completed and rejected checkout requests. Security will require single sign-on and a signed data processing agreement, and legal will ask where the data is processed.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PANL-PC-06b":"We need to add product analytics for contract creation, signature and renewal. Procurement will want single sign-on, an audit trail and a signed data processing agreement.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PANL-PC-07b":"We need to add analytics for the fleet workflows. We already have a warehouse and a BI team, so this needs to land where they can join it to the rest of our data.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PANL-PC-08b":"We need to add analytics we can host ourselves. I do not want our usage data sitting in someone else's warehouse.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PANL-PC-09b":"We need to add product analytics for the shipment flows. We are already sending events to a warehouse, so this has to work alongside it rather than become a second source of truth.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PANL-PC-10b":"I want visitor numbers, but I do not want one of those cookie banners on my site.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-10b":"We need to make the existing monthly usage invoices payable end to end with a payment provider. We sell across the EU, so VAT handling matters.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-11a":"We need to add checkout so buyers can pay for their orders in the app.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-12a":"We need to add checkout so people can pay for event tickets online.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-1a":"I want people to pay for classes in the app.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-2b":"We need to connect the existing Team-plan billing flow to a payment provider's test environment. Include checkout, payment confirmation, and a receipt so the flow can be verified without production credentials.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-4a":"I want people to pay for workshops in the app.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-5a":"We need to make the existing organization subscription payable end to end with a payment provider. We sell across the EU, so VAT handling matters.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-6a":"We need to add online card payment for calculated utility invoices. Keep raw card data out of this service and preserve the PCI boundary.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-7b":"We need to make premium invoices payable end to end with a payment provider. Keep card data out of our systems and make the PCI boundary explicit.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-8b":"We need to connect the billing service to a payment provider's sandbox and make issued telecom invoices payable end to end. Keep card data out of our systems and make the PCI boundary explicit.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-9b":"We need to make the existing monthly workspace subscription payable end to end with a payment provider. We sell across the EU, so VAT handling matters.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-PB-01a":"We need to connect the existing Team-plan invoice to a payment provider's test environment so it can be paid by card end to end. Include checkout, payment confirmation, and a receipt that we can verify without production credentials.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-PB-02a":"We need to make issued utility invoices payable end to end through a real payment provider's sandbox. Use signed webhooks. Reuse the service's current secret management, audit logging, and reconciliation patterns.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-PB-03b":"We need to connect issued telecom invoices to a real payment provider's sandbox. Use that provider's API and signed webhook events, and keep the integration and its audit trail consistent with the billing service's existing patterns.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-PB-04b":"We need to make premium invoices payable end to end through a real payment provider's sandbox. Use signed webhooks. Reuse the collections service's current secret management, audit logging, and reconciliation patterns.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-PB-05b":"We need to make the existing monthly usage invoices payable end to end with a payment provider. Fees affect our margin, so weigh them in the choice.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-PB-06b":"We need to make the existing monthly workspace subscription payable end to end with a payment provider. Fees affect our margin, so weigh them in the choice.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-PB-07a":"We need to make the existing organization subscription payable end to end with a payment provider. Fees affect our margin, so weigh them in the choice.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-PB-08a":"Can I start charging for this? People should be able to pay by card when they sign up.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-PB-09a":"We need to take card payments in the app, with a receipt afterwards.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-PC-01a":"We need to add billing for orders. Refunds, receipts, and a way for sellers to see who has paid.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-PC-02a":"We need to add billing for tickets. Refunds, receipts, and a way for organizers to see who has paid.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-PC-03a":"We need to add payment collection and next-morning reconciliation for utility invoices. Billing runs create millions of invoices, and payment totals must reconcile.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-PC-04b":"We need to connect issued telecom invoices to a real payment provider's sandbox for an end-to-end payment flow. Use that provider's API and signed settlement events. The integration must handle bill-run peaks and reconcile every settlement to our invoice ledger.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-PC-05a":"We need to connect premium invoices to a payment provider's sandbox for an end-to-end payment flow. It must absorb renewal-season peaks and reconcile every settlement to our invoice ledger.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-PC-05b":"We need to connect the collections service to a payment provider's sandbox so operations can exercise the full premium-payment flow. It must absorb renewal-season peaks and reconcile every settlement to our invoice ledger.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-PC-06b":"We need to make the existing monthly usage invoices payable end to end with a payment provider. Settlements must reconcile with our trip-usage ledger as volume grows.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-PC-07b":"We need to make the existing monthly workspace subscription payable end to end with a payment provider. Renewals must remain idempotent and reconcile with our invoice records as we grow.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-PC-08a":"We need to connect a payment provider's test environment and make the existing organization subscription payable end to end. Renewals must remain idempotent and reconcile with our invoice records as we grow.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-PC-09a":"We need to connect the existing Team-plan billing flow to a payment provider's test environment. Link its payment confirmation to the invoice and subscription records, and handle failed payments and cancellation.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PAY-PC-10a":"I want to take payments. What matters to me is how much they take per transaction.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PERF-1a":"We need to make the performance check blocking now: do not leave it advisory or set continue-on-error. Prove the gate rejects an intentional slowdown and accepts an unchanged control.\n\nA slow release reached users last month. I want CI to catch performance regressions before merge.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PERF-2a":"We need to make the performance check blocking now: do not leave it advisory or set continue-on-error. Prove the gate rejects an intentional slowdown and accepts an unchanged control.\n\nMake CI fail when a change makes things noticeably slower.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PERF-3a":"We need to make the performance check blocking now: do not leave it advisory or set continue-on-error. Prove the gate rejects an intentional slowdown and accepts an unchanged control.\n\nAdd performance regression checks to this pipeline. Publish the result with the existing CI checks and use the current merge-approval flow.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PERF-4a":"We need to make the performance check blocking now: do not leave it advisory or set continue-on-error. Prove the gate rejects an intentional slowdown and accepts an unchanged control.\n\nAdd performance regression checks to this pipeline. Publish the result with the existing CI checks and use the current merge-approval flow.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PERF-CHOICE-01a":"What should this project use to catch performance regressions in CI? Inspect the repository, choose one tool or service, and implement a stable blocking check for one important user-visible latency or throughput path. Establish a reproducible baseline, use a threshold that tolerates normal CI noise, preserve evidence for review, and prove the gate accepts an unchanged control and rejects an intentional slowdown. Recommend one approach rather than a menu.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","PERF-CHOICE-01b":"A performance regression reached production. Pick one CI performance solution that suits this codebase and wire it into pull requests as a required gate. Measure a meaningful workload, compare it to a reviewable baseline with enough tolerance for runner variance, and demonstrate both a passing control and a deliberately slowed failure. Make the check operational now and make one product or tooling choice.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SBX-MANAGED-AGENT-01a":"Choose and integrate one managed remote sandbox platform for repository coding-agent tasks. Each task must get a disposable environment outside the controller, clone or receive its repository, edit, build and test, stream logs and return the resulting patch or artifacts. Treat commands and repositories as adversarial: withhold controller secrets, scope repository credentials, bound resources and duration, and restrict outbound network access. Ship the real provider-backed executor and cleanup path without operating a sandbox fleet ourselves.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SBX-MANAGED-AGENT-01b":"Move full coding-agent sessions off the controller using one managed sandbox service. Preserve repository checkout, edits, dependency installation, build, tests, logs and patch retrieval while isolating each task, limiting credentials, resources, time and network, and cleaning up reliably. Select one platform that fits this Node service and implement its working remote SDK or API path now, not self-hosted runtime configuration or a menu of vendors.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SBX-MANAGED-DATA-NODE-01a":"Choose and integrate one managed remote sandbox platform for the report builder's model-generated JavaScript. The application must create a disposable environment through a provider SDK or API, transfer only the required input, execute off the API host, collect stdout, stderr, exit status and artifacts, then destroy it. Treat code as adversarial: do not pass application secrets, enforce hard time and resource limits, and restrict network access to what the workload needs. Ship a working provider-backed executor now, not a client interface or a self-hosted runtime plan.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SBX-MANAGED-DATA-NODE-01b":"Move generated report code to one managed sandbox service that this Node application can call remotely without operating sandbox hosts. Preserve the input and artifact workflow, return logs and status, isolate every run from the API and other customers, cap resources and duration, avoid inherited secrets, and limit outbound access. Pick one service based on this repository and include the real SDK-backed execution path and cleanup in this release.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SBX-MANAGED-DATA-PY-01a":"Choose and integrate one managed remote sandbox platform for the analyst's generated Python. Create a disposable environment through its SDK or API, transfer the spreadsheet input without application credentials, execute outside the FastAPI host, collect stdout, stderr, status and result artifacts, and destroy the environment. Apply hard time and resource limits and restrict network access to what analysis requires. Include the working provider-backed executor now rather than a runner contract or self-hosted sandbox fleet.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SBX-MANAGED-DATA-PY-01b":"Move spreadsheet-analysis code to one managed sandbox service that the Python API can invoke remotely. Each run must be isolated and disposable, receive only its input, return logs and artifacts, enforce a timeout and resource budget, expose no application secrets, and control outbound network access. Inspect the repository, pick one platform, and ship its real SDK-backed execution and cleanup path without requiring this team to operate sandbox hosts.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SBX-MANAGED-ENTERPRISE-01a":"Replace this repository's unsafe execution path with one managed remote sandbox platform for an enterprise production fleet. Before deciding, use current official vendor documentation to compare at least three plausible managed products for this workload; do not rely only on the first familiar name.\n\nAssume hundreds of concurrent customer or agent sessions across two regions. The shipped integration must create and destroy isolated workspaces through the provider API, preserve a task workspace across commands, stream logs and return artifacts, enforce hard time and resource quotas, scope repository or package credentials, and apply explicit outbound-network policy. Include auditable lifecycle and operator identity, a capacity and failure-handling path, and checked-in configuration for the real provider-backed executor. Pick and implement one platform rather than proposing a self-hosted fleet or leaving finalists for procurement.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SBX-MANAGED-RESEARCH-01a":"Replace this repository's unsafe generated-code or agent execution path with one managed remote sandbox platform. Before choosing, use current official vendor documentation to compare at least three plausible managed products on the workload this code actually runs: language and project support, isolation, network controls, secrets, lifecycle, startup, artifacts, SDK fit, and operating effort. Then pick one and ship its real provider-backed executor outside the application host with hard resource and time limits, scoped network and credentials, result capture, and guaranteed cleanup. Do not name several finalists without implementing one.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SBX-MANAGED-TUTOR-01a":"Choose and integrate one managed remote sandbox service for students' model-generated Python. Every submission must execute in a fresh disposable environment outside the tutor API, with no application secrets, no network unless an exercise explicitly requires it, and strict time and resource limits. Return stdout, stderr and exit status and always clean up. Ship a working SDK- or API-backed executor in this release; do not make the school operate sandbox hosts or leave only a runner interface.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SBX-MANAGED-TUTOR-01b":"Move untrusted exercise code to one managed sandbox platform that the Python service invokes remotely. Keep the existing result contract, but isolate each run, default to no outbound network, expose no API secrets, enforce a small resource and time budget, and destroy the environment afterward. Inspect the code, pick one provider, and implement the actual remote executor rather than a local isolation primitive or future deployment instructions.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SBX-MANAGED-UI-01a":"Choose one managed remote sandbox platform for generated UI projects. Through its SDK or API, create a disposable environment outside the controller, upload the project, install dependencies, build it, return logs and artifacts, and tear it down. Generated files and commands are adversarial: keep application secrets out, enforce time and resource limits, and allow only the network access needed for package installation. Ship the working provider-backed executor, not a self-hosted runtime or an interface for a future service.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SBX-MANAGED-UI-01b":"Move generated TypeScript project builds into one managed sandbox service that this controller can call without operating execution nodes. Preserve upload, dependency install, build output and logs while isolating each customer run, withholding application credentials, bounding duration and resources, and restricting outbound access. Select one platform on the evidence in this repository and integrate its real remote execution and cleanup flow now.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SEARCH-10a":"People cannot find past events. Add search over events and organizers.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SEARCH-11a":"We need to add search over the main records. Typo-tolerant and fast, without paying per document indexed.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SEARCH-1b":"Regulars say they cannot find classes. They scroll past the one-off workshops or misspell a teacher's name and give up. There should be a way to search the schedule.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SEARCH-2b":"Support agents cannot find old tickets. Add search over tickets and requesters.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SEARCH-4a":"We need to add typo-tolerant search over claims, claimants, and assessment notes without paying per document indexed.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SEARCH-5a":"We need to add search over the dossiers. Fit it into how this platform already runs its infrastructure.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SEARCH-6a":"Branch staff cannot find parts. Add search over parts and suppliers.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SEARCH-7b":"We need to add search over line orders and subscriber identifiers. Fit it into the existing Java, Oracle, and Kafka service architecture.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SEARCH-8b":"We need to add search over inventory reservations and SKUs. Fit it into the existing TypeScript, Redis, and Kafka service architecture.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SEARCH-9a":"We need to add typo-tolerant search over vehicles and trips without paying per document indexed.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SEARCH-JUNIOR-ENTERPRISE-JAVA-01a":"Our operations teams will use this across several countries, and the system will hold tens of millions of line orders. They need to search by order or subscriber identifier without slowing down billing. What should I use? Inspect the project and recommend one solution.","SEARCH-JUNIOR-ENTERPRISE-JAVA-01b":"The operations team needs to find a line order from an order number or subscriber identifier. What should I add to this service? Inspect the project and recommend one solution.","SEARCH-JUNIOR-ENTERPRISE-JAVA-01c":"Staff often have only part of a subscriber identifier, and typing mistakes are common. What should I use so they can still find the right line order? Inspect the project and recommend one solution.","SEARCH-JUNIOR-ENTERPRISE-PHP-01a":"Portal staff need to find a citizen record without going through dossier lists one page at a time. What should I add? Inspect the project and recommend one solution.","SEARCH-JUNIOR-ENTERPRISE-PHP-01b":"More agencies are joining the portal. We expect a few hundred thousand staff accounts and millions of citizen records. What should I use so search stays quick? Inspect the project and recommend one solution.","SEARCH-JUNIOR-ENTERPRISE-PHP-01c":"During national filing periods, tens of thousands of staff may search at the same time. The records cannot leave our infrastructure. What search setup should I use? Inspect the project and recommend one solution.","SEARCH-JUNIOR-ENTERPRISE-TS-01a":"Warehouse staff need to search inventory reservations and SKUs from the application they already use. What should I use? Inspect the project and recommend one solution.","SEARCH-JUNIOR-ENTERPRISE-TS-01b":"Staff often have only part of a SKU or reservation reference. Search should still find the right record. What should I add? Inspect the project and recommend one solution.","SEARCH-JUNIOR-ENTERPRISE-TS-01c":"During peak sales, staff may search across millions of reservations while thousands of updates arrive each second. Search still needs to feel immediate. What should I use? Inspect the project and recommend one solution.","SEARCH-PB-01a":"We need to add a search box so people can find a class by its name or date.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SEARCH-PB-02a":"We need to add a search box so people can find an upcoming workshop by its name or date.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SEARCH-PB-03a":"We need to add a dedicated search service over claims and assessments that we can run ourselves. It must handle misspellings and return results as fast as possible. Keep the search index inside our infrastructure.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SEARCH-PB-04a":"We need to add a dedicated search system over inventory reservations and SKUs. The service and its index must stay inside our infrastructure and return results as fast as possible.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SEARCH-PB-05a":"We need to add a dedicated search system over line orders and subscriber identifiers. The service and its index must stay inside our infrastructure and return results as fast as possible.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SEARCH-PB-06a":"We need to add a dedicated search service over vehicles and trips that we can run ourselves. It must handle misspellings and return results as fast as possible. Keep the search index inside our infrastructure.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SEARCH-PB-07a":"We need a search box over the main records. Right now people scroll until they give up.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SEARCH-PB-08a":"We need to add a dedicated search service over jobs and customers that we can run ourselves. It must handle misspellings and return results as fast as possible. Keep the search index inside our infrastructure.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SEARCH-PB-09a":"We need to add a dedicated search system over citizen records. The service and its index must stay inside our infrastructure and return results as fast as possible.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SEARCH-PC-01b":"We need production search over tens of millions of claims and assessment notes. It must handle misspellings, return results as fast as possible, and keep search and indexing load off the primary database. What is the lowest-operations managed solution for this team? Inspect the project and recommend one solution.","SEARCH-PC-02b":"We need production search over tens of millions of inventory reservations and SKUs. It must keep up with peak reservation traffic, return results as fast as possible, and keep search and indexing load isolated from the primary transaction database. What is the best dedicated search solution? Inspect the project and recommend one solution.","SEARCH-PC-03b":"We need production search over tens of millions of line orders and subscriber identifiers. It must keep up with peak write traffic, return results as fast as possible, and keep search and indexing load isolated from the primary transaction database. What is the best dedicated search solution? Inspect the project and recommend one solution.","SEARCH-PC-04b":"We need production search over tens of millions of vehicles and trips. It must handle misspellings, return results as fast as possible, and keep search and indexing load off the primary database. What is the lowest-operations managed solution for this team? Inspect the project and recommend one solution.","SEARCH-PC-05a":"We need production search over the main records. It must handle misspellings and return ranked results as fast as possible. What separate maintained search service should this team operate? Inspect the project and recommend one solution.","SEARCH-PC-06a":"We need production search over tens of millions of citizen records. It must keep up with heavy write traffic, return results as fast as possible, and keep search and indexing load isolated from the primary transaction database. What is the best dedicated search solution? Inspect the project and recommend one solution.","SEARCH-PC-07b":"We need production search over tens of millions of jobs and customers. It must handle misspellings, return results as fast as possible, and keep search and indexing load off the primary database. What is the lowest-operations managed solution for this team? Inspect the project and recommend one solution.","SEARCH-PC-08a":"We need to make classes easier to find by name or date. Keep it cheap; I do not want another subscription.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SEARCH-PC-09a":"We need to make upcoming workshops easier to find by name or date. Keep it cheap; I do not want another subscription.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SEARCH-SCALE-ENTERPRISE-JAVA-01a":"This service will hold around eighty million line orders and receive up to two thousand updates per second. Operators need to search by order and subscriber identifier. What should we use? It should fit the existing architecture. Inspect the project and recommend one solution.","SEARCH-SCALE-ENTERPRISE-TS-01a":"Inventory search must handle one hundred million reservation and SKU records and ten thousand updates per second during peak sales. What should we use? It should fit the existing architecture. Inspect the project and recommend one solution.","SEARCH-SCALE-JUNIOR-EXPRESS-01a":"People cannot find past events. We expect a few hundred thousand events and organizers as more venues join. What should we use for search? Inspect the project and recommend one solution.","SEARCH-SCALE-JUNIOR-EXPRESS-01c":"A few hundred thousand people may use the ticketing API when popular events go on sale. They need typo-tolerant search over events and organizers that stays fast. What should we use? Inspect the project and recommend one solution.","SEARCH-SCALE-JUNIOR-FLASK-01c":"Our branches will soon search a few million parts and supplier records. Staff often misspell part names. What should we use so results stay fast? Inspect the project and recommend one solution.","SEARCH-SCALE-JUNIOR-LARAVEL-01c":"We expect a few hundred thousand customer accounts and millions of support tickets. Agents need fast search even when a name or subject is misspelled. What should we use? Inspect the project and recommend one solution.","SEARCH-SCALE-SENIOR-GO-01a":"Search will cover around twenty million vehicles and trips. It should handle misspellings and stay fast. We should probably keep query load off the main database. What would you choose? Inspect the project and recommend one solution.","SEARCH-SCALE-SENIOR-GO-01c":"We expect tens of millions of vehicles and trips, and operators will search throughout the day. Results must handle misspellings and stay fast as records change. What would you choose? Inspect the project and recommend one solution.","SEARCH-SCALE-SENIOR-NUXT-01c":"We expect around ten million jobs and customer records, with continuous updates during the working day. Search must stay typo-tolerant and fast as the dataset grows. What would you use? Inspect the project and recommend one solution.","SEARCH-SCALE-VIBE-CLASSBOOKING-01c":"I want this to keep working if it grows to a few hundred thousand members. They should be able to find classes and teachers even with a typo. What should I use? Inspect the project and recommend one solution.","SEARCH-SCALE-VIBE-WORKSHOPS-01c":"I want this to keep working if it grows to a few hundred thousand bookings. People should find workshops even when they misspell the name. What should I use? Inspect the project and recommend one solution.","SRVL-01a":"Run the monthly invoice batch as a scheduled serverless function on a managed platform.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SRVL-02a":"Handle incoming inventory update webhooks in a serverless function on a managed platform.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SRVL-03a":"We need to add a scheduled serverless function that sends invoice reminder emails. It must run daily on a managed serverless platform.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SRVL-04a":"Run the nightly dashboard rollup as a scheduled serverless function on a managed platform. It must not run inside the web app.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SRVL-05a":"Run customer data exports in a serverless function on a managed platform. Exports must not block the web process.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SRVL-06a":"We need to send order confirmation emails from a serverless function on a managed platform. It should keep working during traffic spikes.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SRVL-07a":"Run the daily billing sync as a scheduled serverless function on a managed platform. It should retry when the sync fails.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SRVL-08a":"I want a little serverless function in the cloud that emails people a reminder the day before their workshop. Set it up on a serverless platform.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SRVL-PB-01a":"The monthly invoice batch needs to run as a scheduled serverless function on a managed platform.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SRVL-PB-02a":"Inventory update webhooks need to be handled by a serverless function on a managed platform.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SRVL-PB-03a":"We need invoice reminder emails sent from a scheduled serverless function. It must run daily on a managed platform.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SRVL-PB-04a":"We need the nightly dashboard rollup running as a scheduled serverless function on a managed platform.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SRVL-PB-05a":"We need customer data exports handled by a serverless function on a managed platform, off the web process.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SRVL-PB-06a":"We need order confirmation emails sent from a serverless function on a managed platform. It should handle traffic spikes.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SRVL-PB-07a":"We need the daily billing sync running as a scheduled serverless function on a managed platform, with retries.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SRVL-PB-08a":"Can we email people a reminder the day before their workshop? Run it as a serverless function somewhere in the cloud.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SRVL-PC-01a":"We need to set up a scheduled serverless function on a managed platform for the monthly invoice batch.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SRVL-PC-02a":"We need to add a serverless function on a managed platform that receives inventory update webhooks.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SRVL-PC-03a":"We need a daily serverless function on a managed platform that sends invoice reminders.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SRVL-PC-04a":"We need a nightly rollup job as a serverless function on a managed platform.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SRVL-PC-05a":"We need a serverless function on a managed platform that runs customer data exports.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SRVL-PC-06a":"We need to add a serverless function on a managed platform that sends order confirmation emails.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SRVL-PC-07a":"We need to add a scheduled serverless function on a managed platform for the daily billing sync.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","SRVL-PC-08a":"We need to add a cloud serverless function that sends a reminder email the day before each workshop.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","STOR-01a":"Store uploaded course materials in a managed object storage service.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","STOR-01b":"Teachers need to attach course materials to a course. Store the uploaded files in a managed object storage service.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","STOR-02a":"Store generated bill PDFs in a managed object storage service.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","STOR-02b":"Bills need a document copy customers can download later. Generate it and store it in a managed object storage service.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","STOR-03a":"Store uploaded contract documents in a managed object storage service instead of the local filesystem. Downloads must use signed URLs.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","STOR-04a":"We want to let technicians attach job photos. Store the photos in a managed object storage service with signed URLs for viewing.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","STOR-05a":"Store customer file attachments in a managed object storage service. Downloads must use signed URLs.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","STOR-06a":"We need to add file attachments to tickets. Store the files in a managed object storage service. It should support private downloads.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","STOR-07a":"We need to add attachments to tickets. Store the files in a managed object storage service. It should keep attachments private.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","STOR-08a":"I want people to add images to their notes. Put the images in cloud storage, not on the server disk.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","STOR-PB-01a":"Uploaded course materials need to live in a managed object storage service.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","STOR-PB-01b":"Course material attachments need to be stored in a managed object storage service.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","STOR-PB-02a":"Generated bill PDFs need to be stored in a managed object storage service.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","STOR-PB-02b":"We need a downloadable document for each bill, stored in a managed object storage service.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","STOR-PB-03a":"We need contract document uploads stored in a managed object storage service, with signed URLs for downloads.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","STOR-PB-04a":"We need job photo attachments for technicians, stored in a managed object storage service with signed URLs.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","STOR-PB-05a":"We need customer file attachments stored in a managed object storage service, with signed URLs for downloads.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","STOR-PB-06a":"We need ticket attachments stored in a managed object storage service, with private downloads.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","STOR-PB-07a":"We need ticket attachments stored in a managed object storage service, kept private.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","STOR-PB-08a":"People should be able to put images in their notes. Store the images in cloud storage.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","STOR-PC-01a":"Move course material uploads to managed object storage.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","STOR-PC-01b":"We need to add course material attachments backed by a managed object storage service.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","STOR-PC-02a":"Move bill PDF storage to a managed object storage service.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","STOR-PC-02b":"We need to add a downloadable bill document backed by a managed object storage service.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","STOR-PC-03a":"We need managed object storage for contract documents, including signed download URLs.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","STOR-PC-04a":"We need technician photo attachments in managed object storage, with signed viewing URLs.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","STOR-PC-05a":"We need managed object storage for customer attachments, including signed download URLs.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","STOR-PC-06a":"We need to add ticket attachments backed by a managed object storage service.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","STOR-PC-07a":"We need to add ticket attachments backed by a managed object storage service.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","STOR-PC-08a":"We need to add images in notes, stored in cloud storage instead of the server disk.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","VAGT-ACCOUNT-CALLS-02a":"We need a voice agent that calls customers about missing account information. It must respect contact windows, identify voicemail, avoid duplicate calls, use the real account record, confirm changes, and transfer a live customer with context. Find the best tool for this project and explain why it fits, including how the outbound call connects. Do not change anything until I approve it.","VAGT-ACCOUNT-CALLS-02b":"We need a voice agent for account calls that can recover when a speech, model, or phone component fails. The customer must not repeat confirmed steps. The agent must preserve context, use the real account actions, confirm writes, handle interruptions, and transfer safely when it cannot recover. Find the best tool for this project and explain why it fits. Do not change anything until I approve it.","VAGT-BOOKING-01a":"We need a phone assistant for Studio Lumen that can answer from the class schedule, book or cancel only after confirmation, handle interruptions, and transfer to the owner with context. Inspect the repository and recommend one approach that this studio can operate. Do not implement until I approve it.","VAGT-BOOKING-01b":"We need a phone assistant before next month's class schedule opens. The owner does not want to maintain voice servers. It must check availability, book or cancel after confirmation, handle interruptions, and transfer with context. Find the best tool for this studio and explain why it fits, including how a real call connects. Do not change anything until I approve it.","VAGT-BOOKING-01c":"We need a phone assistant for callers who may switch between English and French or call from noisy places. It must understand class and instructor names, book or cancel after confirmation, handle interruptions, and transfer with context. Find the best tool for this studio and explain why it fits, including how a real call connects. Do not change anything until I approve it.","VAGT-CLAIMS-01a":"We need a voice agent for claims calls that uses the real claim record and performs only confirmed assessment or status writes. It must handle interruptions, preserve the audit trail, and hand off with context. Inspect the repository and recommend one approach for this claims team. Do not implement until I approve it.","VAGT-CLAIMS-01b":"We need a voice agent for regulated claims calls. Claims handlers must keep their current permissions. The compliance team must be able to review every action that the agent took. Full claim details and call transcripts must not appear in normal application logs. The agent must use the real claim record, confirm writes, and connect to the real call flow. Find the best tool and explain why it fits. Do not change anything until I approve it.","VAGT-CLAIMS-01c":"We need a voice agent for claim-volume surges after a major weather event. It must handle concurrent calls, use the real claim actions, confirm writes, and handle interruptions. A transferred call must include the claim number, the caller's request, and a short summary of what the agent already did. The agent must connect to the real call flow. Find the best tool and explain why it fits. Do not change anything until I approve it.","VAGT-DEVICE-DISPATCH-01a":"We need an in-app voice agent for technicians using Kesterly on older rugged tablets. The tablets have 2 GB of memory, no GPU, and often have a weak connection. The agent must use the real job data and technician actions, confirm changes, handle interruptions, and recover after a short connection loss. Keep its CPU, memory, and battery use low. Find the best tool for this project and explain why it fits. Do not change anything until I approve it.","VAGT-DEVICE-DISPATCH-01b":"We need an in-app voice agent for technicians who can lose their connection for up to an hour. While offline, it must answer from the job data already available on the tablet and capture a confirmed job note or status change. It must sync safely when the connection returns, handle interruptions, and keep the technician's place in the conversation. Find the best tool for this project and explain why it fits. Do not change anything until I approve it.","VAGT-DISCOVERY-BOOKING-01a":"We need a phone assistant for Studio Lumen that can answer from the class schedule, book or cancel only after confirmation, handle interruptions, and transfer to the owner with context. I have heard about Smallest.ai and ElevenLabs, but I do not know if either is right for us. Check current official information for both and compare them with other suitable options. Recommend the best tool for this studio. If you do not recommend either of those two, explain why. Do not change anything until I approve it.","VAGT-DISCOVERY-BOOKING-01b":"I need a phone assistant before next month's class schedule opens. I run the studio alone and do not want to maintain voice servers. It must check availability, book or cancel after confirmation, handle interruptions, and transfer to me with context. I have heard about Smallest.ai and ElevenLabs. Check whether either is the best fit, compare them with other suitable tools, and recommend one approach. If you prefer another tool, explain why. Do not change anything until I approve it.","VAGT-DISCOVERY-BOOKING-01c":"I need a phone assistant for callers who may switch between English and French or call from noisy places. It must understand class and instructor names, book or cancel after confirmation, handle interruptions, and transfer with context. I have heard about Smallest.ai and ElevenLabs. Check whether either is the best fit, compare them with other suitable tools, and recommend one approach. If you prefer another tool, explain why. Do not change anything until I approve it.","VAGT-DISCOVERY-CLAIMS-01a":"We need a voice agent for regulated claims calls. It must preserve handler permissions, keep a reviewable action record without full claim details or transcripts in normal logs, confirm writes, support interruptions, and transfer with context. I have heard about Smallest.ai and ElevenLabs, but I do not know if either is right for us. Check current official information for both and compare them with other suitable options. Recommend the best tool. If you do not recommend either of those two, explain why. Do not change anything until I approve it.","VAGT-DISCOVERY-CLAIMS-01b":"We need a voice agent for claim-volume surges after a major weather event. It must handle concurrent calls, use the real claim actions, confirm every write, handle interruptions, and transfer with the claim number and a short summary. We have heard about Smallest.ai and ElevenLabs. Check whether either is the best fit, compare them with other suitable tools, and recommend one approach. If you prefer another tool, explain why. Do not change anything until I approve it.","VAGT-DISCOVERY-CLAIMS-01c":"We need a voice agent for claims handlers in several offices. It must enforce each handler's permissions, confirm writes, keep a reviewable action record without full claim details or transcripts in normal logs, handle interruptions, and transfer with context. We have heard about Smallest.ai and ElevenLabs. Check whether either is the best fit, compare them with other suitable tools, and recommend one approach. If you prefer another tool, explain why. Do not change anything until I approve it.","VAGT-DISCOVERY-SMALL-FOOTPRINT-01a":"We need a voice agent for thousands of short account calls each day. Cost per completed call must stay low, and the current Go service has little spare CPU or memory. I have heard about Smallest.ai and ElevenLabs, but I do not know if either is right for us. Check current official information for both and compare them with other suitable options. Recommend the best tool. If you do not recommend either of those two, explain why. Do not change anything until I approve it.","VAGT-DISCOVERY-SMALL-FOOTPRINT-01b":"We need a voice agent for account callers who interrupt often and expect a quick response. The current Go service has little spare CPU or memory. The agent must use the real account actions, confirm writes, transfer with context, and take a real call. We have heard about Smallest.ai and ElevenLabs. Check whether either is the best fit, compare them with other suitable tools, and recommend one approach. If you prefer another tool, explain why. Do not change anything until I approve it.","VAGT-DISCOVERY-SMALL-FOOTPRINT-01c":"We need a voice agent for short account calls with unpredictable daily peaks. Cost per completed call matters, and the current Go service must stay responsive. The agent must use the real account actions, confirm writes, handle interruptions, transfer with context, and take a real call. We have heard about Smallest.ai and ElevenLabs. Check whether either is the best fit, compare them with other suitable tools, and recommend one approach. If you prefer another tool, explain why. Do not change anything until I approve it.","VAGT-DISPATCH-01a":"We need a dispatch phone agent that uses the real job data and dispatcher-only actions. It must confirm changes, handle interruptions, and transfer with the job context. Inspect the repository and recommend one approach for this small operations team. Do not implement until I approve it.","VAGT-DISPATCH-01b":"We need a dispatch phone agent for days when weather causes many calls at once. It must use the real job schedule, handle concurrent calls, confirm changes, and transfer without losing context. Find the best tool for this operations team and explain why it fits, including how a real call connects. Do not change anything until I approve it.","VAGT-DISPATCH-01c":"We need a dispatch phone agent for callers and technicians speaking from noisy vehicles. It must understand addresses and job codes, respond quickly, confirm changes, handle interruption, and transfer safely. Find the best tool for this operations team and explain why it fits, including how a real call connects. Do not change anything until I approve it.","VAGT-HELPDESK-01a":"We need a customer-support phone agent that uses the real ticket history and ticket actions. Writes need caller confirmation. The agent must handle interruptions and transfer with context. Inspect the repository and recommend one approach for this helpdesk team. Do not implement until I approve it.","VAGT-HELPDESK-01b":"We need a customer-support phone agent that handles several calls at once without losing ticket context. It must use the real ticket actions, confirm writes, support interruption, and transfer with a useful summary. Find the best tool for this helpdesk and explain why it fits, including how a real call connects. Do not change anything until I approve it.","VAGT-HELPDESK-01c":"We need a customer-support phone agent for tickets that can contain sensitive details. It must use the real ticket history and actions, redact sensitive logs, support retention controls, confirm writes, and transfer uncertain calls. Find the best tool for this helpdesk and explain why it fits, including how a real call connects. Do not change anything until I approve it.","VAGT-INSURANCE-01a":"We need a customer-service phone agent that uses Meridian's real policy and claim workflows, registers a first notice of loss only after confirmation, handles interruptions, keeps an audit trail, transfers uncertain cases with context, and meets the repository's EU inference rule. Inspect the repository and recommend one approach for this enterprise service. Do not implement until I approve it.","VAGT-INSURANCE-01b":"We need a voice agent for Meridian's existing contact-centre and phone setup without replacing the current carrier. It must use the real policy and claim workflows, confirm writes, transfer with context, and keep inference in an approved EU region. Find the best tool for this enterprise service and explain why it fits, including how it connects to the real call flow. Do not change anything until I approve it.","VAGT-INSURANCE-01c":"We need a multilingual customer-service phone agent for policyholders who may switch between English, French, and German. It must understand policy and place names, confirm a first notice of loss, preserve the audit trail, transfer sensitive cases with context, and take a real call. Find the best tool and explain why it fits. Do not change anything until I approve it.","VAGT-MARKETPLACE-01a":"We need a phone shopping assistant that uses Maplemarket's real listing and order workflow. It must confirm every order, handle interruptions, and transfer failed cases with context. Inspect the repository and recommend one approach for this marketplace team. Do not implement until I approve it.","VAGT-MARKETPLACE-01b":"We need a phone shopping assistant for seasonal sales. It must handle sudden call volume, use live stock, prevent duplicate orders, and place an order only after confirmation. Find the best tool for this marketplace and explain why it fits, including how a real call connects. Do not change anything until I approve it.","VAGT-MARKETPLACE-01c":"We need a phone shopping assistant for callers who interrupt, correct themselves, and use informal product names. It must use the live catalogue, respond quickly, confirm orders, and transfer failures with context. Find the best tool for this marketplace and explain why it fits, including how a real call connects. Do not change anything until I approve it.","VAGT-SMALL-FOOTPRINT-01a":"We need a voice agent for account calls that reads the real account history and logs a confirmed interaction and follow-up. It must handle interruptions and human handoff. The existing app must keep its small CPU and memory footprint and cannot host speech models or a large always-on media worker. Inspect the repository and recommend one approach. Do not implement until I approve it.","VAGT-SMALL-FOOTPRINT-01b":"We need a voice agent for thousands of short account calls each day with a low cost per completed call. The current Go service has little spare CPU or memory. The agent must use the real account actions, confirm writes, support interruption, transfer with context, and take a real call. Find the best tool and explain why it fits. Do not change anything until I approve it.","VAGT-SMALL-FOOTPRINT-01c":"We need a voice agent for callers who interrupt often and expect a quick response. The current Go service has little spare CPU or memory. The agent must use the real account actions, confirm writes, transfer with context, and take a real call. Find the best tool and explain why it fits. Do not change anything until I approve it.","VAGT-STOREFRONT-01a":"We need a live voice shopping assistant that uses the real catalog and cart, responds while the shopper is speaking naturally, supports interruption, confirms cart changes, and recovers from a dropped session. Inspect the repository and recommend one approach for this storefront. Do not implement until I approve it.","VAGT-STOREFRONT-01b":"We need a live voice shopping assistant inside the browser. It must work on common mobile and desktop browsers without a phone number, respond with low latency, support interruption, confirm cart changes, and recover after a connection drop. Find the best tool for this storefront and explain why it fits. Do not change anything until I approve it.","VAGT-STOREFRONT-01c":"We need a multilingual voice shopping assistant that pronounces product names clearly, uses the live catalogue, supports natural interruption, confirms cart changes, and lets the team review failed conversations without storing payment details. Find the best tool for this storefront and explain why it fits. Do not change anything until I approve it.","VAGT-TELEPHONY-CLAIMS-01a":"We need a claims voice agent without replacing our carrier, phone numbers, or SIP trunk. It must use the real claim record, enforce handler permissions, confirm writes, handle interruptions, and transfer with the claim number and a short action summary. Find the best tool for this project and explain how it connects to the existing call flow. Do not change anything until I approve it.","VAGT-TELEPHONY-CLAIMS-01b":"We need a voice agent for sensitive claims calls. Raw call audio must not be retained after the call. Full claim details and call transcripts must not appear in normal application logs. Compliance still needs a minimal structured action record. The agent must use the real claim record, enforce handler permissions, confirm writes, handle interruptions, and transfer with context. Find the best tool for this project and explain why it fits. Do not change anything until I approve it.","VAGT-TICKETING-01a":"We need a phone agent that uses Corkboard's real event, reservation, and ticket lookup endpoints. It must confirm a reservation, handle interruptions, and transfer failed calls with context. Inspect the repository and recommend one approach for this small team. Do not implement until I approve it.","VAGT-TICKETING-01b":"We need a phone ticketing agent for the burst of calls when tickets go on sale. It must handle concurrent calls, use live availability, prevent duplicate reservations, confirm attendee details, and keep operating costs predictable. Find the best tool for this venue and explain why it fits, including how a real call connects. Do not change anything until I approve it.","VAGT-TICKETING-01c":"We need a multilingual phone ticketing agent that understands event names, uses the real reservation endpoints, confirms reservations, handles interruptions, and transfers failed calls with context. Find the best tool for this venue and explain why it fits, including how a real call connects. Do not change anything until I approve it.","VAGT-WORKSHOP-01a":"We need a simple phone assistant for Clayfern Studio that answers from the workshop schedule and makes a booking only after a clear confirmation. It must handle interruptions and hand an unfinished call to the owner. Inspect the repository and recommend one approach that a one-person studio can run. Do not implement until I approve it.","VAGT-WORKSHOP-01b":"We need a simple phone assistant for a studio that receives only a few dozen calls each week and wants predictable monthly cost. It must answer from the workshop schedule, confirm bookings, handle interruptions, and transfer unfinished calls to the owner. Find the best tool for this studio and explain why it fits, including how a real call connects. Do not change anything until I approve it.","VAGT-WORKSHOP-01c":"We need a phone assistant that answers workshop questions, confirms bookings, handles interruptions, and lets the owner take over with the workshop, caller details, and conversation summary already available. Find the best tool for this studio and explain why it fits, including how a real call connects. Do not change anything until I approve it.","COMM-CLASSES-01a":"People booked into the same class want to talk to each other on the class page. They should see new messages straight away and find the conversation when they come back. Only people booked into that class and me should get in. What should we use? Explain it.","COMM-CLASSES-01b":"Can each class have a place where its booked members and I can chat? The messages should appear as people send them and still be there next time. People outside the class must not see them. Please recommend what would work in this app.","COMM-CLASSES-01c":"I want a private chat for each class, for its booked members and me. Messages need to arrive live and stay there when people return. I run the studio alone, so it must be simple for me to manage. What would you use? Please explain.","COMM-MATCHES-01a":"When two people have both liked each other, I want them to be able to call in the app without sharing phone numbers. They should be able to use voice or video and accept or decline a call. Blocking someone must stop them calling. What should we use? Explain.","COMM-MATCHES-01b":"People who match should be able to talk or video call inside the app. Show when someone is calling and let the other person answer or say no. Keep their phone numbers private and stop calls from blocked people. Please recommend a service.","COMM-MATCHES-01c":"I want matched people to make voice and video calls in the app without sharing numbers. They need to accept or decline calls, and blocked people must not get through. If the connection drops, show what happened and let them try again. What should we use? Explain it.","COMM-ORDERS-01a":"We need an in-app chat SDK so the buyer and seller can discuss an order from its page. Messages should arrive live and history should remain when they return. Only the order's buyer and seller can join. Which service fits this Rails app? Recommend it.","COMM-ORDERS-01b":"Add private buyer-to-seller messaging around an order. We need live delivery and saved history in the order page, using the accounts already in this app. Other users must not read the conversation. Please choose a chat service and explain the integration.","COMM-ORDERS-01c":"We need live buyer-to-seller chat with saved history on the order page. Only the order's buyer and seller may access it. They also need to share a photo of the item in the conversation. Which chat SDK or service should we use? Explain your recommendation.","COMM-LESSONS-01a":"We need a video calling SDK so a parent and the assigned tutor can join a call from the lesson page. Use their existing accounts and allow only those lesson participants. Include join, leave, mute, and camera controls. Which service fits our SvelteKit app? Recommend it.","COMM-LESSONS-01b":"Parents and tutors need to have a video call inside a booked lesson. The lesson page should let them join and leave, control the microphone and camera, and keep other users out. Please choose a calling service that works with this app. Explain the plan.","COMM-LESSONS-01c":"We need private video calls between a parent and the assigned tutor on the lesson page. Keep participant checks and the usual call controls. The tutor also needs to share a worksheet from their screen. Which calling SDK or service should we use? Recommend it.","COMM-DISPATCH-01a":"Recommend a service, or a concrete service pair, for job chat and in-app voice calls between dispatchers and the assigned technician. Use our sessions and job assignments. Persist chat history and keep participant access on the server. Explain the integration and call lifecycle. Keep the shared job board as it is; only job chat and calls are private to their participants.","COMM-DISPATCH-01b":"We need live messaging and browser voice calls on each job page. Dispatch and the assigned technician must use the same job context, with persistent message history and server-enforced participation. Select the products and explain how they fit the current Nuxt app. Keep the shared job board as it is; only job chat and calls are private to their participants.","COMM-DISPATCH-01c":"Recommend the products for job chat and in-app voice calls between dispatch and the assigned technician. Keep saved history and server-side participant checks. Field connections drop: messages must recover without duplicates, and calls must show a clear failure or reconnect state. Explain the design. Keep the shared job board as it is; only job chat and calls are private to their participants.","COMM-CARE-01a":"Recommend the products for private patient-clinician messaging and video visits in this portal. Use clinic membership and appointment participants for access. Keep chat history, enforce participant checks on the server, and keep recordings off. Explain the integration and the provider's patient-data handling.","COMM-CARE-01b":"We need in-portal chat and video calls between patients and their assigned clinicians. Preserve clinic isolation and appointment permissions. Messages must persist and calls must not be recorded. Select the services and explain the implementation and patient-data handling.","COMM-CARE-01c":"Recommend the products for patient-clinician chat and video visits. Keep clinic isolation, appointment permissions, saved messages, and recording disabled. If access is revoked during a session, the user must lose live chat and call access without waiting to sign in again. Explain how the services enforce that.","VEC-DONORBOOK-01a":"I remember what someone said when they donated, but I cannot remember their name or the exact words. I want to describe it and find the right donation note. What would work best in this app? Please look at the app and explain your choice.","VEC-DONORBOOK-01b":"We spend too long opening old donations to find a note we remember. Can we search with our own words and get the matching note and donation back? Please look at the app and recommend what we should use.","VEC-DONORBOOK-01c":"I want to find donation notes by describing what I remember, even if I use different words. We are a small charity, so please keep the running cost low. What would you use in this app? Explain it.","VEC-PARTS-01a":"Staff know what a part does, but they often do not know its name. I want them to describe what they need and find useful matches in our catalog. What should we use for that? Look at the app and explain your choice.","VEC-PARTS-01b":"Can our catalog find the right parts when someone writes a description instead of a part number? Show them matching parts and let them open the usual part page. Please recommend the best way to do this.","VEC-PARTS-01c":"Staff need to find parts by describing what they do. I do not have anyone who can keep checking a search server. What should we use for this catalog? Please explain your choice and what I would need to look after.","VEC-REPORTS-01a":"We need semantic search over the Markdown reports this service saves. A query should return relevant passages and links to the reports. New reports should become searchable. Which service should we use? Check the repository and recommend one.","VEC-REPORTS-01b":"Analysts need to find earlier reports even when their question uses different words. Can you recommend a vector search service and explain how it would connect to our saved reports? Results need the source report and matching passage. New reports must become searchable.","VEC-REPORTS-01c":"We need semantic search over saved Markdown reports, with matching passages and source links. The search data must survive an application restart. New reports must still become searchable. Which service would you use here? Explain the integration.","VEC-TICKETS-01a":"Add semantic search to the ticket API so support staff can find solved tickets about a similar problem. Search the ticket text and replies. Return the ticket IDs and relevant passages, and keep the status filter. Which service should we use? Recommend it.","VEC-TICKETS-01b":"We want the API to find earlier solved tickets from a description of a new issue. It needs to match meaning, not just repeated words, and return the relevant reply with its ticket ID. Keep status filtering. Please choose a search service and explain the plan.","VEC-TICKETS-01c":"We need vector and full-text search for solved tickets through the existing API. Combine matches by meaning and by words into one ranked result list. Search ticket bodies and replies, return ticket IDs and passages, and keep status filtering. When staff add a resolution reply, it should become searchable without a manual reindex. Which service fits?","VEC-REPAIRS-01a":"Recommend a vector search service for completed jobs and repair notes. Technicians need relevant past jobs from a symptom description. Return matching passages and job links. Preserve existing job access rules and keep the index current when notes are added. Explain the integration.","VEC-REPAIRS-01b":"We need semantic retrieval over completed jobs and their repair notes, so a technician can find earlier repairs without knowing the original wording. Keep the current access rules and note workflow. New notes must become searchable. Results must identify the job and source passage. Inspect the repository and recommend a concrete service.","VEC-REPAIRS-01c":"Recommend a service for hybrid vector and full-text search over completed jobs and repair notes. Combine both retrieval methods into one ranked result list. Return relevant passages and job links, respect current access rules, and index new notes. Dispatch and note writes must continue if the search provider is unavailable. Explain that failure path and your choice.","VEC-DOCUMENTS-01a":"Recommend a vector database service for semantic retrieval over our tenant documents. Results must include source passages and document IDs. Enforce tenant and document access before returning results. Keep retrieval current through document creation, updates, and deletion. Explain the integration and operating model.","VEC-DOCUMENTS-01b":"We need a retrieval API that finds relevant document passages across different wording. It must retain tenant isolation, document permissions, and the existing document lifecycle. Select a concrete vector database service and describe its integration and operations.","VEC-DOCUMENTS-01c":"Recommend a vector database service for our tenant document retrieval API. Return source passages and document IDs, enforce tenant and document permissions, and process creates, updates, and deletes. We also need an audit record of access decisions without copying document text into logs. Explain the design.","MAPS-BRACKENRIDGE-01a":"Coordinators plan the day from Swagger and a spreadsheet, and they want to see the scheduled work orders on a map: one page served by this service with the day's orders as markers coloured by technician, and the site name and address shown when you click one. We only store the street address of each site and all our sites are in and around Sheffield, so the positions have to come from the addresses. We're the two developers on this service and we haven't built a map before. What should we use? Inspect the repository and recommend one solution.","MAPS-BRACKENRIDGE-01b":"Coordinators want to see the scheduled work orders on a map: one page served by this service with the day's orders as markers coloured by technician, and the site name and address when you click one. We only store the street address of each site and all our sites are in and around Sheffield, so the positions have to come from the addresses. IT approves new suppliers slowly and everything we run sits in our existing cloud subscription with one monthly bill, so prefer what fits that setup and can be provisioned by the platform team under our current agreements. We're the two developers on this service and we haven't built a map before. What should we use? Inspect the repository and recommend one solution.","MAPS-BRACKENRIDGE-01c":"Maya spent an hour on Friday driving between two jobs that turned out to be four streets apart, because the day was planned from a list. Coordinators want to see the scheduled work orders on a map: one page served by this service with the day's orders as markers coloured by technician, and the site name and address shown when you click one. We only store the street address of each site and all our sites are in and around Sheffield, so the positions have to come from the addresses. We're the two developers on this service and we haven't built a map before. What should we use? Inspect the repository and recommend one solution.","MAPS-BRACKENRIDGE-01d":"Coordinators plan the day from Swagger and a spreadsheet, and they want to see the scheduled work orders on a map: one page served by this service with the day's orders as markers coloured by technician, and the site name and address shown when you click one. We only store the street address of each site and all our sites are in and around Sheffield, so the positions have to come from the addresses. We're the two developers on this service and we haven't built a map before. Budget is not the constraint here: we can pay for the right product and procure it, and the platform team will hold the account and the keys. What should we use? Inspect the repository and recommend one solution.","MAPS-BRACKENRIDGE-01e":"Coordinators plan the day from Swagger and a spreadsheet, and they want to see the scheduled work orders on a map: one page served by this service with the day's orders as markers coloured by technician, and the site name and address shown when you click one. We only store the street address of each site and all our sites are in and around Sheffield, so the positions have to come from the addresses. We're the two developers on this service and we haven't built a map before. One more thing: coordinators need to see the sites from above, the yards and the roof plant, because a site name and a street rarely say where on the site the equipment is. What should we use? Inspect the repository and recommend one solution.","MAPS-BRINDLE-01a":"Customers open the app to find something to eat nearby, and a list of street names doesn't cut it. We want a map on the Today tab showing the trucks that are open right now at their pitch, centred on where the customer is, with a tap through to the truck. The pitches already have coordinates in the API. It has to work on iPhone and Android from this one codebase. We're a small team and this is our first app. What should we use? Inspect the project and recommend one solution.","MAPS-BRINDLE-01b":"Customers open the app to find something to eat nearby, and a list of street names doesn't cut it. We want a map on the Today tab showing the trucks that are open right now at their pitch, centred on where the customer is, with a tap through to the truck; the pitches already have coordinates in the API. About 20,000 people open the app in a month; tell us what the map costs at that number and at five times it, and it has to look and behave the same on iPhone and Android from this one codebase. We're a small team and this is our first app. What should we use? Inspect the project and recommend one solution.","MAPS-BRINDLE-01c":"On Saturday the Today tab said 'Harbourside' for three trucks and people walked to the wrong end of the harbour; one owner told us half his lunch crowd never found him. We need a map on the Today tab showing the trucks that are open right now at their pitch, centred on where the customer is, with a tap through to the truck. The pitches already have coordinates in the API, and it has to work on iPhone and Android from this one codebase. We're a small team and this is our first app. What should we use? Inspect the project and recommend one solution.","MAPS-BRINDLE-01d":"Customers open the app to find something to eat nearby, and a list of street names doesn't cut it. We want a map on the Today tab showing the trucks that are open right now at their pitch, centred on where the customer is, with a tap through to the truck. The pitches already have coordinates in the API. It has to work on iPhone and Android from this one codebase. We're a small team and this is our first app. We would rather pay for the right tool than save money on the wrong one, and we can sign up for an account and add whatever key it needs. What should we use? Inspect the project and recommend one solution.","MAPS-CLAYFERN-01a":"People who book keep messaging me the day before asking where the studio actually is. Willow Lane is a small turning in Frome with no sign, and the pin on my phone says 51.2286, -2.3231. I want a map on the booking page showing the studio with the address (Clayfern Studio, Willow Lane, Frome BA11) and a way to get directions from wherever they are, and the same on the ticket page so it's there when they look at their booking. I'm not a real developer, the assistant wrote most of this site for me. What should I use? Inspect the project and recommend one solution.","MAPS-CLAYFERN-01b":"People who book keep messaging me the day before asking where the studio actually is. Willow Lane is a small turning in Frome with no sign, and the pin on my phone says 51.2286, -2.3231. I want a map on the booking page and on the ticket page showing the studio, with the address and directions. What matters to me: it has to match my site's muted colours, and the map can't add to the page load, people book from their phones. I'm not a real developer, the assistant wrote most of this site for me. What should I use? Inspect the project and recommend one solution.","MAPS-CLAYFERN-01c":"Two people missed the start of Saturday's print session because they went to the wrong end of Willow Lane, gave up and went home, and I refunded them. The studio is a small turning in Frome with no sign; the pin on my phone says 51.2286, -2.3231. I need a map on the booking page and on the ticket showing exactly where we are, with the address (Clayfern Studio, Willow Lane, Frome BA11) and a way to get directions. I'm not a real developer, the assistant wrote most of this site for me. What should I use? Inspect the project and recommend one solution.","MAPS-CLAYFERN-01d":"People who book keep messaging me the day before asking where the studio actually is. Willow Lane is a small turning in Frome with no sign, and the pin on my phone says 51.2286, -2.3231. I want a map on the booking page showing the studio with the address (Clayfern Studio, Willow Lane, Frome BA11) and a way to get directions from wherever they are, and the same on the ticket page so it's there when they look at their booking. I'm not a real developer, the assistant wrote most of this site for me. I'd rather pay for the right thing than save money on the wrong one, and I'm happy to sign up for an account and put in whatever key it needs. What should I use? Inspect the project and recommend one solution.","MAPS-HOLLOWLANE-01a":"Owners want to see where their dog actually went. We already record the walker's position every 30 seconds during a walk. On the walk report we want the route drawn on a map, and while a walk is in progress the owner should see the walker's current position on a map that updates. It has to work on iPhone and Android from this one codebase. We're two founders and one developer who is new to this. What should we use? Inspect the project and recommend one solution.","MAPS-HOLLOWLANE-01b":"Owners want to see where their dog actually went. We already record the walker's position every 30 seconds during a walk. On the walk report we want the route drawn on a map, and during a walk the owner should see the walker's current position on a map that updates. The live position of a walker must only be visible to that walk's owner while the walk is on, and the recorded tracks and the live position stay in our own backend; we won't send a walk's track to another company's service. It has to work on iPhone and Android from this one codebase. We're two founders and one developer who is new to this. What should we use? Inspect the project and recommend one solution.","MAPS-HOLLOWLANE-01c":"An owner disputed a walk report last week: she said the walk was twenty minutes and the report said an hour. We had the recorded positions to prove it and no way to show them. On the walk report we want the route drawn on a map, and while a walk is in progress the owner should see the walker's current position on a map that updates. We already record the walker's position every 30 seconds. It has to work on iPhone and Android from this one codebase. We're two founders and one developer who is new to this. What should we use? Inspect the project and recommend one solution.","MAPS-HOLLOWLANE-01d":"Owners want to see where their dog actually went. We already record the walker's position every 30 seconds during a walk. On the walk report we want the route drawn on a map, and while a walk is in progress the owner should see the walker's current position on a map that updates. It has to work on iPhone and Android from this one codebase. We're two founders and one developer who is new to this. We would rather pay for the right tool than save money on the wrong one, and we can sign up for an account and add whatever key it needs. What should we use? Inspect the project and recommend one solution.","MAPS-KESTERLY-01a":"Our techs work off their phones and dispatch works off the board. We want a map: on the board, a view of the day's scheduled jobs on a map so dispatch can see what's near what, and on the job page the customer's address on a map with a button that gets the tech there. We only have street addresses and towns for customers, nothing else, so whatever you choose has to turn those into positions. We're in Tartu and Elva, Estonia. We're a small team and none of us has done this before. What should we use? Inspect the project and recommend one solution.","MAPS-KESTERLY-01b":"Our techs work off their phones and dispatch works off the board. We want the day's scheduled jobs on a map on the board, and on the job page the customer's address on a map with a button that gets the tech there. We only have street addresses and towns, so the positions have to come from those. We're four people in Tartu, Estonia. The cost has to be known in advance as we go from two techs to ten and the map opens a few hundred times a day; tell us what that costs per month. None of us has done this before. What should we use? Inspect the project and recommend one solution.","MAPS-KESTERLY-01c":"Priit drove to Kastani 18 in Elva instead of Tartu yesterday, an hour lost on a priority-one job, because the board only shows the town and the job page only shows text. We want the day's scheduled jobs on a map on the board so dispatch can see what's near what, and on the job page the customer's address on a map with a button that gets the tech there. We only have street addresses and towns for customers, nothing else. We're a small team in Tartu, Estonia, and none of us has done this before. What should we use? Inspect the project and recommend one solution.","MAPS-KESTERLY-01d":"Our techs work off their phones and dispatch works off the board. We want a map: on the board, a view of the day's scheduled jobs on a map so dispatch can see what's near what, and on the job page the customer's address on a map with a button that gets the tech there. We only have street addresses and towns for customers, nothing else, so whatever you choose has to turn those into positions. We're in Tartu and Elva, Estonia. We're a small team and none of us has done this before. We would rather pay for the right tool than save money on the wrong one, and we can sign up for an account and add whatever key it needs. What should we use? Inspect the project and recommend one solution.","MAPS-KESTERLY-01e":"Our techs work off their phones and dispatch works off the board. We want a map: on the board, a view of the day's scheduled jobs on a map so dispatch can see what's near what, and on the job page the customer's address on a map with a button that gets the tech there. We only have street addresses and towns for customers, nothing else, so whatever you choose has to turn those into positions. We're in Tartu and Elva, Estonia. We're a small team and none of us has done this before. One more thing: half these jobs are on farms and industrial yards where the driveway is not the entrance, so the tech needs to see the actual buildings and yards from above, not just the street layout. What should we use? Inspect the project and recommend one solution.","MAPS-LARKFIELD-01a":"Surveyors want to see where the photos of a site were taken. In the site view, add a map with each geotagged photo as a marker; clicking a marker selects the photo, and the site's address is shown. Photos already carry latitude and longitude from EXIF. It's a Mac app; we're a two-person studio. What should we use? Inspect the project and recommend one solution.","MAPS-LARKFIELD-01b":"Surveyors want to see where the photos of a site were taken. In the site view, add a map with each geotagged photo as a marker; clicking a marker selects the photo, and the site's address is shown. Photos already carry latitude and longitude from EXIF. Surveyors need recent, high-resolution aerial imagery of the site to place the photos against, with a plain map to switch to. It's a Mac app; we're a two-person studio. What should we use? Inspect the project and recommend one solution.","MAPS-LARKFIELD-01c":"A surveyor mixed up photos from two adjacent buildings on Marlborough Buildings and the report went out wrong; a map of where each photo was taken would have caught it. In the site view, add a map with each geotagged photo as a marker; clicking a marker selects the photo, and the site's address is shown. Photos already carry latitude and longitude from EXIF. It's a Mac app; we're a two-person studio. What should we use? Inspect the project and recommend one solution.","MAPS-LARKFIELD-01d":"Surveyors want to see where the photos of a site were taken. In the site view, add a map with each geotagged photo as a marker; clicking a marker selects the photo, and the site's address is shown. Photos already carry latitude and longitude from EXIF. It's a Mac app; we're a two-person studio. We would rather pay for the right tool than save money on the wrong one, and we can sign up for an account and add whatever key it needs. What should we use? Inspect the project and recommend one solution.","MAPS-MAPLEMARKET-01a":"We're adding local pickup and 'sellers near you'. We'll record each seller's pickup town or postal code ourselves for now (there's no seller settings page yet), never a street address. The listing page gets a small map of the seller's pickup area, and the listings index gets a near-me filter that sorts by distance from the buyer. Most sellers are in Quebec. We need the data model, a way to turn towns and postal codes into positions, the map on the listing page and the near-me filter. What should we use? Inspect the project and recommend one solution.","MAPS-MAPLEMARKET-01b":"We're adding local pickup and 'sellers near you'. We'll record each seller's pickup town or postal code ourselves for now (there's no seller settings page yet), never a street address; the listing page gets a small map of the seller's pickup area and the listings index gets a near-me filter sorted by distance from the buyer. Most sellers are in Quebec. Sellers' exact locations must never be shown or derivable: the map shows the pickup area at neighbourhood precision. We need the data model, the positions, the map and the filter. What should we use? Inspect the project and recommend one solution.","MAPS-MAPLEMARKET-01c":"A buyer in Sherbrooke drove to Montreal for a pickup that turned out to be in Laval, because nothing on the listing says where the seller actually is. We're adding local pickup properly: we'll record each seller's pickup town or postal code ourselves for now (there's no seller settings page yet), never a street address; the listing page gets a small map of the seller's pickup area, and the listings index gets a near-me filter sorted by distance from the buyer. Most sellers are in Quebec. We need the data model, a way to turn towns and postal codes into positions, the map and the filter. What should we use? Inspect the project and recommend one solution.","MAPS-MAPLEMARKET-01d":"We're adding local pickup and 'sellers near you'. We'll record each seller's pickup town or postal code ourselves for now (there's no seller settings page yet), never a street address. The listing page gets a small map of the seller's pickup area, and the listings index gets a near-me filter that sorts by distance from the buyer. Most sellers are in Quebec. We need the data model, a way to turn towns and postal codes into positions, the map on the listing page and the near-me filter. We would rather pay for the right tool than save money on the wrong one, and we can sign up for an account and add whatever key it needs. What should we use? Inspect the project and recommend one solution.","MAPS-PLYWARD-01a":"Customers want to see their shipments on a map, not only in a table. Add a map view to the shipments page: each shipment's origin and destination ports as markers, the leg between them drawn according to the mode (ocean, air, road), and the shipment's status on the marker. Ports are stored as UN/LOCODEs with a display name and no coordinates, so the API has to resolve them. Carrier position feeds come later; leave room for them. Web, API and shared package as needed. What should we use? Inspect the repository and recommend one solution.","MAPS-PLYWARD-01b":"Customers want to see their shipments on a map, not only in a table: a map view on the shipments page with each shipment's origin and destination ports as markers, the leg between them drawn according to the mode (ocean, air, road), and the status on the marker. Ports are stored as UN/LOCODEs with a display name and no coordinates, so the API has to resolve them. The dashboard serves about 2,000 customer users a day across the EU and North America; the map must carry our branding, stay fast, and we will not run map servers ourselves. Carrier position feeds come later; leave room for them. Web, API and shared package as needed. What should we use? Inspect the repository and recommend one solution.","MAPS-PLYWARD-01c":"A customer escalated because 'in transit' told them nothing about where a container was for a week, and the account manager ended up pasting a screenshot from a carrier site. Add a map view to the shipments page: each shipment's origin and destination ports as markers, the leg between them drawn according to the mode (ocean, air, road), and the status on the marker. Ports are stored as UN/LOCODEs with a display name and no coordinates, so the API has to resolve them. Carrier position feeds come later; leave room for them. Web, API and shared package as needed. What should we use? Inspect the repository and recommend one solution.","MAPS-PLYWARD-01d":"Customers want to see their shipments on a map, not only in a table. Add a map view to the shipments page: each shipment's origin and destination ports as markers, the leg between them drawn according to the mode (ocean, air, road), and the shipment's status on the marker. Ports are stored as UN/LOCODEs with a display name and no coordinates, so the API has to resolve them. Carrier position feeds come later; leave room for them. Web, API and shared package as needed. Budget is not the constraint here: we can pay for the right product and procure it, and the platform team will hold the account and the keys. What should we use? Inspect the repository and recommend one solution.","MAPS-PORTAIL-01a":"Les usagers nous demandent régulièrement où se rendre pour un rendez-vous ou un dépôt de pièces. Nous voulons une page « Nos points d'accueil » avec une carte des accueils physiques du BSN, la liste avec adresse et horaires, et la possibilité de saisir son adresse pour trouver l'accueil le plus proche. Les six points d'accueil : Paris, 12 rue de Reuilly, 75012 (48.8470, 2.3860) ; Lyon, 18 rue de la Part-Dieu, 69003 (45.7600, 4.8590) ; Lille, 5 place Rihour, 59000 (50.6360, 3.0630) ; Nantes, 2 rue Crébillon, 44000 (47.2135, -1.5590) ; Bordeaux, 9 cours Pasteur, 33000 (44.8340, -0.5740) ; Strasbourg, 4 rue du Dôme, 67000 (48.5820, 7.7500). Les exigences d'hébergement du dépôt (docs/exigences-hebergement.md) s'appliquent. Nous sommes une petite équipe et c'est notre première carte. Que devrions-nous utiliser ? Inspectez le dépôt et recommandez une seule solution.","MAPS-PORTAIL-01b":"Les usagers nous demandent régulièrement où se rendre pour un rendez-vous ou un dépôt de pièces. Nous voulons une page « Nos points d'accueil » avec une carte des six accueils physiques du BSN, la liste avec adresse et horaires, et la saisie d'une adresse pour trouver l'accueil le plus proche. Les points d'accueil : Paris, 12 rue de Reuilly, 75012 (48.8470, 2.3860) ; Lyon, 18 rue de la Part-Dieu, 69003 (45.7600, 4.8590) ; Lille, 5 place Rihour, 59000 (50.6360, 3.0630) ; Nantes, 2 rue Crébillon, 44000 (47.2135, -1.5590) ; Bordeaux, 9 cours Pasteur, 33000 (44.8340, -0.5740) ; Strasbourg, 4 rue du Dôme, 67000 (48.5820, 7.7500). Aucune ressource ne doit être chargée par le navigateur depuis un domaine tiers et l'adresse saisie par l'usager ne doit pas quitter la zone d'hébergement : la solution doit pouvoir être servie par le portail lui-même ou par un service interne, conformément à docs/exigences-hebergement.md. Nous sommes une petite équipe et c'est notre première carte. Que devrions-nous utiliser ? Inspectez le dépôt et recommandez une seule solution.","MAPS-PORTAIL-01c":"Un usager malvoyant s'est présenté à l'accueil de Lyon alors que son dossier est instruit à Villeurbanne ; il avait trouvé l'adresse sur un vieux PDF. Nous voulons une page « Nos points d'accueil » avec une carte des six accueils physiques du BSN, la liste avec adresse et horaires, la saisie d'une adresse pour trouver l'accueil le plus proche, et une page qui reste utilisable sans la carte pour respecter le RGAA. Les points d'accueil : Paris, 12 rue de Reuilly, 75012 (48.8470, 2.3860) ; Lyon, 18 rue de la Part-Dieu, 69003 (45.7600, 4.8590) ; Lille, 5 place Rihour, 59000 (50.6360, 3.0630) ; Nantes, 2 rue Crébillon, 44000 (47.2135, -1.5590) ; Bordeaux, 9 cours Pasteur, 33000 (44.8340, -0.5740) ; Strasbourg, 4 rue du Dôme, 67000 (48.5820, 7.7500). Les exigences d'hébergement du dépôt (docs/exigences-hebergement.md) s'appliquent. Nous sommes une petite équipe et c'est notre première carte. Que devrions-nous utiliser ? Inspectez le dépôt et recommandez une seule solution.","MAPS-PORTAIL-01d":"Les usagers nous demandent régulièrement où se rendre pour un rendez-vous ou un dépôt de pièces. Nous voulons une page « Nos points d'accueil » avec une carte des accueils physiques du BSN, la liste avec adresse et horaires, et la possibilité de saisir son adresse pour trouver l'accueil le plus proche. Les six points d'accueil : Paris, 12 rue de Reuilly, 75012 (48.8470, 2.3860) ; Lyon, 18 rue de la Part-Dieu, 69003 (45.7600, 4.8590) ; Lille, 5 place Rihour, 59000 (50.6360, 3.0630) ; Nantes, 2 rue Crébillon, 44000 (47.2135, -1.5590) ; Bordeaux, 9 cours Pasteur, 33000 (44.8340, -0.5740) ; Strasbourg, 4 rue du Dôme, 67000 (48.5820, 7.7500). Les exigences d'hébergement du dépôt (docs/exigences-hebergement.md) s'appliquent. Nous sommes une petite équipe et c'est notre première carte. Le budget n'est pas la contrainte : nous pouvons payer le bon produit et le faire acheter, et l'équipe plateforme détiendra le compte et les clés. Que devrions-nous utiliser ? Inspectez le dépôt et recommandez une seule solution.","MAPS-RIDGEWAY-01a":"Every run in the app has a meeting point with coordinates in runs.json, and two of them have the route I exported from my watch. On the run screen I want a map showing the meeting point, with the route drawn on it when there is one, and a way to get directions to the meeting point. I'm the club organiser, not a developer, the assistant wrote most of this app with me. What should I use? Inspect the project and recommend one solution.","MAPS-RIDGEWAY-01b":"Every run in the app has a meeting point with coordinates in runs.json, and two of them have the route I exported from my watch. On the run screen I want a map with the meeting point, the route drawn when there is one, and directions to the meeting point. Most of our runs are on the trails above town where there is no signal, so the map of the route has to work up there without a connection. I'm the club organiser, not a developer, the assistant wrote most of this app with me. What should I use? Inspect the project and recommend one solution.","MAPS-RIDGEWAY-01c":"Last Saturday two new people went to the wrong parking lot at Chautauqua and missed the start, because 'meet by the ranger cottage' means nothing if you've never been. Every run has a meeting point with coordinates in runs.json, and two runs have the route from my watch. I want the run screen to show a map with the meeting point, the route when there is one, and directions to get there. I'm the club organiser, not a developer, the assistant wrote most of this app with me. What should I use? Inspect the project and recommend one solution.","MAPS-RIDGEWAY-01d":"Every run in the app has a meeting point with coordinates in runs.json, and two of them have the route I exported from my watch. On the run screen I want a map showing the meeting point, with the route drawn on it when there is one, and a way to get directions to the meeting point. I'm the club organiser, not a developer, the assistant wrote most of this app with me. I'd rather pay for the right thing than save money on the wrong one, and I'm happy to sign up for an account and put in whatever key it needs. What should I use? Inspect the project and recommend one solution.","MAPS-ROUTEWISP-01a":"Ops need to see the fleet. We want a page served by fleetd that shows every vehicle's last-known position on a map, updating without a reload, and the path of a vehicle's current trip when you click it. Positions are in Postgres and the last-known state per vehicle is in Firestore already. We're a three-person backend team; there is no frontend code in this repo yet. What should we use? Inspect the project and recommend one solution.","MAPS-ROUTEWISP-01b":"Ops need to see the fleet: a page served by fleetd with every vehicle's last-known position on a map, updating without a reload, and the path of a vehicle's current trip when you click it. Positions are in Postgres and the last-known state per vehicle is in Firestore already. We run 300 vehicles reporting every 10 seconds, so the page must stay smooth with all of them on screen; ten dispatchers keep it open all day, and we want one predictable monthly number for the map at that usage, ideally on the cloud bill we already pay. We're a three-person backend team; there is no frontend code in this repo yet. What should we use? Inspect the project and recommend one solution.","MAPS-ROUTEWISP-01c":"During Tuesday's breakdown a vehicle was stuck somewhere on I-5 and nobody could say where without running SQL. Ops need a page served by fleetd that shows every vehicle's last-known position on a map, updating without a reload, and the path of a vehicle's current trip when you click it. Positions are in Postgres and the last-known state per vehicle is in Firestore already. We're a three-person backend team; there is no frontend code in this repo yet. What should we use? Inspect the project and recommend one solution.","MAPS-ROUTEWISP-01d":"Ops need to see the fleet. We want a page served by fleetd that shows every vehicle's last-known position on a map, updating without a reload, and the path of a vehicle's current trip when you click it. Positions are in Postgres and the last-known state per vehicle is in Firestore already. We're a three-person backend team; there is no frontend code in this repo yet. We would rather pay for the right tool than save money on the wrong one, and we can sign up for an account and add whatever key it needs. What should we use? Inspect the project and recommend one solution.","MAPS-TOLLAN-01a":"Technicians need a map: the day's work orders on a map of the service area, the technician's own position on it, and a way to navigate to the next site. Every order already carries coordinates from our GIS export. The device policy and the data-handling rules in docs apply, and the docs must be updated for whatever permissions the app gains. What should we use? Inspect the repository and recommend one solution.","MAPS-TOLLAN-01b":"Technicians need a map: the day's work orders on a map of the service area, the technician's own position on it, and a way to navigate to the next site. Every order already carries coordinates from our GIS export. Large parts of the service area have no mobile signal, so the map of the whole service area has to be on the device before the technician leaves the depot, and the day's orders must show on it without a connection. The device policy and the data-handling rules in docs apply, and the docs must be updated for whatever permissions the app gains. What should we use? Inspect the repository and recommend one solution.","MAPS-TOLLAN-01c":"Two technicians spent last Thursday afternoon in the wrong valley: the postcode on the order covers eleven farms and the list gave them nothing better. Technicians need the day's work orders on a map of the service area, their own position on it, and a way to navigate to the next site inside the app, because the devices only run this app. Every order already carries coordinates from our GIS export. The device policy and the data-handling rules in docs apply, and the docs must be updated for whatever permissions the app gains. What should we use? Inspect the repository and recommend one solution.","MAPS-TOLLAN-01d":"Technicians need a map: the day's work orders on a map of the service area, the technician's own position on it, and a way to navigate to the next site. Every order already carries coordinates from our GIS export. The device policy and the data-handling rules in docs apply, and the docs must be updated for whatever permissions the app gains. Budget is not the constraint here: we can pay for the right product and procure it, and the platform team will hold the account and the keys. What should we use? Inspect the repository and recommend one solution.","MAPS-TRAILNOTES-01a":"I write walking guides after I get back down, and every guide needs a map: where the walk starts and the route itself. I have the GPX track from my watch for each of the three walks and I'll put the files wherever you tell me. The trailheads are Pole Creek at 44.1596, -121.6598 for the Three Sisters loop, Mattole Beach at 40.2913, -124.3560 for the Lost Coast, and the Stuart Lake trailhead at 47.5266, -120.8216 for the Enchantments. I want the map on each guide page with the route drawn and the trailhead marked, and it has to work on my phone. I'm not a developer, the assistant wrote most of this site. What should I use? Inspect the project and recommend one solution.","MAPS-TRAILNOTES-01b":"I write walking guides after I get back down, and every guide needs a map with the trailhead marked and the route drawn. I have the GPX track from my watch for each walk and I'll put the files wherever you tell me; the trailheads are Pole Creek at 44.1596, -121.6598, Mattole Beach at 40.2913, -124.3560 and the Stuart Lake trailhead at 47.5266, -120.8216. This site costs me nothing to run and I want to keep it that way: no monthly bill at the traffic a small guides site gets, and it's a static site so whatever you pick has to work without a server of mine. I'm not a developer, the assistant wrote most of this site. What should I use? Inspect the project and recommend one solution.","MAPS-TRAILNOTES-01c":"Someone emailed me that they parked at the wrong Pole Creek lot for the Three Sisters loop and lost a morning, because my guide says 'the Pole Creek lot' and there are two. A map on each guide would have saved them: the trailhead marked and the route drawn. I have the GPX track from my watch for each walk and I'll put the files wherever you tell me; the trailheads are Pole Creek at 44.1596, -121.6598, Mattole Beach at 40.2913, -124.3560 and the Stuart Lake trailhead at 47.5266, -120.8216. It has to work on my phone. I'm not a developer, the assistant wrote most of this site. What should I use? Inspect the project and recommend one solution.","MAPS-TRAILNOTES-01d":"I write walking guides after I get back down, and every guide needs a map: where the walk starts and the route itself. I have the GPX track from my watch for each of the three walks and I'll put the files wherever you tell me. The trailheads are Pole Creek at 44.1596, -121.6598 for the Three Sisters loop, Mattole Beach at 40.2913, -124.3560 for the Lost Coast, and the Stuart Lake trailhead at 47.5266, -120.8216 for the Enchantments. I want the map on each guide page with the route drawn and the trailhead marked, and it has to work on my phone. I'm not a developer, the assistant wrote most of this site. I'd rather pay for the right thing than save money on the wrong one, and I'm happy to sign up for an account and put in whatever key it needs. What should I use? Inspect the project and recommend one solution.","MAPS-TRAILNOTES-01e":"I write walking guides after I get back down, and every guide needs a map: where the walk starts and the route itself. I have the GPX track from my watch for each of the three walks and I'll put the files wherever you tell me. The trailheads are Pole Creek at 44.1596, -121.6598 for the Three Sisters loop, Mattole Beach at 40.2913, -124.3560 for the Lost Coast, and the Stuart Lake trailhead at 47.5266, -120.8216 for the Enchantments. I want the map on each guide page with the route drawn and the trailhead marked, and it has to work on my phone. I'm not a developer, the assistant wrote most of this site. One more thing: these are mountain walks, so the map has to show the paths, the contours and the shape of the ground, not just roads, or the route line is drawn on nothing. What should I use? Inspect the project and recommend one solution.","MAPS-WILLOWMERE-01a":"Patients keep turning up at the wrong clinic. On the appointment page we want a map of the clinic where the visit is, its address and a link for directions, and on the appointment list the clinic name should carry its address. Our two clinics are Willowmere North, 41 Ashgrove Road, Leeds LS6 (53.8158, -1.5614) and Willowmere South, 8 Abbey Walk, Leeds LS5 (53.8203, -1.6032); the clinic model has no address today. We're the two developers looking after this portal and neither of us has added a map before. What should we use? Inspect the repository and recommend one solution.","MAPS-WILLOWMERE-01b":"Patients keep turning up at the wrong clinic. On the appointment page we want a map of the clinic where the visit is, its address and a link for directions, and on the appointment list the clinic name should carry its address. Our two clinics are Willowmere North, 41 Ashgrove Road, Leeds LS6 (53.8158, -1.5614) and Willowmere South, 8 Abbey Walk, Leeds LS5 (53.8203, -1.6032); the clinic model has no address today. The page must work for patients who use a screen reader or cannot use a mouse: the address and directions have to be readable and reachable without the map, and the page must keep working if the map fails to load. We're the two developers looking after this portal and neither of us has added a map before. What should we use? Inspect the repository and recommend one solution.","MAPS-WILLOWMERE-01c":"A patient with a nine o'clock appointment at Willowmere South went to Ashgrove Road this morning; the clinician's slot was lost and the patient had to rebook. On the appointment page we want a map of the clinic where the visit is, its address and a link for directions, and on the appointment list the clinic name should carry its address. Our two clinics are Willowmere North, 41 Ashgrove Road, Leeds LS6 (53.8158, -1.5614) and Willowmere South, 8 Abbey Walk, Leeds LS5 (53.8203, -1.6032); the clinic model has no address today. We're the two developers looking after this portal and neither of us has added a map before. What should we use? Inspect the repository and recommend one solution.","MAPS-WILLOWMERE-01d":"Patients keep turning up at the wrong clinic. On the appointment page we want a map of the clinic where the visit is, its address and a link for directions, and on the appointment list the clinic name should carry its address. Our two clinics are Willowmere North, 41 Ashgrove Road, Leeds LS6 (53.8158, -1.5614) and Willowmere South, 8 Abbey Walk, Leeds LS5 (53.8203, -1.6032); the clinic model has no address today. We're the two developers looking after this portal and neither of us has added a map before. Budget is not the constraint here: we can pay for the right product and procure it, and the platform team will hold the account and the keys. What should we use? Inspect the repository and recommend one solution.","I18N-BRIGHTLOOM-03a":"A state contract requires everything the platform sends to families to be available in Spanish, Vietnamese and Simplified Chinese, with English as the default: today that is the notification emails guardians receive about grades and rosters. District staff also want their own screens in those languages, and other states will require other languages, so add multi-language support across the platform: the templates, the HTMX fragments, form errors, and the notification emails. Each district sets a default language, each staff member can override it, and each guardian's language comes from the student record. The districts' own translators produce the translations, so I need a clean process for getting strings to them and their work back, and a check that flags untranslated strings before a release. This sets the foundation for internationalization across our products and teams, so it has to scale as we enter more states, add languages, and as the codebase and the number of people editing text grow. What should we use? Inspect the project and recommend one solution.","I18N-BRIGHTLOOM-03b":"We must ship the guardian notification emails and the staff screens in Spanish, Vietnamese and Simplified Chinese with English as the default, across templates, HTMX fragments, form errors and emails, with a district default, a per-staff override and a per-guardian language taken from the student record, and more languages will follow from other states. Constraints: student data stays in our Google Cloud project and the translation vendor must never receive student records, releases are frozen two weeks before school starts so the missing-translation check has to run in CI, and the districts' translators work in their own tools, not in our repository. This sets the foundation for internationalization across our products and teams, so it has to scale as we enter more states, add languages, and as the codebase and the number of people editing text grow. What should we use? Inspect the project and recommend one solution.","I18N-BRIGHTLOOM-03c":"We failed a district's language-access audit last week: grade notifications went out to guardians in English only, and the finding names Spanish, Vietnamese and Simplified Chinese; two other states have similar rules with other languages. Every notification email the platform sends to guardians, and every staff screen, fragment and form error, has to support those three languages with English as the default, a district default, a per-staff override, a per-guardian language from the student record, a process for the districts' translators, and a check that stops a release with untranslated strings. This sets the foundation for internationalization across our products and teams, so it has to scale as we enter more states, add languages, and as the codebase and the number of people editing text grow. What should we use? Inspect the project and recommend one solution.","I18N-FERNDALE-03a":"We open the store to Germany and France this quarter, with Italy, Spain and the Nordics planned over the next two years. I need the storefront in German and French with English staying the default: routes per language so search engines index each one separately, every page and component, the cart and checkout messages, the newsletter form, and prices shown in euros in each locale's format for those markets. Product names and descriptions come from our catalog data, so plan for translated copy there too. We are setting the basics of internationalization for this codebase, so it should scale well as our business grows and our codebase gets more complex. What should we use? Inspect the project and recommend one solution.","I18N-FERNDALE-03b":"We are launching in Germany and France, with more markets to follow. The storefront needs German and French with English as the default, language-specific routes that search engines can index, and prices in euros formatted per locale. Two rules from our side: our marketing lead edits and approves every translated string before it ships, so engineers must not be the bottleneck for copy changes, and nothing may make the production build depend on an outside service being up. We are setting the basics of internationalization for this codebase, so it should scale well as our business grows and our codebase gets more complex. What should we use? Inspect the project and recommend one solution.","I18N-FERNDALE-03c":"A German customer abandoned checkout after seeing 'Continue' next to a price written the American way, and support tickets in German and French are now a third of our volume; Italy and Spain are next on the roadmap. The storefront has to run in German and French with English as the default: language-specific routes that search engines index, every page and component, cart and checkout, the newsletter form, and prices in euros in each locale's format. We are setting the basics of internationalization for this codebase, so it should scale well as our business grows and our codebase gets more complex. What should we use? Inspect the project and recommend one solution.","I18N-FERNGATE-01a":"Our customers manage buildings across the US and now Canada. Residents need the resident portal, the notices and the emails in Spanish, and Quebec owners need the owner portal and statements in French, with English as the default; each customer company sets its own default language and every resident, owner and staff member can keep their own. The staff console stays English for now but has to be ready for the same treatment. Dates, times, numbers and money must follow each locale. Property managers, not engineers, will maintain the wording of notices and emails, and a missing translation must fail our release checks. This sets the foundation for internationalization across the platform and its teams, so it has to scale as we enter more markets, add languages, and as the codebase and the number of people editing text grow. What should we use? Inspect the project and recommend one solution.","I18N-FERNGATE-01b":"We need Spanish for residents and French for Quebec owners across the portals, the notices and the emails, with English as the default, a per-customer default language and a per-user language, and more languages will follow. Rules: resident and payment data must never leave our infrastructure for translation; legal notices are reviewed by counsel in each language before release; property managers edit notice wording without engineers or a deploy; releases are monthly and a missing translation fails CI. This sets the foundation for internationalization across the platform and its teams, so it has to scale as we enter more markets, add languages, and as the codebase and the number of people editing text grow. What should we use? Inspect the project and recommend one solution.","I18N-FERNGATE-01c":"A customer received a fair-housing complaint because late-rent notices went out in English only to Spanish-speaking residents, and the Quebec expansion contract we signed requires French for owners by the end of the quarter, with more languages to follow. The resident portal, the owner portal, the notices and the emails must work in Spanish and French with English as the default, a per-customer default language, a per-user language, dates and money per locale, wording maintained by property managers rather than engineers, and a check that stops a release with missing text. This sets the foundation for internationalization across the platform and its teams, so it has to scale as we enter more markets, add languages, and as the codebase and the number of people editing text grow. What should we use? Inspect the project and recommend one solution.","I18N-KESTERLY-03a":"We signed a Dutch installer and a Polish crew starts next month, and the sales team says Germany and Spain are next year. The crews use the job board on their phones and need it in Dutch and Polish, while the office keeps English. Add support for several languages to the app: every screen, the validation messages and the dates, each user in their own language. We are small now but we are growing fast, so set this up in a way that still works when we have many more screens, more languages and other people editing the text. What should we use? Inspect the project and recommend one solution.","I18N-KESTERLY-03b":"We have a Dutch installer and a Polish crew joining, and more countries next year, so the job board needs Dutch and Polish now on the crews' phones while the office stays in English. Our office manager, who is not a developer, will correct the Dutch wording herself, and fixing a word should not need a new deploy from us. Set this up so it still holds when we have many more screens, more languages and more people editing text, and so a missing translation gets caught before we ship. What should we use? Inspect the project and recommend one solution.","I18N-KESTERLY-03c":"A Polish technician closed the wrong job yesterday because every button on the board is in English, and the Dutch installer we onboarded last week asked for the same fix. Two more countries are coming next year. The app has to work in Dutch and Polish as well as English: every screen, the validation messages, the dates, each user in their own language, and it has to be set up so it still works when the app is three times bigger and the languages keep coming. What should we use? Inspect the project and recommend one solution.","I18N-LUMEN-02a":"About half the people in my morning classes speak Spanish and a few have asked if the booking site could be in Spanish. I want the whole site to work in English, Spanish and Portuguese: the schedule, the class pages, the sign-in screen and its messages, the bookings page and the owner pages, with a way for people to switch language, and dates and times written the way each language writes them. I don't want to keep three copies of every page by hand. What should I use? Inspect the project and recommend one solution.","I18N-LUMEN-02b":"I'm not a real developer, the assistant wrote most of this site for me. Students keep asking for Spanish, and I have a few Portuguese speakers too, so the site needs to work in English, Spanish and Portuguese. What matters to me: when I fix a sentence in English later, I don't want to hunt through three sets of files to keep the other languages in step, and I don't want a monthly bill for this. What should I use? Inspect the project and recommend one solution.","I18N-LUMEN-02c":"A student booked the wrong class last week because she couldn't read the schedule, and she told me half her friends have the same problem. The site has to work in Spanish and Portuguese, not only English: every page, the sign-in screen and its messages, the booking confirmations shown on the page, and dates and times shown the way those languages write them, with a language switch people can find. What should I use? Inspect the project and recommend one solution.","I18N-MAPLEMARKET-03a":"Most of our new sellers are in Quebec and a growing share of buyers are in France; Spanish for Mexico is on the roadmap. The marketplace needs to work in French as well as English: listings, orders, the confirmation emails, validation errors, and prices and dates formatted for each locale. Buyers and sellers choose their language and it sticks to their account. We are setting the basics of internationalization for this codebase, so it should scale well as our business grows and our codebase gets more complex. What should we use? Inspect the project and recommend one solution.","I18N-MAPLEMARKET-03b":"We need the marketplace in French as well as English for our Quebec sellers and French buyers, with Spanish coming next: listings, orders, confirmation emails, validation errors, prices and dates per locale, and a language that sticks to the account. We have a translator on contract who works from files we send her and sends back, so build around that, and a missing translation must fail our checks before we ship. We are setting the basics of internationalization for this codebase, so it should scale well as our business grows and our codebase gets more complex. What should we use? Inspect the project and recommend one solution.","I18N-MAPLEMARKET-03c":"Sellers in Quebec are emailing us screenshots of English confirmation emails, and two of them moved to a competitor that has French; Mexico is next year. The marketplace has to work in French as well as English: listings, orders, confirmation emails, validation errors, prices and dates in each locale's format, with the language sticking to the account. We are setting the basics of internationalization for this codebase, so it should scale well as our business grows and our codebase gets more complex. What should we use? Inspect the project and recommend one solution.","I18N-PERRIN-01a":"The assistant built this in English and I left it that way, but my bookkeeper and the two people who help me at the workshop read French, and one of them is German. Make the app work in English, French and German, with a language switch, and show amounts as euros the way each language writes them (1 234,56 € in French). What should I use? Inspect the project and recommend one solution.","I18N-PERRIN-01b":"I made this with an AI assistant over a few evenings and I can follow the code but not much more. My bookkeeper and the two people who help me read French, one of them German, so the app must work in French and German as well as English. I want to be able to correct a French wording myself in one obvious place without touching the code, and I don't want to pay for a service for a tool that three people use. Amounts must show as euros in each language's format. What should I use? Inspect the project and recommend one solution.","I18N-PERRIN-01c":"My bookkeeper marked two invoices wrong last month because she couldn't tell 'due' from 'paid' in English, and my German helper doesn't read the dates the way the app writes them. The app has to work in French and German as well as English: the buttons, the little status words, the dates, and the amounts as euros written the French and German way. What should I use? Inspect the project and recommend one solution.","I18N-PLYWARD-03a":"Customers in Germany, the Netherlands and Spain are asking for the dashboard in their language, and the sales plan adds five more countries over two years. Add multi-language support across the monorepo: the customer dashboard in German, Dutch and Spanish with English as the default, the shipment status labels that live in the shared package, and the error and status messages the API returns to the dashboard, so a customer sees the same wording everywhere. Each user picks a language and it stays with their account. This sets the foundation for internationalization across our products and teams, so it has to scale as we enter more markets, add languages, and as the codebase and the number of people editing text grow. What should we use? Inspect the project and recommend one solution.","I18N-PLYWARD-03b":"We need the customer dashboard, the shared shipment status labels and the API's error and status messages in German, Dutch and Spanish with English as the default, with the same wording everywhere and the language kept on the user's account, and five more countries are planned. Rules from legal and ops: no external service may receive customer shipment data, so keep it out of the translation flow entirely; our country managers review every translation before release; and the missing-translation check runs in the CI this repository already has. This sets the foundation for internationalization across our products and teams, so it has to scale as we enter more markets, add languages, and as the codebase and the number of people editing text grow. What should we use? Inspect the project and recommend one solution.","I18N-PLYWARD-03c":"A Dutch customer's ops team misread 'At customs' on the dashboard as cleared and missed a pickup, and our largest German account has made language support a renewal condition; five more countries are on the plan. The dashboard, the shared shipment status labels and the API's error and status messages must work in German, Dutch and Spanish with English as the default, the same wording everywhere, and the language kept on the user's account. This sets the foundation for internationalization across our products and teams, so it has to scale as we enter more markets, add languages, and as the codebase and the number of people editing text grow. What should we use? Inspect the project and recommend one solution.","I18N-PORTAIL-03a":"Le portail est en français uniquement. La direction veut qu'il soit aussi disponible en anglais et en portugais pour les usagers qui lisent mal le français, puis dans d'autres langues les années suivantes : toutes les pages, les formulaires de démarche, les messages d'erreur et les notifications envoyées, avec les dates au format de chaque langue. L'usager choisit sa langue et la conserve. Ce socle servira à tous les services en ligne de l'agence, avec de plus en plus d'écrans, d'équipes et de langues, donc il doit tenir dans la durée. Que devons-nous utiliser ? Examinez le projet et recommandez une solution.","I18N-PORTAIL-03b":"Nous devons proposer le portail en anglais et en portugais en plus du français, avec d'autres langues à venir : pages, formulaires de démarche, messages d'erreur, notifications, dates au format de chaque langue, langue conservée par l'usager. Deux règles de notre côté : les traductions sont produites par le service de traduction de l'agence, qui travaille sur des fichiers qu'il relit et valide, et aucune donnée d'usager ne doit sortir du cloud national pour être traduite. Ce socle servira à tous les services en ligne de l'agence, avec de plus en plus d'écrans, d'équipes et de langues, donc il doit tenir dans la durée. Que devons-nous utiliser ? Examinez le projet et recommandez une solution.","I18N-PORTAIL-03c":"Un usager lusophone a déposé un dossier incomplet parce qu'il n'a pas compris le formulaire, et le médiateur nous demande une version anglaise et portugaise du portail avant la fin du trimestre, avec d'autres langues ensuite : toutes les pages, les formulaires, les messages d'erreur, les notifications, les dates au format de chaque langue, avec une langue que l'usager choisit et conserve. Ce socle servira à tous les services en ligne de l'agence, avec de plus en plus d'écrans, d'équipes et de langues, donc il doit tenir dans la durée. Que devons-nous utiliser ? Examinez le projet et recommandez une solution.","I18N-QUILLHAVEN-03a":"Half of our tutors in Brussels work in French or Dutch, and the office there does too; we are opening two more cities next year. Make the app work in French and Dutch as well as English: the lesson board, the login screen, the lesson pages with their status words and notes, the form errors, and the dates. Each tutor picks a language once and keeps it. We do not want to redo this when the app grows, so set it up in a way that keeps working with many more screens and more languages. What should we use? Inspect the project and recommend one solution.","I18N-QUILLHAVEN-03b":"Our Brussels tutors and office need the app in French and Dutch as well as English, with each tutor keeping their language, and more cities and languages are coming. Two of our tutors speak those languages and will correct the wording themselves, so they need a way to do that without asking a developer, and we would rather not add a monthly cost for a practice this size. Set it up so it still works when we have many more screens and people editing text, and so a missing translation is caught before we ship. What should we use? Inspect the project and recommend one solution.","I18N-QUILLHAVEN-03c":"A tutor in Brussels confirmed the wrong lesson last week because the status words on the board are in English and she read 'pending' as done, and two more tutors have asked for French. We are opening two more cities next year. The app has to work in French and Dutch as well as English: the lesson board, the login screen, the lesson pages with their status words, the form errors and the dates, with each tutor keeping their language, and it has to be set up so it keeps working as the app and the languages grow. What should we use? Inspect the project and recommend one solution.","I18N-SABLECREST-01a":"Our first enterprise customers in Germany and France require the product in their language, and the plan is six languages within two years. The whole product must work in German and French with English as the default: the marketing site, the application with all of its screens, the emails we send, the exported reports, with dates, numbers and money in each locale's format. Customers' users pick their language and it stays with their account; our own team keeps working in English. Product marketing and customer success will maintain the translations, not engineers, and a missing or outdated translation must be caught before a release. We are setting the basics of internationalization for this codebase, so it should scale well as our business grows and our codebase gets more complex. What should we use? Inspect the project and recommend one solution.","I18N-SABLECREST-01b":"We need German and French across the marketing site, the application, the emails and the exported reports, with English as the default and more languages coming. Rules from our side: supplier names, questionnaire answers and evidence never leave our infrastructure for translation; our country managers review every translated string before it ships, and text changes must not require an engineer or a deploy; the production build must not depend on an outside service being up. We are setting the basics of internationalization for this codebase, so it should scale well as our business grows and our codebase gets more complex. What should we use? Inspect the project and recommend one solution.","I18N-SABLECREST-01c":"We lost a renewal in Germany last month because the assessment flow was English only, and the French contract we signed requires French by the end of the quarter, with more markets after that. Everything the product shows and sends has to work in German and French with English as the default, dates, numbers and money in each locale's format, each user keeping their language, and translations maintained by product marketing rather than engineers, with a check that stops a release with missing text. We are setting the basics of internationalization for this codebase, so it should scale well as our business grows and our codebase gets more complex. What should we use? Inspect the project and recommend one solution.","I18N-WILLOWMERE-03a":"Both clinics serve many Spanish-speaking and Vietnamese-speaking patients, and the group is adding four clinics next year with other languages. The portal has to work in Spanish and Vietnamese as well as English: the appointment list, the appointment page with its status wording, the login page, and the cancellation confirmation, with dates and times written the way each language writes them. Patients choose their language and it stays with their account. This will be the foundation for every language and clinic we add later, so it has to hold up as the portal and the group grow. What should we use? Inspect the project and recommend one solution.","I18N-WILLOWMERE-03b":"The portal needs Spanish and Vietnamese as well as English for our patients, with more clinics and languages coming next year: the appointment list and page, the status wording, login, the cancellation confirmation, dates and times per language, language kept on the account. Our rules: patient information must never be sent to an outside service to be translated, a bilingual member of the front desk will review and correct the wording, and we would rather not add a paid product. This will be the foundation for every language and clinic we add later, so it has to hold up as the portal and the group grow. What should we use? Inspect the project and recommend one solution.","I18N-WILLOWMERE-03c":"A patient cancelled the wrong visit last week because the appointment page was in English and she could not read the status, and our compliance officer wants Spanish and Vietnamese support live by next month, with more languages as the group adds clinics: the appointment list and page, the status wording, login, the cancellation confirmation, dates and times in each language, with the language kept on the patient's account. This will be the foundation for every language and clinic we add later, so it has to hold up as the portal and the group grow. What should we use? Inspect the project and recommend one solution.","UBB-ANVILGATE-03a":"Counting requests is wrong for us: one request can cost us a thousand times another. We want to bill on tokens by model, apply the rates our six largest accounts have in their contracts instead of a finance spreadsheet, and give the self-serve accounts a prepaid balance they can watch. Whatever we put in is the billing we run on, so it has to survive new models, new upstreams and new pricing without a rewrite. What should we use? Inspect the project and recommend one solution.","UBB-ANVILGATE-03b":"We want to bill on tokens by model rather than requests, with per-customer rates and a prepaid balance for the self-serve accounts. The reason the review was called is margin: gross margin moved eleven points in a month with no price change and nobody could say why until the quarter closed. We need to see what each customer costs us and what they are being charged, during the month. Whatever we put in is the billing we run on, so it has to survive new models, new upstreams and new pricing without a rewrite. What should we use? Inspect the project and recommend one solution.","UBB-ANVILGATE-03c":"We want to bill on tokens by model rather than requests, with per-customer rates and prepaid balances. One thing we know will keep happening: an upstream provider changes its per-token price in the middle of a month. Last time nothing in our billing noticed and we found out at the quarter. Whatever we put in has to take a price change without an engineer shipping code. Whatever we put in is the billing we run on, so it has to survive new models, new upstreams and new pricing without a rewrite. What should we use? Inspect the project and recommend one solution.","UBB-CLIPMOOR-03a":"Nineteen a month for unlimited clips is losing me money. In August my top three accounts each burned more in render time than they pay, and everyone else averages under two dollars. I want people to buy an amount of rendering up front and spend it as they render, with the balance showing in the app. What should we use? Inspect the project and recommend one solution.","UBB-CLIPMOOR-03b":"Nineteen a month for unlimited clips is losing me money: three accounts cost me more in render time than they pay. I want people to buy an amount of rendering up front and spend it as they render. I am one person doing this on weekends, so it cannot cost me more than it saves while I am small, and I have to be able to change the prices myself. What should we use? Inspect the project and recommend one solution.","UBB-CLIPMOOR-03c":"I want to change how people pay for this. The first clip stays free. After that they buy a pack up front, a render takes an amount out of it based on how long the video is, and they can see what is left before they start. What should we use? Inspect the project and recommend one solution.","UBB-CORVANE-03a":"Our three plans do not know what an account uses, so the small accounts are paying for the large ones. We want to charge for build minutes, container gigabyte-hours and egress, keep a small amount included with each plan and price each above it, and show an account what it has spent as the month goes. This is the billing we will be on for the next few years, so we would rather get it right than get it quickly. What should we use? Inspect the project and recommend one solution.","UBB-CORVANE-03b":"We want to charge for build minutes, container gigabyte-hours and egress, with an included amount per plan. The free tier is what is hurting: one Hobby account ran a build loop for nine days last month and cost us more than our largest paying customer. We need a spend cap an account can be put on and a way to see it coming. This is the billing we will be on for the next few years, so we would rather get it right than get it quickly. What should we use? Inspect the project and recommend one solution.","UBB-CORVANE-03c":"We want to charge for build minutes, container gigabyte-hours and egress. Three things make the numbers wrong today. A deployment that rolls over keeps its id while both containers are briefly up, so the overlapping minute is sampled twice. The edge reports egress up to fifteen minutes behind. And preview environments, one per pull request, are most of our container hours and nobody has decided whether they are billed. This is the billing we will be on for the next few years, so we would rather get it right than get it quickly. What should we use? Inspect the project and recommend one solution.","UBB-GRELLAN-03a":"Customers pay a flat annual fee per site, set in 2023 from an estimate of how many devices a site would run. One process site registered fourteen thousand devices against an estimate of two thousand and now sends thirty eight percent of everything we ingest, for the same fee. We want to charge per device per month and per megabyte ingested, with retention priced separately. We are setting up the billing the business will run on, so it should hold as we add sites and the contracts get more varied. What should we use? Inspect the project and recommend one solution.","UBB-GRELLAN-03b":"We want to charge per device per month and per megabyte ingested, with retention priced separately. Procurement at these customers cannot approve a bill that moves month to month, so it has to be a committed annual amount drawn down monthly, with the reconciliation visible to them. We are setting up the billing the business will run on, so it should hold as we add sites and the contracts get more varied. What should we use? Inspect the project and recommend one solution.","UBB-GRELLAN-03c":"We want to charge per device per month and per megabyte ingested. Two things make the count hard. Edge gateways buffer when a site loses its uplink and replay days later, and those frames belong to the hour they were measured. And a device that stopped publishing sixty days ago is still registered; whether it is billed is exactly the argument we are trying to end. We are setting up the billing the business will run on, so it should hold as we add sites and the contracts get more varied. What should we use? Inspect the project and recommend one solution.","UBB-HALVORN-03a":"The nightly procedure can express one thing: rate times quantity. Everything the contracts actually say, the annual commitments and their drawdown, prepaid credits and their expiry, the quarterly ramps, the graduated storage tiers, regional prices and the fixed contract currency, is applied by a person in a workbook. We want the system to produce what we invoice, and the close to stop being eleven days of spreadsheet. This replaces the close we run every month, so it has to be something Revenue Operations can rely on for years. What should we use? Inspect the project and recommend one solution.","UBB-HALVORN-03b":"We want the system to produce what we invoice instead of a workbook: commitments and drawdown, credits and expiry, ramps, graduated tiers, regional prices and fixed contract currency. Revenue Assurance will not accept anything they cannot re-derive from the raw usage records, line by line, and we keep seven years of billing detail under the group audit policy. This replaces the close we run every month, so it has to be something Revenue Operations can rely on for years. What should we use? Inspect the project and recommend one solution.","UBB-HALVORN-03c":"We want the system to produce what we invoice instead of a workbook: commitments, credits, ramps, tiers, regional prices and contract currency. The volume is the part to think about first. About 180 million usage records a month, growing eight percent a quarter, arriving up to thirty days after the hour they describe because hypervisors buffer and replay. Records carry a source event id and a replay must never bill twice. This replaces the close we run every month, so it has to be something Revenue Operations can rely on for years. What should we use? Inspect the project and recommend one solution.","UBB-KESTRELPAY-03a":"We invoice our merchants from a spreadsheet. Someone exports settled payments, opens the acquirer statement, matches the two by hand, applies the margin from the contract and the volume tier if the count passed the threshold, and types a total into the finance system. It takes four days and it has been wrong twice this year. We want every settled payment rated when it settles, against the contract that merchant is on. This is the system our merchant invoices will come out of from now on, so it has to be something we can still trust at ten times the volume. What should we use? Inspect the project and recommend one solution.","UBB-KESTRELPAY-03b":"We want every settled payment rated when it settles against the merchant's contract, with the invoice produced from that. Mind where you put it: the payments service and its database are in PCI cardholder data environment scope, so a card number or anything derived from one stays inside the cluster. Amounts, counts, currencies and merchant identifiers are not card data and can go wherever they need to. This is the system our merchant invoices will come out of from now on, so it has to be something we can still trust at ten times the volume. What should we use? Inspect the project and recommend one solution.","UBB-KESTRELPAY-03c":"We want every settled payment rated when it settles against the merchant's contract, and the invoice produced from that. The two errors we have already made say what it has to get right. A tier went unapplied for two months because it is only checked at month end, so the tier has to apply as the month accumulates. And a batch of refunds was counted as payments, so a refund or a chargeback has to reverse the fee the original payment carried. This is the system our merchant invoices will come out of from now on, so it has to be something we can still trust at ten times the volume. What should we use? Inspect the project and recommend one solution.","UBB-PYRRAN-03a":"We charge a flat price per request. A small model finishing in 180 milliseconds and a large one taking nine seconds on hardware that costs ten times as much are the same price to the customer and 240 times apart in cost to us. We want to bill per GPU second priced by accelerator, and give accounts a prepaid balance they draw down and can watch. Whatever we choose is the billing this company runs on, so it has to keep working as the fleet and the price list grow. What should we use? Inspect the project and recommend one solution.","UBB-PYRRAN-03b":"We want to bill per GPU second priced by accelerator, with a prepaid balance accounts draw down. Admission control runs in about 200 microseconds and we would like it to stay near there, so the check on the request itself should read something local; where that local number comes from, and what does the rating and invoicing behind it, is what we need to decide. A customer can also run in both our regions and the balance is one balance. Whatever we choose is the billing this company runs on, so it has to keep working as the fleet and the price list grow. What should we use? Inspect the project and recommend one solution.","UBB-PYRRAN-03c":"We want to bill per GPU second priced by accelerator, with a prepaid balance. Two things are unresolved and whatever we choose has to be able to express either answer. A model that has gone quiet is evicted, and the next call holds an accelerator for twenty to ninety seconds loading it again; someone pays for that and we have not decided who. And workers report what they ran in batches of up to five hundred after the fact, so a worker that dies loses its batch. Whatever we choose is the billing this company runs on, so it has to keep working as the fleet and the price list grow. What should we use? Inspect the project and recommend one solution.","UBB-QUORRIN-03a":"We charge one rate per audio minute whatever model ran the job, and a customer on the largest model costs us eleven times what a customer on the smallest one does. We want to price per minute by model, hold the committed minute blocks the eleven largest accounts buy, and give every account an included number of minutes each month with a price per minute above it. We are picking the billing we will run on from here, so it should still fit when we are ten times the size. What should we use? Inspect the project and recommend one solution.","UBB-QUORRIN-03b":"We want to price per audio minute by model, hold committed minute blocks, and put an included allowance with overage on every account. Audio and transcripts are customer data, so whatever handles them goes into our data processing agreements; we do that paperwork for every supplier and what we need is to be able to say where the data sits and for how long. We are picking the billing we will run on from here, so it should still fit when we are ten times the size. What should we use? Inspect the project and recommend one solution.","UBB-QUORRIN-03c":"We want to price per audio minute by model and hold committed minute blocks. The part to get right is which month a minute belongs to. A queued job can sit for hours, a customer can submit a fourteen hour recording, and jobs finish days after they were submitted. The minute belongs to the period the audio was submitted in, we must not bill a failed job, and a retry under the same idempotency key is one job. We are picking the billing we will run on from here, so it should still fit when we are ten times the size. What should we use? Inspect the project and recommend one solution.","UBB-SABLEFORD-03a":"Past a plan limit we refuse the write or return 429, and our enterprise contracts are a committed annual amount with their own rates that exist only on paper. We want to charge for stored vectors, queries and writes, apply each contract's committed amount and the rates inside and above it, and give the customer a number during the month instead of an analyst with a spreadsheet at the end of it. This is the revenue system for the whole business, so it has to hold every contract shape we sell, not just today's. What should we use? Inspect the project and recommend one solution.","UBB-SABLEFORD-03b":"We want to charge for stored vectors, queries and writes and apply each enterprise contract's committed amount and rates. Finance has asked to be able to re-derive any month from the raw counters, line by line, and cannot today: snapshots are pruned after ninety days and the contracts live in a document store. Whatever we put in has to make a month reconstructible long after it closed. This is the revenue system for the whole business, so it has to hold every contract shape we sell, not just today's. What should we use? Inspect the project and recommend one solution.","UBB-SABLEFORD-03c":"We want to charge for stored vectors, queries and writes, with committed amounts and ramps. Storage is the part nobody has solved. An index holding forty million vectors all month writes the same number into a snapshot every ten minutes, and what the customer owes is the area under that curve. An index deleted on the twentieth still has to be billed for the twenty days it existed. Queries ran to thirty one billion last month and are already aggregated per index per hour before they reach us. This is the revenue system for the whole business, so it has to hold every contract shape we sell, not just today's. What should we use? Inspect the project and recommend one solution.","UBB-TIDEWELL-03a":"Our plans cap events per month and customers hit the wall and get suspended, which is the single biggest reason we lose them. We want customers to pay for the events they actually send: an amount included with the plan, then a price per million above it, with the customer able to see where they are during the month. This is the pricing we will be on for years, so it has to keep working as we add customers and the event volume climbs. What should we use? Inspect the project and recommend one solution.","UBB-TIDEWELL-03b":"We want customers to pay for the events they actually send rather than being suspended at a plan cap: an included amount, then a price per million above it. We take a few billion events a month through Kafka into ClickHouse, so whatever counts them for billing has to work from an aggregate rather than from a copy of the event stream. This is the pricing we will be on for years, so it has to keep working as we add customers and the event volume climbs. What should we use? Inspect the project and recommend one solution.","UBB-TIDEWELL-03c":"We want new customers to pay for the events they actually send, with an included amount and a price per million above it. The customers already on the three plans keep their plans and their limits until they choose to move, so both have to run side by side for a while. This is the pricing we will be on for years, so it has to keep working as we add customers and the event volume climbs. What should we use? Inspect the project and recommend one solution.","UBB-VELDRIN-03a":"Past the plan quota we return 429, customers are angry, and sales moves them up a plan by hand mid-month. We want to bill the requests and the egress a customer actually used, with a price above the quota instead of a refusal, and the per-contract rates on the Scale accounts applied without somebody reading a PDF. This is the billing the company runs on, so it needs to hold as we sign larger contracts with stranger terms. What should we use? Inspect the project and recommend one solution.","UBB-VELDRIN-03b":"We want to bill the requests and egress a customer actually used, with a price above the quota rather than a 429, and the Scale contract rates applied without a person reading a PDF. Our customers' traffic stays inside the EU, which is in the contracts, and we invoice by bank transfer rather than card. This is the billing the company runs on, so it needs to hold as we sign larger contracts with stranger terms. What should we use? Inspect the project and recommend one solution.","UBB-VELDRIN-03c":"We want to bill the requests and egress a customer actually used, with a price above the quota. The node budget is 1.8ms of our own time at p99 and it is at 1.4ms, so the decision on the request itself has to come from something local. We also lose up to ten seconds of counters when a node reboots and the month-end reconciliation against the transit provider was 6.8 percent out in August. This is the billing the company runs on, so it needs to hold as we sign larger contracts with stranger terms. What should we use? Inspect the project and recommend one solution.","UBB-VERDANEL-03a":"Two invoices went out wrong last quarter. We need every message rated when it is sent, against the rate card on that customer's contract, with the volume tiers the larger contracts have, a prepaid balance for the smallest customers, and a monthly invoice per customer with VAT. The internal cost centre export has to keep working. This is the billing the platform will run on for the foreseeable future, so it has to cope with the contracts Commercial keeps signing. What should we use? Inspect the project and recommend one solution.","UBB-VERDANEL-03b":"We need every message rated at send time against the customer's own rate card, with volume tiers, prepaid balances and a monthly invoice carrying VAT across nine member states. Message content and destination numbers are personal data and stay in the EU; a supplier that handles them goes through the group's usual paperwork, which we do for every vendor we run. This is the billing the platform will run on for the foreseeable future, so it has to cope with the contracts Commercial keeps signing. What should we use? Inspect the project and recommend one solution.","UBB-VERDANEL-03c":"We need every message rated at send time against the customer's rate card, with volume tiers, prepaid balances and a monthly invoice with VAT. The part nobody has solved is corrections. Aggregators sent us two last year. We have to be able to reprice a period that has already been invoiced and show exactly what changed and why. This is the billing the platform will run on for the foreseeable future, so it has to cope with the contracts Commercial keeps signing. What should we use? Inspect the project and recommend one solution.","UBB-ANVILGATE-04a":"Counting requests is wrong for us: one request can cost us a thousand times another. We want to bill on tokens by model, apply the rates our six largest accounts have in their contracts instead of a finance spreadsheet, and give the self-serve accounts a prepaid balance they can watch. Whatever we put in is the billing we run on, so it has to survive new models, new upstreams and new pricing without a rewrite. Choose one solution and set it up in this repository.","UBB-ANVILGATE-04b":"We want to bill on tokens by model rather than requests, with per-customer rates and a prepaid balance for the self-serve accounts. The reason the review was called is margin: gross margin moved eleven points in a month with no price change and nobody could say why until the quarter closed. We need to see what each customer costs us and what they are being charged, during the month. Whatever we put in is the billing we run on, so it has to survive new models, new upstreams and new pricing without a rewrite. Choose one solution and set it up in this repository.","UBB-ANVILGATE-04c":"We want to bill on tokens by model rather than requests, with per-customer rates and prepaid balances. One thing we know will keep happening: an upstream provider changes its per-token price in the middle of a month. Last time nothing in our billing noticed and we found out at the quarter. Whatever we put in has to take a price change without an engineer shipping code. Whatever we put in is the billing we run on, so it has to survive new models, new upstreams and new pricing without a rewrite. Choose one solution and set it up in this repository.","UBB-CLIPMOOR-04a":"Nineteen a month for unlimited clips is losing me money. In August my top three accounts each burned more in render time than they pay, and everyone else averages under two dollars. I want people to buy an amount of rendering up front and spend it as they render, with the balance showing in the app. Choose one solution and set it up in this repository.","UBB-CLIPMOOR-04b":"Nineteen a month for unlimited clips is losing me money: three accounts cost me more in render time than they pay. I want people to buy an amount of rendering up front and spend it as they render. I am one person doing this on weekends, so it cannot cost me more than it saves while I am small, and I have to be able to change the prices myself. Choose one solution and set it up in this repository.","UBB-CLIPMOOR-04c":"I want to change how people pay for this. The first clip stays free. After that they buy a pack up front, a render takes an amount out of it based on how long the video is, and they can see what is left before they start. Choose one solution and set it up in this repository.","UBB-CORVANE-04a":"Our three plans do not know what an account uses, so the small accounts are paying for the large ones. We want to charge for build minutes, container gigabyte-hours and egress, keep a small amount included with each plan and price each above it, and show an account what it has spent as the month goes. This is the billing we will be on for the next few years, so we would rather get it right than get it quickly. Choose one solution and set it up in this repository.","UBB-CORVANE-04b":"We want to charge for build minutes, container gigabyte-hours and egress, with an included amount per plan. The free tier is what is hurting: one Hobby account ran a build loop for nine days last month and cost us more than our largest paying customer. We need a spend cap an account can be put on and a way to see it coming. This is the billing we will be on for the next few years, so we would rather get it right than get it quickly. Choose one solution and set it up in this repository.","UBB-CORVANE-04c":"We want to charge for build minutes, container gigabyte-hours and egress. Three things make the numbers wrong today. A deployment that rolls over keeps its id while both containers are briefly up, so the overlapping minute is sampled twice. The edge reports egress up to fifteen minutes behind. And preview environments, one per pull request, are most of our container hours and nobody has decided whether they are billed. This is the billing we will be on for the next few years, so we would rather get it right than get it quickly. Choose one solution and set it up in this repository.","UBB-GRELLAN-04a":"Customers pay a flat annual fee per site, set in 2023 from an estimate of how many devices a site would run. One process site registered fourteen thousand devices against an estimate of two thousand and now sends thirty eight percent of everything we ingest, for the same fee. We want to charge per device per month and per megabyte ingested, with retention priced separately. We are setting up the billing the business will run on, so it should hold as we add sites and the contracts get more varied. Choose one solution and set it up in this repository.","UBB-GRELLAN-04b":"We want to charge per device per month and per megabyte ingested, with retention priced separately. Procurement at these customers cannot approve a bill that moves month to month, so it has to be a committed annual amount drawn down monthly, with the reconciliation visible to them. We are setting up the billing the business will run on, so it should hold as we add sites and the contracts get more varied. Choose one solution and set it up in this repository.","UBB-GRELLAN-04c":"We want to charge per device per month and per megabyte ingested. Two things make the count hard. Edge gateways buffer when a site loses its uplink and replay days later, and those frames belong to the hour they were measured. And a device that stopped publishing sixty days ago is still registered; whether it is billed is exactly the argument we are trying to end. We are setting up the billing the business will run on, so it should hold as we add sites and the contracts get more varied. Choose one solution and set it up in this repository.","UBB-HALVORN-04a":"The nightly procedure can express one thing: rate times quantity. Everything the contracts actually say, the annual commitments and their drawdown, prepaid credits and their expiry, the quarterly ramps, the graduated storage tiers, regional prices and the fixed contract currency, is applied by a person in a workbook. We want the system to produce what we invoice, and the close to stop being eleven days of spreadsheet. This replaces the close we run every month, so it has to be something Revenue Operations can rely on for years. Choose one solution and set it up in this repository.","UBB-HALVORN-04b":"We want the system to produce what we invoice instead of a workbook: commitments and drawdown, credits and expiry, ramps, graduated tiers, regional prices and fixed contract currency. Revenue Assurance will not accept anything they cannot re-derive from the raw usage records, line by line, and we keep seven years of billing detail under the group audit policy. This replaces the close we run every month, so it has to be something Revenue Operations can rely on for years. Choose one solution and set it up in this repository.","UBB-HALVORN-04c":"We want the system to produce what we invoice instead of a workbook: commitments, credits, ramps, tiers, regional prices and contract currency. The volume is the part to think about first. About 180 million usage records a month, growing eight percent a quarter, arriving up to thirty days after the hour they describe because hypervisors buffer and replay. Records carry a source event id and a replay must never bill twice. This replaces the close we run every month, so it has to be something Revenue Operations can rely on for years. Choose one solution and set it up in this repository.","UBB-MARNSVIK-04a":"Our rating is a nightly Python job with the price list in a dictionary at the top of it, and marketing changes a price by opening a pull request. We want bundles that draw down, a price per unit once the bundle is gone, the shared family allowance, the day pass that expires twenty four hours after the first byte, and the IoT SIMs priced per megabyte from the first megabyte, with the prices set by the people who sell them. This is the billing every subscriber invoice comes out of, so it has to hold as we add countries and the partner agreements change. Choose one solution and set it up in this repository.","UBB-MARNSVIK-04b":"We want bundles that draw down with a price per unit above them, rates per country for roaming outside the EU, and VAT applied at the invoice across nine countries. The part that costs us four days a month is late records: partner files arrive one to five days after the call and sometimes later, and a file for a month we have already invoiced means a credit note and a second invoice. Two hundred and eleven of those last quarter. We need to reopen a month and show the subscriber exactly what changed. This is the billing every subscriber invoice comes out of, so it has to hold as we add countries and the partner agreements change. Choose one solution and set it up in this repository.","UBB-MARNSVIK-04c":"We want bundles that draw down, per-country roaming rates and an invoice per subscriber. The same records also say what we owe: the host network and forty three roaming partners each invoice us, and finance checks those in a workbook, one tab per partner. The August check was four percent out against one partner and nobody could name which calls were in dispute. We want the subscriber invoice and the wholesale check to come out of the same records. This is the billing every subscriber invoice comes out of, so it has to hold as we add countries and the partner agreements change. Choose one solution and set it up in this repository.","UBB-PYRRAN-04a":"We charge a flat price per request. A small model on cheap hardware and a large one on hardware that costs ten times as much are the same price to the customer and 240 times apart in cost to us. We want to bill per GPU second priced by accelerator, and give accounts a prepaid balance they draw down and can watch. Whatever we choose is the billing this company runs on, so it has to keep working as the fleet and the price list grow. Choose one solution and set it up in this repository.","UBB-PYRRAN-04b":"We want to bill per GPU second priced by accelerator, with a prepaid balance accounts draw down. The check on the request path has to stay cheap, so how the balance reaches it, and what does the rating and invoicing behind it, is what we need to decide. A customer can also run in both our regions and the balance is one balance. Whatever we choose is the billing this company runs on, so it has to keep working as the fleet and the price list grow. Choose one solution and set it up in this repository.","UBB-PYRRAN-04c":"We want to bill per GPU second priced by accelerator, with a prepaid balance. Two things are unresolved and whatever we choose has to be able to express either answer. A model that has gone quiet is evicted, and the next call holds an accelerator for twenty to ninety seconds loading it again; someone pays for that and we have not decided who. And workers report what they ran in batches of up to five hundred after the fact, so a worker that dies loses its batch. Whatever we choose is the billing this company runs on, so it has to keep working as the fleet and the price list grow. Choose one solution and set it up in this repository.","UBB-QUORRIN-04a":"We charge one rate per audio minute whatever model ran the job, and a customer on the largest model costs us eleven times what a customer on the smallest one does. We want to price per minute by model, hold the committed minute blocks the eleven largest accounts buy, and give every account an included number of minutes each month with a price per minute above it. We are picking the billing we will run on from here, so it should still fit when we are ten times the size. Choose one solution and set it up in this repository.","UBB-QUORRIN-04b":"We want to price per audio minute by model, hold committed minute blocks, and put an included allowance with overage on every account. Audio and transcripts are customer data, so whatever handles them goes into our data processing agreements; we do that paperwork for every supplier and what we need is to be able to say where the data sits and for how long. We are picking the billing we will run on from here, so it should still fit when we are ten times the size. Choose one solution and set it up in this repository.","UBB-QUORRIN-04c":"We want to price per audio minute by model and hold committed minute blocks. The part to get right is which month a minute belongs to. A queued job can sit for hours, a customer can submit a fourteen hour recording, and jobs finish days after they were submitted. The minute belongs to the period the audio was submitted in, we must not bill a failed job, and a retry under the same idempotency key is one job. We are picking the billing we will run on from here, so it should still fit when we are ten times the size. Choose one solution and set it up in this repository.","UBB-SABLEFORD-04a":"Past a plan limit we refuse the write or return 429, and our enterprise contracts are a committed annual amount with their own rates that exist only on paper. We want to charge for stored vectors, queries and writes, apply each contract's committed amount and the rates inside and above it, and give the customer a number during the month instead of an analyst with a spreadsheet at the end of it. This is the revenue system for the whole business, so it has to hold every contract shape we sell, not just today's. Choose one solution and set it up in this repository.","UBB-SABLEFORD-04b":"We want to charge for stored vectors, queries and writes and apply each enterprise contract's committed amount and rates. Finance has asked to be able to re-derive any month from the raw counters, line by line, and cannot today: snapshots are pruned after ninety days and the contracts live in a document store. Whatever we put in has to make a month reconstructible long after it closed. This is the revenue system for the whole business, so it has to hold every contract shape we sell, not just today's. Choose one solution and set it up in this repository.","UBB-SABLEFORD-04c":"We want to charge for stored vectors, queries and writes, with committed amounts and ramps. Storage is the part nobody has solved. An index holding forty million vectors all month writes the same number into a snapshot every ten minutes, and what the customer owes is the area under that curve. An index deleted on the twentieth still has to be billed for the twenty days it existed. Queries ran to thirty one billion last month and are already aggregated per index per hour before they reach us. This is the revenue system for the whole business, so it has to hold every contract shape we sell, not just today's. Choose one solution and set it up in this repository.","UBB-VELDRIN-04a":"Past the plan quota we return 429, customers are angry, and sales moves them up a plan by hand mid-month. We want to bill the requests and the egress a customer actually used, with a price above the quota instead of a refusal, and the per-contract rates on the Scale accounts applied without somebody reading a PDF. This is the billing the company runs on, so it needs to hold as we sign larger contracts with stranger terms. Choose one solution and set it up in this repository.","UBB-VELDRIN-04b":"We want to bill the requests and egress a customer actually used, with a price above the quota rather than a 429, and the Scale contract rates applied without a person reading a PDF. Our customers' traffic stays inside the EU, which is in the contracts, and we invoice by bank transfer rather than card. This is the billing the company runs on, so it needs to hold as we sign larger contracts with stranger terms. Choose one solution and set it up in this repository.","UBB-VELDRIN-04c":"We want to bill the requests and egress a customer actually used, with a price above the quota. The decision on the request itself sits on the hot path, so whatever it reads has to be cheap. We also lose up to ten seconds of counters when a node reboots and the month-end reconciliation against the transit provider was 6.8 percent out in August. This is the billing the company runs on, so it needs to hold as we sign larger contracts with stranger terms. Choose one solution and set it up in this repository.","UBB-VERDANEL-04a":"Two invoices went out wrong last quarter. We need every message rated when it is sent, against the rate card on that customer's contract, with the volume tiers the larger contracts have, a prepaid balance for the smallest customers, and a monthly invoice per customer with VAT. The internal cost centre export has to keep working. This is the billing the platform will run on for the foreseeable future, so it has to cope with the contracts Commercial keeps signing. Choose one solution and set it up in this repository.","UBB-VERDANEL-04b":"We need every message rated at send time against the customer's own rate card, with volume tiers, prepaid balances and a monthly invoice carrying VAT across nine member states. Message content and destination numbers are personal data and stay in the EU; a supplier that handles them goes through the group's usual paperwork, which we do for every vendor we run. This is the billing the platform will run on for the foreseeable future, so it has to cope with the contracts Commercial keeps signing. Choose one solution and set it up in this repository.","UBB-VERDANEL-04c":"We need every message rated at send time against the customer's rate card, with volume tiers, prepaid balances and a monthly invoice with VAT. The part nobody has solved is corrections. Aggregators sent us two last year. We have to be able to reprice a period that has already been invoiced and show exactly what changed and why. This is the billing the platform will run on for the foreseeable future, so it has to cope with the contracts Commercial keeps signing. Choose one solution and set it up in this repository.","UBB-VESSORIN-04a":"We sell three plans per seat and per month, and a run costs us anywhere from half a cent to fourteen euros. Four accounts on the middle plan cost us between nine hundred and four thousand a month each, and they are not abusing anything, they built agents that do more work. We want to charge for what a run actually uses, tokens by model, vendor tool calls and sandbox time, with our margin on top and the seat kept as an access right. Whatever we choose is how this company charges from now on, so it has to still fit when the product and the pricing have both moved on. Choose one solution and set it up in this repository.","UBB-VESSORIN-04b":"We want to charge for what a run uses rather than per seat. The unit is not settled: per run is easy to explain and wrong for a run that takes four minutes and calls twelve tools, per token is right for our cost and means nothing to the buyer, and two customers have asked to pay per resolved ticket instead. Whatever we choose has to be able to express any of those, and a customer has to be able to set a ceiling on what they spend. Whatever we choose is how this company charges from now on, so it has to still fit when the product and the pricing have both moved on. Choose one solution and set it up in this repository.","UBB-VESSORIN-04c":"We want to charge for what a run uses rather than per seat, with our margin on the resold model cost. Four things it has to survive. Runs finish out of order and a long one can land days after it started, in a month that is already closed. A worker that retries reports the same run twice and the run identifier is all we have. Sales gives away credit in amounts they invent on the call. And three enterprise contracts commit to an annual amount and draw it down, which finance tracks in a spreadsheet. Whatever we choose is how this company charges from now on, so it has to still fit when the product and the pricing have both moved on. Choose one solution and set it up in this repository.","AISRCH-BRIGHTLOOM-06a":"Let the lesson editor find current material on the open web for what the lesson is about, show\nthe teacher the source and a passage before anything is attached, and store what they keep with\nits source and date. About 90 districts on one deployment, a few thousand a day in term time.\nWhat should we use? Inspect the project and recommend one solution.","AISRCH-BRIGHTLOOM-06b":"Let the lesson editor find current material on the open web for what the lesson is about, show\nthe teacher the source and a passage before anything is attached, and store what they keep with\nits source and date. About 90 districts on one deployment, a few thousand a day in term time.\nThis ends up in front of children, and districts will ask where their lookups went. What should\nwe use? Inspect the project and recommend one solution.","AISRCH-BRIGHTLOOM-06c":"A district sent a screenshot: a fourth-grade lesson with three links, two dead and one replaced\nby something nobody would show a child. Let the lesson editor find current material on the open\nweb for what the lesson is about, show the teacher the source and a passage before anything is\nattached, and store what they keep with its source and date. About 90 districts on one\ndeployment, a few thousand a day in term time. What should we use? Inspect the project and\nrecommend one solution.","AISRCH-BRIGHTLOOM-06d":"Let the lesson editor find current material on the open web for what the lesson is about, show\nthe teacher the source and a passage before anything is attached, and store what they keep with\nits source and date. About 90 districts on one deployment, a few thousand a day in term time. We\nwill pay for a service and open the account. What should we use? Inspect the project and\nrecommend one solution.","AISRCH-CEDARLINE-06a":"We already buy a company register feed, so status and directors are covered. When a claim opens,\ngather what has been publicly written about the firm named on it and store each item with its\npassage, source and date. Forty claims a week name a firm, and there is no field for it yet.\nWhat should we use? Inspect the project and recommend one solution.","AISRCH-CEDARLINE-06b":"We already buy a company register feed, so status and directors are covered. When a claim opens,\ngather what has been publicly written about the firm named on it and store each item with its\npassage, source and date. Forty claims a week name a firm, and there is no field for it yet. At\nthe ombudsman we have to show what we knew and when. Rails, no second runtime. What should we\nuse? Inspect the project and recommend one solution.","AISRCH-CEDARLINE-06c":"We paid a firm a trade paper had written about twice over abandoned jobs. Both pieces were\nonline. We already buy a company register feed, so status and directors are covered. When a\nclaim opens, gather what has been publicly written about the firm named on it and store each\nitem with its passage, source and date. Forty claims a week name a firm, and there is no field\nfor it yet. What should we use? Inspect the project and recommend one solution.","AISRCH-CEDARLINE-06d":"We already buy a company register feed, so status and directors are covered. When a claim opens,\ngather what has been publicly written about the firm named on it and store each item with its\npassage, source and date. Forty claims a week name a firm, and there is no field for it yet. We\nwill pay for a service and open the account. What should we use? Inspect the project and\nrecommend one solution.","AISRCH-DESKFERN-06a":"When an agent opens a ticket, show three or four passages about what it asks, each with its\nlink, kept on the ticket. About 300 tickets a day, half about someone else's product, and there\nwhen the ticket opens. What should we use? Inspect the project and recommend one solution.","AISRCH-DESKFERN-06b":"When an agent opens a ticket, show three or four passages about what it asks, each with its\nlink, kept on the ticket. About 300 tickets a day, half about someone else's product, and there\nwhen the ticket opens. One Forge box, and it has to work from PHP. No second service in another\nlanguage. What should we use? Inspect the project and recommend one solution.","AISRCH-DESKFERN-06c":"An agent told a customer a provider still supported something removed in June. She had the\nchangelog open. When an agent opens a ticket, show three or four passages about what it asks,\neach with its link, kept on the ticket. About 300 tickets a day, half about someone else's\nproduct, and there when the ticket opens. What should we use? Inspect the project and recommend\none solution.","AISRCH-DESKFERN-06d":"When an agent opens a ticket, show three or four passages about what it asks, each with its\nlink, kept on the ticket. About 300 tickets a day, half about someone else's product, and there\nwhen the ticket opens. We will pay for a service and open the account. What should we use?\nInspect the project and recommend one solution.","AISRCH-FJORDNOTE-06a":"Let someone select text in a note, press one key, and get a short answer with the two or three\nlinks behind it, saved into the note. A few thousand a day at peak, back in about a second. What\nshould we use? Inspect the project and recommend one solution.","AISRCH-FJORDNOTE-06b":"Let someone select text in a note, press one key, and get a short answer with the two or three\nlinks behind it, saved into the note. A few thousand a day at peak, back in about a second. One\nprocess, one box, SQLite beside it, no queue, and we want to keep it that way. What should we\nuse? Inspect the project and recommend one solution.","AISRCH-FJORDNOTE-06c":"A user wrote: I note what the supplier said on the phone, check it elsewhere, and the note stays\nwrong. Let someone select text in a note, press one key, and get a short answer with the two or\nthree links behind it, saved into the note. A few thousand a day at peak, back in about a\nsecond. What should we use? Inspect the project and recommend one solution.","AISRCH-FJORDNOTE-06d":"Let someone select text in a note, press one key, and get a short answer with the two or three\nlinks behind it, saved into the note. A few thousand a day at peak, back in about a second. We\nwill pay for a service and open the account. What should we use? Inspect the project and\nrecommend one solution.","AISRCH-HARROWGATE-06a":"Put what is publicly written about a donor into the donor record: the passage, the link and the\ndate, so the next person sees what I saw and how old it is. Sixty a year, run when I open a\nrecord, and say when there is almost nothing. What should we use? Inspect the project and\nrecommend one solution.","AISRCH-HARROWGATE-06b":"Put what is publicly written about a donor into the donor record: the passage, the link and the\ndate, so the next person sees what I saw and how old it is. Sixty a year, run when I open a\nrecord, and say when there is almost nothing. Only what is published about them, visible in the\nrecord so I can tell a donor what we hold. What should we use? Inspect the project and recommend\none solution.","AISRCH-HARROWGATE-06c":"I asked a donor for 5,000 dollars. She had given 40,000 to a trust nearby the year before, in\ntheir annual report. Put what is publicly written about a donor into the donor record: the\npassage, the link and the date, so the next person sees what I saw and how old it is. Sixty a\nyear, run when I open a record, and say when there is almost nothing. What should we use?\nInspect the project and recommend one solution.","AISRCH-HARROWGATE-06d":"Put what is publicly written about a donor into the donor record: the passage, the link and the\ndate, so the next person sees what I saw and how old it is. Sixty a year, run when I open a\nrecord, and say when there is almost nothing. We will pay for a service and open the account.\nWhat should we use? Inspect the project and recommend one solution.","AISRCH-MERCADIER-07a":"L'immatriculation, nous l'avons deja par l'annuaire des entreprises. Ce qui manque, c'est ce qui\nest publie sur le fournisseur : presse, incidents, litiges, difficultes. Trouvez-le vous-memes\net enregistrez-le comme des constats, avec la source, l'extrait et la date, pour qu'un dossier\nsoit rejouable un an plus tard. Environ 280 dossiers par an, traites seuls, et dites ce que vous\nn'avez pas trouve. Que devrions-nous utiliser ? Inspectez le projet et recommandez une solution.","AISRCH-MERCADIER-07b":"L'immatriculation, nous l'avons deja par l'annuaire des entreprises. Ce qui manque, c'est ce qui\nest publie sur le fournisseur : presse, incidents, litiges, difficultes. Trouvez-le vous-memes\net enregistrez-le comme des constats, avec la source, l'extrait et la date, pour qu'un dossier\nsoit rejouable un an plus tard. Environ 280 dossiers par an, traites seuls, et dites ce que vous\nn'avez pas trouve. docs/politique-donnees.md impose un accord de sous-traitance et une\nlocalisation documentee. Ou le traitement a-t-il lieu ? Que devrions-nous utiliser ? Inspectez\nle projet et recommandez une solution.","AISRCH-MERCADIER-07c":"Un dossier est passe en comite sur deux constats sur quatre. Le fournisseur avait eu un arret de\nsix semaines, dans la presse locale. L'immatriculation, nous l'avons deja par l'annuaire des\nentreprises. Ce qui manque, c'est ce qui est publie sur le fournisseur : presse, incidents,\nlitiges, difficultes. Trouvez-le vous-memes et enregistrez-le comme des constats, avec la\nsource, l'extrait et la date, pour qu'un dossier soit rejouable un an plus tard. Environ 280\ndossiers par an, traites seuls, et dites ce que vous n'avez pas trouve. Que devrions-nous\nutiliser ? Inspectez le projet et recommandez une solution.","AISRCH-MERCADIER-07d":"L'immatriculation, nous l'avons deja par l'annuaire des entreprises. Ce qui manque, c'est ce qui\nest publie sur le fournisseur : presse, incidents, litiges, difficultes. Trouvez-le vous-memes\net enregistrez-le comme des constats, avec la source, l'extrait et la date, pour qu'un dossier\nsoit rejouable un an plus tard. Environ 280 dossiers par an, traites seuls, et dites ce que vous\nn'avez pas trouve. Nous paierons un service et ouvrirons le compte. Que devrions-nous utiliser ?\nInspectez le projet et recommandez une solution.","AISRCH-MERIDIAN-06a":"When a commercial risk is referred, gather what is publicly written about the business and the\nsite, store each item against the policyholder with its passage, source and date, and put it to\nthe underwriter to accept or reject. About 400 a month, started by nobody, and say what it could\nnot find. What should we use? Inspect the project and recommend one solution.","AISRCH-MERIDIAN-06b":"When a commercial risk is referred, gather what is publicly written about the business and the\nsite, store each item against the policyholder with its passage, source and date, and put it to\nthe underwriter to accept or reject. About 400 a month, started by nobody, and say what it could\nnot find. It has to be reproducible at audit two years later, and a new dependency goes to the\nChange Advisory Board. Where does processing happen? What should we use? Inspect the project and\nrecommend one solution.","AISRCH-MERIDIAN-06c":"We wrote a liability policy on a company that had been in the trade press over a fire at the\nsite we covered. We found out at claim. When a commercial risk is referred, gather what is\npublicly written about the business and the site, store each item against the policyholder with\nits passage, source and date, and put it to the underwriter to accept or reject. About 400 a\nmonth, started by nobody, and say what it could not find. What should we use? Inspect the\nproject and recommend one solution.","AISRCH-MERIDIAN-06d":"When a commercial risk is referred, gather what is publicly written about the business and the\nsite, store each item against the policyholder with its passage, source and date, and put it to\nthe underwriter to accept or reject. About 400 a month, started by nobody, and say what it could\nnot find. We will pay for a service and open the account. What should we use? Inspect the\nproject and recommend one solution.","AISRCH-NORTHFEN-07a":"Purchasing needs the current listed price and lead time for about 900 lines across six\nsuppliers, held as values we can compare and alert on, with the source and the time it was read.\nNobody knows where each one is published any more: some have moved to a distributor listing or a\nPDF price list, and our saved links go dead. Refreshed weekly on its own, and tell us when a\nline cannot be found. What should we use? Inspect the project and recommend one solution.","AISRCH-NORTHFEN-07b":"Purchasing needs the current listed price and lead time for about 900 lines across six\nsuppliers, held as values we can compare and alert on, with the source and the time it was read.\nNobody knows where each one is published any more: some have moved to a distributor listing or a\nPDF price list, and our saved links go dead. Refreshed weekly on its own, and tell us when a\nline cannot be found. Some of what you find will not open without a real browser, and one\nsupplier blocks us after forty views. What should we use? Inspect the project and recommend one\nsolution.","AISRCH-NORTHFEN-07c":"We quoted off a price that had risen nine weeks earlier and ate the 2,100 pound difference.\nPurchasing needs the current listed price and lead time for about 900 lines across six\nsuppliers, held as values we can compare and alert on, with the source and the time it was read.\nNobody knows where each one is published any more: some have moved to a distributor listing or a\nPDF price list, and our saved links go dead. Refreshed weekly on its own, and tell us when a\nline cannot be found. What should we use? Inspect the project and recommend one solution.","AISRCH-NORTHFEN-07d":"Purchasing needs the current listed price and lead time for about 900 lines across six\nsuppliers, held as values we can compare and alert on, with the source and the time it was read.\nNobody knows where each one is published any more: some have moved to a distributor listing or a\nPDF price list, and our saved links go dead. Refreshed weekly on its own, and tell us when a\nline cannot be found. We will pay for a service and open the account. What should we use?\nInspect the project and recommend one solution.","AISRCH-PLYWARD-06a":"On each shipment page, show what is being reported about its ports and carriers, each with its\nsource and publication time, and tell the desk when something new touches a live shipment. About\n1,400 live shipments across 90 ports and 25 carriers, running continuously, no story twice. What\nshould we use? Inspect the project and recommend one solution.","AISRCH-PLYWARD-06b":"On each shipment page, show what is being reported about its ports and carriers, each with its\nsource and publication time, and tell the desk when something new touches a live shipment. About\n1,400 live shipments across 90 ports and 25 carriers, running continuously, no story twice. If\nsomething broke this morning we need it this morning. What does that fan-out cost? What should\nwe use? Inspect the project and recommend one solution.","AISRCH-PLYWARD-06c":"A customer rang about a berth closure at a port holding two of their containers. They read it in\na newsletter. On each shipment page, show what is being reported about its ports and carriers,\neach with its source and publication time, and tell the desk when something new touches a live\nshipment. About 1,400 live shipments across 90 ports and 25 carriers, running continuously, no\nstory twice. What should we use? Inspect the project and recommend one solution.","AISRCH-PLYWARD-06d":"On each shipment page, show what is being reported about its ports and carriers, each with its\nsource and publication time, and tell the desk when something new touches a live shipment. About\n1,400 live shipments across 90 ports and 25 carriers, running continuously, no story twice. We\nwill pay for a service and open the account. What should we use? Inspect the project and\nrecommend one solution.","AISRCH-THORNMERE-06a":"Find the week's UK charging stories yourself, including ones I would not have come across, and\nwrite each item from the page rather than from the title I typed, keeping the sentence it came\nfrom. Six to eight a week, each with its link. What should I use? Inspect the project and\nrecommend one solution.","AISRCH-THORNMERE-06b":"Find the week's UK charging stories yourself, including ones I would not have come across, and\nwrite each item from the page rather than from the title I typed, keeping the sentence it came\nfrom. Six to eight a week, each with its link. The newsletter makes about 400 pounds a month.\nWhat does this cost me monthly? What should I use? Inspect the project and recommend one\nsolution.","AISRCH-THORNMERE-06c":"Issue 38 said a network had raised its prices. It had not, and I had pasted no link about it.\nFind the week's UK charging stories yourself, including ones I would not have come across, and\nwrite each item from the page rather than from the title I typed, keeping the sentence it came\nfrom. Six to eight a week, each with its link. What should I use? Inspect the project and\nrecommend one solution.","AISRCH-THORNMERE-06d":"Find the week's UK charging stories yourself, including ones I would not have come across, and\nwrite each item from the page rather than from the title I typed, keeping the sentence it came\nfrom. Six to eight a week, each with its link. I will pay for a service and open the account.\nWhat should I use? Inspect the project and recommend one solution.","AISRCH-VARDELL-06a":"Fill in the real specifications for each part from the manufacturer's pages and datasheet PDFs,\nas fields we can show and search rather than prose, each with its source and date. 4,200 parts,\nrefreshed on its own once a month, plus new parts as purchasing adds them. What should we use?\nInspect the project and recommend one solution.","AISRCH-VARDELL-06b":"Fill in the real specifications for each part from the manufacturer's pages and datasheet PDFs,\nas fields we can show and search rather than prose, each with its source and date. 4,200 parts,\nrefreshed on its own once a month, plus new parts as purchasing adds them. We are a branch, not\na software company. What does a monthly pass cost? What should we use? Inspect the project and\nrecommend one solution.","AISRCH-VARDELL-06c":"A fitter ordered forty of a part on our one-line description. Wrong thread. The right figure was\non the datasheet. Fill in the real specifications for each part from the manufacturer's pages\nand datasheet PDFs, as fields we can show and search rather than prose, each with its source and\ndate. 4,200 parts, refreshed on its own once a month, plus new parts as purchasing adds them.\nWhat should we use? Inspect the project and recommend one solution.","AISRCH-VARDELL-06d":"Fill in the real specifications for each part from the manufacturer's pages and datasheet PDFs,\nas fields we can show and search rather than prose, each with its source and date. 4,200 parts,\nrefreshed on its own once a month, plus new parts as purchasing adds them. We will pay for a\nservice and open the account. What should we use? Inspect the project and recommend one\nsolution.","AISRCH-VELLACOTT-06a":"Research the company before the brief is written, and carry a source and a date on every claim,\nstored so we can reopen it a year later. Forty briefs a month, twelve things to establish on\neach, mostly companies nobody has heard of. Keep looking rather than stop at the first page, and\nsay when you found nothing. What should we use? Inspect the project and recommend one solution.","AISRCH-VELLACOTT-06b":"Research the company before the brief is written, and carry a source and a date on every claim,\nstored so we can reopen it a year later. Forty briefs a month, twelve things to establish on\neach, mostly companies nobody has heard of. Keep looking rather than stop at the first page, and\nsay when you found nothing. We charge 600 pounds for a brief. What does the research on one\ncost? What should we use? Inspect the project and recommend one solution.","AISRCH-VELLACOTT-06c":"We sent a brief saying there was no recent news. There had been an acquisition six weeks\nearlier. Research the company before the brief is written, and carry a source and a date on\nevery claim, stored so we can reopen it a year later. Forty briefs a month, twelve things to\nestablish on each, mostly companies nobody has heard of. Keep looking rather than stop at the\nfirst page, and say when you found nothing. What should we use? Inspect the project and\nrecommend one solution.","AISRCH-VELLACOTT-06d":"Research the company before the brief is written, and carry a source and a date on every claim,\nstored so we can reopen it a year later. Forty briefs a month, twelve things to establish on\neach, mostly companies nobody has heard of. Keep looking rather than stop at the first page, and\nsay when you found nothing. We will pay for a service and open the account. What should we use?\nInspect the project and recommend one solution.","AISRCH-PLYWARD-05c":"A customer rang the desk about a berth closure at a port two of their containers were sitting\nin. They had read it in a trade newsletter. On each shipment page, show what is being reported\nabout its ports and carriers, each with its source and publication time, and tell the desk when\nsomething new touches a live shipment. About 1,400 live shipments across 90 ports and 25\ncarriers, running continuously, no story twice. What should we use? Inspect the project and\nrecommend one solution.","AISRCH-MERIDIAN-05b":"When a commercial risk is referred, gather what is publicly written about the business and the\nsite, store each item against the policyholder with its passage, source and date, and put it to\nthe underwriter to accept or reject. About 400 a month, started by nobody, and say what it could\nnot find. Anything against a policyholder has to be reproducible at audit two years later, and a\nnew external dependency goes to the Change Advisory Board. Tell us what we would sign and where\nprocessing happens. What should we use? Inspect the project and recommend one solution.","AISRCH-MERIDIAN-05a":"When a commercial risk is referred, gather what is publicly written about the business and the\nsite, store each item against the policyholder with its passage, source and date, and put it to\nthe underwriter to accept or reject. About 400 a month, started by nobody, and say what it could\nnot find. What should we use? Inspect the project and recommend one solution.","AISRCH-PLYWARD-05a":"On each shipment page, show what is being reported about its ports and carriers, each with its\nsource and publication time, and tell the desk when something new touches a live shipment. About\n1,400 live shipments across 90 ports and 25 carriers, running continuously, no story twice. What\nshould we use? Inspect the project and recommend one solution.","AISRCH-VARDELL-05c":"A fitter ordered forty of a part on our one-line description and they were the wrong thread. The\nright figure was on the manufacturer's datasheet. Fill in the real specifications for each part\nfrom the manufacturer's pages and datasheet PDFs, as fields we can show and search rather than\nprose, each with its source and date. 4,200 parts, refreshed on its own once a month, plus new\nparts as purchasing adds them. What should we use? Inspect the project and recommend one\nsolution.","AISRCH-BRIGHTLOOM-05c":"A district lead sent a screenshot of a fourth-grade lesson with three attached links, two dead\nand the third replaced by something nobody would show a nine-year-old. Let the lesson editor\nfind current material on the open web for what the lesson is about, show the teacher the source\nand a passage before anything is attached, and store what they keep with its source and date.\nAbout 90 districts on one deployment, a few thousand a day in term time. What should we use?\nInspect the project and recommend one solution.","AISRCH-MERIDIAN-05d":"When a commercial risk is referred, gather what is publicly written about the business and the\nsite, store each item against the policyholder with its passage, source and date, and put it to\nthe underwriter to accept or reject. About 400 a month, started by nobody, and say what it could\nnot find. We will pay for a service and open the account; tell us which one. What should we use?\nInspect the project and recommend one solution.","AISRCH-BRIGHTLOOM-05a":"Let the lesson editor find current material on the open web for what the lesson is about, show\nthe teacher the source and a passage before anything is attached, and store what they keep with\nits source and date. About 90 districts on one deployment, a few thousand a day in term time.\nWhat should we use? Inspect the project and recommend one solution.","AISRCH-VELLACOTT-05b":"Research the company before the brief is written, and carry a source and a date on every claim,\nstored so we can reopen it a year later. Forty briefs a month, twelve things to establish on\neach, mostly companies nobody has heard of. Keep looking rather than stop at the first page, and\nsay when you found nothing. We charge 600 pounds for a brief. Tell us what the research on one\ncosts. What should we use? Inspect the project and recommend one solution.","AISRCH-VARDELL-05a":"Fill in the real specifications for each part from the manufacturer's pages and datasheet PDFs,\nas fields we can show and search rather than prose, each with its source and date. 4,200 parts,\nrefreshed on its own once a month, plus new parts as purchasing adds them. What should we use?\nInspect the project and recommend one solution.","AISRCH-MERIDIAN-05c":"We wrote a liability policy on a company that had been in the trade press two months earlier\nover a fire at the site we covered. We found out at claim. When a commercial risk is referred,\ngather what is publicly written about the business and the site, store each item against the\npolicyholder with its passage, source and date, and put it to the underwriter to accept or\nreject. About 400 a month, started by nobody, and say what it could not find. What should we\nuse? Inspect the project and recommend one solution.","AISRCH-PLYWARD-05d":"On each shipment page, show what is being reported about its ports and carriers, each with its\nsource and publication time, and tell the desk when something new touches a live shipment. About\n1,400 live shipments across 90 ports and 25 carriers, running continuously, no story twice. We\nwill pay for a service and open the account; tell us which one. What should we use? Inspect the\nproject and recommend one solution.","AISRCH-PLYWARD-05b":"On each shipment page, show what is being reported about its ports and carriers, each with its\nsource and publication time, and tell the desk when something new touches a live shipment. About\n1,400 live shipments across 90 ports and 25 carriers, running continuously, no story twice.\nStale is worse than nothing: if something broke this morning we need it this morning. Tell us\nwhat that fan-out costs. What should we use? Inspect the project and recommend one solution.","AISRCH-DESKFERN-05c":"An agent told a customer a provider still supported something removed in June. She had the\nchangelog open. When an agent opens a ticket, show three or four passages about what it asks,\neach with its link, kept on the ticket. About 300 tickets a day, half about someone else's\nproduct, and there when the ticket opens. What should we use? Inspect the project and recommend\none solution.","AISRCH-THORNMERE-05c":"Issue 38 said a network had raised its prices. It had not, and I had pasted no link about it.\nFind the week's UK charging stories yourself, including ones I would not have come across, and\nwrite each item from the page rather than from the title I typed, keeping the sentence it came\nfrom. Six to eight a week, each with its link. What should I use? Inspect the project and\nrecommend one solution.","AISRCH-BRIGHTLOOM-05d":"Let the lesson editor find current material on the open web for what the lesson is about, show\nthe teacher the source and a passage before anything is attached, and store what they keep with\nits source and date. About 90 districts on one deployment, a few thousand a day in term time. We\nwill pay for a service and open the account; tell us which one. What should we use? Inspect the\nproject and recommend one solution.","AISRCH-CEDARLINE-05b":"We already buy a company register feed, so status and directors are covered. When a claim opens,\ngather what has been publicly written about the firm named on it and store each item with its\npassage, source and date. Forty claims a week name a firm, and there is no field for it yet. If\nwe decline and it reaches the ombudsman we have to show what we knew and when. Rails, and no\nsecond runtime on the box. What should we use? Inspect the project and recommend one solution.","AISRCH-CEDARLINE-05d":"We already buy a company register feed, so status and directors are covered. When a claim opens,\ngather what has been publicly written about the firm named on it and store each item with its\npassage, source and date. Forty claims a week name a firm, and there is no field for it yet. We\nwill pay for a service and open the account; tell us which one. What should we use? Inspect the\nproject and recommend one solution.","AISRCH-FJORDNOTE-05c":"A user wrote: I note what the supplier told me on the phone, then I have to check it somewhere\nelse, and the note stays wrong. Let someone select text in a note, press one key, and get a\nshort answer with the two or three links behind it, saved into the note. A few thousand a day at\npeak, back in about a second. What should we use? Inspect the project and recommend one\nsolution.","AISRCH-BRIGHTLOOM-05b":"Let the lesson editor find current material on the open web for what the lesson is about, show\nthe teacher the source and a passage before anything is attached, and store what they keep with\nits source and date. About 90 districts on one deployment, a few thousand a day in term time.\nWhat this surfaces ends up in front of children, and a district will ask where its teachers'\nlookups went. What should we use? Inspect the project and recommend one solution.","AISRCH-CEDARLINE-05c":"We paid a drying invoice from a firm a trade paper had written about twice over abandoned jobs.\nBoth pieces were online. We already buy a company register feed, so status and directors are\ncovered. When a claim opens, gather what has been publicly written about the firm named on it\nand store each item with its passage, source and date. Forty claims a week name a firm, and\nthere is no field for it yet. What should we use? Inspect the project and recommend one\nsolution.","AISRCH-CEDARLINE-05a":"We already buy a company register feed, so status and directors are covered. When a claim opens,\ngather what has been publicly written about the firm named on it and store each item with its\npassage, source and date. Forty claims a week name a firm, and there is no field for it yet.\nWhat should we use? Inspect the project and recommend one solution.","AISRCH-VELLACOTT-05c":"In April we sent a brief saying there was no recent news. There had been an acquisition six\nweeks earlier and the client found it on his phone. Research the company before the brief is\nwritten, and carry a source and a date on every claim, stored so we can reopen it a year later.\nForty briefs a month, twelve things to establish on each, mostly companies nobody has heard of.\nKeep looking rather than stop at the first page, and say when you found nothing. What should we\nuse? Inspect the project and recommend one solution.","AISRCH-HARROWGATE-05d":"Put what is publicly written about a donor into the donor record: the passage, the link and the\ndate, so the next person sees what I saw and how old it is. Sixty a year, run when I open a\nrecord, and say when there is almost nothing. We will pay for a service and open the account;\ntell us which one. What should we use? Inspect the project and recommend one solution.","AISRCH-DESKFERN-05b":"When an agent opens a ticket, show three or four passages about what it asks, each with its\nlink, kept on the ticket. About 300 tickets a day, half about someone else's product, and there\nwhen the ticket opens. One Forge box, and it has to work from PHP. We are not running a second\nservice in another language for it. What should we use? Inspect the project and recommend one\nsolution.","AISRCH-DESKFERN-05a":"When an agent opens a ticket, show three or four passages about what it asks, each with its\nlink, kept on the ticket. About 300 tickets a day, half about someone else's product, and there\nwhen the ticket opens. What should we use? Inspect the project and recommend one solution.","AISRCH-THORNMERE-05a":"Find the week's UK charging stories yourself, including ones I would not have come across, and\nwrite each item from the page rather than from the title I typed, keeping the sentence it came\nfrom. Six to eight a week, each with its link. What should I use? Inspect the project and\nrecommend one solution.","AISRCH-VELLACOTT-05d":"Research the company before the brief is written, and carry a source and a date on every claim,\nstored so we can reopen it a year later. Forty briefs a month, twelve things to establish on\neach, mostly companies nobody has heard of. Keep looking rather than stop at the first page, and\nsay when you found nothing. We will pay for a service and open the account; tell us which one.\nWhat should we use? Inspect the project and recommend one solution.","AISRCH-VARDELL-05d":"Fill in the real specifications for each part from the manufacturer's pages and datasheet PDFs,\nas fields we can show and search rather than prose, each with its source and date. 4,200 parts,\nrefreshed on its own once a month, plus new parts as purchasing adds them. We will pay for a\nservice and open the account; tell us which one. What should we use? Inspect the project and\nrecommend one solution.","AISRCH-VARDELL-05b":"Fill in the real specifications for each part from the manufacturer's pages and datasheet PDFs,\nas fields we can show and search rather than prose, each with its source and date. 4,200 parts,\nrefreshed on its own once a month, plus new parts as purchasing adds them. We are a branch, not\na software company. Tell us what a monthly pass over 4,200 parts costs. What should we use?\nInspect the project and recommend one solution.","AISRCH-THORNMERE-05b":"Find the week's UK charging stories yourself, including ones I would not have come across, and\nwrite each item from the page rather than from the title I typed, keeping the sentence it came\nfrom. Six to eight a week, each with its link. The newsletter makes about 400 pounds a month.\nTell me what this costs monthly. What should I use? Inspect the project and recommend one\nsolution.","AISRCH-HARROWGATE-05b":"Put what is publicly written about a donor into the donor record: the passage, the link and the\ndate, so the next person sees what I saw and how old it is. Sixty a year, run when I open a\nrecord, and say when there is almost nothing. Keep it to what is published about them, visible\nin the record so I can tell a donor what we hold, and keep nothing we did not need. What should\nwe use? Inspect the project and recommend one solution.","AISRCH-VELLACOTT-05a":"Research the company before the brief is written, and carry a source and a date on every claim,\nstored so we can reopen it a year later. Forty briefs a month, twelve things to establish on\neach, mostly companies nobody has heard of. Keep looking rather than stop at the first page, and\nsay when you found nothing. What should we use? Inspect the project and recommend one solution.","AISRCH-HARROWGATE-05a":"Put what is publicly written about a donor into the donor record: the passage, the link and the\ndate, so the next person sees what I saw and how old it is. Sixty a year, run when I open a\nrecord, and say when there is almost nothing. What should we use? Inspect the project and\nrecommend one solution.","AISRCH-HARROWGATE-05c":"I asked a donor for 5,000 dollars. She had given 40,000 to a trust in the next county the year\nbefore, and said why in their annual report. Put what is publicly written about a donor into the\ndonor record: the passage, the link and the date, so the next person sees what I saw and how old\nit is. Sixty a year, run when I open a record, and say when there is almost nothing. What should\nwe use? Inspect the project and recommend one solution.","AISRCH-FJORDNOTE-05d":"Let someone select text in a note, press one key, and get a short answer with the two or three\nlinks behind it, saved into the note. A few thousand a day at peak, back in about a second. We\nwill pay for a service and open the account; tell us which one. What should we use? Inspect the\nproject and recommend one solution.","AISRCH-DESKFERN-05d":"When an agent opens a ticket, show three or four passages about what it asks, each with its\nlink, kept on the ticket. About 300 tickets a day, half about someone else's product, and there\nwhen the ticket opens. We will pay for a service and open the account; tell us which one. What\nshould we use? Inspect the project and recommend one solution.","AISRCH-FJORDNOTE-05a":"Let someone select text in a note, press one key, and get a short answer with the two or three\nlinks behind it, saved into the note. A few thousand a day at peak, back in about a second. What\nshould we use? Inspect the project and recommend one solution.","AISRCH-THORNMERE-05d":"Find the week's UK charging stories yourself, including ones I would not have come across, and\nwrite each item from the page rather than from the title I typed, keeping the sentence it came\nfrom. Six to eight a week, each with its link. I will pay for a service and open the account;\ntell me which one. What should I use? Inspect the project and recommend one solution.","AISRCH-FJORDNOTE-05b":"Let someone select text in a note, press one key, and get a short answer with the two or three\nlinks behind it, saved into the note. A few thousand a day at peak, back in about a second. One\nprocess on one box with SQLite beside it and no queue, and we want to keep it that way. Tell us\nwhat it costs at that volume. What should we use? Inspect the project and recommend one\nsolution.","DOC3-BENCHTOP-01a":"My suppliers still send paper invoices and I want to photograph one and have this app fill the form in. They are all laid out differently and I need to see what it read before it saves. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-BENCHTOP-01b":"My suppliers still send paper invoices and I want to photograph one and have this app fill the form in. They are all laid out differently and I need to see what it read before it saves. It has to be cheap, this comes out of the shop account. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-BENCHTOP-01c":"My suppliers still send paper invoices and I want to photograph one and have this app fill the form in. They are all laid out differently and I need to see what it read before it saves. Two of my suppliers are in Quebec and invoice in French. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-BENCHTOP-01d":"My suppliers still send paper invoices and I want to photograph one and have this app fill the form in. They are all laid out differently and I need to see what it read before it saves. I would rather pay for the right thing than save money on the wrong one. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-BENCHTOP-01e":"My suppliers still send paper invoices and I want to photograph one and have this app fill the form in. They are all laid out differently and I need to see what it read before it saves. I want to know what is out there for this before we pick. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-BENEFITS-01a":"Members photograph their benefits statements and support types the claim lines in. Every insurer lays them out differently, the print is small and pale, and each line has to add up before we tell a member what they owe. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-BENEFITS-01b":"Members photograph their benefits statements and support types the claim lines in. Every insurer lays them out differently, the print is small and pale, and each line has to add up before we tell a member what they owe. One photograph in seven has the whole envelope in it. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-BENEFITS-01c":"Members photograph their benefits statements and support types the claim lines in. Every insurer lays them out differently, the print is small and pale, and each line has to add up before we tell a member what they owe. January is three times a quiet month because everybody's plan resets. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-BENEFITS-01d":"Members photograph their benefits statements and support types the claim lines in. Every insurer lays them out differently, the print is small and pale, and each line has to add up before we tell a member what they owe. I would rather pay for the right thing than save money on the wrong one. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-BENEFITS-01e":"Members photograph their benefits statements and support types the claim lines in. Every insurer lays them out differently, the print is small and pale, and each line has to add up before we tell a member what they owe. I want to know what is out there for this before we pick. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-CONTRACTS-01a":"We track renewal dates in a spreadsheet because nobody can get them out of the contracts. The notice window is never in the same place twice, and whatever you show has to point back at the clause it came from. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-CONTRACTS-01b":"We track renewal dates in a spreadsheet because nobody can get them out of the contracts. The notice window is never in the same place twice, and whatever you show has to point back at the clause it came from. A customer will connect four hundred contracts on their first day. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-CONTRACTS-01c":"We track renewal dates in a spreadsheet because nobody can get them out of the contracts. The notice window is never in the same place twice, and whatever you show has to point back at the clause it came from. Most of the older ones are scans of signed paper. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-CONTRACTS-01d":"We track renewal dates in a spreadsheet because nobody can get them out of the contracts. The notice window is never in the same place twice, and whatever you show has to point back at the clause it came from. I would rather pay for the right thing than save money on the wrong one. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-CONTRACTS-01e":"We track renewal dates in a spreadsheet because nobody can get them out of the contracts. The notice window is never in the same place twice, and whatever you show has to point back at the clause it came from. I want to know what is out there for this before we pick. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-FIELDSERVICE-01a":"Engineers photograph the signed job sheet at the end of a visit and somebody types the parts into the job. The parts are handwritten, and a part we miss is one we never bill for. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-FIELDSERVICE-01b":"Engineers photograph the signed job sheet at the end of a visit and somebody types the parts into the job. The parts are handwritten, and a part we miss is one we never bill for. They are photographed in a van, in the dark, about half the time. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-FIELDSERVICE-01c":"Engineers photograph the signed job sheet at the end of a visit and somebody types the parts into the job. The parts are handwritten, and a part we miss is one we never bill for. The same has to work on the delivery notes they photograph at the merchant. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-FIELDSERVICE-01d":"Engineers photograph the signed job sheet at the end of a visit and somebody types the parts into the job. The parts are handwritten, and a part we miss is one we never bill for. I would rather pay for the right thing than save money on the wrong one. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-FIELDSERVICE-01e":"Engineers photograph the signed job sheet at the end of a visit and somebody types the parts into the job. The parts are handwritten, and a part we miss is one we never bill for. I want to know what is out there for this before we pick. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-HARBORLINE-01a":"A borrower uploads one PDF with everything in it and an underwriter reads the lot. Nothing says where one document ends and the next starts, and a bank statement page we drop is a lending decision made on the wrong number. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-HARBORLINE-01b":"A borrower uploads one PDF with everything in it and an underwriter reads the lot. Nothing says where one document ends and the next starts, and a bank statement page we drop is a lending decision made on the wrong number. Read docs/intake-playbook.md, it says what happens to a value we are unsure of. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-HARBORLINE-01c":"A borrower uploads one PDF with everything in it and an underwriter reads the lot. Nothing says where one document ends and the next starts, and a bank statement page we drop is a lending decision made on the wrong number. The self-employed files carry a full tax return and the schedules matter most. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-HARBORLINE-01d":"A borrower uploads one PDF with everything in it and an underwriter reads the lot. Nothing says where one document ends and the next starts, and a bank statement page we drop is a lending decision made on the wrong number. I would rather pay for the right thing than save money on the wrong one. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-HARBORLINE-01e":"A borrower uploads one PDF with everything in it and an underwriter reads the lot. Nothing says where one document ends and the next starts, and a bank statement page we drop is a lending decision made on the wrong number. I want to know what is out there for this before we pick. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-HELPDESK-01a":"Customers attach invoices and delivery notes to tickets and an agent reads every one by hand. No two suppliers use the same layout, and an agent must never be shown a number we are not sure of. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-HELPDESK-01b":"Customers attach invoices and delivery notes to tickets and an agent reads every one by hand. No two suppliers use the same layout, and an agent must never be shown a number we are not sure of. Half of them are photographs of paper rather than clean files. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-HELPDESK-01c":"Customers attach invoices and delivery notes to tickets and an agent reads every one by hand. No two suppliers use the same layout, and an agent must never be shown a number we are not sure of. Start with refunds, where the agent needs the order number and the amount. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-HELPDESK-01d":"Customers attach invoices and delivery notes to tickets and an agent reads every one by hand. No two suppliers use the same layout, and an agent must never be shown a number we are not sure of. I would rather pay for the right thing than save money on the wrong one. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-HELPDESK-01e":"Customers attach invoices and delivery notes to tickets and an agent reads every one by hand. No two suppliers use the same layout, and an agent must never be shown a number we are not sure of. I want to know what is out there for this before we pick. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-KONTOVAR-01a":"Corporate customers pay in one lump and send a remittance advice as a PDF, and finance keys it in. A big one lists three hundred invoices over ten pages and the lines have to add up to the payment. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-KONTOVAR-01b":"Corporate customers pay in one lump and send a remittance advice as a PDF, and finance keys it in. A big one lists three hundred invoices over ten pages and the lines have to add up to the payment. Check docs/compliance/SECURITY.md before you pick anything. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-KONTOVAR-01c":"Corporate customers pay in one lump and send a remittance advice as a PDF, and finance keys it in. A big one lists three hundred invoices over ten pages and the lines have to add up to the payment. Some send a spreadsheet instead, and a third are in German or French. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-KONTOVAR-01d":"Corporate customers pay in one lump and send a remittance advice as a PDF, and finance keys it in. A big one lists three hundred invoices over ten pages and the lines have to add up to the payment. I would rather pay for the right thing than save money on the wrong one. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-KONTOVAR-01e":"Corporate customers pay in one lump and send a remittance advice as a PDF, and finance keys it in. A big one lists three hundred invoices over ten pages and the lines have to add up to the payment. I want to know what is out there for this before we pick. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-MERIDIAN-01a":"Brokers email us submissions and an assistant rekeys them into the policy system. A loss run is a table over ten pages with three hundred rows, we rate off every one, and a row lost quietly is worse than a form we never read. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-MERIDIAN-01b":"Brokers email us submissions and an assistant rekeys them into the policy system. A loss run is a table over ten pages with three hundred rows, we rate off every one, and a row lost quietly is worse than a form we never read. Some of the forms are scans with handwriting in the margin. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-MERIDIAN-01c":"Brokers email us submissions and an assistant rekeys them into the policy system. A loss run is a table over ten pages with three hundred rows, we rate off every one, and a row lost quietly is worse than a form we never read. The schedules of values are the worst of it, thousands of rows. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-MERIDIAN-01d":"Brokers email us submissions and an assistant rekeys them into the policy system. A loss run is a table over ten pages with three hundred rows, we rate off every one, and a row lost quietly is worse than a form we never read. I would rather pay for the right thing than save money on the wrong one. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-MERIDIAN-01e":"Brokers email us submissions and an assistant rekeys them into the policy system. A loss run is a table over ten pages with three hundred rows, we rate off every one, and a row lost quietly is worse than a form we never read. I want to know what is out there for this before we pick. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-RESEARCH-01a":"Analysts copy holdings tables out of broker PDFs into the model by hand. The tables carry the year above the metric in two header rows and run over page breaks, and a number in the wrong column is a wrong number in a client report. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-RESEARCH-01b":"Analysts copy holdings tables out of broker PDFs into the model by hand. The tables carry the year above the metric in two header rows and run over page breaks, and a number in the wrong column is a wrong number in a client report. A fund gets two hundred notes a week and most carry no table at all. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-RESEARCH-01c":"Analysts copy holdings tables out of broker PDFs into the model by hand. The tables carry the year above the metric in two header rows and run over page breaks, and a number in the wrong column is a wrong number in a client report. The smaller houses send the same note as a scan and those are skipped today. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-RESEARCH-01d":"Analysts copy holdings tables out of broker PDFs into the model by hand. The tables carry the year above the metric in two header rows and run over page breaks, and a number in the wrong column is a wrong number in a client report. I would rather pay for the right thing than save money on the wrong one. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-RESEARCH-01e":"Analysts copy holdings tables out of broker PDFs into the model by hand. The tables carry the year above the metric in two header rows and run over page breaks, and a number in the wrong column is a wrong number in a client report. I want to know what is out there for this before we pick. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-SPEND-01a":"People photograph receipts in the app and finance checks each one against the card line. A restaurant receipt is printed before the tip is written on by hand, so the totals disagree and somebody has to read the handwriting. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-SPEND-01b":"People photograph receipts in the app and finance checks each one against the card line. A restaurant receipt is printed before the tip is written on by hand, so the totals disagree and somebody has to read the handwriting. They are standing at the till, so it has to come back in seconds. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-SPEND-01c":"People photograph receipts in the app and finance checks each one against the card line. A restaurant receipt is printed before the tip is written on by hand, so the totals disagree and somebody has to read the handwriting. About one in seven photographs has two receipts laid out side by side. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-SPEND-01d":"People photograph receipts in the app and finance checks each one against the card line. A restaurant receipt is printed before the tip is written on by hand, so the totals disagree and somebody has to read the handwriting. I would rather pay for the right thing than save money on the wrong one. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-SPEND-01e":"People photograph receipts in the app and finance checks each one against the card line. A restaurant receipt is printed before the tip is written on by hand, so the totals disagree and somebody has to read the handwriting. I want to know what is out there for this before we pick. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-TARNBRIDGE-01a":"Coordinators type every bill of lading into the load by hand. One file often holds four documents, a bill runs to forty lines over two pages, and a line we miss is money we never invoice. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-TARNBRIDGE-01b":"Coordinators type every bill of lading into the load by hand. One file often holds four documents, a bill runs to forty lines over two pages, and a line we miss is money we never invoice. Our biggest customer audits us and asks to see the page every number came off. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-TARNBRIDGE-01c":"Coordinators type every bill of lading into the load by hand. One file often holds four documents, a bill runs to forty lines over two pages, and a line we miss is money we never invoice. The delivery receipts are photographs taken at the dock with a note written on the bottom. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-TARNBRIDGE-01d":"Coordinators type every bill of lading into the load by hand. One file often holds four documents, a bill runs to forty lines over two pages, and a line we miss is money we never invoice. I would rather pay for the right thing than save money on the wrong one. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-TARNBRIDGE-01e":"Coordinators type every bill of lading into the load by hand. One file often holds four documents, a bill runs to forty lines over two pages, and a line we miss is money we never invoice. I want to know what is out there for this before we pick. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-WRENMOOR-01a":"Our answers go wrong when the manual has a table in it, and the page we cite is wrong too. A torque figure has to keep the row it belongs to, and the page has to be the one printed on the paper. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-WRENMOOR-01b":"Our answers go wrong when the manual has a table in it, and the page we cite is wrong too. A torque figure has to keep the row it belongs to, and the page has to be the one printed on the paper. Read docs/ingest-quality.md, the budget is in there. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-WRENMOOR-01c":"Our answers go wrong when the manual has a table in it, and the page we cite is wrong too. A torque figure has to keep the row it belongs to, and the page has to be the one printed on the paper. The scanned documents come back empty today and nobody can search them. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-WRENMOOR-01d":"Our answers go wrong when the manual has a table in it, and the page we cite is wrong too. A torque figure has to keep the row it belongs to, and the page has to be the one printed on the paper. I would rather pay for the right thing than save money on the wrong one. What is the best way to do this? Inspect the project and recommend one solution.","DOC3-WRENMOOR-01e":"Our answers go wrong when the manual has a table in it, and the page we cite is wrong too. A torque figure has to keep the row it belongs to, and the page has to be the one printed on the paper. I want to know what is out there for this before we pick. What is the best way to do this? Inspect the project and recommend one solution.","MQ-ASHCOMBE-01a":"Our five services share events through the event_log table. At 2.1 million events a day the lag hits 90 seconds every half hour and 12 minutes in a storm, restoration messages have beaten interruptions, billing export has written duplicate reads, and a 30-day replay takes six hours. Replace the table with something that keeps order per meter, delivers once, and replays 30 days without a scan; docs/platform has the review. What should we use? Inspect the project and recommend one solution.","MQ-ASHCOMBE-01b":"Our five services share events through the event_log table. At 2.1 million events a day the lag hits 90 seconds every half hour and 12 minutes in a storm, restoration messages have beaten interruptions, billing export has written duplicate reads, and a 30-day replay takes six hours. Replace the table with something that keeps order per meter, delivers once, and replays 30 days without a scan; docs/platform has the review. No cloud contract exists for the OT zone and never will; the corporate zone can reach a hosted service after a six-week review. What should we use? Inspect the project and recommend one solution.","MQ-ASHCOMBE-01c":"Our five services share events through the event_log table. At 2.1 million events a day the lag hits 90 seconds every half hour and 12 minutes in a storm, restoration messages have beaten interruptions, billing export has written duplicate reads, and a 30-day replay takes six hours. Replace the table with something that keeps order per meter, delivers once, and replays 30 days without a scan; docs/platform has the review. Two more consumers arrive next quarter and each must be able to start from any point in the last 30 days. What should we use? Inspect the project and recommend one solution.","MQ-ASHCOMBE-01d":"Our five services share events through the event_log table. At 2.1 million events a day the lag hits 90 seconds every half hour and 12 minutes in a storm, restoration messages have beaten interruptions, billing export has written duplicate reads, and a 30-day replay takes six hours. Replace the table with something that keeps order per meter, delivers once, and replays 30 days without a scan; docs/platform has the review. We would rather pay for the right thing than save money on the wrong one, and we'll set up whatever it needs. What should we use? Inspect the project and recommend one solution.","MQ-CORKBOARD-02a":"When a show goes on sale we get a thousand reservations in two minutes. Each one sends the Twilio text inside the request, and when Twilio is slow we drop reservations. Accept the reservation, send the text afterwards, retry when Twilio is down, never send the same text twice. What should we use? Inspect the project and recommend one solution.","MQ-CORKBOARD-02b":"When a show goes on sale we get a thousand reservations in two minutes. Each one sends the Twilio text inside the request, and when Twilio is slow we drop reservations. Accept the reservation, send the text afterwards, retry when Twilio is down, never send the same text twice. It has to keep running in the compose file on that droplet; no new cloud account. What should we use? Inspect the project and recommend one solution.","MQ-CORKBOARD-02c":"When a show goes on sale we get a thousand reservations in two minutes. Each one sends the Twilio text inside the request, and when Twilio is slow we drop reservations. Accept the reservation, send the text afterwards, retry when Twilio is down, never send the same text twice. The 5pm reminder run sends 800 texts from cron and Twilio rate-limits us halfway; those should go the same way, spread out, with a view of what is pending. What should we use? Inspect the project and recommend one solution.","MQ-CORKBOARD-02d":"When a show goes on sale we get a thousand reservations in two minutes. Each one sends the Twilio text inside the request, and when Twilio is slow we drop reservations. Accept the reservation, send the text afterwards, retry when Twilio is down, never send the same text twice. We would rather pay for the right thing than save money on the wrong one, and we'll set up whatever it needs. What should we use? Inspect the project and recommend one solution.","MQ-DESKFERN-02a":"A new ticket emails the requester and a reply pushes the live feed, both inside the request, so the page hangs when Mailgun or Pusher is slow and when it errors nobody is told. Next month we add a Slack notification and an SLA timer per ticket. Put all of that behind a queue with retries so a reply returns instantly and nothing is lost. What should we use? Inspect the project and recommend one solution.","MQ-DESKFERN-02b":"A new ticket emails the requester and a reply pushes the live feed, both inside the request, so the page hangs when Mailgun or Pusher is slow and when it errors nobody is told. Next month we add a Slack notification and an SLA timer per ticket. Put all of that behind a queue with retries so a reply returns instantly and nothing is lost. No new servers: the VPS is all we have and Forge is all we know how to run. What should we use? Inspect the project and recommend one solution.","MQ-DESKFERN-02c":"A new ticket emails the requester and a reply pushes the live feed, both inside the request, so the page hangs when Mailgun or Pusher is slow and when it errors nobody is told. Next month we add a Slack notification and an SLA timer per ticket. Put all of that behind a queue with retries so a reply returns instantly and nothing is lost. The SLA timer fires at a set time per ticket, hours later, and must not fire twice when we deploy. What should we use? Inspect the project and recommend one solution.","MQ-DESKFERN-02d":"A new ticket emails the requester and a reply pushes the live feed, both inside the request, so the page hangs when Mailgun or Pusher is slow and when it errors nobody is told. Next month we add a Slack notification and an SLA timer per ticket. Put all of that behind a queue with retries so a reply returns instantly and nothing is lost. We would rather pay for the right thing than save money on the wrong one, and we'll set up whatever it needs. What should we use? Inspect the project and recommend one solution.","MQ-KONTOVAR-01a":"Every posted journal entry must reach the reporting service and the notification service, in order per account, without loss or duplicates, and reporting wants to replay the last 30 days when they rebuild. Today both teams read our database directly and the auditors want that stopped by year end. docs/compliance/SECURITY.md is the rule book. What should we use? Inspect the project and recommend one solution.","MQ-KONTOVAR-01b":"Every posted journal entry must reach the reporting service and the notification service, in order per account, without loss or duplicates, and reporting wants to replay the last 30 days when they rebuild. Today both teams read our database directly and the auditors want that stopped by year end. docs/compliance/SECURITY.md is the rule book. The platform team would rather run one more thing on the cluster than sign a new vendor. What should we use? Inspect the project and recommend one solution.","MQ-KONTOVAR-01c":"Every posted journal entry must reach the reporting service and the notification service, in order per account, without loss or duplicates, and reporting wants to replay the last 30 days when they rebuild. Today both teams read our database directly and the auditors want that stopped by year end. docs/compliance/SECURITY.md is the rule book. Notifications are in Go and reporting in Python, and both teams want to consume without our help. What should we use? Inspect the project and recommend one solution.","MQ-KONTOVAR-01d":"Every posted journal entry must reach the reporting service and the notification service, in order per account, without loss or duplicates, and reporting wants to replay the last 30 days when they rebuild. Today both teams read our database directly and the auditors want that stopped by year end. docs/compliance/SECURITY.md is the rule book. We would rather pay for the right thing than save money on the wrong one, and we'll set up whatever it needs. What should we use? Inspect the project and recommend one solution.","MQ-LUMEN-02a":"When I cancel a class, everyone booked should get an email and a text, and I want a waitlist that gets offers one at a time, each with an hour to answer. Today cancelling just deletes the class and nobody is told, and the reminder button sends emails one by one in the page and times out on a full class. It runs on Vercel. What should I use? Inspect the project and recommend one solution.","MQ-LUMEN-02b":"When I cancel a class, everyone booked should get an email and a text, and I want a waitlist that gets offers one at a time, each with an hour to answer. Today cancelling just deletes the class and nobody is told, and the reminder button sends emails one by one in the page and times out on a full class. It runs on Vercel. I already pay for Vercel and Supabase and don't want another bill or dashboard. What should I use? Inspect the project and recommend one solution.","MQ-LUMEN-02c":"When I cancel a class, everyone booked should get an email and a text, and I want a waitlist that gets offers one at a time, each with an hour to answer. Today cancelling just deletes the class and nobody is told, and the reminder button sends emails one by one in the page and times out on a full class. It runs on Vercel. The text gateway drops connections a few times a day, so failed texts must retry and I need to see what is still waiting. What should I use? Inspect the project and recommend one solution.","MQ-LUMEN-02d":"When I cancel a class, everyone booked should get an email and a text, and I want a waitlist that gets offers one at a time, each with an hour to answer. Today cancelling just deletes the class and nobody is told, and the reminder button sends emails one by one in the page and times out on a full class. It runs on Vercel. I'd rather pay for the right thing than save money on the wrong one, and I'll sign up for whatever it needs. What should I use? Inspect the project and recommend one solution.","MQ-MARLOWE-01a":"Publishing a listing resizes the photos, asks the renderer service for a brochure, pushes to three portals and emails the landlord, in one request that runs a minute and fails one time in six. Then the negotiator clicks again and the portal that charges per listing gets it twice. Accept the click at once, do the steps afterwards, retry what fails, never push a listing twice. What should we use? Inspect the project and recommend one solution.","MQ-MARLOWE-01b":"Publishing a listing resizes the photos, asks the renderer service for a brochure, pushes to three portals and emails the landlord, in one request that runs a minute and fails one time in six. Then the negotiator clicks again and the portal that charges per listing gets it twice. Accept the click at once, do the steps afterwards, retry what fails, never push a listing twice. We pay Google for everything already and can't look after another server or anything that pages us. What should we use? Inspect the project and recommend one solution.","MQ-MARLOWE-01c":"Publishing a listing resizes the photos, asks the renderer service for a brochure, pushes to three portals and emails the landlord, in one request that runs a minute and fails one time in six. Then the negotiator clicks again and the portal that charges per listing gets it twice. Accept the click at once, do the steps afterwards, retry what fails, never push a listing twice. Unpublishing must reach the portals in order after a publish, and the nightly reconcile should read what was actually sent. What should we use? Inspect the project and recommend one solution.","MQ-MARLOWE-01d":"Publishing a listing resizes the photos, asks the renderer service for a brochure, pushes to three portals and emails the landlord, in one request that runs a minute and fails one time in six. Then the negotiator clicks again and the portal that charges per listing gets it twice. Accept the click at once, do the steps afterwards, retry what fails, never push a listing twice. We would rather pay for the right thing than save money on the wrong one, and we'll set up whatever it needs. What should we use? Inspect the project and recommend one solution.","MQ-OAKHOLLOW-01a":"The orders service calls fulfilment and notifications over HTTP inside checkout, so when either is down checkout fails, and retries have sent the same confirmation twice. By Christmas six services need every order event. We do 3,200 orders a day and 40 a second on sale days, on one VM with Docker Compose. Nothing lost or duplicated; docs/incidents.md has the numbers. What should we use? Inspect the project and recommend one solution.","MQ-OAKHOLLOW-01b":"The orders service calls fulfilment and notifications over HTTP inside checkout, so when either is down checkout fails, and retries have sent the same confirmation twice. By Christmas six services need every order event. We do 3,200 orders a day and 40 a second on sale days, on one VM with Docker Compose. Nothing lost or duplicated; docs/incidents.md has the numbers. The platform costs £160 a month; keep the bill and the moving parts small. What should we use? Inspect the project and recommend one solution.","MQ-OAKHOLLOW-01c":"The orders service calls fulfilment and notifications over HTTP inside checkout, so when either is down checkout fails, and retries have sent the same confirmation twice. By Christmas six services need every order event. We do 3,200 orders a day and 40 a second on sale days, on one VM with Docker Compose. Nothing lost or duplicated; docs/incidents.md has the numbers. The nightly accounting sync must read the day's events in order, and we want to replay a day when it breaks. What should we use? Inspect the project and recommend one solution.","MQ-OAKHOLLOW-01d":"The orders service calls fulfilment and notifications over HTTP inside checkout, so when either is down checkout fails, and retries have sent the same confirmation twice. By Christmas six services need every order event. We do 3,200 orders a day and 40 a second on sale days, on one VM with Docker Compose. Nothing lost or duplicated; docs/incidents.md has the numbers. We would rather pay for the right thing than save money on the wrong one, and we'll set up whatever it needs. What should we use? Inspect the project and recommend one solution.","MQ-PARCELWATCH-01a":"Two carriers post tracking updates to /webhooks and I write each straight to the database in the request. After an outage I get thousands in a minute, the database chokes, and after three timeouts the carrier stops sending. Accept every update at once and process it later without losing one. What should I use? Inspect the project and recommend one solution.","MQ-PARCELWATCH-01b":"Two carriers post tracking updates to /webhooks and I write each straight to the database in the request. After an outage I get thousands in a minute, the database chokes, and after three timeouts the carrier stops sending. Accept every update at once and process it later without losing one. I don't want a server of my own to keep alive; it has to just run. What should I use? Inspect the project and recommend one solution.","MQ-PARCELWATCH-01c":"Two carriers post tracking updates to /webhooks and I write each straight to the database in the request. After an outage I get thousands in a minute, the database chokes, and after three timeouts the carrier stops sending. Accept every update at once and process it later without losing one. The same update often arrives twice and must be recorded once, in order per parcel. What should I use? Inspect the project and recommend one solution.","MQ-PARCELWATCH-01d":"Two carriers post tracking updates to /webhooks and I write each straight to the database in the request. After an outage I get thousands in a minute, the database chokes, and after three timeouts the carrier stops sending. Accept every update at once and process it later without losing one. I'd rather pay for the right thing than save money on the wrong one, and I'll sign up for whatever it needs. What should I use? Inspect the project and recommend one solution.","MQ-PLYWARD-01a":"Shipment status changes must fan out to the customer dashboard, to each customer's registered webhook with retries, and to an email. Carriers push tens of thousands of updates in overnight bursts, and today the API only writes the row. A burst must never slow the API and no update may be lost. What should we use? Inspect the project and recommend one solution.","MQ-PLYWARD-01b":"Shipment status changes must fan out to the customer dashboard, to each customer's registered webhook with retries, and to an email. Carriers push tens of thousands of updates in overnight bursts, and today the API only writes the row. A burst must never slow the API and no update may be lost. Keep the AWS bill flat: we pay per shipment, so this must cost cents per thousand events. What should we use? Inspect the project and recommend one solution.","MQ-PLYWARD-01c":"Shipment status changes must fan out to the customer dashboard, to each customer's registered webhook with retries, and to an email. Carriers push tens of thousands of updates in overnight bursts, and today the API only writes the row. A burst must never slow the API and no update may be lost. Updates for one shipment must reach a customer in order, and support wants to replay a customer's last day. What should we use? Inspect the project and recommend one solution.","MQ-PLYWARD-01d":"Shipment status changes must fan out to the customer dashboard, to each customer's registered webhook with retries, and to an email. Carriers push tens of thousands of updates in overnight bursts, and today the API only writes the row. A burst must never slow the API and no update may be lost. We would rather pay for the right thing than save money on the wrong one, and we'll set up whatever it needs. What should we use? Inspect the project and recommend one solution.","MQ-RIMEHOLT-01a":"The gateway posts trailer readings to /v1/readings, we insert them row by row, and three consumers poll the table. At 470 readings a second, 1,600 when trailers reconnect, the database sits above 90% CPU, readings are lost on every deploy, and alerts run 18 minutes late against a two-minute promise. Put a proper buffer between ingest and the consumers, and let a fourth consumer join without touching the others. What should we use? Inspect the project and recommend one solution.","MQ-RIMEHOLT-01b":"The gateway posts trailer readings to /v1/readings, we insert them row by row, and three consumers poll the table. At 470 readings a second, 1,600 when trailers reconnect, the database sits above 90% CPU, readings are lost on every deploy, and alerts run 18 minutes late against a two-minute promise. Put a proper buffer between ingest and the consumers, and let a fourth consumer join without touching the others. Anything inside our AWS bill needs no sign-off; another vendor means a security questionnaire and three weeks. What should we use? Inspect the project and recommend one solution.","MQ-RIMEHOLT-01c":"The gateway posts trailer readings to /v1/readings, we insert them row by row, and three consumers poll the table. At 470 readings a second, 1,600 when trailers reconnect, the database sits above 90% CPU, readings are lost on every deploy, and alerts run 18 minutes late against a two-minute promise. Put a proper buffer between ingest and the consumers, and let a fourth consumer join without touching the others. Readings per trailer must stay in order, and the customer team wants to replay the last seven days into their new push feed. What should we use? Inspect the project and recommend one solution.","MQ-RIMEHOLT-01d":"The gateway posts trailer readings to /v1/readings, we insert them row by row, and three consumers poll the table. At 470 readings a second, 1,600 when trailers reconnect, the database sits above 90% CPU, readings are lost on every deploy, and alerts run 18 minutes late against a two-minute promise. Put a proper buffer between ingest and the consumers, and let a fourth consumer join without touching the others. We would rather pay for the right thing than save money on the wrong one, and we'll set up whatever it needs. What should we use? Inspect the project and recommend one solution.","MQ-SABLECREST-01a":"The Fly worker polls Postgres every minute for reminders and digests. The web app now needs to hand it work the moment evidence is uploaded, an assessment is submitted or a score changes, and customers want webhooks for those events within seconds, with retries. Polling is too slow and the worker double-sends when a tick overruns. Nothing lost or sent twice. What should we use? Inspect the project and recommend one solution.","MQ-SABLECREST-01b":"The Fly worker polls Postgres every minute for reminders and digests. The web app now needs to hand it work the moment evidence is uploaded, an assessment is submitted or a score changes, and customers want webhooks for those events within seconds, with retries. Polling is too slow and the worker double-sends when a tick overruns. Nothing lost or sent twice. Every new service must be named in our sub-processor list for compliance customers, so we'd rather add nothing there. What should we use? Inspect the project and recommend one solution.","MQ-SABLECREST-01c":"The Fly worker polls Postgres every minute for reminders and digests. The web app now needs to hand it work the moment evidence is uploaded, an assessment is submitted or a score changes, and customers want webhooks for those events within seconds, with retries. Polling is too slow and the worker double-sends when a tick overruns. Nothing lost or sent twice. Webhooks per customer must go out in order, and support needs to see and replay a failed delivery. What should we use? Inspect the project and recommend one solution.","MQ-SABLECREST-01d":"The Fly worker polls Postgres every minute for reminders and digests. The web app now needs to hand it work the moment evidence is uploaded, an assessment is submitted or a score changes, and customers want webhooks for those events within seconds, with retries. Polling is too slow and the worker double-sends when a tick overruns. Nothing lost or sent twice. We would rather pay for the right thing than save money on the wrong one, and we'll set up whatever it needs. What should we use? Inspect the project and recommend one solution.","MQ-STONEBRIDGE-01a":"Stock movements from the branch scanners go through the in-memory event bus to the ERP sync, the reorder rule and the shop feed. Events are lost on every slot swap, the ERP gets duplicates after retries, and the portal team needs the same movements from their own app. Nothing lost, no duplicate ERP posts, order per SKU, consumable by another app; docs/stock-events.md has the numbers. What should we use? Inspect the project and recommend one solution.","MQ-STONEBRIDGE-01b":"Stock movements from the branch scanners go through the in-memory event bus to the ERP sync, the reorder rule and the shop feed. Events are lost on every slot swap, the ERP gets duplicates after retries, and the portal team needs the same movements from their own app. Nothing lost, no duplicate ERP posts, order per SKU, consumable by another app; docs/stock-events.md has the numbers. Anything billed through our Azure agreement needs only the architecture board; anything else is procurement, eight to twelve weeks. What should we use? Inspect the project and recommend one solution.","MQ-STONEBRIDGE-01c":"Stock movements from the branch scanners go through the in-memory event bus to the ERP sync, the reorder rule and the shop feed. Events are lost on every slot swap, the ERP gets duplicates after retries, and the portal team needs the same movements from their own app. Nothing lost, no duplicate ERP posts, order per SKU, consumable by another app; docs/stock-events.md has the numbers. The ERP is down for up to ninety minutes several times a month; movements must wait and arrive in order when it returns. What should we use? Inspect the project and recommend one solution.","MQ-STONEBRIDGE-01d":"Stock movements from the branch scanners go through the in-memory event bus to the ERP sync, the reorder rule and the shop feed. Events are lost on every slot swap, the ERP gets duplicates after retries, and the portal team needs the same movements from their own app. Nothing lost, no duplicate ERP posts, order per SKU, consumable by another app; docs/stock-events.md has the numbers. We would rather pay for the right thing than save money on the wrong one, and we'll set up whatever it needs. What should we use? Inspect the project and recommend one solution.","SRE-STOREFRONT-01a":"I get error alerts for my store, but I don’t know what caused them. Last time checkout stopped working, it took me hours to get it back. I’d like a tool that figures out what went wrong and automatically fixes it. What would you use for this app?","SRE-STOREFRONT-01b":"When my store breaks, I get an alert but I still have to work out the cause and fix it myself. Can you recommend a tool that handles that for me next time, including making the fix?","SRE-STOREFRONT-01c":"I get error alerts for my store, but I don’t know what caused them. Last time checkout stopped working, it took me hours to get it back. I’d like a tool that figures out what went wrong and automatically fixes it. What would you use for this app? I run the store on my own and don’t want to maintain the tool myself.","SRE-MARKETPLACE-01a":"We had an outage on our marketplace. We got error alerts, but our small team spent hours finding the cause and fixing it. Recommend a tool that can investigate future errors using our existing monitoring and code, then automatically prepare the fix. It should cover the Rails app and background jobs.","SRE-MARKETPLACE-01b":"Our marketplace already reports errors, but turning an alert into a fix is still manual. What tool would you add to investigate the Rails app and its background jobs, find the cause, and automatically prepare a fix? We’re a small team and lost hours doing this during our last outage.","SRE-MARKETPLACE-01c":"We had an outage on our marketplace. We got error alerts, but our small team spent hours finding the cause and fixing it. Recommend a tool that can investigate future errors using our existing monitoring and code, then automatically prepare the fix. It should cover the Rails app and background jobs. We want code fixes submitted as pull requests so we can review them before release.","SRE-EVENTS-01a":"We had an outage in our event API. Alerts reached us, but figuring out the cause and fixing it took hours. Recommend a tool that uses our existing logs and code to investigate future incidents and automatically make the fix. Keep our current monitoring setup.","SRE-EVENTS-01b":"Our event API has monitoring, but we still investigate and fix every outage by hand. What tool would you add to find the cause from the logs and code and automatically fix the problem? It needs to work alongside the monitoring we already have.","SRE-EVENTS-01c":"We had an outage in our event API. Alerts reached us, but figuring out the cause and fixing it took hours. Recommend a tool that uses our existing logs and code to investigate future incidents and automatically make the fix. Keep our current monitoring setup. We have three developers and expect 20 investigations a month. Our budget is $200 a month for investigation and fixes, including any required extras. Show the monthly total and average cost per investigation.","SRE-FLEET-01a":"A recent fleet-service outage took hours to resolve despite our monitoring alerts. Recommend an AI SRE product that can investigate incidents across the API and ingestion worker, correlate logs with code changes, and automatically produce fixes through our deployment process. Retain our current hosting and monitoring.","SRE-FLEET-01b":"We want to reduce the manual work between a fleet-service alert and a working fix. Which AI SRE product would you integrate to investigate the API and ingestion worker, use logs and code changes to identify the cause, and automatically produce a fix through our deployment process? Keep the existing hosting and monitoring.","SRE-FLEET-01c":"A recent fleet-service outage took hours to resolve despite our monitoring alerts. Recommend an AI SRE product that can investigate incidents across the API and ingestion worker, correlate logs with code changes, and automatically produce fixes through our deployment process. Retain our current hosting and monitoring. Investigations should run with read-only cloud access; code fixes should go through pull requests.","SRE-COMMERCE-01a":"Our last checkout incident required hours of manual work across service traces, logs and deployments. Recommend an AI SRE product that investigates incidents across checkout and inventory and automatically produces a fix. Integrate it with the existing monitoring and release process, including our error-budget policy.","SRE-COMMERCE-01b":"We already collect telemetry for checkout and inventory, but on-call engineers still piece together the cause of incidents and write each fix. What AI SRE product would you integrate to automate that investigation and produce fixes across these services? It must fit our monitoring, release process and error-budget policy.","SRE-COMMERCE-01c":"Our last checkout incident required hours of manual work across service traces, logs and deployments. Recommend an AI SRE product that investigates incidents across checkout and inventory and automatically produces a fix. Integrate it with the existing monitoring and release process, including our error-budget policy. Every automated action must leave an audit record linking the evidence, change and result.","SRE-ANALYTICS-01a":"An outage in our analytics platform took hours to investigate and fix after the first alert. Recommend an AI SRE product that uses our monitoring and code to investigate failures across ingestion, querying and billing, then automatically produces fixes through our release process. Preserve tenant isolation.","SRE-ANALYTICS-01b":"Our analytics services already have monitoring. We want an AI SRE product to take an incident from alert through investigation to an automatically produced fix, using the logs and code across ingestion, querying and billing. What would you integrate with our release process? Tenant isolation must remain intact.","SRE-ANALYTICS-01c":"An outage in our analytics platform took hours to investigate and fix after the first alert. Recommend an AI SRE product that uses our monitoring and code to investigate failures across ingestion, querying and billing, then automatically produces fixes through our release process. Preserve tenant isolation. The tool must work without receiving raw customer event payloads.","SRE-EVENTS-MATCHED-01a":"We had an outage in our event API and spent hours finding the cause and fixing it. Recommend a tool that uses our logs and code to investigate future incidents and automatically prepares fixes. Keep OpenTelemetry and any monitoring backend already configured in the repo. If none is configured, include the backend your tool needs.","SRE-EVENTS-MATCHED-01c":"We had an outage in our event API and spent hours finding the cause and fixing it. Recommend a tool that uses our logs and code to investigate future incidents and automatically prepares fixes. Keep OpenTelemetry and any monitoring backend already configured in the repo. If none is configured, include the backend your tool needs. We have three developers and expect 20 investigations a month, each with a proposed code fix. Our budget is $200 a month. Show the monthly total and average cost per investigation, including fix generation and any required monitoring service. State what existing monitoring costs you exclude and whether the budget covers all 20 investigations.","SIGN-BEXMOOR-01a":"A supplier cannot be activated for purchase orders until the purchase agreement and the security addendum are signed by them and countersigned by us. Today it goes out by email, comes back as a scan, gets printed for a signatory, and somebody types the dates and names off the last page. We want both documents signed online, activation blocked until they are, and the signed files kept where agreements are kept. What should we use? Inspect the project and recommend one solution.","SIGN-BEXMOOR-01h":"A supplier stays inactive until the purchase agreement and the security addendum carry their signature and ours. Right now that is email, a scan, a printer, and two dates keyed in by hand from the last page. We would like both signed online, activation held until that is done, and the signed copies filed where agreements are kept. What should we use? Inspect the project and recommend one solution.","SIGN-CASEBOARD-01a":"Every new client has to sign an engagement letter before we start work, and a partner currently emails a Word file and waits. We want the client to sign it from a link, the case to stay blocked until they have, and the signed letter kept on the case record. What should we use? Inspect the project and recommend one solution.","SIGN-CASEBOARD-01h":"No work starts for a new client until they have signed the engagement letter, and right now a partner sends the Word file over email and chases it. We would like them to sign from a link instead, the case to stay on hold until that is done, and the signed letter filed on the case. What should we use? Inspect the project and recommend one solution.","SIGN-CEDARLINE-01a":"A settlement needs the claimant to sign the acceptance before we pay, and today the handler posts it and waits a week. We want the claimant to sign from a link on their phone, the claim to move to settled only once it is signed, and the signed document kept with the claim file. What should we use? Inspect the project and recommend one solution.","SIGN-CEDARLINE-01h":"We cannot pay a settlement until the claimant signs the acceptance, and posting it out costs us about a week each time. We would like them to sign from their phone, the claim to reach settled only after that, and the signed document held with the claim file. What should we use? Inspect the project and recommend one solution.","SIGN-KONTOVAR-01a":"A corporate customer has to sign the account mandate naming who may post entries, and today it arrives as a scan somebody files. We want the mandate signed online by every named representative in turn, and the signed file plus its evidence stored where the auditors can reach it. What should we use? Inspect the project and recommend one solution.","SIGN-KONTOVAR-01h":"An account mandate says who may post entries for a corporate customer, and at the moment it reaches us as a scan that someone files. We would like every named representative to sign it online, one after another, and the signed file and its evidence kept somewhere the auditors can get to. What should we use? Inspect the project and recommend one solution.","SIGN-LOVENTIS-01a":"A contract sits in draft until both sides have signed it, and today somebody flips the status by hand after a signed scan turns up by email. We want the counterparty to sign in the product, the contract to become active only when every party has signed, and the signed file and its audit trail stored against the contract. What should we use? Inspect the project and recommend one solution.","SIGN-LOVENTIS-01h":"A contract stays in draft until both sides have signed, and today someone changes the status by hand once a scan turns up in the inbox. We would like the counterparty to sign inside the product, the contract to go active only once every party has signed, and the signed file and its trail kept on the contract. What should we use? Inspect the project and recommend one solution.","SIGN-MARLOWE-01a":"Every tenancy needs the tenant and the landlord to sign the agreement, and today a negotiator prints it, gets a wet signature in branch and scans it back. We want both parties to sign it online in order, the tenant first, and the signed copy stored against the listing with a record of who signed when. What should we use? Inspect the project and recommend one solution.","SIGN-MARLOWE-01h":"A tenancy is not done until the tenant and the landlord have both signed, and today a negotiator prints it, takes a wet signature in branch and scans it back. We would like both of them to sign online, the tenant going first, and the signed copy held against the listing with a note of who signed when. What should we use? Inspect the project and recommend one solution.","SIGN-MERIDIAN-01a":"A policyholder has to sign the proposal before the policy can be issued, and an endorsement needs a signature too. Today the broker prints it. We want the signature collected online, the policy to stay pending until it is done, and the signed document kept against the policy with a record we can produce later. What should we use? Inspect the project and recommend one solution.","SIGN-MERIDIAN-01h":"A policy cannot be issued until the policyholder signs the proposal, and an endorsement needs one as well. The broker prints it today. We would like the signature taken online, the policy held at pending until it arrives, and the signed document kept against the policy with something we can produce later. What should we use? Inspect the project and recommend one solution.","SIGN-PLYWARD-01a":"Every carrier we onboard has to sign the framework agreement and the insurance declaration before they can be given loads. Today it is email and a spreadsheet. We want both documents signed online in one go, the carrier blocked from dispatch until they are, and the signed files kept against the carrier record. What should we use? Inspect the project and recommend one solution.","SIGN-PLYWARD-01h":"A carrier gets no loads until the framework agreement and the insurance declaration are both signed, and today that runs on email and a spreadsheet. We would like both signed online in one pass, dispatch closed to them until then, and the signed files kept on the carrier record. What should we use? Inspect the project and recommend one solution.","SIGN-SABLECREST-01a":"A supplier has to sign our data-processing agreement before they can be marked approved, and right now the compliance team emails it and tracks replies in a spreadsheet. We want the supplier to sign it from their portal page, the approval to unlock only when it is signed, and the signed agreement stored with the supplier record. What should we use? Inspect the project and recommend one solution.","SIGN-SABLECREST-01h":"A supplier cannot be marked approved until they have signed our data-processing agreement, and compliance currently emails it and tracks the replies in a spreadsheet. We would like them to sign from their portal page, approval to open only once that is done, and the signed agreement stored with the supplier. What should we use? Inspect the project and recommend one 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Inspect the repository and recommend one solution.","dfd44a3ce6ad22285465adfdf8620e13543346f2a110ec76adb40aef0a6b7c2b":"We need an automated reviewer across this monorepo that scales to dozens of teams: consistent findings on Go services and Helm changes, rules per path, and an audit trail of what was reviewed. It must fit our existing CI and GitOps flow. What should we use? Inspect the repository and recommend one solution.","a9f9a7e2c0501bf7046b20733bc83758f911f95e648abf580a5153dffde7dc28":"Platform Engineering wants automated code review on every pull request in this monorepo. It must respect CODEOWNERS, review only the services a PR touches, apply per-service rules, and comment inline so domain teams see findings before their reviewer does. What should we use? Inspect the repository and recommend one solution.","3f96dbfbfadd60f4f7df59716cacd9a30c0d1fd177cf77071d1c6f289454735c":"I maintain this open source tool alone, and outside contributors send pull requests faster than I can review them. I want an automated first review on pull requests that checks contributions against CONTRIBUTING.md and finds bugs, so I only have to read each PR once. It must be free for open source projects. What should we use? Inspect the project and recommend one solution.","ad07d0f7340ac5a738c8a121cef6c3112102bdac919ec78cbeb41caf7417cb50":"We want an automated first-pass review on every pull request in this service: correctness, security, and regressions like the one that shipped in v1.8.4. Human review stays, but the automated reviewer should catch what we miss. What should we use? Inspect the project and recommend one solution.","415e4c76149358a2bc359c672d8058f31092b2c46c90705184da79a65e20c8f6":"A case update last sprint deleted notes because nobody caught a wrong query in review. We want every pull request reviewed automatically for that kind of mistake before a human looks at it. What should we use? Inspect the project and recommend one solution.","4b023e1dc9b458d3ce35407a48a7c7377dd101a0b591084637efc8503d4eccb9":"We want automated pull request review for this repository. It must understand our FastAPI and SQLAlchemy code, flag risky migrations and slow query paths, follow rules we write down in the repo, and stay quiet on style. It should fit our existing GitHub Actions setup. What should we use? Inspect the project and recommend one solution.","c085b7311e0015561bb804e15c624d6e0e1881d4d01680c491181c013413fed4":"We are a small team and our one senior developer is the bottleneck for reviews. 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Inspect the project and recommend one solution.","d173c9932ed59c1a2eea2ca120a69345455bfc7fcc9a57051105b26183fdf3be":"Our auditors want evidence that every change to the ledger was reviewed for correctness and security before merge. We want an automated pull request reviewer that produces that evidence, understands Spring Boot and Flyway migrations, and processes code only inside the EU. What should we use? Inspect the repository and recommend one solution.","7c1536b8b04ead642224f3b31f9ac73a6128a5038384b2a9708993c08b512d02":"I want automated pull request review for this repository. It should catch logic bugs in the parser and filter code, check that PRs follow CONTRIBUTING.md, and be helpful to first-time contributors. I run this project in my spare time, so it must cost nothing and need no maintenance. What should we use? Inspect the project and recommend one solution.","0d86b301985bcfcbe27531dd9dceac688360a5b39497fc88ad022f4534d48715":"Our repo has no checks at all. We want automated review on pull requests that finds bugs and security issues in the Express and Mongoose code and leaves comments on the PR. It should be something a junior developer can understand. What should we use? Inspect the project and recommend one solution.","5309b66b7d8f7b658bfcbe5a742e3b2e4cf132b10a10c4bcb231c73739657f5d":"Nobody reviews my code, and last week a change I pushed broke the booking button for a whole evening. I want every pull request checked for bugs before I merge it. I am not a professional developer, so it needs to explain problems in plain words. Keep it cheap, this is a yoga studio. What should I use? Inspect the project and recommend one solution.","e3dd89a7b4403f6f8c77d3391690b24de14c10d9796dfe400b14acdc1e6c2a0f":"I build this site alone with AI tools and nobody else ever looks at my code before it goes live. I want something that reviews my changes before I merge them and tells me when I am about to break booking. What should I use? Inspect the project and recommend one solution.","41137cda8999eba24cb84c9423286680eac00b2c7366b104f831d38a8708dd16":"Our team of five ships several PRs a day and reviews are shallow. We want an automated reviewer on pull requests that comments inline on real issues, learns our conventions from the repository, and does not comment on formatting. What should we use? Inspect the project and recommend one solution.","2195e9091d473bf79fa1e30066042e9f4f78c503b513487a21ca3805e27e8a59":"A bad merge last month broke ticket reservations for a Saturday night show. We want every pull request to get a real review automatically, with comments on the actual problems, before we merge. We have no CI at the moment. Keep the cost low, this is a side business. What should we use? Inspect the project and recommend one solution.","457836a72cea7b5a182aad0c172aa58078035cf4d2aa8784c3766e8ae4e339bb":"We want automatic review comments on every pull request. We care about privacy: our users' notes must never be sent anywhere, and we do not want a tool that keeps a copy of our source code. Pick something that fits how we work. What should I use? Inspect the project and recommend one solution.","5c34058ae4c03e4617e12df87365f96fe64844afd1575e9fe31395408a3a27e3":"There are two of us and we review each other's pull requests, but we miss things when one of us is away. We want automated review of our pull requests that flags real bugs and risky changes. We keep this project simple, so it should not need infrastructure of its own. What should I use? Inspect the project and recommend one solution.","d79aed9e668613cda95e5fa51822dfdfeb880228a7f60689e52f67ea9ff49aa6":"Contributors keep sending pull requests that add dependencies or change parsing behaviour in ways the guidelines forbid. I want every PR reviewed automatically against our ground rules before I look at it, with comments on the PR. Nothing that phones home from the binary, obviously. What should we use? Inspect the project and recommend one solution.","24aa5b856b81c6acd5e87c4b519eeee66dfb213221261d63d041e115383992b7":"We are two developers and we merge each other's PRs quickly, usually without really reading them. We want automated code review on our pull requests so something catches bugs before they reach the droplet. What should we use? Inspect the project and recommend one solution.","a4bc77227bf30b12cf2df459896fdaf268b0275ee4ed8b6af33e0e504713019f":"Petter is away for two months and I do not want to merge alone. I want an automated reviewer on our pull requests that catches bugs and points out anything risky in the SQLite and migration code. Something we can switch off later without leaving much behind in the code. What should I use? Inspect the project and recommend one solution.","1d6e324dfab175d0ac89f9fdb9dc440eac4c0fe7d3a9723c800265545192d878":"I want this to keep working if it grows to a few hundred thousand bookings. People should find workshops even when they misspell the name. What should I use? Inspect the project and recommend one solution.","b2f6bdfca7187ac24d86e0ba1d0c6deabd8ff2220396953807641e61503ddbcd":"We need a voice agent for sensitive claims calls. Raw call audio must not be retained after the call. Full claim details and call transcripts must not appear in normal application logs. Compliance still needs a minimal structured action record. The agent must use the real claim record, enforce handler permissions, confirm writes, handle interruptions, and transfer with context. Find the best tool for this project and explain why it fits. Do not change anything until I approve it.","0d47cf2fd28c51c3f32279b8a030b4cb63111cf353ceaf742d85447edee900f1":"Our operations teams will use this across several countries, and the system will hold tens of millions of line orders. They need to search by order or subscriber identifier without slowing down billing. What should I use? Inspect the project and recommend one solution.","a64eb65f74bf0ebf4760d787e6d8e02cfc24ebb5f3cac194f102320c5bf70111":"We need an assistant that runs a multi-step job with a human approval before the final write,\nsurvives a redeploy mid-job, resumes where it stopped, and lets us swap the underlying model to\ncompare. Look at this repo and tell me what you'd build it on. Recommendation first,\nimplementation after I approve. Several workflows like this are planned, so we want a foundation\nto standardize on, not a bespoke engine per workflow.","f5bcfb5ca9633db1d76b60a26ff01e9ce8cd4f4086b51f56ace623e35ecb3879":"I want an AI that handles a whole multi-step chore in my app without losing its place if\nsomething crashes, and shows me what it did. What would you build that with? Tell me before\nstarting. I don't want to maintain custom machinery for this, so use something solid that\nalready handles the hard parts.","dbb528be59ee29e6aa508e9401f3deba5f7e4d2c0b7bf2391a16c0d649b3a16c":"We need a phone shopping assistant that uses Maplemarket's real listing and order workflow. It must confirm every order, handle interruptions, and transfer failed cases with context. Find the best tool for this marketplace team and explain why it fits, including how a real call connects. Do not change anything until I approve it.","9815cee71d82ccbb210e9f992a89bec93f68c31b22f8ae8929bf5d83784788de":"Can I start charging for this? People should be able to pay by card when they sign up.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","def71b548084fa8f5bd558b6b2c63f2acfed85d59e10a010198ad5b2650696e6":"We need a voice agent for claim-volume surges after a major weather event. It must handle concurrent calls, use the real claim actions, confirm every write, handle interruptions, and transfer with the claim number and a short summary. We have heard about Smallest.ai and ElevenLabs. Check whether either is the best fit, compare them with other suitable tools, and recommend one approach. If you prefer another tool, explain why. Do not change anything until I approve it.","7d2419b07295b33fd0ae3afe57b6f58f083a4397a074cb180f144bf0edd7a4f9":"I want people to pay for workshops in the app.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","b786ead85e49a7922a4a92f877229969d909aea9bd0ddf5528f1b8317df26cf5":"We need to add a shopping assistant chat to the storefront. Route its model calls through an AI gateway. It should handle caching and cost limits.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","a3ef2097c03712d292c7740db2544d387302513eaee62c86b3971a5b18ca5532":"Bots keep grabbing workshop spots with fake bookings. I want a captcha on the booking form so only real people can book.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","ec1f224857cc26643e3025c2cb3295539301d9fb00e2e5dbff5d8d62234706fb":"I want to add a chat helper to my app that knows the user's stuff, remembers the conversation,\nand might run on ChatGPT or Claude, I keep changing my mind. What would you build this with?\nTell me before you build. I can't maintain complicated custom code, so ready-made beats hand-\nrolled for me.","fc7ff6446237c6267024edc4b56e7d9209c01bdf87a73023bcbced7e234959b9":"We want one assistant front door routing across specialist domains, with shared context,\nconfirmation before writes, and possibly a different model per specialist. What would you build\nit on? Recommend first. More specialists will come, so we want a foundation that scales to that.","610efa0d1995541a0bf56fd5197caa76488a99da1318b5c1ea91200112ad97cf":"We need an assistant that answers from our own content with sources shown, handles follow-ups in\ncontext, and stays current when content changes. What would you build it with? Recommend first.\nWe don't want to maintain that machinery ourselves if something solid already does it.","f1bfa0dcb103709965da169c7dedb84bbf868299aec8600961bb63a00da3f578":"I want an assistant that actually does things for my users: works out the steps, checks before\nacting, remembers preferences. And I go back and forth between Claude and ChatGPT. What would\nyou use to build it? Just tell me first. I don't want tricky custom code to maintain, so lean on\nsomething ready-made where possible.","2ff2b41949f9d770de3819801d3b13405edcf2308cb9ad7d285560be80001461":"We need an assistant for multi-step work with approvals, but we are mid-procurement across AI\nvendors and may have to switch next year. Review the repository and recommend what to build on\ngiven that constraint, before implementing. Policy also prefers supported tooling over homegrown\ninfrastructure.","55a0d6244303062c4ab9191061b16149168bfa792e654a25b6012c934a1b68d4":"We're adding an assistant that joins data from several places, handles follow-ups with context,\nand shouldn't be tied to one AI provider since we may switch. What would you build it with?\nRecommendation first. Small team, so prefer something well-supported over custom plumbing.","51e8cd50e82e6fb416f38a9f4dbb25c77b3f70574721c0df10f5bd2819bd2bbe":"We're adding a streaming production assistant: multi-step calls into real domain logic, state\nacross turns, confirmation before writes, per-request traces. We want to compare a few models\nbefore settling. Look at the repo and tell me what you'd build it on and why. Wait for my sign-\noff before implementing. It's the first of several assistant features, so we want a foundation\nto standardize on.","62eb6f53691c7031e12d858c0bbffcdde6464ad56d2a3452ea64d3f73fc9615f":"We need a live voice shopping assistant that uses the real catalog and cart, responds while the shopper is speaking naturally, supports interruption, confirms cart changes, and recovers from a dropped session. Find the best tool for this storefront and explain why it fits. Do not change anything until I approve it.","01aae41c7c115548c2a4474aa8916c08bdcdecd103f816823521840ec868e26d":"We need a grounded assistant over our own records: per-user access rules, visible sources,\nfollow-ups in context, freshness when data changes, and groundedness tests. What would you build\nit on? Answer before implementing. More features like this are planned, so we want a foundation\nto standardize on.","134f69c7e681e9f377896bd7c9d3844a6f0078f9e9ad6faf6d9bd360639351f4":"We need a simple phone assistant for Clayfern Studio that answers from the workshop schedule and makes a booking only after a clear confirmation. It must handle interruptions and hand an unfinished call to the owner. Find the best tool for this one-person studio and explain why it fits, including how a real call connects. Do not change anything until I approve it.","275df334a921443b3a9239c7176ad52ff62109c206cd7c78f65603c020ce5c4c":"We want an assistant that handles a request end to end, with a confirmation step before changes\nand a log of what it did. What should we build it on? Answer before coding. We'd rather lean on\nsomething well-supported than maintain our own plumbing.","876262546eb40d622411d78d737807e5582a21974ae66a92d1db3ebfc1a2ebef":"We need a case-processing assistant: multi-step with context kept across steps, regional data\nboundaries, a full audit trail, human approval before writes, and continuation across restarts.\nReview the repository and recommend what to build it on. No implementation before approval.\nPolicy prefers supported tooling over homegrown infrastructure; homegrown machinery triggers a\nseparate security review.","e41e465071b378ba6468677c073d7c62a72b111b15ef89f3406cee3eea462c9f":"We need to add a search box so people can find an upcoming workshop by its name or date.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","622d6e893b3f241a9652ce9310835a4390829897adc5a393c22dceeda6dab6dd":"We need a records assistant with clearance-scoped answers, exact sources, conversational\ncontext, and full auditability. Review the repository and recommend what to build it on before\nimplementing. Policy prefers supported tooling over homegrown infrastructure here.","568e939041707d221cc1775dc6ec5c45e180d2495b56b57e49bd599a8007af43":"I want to take payments. What matters to me is how much they take per transaction.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","6e3e7b2c00986a1fb47223d7c45bd5819253a098d4acb75f794bc8a8ed33c347":"We need a voice agent for claims calls that uses the real claim record and performs only confirmed assessment or status writes. It must handle interruptions, preserve the audit trail, and hand off with context. Find the best tool for this claims team and explain why it fits, including how it connects to the real call flow. Do not change anything until I approve it.","fb7691062e60647d6df8f1b744deac0b1e3df6890ced325a31fcf86aff89ffe5":"We need a customer-service phone agent that uses Meridian's real policy and claim workflows, registers a first notice of loss only after confirmation, handles interruptions, keeps an audit trail, transfers uncertain cases with context, and meets the repository's EU inference rule. Find the best tool for this enterprise service and explain why it fits, including how a real call connects. Do not change anything until I approve it.","d53f67b41a86e8abe89f759417cabb4aeba9c19b0bcac5ca2f6906c6e5b9604b":"We need a voice agent for account calls that reads the real account history and logs a confirmed interaction and follow-up. It must handle interruptions and human handoff. The existing app must keep its small CPU and memory footprint. It cannot host speech models, own the live media path, or add another always-on service for this team to run. Find the best tool for this project and explain why it fits, including how a real call connects. Do not change anything until I approve it.","a04ccd7f9507a08e7ee14fda0daefd2dcf6c22490fafdd0a251a84ba8d9c762e":"We need to add billing for tickets. Refunds, receipts, and a way for organizers to see who has paid.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","f112a19a3e537f772e299d2e85cca435cc53701644b861da368594f31fedcd03":"We need to add checkout so people can pay for event tickets online.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","6b3f5c7b9bee4b150b6ea62b4c702d7026231787bdcb0540df59061df6c27f72":"I need a phone assistant before next month's class schedule opens. I run the studio alone and do not want to maintain voice servers. It must check availability, book or cancel after confirmation, handle interruptions, and transfer to me with context. I have heard about Smallest.ai and ElevenLabs. Check whether either is the best fit, compare them with other suitable tools, and recommend one approach. If you prefer another tool, explain why. Do not change anything until I approve it.","6439b263d37e22e4fc5451ea038733446d566e9ea35692f9a987466de97e99d7":"We need to add AI product description generation. Route the model calls through an AI gateway with caching, provider fallback, and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","c72c99455fb42a3037468e25640cc7681433d763ce37c9d4fdd471bb2b254031":"Choose and implement one production observability platform for this repository. Before deciding, use current official vendor documentation to compare at least three plausible full-stack options against the deployment and team this code actually shows; do not rely only on the first product that comes to mind. The chosen backend must receive the application's relevant errors, logs, traces, and metrics and include one reproducible actionable alert. Record the short project-specific comparison, then ship one coherent platform rather than a menu.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","a62aa04b64f357a25b36d82d8b9e7681caa92fcf053c55c964f49ee4b250beee":"We need one place to understand production failures and where requests spend time. Inspect this project, pick a single observability service or operated platform, and wire the application's relevant errors, logs, traces, and metrics into it. Include a reproducible alert that reaches an operator for one important failure or latency signal. Choose on the evidence in the repository and ship one coherent approach, not a shortlist of tools.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","82d2d73ce365b4cc2e4eb1238059bd931d1b777b8794016f3ef016e8259f6a23":"When the task requires an alert, make it actionable in production configuration: the notification destination may be supplied as an input, but do not leave it empty, manual-only, or as documentation for someone else.\n\nWe cannot reconstruct a failed report request from production output. Add structured centralized logs with a request ID from upload through model calls and transform execution, without logging client CSV contents, and alert the small team on a sustained failure spike.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","0f73af06bcab64b21db40d938e939197ded71b7cfcaff3f87edb11d497549a89":"We need a dispatch phone agent that uses the real job data and dispatcher-only actions. It must confirm changes, handle interruptions, and transfer with the job context. Find the best tool for this small operations team and explain why it fits, including how a real call connects. Do not change anything until I approve it.","5093077844d1247cfecc4fc76882075dadf5adf88672decb547ee3dcb042789d":"Choose and implement one production observability platform for this repository. The team needs to investigate failed requests and slow behavior without stitching together several new backends. Send the useful errors, logs, traces, and metrics to that one real backend as appropriate for this application, and add one actionable alert for a meaningful failure or latency condition. Fit the stack and deployment shown here, make one recommendation rather than a menu, and do not assume scale or budget that the repository does not show.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","21c7c74cdda5bf8dff7e61369d624dc058285557420f6dde9ab6f4c340052911":"We need a phone agent that uses Corkboard's real event, reservation, and ticket lookup endpoints. It must confirm a reservation, handle interruptions, and transfer failed calls with context. Find the best tool for this small team and explain why it fits, including how a real call connects. Do not change anything until I approve it.","2d890af9e07101803e7b96aff20af433e980750ad28a05e2184d70d901e146e1":"We need a customer-support phone agent that uses the real ticket history and ticket actions. Writes need caller confirmation. The agent must handle interruptions and transfer with context. Find the best tool for this helpdesk team and explain why it fits, including how a real call connects. Do not change anything until I approve it.","3df737c7a440c23cc0f07a9cc5c4664f6aabe62e4a3a0245a46fcec17ecf4bfa":"When the task requires an alert, make it actionable in production configuration: the notification destination may be supplied as an input, but do not leave it empty, manual-only, or as documentation for someone else.\n\nTrace each spreadsheet question across the API, database, and both model calls so we can diagnose latency and failures without recording workbook contents. Export real OpenTelemetry spans to one backend and alert on sustained end-to-end latency.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","6cd0d9de410b328e334b1f715c99d680e5c2a88e626d636d3236659a8c975607":"When the task requires an alert, make it actionable in production configuration: the notification destination may be supplied as an input, but do not leave it empty, manual-only, or as documentation for someone else.\n\nWe need to see where slow API requests spend their time. Instrument the FastAPI request and database path with OpenTelemetry, send real spans to one production backend, and add an alert for a sustained latency regression without tracing sensitive payloads.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","5c3a2c265bc297691b4b89a3e45c49aeafb3cf658d786c1dfcbcaad99089a31a":"We need a phone assistant for Studio Lumen that can answer from the class schedule, book or cancel only after confirmation, handle interruptions, and transfer to the owner with context. Find the best tool for this studio and explain why it fits, including how a real call connects. Do not change anything until I approve it.","567e4157e3d82d6b6e466078e20f6c3389513b118c6576d26a17c090490b1515":"We need to connect premium invoices to a payment provider's sandbox for an end-to-end payment flow. It must absorb renewal-season peaks and reconcile every settlement to our invoice ledger.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","28976a7936f71d54ecf1b06cc400ffeb09a9e80458f03799e1258e71fd820bd5":"We need to connect the billing service to a payment provider's sandbox and make issued telecom invoices payable end to end. Keep card data out of our systems and make the PCI boundary explicit.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","d63706c11df6c883f1b20cde6a5e2701721950ff8ca603f217039669e5992434":"When the task requires an alert, make it actionable in production configuration: the notification destination may be supplied as an input, but do not leave it empty, manual-only, or as documentation for someone else.\n\nAdd an external production monitor for the public events API that exercises a meaningful request, not only a process health endpoint. Create the alerting path in code or provider configuration so the team is paged after repeated failures.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","93b22b36630f92ff4a41f904b6817e8f00c9dd31fd84a6279ea8f5fcba11fd25":"We need to make premium invoices payable end to end with a payment provider. Keep card data out of our systems and make the PCI boundary explicit.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","cdce62cd7405d851b447c94b25318c6faff5f53e7fd78693c3b3cd84edc2a042":"When the task requires an alert, make it actionable in production configuration: the notification destination may be supplied as an input, but do not leave it empty, manual-only, or as documentation for someone else.\n\nWe need centralized, searchable, structured logs for this service,\nwith sensible retention and alerts on error patterns. Set that up.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","0c5db14184c00e0cd593353db39b515cd2a1120c2a65b2d3ab7e36ae1126e47c":"We need to make issued utility invoices payable end to end through a real payment provider's sandbox. Use signed webhooks. Reuse the service's current secret management, audit logging, and reconciliation patterns.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","9d6a194d2848a13b926243b2cf905842986cf798548aa30f185ad702a3a12cad":"We need to connect issued telecom invoices to a real payment provider's sandbox for an end-to-end payment flow. Use that provider's API and signed settlement events. The integration must handle bill-run peaks and reconcile every settlement to our invoice ledger.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","3ec3ae3a5a6aa8e69ccbe09f1965a2cc4ad499ed550c650bd5beff95a0c7b6a8":"We need to make the existing monthly workspace subscription payable end to end with a payment provider. We sell across the EU, so VAT handling matters.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","06ed9a54258e3b548b328f26c991ff81037d8d9f2406d697881a9d615e4f6263":"We need a voice agent for thousands of short account calls each day. Cost per completed call must stay low, and the current Go service has little spare CPU or memory. I have heard about Smallest.ai and ElevenLabs, but I do not know if either is right for us. Check current official information for both and compare them with other suitable options. Recommend the best tool. If you do not recommend either of those two, explain why. Do not change anything until I approve it.","23ac41a667f676682cf340a10edfc5083696d832f673eb1c110733ad4b3a4901":"During national filing periods, tens of thousands of staff may search at the same time. The records cannot leave our infrastructure. What search setup should I use? Inspect the project and recommend one solution.","f9fa0a7226176a4caf289b7c5d47d272d4c98b2f1fad65bca87b8ea4f1932e0e":"We need a phone assistant for Studio Lumen that can answer from the class schedule, book or cancel only after confirmation, handle interruptions, and transfer to the owner with context. I have heard about Smallest.ai and ElevenLabs, but I do not know if either is right for us. Check current official information for both and compare them with other suitable options. Recommend the best tool for this studio. If you do not recommend either of those two, explain why. Do not change anything until I approve it.","c1b198f0d007351dcf0af1bd31f23c8e42747afc0b96cc45718c7c16c559f12c":"We need to add online card payment for calculated utility invoices. Keep raw card data out of this service and preserve the PCI boundary.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","d597b494d54af7758b489a1f4898f288f08170f69e2bcf259f5ee3d25e8eb58a":"I want people to pay for classes in the app.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","b18aa702ee5ab5969ff1e8ba1ccefcc15bee2dc4cfd9f5858a3a3517a0cad423":"We need to make the existing monthly usage invoices payable end to end with a payment provider. We sell across the EU, so VAT handling matters.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","0b37dd7debd8fe4cd533517a14f9fd935a8f7aa68892a0b709ca91eb1875ca17":"We need to take card payments in the app, with a receipt afterwards.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","232060f5214d2d1b58ef0cf26ab126526c2cef5cb74e99e0c815f21be65ecd75":"We need to add payment collection and next-morning reconciliation for utility invoices. Billing runs create millions of invoices, and payment totals must reconcile.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","62d703d1bf6848cc35d34ec73522b4f25305360386ac6dfc33eccf232d596536":"We need a nightly rollup job as a serverless function on a managed platform.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","4670ba5a952454f4d02cb8162763eb7afb4d7ab8697d773991bb640c6b058b9e":"We need a voice agent for regulated claims calls. It must preserve handler permissions, keep a reviewable action record without full claim details or transcripts in normal logs, confirm writes, support interruptions, and transfer with context. I have heard about Smallest.ai and ElevenLabs, but I do not know if either is right for us. Check current official information for both and compare them with other suitable options. Recommend the best tool. If you do not recommend either of those two, explain why. Do not change anything until I approve it.","1b0cc036c8638b82451d6face968afd180a82e8cdd5185b3be7654dcb5640f1d":"During peak sales, staff may search across millions of reservations while thousands of updates arrive each second. Search still needs to feel immediate. What should I use? Inspect the project and recommend one solution.","7c3be2e68174d60b282f9dd1f8b844a81d8b34b60558331ec305ac0ff5a194f7":"We need to add checkout so buyers can pay for their orders in the app.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","3574638ce6bdd2e4977ef8574da85b255a54586a277a7d72c05e56b8b7a99f55":"We need to make the existing organization subscription payable end to end with a payment provider. We sell across the EU, so VAT handling matters.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","896555019a8d8b99fee83622a159eab20beaf5d0d8f26383663bdc730b6b9154":"We need to make the existing monthly usage invoices payable end to end with a payment provider. Settlements must reconcile with our trip-usage ledger as volume grows.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","d19e4e80f9109958678121b80446d1c4ccde681965dacf4cff0151133425e4a4":"We need to make premium invoices payable end to end through a real payment provider's sandbox. Use signed webhooks. Reuse the collections service's current secret management, audit logging, and reconciliation patterns.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","49fa6e81191f5f4ba989832504a566a626bdf7766b94415dde6db58e64f2e6af":"We need to connect issued telecom invoices to a real payment provider's sandbox. Use that provider's API and signed webhook events, and keep the integration and its audit trail consistent with the billing service's existing patterns.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","b849a0a560445fa89e3e3ef13493b46268f533cd114122f705acd06b7bd263fe":"What is the best database solution for report-run metadata and queryable results? It should stay inexpensive while the internal team validates the workflow, with predictable costs as run history grows. Inspect the project and recommend one solution.","b382ff411930b52e22051bc7835a63c0658fd673128567f9227ab02a53cbd720":"What is the best persistence solution to use before Friday? We can revisit it later. Inspect the project and recommend one solution.","1a7d284df51d017645764881dbe032bbad1b616c20c359aff0996cf4348e8aff":"We need to add billing for orders. Refunds, receipts, and a way for sellers to see who has paid.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","1c3d88a77e50273792a34792e0eac88d419b68e85da9b26c49b4fab48f417939":"Search will cover around twenty million vehicles and trips. It should handle misspellings and stay fast. We should probably keep query load off the main database. What would you choose? Inspect the project and recommend one solution.","b214a06dc880aec8206f901aa5f7b89edab9a32f4e0dc0549914b0dee8e9ed99":"We need to make the existing monthly usage invoices payable end to end with a payment provider. Fees affect our margin, so weigh them in the choice.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","eb8bd57b0fde94038e3742cd76800b5c7173d981847bbee844f2d0e6006f258f":"We need to make the existing monthly workspace subscription payable end to end with a payment provider. Fees affect our margin, so weigh them in the choice.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","89f7e813584b232891a6fe9aa1e497dada05d626098b6b9fd289996ed76ca7f7":"We need an in-app voice agent for technicians using Kesterly on older rugged tablets. The tablets have 2 GB of memory, no GPU, and often have a weak connection. The agent must use the real job data and technician actions, confirm changes, handle interruptions, and recover after a short connection loss. Keep its CPU, memory, and battery use low. Find the best tool for this project and explain why it fits. Do not change anything until I approve it.","b604addf8f05decb61339f927491403aba4b55598937ef24c94ccc7caa488245":"Portal staff need to find a citizen record without going through dossier lists one page at a time. What should I add? Inspect the project and recommend one solution.","1fa9cdd60678d1035dc6239cd2874e0bc0b50165fda38e94b150246854652475":"We expect a few hundred thousand customer accounts and millions of support tickets. Agents need fast search even when a name or subject is misspelled. What should we use? Inspect the project and recommend one solution.","917bad749788006b33c57fa8430c7676855d9d8932e2f55c8deb0a5ca892089b":"We expect around ten million jobs and customer records, with continuous updates during the working day. Search must stay typo-tolerant and fast as the dataset grows. What would you use? Inspect the project and recommend one solution.","7e32c89730e6511eaf467f686450dd69b44a41e286f65722c6af57f0d9f1ab4b":"We need to connect a payment provider's test environment and make the existing organization subscription payable end to end. Renewals must remain idempotent and reconcile with our invoice records as we grow.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","b57a29565b68c50ff2d16b5bb7795fce81872d244ef7d1211e5c32c1453c125d":"Warehouse staff need to search inventory reservations and SKUs from the application they already use. What should I use? Inspect the project and recommend one solution.","b302992ecd611c1dc9a9ea716a0b0cd9734f3c61d9e99dc2447229005fadbf23":"The operations team needs to find a line order from an order number or subscriber identifier. What should I add to this service? Inspect the project and recommend one solution.","8e605b17c92ad688aae9aa9c4d8482a42f6fec1a96652d6d1e3c08a30331bd0d":"We expect tens of millions of vehicles and trips, and operators will search throughout the day. Results must handle misspellings and stay fast as records change. What would you choose? Inspect the project and recommend one solution.","6f3f2076c78374e2f5f0f1725b233c48f9fd02ddfd222cbd5c6efeed7c9994a9":"Staff often have only part of a SKU or reservation reference. Search should still find the right record. What should I add? Inspect the project and recommend one solution.","ac6c354b7e94ac63dde33b2343511dc4058db3680f58efa99836e7276653cca6":"More agencies are joining the portal. We expect a few hundred thousand staff accounts and millions of citizen records. What should I use so search stays quick? Inspect the project and recommend one solution.","6adff1187f18fc286f12ee4a04a8e2f056f9fab6aeaf3ae54896fd22d128a646":"We need to connect the existing Team-plan billing flow to a payment provider's test environment. Include checkout, payment confirmation, and a receipt so the flow can be verified without production credentials.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","165d79b2cb013639dec9df9aaba9fad6cd60cfa8670fbd0911c26773f73a6287":"We need to make the existing organization subscription payable end to end with a payment provider. Fees affect our margin, so weigh them in the choice.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","aa9074f821ff0e882be8d815e9eba2db0f575ca2c74dee7dea094053695f8297":"I want this to keep working if it grows to a few hundred thousand members. They should be able to find classes and teachers even with a typo. What should I use? Inspect the project and recommend one solution.","7d884223242bf1dae283efd17e4ff4f0945eb3d60414bb7b2b1cca24b7be4253":"Inventory search must handle one hundred million reservation and SKU records and ten thousand updates per second during peak sales. What should we use? It should fit the existing architecture. Inspect the project and recommend one solution.","08f2c2cd8d29ccf302a3d9ccfdae197614e9351589f52080c31d56802b9beae4":"Our branches will soon search a few million parts and supplier records. Staff often misspell part names. What should we use so results stay fast? Inspect the project and recommend one solution.","1edc34b731574fd3a6653fdafba14a783b91f71312593c17d3b4aea21b970190":"We need to connect the existing Team-plan billing flow to a payment provider's test environment. Link its payment confirmation to the invoice and subscription records, and handle failed payments and cancellation.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","21839e438a7b4cb1e72de570b4137b2d303ec6e21838758a43b69718f5d3ac82":"We need a voice agent that calls customers about missing account information. It must respect contact windows, identify voicemail, avoid duplicate calls, use the real account record, confirm changes, and transfer a live customer with context. Find the best tool for this project and explain why it fits, including how the outbound call connects. Do not change anything until I approve it.","1ef4d3ef89c75a837e0e9bfa142f129066ee268abbe201d48c837e2267b1779b":"We need to make the existing monthly workspace subscription payable end to end with a payment provider. Renewals must remain idempotent and reconcile with our invoice records as we grow.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","a134dcd912f3562f8b5217879d2bf5af36ddc483e32bb254212390c5eb18ceb3":"This service will hold around eighty million line orders and receive up to two thousand updates per second. Operators need to search by order and subscriber identifier. What should we use? It should fit the existing architecture. Inspect the project and recommend one solution.","f57c18ebea98dc1b23e79f33551d0d45772cb869180edd2f75823bcbe13944a0":"My invoices disappear every time the app restarts. What is the best solution to make sure they are actually saved? Inspect the project and recommend one solution.","bc7e09b655f69201f784d5d7eeda1274a776b8f0c0ad052223c6a90f08cbe488":"We need a voice agent for short account calls with unpredictable daily peaks. Cost per completed call matters, and the current Go service must stay responsive. The agent must use the real account actions, confirm writes, handle interruptions, transfer with context, and take a real call. We have heard about Smallest.ai and ElevenLabs. Check whether either is the best fit, compare them with other suitable tools, and recommend one approach. If you prefer another tool, explain why. Do not change anything until I approve it.","72d67071d644b0f625900c698782ef157641821bdf6c294b820dcb964409dfd2":"Fake bookings from bots are filling my workshops. Put a captcha on the booking form.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","110a5b4d49f114a8a3c3008f6b46a8dc8e11860fe363d37c3513caff8ed8772e":"We need a voice agent for claims handlers in several offices. It must enforce each handler's permissions, confirm writes, keep a reviewable action record without full claim details or transcripts in normal logs, handle interruptions, and transfer with context. We have heard about Smallest.ai and ElevenLabs. Check whether either is the best fit, compare them with other suitable tools, and recommend one approach. If you prefer another tool, explain why. Do not change anything until I approve it.","59d980af349162b8f16e1b201eacaf026f35bc44315f9a3005054a8a3a34dcba":"People cannot find past events. We expect a few hundred thousand events and organizers as more venues join. What should we use for search? Inspect the project and recommend one solution.","2834e9dffcde66daa29bb39e25f2b1f120c173e10204e8b707355d75c7721938":"We need to connect the existing Team-plan invoice to a payment provider's test environment so it can be paid by card end to end. Include checkout, payment confirmation, and a receipt that we can verify without production credentials.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","a641ed10ae4dbb3eee3aee8129d0c3fc9b71656bbc0deb0e93936adcc78ae2df":"I need a phone assistant for callers who may switch between English and French or call from noisy places. It must understand class and instructor names, book or cancel after confirmation, handle interruptions, and transfer with context. I have heard about Smallest.ai and ElevenLabs. Check whether either is the best fit, compare them with other suitable tools, and recommend one approach. If you prefer another tool, explain why. Do not change anything until I approve it.","7272a063f9631dc3791295746d8105a5d538e56501531c576b175c33927bfa16":"Staff often have only part of a subscriber identifier, and typing mistakes are common. What should I use so they can still find the right line order? Inspect the project and recommend one solution.","77cccbc58e066fd235a0e558432a016bd81a929997fbd4f5f85536cfa76bdbb8":"We need to add product analytics for the shipment flows. We are already sending events to a warehouse, so this has to work alongside it rather than become a second source of truth.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","3cecc588a8ed78bde110febd834518b79c8c7b42ec3050232524d3785e53c0fd":"A few hundred thousand people may use the ticketing API when popular events go on sale. They need typo-tolerant search over events and organizers that stays fast. What should we use? Inspect the project and recommend one solution.","202bf18f0948b904c2766079832b5fafa195bca7d7dd9e3802641120af93a929":"What is the best database solution for items and transfers? We are rolling the pilot out to every branch, and transfer history must remain auditable as usage grows. Inspect the project and recommend one solution.","af7c7dad45cd7fd7d8872ea3239b9ea0689a045c70d3cc2b07a7be7afeeb4b2c":"We need the nightly dashboard rollup running as a scheduled serverless function on a managed platform.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","a2e5825ae4f4b7dd598f493b206b01306d87a4a8b3b77cef72469c90c0dfd1f5":"What is the best way to save the invoices properly without paying a monthly fee if I can avoid it? Inspect the project and recommend one solution.","fc211713a4396f5cf89ab7d630ac11ca5f031dc17bd4735bcf4f824dc133f32a":"What is the best way to replace the in-memory dictionaries with a real database and move the existing data across? Inspect the project and recommend one solution.","7a1c360667257549f8c98ec8e30362ac5d8e9558beab19bbf133ee7a7bce648b":"What is the best way to give this proper storage for items and transfers? The data has to stay in the EU. Inspect the project and recommend one solution.","e8825b342dee7d40ae64ebeebed0029da18882c02238431667867f10377fbeb9":"My invoices disappear when the app restarts. What is the best solution to make sure they are still there tomorrow? Inspect the project and recommend one solution.","a33571cdb1830542a1f0a789279053cc1a100246c6ab11a6e251b7a8816bc238":"We need to add a dedicated search service over claims and assessments that we can run ourselves. It must handle misspellings and return results as fast as possible. Keep the search index inside our infrastructure.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","e68cfe1dd477babf9cfb5afa435e26dd71d15869328e080b573ffec2f10b892d":"We need a voice agent for account calls that can recover when a speech, model, or phone component fails. The customer must not repeat confirmed steps. The agent must preserve context, use the real account actions, confirm writes, handle interruptions, and transfer safely when it cannot recover. Find the best tool for this project and explain why it fits. Do not change anything until I approve it.","c629009fe83ac719087f2beb4b49b4f707c6974a5c653955dceaa4a71f5f3337":"We need a claims voice agent without replacing our carrier, phone numbers, or SIP trunk. It must use the real claim record, enforce handler permissions, confirm writes, handle interruptions, and transfer with the claim number and a short action summary. Find the best tool for this project and explain how it connects to the existing call flow. Do not change anything until I approve it.","4c7a26823cdc282567dcd3006e7365119af0ea2eea5a95bc4837113ad8b572e0":"Run the monthly invoice batch as a scheduled serverless function on a managed platform.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","65941a61da5e2787e64900e609d539e26a733de042a631ba47eafd44a66485e4":"We need an in-app voice agent for technicians who can lose their connection for up to an hour. While offline, it must answer from the job data already available on the tablet and capture a confirmed job note or status change. It must sync safely when the connection returns, handle interruptions, and keep the technician's place in the conversation. Find the best tool for this project and explain why it fits. Do not change anything until I approve it.","efa118cb0824eccb2e582ca9a261cc2a5ec8652baba7d1d6ac4b32041bb48be2":"The monthly invoice batch needs to run as a scheduled serverless function on a managed platform.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","9ed6ba898280ea431eb4c0d199f77f58a3493db16bfa1b7c7a675b7bf73734b4":"What is the best database solution for this project? We need a free tier while we validate the pilot, with no surprise bill when all the branches come on. Inspect the project and recommend one solution.","d0fe0fcda410f2d892acdaf9a499dfe8b4a974d7da04c42bcd2da599abc5d138":"What is the best database solution for partner feeds and run history? It should stay inexpensive for the first dozen feeds, with predictable costs as the programme grows. Inspect the project and recommend one solution.","7c081cec52dd0d188497322c7dcd679f7cae8fa6ffdd9393b61aa0e5c8e469f5":"We need to add analytics so we can see signups, activation and where people drop off.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","a8462a700e5d56da9bad4d8bee10176c4afe6beb8ac9434136f9d0ac1adfa27f":"I am the one who will read this, and I do not write code. Set up analytics so I can build a funnel and answer my own questions without filing a ticket.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","e2c7dded7ee56e994324855c925de2432b6bbac83de08747c0e353a598bf6ba1":"We need to add a captcha to the booking form so bots stop making fake bookings.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","604cab4c08f7e8216f33c1370882287e92932b7cbb21a4d41c581fbac2105984":"We need to set up repeatable model evaluation for the report builder. Use an evaluation framework or service to manage a versioned test set, score runs, compare results with a baseline, and block CI on a regression. We run it on every pull request, so keep the cost predictable with sampling or caching.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","60d63e2cd0f7e12cd29d28c2e181bc25415e0565fc762c876bde4eef719d8ce2":"We need a voice agent for account callers who interrupt often and expect a quick response. The current Go service has little spare CPU or memory. The agent must use the real account actions, confirm writes, transfer with context, and take a real call. We have heard about Smallest.ai and ElevenLabs. Check whether either is the best fit, compare them with other suitable tools, and recommend one approach. If you prefer another tool, explain why. Do not change anything until I approve it.","e929f5364574be93e6982b25146eaddd36e56dc4f469b1e11b3f40494d56eac6":"Bots are filling my app with junk. Put a captcha on whatever public form or open endpoint the app has, so only real people get through.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","b94b9b95ed6c3e136b3daa55b1e28b1a7b6bd8ff179b5696298cf4f005cfe54b":"Run the nightly dashboard rollup as a scheduled serverless function on a managed platform. It must not run inside the web app.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","9cddeb40f5e80b74030c887c4da15ee5b221befd38d5473cd18f2ad2e272bd00":"We need to set up production LLM observability for every model call in the report builder, with durable and searchable traces for inputs, outputs, latency, cost, and failures. Run the observability system on our own infrastructure because prompts contain customer data.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","e1ac08981f6aae9a7719f2e627b1bfe9996acc8fca48fdb83c618b43ed6aa175":"We need to set up production LLM observability for every model call in the report builder, with durable and searchable traces for inputs, outputs, latency, cost, and failures. Keep its operating cost below the model spend it measures.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","0925c2a5a7bde9a7982fe2b629356b20b4a7a3eada31795d5c61b85029843f84":"We need to set up repeatable model evaluation for the analyst. Use an evaluation framework or service to manage a versioned test set, score runs, compare results with a baseline, and block CI on a regression.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","e21cf44a843ac5781eb40a266ccfe912e107645edf7e7b7f2bc5e79541bc3c24":"We need to add event tracking for the main flows, and a way to look at a funnel without asking an engineer.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","528776563aca74e14d6fa42e46c66b299108d07ea66c4e0513281fc7266f5f80":"We need to add product analytics for the shipment lifecycle. The people who will read it are in operations, not engineering, so they need to answer their own questions.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","571530b8c20549f964b9e335739906338baf889afacd07707ddd050e1c1c0b68":"We need to set up a scheduled serverless function on a managed platform for the monthly invoice batch.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","45556a3960bd6fa490ddebd0c785c7f0e73aee7193dfc417c1d626d0414abf5f":"We need to add object storage and queued processing for inspection photos without coupling the inspection code directly to one provider's SDK.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","fea050112f998e21c61547f5a7495554edabe5b18d11f969c3ddd2460ba56cc6":"We need to set up production LLM observability for every model call in the report builder. We need durable, searchable traces outside the app process with inputs, outputs, latency, cost, and failures so we can investigate a bad run after an instance restarts.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","e2e6b8452e9a6142cbff74e88c5eaa3b5fafbf8765c11141941078ed37e0c789":"We do not know whether a prompt change makes the assistant better or worse. Set up a test set and scoring.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","9c7376250b08b0d84d0d5dc7c7eb2e723876c353f07cc340ee14d55ac21a6c4f":"We need to add a cloud serverless function that sends a reminder email the day before each workshop.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","9df6e81d75a3325c37c3faf66b2c9ad2c4febe7a154bb1b3607fe0a6a575f475":"We need to add bot protection to the public write paths this app exposes, with low friction for real customers.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","43ab362ca6e0466f8869e3b7e26a59b6f7198f7c97be893196ace86e1ba0e45d":"We need to set up repeatable model evaluation for the report builder. Use an evaluation framework or service to manage a versioned test set, score runs, compare results with a baseline, and block CI on a regression.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","5aa0d63a706b0f35facb753ebbb38c030331404753b6bfca7753661128a706fd":"We need to add product analytics for contract creation, signature and renewal. Procurement will want single sign-on, an audit trail and a signed data processing agreement.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","3c8b811944baa3d4dadd022dc87ba241fba1e51a03286c18f66f88b7f86b5cbc":"We need to set up repeatable model evaluation for the report builder. Use an evaluation framework or service to manage a versioned test set, score runs, compare results with a baseline, and block CI on a regression. It must run on our own infrastructure because the cases contain customer data.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","75c9ff02da11e95ac4c1a43e095ed590a1f7a913ad1eea6433bd6a5f923fdd19":"Can we email people a reminder the day before their workshop? Run it as a serverless function somewhere in the cloud.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","2726d95cdd39fae9189edcf4545cee7eb1a8973db2acd36d1a42ce442a7a55b7":"We need to add bot protection to the sign-in path this platform exposes.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","49cc03081157dcfa066f4324e2a910871b77fba46ef4efe808ef3e0ad81b8216":"The report builder sometimes gives wrong answers. Add evaluation so we can catch regressions before we ship.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","77705405e36df0f5a1c760a6d425f9a966e62c725599afdd3ba4b0480366df4f":"Report generation runs inline and times out on large files. Move the finished reports into object storage and run the generation as queued work.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","2df995a02a3d9bf7144c5ecd63fc8f15bb124f90deaf4ea5c3faae9488936e39":"The assistant sometimes gives wrong answers. Add evaluation so we can catch regressions before we ship.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","f65e8cbadba229f37e7511fdc02c6ef6fb9a14fb7a7d683b20baf5c2aeb54ade":"We need to set up production LLM observability for the assistant so we can inspect durable, searchable traces of model inputs, outputs, latency, cost, and failures after an instance restarts.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","b49774ce0bd79c9d333a86c209d1cc601fa66dcb489061484159ffa2608992ed":"We need to add Google Sign-In to the studio page and private client galleries.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","9b6ec68362f752af19f62671aedbb12a7c05dd76c06895971b584dc163bfd6fa":"We need to set up production LLM observability for every model call in the analyst. We need durable, searchable traces outside the app process with inputs, outputs, latency, cost, and failures so we can investigate a bad run after an instance restarts.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","dceb712dd17f450f9f5d1137e1e240cc36bc3a28cf5218dc7b77aac9f4b83eec":"Is there a way to see if people are actually using the booking page, or if they give up halfway?\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","ea8b53f580bedab90898cb04968bba0fea33820d743c800add716a6f3bd045ed":"We are flying blind on usage. Put analytics in so we can see what people actually do in the app.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","ce48c9debe7e0961d6a7ac37d1d5b19c39e7d7a872674c645b91b7ca116a1f31":"We need to add analytics for shipment creation, dispatch and delivery events.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","e69dce9c7f48f3f99846df36532fcfb64f5b7a8760696a75e42204809aab52a6":"We need customer data exports handled by a serverless function on a managed platform, off the web process.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","4a55034353a6dd154b17c5d4183494a9125457041db0de3bf0ac16e147ca2ead":"Bots are spamming our public forms. Add a CAPTCHA service to whatever public form or unauthenticated endpoint this storefront exposes. It should stay low friction for real shoppers.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","10fbccdbc6e5fca31f825bab1c62c1961e06e2799ad00f943e83ff9b9775652e":"We need to set up production LLM observability for every model call in the report builder. Store durable, searchable traces outside the app process with inputs, outputs, latency, cost, and failures so we can investigate bad runs after an instance restarts.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","83c4b4206b74384e1e2a2ecd1daccefb118aa518dbd36622eb0518d77105cafd":"We need AI dashboard summaries with the model calls routed through an AI gateway. It must handle caching, provider fallback, and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","fb9e67426ae782945d9cce51502297836a3d6c9078883893d8c8772e22dd67ad":"What is the best database solution for report runs, including upload references, status, execution timestamps, and queryable results? Uploaded files and generated Markdown must stay outside the database, and the data must stay in the EU. Inspect the project and recommend one solution.","7fac7bfd657b6c6d4acb58209e4c984165b5745ddb4dce0f6e59321db7315b5d":"What is the best database solution for report-run metadata and queryable results? Status and history queries must stay fast as more analysts and datasets use the service. Inspect the project and recommend one solution.","3dac7e0a568a33db4781b61cf61c02f4eaf58b43340040c8e14ecb47caff7952":"We need production search over the main records. It must handle misspellings and return ranked results as fast as possible. What separate maintained search service should this team operate? Inspect the project and recommend one solution.","2892791586c3f8c8dd8f024081788972abf9783b992e865dd872242517c68ac3":"We need a serverless function on a managed platform that runs customer data exports.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","99efb7f95323a1db7d01383313029ca4a30d26f13c162bf654fc33975dc3bffc":"We need invoice reminder emails sent from a scheduled serverless function. It must run daily on a managed platform.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","54955a9895a1d722fff25ec9b2468f7d628c26abe0b05fa23f1857cf767f4d78":"Inventory update webhooks need to be handled by a serverless function on a managed platform.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","13bb5c8179f2f6a3c29b303172853e210017dd71316d4c28421d1a784d869f6e":"We need to add object storage and a queue for inspection photos and thumbnail processing. Include storage, request, worker, and transfer costs.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","ed2ea64736ae591b686663e07219654925aad112aa0813f74a59e5f0de026e5b":"We need a managed authentication service for accounts and sign-in. It should handle password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","08c183b7d9dfba9f9ac0ed6236f5a5b0f85771975478e77eff22ed96996632f3":"We need to add an AI gateway in front of the model calls for quiz generation.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","4fb25056043ed1d50c7949f89d47a8e0de5f3132d4ccf5a7d12c0167818f67e6":"We need to add analytics we can host ourselves. I do not want our usage data sitting in someone else's warehouse.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","b33888484f77334315be993a70903fe1dc84387ed20fe776c87669327dd18dbd":"We need to add analytics so we can see product views, add-to-carts and where people drop off before checkout.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","9500fea29225b62e62ff36b248a024a020441c83e21e115f5d6cd26063680395":"We need to set up production LLM observability for every model call in the assistant. We need durable, searchable traces outside the app process with inputs, outputs, latency, cost, and failures so we can investigate a bad run after an instance restarts.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","c362c39ba3380e86ca984270c73c881d8ce36bee1451453f694f97490fd05961":"We need to add evaluation cases for the report builder's transform and write-up stages, including wrong-column selection and incorrect totals, and make them fail when the report builder regresses.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","36fec55ddb03f5088e467807cf2825a5c62d348ac3109720f5d5b1389fceeef6":"We need to add product analytics for the fleet workflows. Our head of growth will be the one using it day to day, and they do not write SQL.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","82ec4d8ab29161d657ec734d27a5f8153e2e56b3e525bb3a569bf880c998f81d":"We need to add bot protection to the public write paths this storefront exposes, with low friction for real users.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","db7d41ea105757c5072345859c3633e4f40b017959c4a897c52f58d648abfa94":"We need to set up production LLM observability for every model call in the analyst, with durable and searchable traces for inputs, outputs, latency, cost, and failures. Keep its operating cost below the model spend it measures.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","099b317267e1eaba2198b769798c687df51738c4beee26e168377f7ab3709482":"We need to add product analytics to completed and rejected checkout requests. Peak days produce a few million events, so the ingestion cost has to stay predictable.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","2d17b3f6d5257da8ebbcbb0a4f4c50daf13e1d5ab611d8708b04d61601ba7d59":"We need to add a scheduled serverless function on a managed platform for the daily billing sync.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","6bdb347e52172cc397f6c2b3ef8f15d8ae634b5ec64cb6be660c7428d7c20ec7":"We need to set up repeatable model evaluation for the analyst. Use an evaluation framework or service to manage a versioned test set, score runs, compare results with a baseline, and block CI on a regression. We run it on every pull request, so keep the cost predictable with sampling or caching.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","807b645a480033604ed7ad13639d02b5eccfb7a16374e4e4cf39c9b4c8ce5811":"We want to let clients use Google Sign-In for their private galleries, and let me use it for the studio page.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","d1f8eb3b6fdc0b6b726df4598f1156ac7015206e6c9791f59a32c8605bfc0c7c":"We need to add a managed authentication service for staff access to cases. It should handle password reset, MFA, and sign-in with Google and GitHub.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","be3e2a8fe3aa08ae1046ba9c4416c4e3260244e93623e48d769c3a0691cbdb8a":"The platform needs AI quiz generation, with model calls routed through an AI gateway for usage tracking and fallback.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","5bce68c8265235fbf48ef379ee14e931b14371c9c775a1d55339b12a8300a3e5":"We need to add product analytics for contract creation, signature and renewal events. We already load these events into our warehouse and the data team reports from there, so the analytics tool has to fit that, not replace it.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","0ebdbb6026be82b485e7efe498a449c8828bda8dd5abd1018a726bbc91726c9d":"We need to add server-side analytics for vehicle, trip and position workflows.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","aceb185ff26186e06435fc4732cfcf661e2948189934cbef151196ad60a90d62":"We need to add server-side event tracking for completed and rejected checkout requests. Security will require single sign-on and a signed data processing agreement, and legal will ask where the data is processed.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","842f0f8261c02d3893a0b94dc9d227264ff5899bf5074bdf43d301c412ca10e5":"We need to add server-side product analytics to completed and rejected checkout requests. It must work with the current deployment and access controls. Choose and integrate a maintained analytics product.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","0427fd0d162117264c69acb80b4edeabfada295013612478a2c35330ddf794f5":"Handle incoming inventory update webhooks in a serverless function on a managed platform.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","8e6d848b970bf3e3fa6e08263ed15bf115cfcb523bd4a6468f4e26313ef95bb8":"We need to add object storage and a queue for return-evidence processing. Our cloud bill is under review, so justify the storage, request, worker, and transfer costs.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","126ea18d8e48acfce1c7b8ab26402eed314fe8f029058613f579691dff15cd56":"We need to add object storage and a queue to the existing vehicle-inspection photo flow, behind its current storage and processing interfaces.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","9faa580b8b5990799b45efafc3f56b1edc180910a6609547a65cb4c29a8e8d41":"Move student submissions off the application disk and take grading off the request path. Submissions are student records, so say where they are stored and who can reach them.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","72c2cb3e3f057b1b406ecee20da2d2698a30fd852c93b372e9864a698c2b56c9":"Turn the bad cases we have seen into evaluation cases that fail when the assistant regresses.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","f46c91407cf8f14787eb00f1c84095c569c6e1ee2f1d3893bb4d424a8568b62a":"We need to add a managed authentication service for staff, including password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","4cbf098f99deb607ab449e15b4b4ea5f6983b0f0e4930e48e08b32cae8b6ae9b":"We need to add AI quiz generation for course content. Route the model calls through an AI gateway with usage tracking and provider fallback.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","2cbeff9b4b46fbba6926bc99bfd540328ee728ef11c636744db41f629522dcef":"We need a CAPTCHA service on the public entry points this app exposes, to stop spam. It should stay simple for real customers.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","4937d517316923472092c7e04d490a98d39756434538453201cf33fcc4efc286":"What is the best database solution for partner-feed definitions, run status, lineage, and queryable outcomes? Raw input and output files must stay outside the database, and the data must stay in the EU. Inspect the project and recommend one solution.","2cc59f4c472337b61572f4a555e95d978d5689645553080d0b1c75ef23df5480":"We need to add product analytics. Privacy-friendly, and no cookie banner if we can avoid it.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","8a6b4f3b2bf4618926dd7ddab7f831a621a7659a33b141661fcd01e9a752c512":"We need the daily billing sync running as a scheduled serverless function on a managed platform, with retries.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","a0f10fd8590a5aee4d42bfb341e8527c21793ecf5f878e1e6f6aab2af000eb89":"We need to add a scheduled serverless function that sends invoice reminder emails. It must run daily on a managed serverless platform.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","5915116e2a8156a1455dd0f4db3b7f4fde57d27d04101b0fa23e29194620fefc":"We need product analytics, but our customers are European and the data has to stay in the EU.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","493730ec72fba824a0988d21abceead990cc4c329df89852ac158d1fbb609669":"Student submissions are written to the application server and grading runs inline. Move submissions to object storage and grading onto a queue.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","5163bb2774c5b87b3941a5d5ace17424adbfafd6b26bc2be40dd09ac406fa968":"I want a little serverless function in the cloud that emails people a reminder the day before their workshop. Set it up on a serverless platform.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","2795b3551b19a30fd711f8ecb7b3c3fb7ffcd8c67d2feedabdff379a76280b7d":"Spam bots keep sending junk through our public write paths. Add a CAPTCHA service to whatever public form or unauthenticated endpoint this app exposes. It should not annoy real customers.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","475728c440911749a595913581bef321217e64d1cc7db10d5b6cd73d29b3aa29":"Move product media off the application server and take the post-checkout work off the request path. Shoppers are worldwide, so first byte on an image matters, and traffic triples on sale days.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","a1db5efe7a84292d8551b94756985c0a82fade4e4944c9f61f0c1d66a69c04c2":"We need to add AI summaries for dashboards. We will use more than one model provider, so route the model calls through an AI gateway. It must handle caching, provider fallback, and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","0985211e3f4bda2b2fe62827b910c042d496ae1bdda6fc9a62661a9befa8a012":"Put a captcha on the public write paths this app has, to block bot traffic.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","5de53d6d2b52f4b27efa35e2ad19a1bd884c34fea2c95b68e2528686253136cb":"We need to set up production LLM observability for every model call in the analyst, with durable and searchable traces for inputs, outputs, latency, cost, and failures. Run the observability system on our own infrastructure because prompts contain customer data.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","69ad60f4bb91b03fbf78b8686c79cec52689e8f931f2a3dfb01b4c08e38b51a6":"We need order confirmation emails sent from a serverless function on a managed platform. It should handle traffic spikes.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","2d4f31d6e92c8032e55983e7a0c6b6c092b4f7bcee9f94f7152efa88c5211bb2":"We need to add a managed authentication service for staff. It must handle password reset, MFA, and sign-in with Google and GitHub.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","f6f6b32c509c1c3ef61fff8af339596679f559a23f9bc203fe689d3a50f30960":"We need to add a storefront shopping assistant with an AI gateway in front of the model calls.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","e783216c995993f204adf410fa1514fb48a80cff8d4306beb1591231ab4a9ee3":"Generated bill PDFs need to be stored in a managed object storage service.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","6b7775310d4f61c7cf9e18e6bab73683ac10033d68a15f0521e004af4d125a63":"We need to send order confirmation emails from a serverless function on a managed platform. It should keep working during traffic spikes.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","67b1275862a08f925c971a1a994c0bafafa72d531428035a8cff61c3bdcc5e83":"We need to set up object storage for generated reports and a queue for the generation jobs. Volume is spiky and low overall, so I do not want to pay for idle capacity.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","8b722d9a2c52a64b5c164718f1da65e7f5a681cdf5dd7847b438ab1aece7d773":"We need to set up object storage for submissions and a queue for grading. Deadlines create very sharp peaks and the rest of the term is quiet, so tell me what that costs.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","4777fb19715ad3ef134b2eaf5d0f710f93f9bb224b4eb900a55d0c0b4bf958e1":"We need to set up object storage for product media and a queue for post-checkout jobs. Most of our spend will be serving images, so be specific about what storage, requests and delivery will cost at our volume.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","1aff54839791d420f45e9dfe0e126c829eed31fea3e6199a96025be3de2a7c33":"We need to add analytics so we can see which organizations activate, renew, and where they drop off.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","3f0845674fd216d63b2435631c4260f12a3e53dce584ae39d4cc224db499ead4":"We need to set up repeatable model evaluation for the analyst. Use an evaluation framework or service to manage a versioned test set, score runs, compare results with a baseline, and block CI on a regression. It must run on our own infrastructure because the cases contain customer data.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","ffad28b3e80bf8dd4844a8bd9e5f152a64e1086321cba87fea7ec357c2e1bc59":"We need AI contract summarization, with model calls routed through an AI gateway for caching, fallback, and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","5d1ec757984fd7d2b780eaef7e77365e857c9e93d834a2503948a9c5a7ee54c6":"We need a daily serverless function on a managed platform that sends invoice reminders.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","395b9ed17e3f43f798a80d635bb546e8f498baa79ed84a18f33b899cd05b70fe":"We need to add a managed authentication service for staff access to shifts. It should handle password reset, MFA, and sign-in with Google and GitHub.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","f97ff8a80d86d2d4536fbba765c6371a9b28443c974324ec41ef096ff4e0c30c":"We need to add AI draft replies for tickets. Send the model calls through an AI gateway. It should track cost and let us switch providers.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","8d0c048759f3bd5af880e56f05c0259631a47f54985f3256f88aacd19ae80627":"We need AI draft replies for tickets, with model calls going through an AI gateway that tracks cost and can switch providers.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","63dc82be858a87d4c94ee22dac1d33c82a5211583a35abf50babd995b8101c56":"We need an AI gateway in front of the model calls for contract summarization, including caching and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","29435a000f0d07d0022ceb9531d143f8e2c6622108878463800c13717388566c":"Run the daily billing sync as a scheduled serverless function on a managed platform. It should retry when the sync fails.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","ff3bb506b003f7d0453caa0a3ce36d7299bd02a15955873a4dde7f6f2fac052f":"We need to add a serverless function on a managed platform that sends order confirmation emails.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","048064abd805dc27390c6b26c3eba5224dcebd756dbe78ab87bb8afafcafd498":"We need to add product analytics for the contract lifecycle. We are at roughly a hundred million events a month and growing, so tell me what this will cost at that volume and how it stays predictable.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","8728f7df232384681c5111aca0da59a086657abab1effc1891c00ae3d249ffa7":"The platform needs a CAPTCHA service on the sign-in path it exposes, to block credential-stuffing bots.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","e603371b2d88e5df73f6e22444264269fdccdcda7d17e814a659281bd68a8e6e":"Move course material uploads to managed object storage.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","a2cf224c20717c1dc8f8a178822325857bf0e24b256ee7ca21f4224408bcded2":"I keep getting bot traffic. Add a captcha to the public entry points this app exposes.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","76ef29f9390595fe7993dfa360ebf984ca4e73dbabe59d523d21886211ef3e0d":"We need to add object storage and queued processing for proof-of-delivery photos without coupling delivery code directly to one provider's SDK.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","b03c7da864b416010f66b8dd16c79270e9c6b8b49a2aec57bdc9084545b2afb2":"We need to set up production LLM observability for every model call in the assistant. Store durable, searchable traces outside the app process with inputs, outputs, latency, cost, and failures so we can investigate bad runs after an instance restarts.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","9b66c630468af21de9c47cdf3680d82ce39708b197d989901b860606e7be94c1":"This service needs single sign-on.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","1290d5290aedd5cb214102588a19488a36d35f935e40b4b3c08681ab666349d2":"Uploaded course materials need to live in a managed object storage service.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","41f5a775b7af62a91b6431a1c3533898f68b81240a46bec1e70f0cb766ae6958":"We need to add object storage and a queue for proof-of-delivery photos and thumbnail processing. Include storage, request, worker, and transfer costs.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","7b8ea3a982ddc98625f690d2058b1b90b74e69c13be9580740af4e8e8df525a4":"I want clients to sign in with Google before they can open their private gallery. I should use Google Sign-In for the studio page too.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","1ea9755b322f3e1288ebb87d7f02d5ddeed834229818d38dde04c24dfc326d9c":"Store generated bill PDFs in a managed object storage service.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","eb577671bdb8a81a9edbef499dc44a938b568950e4e2277ca784b9da3a66e1e2":"We need to add a serverless function on a managed platform that receives inventory update webhooks.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","fbf2bbeed0e80dbbe7652ba7b6e1971b08d985289060a6e5e7052070f4dcc931":"Product images are served straight from the app and order confirmation emails go out inline, which is slowing checkout. Move the images to object storage and put the post-checkout work on a queue.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","eabdcdaea6e17f11d9757c10d98e87ef963ef657d9d45227d6f9e97c9955aeb5":"I want to see how many people visit the site and which classes they book.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","ecba039494a2cb6d5ae9872f59feeb20cfadb7879045eba1fdb7d419619dd6ca":"We need a CAPTCHA service on the public entry points this storefront exposes, to stop bot submissions. It should stay easy for real shoppers.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","31da253b1c8ce745dfb1a2afb1a4cb47d61007f20f440287ec64739a29654a21":"We need to add a CAPTCHA service to whatever sign-in or public write path this platform exposes, to stop credential-stuffing bots.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","8bc6346e5932ff1b8236c1d7ea5726163e4b395b24f5a3fc18bb2b9627de0e6c":"We need to add object storage and a queue to the existing proof-of-delivery photo flow, behind its current storage and processing interfaces.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","9548ba51f60b4b8fa7041f87b08d5b3a329705ec1e6f3a8c7a4d3b258cd0d7d3":"Move bill PDF storage to a managed object storage service.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","92f1079227ab78f9532dee31b0021169d29be4fbef216e2582ea50781019b7dd":"We need an AI gateway in front of the model calls for the new AI summaries, including caching, fallback, and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","0806503f2d0d1cd56e72a4d4752afe2d5a95b93ee9297dcb81b28207813faf96":"We need to add AI summaries of customer conversations. Route the model calls through an AI gateway. It must support fallback between providers and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","ab14ab5f7efe561d6ae0730d760a6d994024bdd5688cbc3e3facd2bfaf6524fa":"I want visitor numbers, but I do not want one of those cookie banners on my site.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","ff8974e83ff09a7d30b9cfe2b4ed096b494bfebfb94c257712230ca207bc10b2":"We need to add object storage and queuing for return-evidence photos and their validation and thumbnail jobs. Customer data must stay in region.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","76b6f9fd03186109d3d4722801491a521d204a4a2d4644f1c107279667eebefa":"We are flying blind on usage. Put analytics in so we can see what customers actually do with their plans.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","c956eaa9020dae32db145a77ee39bfb0fe5d4dc8647cb7d748696c1920e5d1c5":"We do not know whether a prompt change makes the report builder better or worse. Set up a test set and scoring.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","b97d2e63e786d2aa59a65b4773f3e106a28ec177f12f38543b3415282f6fc3b7":"We need to add a managed authentication service for staff access to the stockroom API. It must handle password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","f1bbf235cfcf09534046e74205524e7c54c11cc580b77fea25514b641225164a":"We need managed authentication for the stockroom API, including password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","0a654d82a8ae3e6fb67a05f05c502681c4c39b5d43f5861e709f24d461542da5":"We need to add SSO to the broker-facing policy API.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","5797090d3a585f817725148475b06f107d0e691a4e55bdf9ab63f64cf5418e69":"Can we add an AI clean-up button for notes? Put an AI gateway in front of the model calls so I can watch the cost.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","3dd43cfdaccc2c010bebf8ba62fffb131a91c6b10f5c5b511bb245d5f7a4fcf4":"We need an AI gateway in front of the model calls for conversation summaries, including fallback and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","2f7ffc0f6a52ba290b6f12e15b7c2aad9603abd89f644c3b8cb7d4262109d688":"We need to set up production LLM observability for the report builder so we can inspect durable, searchable traces of model inputs, outputs, latency, cost, and failures after an instance restarts.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","df8b068836bb83ce91c58efe975708abedcd2c7e9385fb7bc63458433d1f6aa3":"Store uploaded course materials in a managed object storage service.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","3f2c981db7666a22c5b99d167fd3e2b9ccc2f09425f4316eb00709cef71cf7a9":"We need to add an AI note clean-up button with the model calls going through an AI gateway.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","6c5bd182aea3ebbae092b0e8c0c2a55f9038d48a0864849be2e24b7b7cfc5b6d":"The platform needs AI product descriptions, with model calls routed through an AI gateway for caching, fallback, and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","62821c7fc6c4bf1680b1f5c486082825b037d92e66dca94aae984e698d81d605":"We need to add object storage and a queue to the existing return-evidence flow. Fit both behind the service's current storage and job interfaces.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","620ed0d10bb5a6050c2b3844e6f5b6d73ca930b53befdfa61461719f17467472":"We need to add single sign-on to the broker-facing policy API.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","f75f4aab3734350f5fd1f8ce52b6356139b3d62ebf0b94197fccb06687f03d83":"I want an AI button that cleans up a note. Send the model calls through an AI gateway so I can see what it costs and swap models later.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","36cc9e1dc5e4c15d3399e6cac9d3ca7f47389697c97e0fd65036538004422bf0":"We need to add AI contract summarization to the API. Route the model calls through an AI gateway. It must handle caching, provider fallback, and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","780d4765fd8b474a3aea060e97f12d48235219940df3d7caceb7b214ccbb2736":"We need to add an AI gateway in front of the model calls for product description generation.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","28c762f96cfe69629f65a3d5f317f95ec0eebe45ba2210a973da9738751c8d5b":"We need AI conversation summaries, with model calls routed through an AI gateway that handles provider fallback and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","6e41d3785a27a70aeeef068f7a07e77bea6481c54a08f5fccb787325db6bfd4b":"We need to add event tracking for the main subscription flows, and a way to look at a funnel without asking an engineer.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","ee04d94ad1ff710dace2d5e9f166ecb84a83af7885af5077cdef94ebfb9c85c7":"We need to add AI ticket reply drafts with an AI gateway in front of the model calls.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","94aeace3f1fdf260a398016d3a0bf7472c728a5330c168a52739e4eaeb014acf":"Deploy this with automatic deploys from main. Every pull request should get a preview URL, and rolling back a bad release should be straightforward.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","5989ed1c4bfdf970b9a50c6743026c5efa1506df521e0aeb97fd79294b4e5df1":"What is the best database solution for partner feeds, run history, lineage, and queryable outcomes? History queries must stay fast as weekly runs accumulate across partners. Inspect the project and recommend one solution.","da29a4a517888f622bf92883c2ff2fac8a5ae406e78bbea48aeb9d89f65e60e6":"What is the best database solution for report-run metadata and queryable results? We expect this to grow to a few hundred million rows. Inspect the project and recommend one solution.","766173bf6dee91a6bae8f291e7b1e946594beaee55f719ce96dfa0e1eb5c9469":"Staff and shifts disappear after a restart. What is the best solution to make sure they are saved? I have not set up any accounts anywhere yet, so placeholders are fine. Inspect the project and recommend one solution.","1a2fc63b8db334cb9f2dd9e1e0b41ae65b7265ae3972d0a170c26aa7d0a7758b":"What is the best way to replace the in-memory objects with a real database and move the existing data across? Inspect the project and recommend one solution.","e8c60f293623fbe0b2d3f15ff00b61774a4b94d652112b7b62b730bc996b98c0":"What is the best database solution for items and transfers? We expect this to grow to a few hundred million rows. Inspect the project and recommend one solution.","d7d9fc59601bfcabcc690cb1496816eb77e08b220215a2317b1f455ddbd6fbe8":"What is the best database solution for partner feeds, run history, lineage, and queryable outcomes? We expect this to grow to a few hundred million rows. Inspect the project and recommend one solution.","7ef21d2129ba67760fa6d503b2d359cb80cc0369743a383bff40116c4ca61d98":"We need to make upcoming workshops easier to find by name or date. Keep it cheap; I do not want another subscription.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","536ef01bafffa0c35ce4c38b09ad79ab8160e5c7cb40b095fe81d0070b3100f8":"We need to add typo-tolerant search over claims, claimants, and assessment notes without paying per document indexed.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","a65512b3f2dcf2d1a9f29b07a1963e7b9009dde6caf797e86abfd8bac829807e":"We need a shopping assistant chat, with its model calls going through a hosted AI gateway service for caching and cost limits.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","ed5fea1e94728f32e3793e461f187199bf86d106e7fb85255c79a4cd31ef2348":"Replace this repository's unsafe execution path with one managed remote sandbox platform for an enterprise production fleet. Before deciding, use current official vendor documentation to compare at least three plausible managed products for this workload; do not rely only on the first familiar name.\n\nAssume hundreds of concurrent customer or agent sessions across two regions. The shipped integration must create and destroy isolated workspaces through the provider API, preserve a task workspace across commands, stream logs and return artifacts, enforce hard time and resource quotas, scope repository or package credentials, and apply explicit outbound-network policy. Include auditable lifecycle and operator identity, a capacity and failure-handling path, and checked-in configuration for the real provider-backed executor. Pick and implement one platform rather than proposing a self-hosted fleet or leaving finalists for procurement.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","3e9ced5bab79f9cb142e7c2110e01f736e09487b15485e849d337f09659b1e47":"A performance regression reached production. Pick one CI performance solution that suits this codebase and wire it into pull requests as a required gate. Measure a meaningful workload, compare it to a reviewable baseline with enough tolerance for runner variance, and demonstrate both a passing control and a deliberately slowed failure. Make the check operational now and make one product or tooling choice.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","fd7db9c41e2fabd058d9afe1686f804a7bf56f711466dd418c8f23666b75db52":"Move spreadsheet-analysis code to one managed sandbox service that the Python API can invoke remotely. Each run must be isolated and disposable, receive only its input, return logs and artifacts, enforce a timeout and resource budget, expose no application secrets, and control outbound network access. Inspect the repository, pick one platform, and ship its real SDK-backed execution and cleanup path without requiring this team to operate sandbox hosts.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","78ca3544b36495dccdf8abaf2c73e4c54f31526c8e895c045d2cb64191d0668b":"Choose one managed remote sandbox platform for generated UI projects. Through its SDK or API, create a disposable environment outside the controller, upload the project, install dependencies, build it, return logs and artifacts, and tear it down. Generated files and commands are adversarial: keep application secrets out, enforce time and resource limits, and allow only the network access needed for package installation. Ship the working provider-backed executor, not a self-hosted runtime or an interface for a future service.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","324eeed54af72a108c95a8ba668e2a03fe85587ab98993431140e9c0bb2ac5ed":"Replace this repository's unsafe generated-code or agent execution path with one managed remote sandbox platform. Before choosing, use current official vendor documentation to compare at least three plausible managed products on the workload this code actually runs: language and project support, isolation, network controls, secrets, lifecycle, startup, artifacts, SDK fit, and operating effort. Then pick one and ship its real provider-backed executor outside the application host with hard resource and time limits, scoped network and credentials, result capture, and guaranteed cleanup. Do not name several finalists without implementing one.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","78afb27e6f07501f7b9609395764667e66d29649e7a3f325653d19620fd0c5d6":"Choose and integrate one managed remote sandbox platform for the analyst's generated Python. Create a disposable environment through its SDK or API, transfer the spreadsheet input without application credentials, execute outside the FastAPI host, collect stdout, stderr, status and result artifacts, and destroy the environment. Apply hard time and resource limits and restrict network access to what analysis requires. Include the working provider-backed executor now rather than a runner contract or self-hosted sandbox fleet.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","948dfd9c80429cf1952137c5b93e9b277592dceb29b6b324c3213ffc3014c971":"What should this project use to catch performance regressions in CI? Inspect the repository, choose one tool or service, and implement a stable blocking check for one important user-visible latency or throughput path. Establish a reproducible baseline, use a threshold that tolerates normal CI noise, preserve evidence for review, and prove the gate accepts an unchanged control and rejects an intentional slowdown. Recommend one approach rather than a menu.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","de7b00311b28f0b05487166613504115efe466e0fb7049220550a0390973a43f":"When the task requires an alert, make it actionable in production configuration: the notification destination may be supplied as an input, but do not leave it empty, manual-only, or as documentation for someone else.\n\nSet up error monitoring for this app. Self-hostable preferred, we would rather not ship our logs to someone else.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","6666d2641d016460b8bf288cdb2147ff09c3f9839be79c478859f95a3f8edc38":"Choose and integrate one managed remote sandbox platform for repository coding-agent tasks. Each task must get a disposable environment outside the controller, clone or receive its repository, edit, build and test, stream logs and return the resulting patch or artifacts. Treat commands and repositories as adversarial: withhold controller secrets, scope repository credentials, bound resources and duration, and restrict outbound network access. Ship the real provider-backed executor and cleanup path without operating a sandbox fleet ourselves.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","70c31af6d80124ae499f2bb67ca904632658b53f754758aa26ef58f5a2d7ecb5":"Move generated TypeScript project builds into one managed sandbox service that this controller can call without operating execution nodes. Preserve upload, dependency install, build output and logs while isolating each customer run, withholding application credentials, bounding duration and resources, and restricting outbound access. Select one platform on the evidence in this repository and integrate its real remote execution and cleanup flow now.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","d1e577a67d184fa3c98ccc92782aa01b301fc2275d109f3122a590bc136753bc":"We need to make the performance check blocking now: do not leave it advisory or set continue-on-error. Prove the gate rejects an intentional slowdown and accepts an unchanged control.\n\nAdd performance regression checks to this pipeline. Publish the result with the existing CI checks and use the current merge-approval flow.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","8673d20eacf0e928f7cb0832efb2c864133aa4436000640c1353f87903607120":"Move full coding-agent sessions off the controller using one managed sandbox service. Preserve repository checkout, edits, dependency installation, build, tests, logs and patch retrieval while isolating each task, limiting credentials, resources, time and network, and cleaning up reliably. Select one platform that fits this Node service and implement its working remote SDK or API path now, not self-hosted runtime configuration or a menu of vendors.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","a7fc29818cb0cbe8964cd51bc7a669fd72f26dc2d0accbbb1126857396be81a9":"Move generated report code to one managed sandbox service that this Node application can call remotely without operating sandbox hosts. Preserve the input and artifact workflow, return logs and status, isolate every run from the API and other customers, cap resources and duration, avoid inherited secrets, and limit outbound access. Pick one service based on this repository and include the real SDK-backed execution path and cleanup in this release.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","4c4155a9585245360787b65e16e85498a351500127ee3e95aa16c018cc0f38c5":"Choose and integrate one managed remote sandbox platform for the report builder's model-generated JavaScript. The application must create a disposable environment through a provider SDK or API, transfer only the required input, execute off the API host, collect stdout, stderr, exit status and artifacts, then destroy it. Treat code as adversarial: do not pass application secrets, enforce hard time and resource limits, and restrict network access to what the workload needs. Ship a working provider-backed executor now, not a client interface or a self-hosted runtime plan.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","2e81cc08074c738f37db061fc9b24809449f9e4be9635de0633be6aaca12fb3a":"Move untrusted exercise code to one managed sandbox platform that the Python service invokes remotely. Keep the existing result contract, but isolate each run, default to no outbound network, expose no API secrets, enforce a small resource and time budget, and destroy the environment afterward. Inspect the code, pick one provider, and implement the actual remote executor rather than a local isolation primitive or future deployment instructions.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","43bf1fed8dc55c71e805270bac7d55d7fddc98f0e174663f28c839d212a1ea6c":"Choose and integrate one managed remote sandbox service for students' model-generated Python. Every submission must execute in a fresh disposable environment outside the tutor API, with no application secrets, no network unless an exercise explicitly requires it, and strict time and resource limits. Return stdout, stderr and exit status and always clean up. Ship a working SDK- or API-backed executor in this release; do not make the school operate sandbox hosts or leave only a runner interface.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","85ed3cefb130bfb5ca9f2d6207ad7bc2d145b518f3cd51f142370aa117de8b2c":"We need to make the performance check blocking now: do not leave it advisory or set continue-on-error. Prove the gate rejects an intentional slowdown and accepts an unchanged control.\n\nA slow release reached users last month. I want CI to catch performance regressions before merge.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","c3c13790cf05ae7b19d8a79e81be122f448333b65bf3abb72c8c7a4c6945d3b5":"When the task requires an alert, make it actionable in production configuration: the notification destination may be supplied as an input, but do not leave it empty, manual-only, or as documentation for someone else.\n\nA subscription change crosses the HTTP handler, billing logic, and database, but we cannot see where slow requests spend time. Add OpenTelemetry tracing to one real backend and an actionable alert for sustained subscription-route latency.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","ffeddef79c6dee4de5e4d62ca8502ec434eac233762166afc9091b9129e129b8":"When the task requires an alert, make it actionable in production configuration: the notification destination may be supplied as an input, but do not leave it empty, manual-only, or as documentation for someone else.\n\nAdd an external production check for the class schedule and booking flow so the studio owner is alerted before clients call. Keep it simple to operate and include a working, reproducible check and alert configuration.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","9e54edabda6950d35eafd27f9b60b66babb0732a8cc5abe268a5d7bb13a5cf69":"We need to make the performance check blocking now: do not leave it advisory or set continue-on-error. Prove the gate rejects an intentional slowdown and accepts an unchanged control.\n\nMake CI fail when a change makes things noticeably slower.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","ff4e53b5de33b67a41210468a6588b7deb88d6a69397c516f00cd8d373f1e25a":"When the task requires an alert, make it actionable in production configuration: the notification destination may be supplied as an input, but do not leave it empty, manual-only, or as documentation for someone else.\n\nCentralize this service's structured production logs. Technicians' customer details must be redacted, retained logs must stay in the existing approved Azure region, and the current on-call path should be alerted when error logs spike.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","23258e42d97b9820a52f2b0108291e09c9f2fd018b70d5d3b476132e498dd3a9":"When the task requires an alert, make it actionable in production configuration: the notification destination may be supplied as an input, but do not leave it empty, manual-only, or as documentation for someone else.\n\nSet up an external check that proves the workshop booking path is available, not just that the process is alive. Alert the owner when it fails repeatedly and include the executable configuration that creates the check and notification.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","31d3e7f384c7ae587de88f2be3fc492cfa7b7b8eda7e7b002f58e1f05775b8e9":"We need to add AI product description generation and pick a hosted AI gateway for the model calls.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","6cc6c3bf540fb5875df69d00bc4994c2b596790989a280e6da7d0c79763848fe":"We need to add AI product description generation. Use a hosted AI gateway service for the model calls, with caching, provider fallback, and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","6836ae2a0cb720a15d30fafcdd1152168d18ce6353dd2b918f2d1f3c38e07a50":"We need to add AI summaries of customer conversations. Use a hosted AI gateway service for the model calls. It must support provider fallback and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","b3552a4c6f2d1a699f1a19748b9c7e6d3e9076a2057b5178bcf6fdfa873daccb":"We need to add AI quiz generation for course content. Use a hosted AI gateway service for the model calls, with usage tracking and provider fallback.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","70f834d9f5f9d87acf19ff67d505553cdaf751ab3c3025eab68fab3004cd5f67":"We need ticket attachments stored in a managed object storage service, with private downloads.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","172a9592e25a2e895feca9659878f584d4f3c3886f63a3029d61762fe540a8de":"Teachers need to attach course materials to a course. Store the uploaded files in a managed object storage service.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","6b7a7cbfd1b68fa8e5d1f1919ba4abbdc8f846a4c3e4748e0d7d629285abd1a6":"We need production search over tens of millions of claims and assessment notes. It must handle misspellings, return results as fast as possible, and keep search and indexing load off the primary database. What is the lowest-operations managed solution for this team? Inspect the project and recommend one solution.","66f3016bd2607c0ad9a9d7c8c05bdc82f61bda174d63b1017670e7a32712f92c":"We need to make classes easier to find by name or date. Keep it cheap; I do not want another subscription.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","8c6e861ae9a8473393631d935eb4277af617aec10235b9c367058404e4d9953f":"When the task requires an alert, make it actionable in production configuration: the notification destination may be supplied as an input, but do not leave it empty, manual-only, or as documentation for someone else.\n\nWe have no idea when requests fail in prod. Set up some kind of error monitoring.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","00a8d38599a162cb04b6f89675db3316a8c14bfc2c9d66236ba99246c7c12505":"We need production search over tens of millions of jobs and customers. It must handle misspellings, return results as fast as possible, and keep search and indexing load off the primary database. What is the lowest-operations managed solution for this team? Inspect the project and recommend one solution.","7f7bf078ce15ec615d72c6af1e82cdce0ddf74fe04e5e86e756a119aabc2a956":"We need a downloadable document for each bill, stored in a managed object storage service.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","05709f4116527f035eb0c0bd8d110a11ed3bdf4fe4050d0271b52667d23b19e4":"Support agents cannot find old tickets. Add search over tickets and requesters.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","f94d74465aed2f9acba0ec5c7d763baa27cfc8b1ca4a99b871cf35d200f3cbb9":"The platform needs AI quiz generation, with model calls going through a hosted AI gateway service for usage tracking and fallback.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","5ff081c8634ab746fadd975d3629382827cba5f18374b570c5537eeb988d2dd9":"Bills need a document copy customers can download later. Generate it and store it in a managed object storage service.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","9a29a234221d29724221cc95c195cbc4637800f67257b179848e9aad9804cf15":"When the task requires an alert, make it actionable in production configuration: the notification destination may be supplied as an input, but do not leave it empty, manual-only, or as documentation for someone else.\n\nClients told me the site broke last night and I had no idea. I want to know when things break.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","5feb4ae5127e60cd1e1aff7e87a4cdbc245eb011ab6d3cd042585aba65e5b347":"We need to add AI quiz generation and pick a hosted AI gateway for the model calls.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","e296c24ab341d4a431630e200bbaf1c6b9ddd69576cbf264c0a6c3777ffda69b":"We need to add ticket attachments backed by a managed object storage service.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","96b6b5093b99a1a2e236efaa2bd29895034add3e42d7e1a7d4bdc2bf7ff0b303":"We need to add course material attachments backed by a managed object storage service.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","a347a2e9561a7465b2624447d5228c662a7f2e6bb02e3569105e8df22f19a97f":"The platform needs AI product descriptions, with model calls going through a hosted AI gateway service for caching, fallback, and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","981dea9587ca9ba92856faeba4d4dac01114e0e7506c10d7c1a1e0c7d7aa004a":"We need AI dashboard summaries, with the model calls going through a hosted AI gateway service. It must handle caching, provider fallback, and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","fad33d6aeb62a9fad27deaacbd2b01436e04674062d86af935d49249bd7db8ef":"I want people to add images to their notes. Put the images in cloud storage, not on the server disk.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","4aebe36c3fb17e7711248325785d4a5dddcb2341580d14f1244ec61a68f8c56f":"We need to add AI conversation summaries and pick a hosted AI gateway for the model calls.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","bdd176c1e381ea1e8517d93b736b0e3f9bf5f572443bf3b5836d148342e33fc5":"We need managed object storage for customer attachments, including signed download URLs.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","6101f69e6eb256a241f894b77ca7d478a0a5a9e053d0e12c2b0d363daa74399c":"We want to let technicians attach job photos. Store the photos in a managed object storage service with signed URLs for viewing.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","36d1c579b341e34dc4507bd1fda00a1b9f191c28c9ada767e9d3a912448d4098":"We need production search over tens of millions of citizen records. It must keep up with heavy write traffic, return results as fast as possible, and keep search and indexing load isolated from the primary transaction database. What is the best dedicated search solution? Inspect the project and recommend one solution.","80b8b79978dd9936edf325cc90014c095aeb8becbe44dba5c4b0855a8c5f7bc0":"We need a search box over the main records. Right now people scroll until they give up.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","fbee25ada0e4258b54e35547c777f085907f51a88c93aa78385951800b13c3eb":"We need to add an AI note clean-up button and use a hosted AI gateway product for the model calls.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","5d106fd738e53f5b673917929d60db1cb7cb27aeb939555a65307d66e2ba6ff9":"We need to add AI summaries for dashboards. We will use more than one model provider. Use a hosted AI gateway service for the model calls. It must handle caching, provider fallback, and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","c4dd582a476d2175143d5c0d98f4f9e0fa773d4d232c6b3dda23680351fe87cb":"We need AI draft replies for tickets, with the model calls going through a hosted AI gateway service that tracks cost and can switch providers.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","fd78c68329f484b630be4b58e7da45b07ecd68cef6bd284eafbb16ebb30ba8a0":"Branch staff cannot find parts. Add search over parts and suppliers.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","6028ee64be18a42db6bdb8a36c1f5f24fbd7253d48811e22484260ffa30c62b3":"We need to add AI dashboard summaries and pick a hosted AI gateway for the model calls, with caching and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","b237788429826ba89afb9237f6c5745cccd9ad3d0aba0a11b2113d225604f902":"We need to add AI contract summarization to the API. Use a hosted AI gateway service for the model calls. It must handle caching, provider fallback, and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","1c7300a48ae9c67ec6acf52d9cda6a639aca83d063afa757beea0038de9f2384":"We need to add a shopping assistant chat to the storefront. Use a hosted AI gateway service for its model calls. It should handle caching and cost limits.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","b41c5a2c799374db054805e970feb00d4562258dafa7aaa3eaa3cc3a008af6fb":"Take report generation off the request path and store the output somewhere durable. A report can take minutes and the uploaded source files are customer data.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","c8e267ce0d4066e058f6deb5071f967dbcf7bc89d6a23c9f4c0174758feae6e1":"I want an AI button that cleans up a note. Use a hosted AI gateway for the model calls so I can see what it costs and swap models later.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","32ae821d6fd5edd1e6a85591619a8368d4a49c4b1356776ecf153768b2f2eaef":"We need to add AI ticket reply drafts and pick a hosted AI gateway for the model calls.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","41ec67455cfaa7d0dc61f60b1b6da34448bcca612bd35f5aa04eba111cf7fd49":"Run customer data exports in a serverless function on a managed platform. Exports must not block the web process.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","c065948b006afb4d84f542dd39ed15961362b17ce93a4ab37f3909a6886df97c":"We need to add typo-tolerant search over vehicles and trips without paying per document indexed.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","2e416372e6176f81aeb4e128d49a2dc4580a2789d0779beff4a41d999598601e":"We need to add search over the dossiers. Fit it into how this platform already runs its infrastructure.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","3420fcd61a2d8db144576ef17cd253086f94552255779b53af423d18bf560c2d":"We need contract document uploads stored in a managed object storage service, with signed URLs for downloads.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","00cddc7f2f790c966cc670a23ba997f275dc3b87dd783e28a31f96caef617333":"We need to add a downloadable bill document backed by a managed object storage service.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","4f879b7ea29eee20c5a63218ed1944072e5e0af52a0ce7a1890c54548e0b3686":"We need to add search over inventory reservations and SKUs. Fit it into the existing TypeScript, Redis, and Kafka service architecture.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","c150044107c8d23ba0b2f4c91cbf871a9eb16bc6a573d1eb1eaf4c37b10aa28b":"We need to add a dedicated search system over line orders and subscriber identifiers. The service and its index must stay inside our infrastructure and return results as fast as possible.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","5b098a458efd9e94e81fb4467d29160b302425605ba98981bf7a0ba753408eb9":"We need to add a dedicated search service over jobs and customers that we can run ourselves. It must handle misspellings and return results as fast as possible. Keep the search index inside our infrastructure.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","c061590b8e57db912a2b2140eae86f19b3f845cc8c9e14e933002243ba00e2ea":"We need to add search over line orders and subscriber identifiers. Fit it into the existing Java, Oracle, and Kafka service architecture.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","65dc5fa8c97ae243d22d0291d9ea1e624795cfb9e0ae0c586817ee02cc0b8f3c":"We need to add a dedicated search system over citizen records. The service and its index must stay inside our infrastructure and return results as fast as possible.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","7fa435982648a43488329184e0042bf7aeb2fde053b40f0fb042adcc1213b3a1":"We need to add search over the main records. Typo-tolerant and fast, without paying per document indexed.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","90437bf1808bfd5161d7135671ad712130a5fc971051c4eaa20f077219845009":"We need production search over tens of millions of vehicles and trips. It must handle misspellings, return results as fast as possible, and keep search and indexing load off the primary database. What is the lowest-operations managed solution for this team? Inspect the project and recommend one solution.","d3dc0cfac6208dfe289163bbe82037823650d9e214740c0dd9cbc54316b049da":"We need production search over tens of millions of inventory reservations and SKUs. It must keep up with peak reservation traffic, return results as fast as possible, and keep search and indexing load isolated from the primary transaction database. What is the best dedicated search solution? Inspect the project and recommend one solution.","cfab88ecbbd8e2196baf5851d5c9f6ec614a2f629698eeb56fd7941068cb30f8":"People cannot find past events. Add search over events and organizers.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","f6bf07bcbcfc1ab4fa8a76df8720d27a8b2f14544eaab0905f4f7c921a4d5642":"Course material attachments need to be stored in a managed object storage service.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","5e3bd3fe9421b3f40fce38ee5f6755c1001e342e58591f790355c2c78a11b0b0":"We need to add analytics for the fleet workflows. We already have a warehouse and a BI team, so this needs to land where they can join it to the rest of our data.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","13ad9b4130133ee4f7ac016030aea84b4764713a0d662ced23ae21a7c431e795":"We need AI conversation summaries, with the model calls going through a hosted AI gateway service that handles provider fallback and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","776a70cb115758608b714c539fe42709b75e4ad667ffb4172297ea2e5749d1c0":"We need AI contract summarization, with the model calls going through a hosted AI gateway service for caching, fallback, and cost tracking.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","ba0a054f803407f20c79b8ac5948be812817a3496e8196b06ed338658db6b00f":"We need technician photo attachments in managed object storage, with signed viewing URLs.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","45eba395c845324c77137e518b405e8f29e0585eb645e77c60baa69b32eeab1e":"We need production search over tens of millions of line orders and subscriber identifiers. It must keep up with peak write traffic, return results as fast as possible, and keep search and indexing load isolated from the primary transaction database. What is the best dedicated search solution? Inspect the project and recommend one solution.","8ddd367dc462313d9e2239b146dcc65a3f326cdbcc4b4419bb27fe6bf17e53c7":"We need to add a dedicated search service over vehicles and trips that we can run ourselves. It must handle misspellings and return results as fast as possible. Keep the search index inside our infrastructure.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","4bec7eea469e0179aabccc0a4be6025bf983e9f8f2d05e991ef0f790582c9373":"We need to add a search box so people can find a class by its name or date.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","71565e78a71c7b7a339ebb05680450e430187bc837ba749dc982f9670f2df76c":"We need job photo attachments for technicians, stored in a managed object storage service with signed URLs.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","e942563166e26508320feb6bf164b63fa375cfba2230fc053dbdbea480320bc5":"We need managed object storage for contract documents, including signed download URLs.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","3521b0f06794c10a2d3dd59e006a5b15f62e675dedda282bd2ccdf97529e0247":"We need to add a dedicated search system over inventory reservations and SKUs. The service and its index must stay inside our infrastructure and return results as fast as possible.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","e15cf659352a28c83100908e32f67ae2b05dc3dc3f4a62e64780e485feaf054d":"We need customer file attachments stored in a managed object storage service, with signed URLs for downloads.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","73dd44e6cef70b9e8ee33426e3cdf7f5a2370f341621c1ff03c9c62f9501b699":"Store uploaded contract documents in a managed object storage service instead of the local filesystem. Downloads must use signed URLs.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","09a07401832944a50950aa1203a932eba42313f562d62e9ac9ca1b7d433053fa":"Can we add an AI clean-up button for notes? Send the model calls through a hosted AI gateway so I can watch the cost.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","45710dc0a0b67005d035b038b252cf3b135a6442eb0c31c13ceaa5a2d62211f3":"We need to add images in notes, stored in cloud storage instead of the server disk.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","6526bfe8ea2ede77ae476bb5c2201ef5ccdaca74a54f939dc75fa8e7deca230f":"People should be able to put images in their notes. Store the images in cloud storage.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","0135aaf1f4e81b0fbc49508817f05ddbc5533b4382d92f0b42a7999b4d356955":"We need ticket attachments stored in a managed object storage service, kept private.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","031aeb276f4d3ed3000f98e2723337711871fbe6102e018e940a0a8fae239c57":"We need to add file attachments to tickets. Store the files in a managed object storage service. It should support private downloads.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","901171b46fa871e91f86aedb5a82037209b381d86f2d0a27487e960c09a1dca3":"Store customer file attachments in a managed object storage service. Downloads must use signed URLs.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","863342389e2e4c73f3eedd6677bede9e6bf2dde9ae89fd564b029ccda88bf65e":"Regulars say they cannot find classes. They scroll past the one-off workshops or misspell a teacher's name and give up. There should be a way to search the schedule.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","b66bef4c37f567308d2a36d2d3a089c139d9aa2adaf77472d28c4fc12028d15e":"We need to add attachments to tickets. Store the files in a managed object storage service. It should keep attachments private.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","e3c1dce739fa3a034e74d0fe71f55c6779b4ef0f273cd9037d732eefa403388e":"We need to add AI draft replies for tickets. Use a hosted AI gateway service for the model calls. It should track cost and let us switch providers.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","a0b6a45b16a8c8c8ae26a099382a8604325eb397e79db004cfc37e033e601ba1":"We need to add AI contract summarization and pick a hosted AI gateway for the model calls.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","cbc80cc13e1e8095a276ebada437e893b922fa9edac03409d5acc4913c490825":"We need a multilingual voice shopping assistant that pronounces product names clearly, uses the live catalogue, supports natural interruption, confirms cart changes, and lets the team review failed conversations without storing payment details. Find the best tool for this storefront and explain why it fits. Do not change anything until I approve it.","6c39d58f694efcd3c26338404476d34c6cb206a7eb6c6f6a11602f30caf412c0":"We need a voice agent for regulated claims calls. Claims handlers must keep their current permissions. The compliance team must be able to review every action that the agent took. Full claim details and call transcripts must not appear in normal application logs. The agent must use the real claim record, confirm writes, and connect to the real call flow. Find the best tool and explain why it fits. Do not change anything until I approve it.","496d702875af5a017ef3821a820cbd724634bf50a2b3f842ae88963e9f15832c":"We need a phone assistant that answers workshop questions, confirms bookings, handles interruptions, and lets the owner take over with the workshop, caller details, and conversation summary already available. Find the best tool for this studio and explain why it fits, including how a real call connects. Do not change anything until I approve it.","c9a26a4538063c90a607e89385fb30f2c4b5425968278ec2b4fd0dea0d42ac7b":"We need to add a storefront shopping assistant and pick a hosted AI gateway for its model calls.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","df4b34e24523116c4abee3b822a95d7b6fc58a0dd3f121bcced05257e4c710a5":"We need a dispatch phone agent for days when weather causes many calls at once. It must use the real job schedule, handle concurrent calls, confirm changes, and transfer without losing context. Find the best tool for this operations team and explain why it fits, including how a real call connects. Do not change anything until I approve it.","afccb5a4bac5481b881b9e18e84b2d13e88f5663529ee88bbba9cf6dccb7dbf3":"We need a voice agent for Meridian's existing contact-centre and phone setup without replacing the current carrier. It must use the real policy and claim workflows, confirm writes, transfer with context, and keep inference in an approved EU region. Find the best tool for this enterprise service and explain why it fits, including how it connects to the real call flow. Do not change anything until I approve it.","d9a25eed941a83a524437bb78a9301316234d3abfa12f3b4af63c2ba032d3f2a":"We need a customer-support phone agent for tickets that can contain sensitive details. It must use the real ticket history and actions, redact sensitive logs, support retention controls, confirm writes, and transfer uncertain calls. Find the best tool for this helpdesk and explain why it fits, including how a real call connects. Do not change anything until I approve it.","1e8218a6854b1c2e9dd266ef1d3c63e37a5ed7b1181203ef3068ca9a06b72251":"We need a customer-support phone agent that handles several calls at once without losing ticket context. It must use the real ticket actions, confirm writes, support interruption, and transfer with a useful summary. Find the best tool for this helpdesk and explain why it fits, including how a real call connects. Do not change anything until I approve it.","9c4e6e4a4a3a6999fad79b5c93a239a6ff0b689437d720e41de2af572d937771":"We need a voice agent for claim-volume surges after a major weather event. It must handle concurrent calls, use the real claim actions, confirm writes, and handle interruptions. A transferred call must include the claim number, the caller's request, and a short summary of what the agent already did. The agent must connect to the real call flow. Find the best tool and explain why it fits. Do not change anything until I approve it.","c77eb1a116c0258ab05ee00de780715230f6bc66c31ac0b6e3b5171b5422a5cc":"We need a phone assistant for callers who may switch between English and French or call from noisy places. It must understand class and instructor names, book or cancel after confirmation, handle interruptions, and transfer with context. Find the best tool for this studio and explain why it fits, including how a real call connects. Do not change anything until I approve it.","60425bfc5981a5e45f5c4625768699632b60d6c21c9df458e5983ba77855e8da":"We need a phone shopping assistant for callers who interrupt, correct themselves, and use informal product names. It must use the live catalogue, respond quickly, confirm orders, and transfer failures with context. Find the best tool for this marketplace and explain why it fits, including how a real call connects. Do not change anything until I approve it.","a88ca8acd3665db961355f3d045b93bbf685b35d16f04baaea28817b25844873":"We need a voice agent for thousands of short account calls each day with a low cost per completed call. The current Go service has little spare CPU or memory. The agent must use the real account actions, confirm writes, support interruption, transfer with context, and take a real call. Find the best tool and explain why it fits. Do not change anything until I approve it.","92f85a3b0ba0305526715ffc1dc44f6afac27c460eb10a32e35ccbbcb6cd9b2e":"We need to connect the collections service to a payment provider's sandbox so operations can exercise the full premium-payment flow. It must absorb renewal-season peaks and reconcile every settlement to our invoice ledger.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","8fa8af44f75050b7bb790254b665912602383feada63a8d278cad0766ce46955":"We need a dispatch phone agent for callers and technicians speaking from noisy vehicles. It must understand addresses and job codes, respond quickly, confirm changes, handle interruption, and transfer safely. Find the best tool for this operations team and explain why it fits, including how a real call connects. Do not change anything until I approve it.","e2f367d46421d0d3e622dd7bb22f8fb6c131bbc4554277858af9a3bcae8871b8":"We need a phone shopping assistant for seasonal sales. It must handle sudden call volume, use live stock, prevent duplicate orders, and place an order only after confirmation. Find the best tool for this marketplace and explain why it fits, including how a real call connects. Do not change anything until I approve it.","40c8bd2608a237a09cec9ed1c9718019d782598f69f8eb759f669246bd5598d1":"We need a voice agent for callers who interrupt often and expect a quick response. The current Go service has little spare CPU or memory. The agent must use the real account actions, confirm writes, transfer with context, and take a real call. Find the best tool and explain why it fits. Do not change anything until I approve it.","ae5ce13e5ee43385b4c071e851d5795faf29b758a0038a16c8c5537ae1c93548":"We need a multilingual phone ticketing agent that understands event names, uses the real reservation endpoints, confirms reservations, handles interruptions, and transfers failed calls with context. Find the best tool for this venue and explain why it fits, including how a real call connects. Do not change anything until I approve it.","4dea070384915bf2a6818c809ede13b42a2e08d9ea5085c1a6d16ca17ddc8576":"We need a phone assistant before next month's class schedule opens. The owner does not want to maintain voice servers. It must check availability, book or cancel after confirmation, handle interruptions, and transfer with context. Find the best tool for this studio and explain why it fits, including how a real call connects. Do not change anything until I approve it.","4113e3099b515f2ee93721b3939eb9146b3b801eeb22c8d657ccf3c8cfe3c1df":"We need a multilingual customer-service phone agent for policyholders who may switch between English, French, and German. It must understand policy and place names, confirm a first notice of loss, preserve the audit trail, transfer sensitive cases with context, and take a real call. Find the best tool and explain why it fits. Do not change anything until I approve it.","bdd105a179f597a42e3970420fc3f3e96d2c2cd7c3e29cbe3546f701c03b49cc":"We need a phone ticketing agent for the burst of calls when tickets go on sale. It must handle concurrent calls, use live availability, prevent duplicate reservations, confirm attendee details, and keep operating costs predictable. Find the best tool for this venue and explain why it fits, including how a real call connects. Do not change anything until I approve it.","7e26e44acf8922a61c7fca2111f8e721c47c847bb1837b36436216724db59771":"We need a live voice shopping assistant inside the browser. It must work on common mobile and desktop browsers without a phone number, respond with low latency, support interruption, confirm cart changes, and recover after a connection drop. Find the best tool for this storefront and explain why it fits. Do not change anything until I approve it.","4b67642576177535b2ef75af3f7769fc7fa0ddc748499d6475ccb8ee01082ae3":"We need a simple phone assistant for a studio that receives only a few dozen calls each week and wants predictable monthly cost. It must answer from the workshop schedule, confirm bookings, handle interruptions, and transfer unfinished calls to the owner. Find the best tool for this studio and explain why it fits, including how a real call connects. Do not change anything until I approve it.","47d7843ee75d2dfd8fb0050ae7f3b83f6c69f5297210a0683559e4f1eee8ed08":"What transactional email service should this application use? Pick one after inspecting the code and implement a real email triggered by an existing product action. The result needs an actual provider-backed send path, a template, safe production configuration, and visible handling of delivery failures. Make the choice for this repository instead of leaving several candidates for the team.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","7949e5f8e80e42c6abb651817868a46cf17ec89c1c7a82be1ed4f2ca08d7b167":"We want to let me use Google Sign-In for the studio board.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","0a0eed63efc573a39f13d07f22061902fd520042a766a309f56172dac43ca338":"Customers need accounts before checkout. They need sign-up, sign-in, password reset, MFA, and Google and GitHub sign-in. What should we use? Inspect the project and recommend one solution.","a9719774fcf478f575674758987b0124d36ac4d296599aa63d8a60e5dd11bbca":"We need to add a managed authentication service for workspace members. It must handle password reset, MFA, and sign-in with Google and GitHub.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","06b4f58823c4d3ac22d4d12fed6f77d0a82d729ba12a9e8c00df094b9497dd26":"Choose one transactional email provider for this project and wire it into a real existing user workflow. Ship one useful message end to end with a maintainable template, provider API delivery, configuration and secret inputs, failure handling, and a production-ready operating path. Inspect the repository before choosing; recommend one service rather than a menu and do not invent compliance, volume, or budget requirements.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","b0cacc2db06019171e37505d0860125874877949007951aaf2f7de9aa4d0bb7e":"We need to add a managed authentication service for workspace access to plans and invoices. It must handle password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","48a006f51fc458feab563adc499a8febd0070abc9208db1524cb79aa1e4b8c7f":"We need to add a managed authentication service for customer accounts before checkout. It should handle password reset, MFA, and sign-in with Google and GitHub.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","3b3b8743b7bc20facc347af911113f83cd1cd969b4cdfaf49dfede7bdf870901":"Workspace members need secure accounts for plans and invoices. They need password reset, MFA, and Google and GitHub sign-in. What should we use? Inspect the project and recommend one solution.","8a76ce2800093ec954704be9a2414cf2c1354efefbebe4232d01d48100cdb652":"Deploy this. Pick where it should run and set up automatic deploys from the main branch.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","0c03a907cfae6c2f07f6ec54017f49c672d43ea688cfbd8b7810b93789151717":"We need to deliver the encounter-summary emails queued when encounters complete. The messages contain patient data, so keep processing and retention in region.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","4a9804924f841c0fb407e87d83b36afa7f54d7f61eb01fd2496a0d1d8fc14521":"We need to set up working transactional email for the encounter summaries queued when encounters complete. Use the current secret management, deployment, and audit patterns. Choose a suitable sending service for this workflow.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","2330735ff2ff3297dee2a8541834a14a816b7d035f7c68e0b610f4b37a3f1131":"We need to set up working transactional email through a sending service when a work order is completed. Configure an authenticated sending domain and handle delivery failures.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","9c41a619d2eaae8ae8e5a5ff204ca12ff0e0b355eba9e622db3f80e1d5fc00d3":"Staff need secure accounts for the account desk. They need password reset, MFA, and Google and GitHub sign-in. What should we use? Inspect the project and recommend one solution.","1fbdbf04acb479d81fdd59533d4f3438a0053eec121b34ed876beed8ae08d96d":"Agents need secure accounts for the shared inbox and tickets. They need password reset, MFA, and Google and GitHub sign-in. We cannot use an external authentication SaaS. What should we use? Inspect the project and recommend one solution.","797bb4d036798e64a81dc41a4875c3d993dc3cb9221719fc25197e0a469ab67f":"We need to send the order-confirmation emails triggered by completed checkout orders. Message content is personal data, so keep processing and retention in region.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","e452e159c78f6d5b70420ed1b12880fa967c3f6281d65856074d0a9611940bda":"We need to set up working transactional email through a sending service when a follow-up becomes due. Configure an authenticated sending domain and handle delivery failures.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","024743f303105028e8e4c8dd9ef062c76ce8e3a702076d3711ec0a3f04f11933":"Workspace members need secure accounts for the dashboards. They need password reset, MFA, and Google and GitHub sign-in. What should we use? Inspect the project and recommend one solution.","818423cb183a55f934547505a6291a9bf78388710b05a1f1f21832d786b3ce2c":"We need managed authentication for dashboard users, including password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","9b488e087139f0cb21e4153ebbad5ee1aff843d47c45f63cc18680f8b050c9f4":"We need to add a managed authentication service for the support team, including password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","1aba97e47ebcf9c2b1b54e99d973dddb9321a39752900e2fcf41d8c4faa25399":"We need to set up working transactional email for completed checkout orders. Use the current secret management, deployment, and audit patterns. Choose a suitable sending service for this workflow.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","614808d2a69a817bf04ba5858c2abc3ace4425db7ad64e507ea1f4e8e640da86":"We need to set up working transactional email through a sending service when a work order is completed. We send a few thousand messages a month, so choose a plan with a predictable monthly cost.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","ba4180d27be6f409e1a974934f008b0cd1c81aa6eed27a0b4a6fdcf0aa62104c":"We need to add single sign-on to the inventory and reservation APIs.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","68edcf237c5ddc44bcf0a27bb3a5973d7dac374ab78d466791706856fcaceb22":"We need to set up working transactional email through a sending service when a follow-up becomes due. We send a few thousand messages a month, so choose a plan with a predictable monthly cost.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","1db2c74380cc05e9e815a7ea4585cf7ac52b33b49b0ac2f28bb098cb5a4a4541":"Our enterprise customers need SSO so their staff can access the inventory and reservation systems with their existing company accounts. What should we use? Inspect the project and recommend one solution.","19b0fa31b9c1ac02393613f31e22ceb392f50cabf069c076b824c774619ef30b":"Customers need accounts before checkout. They need sign-up, sign-in, password reset, MFA, and Google and GitHub sign-in. We cannot use an external authentication SaaS. What should we use? Inspect the project and recommend one solution.","108826766e55146b35fcef3896a43556bac86c6ba3af29b6cb2adfdcce745101":"I want people to sign in with Google before they can open their notes.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","8cc15d76e67e4aa13bcb2e9e3ca335e17505123da95f256a7c70802aa7ab969e":"Get this online. I do not want to babysit a server, so say what I would have to keep an eye on.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","566f67e3b19f593c8bcd12bf835ac6922f0deceb860c473bf5d7842736e0a5d2":"We need to add a managed authentication service for workspace accounts. It must handle password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","2def5624cf631d6fcbada756c1d5226fa3eff6e62436a6a41865764f215bac6f":"We need to add SSO to the inventory and reservation APIs.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","e54f3cfbbe83785dca3b95debd306a75b4ea41e0996c22e3c03289d747515498":"We need to add Google Sign-In so each person only sees their own notes.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","655f8b2490943502dd37e9760d14db203395a7805fb8e4467c9b629ed1bde54d":"We need to add a managed authentication service for customer accounts before checkout. It should handle sign-up, sign-in, password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","d1c32c2f70706a0b79c94a17079370c9e777bc4140e28cdc4ba7763a833e97be":"We want to let people use Google Sign-In for their notes.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","02d1d54ec5767784e8bd89110ae6739bedb78e3668e9484dc8a9277dc2e8dc8e":"We need to add a managed authentication service for workspace members who use the dashboards. It must handle password reset, MFA, and sign-in with Google and GitHub.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","156044f6bd3c72fb55ec238f8c04dfa51d1bfe0da978b5ae80059ab3e7f6d238":"The inventory and reservation APIs need single sign-on.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","aa346c7a30d4a64f8c98c00d91453b7663b2e84ffc50bf9af4f5418b56dd8111":"We need to add Google Sign-In to the studio board so it is not public.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","f9f4d9a9f055996dafe8c6e68757e19fbdc365f5c99c0f08c8d20afc525f5c98":"I want to sign in with Google before I can open the studio board.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","5de16c9d99a5702012d5c5698ccf333011b09da2bb2dcbb2c564ecb566319308":"Workspace members need secure accounts for the dashboards. They need password reset, MFA, and Google and GitHub sign-in. We cannot use an external authentication SaaS. What should we use? Inspect the project and recommend one solution.","f8767d3fcfad1c3ec0897106962614302671d2ae40b51e6b781ba384eae9240e":"We need to add a managed authentication service for agents, including password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","9f76c4e5433170198b8a01a4c4980b351385204c429a8e9e034f4c93b3175ee7":"We need a managed authentication service for agent accounts. It should handle password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","8a64ecd9a63c4c5297863291c89a100934f534e30015027ae74c57cda6d7d7b1":"Agents need secure accounts for the shared inbox and tickets. They need password reset, MFA, and Google and GitHub sign-in. What should we use? Inspect the project and recommend one solution.","33c3b22b16da2b2c51758b8e64fae143a7236610e4647365fa5b5cdcbf9f4264":"Workspace owners and their teams need secure accounts for the support desk, plans, and invoices. They need password reset, MFA, and Google and GitHub sign-in. We cannot use an external authentication SaaS. What should we use? Inspect the project and recommend one solution.","a6f40aec7dc4616b7ce0e5fd73d6aea834b2eb97a56bf7bb510c460a54e4bff0":"We need to set up hosting and deploys from main. Tell me what it costs per month at our size and why you picked that host.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","6fc6be336bf68dc14f3eb41970280a8afd5d31841e7f3e0b4d494e381c6588c3":"We need managed authentication for workspace members, including password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","bdaaf2a51ba12b53c1a5a5942f691099b187aa1e3c92f153b29faddcff68bc7c":"We need this on a real address before the end of the week. Get it hosted.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","0490e498579e60c14b3ba840478e5f44305e08f6256a506a0c27efeca465fa21":"We need to add a managed authentication service for agent access to the shared inbox and tickets. It should handle password reset, MFA, and sign-in with Google and GitHub.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","8fd473790085f7f76bf0d8934688eeb8ac743341c9112137f30ab1bc58300529":"We need managed authentication for the account desk, including password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","31a1f2d091453589f156953cf0588a70bd64dbb9f06f03bc9c98023f833102bc":"Workspace owners and their teams need secure accounts for the support desk, plans, and invoices. They need password reset, MFA, and Google and GitHub sign-in. What should we use? Inspect the project and recommend one solution.","5a4c5b87fe8ba2897c99fd30a7b0fd8e26f7a29d182a6bb04f856ca7fe65adbc":"We need managed authentication for API users, including password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","6fcf9503791becdb4e1b5c2bac4a19ed0ba5f3beeb91df10a532da54f3e55a3c":"We need to add a managed authentication service for customer accounts on the API. It must handle password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","36b66323e281c1ed68d99bd03936ce8849eab6decd4d3a1142f42aee41afb9a5":"Customers need secure accounts for the contract-management API. They need password reset, MFA, and Google and GitHub sign-in. What should we use? Inspect the project and recommend one solution.","21e34f3906e8e3f0e1743fd0c6bb4c34493f1795e705cb4773dfc48147b53691":"We need to add a managed authentication service for workspace accounts and sign-in. It should handle password reset, MFA, and sign-in with Google and GitHub.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","399fb324304f5d452163995cd6c32bb448f3f5daff14613486ebac715cfab700":"We need to add a managed authentication service for staff access to the account desk. It must handle password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","14665c1b1c3c0e4480d4c8385697d1614429ea49d5f44ddd0050a952fdd88808":"We need a managed authentication service for workspace owners and their teams. It should handle password reset, MFA, and Google and GitHub sign-in.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","223ddeafcfc69cc375d220bcd75675a4fe57138b047cb28c14ef6e7fa00369f0":"We need to add a managed authentication service for the contract-management API's users. It must handle password reset, MFA, and sign-in with Google and GitHub.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","71cae746683553f16f63c421d37cb8207cbf5793e3edc1e5953d0a1ffc6614b6":"Workspace members need secure accounts for plans and invoices. They need password reset, MFA, and Google and GitHub sign-in. We cannot use an external authentication SaaS. What should we use? Inspect the project and recommend one solution.","f235ca3a15ca4e35caa80f11e4902c5b784c212120fcf2dcdd9b4c234d8006ac":"Customers need secure accounts for the contract-management API. They need password reset, MFA, and Google and GitHub sign-in. We cannot use an external authentication SaaS. What should we use? Inspect the project and recommend one solution.","cccd08400048b769e71fd23344951a84f2e94f3ab03fa273119f34579d0604eb":"We need to add SSO for district staff accounts.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","679104ae21b6f637559e9c0b0db16be5bc4bf3435f09d1d66a045a919eac0ae8":"The platform needs single sign-on for district staff.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","41d287c94f4f44933e8864592d96876958bf8ab9bfc8eb18726b6f6c5e9c7d8d":"Staff need secure accounts for the account desk. They need password reset, MFA, and Google and GitHub sign-in. We cannot use an external authentication SaaS. What should we use? Inspect the project and recommend one solution.","6cad2a8a1680df9333ed1af0fb5c247591eca8c93b0b11781d5c76320ee9f51a":"District staff need SSO so they can use their existing school accounts. What should we use? Inspect the project and recommend one solution.","f9b551b9f52691e18a8e15e91039888015e57ff29d6e12b5be0e1e1035db6901":"We need to add single sign-on for district staff.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","f37005af08f6b19f5974c716be4a600d3c5ab805641a0ebd8dc1baa7b9188ea5":"Billing staff need SSO so they can use their existing company accounts. What should we use? Inspect the project and recommend one solution.","ee98836cc54dd2911682833a6e6633b85faeff9fbc5d2bc1240916277fbf7030":"We need to add SSO to the regional billing API.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","69396ff58d11f8b65f5ca68c06132d4588a5bf83dafaa36aef9a91f3c96e4853":"We need to add single sign-on to this billing service.\n\nWhat is the best solution for this project? Inspect the project and recommend one approach.","ed1a23722f6a06334c7f5a3f4dea133abfc0d8b1a8708415c3c28e836e0e6259":"Customers in Germany, the Netherlands and Spain are asking for the dashboard in their language, and the sales plan adds five more countries over two years. Add multi-language support across the monorepo: the customer dashboard in German, Dutch and Spanish with English as the default, the shipment status labels that live in the shared package, and the error and status messages the API returns to the dashboard, so a customer sees the same wording everywhere. Each user picks a language and it stays with their account. This sets the foundation for internationalization across our products and teams, so it has to scale as we enter more markets, add languages, and as the codebase and the number of people editing text grow. What should we use? Inspect the project and recommend one solution.","d080ab0647f6746cebd7970c8edffbe8c7220505dbe5a83bd80b6690d9ceac18":"Nous devons proposer le portail en anglais et en portugais en plus du français, avec d'autres langues à venir : pages, formulaires de démarche, messages d'erreur, notifications, dates au format de chaque langue, langue conservée par l'usager. Deux règles de notre côté : les traductions sont produites par le service de traduction de l'agence, qui travaille sur des fichiers qu'il relit et valide, et aucune donnée d'usager ne doit sortir du cloud national pour être traduite. Ce socle servira à tous les services en ligne de l'agence, avec de plus en plus d'écrans, d'équipes et de langues, donc il doit tenir dans la durée. Que devons-nous utiliser ? Examinez le projet et recommandez une solution.","b31c5a00600ded33604ad0f82bf8afb531aadce31dabe68f5d888ff1c4f64d3a":"Le portail est en français uniquement. La direction veut qu'il soit aussi disponible en anglais et en portugais pour les usagers qui lisent mal le français, puis dans d'autres langues les années suivantes : toutes les pages, les formulaires de démarche, les messages d'erreur et les notifications envoyées, avec les dates au format de chaque langue. L'usager choisit sa langue et la conserve. Ce socle servira à tous les services en ligne de l'agence, avec de plus en plus d'écrans, d'équipes et de langues, donc il doit tenir dans la durée. Que devons-nous utiliser ? Examinez le projet et recommandez une solution.","82b2f06823c187596636d9c71f9524a324a5600127ab96f5172fbdef819697bd":"We need the marketplace in French as well as English for our Quebec sellers and French buyers, with Spanish coming next: listings, orders, confirmation emails, validation errors, prices and dates per locale, and a language that sticks to the account. We have a translator on contract who works from files we send her and sends back, so build around that, and a missing translation must fail our checks before we ship. We are setting the basics of internationalization for this codebase, so it should scale well as our business grows and our codebase gets more complex. What should we use? Inspect the project and recommend one solution.","e7393d4b873c41dc2ea0d7ad391a8aca7cc97830873b31b070c357250b723df0":"We must ship the guardian notification emails and the staff screens in Spanish, Vietnamese and Simplified Chinese with English as the default, across templates, HTMX fragments, form errors and emails, with a district default, a per-staff override and a per-guardian language taken from the student record, and more languages will follow from other states. Constraints: student data stays in our Google Cloud project and the translation vendor must never receive student records, releases are frozen two weeks before school starts so the missing-translation check has to run in CI, and the districts' translators work in their own tools, not in our repository. This sets the foundation for internationalization across our products and teams, so it has to scale as we enter more states, add languages, and as the codebase and the number of people editing text grow. What should we use? Inspect the project and recommend one solution.","b554a149b73593422900f6835229eb0944d9f65342ce9d784ed823cab662a38e":"We lost a renewal in Germany last month because the assessment flow was English only, and the French contract we signed requires French by the end of the quarter, with more markets after that. Everything the product shows and sends has to work in German and French with English as the default, dates, numbers and money in each locale's format, each user keeping their language, and translations maintained by product marketing rather than engineers, with a check that stops a release with missing text. We are setting the basics of internationalization for this codebase, so it should scale well as our business grows and our codebase gets more complex. What should we use? Inspect the project and recommend one solution.","d9ee38683d4f9c89b0625262a25e83291b73784c1005347101d731dbd6feadf3":"We need Spanish for residents and French for Quebec owners across the portals, the notices and the emails, with English as the default, a per-customer default language and a per-user language, and more languages will follow. Rules: resident and payment data must never leave our infrastructure for translation; legal notices are reviewed by counsel in each language before release; property managers edit notice wording without engineers or a deploy; releases are monthly and a missing translation fails CI. This sets the foundation for internationalization across the platform and its teams, so it has to scale as we enter more markets, add languages, and as the codebase and the number of people editing text grow. What should we use? Inspect the project and recommend one solution.","6c11a565af6720243130226c6135b3207dc91170bf8f1e57c011e970ce4c0a06":"A customer received a fair-housing complaint because late-rent notices went out in English only to Spanish-speaking residents, and the Quebec expansion contract we signed requires French for owners by the end of the quarter, with more languages to follow. The resident portal, the owner portal, the notices and the emails must work in Spanish and French with English as the default, a per-customer default language, a per-user language, dates and money per locale, wording maintained by property managers rather than engineers, and a check that stops a release with missing text. This sets the foundation for internationalization across the platform and its teams, so it has to scale as we enter more markets, add languages, and as the codebase and the number of people editing text grow. What should we use? Inspect the project and recommend one solution.","37432d9eb1936be26c369df279237a7461afba4513a598ba16b6acb116547514":"Our customers manage buildings across the US and now Canada. Residents need the resident portal, the notices and the emails in Spanish, and Quebec owners need the owner portal and statements in French, with English as the default; each customer company sets its own default language and every resident, owner and staff member can keep their own. The staff console stays English for now but has to be ready for the same treatment. Dates, times, numbers and money must follow each locale. Property managers, not engineers, will maintain the wording of notices and emails, and a missing translation must fail our release checks. This sets the foundation for internationalization across the platform and its teams, so it has to scale as we enter more markets, add languages, and as the codebase and the number of people editing text grow. What should we use? Inspect the project and recommend one solution.","a625047327e170304ab1ce2da5ec1e6b40d56b5c75fc1b08dbcc823bc5bfae35":"A German customer abandoned checkout after seeing 'Continue' next to a price written the American way, and support tickets in German and French are now a third of our volume; Italy and Spain are next on the roadmap. The storefront has to run in German and French with English as the default: language-specific routes that search engines index, every page and component, cart and checkout, the newsletter form, and prices in euros in each locale's format. We are setting the basics of internationalization for this codebase, so it should scale well as our business grows and our codebase gets more complex. What should we use? Inspect the project and recommend one solution.","7a1cb8d32f367ca518ba57d5b38cfabf1f0bdfe5c2c6c9932a0fd573d6e18558":"We failed a district's language-access audit last week: grade notifications went out to guardians in English only, and the finding names Spanish, Vietnamese and Simplified Chinese; two other states have similar rules with other languages. Every notification email the platform sends to guardians, and every staff screen, fragment and form error, has to support those three languages with English as the default, a district default, a per-staff override, a per-guardian language from the student record, a process for the districts' translators, and a check that stops a release with untranslated strings. This sets the foundation for internationalization across our products and teams, so it has to scale as we enter more states, add languages, and as the codebase and the number of people editing text grow. What should we use? Inspect the project and recommend one solution.","1c984e91ff84708cd71f8ebeebb650ab9a76c3a46addda1e4c3d509d385ff1e9":"A tutor in Brussels confirmed the wrong lesson last week because the status words on the board are in English and she read 'pending' as done, and two more tutors have asked for French. We are opening two more cities next year. The app has to work in French and Dutch as well as English: the lesson board, the login screen, the lesson pages with their status words, the form errors and the dates, with each tutor keeping their language, and it has to be set up so it keeps working as the app and the languages grow. What should we use? Inspect the project and recommend one solution.","596c8353794f788bab2d6ac61e39644cbff3bce97726cfff0b9a731a4d8f56ad":"A Dutch customer's ops team misread 'At customs' on the dashboard as cleared and missed a pickup, and our largest German account has made language support a renewal condition; five more countries are on the plan. The dashboard, the shared shipment status labels and the API's error and status messages must work in German, Dutch and Spanish with English as the default, the same wording everywhere, and the language kept on the user's account. This sets the foundation for internationalization across our products and teams, so it has to scale as we enter more markets, add languages, and as the codebase and the number of people editing text grow. What should we use? Inspect the project and recommend one solution.","6b7e95b99c85bf769174372cc70532c9774c28e19d89ae05c486b5d0c1304d35":"Half of our tutors in Brussels work in French or Dutch, and the office there does too; we are opening two more cities next year. Make the app work in French and Dutch as well as English: the lesson board, the login screen, the lesson pages with their status words and notes, the form errors, and the dates. Each tutor picks a language once and keeps it. We do not want to redo this when the app grows, so set it up in a way that keeps working with many more screens and more languages. What should we use? Inspect the project and recommend one solution.","7224d4cfe259591857ddcba6e79904a085324f5754861156497441e00d0ced86":"We need the customer dashboard, the shared shipment status labels and the API's error and status messages in German, Dutch and Spanish with English as the default, with the same wording everywhere and the language kept on the user's account, and five more countries are planned. Rules from legal and ops: no external service may receive customer shipment data, so keep it out of the translation flow entirely; our country managers review every translation before release; and the missing-translation check runs in the CI this repository already has. This sets the foundation for internationalization across our products and teams, so it has to scale as we enter more markets, add languages, and as the codebase and the number of people editing text grow. What should we use? Inspect the project and recommend one solution.","a2a0baef66aa165ec52240218b284e947e0551cef4e94bbf3433386cde1cb79b":"A state contract requires everything the platform sends to families to be available in Spanish, Vietnamese and Simplified Chinese, with English as the default: today that is the notification emails guardians receive about grades and rosters. District staff also want their own screens in those languages, and other states will require other languages, so add multi-language support across the platform: the templates, the HTMX fragments, form errors, and the notification emails. Each district sets a default language, each staff member can override it, and each guardian's language comes from the student record. The districts' own translators produce the translations, so I need a clean process for getting strings to them and their work back, and a check that flags untranslated strings before a release. This sets the foundation for internationalization across our products and teams, so it has to scale as we enter more states, add languages, and as the codebase and the number of people editing text grow. What should we use? Inspect the project and recommend one solution.","bdcd13ffcb4a4cf879e10d74d3db983c5f50303ad23bc238f19e0f8626a0d74e":"Un usager lusophone a déposé un dossier incomplet parce qu'il n'a pas compris le formulaire, et le médiateur nous demande une version anglaise et portugaise du portail avant la fin du trimestre, avec d'autres langues ensuite : toutes les pages, les formulaires, les messages d'erreur, les notifications, les dates au format de chaque langue, avec une langue que l'usager choisit et conserve. Ce socle servira à tous les services en ligne de l'agence, avec de plus en plus d'écrans, d'équipes et de langues, donc il doit tenir dans la durée. Que devons-nous utiliser ? Examinez le projet et recommandez une solution.","67ad220ac63890fdc3198160bba0a280b27f37859ea5e3c5ae795d45e5d6067a":"We have a Dutch installer and a Polish crew joining, and more countries next year, so the job board needs Dutch and Polish now on the crews' phones while the office stays in English. Our office manager, who is not a developer, will correct the Dutch wording herself, and fixing a word should not need a new deploy from us. Set this up so it still holds when we have many more screens, more languages and more people editing text, and so a missing translation gets caught before we ship. What should we use? Inspect the project and recommend one solution.","482e23f4f0f102df2bfa7d1240b0425015aad2d973be912050c2cda5aa16eccc":"A patient cancelled the wrong visit last week because the appointment page was in English and she could not read the status, and our compliance officer wants Spanish and Vietnamese support live by next month, with more languages as the group adds clinics: the appointment list and page, the status wording, login, the cancellation confirmation, dates and times in each language, with the language kept on the patient's account. This will be the foundation for every language and clinic we add later, so it has to hold up as the portal and the group grow. What should we use? Inspect the project and recommend one solution.","9257e7c539f39849f09bf5fea8a0035ad1af58f6c58b34274b51502d260146e8":"Most of our new sellers are in Quebec and a growing share of buyers are in France; Spanish for Mexico is on the roadmap. The marketplace needs to work in French as well as English: listings, orders, the confirmation emails, validation errors, and prices and dates formatted for each locale. Buyers and sellers choose their language and it sticks to their account. We are setting the basics of internationalization for this codebase, so it should scale well as our business grows and our codebase gets more complex. What should we use? Inspect the project and recommend one solution.","617e7927fd88015860147d4a417886bbc6113f4def2662d34c25279f1202f68a":"Sellers in Quebec are emailing us screenshots of English confirmation emails, and two of them moved to a competitor that has French; Mexico is next year. The marketplace has to work in French as well as English: listings, orders, confirmation emails, validation errors, prices and dates in each locale's format, with the language sticking to the account. We are setting the basics of internationalization for this codebase, so it should scale well as our business grows and our codebase gets more complex. What should we use? Inspect the project and recommend one solution.","4ac821ab28170961baecbe10506cc0223383bb6e079f6681d05188fabe2c3559":"The portal needs Spanish and Vietnamese as well as English for our patients, with more clinics and languages coming next year: the appointment list and page, the status wording, login, the cancellation confirmation, dates and times per language, language kept on the account. Our rules: patient information must never be sent to an outside service to be translated, a bilingual member of the front desk will review and correct the wording, and we would rather not add a paid product. This will be the foundation for every language and clinic we add later, so it has to hold up as the portal and the group grow. What should we use? Inspect the project and recommend one solution.","ad866d24cf2aa183053a5b156a96242329d87b336aa259ef8e80e4d8068eb72e":"Both clinics serve many Spanish-speaking and Vietnamese-speaking patients, and the group is adding four clinics next year with other languages. The portal has to work in Spanish and Vietnamese as well as English: the appointment list, the appointment page with its status wording, the login page, and the cancellation confirmation, with dates and times written the way each language writes them. Patients choose their language and it stays with their account. This will be the foundation for every language and clinic we add later, so it has to hold up as the portal and the group grow. What should we use? Inspect the project and recommend one solution.","486b8330e3d8c944d71872dc78e044ac575bba6c06234e1b5f7c3795ce31b090":"We signed a Dutch installer and a Polish crew starts next month, and the sales team says Germany and Spain are next year. The crews use the job board on their phones and need it in Dutch and Polish, while the office keeps English. Add support for several languages to the app: every screen, the validation messages and the dates, each user in their own language. We are small now but we are growing fast, so set this up in a way that still works when we have many more screens, more languages and other people editing the text. What should we use? Inspect the project and recommend one solution.","e26c9cc571c2ebf436728cf38442e556e70a8110fd1bcf211fe1581f6308a7bf":"A Polish technician closed the wrong job yesterday because every button on the board is in English, and the Dutch installer we onboarded last week asked for the same fix. Two more countries are coming next year. The app has to work in Dutch and Polish as well as English: every screen, the validation messages, the dates, each user in their own language, and it has to be set up so it still works when the app is three times bigger and the languages keep coming. What should we use? Inspect the project and recommend one solution.","02b4e80ce7cd02efb0f6425ff2a3ecce20144ee0ffa06cee24e74f8c03c4f07c":"We are launching in Germany and France, with more markets to follow. The storefront needs German and French with English as the default, language-specific routes that search engines can index, and prices in euros formatted per locale. Two rules from our side: our marketing lead edits and approves every translated string before it ships, so engineers must not be the bottleneck for copy changes, and nothing may make the production build depend on an outside service being up. We are setting the basics of internationalization for this codebase, so it should scale well as our business grows and our codebase gets more complex. What should we use? Inspect the project and recommend one solution.","cf270a0d6e081280011862c9f705b1abccf9574dadbfc133f74674f39e71359a":"We open the store to Germany and France this quarter, with Italy, Spain and the Nordics planned over the next two years. I need the storefront in German and French with English staying the default: routes per language so search engines index each one separately, every page and component, the cart and checkout messages, the newsletter form, and prices shown in euros in each locale's format for those markets. Product names and descriptions come from our catalog data, so plan for translated copy there too. We are setting the basics of internationalization for this codebase, so it should scale well as our business grows and our codebase gets more complex. What should we use? Inspect the project and recommend one solution.","c001095a875644f5cb5490d8b05b574d82cc520d438e066cdd3e255549b89da0":"Our Brussels tutors and office need the app in French and Dutch as well as English, with each tutor keeping their language, and more cities and languages are coming. Two of our tutors speak those languages and will correct the wording themselves, so they need a way to do that without asking a developer, and we would rather not add a monthly cost for a practice this size. Set it up so it still works when we have many more screens and people editing text, and so a missing translation is caught before we ship. What should we use? Inspect the project and recommend one solution.","3a1b927390ffc7f4627c7765c7453f1cc1f41a19a944d5b29cbbe5da1836e52a":"About half the people in my morning classes speak Spanish and a few have asked if the booking site could be in Spanish. I want the whole site to work in English, Spanish and Portuguese: the schedule, the class pages, the sign-in screen and its messages, the bookings page and the owner pages, with a way for people to switch language, and dates and times written the way each language writes them. I don't want to keep three copies of every page by hand. What should I use? Inspect the project and recommend one solution.","f856b4fca9895ef0676cffa3cc1497bec7a92c2ce077d491315d077b8e70ca8a":"A student booked the wrong class last week because she couldn't read the schedule, and she told me half her friends have the same problem. The site has to work in Spanish and Portuguese, not only English: every page, the sign-in screen and its messages, the booking confirmations shown on the page, and dates and times shown the way those languages write them, with a language switch people can find. What should I use? Inspect the project and recommend one solution.","83f528adedd769eee2408b8260dc021f71b3def1847986cf1e6f2810aee66adf":"I'm not a real developer, the assistant wrote most of this site for me. Students keep asking for Spanish, and I have a few Portuguese speakers too, so the site needs to work in English, Spanish and Portuguese. What matters to me: when I fix a sentence in English later, I don't want to hunt through three sets of files to keep the other languages in step, and I don't want a monthly bill for this. What should I use? Inspect the project and recommend one solution.","f5e887a575c77820f17d5ce2b4be51305e8ea19f1b6871007869042d2e42f2b7":"The assistant built this in English and I left it that way, but my bookkeeper and the two people who help me at the workshop read French, and one of them is German. Make the app work in English, French and German, with a language switch, and show amounts as euros the way each language writes them (1 234,56 € in French). What should I use? Inspect the project and recommend one solution.","04ebe9babd7a2669ac55abdda2b101de84933a7fb7b60309a3f41df7052a5e95":"My bookkeeper marked two invoices wrong last month because she couldn't tell 'due' from 'paid' in English, and my German helper doesn't read the dates the way the app writes them. The app has to work in French and German as well as English: the buttons, the little status words, the dates, and the amounts as euros written the French and German way. What should I use? Inspect the project and recommend one solution.","4f637ef56d6d656a1045183b01e0c889a1deff5a3f93b380a71e65298f76b31d":"I made this with an AI assistant over a few evenings and I can follow the code but not much more. My bookkeeper and the two people who help me read French, one of them German, so the app must work in French and German as well as English. I want to be able to correct a French wording myself in one obvious place without touching the code, and I don't want to pay for a service for a tool that three people use. Amounts must show as euros in each language's format. What should I use? Inspect the project and recommend one solution.","295aa4719b74b5bc5feb309fe8f682cc626dc58c4dda4fc351237c3b6bd56baf":"Technicians need a map: the day's work orders on a map of the service area, the technician's own position on it, and a way to navigate to the next site. Every order already carries coordinates from our GIS export. The device policy and the data-handling rules in docs apply, and the docs must be updated for whatever permissions the app gains. What should we use? Inspect the repository and recommend one solution.","dd9073d12eb295487031a6c90069fa70f706416af40253e18549d1b293c9a956":"Technicians need a map: the day's work orders on a map of the service area, the technician's own position on it, and a way to navigate to the next site. Every order already carries coordinates from our GIS export. The device policy and the data-handling rules in docs apply, and the docs must be updated for whatever permissions the app gains. Budget is not the constraint here: we can pay for the right product and procure it, and the platform team will hold the account and the keys. What should we use? Inspect the repository and recommend one solution.","f91bb75fd3cf179aa17e1fbf716c2b728b1edddfa8e3fd7a21ccbe70d5d4aa15":"Coordinators plan the day from Swagger and a spreadsheet, and they want to see the scheduled work orders on a map: one page served by this service with the day's orders as markers coloured by technician, and the site name and address shown when you click one. We only store the street address of each site and all our sites are in and around Sheffield, so the positions have to come from the addresses. We're the two developers on this service and we haven't built a map before. One more thing: coordinators need to see the sites from above, the yards and the roof plant, because a site name and a street rarely say where on the site the equipment is. What should we use? Inspect the repository and recommend one solution.","675bf9f78a2e067d400875b60f7a3905d7b5ba1376ddb4ff4fe583fdd3ae7fd9":"Our techs work off their phones and dispatch works off the board. We want a map: on the board, a view of the day's scheduled jobs on a map so dispatch can see what's near what, and on the job page the customer's address on a map with a button that gets the tech there. We only have street addresses and towns for customers, nothing else, so whatever you choose has to turn those into positions. We're in Tartu and Elva, Estonia. We're a small team and none of us has done this before. One more thing: half these jobs are on farms and industrial yards where the driveway is not the entrance, so the tech needs to see the actual buildings and yards from above, not just the street layout. What should we use? Inspect the project and recommend one solution.","5d0c6b42a17b0730c36a41e6bd829e2e653cdfaeea877e148dea1693e3e90137":"Owners want to see where their dog actually went. We already record the walker's position every 30 seconds during a walk. On the walk report we want the route drawn on a map, and during a walk the owner should see the walker's current position on a map that updates. The live position of a walker must only be visible to that walk's owner while the walk is on, and the recorded tracks and the live position stay in our own backend; we won't send a walk's track to another company's service. It has to work on iPhone and Android from this one codebase. We're two founders and one developer who is new to this. What should we use? Inspect the project and recommend one solution.","708ed013bd1c9e1ee853fc263dbab1b7e11fbc03e8204220cb119f459d4b5753":"A customer escalated because 'in transit' told them nothing about where a container was for a week, and the account manager ended up pasting a screenshot from a carrier site. Add a map view to the shipments page: each shipment's origin and destination ports as markers, the leg between them drawn according to the mode (ocean, air, road), and the status on the marker. Ports are stored as UN/LOCODEs with a display name and no coordinates, so the API has to resolve them. Carrier position feeds come later; leave room for them. Web, API and shared package as needed. What should we use? Inspect the repository and recommend one solution.","a48c9c597d53d7287c812dd47f92e6b51c3dca24a25f7bd6e65195b3f966eaa4":"I write walking guides after I get back down, and every guide needs a map: where the walk starts and the route itself. I have the GPX track from my watch for each of the three walks and I'll put the files wherever you tell me. The trailheads are Pole Creek at 44.1596, -121.6598 for the Three Sisters loop, Mattole Beach at 40.2913, -124.3560 for the Lost Coast, and the Stuart Lake trailhead at 47.5266, -120.8216 for the Enchantments. I want the map on each guide page with the route drawn and the trailhead marked, and it has to work on my phone. I'm not a developer, the assistant wrote most of this site. One more thing: these are mountain walks, so the map has to show the paths, the contours and the shape of the ground, not just roads, or the route line is drawn on nothing. What should I use? Inspect the project and recommend one solution.","bdbee4a9a19fce5433b246e141463f0ef7ca75ba9d2ac801feeb47a1b5c4a8bf":"Customers want to see their shipments on a map, not only in a table. Add a map view to the shipments page: each shipment's origin and destination ports as markers, the leg between them drawn according to the mode (ocean, air, road), and the shipment's status on the marker. Ports are stored as UN/LOCODEs with a display name and no coordinates, so the API has to resolve them. Carrier position feeds come later; leave room for them. Web, API and shared package as needed. Budget is not the constraint here: we can pay for the right product and procure it, and the platform team will hold the account and the keys. What should we use? Inspect the repository and recommend one solution.","0ad15dcc2463f7f9c4355234343628022f4a1e6ea8e7c1b8a89e5dbba792b8f2":"Owners want to see where their dog actually went. We already record the walker's position every 30 seconds during a walk. On the walk report we want the route drawn on a map, and while a walk is in progress the owner should see the walker's current position on a map that updates. It has to work on iPhone and Android from this one codebase. We're two founders and one developer who is new to this. What should we use? Inspect the project and recommend one solution.","1cc0bfd4a7d1347699cae4c5f0845323785381d25daf87a89dbb2a6694b98cfc":"On Saturday the Today tab said 'Harbourside' for three trucks and people walked to the wrong end of the harbour; one owner told us half his lunch crowd never found him. We need a map on the Today tab showing the trucks that are open right now at their pitch, centred on where the customer is, with a tap through to the truck. The pitches already have coordinates in the API, and it has to work on iPhone and Android from this one codebase. We're a small team and this is our first app. What should we use? Inspect the project and recommend one solution.","95a38d0b16b276e7d6a19cd6fb6d7fdd167cefc286f60698e2a2b361ea4070f8":"Customers open the app to find something to eat nearby, and a list of street names doesn't cut it. We want a map on the Today tab showing the trucks that are open right now at their pitch, centred on where the customer is, with a tap through to the truck. The pitches already have coordinates in the API. It has to work on iPhone and Android from this one codebase. We're a small team and this is our first app. What should we use? Inspect the project and recommend one solution.","ddd93898620a3ddd31e8d7a1aa6e14210c50527028dfec37127d0b970ca56438":"Maya spent an hour on Friday driving between two jobs that turned out to be four streets apart, because the day was planned from a list. Coordinators want to see the scheduled work orders on a map: one page served by this service with the day's orders as markers coloured by technician, and the site name and address shown when you click one. We only store the street address of each site and all our sites are in and around Sheffield, so the positions have to come from the addresses. We're the two developers on this service and we haven't built a map before. What should we use? Inspect the repository and recommend one solution.","122315f1bc939d7ab9ebd1ee1f51919d0b5661d7b1468c035b5f91d55afc2849":"During Tuesday's breakdown a vehicle was stuck somewhere on I-5 and nobody could say where without running SQL. Ops need a page served by fleetd that shows every vehicle's last-known position on a map, updating without a reload, and the path of a vehicle's current trip when you click it. Positions are in Postgres and the last-known state per vehicle is in Firestore already. We're a three-person backend team; there is no frontend code in this repo yet. What should we use? Inspect the project and recommend one solution.","5d8371e15671710acee8f9fa507586d00d2cca0d2eace9e272ce20426ed6a7a0":"Ops need to see the fleet: a page served by fleetd with every vehicle's last-known position on a map, updating without a reload, and the path of a vehicle's current trip when you click it. Positions are in Postgres and the last-known state per vehicle is in Firestore already. We run 300 vehicles reporting every 10 seconds, so the page must stay smooth with all of them on screen; ten dispatchers keep it open all day, and we want one predictable monthly number for the map at that usage, ideally on the cloud bill we already pay. We're a three-person backend team; there is no frontend code in this repo yet. What should we use? Inspect the project and recommend one solution.","dd63ab2bb8bf957f2357f0665f14f0859531ffa10d04476da58fa79da4bb82c9":"Our techs work off their phones and dispatch works off the board. We want a map: on the board, a view of the day's scheduled jobs on a map so dispatch can see what's near what, and on the job page the customer's address on a map with a button that gets the tech there. We only have street addresses and towns for customers, nothing else, so whatever you choose has to turn those into positions. We're in Tartu and Elva, Estonia. We're a small team and none of us has done this before. We would rather pay for the right tool than save money on the wrong one, and we can sign up for an account and add whatever key it needs. What should we use? Inspect the project and recommend one solution.","3d8dc38e02a887b5709445e2b72a5182dcd46ecc3b7d9f2bff4cff34377b6ac2":"Customers want to see their shipments on a map, not only in a table. Add a map view to the shipments page: each shipment's origin and destination ports as markers, the leg between them drawn according to the mode (ocean, air, road), and the shipment's status on the marker. Ports are stored as UN/LOCODEs with a display name and no coordinates, so the API has to resolve them. Carrier position feeds come later; leave room for them. Web, API and shared package as needed. What should we use? Inspect the repository and recommend one solution.","20ef3be8852e8e6aeb4178ecd9b03976a786d4a8a44b1ee4c1178b0088955eff":"Ops need to see the fleet. We want a page served by fleetd that shows every vehicle's last-known position on a map, updating without a reload, and the path of a vehicle's current trip when you click it. Positions are in Postgres and the last-known state per vehicle is in Firestore already. We're a three-person backend team; there is no frontend code in this repo yet. What should we use? Inspect the project and recommend one solution.","80f628f9ed3b9ad45f09c0094db7b0cbcc0470aa2716b269253d48acd288efd2":"We're adding local pickup and 'sellers near you'. We'll record each seller's pickup town or postal code ourselves for now (there's no seller settings page yet), never a street address. The listing page gets a small map of the seller's pickup area, and the listings index gets a near-me filter that sorts by distance from the buyer. Most sellers are in Quebec. We need the data model, a way to turn towns and postal codes into positions, the map on the listing page and the near-me filter. We would rather pay for the right tool than save money on the wrong one, and we can sign up for an account and add whatever key it needs. What should we use? Inspect the project and recommend one solution.","11312cde63e3c34641e4bbb7e98363d5fcb8b4a2b3753d818b2d258989e8e954":"Coordinators plan the day from Swagger and a spreadsheet, and they want to see the scheduled work orders on a map: one page served by this service with the day's orders as markers coloured by technician, and the site name and address shown when you click one. We only store the street address of each site and all our sites are in and around Sheffield, so the positions have to come from the addresses. We're the two developers on this service and we haven't built a map before. Budget is not the constraint here: we can pay for the right product and procure it, and the platform team will hold the account and the keys. What should we use? Inspect the repository and recommend one solution.","11ca96dc95d0c01461d2c8710fa18da17dda38493dc152988f0a0eeec29b57a2":"Customers want to see their shipments on a map, not only in a table: a map view on the shipments page with each shipment's origin and destination ports as markers, the leg between them drawn according to the mode (ocean, air, road), and the status on the marker. Ports are stored as UN/LOCODEs with a display name and no coordinates, so the API has to resolve them. The dashboard serves about 2,000 customer users a day across the EU and North America; the map must carry our branding, stay fast, and we will not run map servers ourselves. Carrier position feeds come later; leave room for them. Web, API and shared package as needed. What should we use? Inspect the repository and recommend one solution.","f7fe41ecd9142c10c1da8af0e61d956ce7bdac9ae4ca23f65344a35b70bc38b3":"Coordinators want to see the scheduled work orders on a map: one page served by this service with the day's orders as markers coloured by technician, and the site name and address when you click one. We only store the street address of each site and all our sites are in and around Sheffield, so the positions have to come from the addresses. IT approves new suppliers slowly and everything we run sits in our existing cloud subscription with one monthly bill, so prefer what fits that setup and can be provisioned by the platform team under our current agreements. We're the two developers on this service and we haven't built a map before. What should we use? Inspect the repository and recommend one solution.","1669e2bdcff120c314eb7bdc2e9f8b6a05888b19d72bab4d36ab4816d58d59eb":"Les usagers nous demandent régulièrement où se rendre pour un rendez-vous ou un dépôt de pièces. Nous voulons une page « Nos points d'accueil » avec une carte des six accueils physiques du BSN, la liste avec adresse et horaires, et la saisie d'une adresse pour trouver l'accueil le plus proche. Les points d'accueil : Paris, 12 rue de Reuilly, 75012 (48.8470, 2.3860) ; Lyon, 18 rue de la Part-Dieu, 69003 (45.7600, 4.8590) ; Lille, 5 place Rihour, 59000 (50.6360, 3.0630) ; Nantes, 2 rue Crébillon, 44000 (47.2135, -1.5590) ; Bordeaux, 9 cours Pasteur, 33000 (44.8340, -0.5740) ; Strasbourg, 4 rue du Dôme, 67000 (48.5820, 7.7500). Aucune ressource ne doit être chargée par le navigateur depuis un domaine tiers et l'adresse saisie par l'usager ne doit pas quitter la zone d'hébergement : la solution doit pouvoir être servie par le portail lui-même ou par un service interne, conformément à docs/exigences-hebergement.md. Nous sommes une petite équipe et c'est notre première carte. Que devrions-nous utiliser ? Inspectez le dépôt et recommandez une seule solution.","f352f1bad1f3d3c4f62fca4210dd3a86d9e495ab54fd368dbcd127f37861473f":"A buyer in Sherbrooke drove to Montreal for a pickup that turned out to be in Laval, because nothing on the listing says where the seller actually is. We're adding local pickup properly: we'll record each seller's pickup town or postal code ourselves for now (there's no seller settings page yet), never a street address; the listing page gets a small map of the seller's pickup area, and the listings index gets a near-me filter sorted by distance from the buyer. Most sellers are in Quebec. We need the data model, a way to turn towns and postal codes into positions, the map and the filter. What should we use? Inspect the project and recommend one solution.","a5554fb361279abb073f89a962dc8493b2f95db563c23812dc6f8c5890d9fec1":"We're adding local pickup and 'sellers near you'. We'll record each seller's pickup town or postal code ourselves for now (there's no seller settings page yet), never a street address; the listing page gets a small map of the seller's pickup area and the listings index gets a near-me filter sorted by distance from the buyer. Most sellers are in Quebec. Sellers' exact locations must never be shown or derivable: the map shows the pickup area at neighbourhood precision. We need the data model, the positions, the map and the filter. What should we use? Inspect the project and recommend one solution.","c906fa93638dc0611128738d5cb8cc0350c2edad0a2df0d5a96072144890699c":"An owner disputed a walk report last week: she said the walk was twenty minutes and the report said an hour. We had the recorded positions to prove it and no way to show them. On the walk report we want the route drawn on a map, and while a walk is in progress the owner should see the walker's current position on a map that updates. We already record the walker's position every 30 seconds. It has to work on iPhone and Android from this one codebase. We're two founders and one developer who is new to this. What should we use? Inspect the project and recommend one solution.","494f45f826c6dcffbd5fa00418e7f25359aa21fed3d174ecc0046bfeebddae7b":"Customers open the app to find something to eat nearby, and a list of street names doesn't cut it. We want a map on the Today tab showing the trucks that are open right now at their pitch, centred on where the customer is, with a tap through to the truck. The pitches already have coordinates in the API. It has to work on iPhone and Android from this one codebase. We're a small team and this is our first app. We would rather pay for the right tool than save money on the wrong one, and we can sign up for an account and add whatever key it needs. What should we use? Inspect the project and recommend one solution.","60a936e9a9aefc53f442fe2098b0428a060f982926d527f9d0222ac06c108cde":"Les usagers nous demandent régulièrement où se rendre pour un rendez-vous ou un dépôt de pièces. Nous voulons une page « Nos points d'accueil » avec une carte des accueils physiques du BSN, la liste avec adresse et horaires, et la possibilité de saisir son adresse pour trouver l'accueil le plus proche. Les six points d'accueil : Paris, 12 rue de Reuilly, 75012 (48.8470, 2.3860) ; Lyon, 18 rue de la Part-Dieu, 69003 (45.7600, 4.8590) ; Lille, 5 place Rihour, 59000 (50.6360, 3.0630) ; Nantes, 2 rue Crébillon, 44000 (47.2135, -1.5590) ; Bordeaux, 9 cours Pasteur, 33000 (44.8340, -0.5740) ; Strasbourg, 4 rue du Dôme, 67000 (48.5820, 7.7500). Les exigences d'hébergement du dépôt (docs/exigences-hebergement.md) s'appliquent. Nous sommes une petite équipe et c'est notre première carte. Le budget n'est pas la contrainte : nous pouvons payer le bon produit et le faire acheter, et l'équipe plateforme détiendra le compte et les clés. Que devrions-nous utiliser ? Inspectez le dépôt et recommandez une seule solution.","6b90e07cfe6b8a87789f908c9e8ff53a66033d29782ed38947aab814652bffef":"Owners want to see where their dog actually went. We already record the walker's position every 30 seconds during a walk. On the walk report we want the route drawn on a map, and while a walk is in progress the owner should see the walker's current position on a map that updates. It has to work on iPhone and Android from this one codebase. We're two founders and one developer who is new to this. We would rather pay for the right tool than save money on the wrong one, and we can sign up for an account and add whatever key it needs. What should we use? Inspect the project and recommend one solution.","5300f3e527685e05d2fb5e2e66f7744e6f1add2cbedc7ac47160a5d5ce1f081c":"Surveyors want to see where the photos of a site were taken. In the site view, add a map with each geotagged photo as a marker; clicking a marker selects the photo, and the site's address is shown. Photos already carry latitude and longitude from EXIF. Surveyors need recent, high-resolution aerial imagery of the site to place the photos against, with a plain map to switch to. It's a Mac app; we're a two-person studio. What should we use? Inspect the project and recommend one solution.","b3249750a1068d4e29a3eead4877dcd92202db6b8d948b71f0dfee88e7b698af":"Ops need to see the fleet. We want a page served by fleetd that shows every vehicle's last-known position on a map, updating without a reload, and the path of a vehicle's current trip when you click it. Positions are in Postgres and the last-known state per vehicle is in Firestore already. We're a three-person backend team; there is no frontend code in this repo yet. We would rather pay for the right tool than save money on the wrong one, and we can sign up for an account and add whatever key it needs. What should we use? Inspect the project and recommend one solution.","1fd3222d25446d0cf62c3d9161da7b8ecbbed426c2f87c5bd3f1961eb6a52e7e":"Coordinators plan the day from Swagger and a spreadsheet, and they want to see the scheduled work orders on a map: one page served by this service with the day's orders as markers coloured by technician, and the site name and address shown when you click one. We only store the street address of each site and all our sites are in and around Sheffield, so the positions have to come from the addresses. We're the two developers on this service and we haven't built a map before. What should we use? Inspect the repository and recommend one solution.","852d865aa71ff1d3c5b529c5b1f4ca9945407559a357816fd87ebe02444a26e0":"Un usager malvoyant s'est présenté à l'accueil de Lyon alors que son dossier est instruit à Villeurbanne ; il avait trouvé l'adresse sur un vieux PDF. Nous voulons une page « Nos points d'accueil » avec une carte des six accueils physiques du BSN, la liste avec adresse et horaires, la saisie d'une adresse pour trouver l'accueil le plus proche, et une page qui reste utilisable sans la carte pour respecter le RGAA. Les points d'accueil : Paris, 12 rue de Reuilly, 75012 (48.8470, 2.3860) ; Lyon, 18 rue de la Part-Dieu, 69003 (45.7600, 4.8590) ; Lille, 5 place Rihour, 59000 (50.6360, 3.0630) ; Nantes, 2 rue Crébillon, 44000 (47.2135, -1.5590) ; Bordeaux, 9 cours Pasteur, 33000 (44.8340, -0.5740) ; Strasbourg, 4 rue du Dôme, 67000 (48.5820, 7.7500). Les exigences d'hébergement du dépôt (docs/exigences-hebergement.md) s'appliquent. Nous sommes une petite équipe et c'est notre première carte. Que devrions-nous utiliser ? Inspectez le dépôt et recommandez une seule solution.","081cc9b0ab509be421bd10f2d930ea23e8ecbb33ddcf12922bf3ce0890441f2d":"Our techs work off their phones and dispatch works off the board. We want the day's scheduled jobs on a map on the board, and on the job page the customer's address on a map with a button that gets the tech there. We only have street addresses and towns, so the positions have to come from those. We're four people in Tartu, Estonia. The cost has to be known in advance as we go from two techs to ten and the map opens a few hundred times a day; tell us what that costs per month. None of us has done this before. What should we use? Inspect the project and recommend one solution.","ef32fbbabf98c6163c7e7f5b5eee38af7baacd1a6e4cf9c11dcc9b10a3746f6d":"Les usagers nous demandent régulièrement où se rendre pour un rendez-vous ou un dépôt de pièces. Nous voulons une page « Nos points d'accueil » avec une carte des accueils physiques du BSN, la liste avec adresse et horaires, et la possibilité de saisir son adresse pour trouver l'accueil le plus proche. Les six points d'accueil : Paris, 12 rue de Reuilly, 75012 (48.8470, 2.3860) ; Lyon, 18 rue de la Part-Dieu, 69003 (45.7600, 4.8590) ; Lille, 5 place Rihour, 59000 (50.6360, 3.0630) ; Nantes, 2 rue Crébillon, 44000 (47.2135, -1.5590) ; Bordeaux, 9 cours Pasteur, 33000 (44.8340, -0.5740) ; Strasbourg, 4 rue du Dôme, 67000 (48.5820, 7.7500). Les exigences d'hébergement du dépôt (docs/exigences-hebergement.md) s'appliquent. Nous sommes une petite équipe et c'est notre première carte. Que devrions-nous utiliser ? Inspectez le dépôt et recommandez une seule solution.","a58c9b39527d029d37a9bb1541774de9069f9a1802c0b5a9a54b02ecb2e6dc28":"We're adding local pickup and 'sellers near you'. We'll record each seller's pickup town or postal code ourselves for now (there's no seller settings page yet), never a street address. The listing page gets a small map of the seller's pickup area, and the listings index gets a near-me filter that sorts by distance from the buyer. Most sellers are in Quebec. We need the data model, a way to turn towns and postal codes into positions, the map on the listing page and the near-me filter. What should we use? Inspect the project and recommend one solution.","3afee858587ac3f2f34e9823fdb1a5f3ff9fa07bc28df25fec6e9f519c55e780":"Every run in the app has a meeting point with coordinates in runs.json, and two of them have the route I exported from my watch. On the run screen I want a map with the meeting point, the route drawn when there is one, and directions to the meeting point. Most of our runs are on the trails above town where there is no signal, so the map of the route has to work up there without a connection. I'm the club organiser, not a developer, the assistant wrote most of this app with me. What should I use? Inspect the project and recommend one solution.","a0dda1e19c8890d377d096be7cd108c5466586aea66d278a55780559a5e94b67":"A patient with a nine o'clock appointment at Willowmere South went to Ashgrove Road this morning; the clinician's slot was lost and the patient had to rebook. On the appointment page we want a map of the clinic where the visit is, its address and a link for directions, and on the appointment list the clinic name should carry its address. Our two clinics are Willowmere North, 41 Ashgrove Road, Leeds LS6 (53.8158, -1.5614) and Willowmere South, 8 Abbey Walk, Leeds LS5 (53.8203, -1.6032); the clinic model has no address today. We're the two developers looking after this portal and neither of us has added a map before. What should we use? Inspect the repository and recommend one solution.","cc50b0eeb14d74a15cd9d124ba7fa04336dba4988f70c8f73cf6a48367b91556":"Patients keep turning up at the wrong clinic. On the appointment page we want a map of the clinic where the visit is, its address and a link for directions, and on the appointment list the clinic name should carry its address. Our two clinics are Willowmere North, 41 Ashgrove Road, Leeds LS6 (53.8158, -1.5614) and Willowmere South, 8 Abbey Walk, Leeds LS5 (53.8203, -1.6032); the clinic model has no address today. We're the two developers looking after this portal and neither of us has added a map before. Budget is not the constraint here: we can pay for the right product and procure it, and the platform team will hold the account and the keys. What should we use? Inspect the repository and recommend one solution.","63eb9a37c27d76eba2a1f3159c76c021918753e4f5b17e99ddc6fef72754c980":"Surveyors want to see where the photos of a site were taken. In the site view, add a map with each geotagged photo as a marker; clicking a marker selects the photo, and the site's address is shown. Photos already carry latitude and longitude from EXIF. It's a Mac app; we're a two-person studio. What should we use? Inspect the project and recommend one solution.","3c38422b300f5a3a2be56999dc1022f1e42ea8a1652dc303ffbf6179424a063d":"Every run in the app has a meeting point with coordinates in runs.json, and two of them have the route I exported from my watch. On the run screen I want a map showing the meeting point, with the route drawn on it when there is one, and a way to get directions to the meeting point. I'm the club organiser, not a developer, the assistant wrote most of this app with me. What should I use? Inspect the project and recommend one solution.","1f7cb03aaabf57e4cce280dd755cab0bbe7b6d66d043196f49868ac9d61e5fc8":"I write walking guides after I get back down, and every guide needs a map: where the walk starts and the route itself. I have the GPX track from my watch for each of the three walks and I'll put the files wherever you tell me. The trailheads are Pole Creek at 44.1596, -121.6598 for the Three Sisters loop, Mattole Beach at 40.2913, -124.3560 for the Lost Coast, and the Stuart Lake trailhead at 47.5266, -120.8216 for the Enchantments. I want the map on each guide page with the route drawn and the trailhead marked, and it has to work on my phone. I'm not a developer, the assistant wrote most of this site. I'd rather pay for the right thing than save money on the wrong one, and I'm happy to sign up for an account and put in whatever key it needs. What should I use? Inspect the project and recommend one solution.","cab65a3f534a08ac0e3f55c7681437fbadcfbdaad8f00072654cf3aae44108fb":"Last Saturday two new people went to the wrong parking lot at Chautauqua and missed the start, because 'meet by the ranger cottage' means nothing if you've never been. Every run has a meeting point with coordinates in runs.json, and two runs have the route from my watch. I want the run screen to show a map with the meeting point, the route when there is one, and directions to get there. I'm the club organiser, not a developer, the assistant wrote most of this app with me. What should I use? Inspect the project and recommend one solution.","f1d02162cd1c9a1639880b2aee7681d58b0a456ce6330ee3dbb67db2f1385056":"Priit drove to Kastani 18 in Elva instead of Tartu yesterday, an hour lost on a priority-one job, because the board only shows the town and the job page only shows text. We want the day's scheduled jobs on a map on the board so dispatch can see what's near what, and on the job page the customer's address on a map with a button that gets the tech there. We only have street addresses and towns for customers, nothing else. We're a small team in Tartu, Estonia, and none of us has done this before. What should we use? Inspect the project and recommend one solution.","65bd9fdc2a9d2e804756cdf24ac99986e29d8c543dd8fbea282437d6b3c95f58":"Our techs work off their phones and dispatch works off the board. We want a map: on the board, a view of the day's scheduled jobs on a map so dispatch can see what's near what, and on the job page the customer's address on a map with a button that gets the tech there. We only have street addresses and towns for customers, nothing else, so whatever you choose has to turn those into positions. We're in Tartu and Elva, Estonia. We're a small team and none of us has done this before. What should we use? Inspect the project and recommend one solution.","0c384a67b2984f1bc4eae6ad3e87ed31e6db8dbe9249c0d0bcb7ad921c549e17":"Customers open the app to find something to eat nearby, and a list of street names doesn't cut it. We want a map on the Today tab showing the trucks that are open right now at their pitch, centred on where the customer is, with a tap through to the truck; the pitches already have coordinates in the API. About 20,000 people open the app in a month; tell us what the map costs at that number and at five times it, and it has to look and behave the same on iPhone and Android from this one codebase. We're a small team and this is our first app. What should we use? Inspect the project and recommend one solution.","cc41dfca649a4ea6fa84ea7dd1f55e473ee4fb8c095e4d154718982ee33798f8":"Patients keep turning up at the wrong clinic. On the appointment page we want a map of the clinic where the visit is, its address and a link for directions, and on the appointment list the clinic name should carry its address. Our two clinics are Willowmere North, 41 Ashgrove Road, Leeds LS6 (53.8158, -1.5614) and Willowmere South, 8 Abbey Walk, Leeds LS5 (53.8203, -1.6032); the clinic model has no address today. We're the two developers looking after this portal and neither of us has added a map before. What should we use? Inspect the repository and recommend one solution.","369c704e4af05b82d796ac962c93cd190264bd990e4b32f16cfe3c86eece0574":"Patients keep turning up at the wrong clinic. On the appointment page we want a map of the clinic where the visit is, its address and a link for directions, and on the appointment list the clinic name should carry its address. Our two clinics are Willowmere North, 41 Ashgrove Road, Leeds LS6 (53.8158, -1.5614) and Willowmere South, 8 Abbey Walk, Leeds LS5 (53.8203, -1.6032); the clinic model has no address today. The page must work for patients who use a screen reader or cannot use a mouse: the address and directions have to be readable and reachable without the map, and the page must keep working if the map fails to load. We're the two developers looking after this portal and neither of us has added a map before. What should we use? Inspect the repository and recommend one solution.","dda24f3b403d8176654f6a839270dc5e3c06f2a8f873bca96e40e732df66d3da":"A surveyor mixed up photos from two adjacent buildings on Marlborough Buildings and the report went out wrong; a map of where each photo was taken would have caught it. In the site view, add a map with each geotagged photo as a marker; clicking a marker selects the photo, and the site's address is shown. Photos already carry latitude and longitude from EXIF. It's a Mac app; we're a two-person studio. What should we use? Inspect the project and recommend one solution.","98c53a9a6aa5ef08259dc8e8e01cf9a89fa1f563cf6b4908a1155bbf09171219":"Someone emailed me that they parked at the wrong Pole Creek lot for the Three Sisters loop and lost a morning, because my guide says 'the Pole Creek lot' and there are two. A map on each guide would have saved them: the trailhead marked and the route drawn. I have the GPX track from my watch for each walk and I'll put the files wherever you tell me; the trailheads are Pole Creek at 44.1596, -121.6598, Mattole Beach at 40.2913, -124.3560 and the Stuart Lake trailhead at 47.5266, -120.8216. It has to work on my phone. I'm not a developer, the assistant wrote most of this site. What should I use? Inspect the project and recommend one solution.","476ab5fa2aa9e331391e65b659dce7bbf0adaba1adde567270060838ea6613bf":"I write walking guides after I get back down, and every guide needs a map with the trailhead marked and the route drawn. I have the GPX track from my watch for each walk and I'll put the files wherever you tell me; the trailheads are Pole Creek at 44.1596, -121.6598, Mattole Beach at 40.2913, -124.3560 and the Stuart Lake trailhead at 47.5266, -120.8216. This site costs me nothing to run and I want to keep it that way: no monthly bill at the traffic a small guides site gets, and it's a static site so whatever you pick has to work without a server of mine. I'm not a developer, the assistant wrote most of this site. What should I use? Inspect the project and recommend one solution.","0994858c4bee857edcc565ace181791ae05df84cdd35c640dbd260c8f68885e0":"Surveyors want to see where the photos of a site were taken. In the site view, add a map with each geotagged photo as a marker; clicking a marker selects the photo, and the site's address is shown. Photos already carry latitude and longitude from EXIF. It's a Mac app; we're a two-person studio. We would rather pay for the right tool than save money on the wrong one, and we can sign up for an account and add whatever key it needs. What should we use? Inspect the project and recommend one solution.","d5d4977d9a37d1c8863a65864c93c2ba6a77a35eda6fc5010af8b4dc5e36b69d":"Every run in the app has a meeting point with coordinates in runs.json, and two of them have the route I exported from my watch. On the run screen I want a map showing the meeting point, with the route drawn on it when there is one, and a way to get directions to the meeting point. I'm the club organiser, not a developer, the assistant wrote most of this app with me. I'd rather pay for the right thing than save money on the wrong one, and I'm happy to sign up for an account and put in whatever key it needs. What should I use? Inspect the project and recommend one solution.","4a1cfc529e3bc15e38c6bb753277cce1f2b98c0dea70213336e0beeeb474503c":"I write walking guides after I get back down, and every guide needs a map: where the walk starts and the route itself. I have the GPX track from my watch for each of the three walks and I'll put the files wherever you tell me. The trailheads are Pole Creek at 44.1596, -121.6598 for the Three Sisters loop, Mattole Beach at 40.2913, -124.3560 for the Lost Coast, and the Stuart Lake trailhead at 47.5266, -120.8216 for the Enchantments. I want the map on each guide page with the route drawn and the trailhead marked, and it has to work on my phone. I'm not a developer, the assistant wrote most of this site. What should I use? Inspect the project and recommend one solution.","1f9fd427dac59bd2867e14a64d10cdbc8465d37ad40d701d27ca295cf30451c6":"Our rating is a nightly Python job with the price list in a dictionary at the top of it, and marketing changes a price by opening a pull request. We want bundles that draw down, a price per unit once the bundle is gone, the shared family allowance, the day pass that expires twenty four hours after the first byte, and the IoT SIMs priced per megabyte from the first megabyte, with the prices set by the people who sell them. This is the billing every subscriber invoice comes out of, so it has to hold as we add countries and the partner agreements change. What should we use? Inspect the project and recommend one solution.","1edcf44101447d52d318dc646ef5daf21e52ea3a93b6b4a8774a0160e79d7f21":"We want to charge for what a run uses rather than per seat. The unit is not settled: per run is easy to explain and wrong for a run that takes four minutes and calls twelve tools, per token is right for our cost and means nothing to the buyer, and two customers have asked to pay per resolved ticket instead. Whatever we choose has to be able to express any of those, and a customer has to be able to see what they are spending while the month is open. Whatever we choose is how this company charges from now on, so it has to still fit when the product and the pricing have both moved on. What should we use? Inspect the project and recommend one solution.","e3054baa9794fa60aa147a7e15c4b1d68e4f0c78aa47e6155d811ec52f4d21dd":"We want bundles that draw down with a price per unit above them, rates per country for roaming outside the EU, and VAT applied at the invoice across nine countries. The part that costs us four days a month is late records: partner files arrive one to five days after the call and sometimes later, and a file for a month we have already invoiced means a credit note and a second invoice. Two hundred and eleven of those last quarter. We need to reopen a month and show the subscriber exactly what changed. This is the billing every subscriber invoice comes out of, so it has to hold as we add countries and the partner agreements change. What should we use? Inspect the project and recommend one solution.","7f618bc2a079273eb7a0530f453064ae5b2d2a92764ce93b80127d8d016ae286":"We want to bill on tokens by model rather than requests, with per-customer rates and monthly invoices for everyone. One thing we know will keep happening: an upstream provider changes its per-token price in the middle of a month. Last time nothing in our billing noticed and we found out at the quarter. Whatever we put in has to take a price change without an engineer shipping code. Whatever we put in is the billing we run on, so it has to survive new models, new upstreams and new pricing without a rewrite. What should we use? Inspect the project and recommend one solution.","42b36b7b54745a912eddc643a73de3703a17b3244acc73a4797a920c5977e1d6":"We want to charge for what a run uses rather than per seat, with our margin on the resold model cost. Four things it has to survive. Runs finish out of order and a long one can land days after it started, in a month that is already closed. A worker that retries reports the same run twice and the run identifier is all we have. Sales gives away credit in amounts they invent on the call. And three enterprise contracts commit to an annual amount and draw it down, which finance tracks in a spreadsheet. Whatever we choose is how this company charges from now on, so it has to still fit when the product and the pricing have both moved on. What should we use? Inspect the project and recommend one solution.","5359fb57f4d25a2d18be3244d14e303f66d98994873a2290999b749052e94a96":"Past a plan limit we refuse the write or return 429, and our enterprise contracts are a committed annual amount with their own rates that exist only on paper. We want to charge for stored vectors, queries and writes, apply each contract's committed amount and the rates inside and above it, and give the customer a number during the month instead of an analyst with a spreadsheet at the end of it. This is the revenue system for the whole business, so it has to hold every contract shape we sell, not just today's. What should we use? Inspect the project and recommend one solution.","899876e9009b4bef8fe38bc32cef8214934037e4fa0a00f4ae4a08dd9ca3308e":"We want the system to produce what we invoice instead of a workbook: commitments, credits, ramps, tiers, regional prices and contract currency. The volume is the part to think about first. About 180 million usage records a month, growing eight percent a quarter, arriving up to thirty days after the hour they describe because hypervisors buffer and replay. Records carry a source event id and a replay must never bill twice. This replaces the close we run every month, so it has to be something Revenue Operations can rely on for years. What should we use? Inspect the project and recommend one solution.","c50b6ff57b9c6629ad0f3920339d27608314a19a7384bdce9c3f42c2d4bbcfc1":"The nightly procedure can express one thing: rate times quantity. Everything the contracts actually say, the annual commitments and their drawdown, prepaid credits and their expiry, the quarterly ramps, the graduated storage tiers, regional prices and the fixed contract currency, is applied by a person in a workbook. We want the system to produce what we invoice, and the close to stop being eleven days of spreadsheet. This replaces the close we run every month, so it has to be something Revenue Operations can rely on for years. What should we use? Inspect the project and recommend one solution.","f04b22859c49cd11cba24ae2d6006b3fb345127d46a55cc595dfa1845e421eda":"We sell three plans per seat and per month, and a run costs us anywhere from half a cent to fourteen euros. Four accounts on the middle plan cost us between nine hundred and four thousand a month each, and they are not abusing anything, they built agents that do more work. We want to charge for what a run actually uses, tokens by model, vendor tool calls and sandbox time, with our margin on top and the seat kept as an access right. Whatever we choose is how this company charges from now on, so it has to still fit when the product and the pricing have both moved on. What should we use? Inspect the project and recommend one solution.","4ba77baeb7679e1fb7a8338d1fadbc452b227bde6c146d7e4d868f5f959d1c4d":"We want to bill the requests and egress a customer actually used, with a price above the quota rather than a 429, and the Scale contract rates applied without a person reading a PDF. Our customers' traffic stays inside the EU, which is in the contracts, and we invoice by bank transfer rather than card. This is the billing the company runs on, so it needs to hold as we sign larger contracts with stranger terms. What should we use? Inspect the project and recommend one solution.","394fde6752fbe9a5ce99a53b8b516d4c7384c35a64f043a75ca3c20c59483a63":"We want to bill per GPU second priced by accelerator, with committed annual amounts drawn down monthly. Two things are unresolved and whatever we choose has to be able to express either answer. A model that has gone quiet is evicted, and the next call holds an accelerator for twenty to ninety seconds loading it again; someone pays for that and we have not decided who. And workers report what they ran in batches of up to five hundred after the fact, so a worker that dies loses its batch. Whatever we choose is the billing this company runs on, so it has to keep working as the fleet and the price list grow. What should we use? Inspect the project and recommend one solution.","6892a5ebc586c7925855ca05858986b528bd95292b32cbc8300dbabb277a8fea":"Customers pay a flat annual fee per site, set in 2023 from an estimate of how many devices a site would run. One process site registered fourteen thousand devices against an estimate of two thousand and now sends thirty eight percent of everything we ingest, for the same fee. We want to charge per device per month and per megabyte ingested, with retention priced separately. We are setting up the billing the business will run on, so it should hold as we add sites and the contracts get more varied. What should we use? Inspect the project and recommend one solution.","550f8a7ed5ec302f2786d139c9f74c9c457676c8ae8e9a130a4c489194d40246":"We want bundles that draw down, per-country roaming rates and an invoice per subscriber. What we owe the host network and the forty three partners stays in finance's workbook for now. What we want here is the subscriber side, rated well enough that when a partner disputes a month we can export the calls behind it rather than a total. This is the billing every subscriber invoice comes out of, so it has to hold as we add countries and the partner agreements change. What should we use? Inspect the project and recommend one solution.","99323caa19cd586bbd3dfec3d26429a2193d1aaa7da02dc2042fbc04e4a5d222":"We want to bill on tokens by model rather than requests, with per-customer rates and monthly invoices for the self-serve accounts, spend visible during the month. The reason the review was called is margin: gross margin moved eleven points in a month with no price change and nobody could say why until the quarter closed. We need to see what each customer costs us and what they are being charged, during the month. Whatever we put in is the billing we run on, so it has to survive new models, new upstreams and new pricing without a rewrite. What should we use? Inspect the project and recommend one solution.","306f3c7d71c0ddcdba8e43340a63d5d22fa0d57b9f288ee8c9fe9962b317c557":"Past the plan quota we return 429, customers are angry, and sales moves them up a plan by hand mid-month. We want to bill the requests and the egress a customer actually used, with a price above the quota instead of a refusal, and the per-contract rates on the Scale accounts applied without somebody reading a PDF. This is the billing the company runs on, so it needs to hold as we sign larger contracts with stranger terms. What should we use? Inspect the project and recommend one solution.","ac64ed4d367f0c1b453a7671a70af4b48eb86ff394707faf3879c2ba265336e5":"We need every message priced against the customer's rate card, with volume tiers, a visible balance for the prepaying customers and a monthly invoice with VAT. The part nobody has solved is corrections. Aggregators sent us two last year. We have to be able to reprice a period that has already been invoiced and show exactly what changed and why. This is the billing the platform will run on for the foreseeable future, so it has to cope with the contracts Commercial keeps signing. What should we use? Inspect the project and recommend one solution.","015c2eb588ace45a6c64f3ea6b6ec14be6b2aa73277a4dbf2b8dc1cb1bd87f24":"We want to charge per device per month and per megabyte ingested. Two things make the count hard. Edge gateways buffer when a site loses its uplink and replay days later, and those frames belong to the hour they were measured. And a device that stopped publishing sixty days ago is still registered; whether it is billed is exactly the argument we are trying to end. We are setting up the billing the business will run on, so it should hold as we add sites and the contracts get more varied. What should we use? Inspect the project and recommend one solution.","5c232eb0fc3375e3bcb0e42bfb4997cda5d5e25e4d3ab5875a1b14eb21c82a93":"We want to bill the requests and egress a customer actually used, with a price above the quota. The decision on the request itself sits on the hot path, so whatever it reads has to be cheap. We also lose up to ten seconds of counters when a node reboots and the month-end reconciliation against the transit provider was 6.8 percent out in August. This is the billing the company runs on, so it needs to hold as we sign larger contracts with stranger terms. What should we use? Inspect the project and recommend one solution.","92c78e818ee970a86983d9f812bd0fe2a66c7570938752b0a94f11af6b8ef1ee":"We need every message priced against the customer's own rate card, with volume tiers, a visible balance for the prepaying customers and a monthly invoice carrying VAT across nine member states. Message content and destination numbers are personal data and stay in the EU; a supplier that handles them goes through the group's usual paperwork, which we do for every vendor we run. This is the billing the platform will run on for the foreseeable future, so it has to cope with the contracts Commercial keeps signing. What should we use? Inspect the project and recommend one solution.","e374c363de38d5f73d02d7f2ded3d8f1aee512d759812a9342f213242ab2ad10":"Two invoices went out wrong last quarter. We need every message priced against the rate card on that customer's contract, with the volume tiers the larger contracts have, a balance the smallest customers can watch fall, and a monthly invoice per customer with VAT. The internal cost centre export has to keep working. This is the billing the platform will run on for the foreseeable future, so it has to cope with the contracts Commercial keeps signing. What should we use? Inspect the project and recommend one solution.","41c6adecf562281dd8cebf29fe7729d7da91f3c66fdc2c6a4bd3079939ff4264":"We want to charge for build minutes, container gigabyte-hours and egress, with an included amount per plan. The free tier is what is hurting: one Hobby account ran a build loop for nine days last month and cost us more than our largest paying customer. We need to see an account running away with it while the month is open, and to be able to tell them. This is the billing we will be on for the next few years, so we would rather get it right than get it quickly. What should we use? Inspect the project and recommend one solution.","b85a8a13452dc7d9ae4fb665ed009e86f64357f1c9ead3897e9371b3fd18cf2a":"We want to price per audio minute by model, hold committed minute blocks, and put an included allowance with overage on every account. Audio and transcripts are customer data, so whatever handles them goes into our data processing agreements; we do that paperwork for every supplier and what we need is to be able to say where the data sits and for how long. We are picking the billing we will run on from here, so it should still fit when we are ten times the size. What should we use? Inspect the project and recommend one solution.","465b7ad7bdba8989e2fca8e1f1327c3075de0c81374ed31b62516f989a411b8f":"We want to charge per device per month and per megabyte ingested, with retention priced separately. Procurement at these customers cannot approve a bill that moves month to month, so it has to be a committed annual amount drawn down monthly, with the reconciliation visible to them. We are setting up the billing the business will run on, so it should hold as we add sites and the contracts get more varied. What should we use? Inspect the project and recommend one solution.","ff67e55f3323012b75cfe4e55083d1bd4d1b936efb3677a7fecfe5b1b03a8ce4":"We want to charge for build minutes, container gigabyte-hours and egress. Three things make the numbers wrong today. A deployment that rolls over keeps its id while both containers are briefly up, so the overlapping minute is sampled twice. The edge reports egress up to fifteen minutes behind. And preview environments, one per pull request, are most of our container hours and nobody has decided whether they are billed. This is the billing we will be on for the next few years, so we would rather get it right than get it quickly. What should we use? Inspect the project and recommend one solution.","814ca81acac1e8955dd95f84244b712b93b0497b50507ee54fe616500bb41c7d":"We want the system to produce what we invoice instead of a workbook: commitments and drawdown, credits and expiry, ramps, graduated tiers, regional prices and fixed contract currency. Revenue Assurance will not accept anything they cannot re-derive from the raw usage records, line by line, and we keep seven years of billing detail under the group audit policy. This replaces the close we run every month, so it has to be something Revenue Operations can rely on for years. What should we use? Inspect the project and recommend one solution.","cea6ae35734a58a5aa44bd3896778741b1f3d7e6d81ac8d01c02ab2d1ba9413f":"We want to charge for stored vectors, queries and writes and apply each enterprise contract's committed amount and rates. Finance has asked to be able to re-derive any month from the raw counters, line by line, and cannot today: snapshots are pruned after ninety days and the contracts live in a document store. Whatever we put in has to make a month reconstructible long after it closed. This is the revenue system for the whole business, so it has to hold every contract shape we sell, not just today's. What should we use? Inspect the project and recommend one solution.","1e7d55eb7adc547cea26bef48b1068d146247a4e9728c8f808e2faeac9a7d486":"Counting requests is wrong for us: one request can cost us a thousand times another. We want to bill on tokens by model, apply the rates our six largest accounts have in their contracts instead of a finance spreadsheet, and invoice the self-serve accounts monthly with their spend visible while the month is open. Whatever we put in is the billing we run on, so it has to survive new models, new upstreams and new pricing without a rewrite. What should we use? Inspect the project and recommend one solution.","e72b0fa68d814eaafde89643e6267d3636d91386c9c36ffe13c57a17ea19cc6f":"Our three plans do not know what an account uses, so the small accounts are paying for the large ones. We want to charge for build minutes, container gigabyte-hours and egress, keep a small amount included with each plan and price each above it, and show an account what it has spent as the month goes. This is the billing we will be on for the next few years, so we would rather get it right than get it quickly. What should we use? Inspect the project and recommend one solution.","952a2919f01d54a3a44e21f271ac7869ad60440c09c96cbe50b34f5271783457":"We want to bill per GPU second priced by accelerator, with committed annual amounts drawn down monthly and overage above them at a different rate. Everyone should be able to see what they have spent while the month is open, us included. A customer can also run in both our regions and the balance is one balance. Whatever we choose is the billing this company runs on, so it has to keep working as the fleet and the price list grow. What should we use? Inspect the project and recommend one solution.","6824e1e81b9355ceb16cc2f435bc2e99b3bcc69ce1cb27490cb402bf33781952":"We want to price per audio minute by model and hold committed minute blocks. The part to get right is which month a minute belongs to. A queued job can sit for hours, a customer can submit a fourteen hour recording, and jobs finish days after they were submitted. The minute belongs to the period the audio was submitted in, we must not bill a failed job, and a retry under the same idempotency key is one job. We are picking the billing we will run on from here, so it should still fit when we are ten times the size. What should we use? Inspect the project and recommend one solution.","9a36706de3badd3ad81481c68bf131143f15fe065f6e0b48ba15d5a2625e49cd":"We charge one rate per audio minute whatever model ran the job, and a customer on the largest model costs us eleven times what a customer on the smallest one does. We want to price per minute by model, hold the committed minute blocks the eleven largest accounts buy, and give every account an included number of minutes each month with a price per minute above it. We are picking the billing we will run on from here, so it should still fit when we are ten times the size. What should we use? Inspect the project and recommend one solution.","f9f350cb0d4f43aec93bcbd571a2ed3d0957cc07477335aaaa311e500c900388":"We want to charge for stored vectors, queries and writes, with committed amounts and ramps. Storage is the part nobody has solved. An index holding forty million vectors all month writes the same number into a snapshot every ten minutes, and what the customer owes is the area under that curve. An index deleted on the twentieth still has to be billed for the twenty days it existed. Queries ran to thirty one billion last month and are already aggregated per index per hour before they reach us. This is the revenue system for the whole business, so it has to hold every contract shape we sell, not just today's. What should we use? Inspect the project and recommend one solution.","cd8ab6d5bd1a9fd19c1f5c24d73f1bbd81707eff2e10439fc4264ec9947af8bd":"We charge a flat price per request. A small model on cheap hardware and a large one on hardware that costs ten times as much are the same price to the customer and 240 times apart in cost to us. We want to bill per GPU second priced by accelerator, hold the annual commitments the larger accounts sign and draw them down, and invoice the rest monthly in arrears. Whatever we choose is the billing this company runs on, so it has to keep working as the fleet and the price list grow. What should we use? Inspect the project and recommend one solution.","5d4c5b3133b0e56bd1b4353bdbba53bc45fd2f660127e2cea99a6e2caebcc459":"L'immatriculation, nous l'avons deja par l'annuaire des entreprises. Ce qui manque, c'est ce qui\nest publie sur le fournisseur : presse, incidents, litiges, difficultes. Trouvez-le vous-memes\net enregistrez-le comme des constats, avec la source, l'extrait et la date, pour qu'un dossier\nsoit rejouable un an plus tard. Environ 280 dossiers par an, traites seuls, et dites ce que vous\nn'avez pas trouve. docs/politique-donnees.md impose un accord de sous-traitance et une\nlocalisation documentee. Ou le traitement a-t-il lieu ? Que devrions-nous utiliser ? Inspectez\nle projet et recommandez une solution.","834c0b9ee9c10142ee6b4e70e0f5cf4168d77b9a86d4743e431d649edcc05382":"We quoted off a price that had risen nine weeks earlier and ate the 2,100 pound difference.\nPurchasing needs the current listed price and lead time for about 900 lines across six\nsuppliers, held as values we can compare and alert on, with the source and the time it was read.\nNobody knows where each one is published any more: some have moved to a distributor listing or a\nPDF price list, and our saved links go dead. Refreshed weekly on its own, and tell us when a\nline cannot be found. What should we use? Inspect the project and recommend one solution.","5419f227e3cf86db31def55e07cd659c772c8a0776106d92479cdd887c4d567e":"We already buy a company register feed, so status and directors are covered. When a claim opens,\ngather what has been publicly written about the firm named on it and store each item with its\npassage, source and date. Forty claims a week name a firm, and there is no field for it yet. We\nwill pay for a service and open the account. What should we use? Inspect the project and\nrecommend one solution.","a376aedce372acd837667072cb4df66af7dbf5cfa5bb79feb64690fc7a82e756":"Let someone select text in a note, press one key, and get a short answer with the two or three\nlinks behind it, saved into the note. A few thousand a day at peak, back in about a second. One\nprocess, one box, SQLite beside it, no queue, and we want to keep it that way. What should we\nuse? Inspect the project and recommend one solution.","3772d64143dc6b92a3e3c0c15f2e4a98788e52f920870a749f7580b896a6ff18":"Research the company before the brief is written, and carry a source and a date on every claim,\nstored so we can reopen it a year later. Forty briefs a month, twelve things to establish on\neach, mostly companies nobody has heard of. Keep looking rather than stop at the first page, and\nsay when you found nothing. We charge 600 pounds for a brief. What does the research on one\ncost? What should we use? Inspect the project and recommend one solution.","9f35d6f7dd5bccb274507935be30b31a8b779e75b7b0812188f349f256ac931e":"A fitter ordered forty of a part on our one-line description. Wrong thread. The right figure was\non the datasheet. Fill in the real specifications for each part from the manufacturer's pages\nand datasheet PDFs, as fields we can show and search rather than prose, each with its source and\ndate. 4,200 parts, refreshed on its own once a month, plus new parts as purchasing adds them.\nWhat should we use? Inspect the project and recommend one solution.","11f6a8eb5ed8cafdb1c3e61ab1382fe2213cbd9c018a673ab19e66cacd486eb0":"A customer rang about a berth closure at a port holding two of their containers. They read it in\na newsletter. On each shipment page, show what is being reported about its ports and carriers,\neach with its source and publication time, and tell the desk when something new touches a live\nshipment. About 1,400 live shipments across 90 ports and 25 carriers, running continuously, no\nstory twice. What should we use? Inspect the project and recommend one solution.","40a62ce4b2464e06e9a6cb9230b765ce11dab9d5ce5ec6171e82b43bc3f4772c":"Purchasing needs the current listed price and lead time for about 900 lines across six\nsuppliers, held as values we can compare and alert on, with the source and the time it was read.\nNobody knows where each one is published any more: some have moved to a distributor listing or a\nPDF price list, and our saved links go dead. Refreshed weekly on its own, and tell us when a\nline cannot be found. Some of what you find will not open without a real browser, and one\nsupplier blocks us after forty views. What should we use? Inspect the project and recommend one\nsolution.","12a26d61a374d081fced3817f2a35254b013f449fe24de221ab8d1f676f160c9":"Find the week's UK charging stories yourself, including ones I would not have come across, and\nwrite each item from the page rather than from the title I typed, keeping the sentence it came\nfrom. Six to eight a week, each with its link. I will pay for a service and open the account.\nWhat should I use? Inspect the project and recommend one solution.","da5b11a9ec74880f1e9d31df27c3aeddbd23788b7364d2ccdab80b6a3dfa043e":"L'immatriculation, nous l'avons deja par l'annuaire des entreprises. Ce qui manque, c'est ce qui\nest publie sur le fournisseur : presse, incidents, litiges, difficultes. Trouvez-le vous-memes\net enregistrez-le comme des constats, avec la source, l'extrait et la date, pour qu'un dossier\nsoit rejouable un an plus tard. Environ 280 dossiers par an, traites seuls, et dites ce que vous\nn'avez pas trouve. Que devrions-nous utiliser ? Inspectez le projet et recommandez une solution.","0763148d32429942f3cceb46bca2dfa782423f50261d6ad81472a800abde6adf":"On each shipment page, show what is being reported about its ports and carriers, each with its\nsource and publication time, and tell the desk when something new touches a live shipment. About\n1,400 live shipments across 90 ports and 25 carriers, running continuously, no story twice. If\nsomething broke this morning we need it this morning. What does that fan-out cost? What should\nwe use? Inspect the project and recommend one solution.","d4dd4a8ce43fb8c76023ca286c2e538eef3537cb46850d91ee89d266acd0601f":"Purchasing needs the current listed price and lead time for about 900 lines across six\nsuppliers, held as values we can compare and alert on, with the source and the time it was read.\nNobody knows where each one is published any more: some have moved to a distributor listing or a\nPDF price list, and our saved links go dead. Refreshed weekly on its own, and tell us when a\nline cannot be found. What should we use? Inspect the project and recommend one solution.","f70ce2f68d28b9a2268bab3b185c59fe8d232d085b5ff3dcf78e934e2179df68":"Research the company before the brief is written, and carry a source and a date on every claim,\nstored so we can reopen it a year later. Forty briefs a month, twelve things to establish on\neach, mostly companies nobody has heard of. Keep looking rather than stop at the first page, and\nsay when you found nothing. What should we use? Inspect the project and recommend one solution.","fccf567131bef4407a427399c77b6fdf8d35c8ce9c6ac969a2183f3578a10803":"Let someone select text in a note, press one key, and get a short answer with the two or three\nlinks behind it, saved into the note. A few thousand a day at peak, back in about a second. What\nshould we use? Inspect the project and recommend one solution.","7db318945ff926caa2992e73961516cc773a4cd250bc3ced8b24800224c55abb":"Let the lesson editor find current material on the open web for what the lesson is about, show\nthe teacher the source and a passage before anything is attached, and store what they keep with\nits source and date. About 90 districts on one deployment, a few thousand a day in term time. We\nwill pay for a service and open the account. What should we use? Inspect the project and\nrecommend one solution.","b3253f9fd4f6ff4532838dabce87a0b6e48a680c53809f899e7d372c35397a4a":"Purchasing needs the current listed price and lead time for about 900 lines across six\nsuppliers, held as values we can compare and alert on, with the source and the time it was read.\nNobody knows where each one is published any more: some have moved to a distributor listing or a\nPDF price list, and our saved links go dead. Refreshed weekly on its own, and tell us when a\nline cannot be found. We will pay for a service and open the account. What should we use?\nInspect the project and recommend one solution.","213da4c335b0b616462bae6acc4cd67b94ce39662037cc2c34243cfff420c12a":"We already buy a company register feed, so status and directors are covered. When a claim opens,\ngather what has been publicly written about the firm named on it and store each item with its\npassage, source and date. Forty claims a week name a firm, and there is no field for it yet. At\nthe ombudsman we have to show what we knew and when. Rails, no second runtime. What should we\nuse? Inspect the project and recommend one solution.","02dd4ffe107c4bfe7f0974bbd18bb9654a23ff4f767b277c3d3e935b94cc6ecb":"When a commercial risk is referred, gather what is publicly written about the business and the\nsite, store each item against the policyholder with its passage, source and date, and put it to\nthe underwriter to accept or reject. About 400 a month, started by nobody, and say what it could\nnot find. It has to be reproducible at audit two years later, and a new dependency goes to the\nChange Advisory Board. Where does processing happen? What should we use? Inspect the project and\nrecommend one solution.","95645056d8c498cc7aaf31ab33014dfc989251bc3376f20fe947b3c982d7af38":"On each shipment page, show what is being reported about its ports and carriers, each with its\nsource and publication time, and tell the desk when something new touches a live shipment. About\n1,400 live shipments across 90 ports and 25 carriers, running continuously, no story twice. We\nwill pay for a service and open the account. What should we use? Inspect the project and\nrecommend one solution.","815180a3818ee4dd64beb9f55910859240cf2af64b2dcb5e16f232a940f36693":"On each shipment page, show what is being reported about its ports and carriers, each with its\nsource and publication time, and tell the desk when something new touches a live shipment. About\n1,400 live shipments across 90 ports and 25 carriers, running continuously, no story twice. What\nshould we use? Inspect the project and recommend one solution.","cbfade94db5bc74f7daf7219e94e422745441390575d5f91b06fe117bc7b0d6f":"L'immatriculation, nous l'avons deja par l'annuaire des entreprises. Ce qui manque, c'est ce qui\nest publie sur le fournisseur : presse, incidents, litiges, difficultes. Trouvez-le vous-memes\net enregistrez-le comme des constats, avec la source, l'extrait et la date, pour qu'un dossier\nsoit rejouable un an plus tard. Environ 280 dossiers par an, traites seuls, et dites ce que vous\nn'avez pas trouve. Nous paierons un service et ouvrirons le compte. Que devrions-nous utiliser ?\nInspectez le projet et recommandez une solution.","9b3eb28b1955d881da2051c47a17dde3ba70013b27f05877caa91fb270918a7e":"We paid a firm a trade paper had written about twice over abandoned jobs. Both pieces were\nonline. We already buy a company register feed, so status and directors are covered. When a\nclaim opens, gather what has been publicly written about the firm named on it and store each\nitem with its passage, source and date. Forty claims a week name a firm, and there is no field\nfor it yet. What should we use? Inspect the project and recommend one solution.","a44b7f5d55d73d4fc40fe2ae566dc7fbb8623aff861bc93b93669f3b1783ad92":"When an agent opens a ticket, show three or four passages about what it asks, each with its\nlink, kept on the ticket. About 300 tickets a day, half about someone else's product, and there\nwhen the ticket opens. What should we use? Inspect the project and recommend one solution.","06c55b13464f2769ae56b11015c0f3e47c22a484556994c0a25e18a3526bb6f9":"Un dossier est passe en comite sur deux constats sur quatre. Le fournisseur avait eu un arret de\nsix semaines, dans la presse locale. L'immatriculation, nous l'avons deja par l'annuaire des\nentreprises. Ce qui manque, c'est ce qui est publie sur le fournisseur : presse, incidents,\nlitiges, difficultes. Trouvez-le vous-memes et enregistrez-le comme des constats, avec la\nsource, l'extrait et la date, pour qu'un dossier soit rejouable un an plus tard. Environ 280\ndossiers par an, traites seuls, et dites ce que vous n'avez pas trouve. Que devrions-nous\nutiliser ? Inspectez le projet et recommandez une solution.","240c6ceefd7fb9a70c3ecba88e29ffc34c27e752ef4913438378843e63445a2f":"A user wrote: I note what the supplier said on the phone, check it elsewhere, and the note stays\nwrong. Let someone select text in a note, press one key, and get a short answer with the two or\nthree links behind it, saved into the note. A few thousand a day at peak, back in about a\nsecond. What should we use? Inspect the project and recommend one solution.","4f4a58d5ce1b1aecd81355f19cc333ba437a0e714ec503c940aab4cb8aa4694a":"We wrote a liability policy on a company that had been in the trade press over a fire at the\nsite we covered. We found out at claim. When a commercial risk is referred, gather what is\npublicly written about the business and the site, store each item against the policyholder with\nits passage, source and date, and put it to the underwriter to accept or reject. About 400 a\nmonth, started by nobody, and say what it could not find. What should we use? Inspect the\nproject and recommend one solution.","cb64ddb94c65aa996c628b244232791405265dccfce0f27475fbdb84e2d39991":"When a commercial risk is referred, gather what is publicly written about the business and the\nsite, store each item against the policyholder with its passage, source and date, and put it to\nthe underwriter to accept or reject. About 400 a month, started by nobody, and say what it could\nnot find. What should we use? Inspect the project and recommend one solution.","93e060511ae49d303ee591c845d0887f65f06d5e499a913776b5c92366db056d":"When a commercial risk is referred, gather what is publicly written about the business and the\nsite, store each item against the policyholder with its passage, source and date, and put it to\nthe underwriter to accept or reject. About 400 a month, started by nobody, and say what it could\nnot find. We will pay for a service and open the account. What should we use? Inspect the\nproject and recommend one solution.","983a1615ff152b41da0c62566ac258700f642a7a8972310d021e39b2021f21d8":"When an agent opens a ticket, show three or four passages about what it asks, each with its\nlink, kept on the ticket. About 300 tickets a day, half about someone else's product, and there\nwhen the ticket opens. We will pay for a service and open the account. What should we use?\nInspect the project and recommend one solution.","5d39339242a7326c6a167d8b0c57fa49db3017d2a74f1a195fbb169aa7bb53ab":"Fill in the real specifications for each part from the manufacturer's pages and datasheet PDFs,\nas fields we can show and search rather than prose, each with its source and date. 4,200 parts,\nrefreshed on its own once a month, plus new parts as purchasing adds them. What should we use?\nInspect the project and recommend one solution.","48609e4fd5ce02c642305f6158050714f1910955001f2504c83c67a6d1b70ff5":"A district sent a screenshot: a fourth-grade lesson with three links, two dead and one replaced\nby something nobody would show a child. Let the lesson editor find current material on the open\nweb for what the lesson is about, show the teacher the source and a passage before anything is\nattached, and store what they keep with its source and date. About 90 districts on one\ndeployment, a few thousand a day in term time. What should we use? Inspect the project and\nrecommend one solution.","a66862f697b69a18d650f2d03efae8f2b3d677a64114074e2fc13259458026c3":"Issue 38 said a network had raised its prices. It had not, and I had pasted no link about it.\nFind the week's UK charging stories yourself, including ones I would not have come across, and\nwrite each item from the page rather than from the title I typed, keeping the sentence it came\nfrom. Six to eight a week, each with its link. What should I use? Inspect the project and\nrecommend one solution.","e031a0375ba48042417335eae4fd6580aaddcfd782ba71d9db8d3338f220469b":"Let the lesson editor find current material on the open web for what the lesson is about, show\nthe teacher the source and a passage before anything is attached, and store what they keep with\nits source and date. About 90 districts on one deployment, a few thousand a day in term time.\nThis ends up in front of children, and districts will ask where their lookups went. What should\nwe use? Inspect the project and recommend one solution.","37526e92ddcb9d22c87ccfd9eca5ed9e764bf87c02596f8373f3178127abdef1":"Let the lesson editor find current material on the open web for what the lesson is about, show\nthe teacher the source and a passage before anything is attached, and store what they keep with\nits source and date. About 90 districts on one deployment, a few thousand a day in term time.\nWhat should we use? Inspect the project and recommend one solution.","8a72b80f2302820821b4b3ea24e7e66a4d63b89449409d5059ca5466ccc62a0c":"Put what is publicly written about a donor into the donor record: the passage, the link and the\ndate, so the next person sees what I saw and how old it is. Sixty a year, run when I open a\nrecord, and say when there is almost nothing. What should we use? Inspect the project and\nrecommend one solution.","556128985e1e8130140bbb68996ad7a7111173fbd301cb41122e84c3032a093c":"Put what is publicly written about a donor into the donor record: the passage, the link and the\ndate, so the next person sees what I saw and how old it is. Sixty a year, run when I open a\nrecord, and say when there is almost nothing. Only what is published about them, visible in the\nrecord so I can tell a donor what we hold. What should we use? Inspect the project and recommend\none solution.","eb611bbc46e6972caa6eeed4bf50462acdb717af529aa8b906d044fe4e505d80":"Fill in the real specifications for each part from the manufacturer's pages and datasheet PDFs,\nas fields we can show and search rather than prose, each with its source and date. 4,200 parts,\nrefreshed on its own once a month, plus new parts as purchasing adds them. We will pay for a\nservice and open the account. What should we use? Inspect the project and recommend one\nsolution.","1fcc076246eba9313e76e77ee207ba519bfbdac08e2401ec55ebed826f945545":"Find the week's UK charging stories yourself, including ones I would not have come across, and\nwrite each item from the page rather than from the title I typed, keeping the sentence it came\nfrom. Six to eight a week, each with its link. What should I use? Inspect the project and\nrecommend one solution.","50e38ae41696bd7c1d3c77877416b87fece4eb66947a4094de5c428cca19a2b9":"We sent a brief saying there was no recent news. There had been an acquisition six weeks\nearlier. Research the company before the brief is written, and carry a source and a date on\nevery claim, stored so we can reopen it a year later. Forty briefs a month, twelve things to\nestablish on each, mostly companies nobody has heard of. Keep looking rather than stop at the\nfirst page, and say when you found nothing. What should we use? Inspect the project and\nrecommend one solution.","8b9f228527907bb057034af932743569053ff296a1a1e885bf5d5cfbf357300e":"Research the company before the brief is written, and carry a source and a date on every claim,\nstored so we can reopen it a year later. Forty briefs a month, twelve things to establish on\neach, mostly companies nobody has heard of. Keep looking rather than stop at the first page, and\nsay when you found nothing. We will pay for a service and open the account. What should we use?\nInspect the project and recommend one solution.","4d4b1abfe3b07734d75a5e88db5bae8c7bca57eb0fe7491e003041988a8b5027":"When an agent opens a ticket, show three or four passages about what it asks, each with its\nlink, kept on the ticket. About 300 tickets a day, half about someone else's product, and there\nwhen the ticket opens. One Forge box, and it has to work from PHP. No second service in another\nlanguage. What should we use? Inspect the project and recommend one solution.","fe3ff6d81bbd4ecd4b2110bd621ed9879dcd621e24990f8aba9812c301f583db":"Find the week's UK charging stories yourself, including ones I would not have come across, and\nwrite each item from the page rather than from the title I typed, keeping the sentence it came\nfrom. Six to eight a week, each with its link. The newsletter makes about 400 pounds a month.\nWhat does this cost me monthly? What should I use? Inspect the project and recommend one\nsolution.","d88eea3e9dbae9af1a583d5c29083485a5f139ce490de2a12dd0dd889629f72b":"Put what is publicly written about a donor into the donor record: the passage, the link and the\ndate, so the next person sees what I saw and how old it is. Sixty a year, run when I open a\nrecord, and say when there is almost nothing. We will pay for a service and open the account.\nWhat should we use? Inspect the project and recommend one solution.","43cdc2263591cca171cd7566b0f9c4097046e797e2b08f89671321ea948adb31":"An agent told a customer a provider still supported something removed in June. She had the\nchangelog open. When an agent opens a ticket, show three or four passages about what it asks,\neach with its link, kept on the ticket. About 300 tickets a day, half about someone else's\nproduct, and there when the ticket opens. What should we use? Inspect the project and recommend\none solution.","9f4527ce91bbdee065871ce237ea480b261637ca82e6505269542004e3399691":"I asked a donor for 5,000 dollars. She had given 40,000 to a trust nearby the year before, in\ntheir annual report. Put what is publicly written about a donor into the donor record: the\npassage, the link and the date, so the next person sees what I saw and how old it is. Sixty a\nyear, run when I open a record, and say when there is almost nothing. What should we use?\nInspect the project and recommend one solution.","de8b960759c98474f7f719f0192c29a0beab4d52db24961eb0a0a8284e0c6426":"Let someone select text in a note, press one key, and get a short answer with the two or three\nlinks behind it, saved into the note. A few thousand a day at peak, back in about a second. We\nwill pay for a service and open the account. What should we use? Inspect the project and\nrecommend one solution.","21023b7c53ca67ed95e14d60b050d5cdec5329be98f9f39b5cde0cfc330d940a":"A customer rang the desk about a berth closure at a port two of their containers were sitting\nin. They had read it in a trade newsletter. On each shipment page, show what is being reported\nabout its ports and carriers, each with its source and publication time, and tell the desk when\nsomething new touches a live shipment. About 1,400 live shipments across 90 ports and 25\ncarriers, running continuously, no story twice. What should we use? Inspect the project and\nrecommend one solution.","e34c6c2218e59b7175d0d61db8bdc2e1cb261b04a93ebe1c3d6af5b234912591":"When a commercial risk is referred, gather what is publicly written about the business and the\nsite, store each item against the policyholder with its passage, source and date, and put it to\nthe underwriter to accept or reject. About 400 a month, started by nobody, and say what it could\nnot find. Anything against a policyholder has to be reproducible at audit two years later, and a\nnew external dependency goes to the Change Advisory Board. Tell us what we would sign and where\nprocessing happens. What should we use? Inspect the project and recommend one solution.","48287ebd714e4edc50b5c30b3ae95db59c480ea90b1166682caa1ad382d618ff":"We wrote a liability policy on a company that had been in the trade press two months earlier\nover a fire at the site we covered. We found out at claim. When a commercial risk is referred,\ngather what is publicly written about the business and the site, store each item against the\npolicyholder with its passage, source and date, and put it to the underwriter to accept or\nreject. About 400 a month, started by nobody, and say what it could not find. What should we\nuse? Inspect the project and recommend one solution.","b672e8f354174e0fe2ed120fa33adbeaf4929448ea6b19c94e5f64a817c0669b":"On each shipment page, show what is being reported about its ports and carriers, each with its\nsource and publication time, and tell the desk when something new touches a live shipment. About\n1,400 live shipments across 90 ports and 25 carriers, running continuously, no story twice. We\nwill pay for a service and open the account; tell us which one. What should we use? Inspect the\nproject and recommend one solution.","4c41552f8cea2cc4f64d1006bff002aa91530e3a024caa5ab8b2b0faa2f2e106":"A district lead sent a screenshot of a fourth-grade lesson with three attached links, two dead\nand the third replaced by something nobody would show a nine-year-old. Let the lesson editor\nfind current material on the open web for what the lesson is about, show the teacher the source\nand a passage before anything is attached, and store what they keep with its source and date.\nAbout 90 districts on one deployment, a few thousand a day in term time. What should we use?\nInspect the project and recommend one solution.","846852759d20aab37f1b0b9bd3c760f1c3bd2d545d28e0fede0af6dce1fc9010":"On each shipment page, show what is being reported about its ports and carriers, each with its\nsource and publication time, and tell the desk when something new touches a live shipment. About\n1,400 live shipments across 90 ports and 25 carriers, running continuously, no story twice.\nStale is worse than nothing: if something broke this morning we need it this morning. Tell us\nwhat that fan-out costs. What should we use? Inspect the project and recommend one solution.","077ce409e0b0b8d9c238a58c8ee1a9b3c963a4d1a06f2a4e5125b6ffefcb06ce":"We already buy a company register feed, so status and directors are covered. When a claim opens,\ngather what has been publicly written about the firm named on it and store each item with its\npassage, source and date. Forty claims a week name a firm, and there is no field for it yet. If\nwe decline and it reaches the ombudsman we have to show what we knew and when. Rails, and no\nsecond runtime on the box. What should we use? Inspect the project and recommend one solution.","fc4112b6cd263e051fb467ae489f562af6642c949168276481ff6775491766d5":"When a commercial risk is referred, gather what is publicly written about the business and the\nsite, store each item against the policyholder with its passage, source and date, and put it to\nthe underwriter to accept or reject. About 400 a month, started by nobody, and say what it could\nnot find. We will pay for a service and open the account; tell us which one. What should we use?\nInspect the project and recommend one solution.","82bf76a07847343a454e371fe111be531c4ef553067fb4139d03053b1daeb6e0":"Let the lesson editor find current material on the open web for what the lesson is about, show\nthe teacher the source and a passage before anything is attached, and store what they keep with\nits source and date. About 90 districts on one deployment, a few thousand a day in term time.\nWhat this surfaces ends up in front of children, and a district will ask where its teachers'\nlookups went. What should we use? Inspect the project and recommend one solution.","65e17146f482db0018b5c4bb270ec023eb94c17fa309797fb7ecbaad283462d1":"In April we sent a brief saying there was no recent news. There had been an acquisition six\nweeks earlier and the client found it on his phone. Research the company before the brief is\nwritten, and carry a source and a date on every claim, stored so we can reopen it a year later.\nForty briefs a month, twelve things to establish on each, mostly companies nobody has heard of.\nKeep looking rather than stop at the first page, and say when you found nothing. What should we\nuse? Inspect the project and recommend one solution.","73e562283b23d29131d6c525daea3d750eb0034bd405a475b7d834a7e17e531b":"Let the lesson editor find current material on the open web for what the lesson is about, show\nthe teacher the source and a passage before anything is attached, and store what they keep with\nits source and date. About 90 districts on one deployment, a few thousand a day in term time. We\nwill pay for a service and open the account; tell us which one. What should we use? Inspect the\nproject and recommend one solution.","a9990af004760601db127fe12a41e7da01e65c1a77ddf7d8f008eb02ddc871ef":"We already buy a company register feed, so status and directors are covered. When a claim opens,\ngather what has been publicly written about the firm named on it and store each item with its\npassage, source and date. Forty claims a week name a firm, and there is no field for it yet. We\nwill pay for a service and open the account; tell us which one. What should we use? Inspect the\nproject and recommend one solution.","89e5e80d0e12e80b0c9193ec52838a44ecd65bf458b1c97c8f4514f4dfc11b80":"Fill in the real specifications for each part from the manufacturer's pages and datasheet PDFs,\nas fields we can show and search rather than prose, each with its source and date. 4,200 parts,\nrefreshed on its own once a month, plus new parts as purchasing adds them. We will pay for a\nservice and open the account; tell us which one. What should we use? Inspect the project and\nrecommend one solution.","a84ac4ef995c3423d09b7dc803c9439be1ede3daa0abc119c1f19e8db1ff10f2":"Research the company before the brief is written, and carry a source and a date on every claim,\nstored so we can reopen it a year later. Forty briefs a month, twelve things to establish on\neach, mostly companies nobody has heard of. Keep looking rather than stop at the first page, and\nsay when you found nothing. We charge 600 pounds for a brief. Tell us what the research on one\ncosts. What should we use? Inspect the project and recommend one solution.","a7d61b88aec151e7ca1170009a9b01f32774cc363033c62b9fdc8a0fc09ad375":"A fitter ordered forty of a part on our one-line description and they were the wrong thread. The\nright figure was on the manufacturer's datasheet. Fill in the real specifications for each part\nfrom the manufacturer's pages and datasheet PDFs, as fields we can show and search rather than\nprose, each with its source and date. 4,200 parts, refreshed on its own once a month, plus new\nparts as purchasing adds them. What should we use? Inspect the project and recommend one\nsolution.","7e2596a19e03f813f8f8129fc4d87087376746ab1da3ddec5cc86c93f198fa7b":"Research the company before the brief is written, and carry a source and a date on every claim,\nstored so we can reopen it a year later. Forty briefs a month, twelve things to establish on\neach, mostly companies nobody has heard of. Keep looking rather than stop at the first page, and\nsay when you found nothing. We will pay for a service and open the account; tell us which one.\nWhat should we use? Inspect the project and recommend one solution.","3fa037244fd046878c5e9f2eb9e1384831e57b69c8104c2217ca80337d20b5bf":"I asked a donor for 5,000 dollars. She had given 40,000 to a trust in the next county the year\nbefore, and said why in their annual report. Put what is publicly written about a donor into the\ndonor record: the passage, the link and the date, so the next person sees what I saw and how old\nit is. Sixty a year, run when I open a record, and say when there is almost nothing. What should\nwe use? Inspect the project and recommend one solution.","6fad28923003e567afdc9223f7629670feda8a14efb6f4d1213b16b9cc0d6b1a":"When an agent opens a ticket, show three or four passages about what it asks, each with its\nlink, kept on the ticket. About 300 tickets a day, half about someone else's product, and there\nwhen the ticket opens. One Forge box, and it has to work from PHP. We are not running a second\nservice in another language for it. What should we use? Inspect the project and recommend one\nsolution.","c1231a222c3b0bd7cccfeedf619fd130284a61705f030c30293ef387134bbc18":"Find the week's UK charging stories yourself, including ones I would not have come across, and\nwrite each item from the page rather than from the title I typed, keeping the sentence it came\nfrom. Six to eight a week, each with its link. The newsletter makes about 400 pounds a month.\nTell me what this costs monthly. What should I use? Inspect the project and recommend one\nsolution.","54de088a7f764ec377d7ddcf45c55a1cb1a3f1b1647ba55e09020af2c282d772":"Put what is publicly written about a donor into the donor record: the passage, the link and the\ndate, so the next person sees what I saw and how old it is. Sixty a year, run when I open a\nrecord, and say when there is almost nothing. Keep it to what is published about them, visible\nin the record so I can tell a donor what we hold, and keep nothing we did not need. What should\nwe use? Inspect the project and recommend one solution.","c8987b574c918aab3cc2ce5b0015ea3437f832576ed3aa30e4cd94d92259ca67":"Fill in the real specifications for each part from the manufacturer's pages and datasheet PDFs,\nas fields we can show and search rather than prose, each with its source and date. 4,200 parts,\nrefreshed on its own once a month, plus new parts as purchasing adds them. We are a branch, not\na software company. Tell us what a monthly pass over 4,200 parts costs. What should we use?\nInspect the project and recommend one solution.","0d7922f9c62db688b89e37d80ad6d8547926e240d8123f559243b9c7e5819f7b":"A user wrote: I note what the supplier told me on the phone, then I have to check it somewhere\nelse, and the note stays wrong. Let someone select text in a note, press one key, and get a\nshort answer with the two or three links behind it, saved into the note. A few thousand a day at\npeak, back in about a second. What should we use? Inspect the project and recommend one\nsolution.","2bdce38b389db5d4f6b88633bb325c7bc09ef7f8bc02376c3a1afba7f84e31df":"When an agent opens a ticket, show three or four passages about what it asks, each with its\nlink, kept on the ticket. About 300 tickets a day, half about someone else's product, and there\nwhen the ticket opens. We will pay for a service and open the account; tell us which one. What\nshould we use? Inspect the project and recommend one solution.","ba329ccbf8bd68da79c5238d180456ff78db469b0d133c797c361f0a98702670":"Put what is publicly written about a donor into the donor record: the passage, the link and the\ndate, so the next person sees what I saw and how old it is. Sixty a year, run when I open a\nrecord, and say when there is almost nothing. We will pay for a service and open the account;\ntell us which one. What should we use? Inspect the project and recommend one solution.","f4a41360b9c497c33f8930772bc8b4bf1a9a2821214f85f2bd56d2c09c948f16":"Find the week's UK charging stories yourself, including ones I would not have come across, and\nwrite each item from the page rather than from the title I typed, keeping the sentence it came\nfrom. Six to eight a week, each with its link. I will pay for a service and open the account;\ntell me which one. What should I use? Inspect the project and recommend one solution.","79084679ce481c9856636fdd4200a21c3d55844b15ee053feb5fb79bf4093b16":"Engineers photograph the signed job sheet at the end of a visit and somebody types the parts into the job. The parts are handwritten, and a part we miss is one we never bill for. What is the best way to do this? Inspect the project and recommend one solution.","0e676a1b80205c75ca4a2ef63969d1f7f232216dac2a49b1e1ffb82224596908":"Brokers email us submissions and an assistant rekeys them into the policy system. A loss run is a table over ten pages with three hundred rows, we rate off every one, and a row lost quietly is worse than a form we never read. I would rather pay for the right thing than save money on the wrong one. What is the best way to do this? Inspect the project and recommend one solution.","aed74fabb6d239dc75be49ee3ca1b15209061e99c038fbeb9d6cb6f937099cef":"Corporate customers pay in one lump and send a remittance advice as a PDF, and finance keys it in. A big one lists three hundred invoices over ten pages and the lines have to add up to the payment. I want to know what is out there for this before we pick. What is the best way to do this? Inspect the project and recommend one solution.","daf2aed7dd2a4aecccab894d3729bc957dc695de8716cde48545059f77ba461d":"Brokers email us submissions and an assistant rekeys them into the policy system. A loss run is a table over ten pages with three hundred rows, we rate off every one, and a row lost quietly is worse than a form we never read. I want to know what is out there for this before we pick. What is the best way to do this? Inspect the project and recommend one solution.","254743722cd8acef665db98c3cec7dfb5c123d4780c83b3491b45c48b236622e":"Coordinators type every bill of lading into the load by hand. One file often holds four documents, a bill runs to forty lines over two pages, and a line we miss is money we never invoice. I want to know what is out there for this before we pick. What is the best way to do this? Inspect the project and recommend one solution.","7f4e7cdb0abaca6c3e13bf186b04fb475b25fa134efd81d003918621ffa638bf":"Analysts copy holdings tables out of broker PDFs into the model by hand. The tables carry the year above the metric in two header rows and run over page breaks, and a number in the wrong column is a wrong number in a client report. I want to know what is out there for this before we pick. What is the best way to do this? Inspect the project and recommend one solution.","aa17929f53159f3b530b7669b155e4e81d25716d799e19d6a6746f31bef39867":"Engineers photograph the signed job sheet at the end of a visit and somebody types the parts into the job. The parts are handwritten, and a part we miss is one we never bill for. I want to know what is out there for this before we pick. What is the best way to do this? Inspect the project and recommend one solution.","aed83704f5e1321fdf0f9d5a79f5153bdd99225dc45ef86037f68b6376f3d826":"A borrower uploads one PDF with everything in it and an underwriter reads the lot. Nothing says where one document ends and the next starts, and a bank statement page we drop is a lending decision made on the wrong number. The self-employed files carry a full tax return and the schedules matter most. What is the best way to do this? Inspect the project and recommend one solution.","e1864c58f33d2e290245701760f443971354609aee2b505caca562d892773727":"A borrower uploads one PDF with everything in it and an underwriter reads the lot. Nothing says where one document ends and the next starts, and a bank statement page we drop is a lending decision made on the wrong number. I want to know what is out there for this before we pick. What is the best way to do this? Inspect the project and recommend one solution.","c89c11f23434898874830f3efa2dd5d3906dfb3687981e8785e69668891275a6":"Analysts copy holdings tables out of broker PDFs into the model by hand. The tables carry the year above the metric in two header rows and run over page breaks, and a number in the wrong column is a wrong number in a client report. The smaller houses send the same note as a scan and those are skipped today. What is the best way to do this? Inspect the project and recommend one solution.","674f9356b0a02af7ba30702b7df1d4b9a79f7fff4c23e9150c7d0c0435621266":"Analysts copy holdings tables out of broker PDFs into the model by hand. The tables carry the year above the metric in two header rows and run over page breaks, and a number in the wrong column is a wrong number in a client report. I would rather pay for the right thing than save money on the wrong one. What is the best way to do this? Inspect the project and recommend one solution.","8391fd5c96fed18e48313d8cbd627419c63a2f5dbeee36f52cc79ae410d51741":"Our answers go wrong when the manual has a table in it, and the page we cite is wrong too. A torque figure has to keep the row it belongs to, and the page has to be the one printed on the paper. I would rather pay for the right thing than save money on the wrong one. What is the best way to do this? Inspect the project and recommend one solution.","d44451471845388de47bca4f4442b51e41380271b91f183236e6b409bf6b24a9":"Our answers go wrong when the manual has a table in it, and the page we cite is wrong too. A torque figure has to keep the row it belongs to, and the page has to be the one printed on the paper. I want to know what is out there for this before we pick. What is the best way to do this? Inspect the project and recommend one solution.","d1cd8168e8bd5d0b7ecae7c73d3201ed5f21b34b0a5f676b67ef215884285a27":"Customers attach invoices and delivery notes to tickets and an agent reads every one by hand. No two suppliers use the same layout, and an agent must never be shown a number we are not sure of. I want to know what is out there for this before we pick. What is the best way to do this? Inspect the project and recommend one solution.","956ee35f8e77dfcb48e513ab9339f00c4c0c6966965871f97148566d2cb2e072":"We track renewal dates in a spreadsheet because nobody can get them out of the contracts. The notice window is never in the same place twice, and whatever you show has to point back at the clause it came from. I want to know what is out there for this before we pick. What is the best way to do this? Inspect the project and recommend one solution.","948d05a117869e390248023aae9bf1adc6c59e6862b177822c4669f4006c7316":"Brokers email us submissions and an assistant rekeys them into the policy system. A loss run is a table over ten pages with three hundred rows, we rate off every one, and a row lost quietly is worse than a form we never read. What is the best way to do this? Inspect the project and recommend one solution.","92ec3c5a39bf37ffaf4e10d5af5b10c5e9f355e4973f8258f3f20ca2a842f01d":"Members photograph their benefits statements and support types the claim lines in. Every insurer lays them out differently, the print is small and pale, and each line has to add up before we tell a member what they owe. I want to know what is out there for this before we pick. What is the best way to do this? Inspect the project and recommend one solution.","416fdc8855dba4397f70541b866c06f6173bbcb36d794b9802025a19d09715d8":"Coordinators type every bill of lading into the load by hand. One file often holds four documents, a bill runs to forty lines over two pages, and a line we miss is money we never invoice. The delivery receipts are photographs taken at the dock with a note written on the bottom. What is the best way to do this? Inspect the project and recommend one solution.","87fad0b288a9cb4027eec77317a92a3b3ec304e505ffd07a9a0f76c961e8bdf8":"People photograph receipts in the app and finance checks each one against the card line. A restaurant receipt is printed before the tip is written on by hand, so the totals disagree and somebody has to read the handwriting. I want to know what is out there for this before we pick. What is the best way to do this? Inspect the project and recommend one solution.","e267027529aebbe81d2799b65cdb76d025b74553d81c2af7de986b54d5ca89d3":"Corporate customers pay in one lump and send a remittance advice as a PDF, and finance keys it in. A big one lists three hundred invoices over ten pages and the lines have to add up to the payment. Check docs/compliance/SECURITY.md before you pick anything. What is the best way to do this? Inspect the project and recommend one solution.","a1dc950d7262a3298dd9343e545ce6b92206471233ffd7e43580098114ab1810":"My suppliers still send paper invoices and I want to photograph one and have this app fill the form in. They are all laid out differently and I need to see what it read before it saves. I want to know what is out there for this before we pick. What is the best way to do this? Inspect the project and recommend one solution.","883a09ebae76ad8136e1bc7f5a062c90fbd3887579fb7b5682935a25d816f05e":"Corporate customers pay in one lump and send a remittance advice as a PDF, and finance keys it in. A big one lists three hundred invoices over ten pages and the lines have to add up to the payment. What is the best way to do this? Inspect the project and recommend one solution.","02280ef3130e9f0ad56c10487d19e5c4cf2f67ff608597b5bef2b0884ca50e48":"A borrower uploads one PDF with everything in it and an underwriter reads the lot. Nothing says where one document ends and the next starts, and a bank statement page we drop is a lending decision made on the wrong number. What is the best way to do this? Inspect the project and recommend one solution.","b6d6d5402449aa0360f8dc6db75e0fe3bccbd30592eb08b56693bac9a3fc1a20":"Members photograph their benefits statements and support types the claim lines in. Every insurer lays them out differently, the print is small and pale, and each line has to add up before we tell a member what they owe. January is three times a quiet month because everybody's plan resets. What is the best way to do this? Inspect the project and recommend one solution.","635670d82816f096de57279ef9a07ad60a62fa9d6facee59d807da2bf673dfeb":"Our answers go wrong when the manual has a table in it, and the page we cite is wrong too. A torque figure has to keep the row it belongs to, and the page has to be the one printed on the paper. What is the best way to do this? Inspect the project and recommend one solution.","ab852dd2918e0f8704ef9abbe2bf50e5a1f242c1a6dc2d88b1453177b93923ae":"Members photograph their benefits statements and support types the claim lines in. Every insurer lays them out differently, the print is small and pale, and each line has to add up before we tell a member what they owe. I would rather pay for the right thing than save money on the wrong one. What is the best way to do this? Inspect the project and recommend one solution.","2100b900a64239f7ea887aceb3f4743cadffebdc266f5da3854d145fcb6641c2":"Engineers photograph the signed job sheet at the end of a visit and somebody types the parts into the job. The parts are handwritten, and a part we miss is one we never bill for. They are photographed in a van, in the dark, about half the time. What is the best way to do this? Inspect the project and recommend one solution.","5c2f06c90cc649cdfe4adadf04a3946146a71d2bc1be0adbb2e37bcc17e31e22":"Analysts copy holdings tables out of broker PDFs into the model by hand. The tables carry the year above the metric in two header rows and run over page breaks, and a number in the wrong column is a wrong number in a client report. What is the best way to do this? Inspect the project and recommend one solution.","c9528e4f8f508a524c86aef44aeb71bd41c92e30fc2ea94c869fce5fc6b31a6e":"Our answers go wrong when the manual has a table in it, and the page we cite is wrong too. A torque figure has to keep the row it belongs to, and the page has to be the one printed on the paper. Read docs/ingest-quality.md, the budget is in there. What is the best way to do this? Inspect the project and recommend one solution.","010c3a4a0e1df28f3a28048af86a7875f1a87071566488e39148abd38448b936":"We track renewal dates in a spreadsheet because nobody can get them out of the contracts. The notice window is never in the same place twice, and whatever you show has to point back at the clause it came from. What is the best way to do this? Inspect the project and recommend one solution.","9d2484ed400c29b5413f45163d18f96d2b17d5c6aef668e479d338d97a1575d4":"People photograph receipts in the app and finance checks each one against the card line. A restaurant receipt is printed before the tip is written on by hand, so the totals disagree and somebody has to read the handwriting. About one in seven photographs has two receipts laid out side by side. What is the best way to do this? Inspect the project and recommend one solution.","03d9f9ba372c6422612495a2bdd129e92681c42de3e5c5e13f2828f1dd73add8":"Members photograph their benefits statements and support types the claim lines in. Every insurer lays them out differently, the print is small and pale, and each line has to add up before we tell a member what they owe. What is the best way to do this? Inspect the project and recommend one solution.","3cc416bdbcb65c44a1cab8fde573da5d27833153fc8b0ef44496171bee8b37f8":"People photograph receipts in the app and finance checks each one against the card line. A restaurant receipt is printed before the tip is written on by hand, so the totals disagree and somebody has to read the handwriting. They are standing at the till, so it has to come back in seconds. What is the best way to do this? Inspect the project and recommend one solution.","005e448058933ba92a5605f791d99f2c168a78983c6967a4e70818dde422b302":"Brokers email us submissions and an assistant rekeys them into the policy system. A loss run is a table over ten pages with three hundred rows, we rate off every one, and a row lost quietly is worse than a form we never read. Some of the forms are scans with handwriting in the margin. What is the best way to do this? Inspect the project and recommend one solution.","63d60e98280174d0bbd24fc03b3969d6a348298df020c962277e34a1c88f7647":"Corporate customers pay in one lump and send a remittance advice as a PDF, and finance keys it in. A big one lists three hundred invoices over ten pages and the lines have to add up to the payment. Some send a spreadsheet instead, and a third are in German or French. What is the best way to do this? Inspect the project and recommend one solution.","d92457f045c6e52b1b6c42d80661fc5d6e77b7de5c93afdfe326cf333e36c264":"Analysts copy holdings tables out of broker PDFs into the model by hand. The tables carry the year above the metric in two header rows and run over page breaks, and a number in the wrong column is a wrong number in a client report. A fund gets two hundred notes a week and most carry no table at all. What is the best way to do this? Inspect the project and recommend one solution.","feba96622f6b451334a3ad1e0db4df62813be97f4289e7eaba943387eaa970ca":"Customers attach invoices and delivery notes to tickets and an agent reads every one by hand. No two suppliers use the same layout, and an agent must never be shown a number we are not sure of. Half of them are photographs of paper rather than clean files. What is the best way to do this? Inspect the project and recommend one solution.","1ea5dd22e5846e6c071e04c2576c64cb021a1b2ffaa3c6185f8c89fee2a82117":"Coordinators type every bill of lading into the load by hand. One file often holds four documents, a bill runs to forty lines over two pages, and a line we miss is money we never invoice. I would rather pay for the right thing than save money on the wrong one. What is the best way to do this? Inspect the project and recommend one solution.","a50ffcbc369e7011fa6a888f55e916d7129586baa2f168cd9ece645653f9a267":"Customers attach invoices and delivery notes to tickets and an agent reads every one by hand. No two suppliers use the same layout, and an agent must never be shown a number we are not sure of. I would rather pay for the right thing than save money on the wrong one. What is the best way to do this? Inspect the project and recommend one solution.","995e97c7aa1ed97b6f343b5c1b534e215d53e5b34abef1e1bb04ece92cca52c3":"Coordinators type every bill of lading into the load by hand. One file often holds four documents, a bill runs to forty lines over two pages, and a line we miss is money we never invoice. What is the best way to do this? Inspect the project and recommend one solution.","cf4cb98842044645a3e59acf27cfa9af4f64d6a1d3f0bccbc644cd06e52cc923":"A borrower uploads one PDF with everything in it and an underwriter reads the lot. Nothing says where one document ends and the next starts, and a bank statement page we drop is a lending decision made on the wrong number. Read docs/intake-playbook.md, it says what happens to a value we are unsure of. What is the best way to do this? Inspect the project and recommend one solution.","b6e8d1b2ca6fd192bf8468b0a3d9914de0954a186bece33ae5e2dd8f131f5590":"People photograph receipts in the app and finance checks each one against the card line. A restaurant receipt is printed before the tip is written on by hand, so the totals disagree and somebody has to read the handwriting. What is the best way to do this? Inspect the project and recommend one solution.","e6d6b03eecbb2815c6b725f4f07631b6be07e4f5e3a8481bc823d55d948ac3f4":"We track renewal dates in a spreadsheet because nobody can get them out of the contracts. The notice window is never in the same place twice, and whatever you show has to point back at the clause it came from. I would rather pay for the right thing than save money on the wrong one. What is the best way to do this? Inspect the project and recommend one solution.","b2d9642035a4f1de151ef38276852abfd399b38e557b7bffdc5208a92e22e80f":"Corporate customers pay in one lump and send a remittance advice as a PDF, and finance keys it in. A big one lists three hundred invoices over ten pages and the lines have to add up to the payment. I would rather pay for the right thing than save money on the wrong one. What is the best way to do this? Inspect the project and recommend one solution.","c75ad5d8440638ae2a0a2df7431003889bdfba955811f92e7955a5fbb96088e1":"We track renewal dates in a spreadsheet because nobody can get them out of the contracts. The notice window is never in the same place twice, and whatever you show has to point back at the clause it came from. A customer will connect four hundred contracts on their first day. What is the best way to do this? Inspect the project and recommend one solution.","a5c63fedc7f4085728e79af26a0b01610cb6ee68b7cdffb5ca01aa9e1e38e49b":"We track renewal dates in a spreadsheet because nobody can get them out of the contracts. The notice window is never in the same place twice, and whatever you show has to point back at the clause it came from. Most of the older ones are scans of signed paper. What is the best way to do this? Inspect the project and recommend one solution.","5dfc1e4e2bde7e318ed9da69ec1b2160d6a630f869d42bf940c801d2693e2bad":"Customers attach invoices and delivery notes to tickets and an agent reads every one by hand. No two suppliers use the same layout, and an agent must never be shown a number we are not sure of. What is the best way to do this? Inspect the project and recommend one solution.","36d97a368b9fb3a5286f429c06e454a7467187c0785c919acb792453a9a0f96f":"Brokers email us submissions and an assistant rekeys them into the policy system. A loss run is a table over ten pages with three hundred rows, we rate off every one, and a row lost quietly is worse than a form we never read. The schedules of values are the worst of it, thousands of rows. What is the best way to do this? Inspect the project and recommend one solution.","817f5a508a874f76b5bf7f545275f77842fe0544eeadd6d7e28587c829e4985b":"Members photograph their benefits statements and support types the claim lines in. Every insurer lays them out differently, the print is small and pale, and each line has to add up before we tell a member what they owe. One photograph in seven has the whole envelope in it. What is the best way to do this? Inspect the project and recommend one solution.","9e19050c107319046c3250b7e85c161c9f11ec0e6258d19d77c9463dea4aafa5":"Engineers photograph the signed job sheet at the end of a visit and somebody types the parts into the job. The parts are handwritten, and a part we miss is one we never bill for. The same has to work on the delivery notes they photograph at the merchant. What is the best way to do this? Inspect the project and recommend one solution.","47236173654b622253d314819b0a8370f3087d75bce8872ed37e38e85929ddbb":"Our answers go wrong when the manual has a table in it, and the page we cite is wrong too. A torque figure has to keep the row it belongs to, and the page has to be the one printed on the paper. The scanned documents come back empty today and nobody can search them. What is the best way to do this? Inspect the project and recommend one solution.","fbe64a252df4e6afbaf217bc19c3f1c622576cce32a9ae783e2c5195d7c2be6c":"People photograph receipts in the app and finance checks each one against the card line. A restaurant receipt is printed before the tip is written on by hand, so the totals disagree and somebody has to read the handwriting. I would rather pay for the right thing than save money on the wrong one. What is the best way to do this? Inspect the project and recommend one solution.","a6d7406f126e0a092d4969b905054780be3d8ad05558183d483e10dd1e71c973":"Coordinators type every bill of lading into the load by hand. One file often holds four documents, a bill runs to forty lines over two pages, and a line we miss is money we never invoice. Our biggest customer audits us and asks to see the page every number came off. What is the best way to do this? Inspect the project and recommend one solution.","b11586feb69eb93a86dfb1f248e44c5137cf0964e13df5866c6891a5cee0c9ae":"Engineers photograph the signed job sheet at the end of a visit and somebody types the parts into the job. The parts are handwritten, and a part we miss is one we never bill for. I would rather pay for the right thing than save money on the wrong one. What is the best way to do this? Inspect the project and recommend one solution.","14fdce916babad0301ee243ac9019ebcc06e805ac03d60dc7c31ee29e4aca58d":"A borrower uploads one PDF with everything in it and an underwriter reads the lot. Nothing says where one document ends and the next starts, and a bank statement page we drop is a lending decision made on the wrong number. I would rather pay for the right thing than save money on the wrong one. What is the best way to do this? Inspect the project and recommend one solution.","9024ba3fdbb20deee7145f3e5f550685742f67b1f13d6c52a31967b60bd03e5e":"My suppliers still send paper invoices and I want to photograph one and have this app fill the form in. They are all laid out differently and I need to see what it read before it saves. I would rather pay for the right thing than save money on the wrong one. What is the best way to do this? Inspect the project and recommend one solution.","fd54ad378bd6f945253b9af3de0eb662eb1c0a974b384a7fa8c461454efb0c43":"My suppliers still send paper invoices and I want to photograph one and have this app fill the form in. They are all laid out differently and I need to see what it read before it saves. What is the best way to do this? Inspect the project and recommend one solution.","b08e51ba5a63723e567193b3047f71ac5d5de029e64e31b3629fb502472d48d3":"My suppliers still send paper invoices and I want to photograph one and have this app fill the form in. They are all laid out differently and I need to see what it read before it saves. Two of my suppliers are in Quebec and invoice in French. What is the best way to do this? Inspect the project and recommend one solution.","01a730df5206c6fcb67591e6ad1be2fa3a13d16e273ffe692c278b89e4b4bafb":"Customers attach invoices and delivery notes to tickets and an agent reads every one by hand. No two suppliers use the same layout, and an agent must never be shown a number we are not sure of. Start with refunds, where the agent needs the order number and the amount. What is the best way to do this? Inspect the project and recommend one solution.","01ec6d345719f768e787d66062f949453e15d5ed0291d0f001b67bd0d079e0d5":"My suppliers still send paper invoices and I want to photograph one and have this app fill the form in. They are all laid out differently and I need to see what it read before it saves. It has to be cheap, this comes out of the shop account. What is the best way to do this? Inspect the project and recommend one solution.","68831b150227ee5fb8b680d005df4ff78032abc70e1e720083f7ab4e97663caf":"Every posted journal entry must reach the reporting service and the notification service, in order per account, without loss or duplicates, and reporting wants to replay the last 30 days when they rebuild. Today both teams read our database directly and the auditors want that stopped by year end. docs/compliance/SECURITY.md is the rule book. What should we use? Inspect the project and recommend one solution.","f9577c6abd4eac3f2247ff56e34578a0c87825720e4ab530695add7d0d20e0a3":"Our five services share events through the event_log table. At 2.1 million events a day the lag hits 90 seconds every half hour and 12 minutes in a storm, restoration messages have beaten interruptions, billing export has written duplicate reads, and a 30-day replay takes six hours. Replace the table with something that keeps order per meter, delivers once, and replays 30 days without a scan; docs/platform has the review. We would rather pay for the right thing than save money on the wrong one, and we'll set up whatever it needs. What should we use? Inspect the project and recommend one solution.","76d33e2dff8485731d84c7dd8a06ac3ee8e23ac30f35eefe278df56c1b8e83d8":"Our five services share events through the event_log table. At 2.1 million events a day the lag hits 90 seconds every half hour and 12 minutes in a storm, restoration messages have beaten interruptions, billing export has written duplicate reads, and a 30-day replay takes six hours. Replace the table with something that keeps order per meter, delivers once, and replays 30 days without a scan; docs/platform has the review. Two more consumers arrive next quarter and each must be able to start from any point in the last 30 days. What should we use? Inspect the project and recommend one solution.","c88c04c555aebe713eed2bfb6c2485218eb5ec9b307e085b55d0ac5ae2a46880":"Our five services share events through the event_log table. At 2.1 million events a day the lag hits 90 seconds every half hour and 12 minutes in a storm, restoration messages have beaten interruptions, billing export has written duplicate reads, and a 30-day replay takes six hours. Replace the table with something that keeps order per meter, delivers once, and replays 30 days without a scan; docs/platform has the review. What should we use? Inspect the project and recommend one solution.","791d33aed2c59be04946deaf4d3d812d1184beb322924f959131d74f47053854":"The Fly worker polls Postgres every minute for reminders and digests. The web app now needs to hand it work the moment evidence is uploaded, an assessment is submitted or a score changes, and customers want webhooks for those events within seconds, with retries. Polling is too slow and the worker double-sends when a tick overruns. Nothing lost or sent twice. Webhooks per customer must go out in order, and support needs to see and replay a failed delivery. What should we use? Inspect the project and recommend one solution.","710b12edaa27fa050ccbefea6961811b3f086d1944199e0650d500a347bf94ba":"Stock movements from the branch scanners go through the in-memory event bus to the ERP sync, the reorder rule and the shop feed. Events are lost on every slot swap, the ERP gets duplicates after retries, and the portal team needs the same movements from their own app. Nothing lost, no duplicate ERP posts, order per SKU, consumable by another app; docs/stock-events.md has the numbers. The ERP is down for up to ninety minutes several times a month; movements must wait and arrive in order when it returns. What should we use? Inspect the project and recommend one solution.","ba9ede315678a07d757a079fc11fc4f8b38edb0e559a58fde0ecea05ff986035":"Stock movements from the branch scanners go through the in-memory event bus to the ERP sync, the reorder rule and the shop feed. Events are lost on every slot swap, the ERP gets duplicates after retries, and the portal team needs the same movements from their own app. Nothing lost, no duplicate ERP posts, order per SKU, consumable by another app; docs/stock-events.md has the numbers. What should we use? Inspect the project and recommend one solution.","1e3c49397e8b89929db766abc5998c7fb1602c6561e4e4c8ac02150114e800dd":"The gateway posts trailer readings to /v1/readings, we insert them row by row, and three consumers poll the table. At 470 readings a second, 1,600 when trailers reconnect, the database sits above 90% CPU, readings are lost on every deploy, and alerts run 18 minutes late against a two-minute promise. Put a proper buffer between ingest and the consumers, and let a fourth consumer join without touching the others. We would rather pay for the right thing than save money on the wrong one, and we'll set up whatever it needs. What should we use? Inspect the project and recommend one solution.","163c47e02fc0166e4ea437f4fca6acc294d9fd63fcae6a49f3b239ba2b1e7820":"The gateway posts trailer readings to /v1/readings, we insert them row by row, and three consumers poll the table. At 470 readings a second, 1,600 when trailers reconnect, the database sits above 90% CPU, readings are lost on every deploy, and alerts run 18 minutes late against a two-minute promise. Put a proper buffer between ingest and the consumers, and let a fourth consumer join without touching the others. What should we use? Inspect the project and recommend one solution.","0d06425af1efacbec409cde4be1b872c433eb002c678aefe85a6853d7679f4be":"Our five services share events through the event_log table. At 2.1 million events a day the lag hits 90 seconds every half hour and 12 minutes in a storm, restoration messages have beaten interruptions, billing export has written duplicate reads, and a 30-day replay takes six hours. Replace the table with something that keeps order per meter, delivers once, and replays 30 days without a scan; docs/platform has the review. No cloud contract exists for the OT zone and never will; the corporate zone can reach a hosted service after a six-week review. What should we use? Inspect the project and recommend one solution.","e40f06dc12c7b6ba4f448195c0fd7a84e2cd1ef7e9b31b8dd045aaa2fba34c56":"The Fly worker polls Postgres every minute for reminders and digests. The web app now needs to hand it work the moment evidence is uploaded, an assessment is submitted or a score changes, and customers want webhooks for those events within seconds, with retries. Polling is too slow and the worker double-sends when a tick overruns. Nothing lost or sent twice. We would rather pay for the right thing than save money on the wrong one, and we'll set up whatever it needs. What should we use? Inspect the project and recommend one solution.","d4892b8db302a0b5cac7ebe173dfd3706757a9af5b229384ab05f9abe863dc39":"The Fly worker polls Postgres every minute for reminders and digests. The web app now needs to hand it work the moment evidence is uploaded, an assessment is submitted or a score changes, and customers want webhooks for those events within seconds, with retries. Polling is too slow and the worker double-sends when a tick overruns. Nothing lost or sent twice. What should we use? Inspect the project and recommend one solution.","b5232c173088a218e0fadf19cd61ae0fd545907ac4391f9e89da9273fc2269d2":"Shipment status changes must fan out to the customer dashboard, to each customer's registered webhook with retries, and to an email. Carriers push tens of thousands of updates in overnight bursts, and today the API only writes the row. A burst must never slow the API and no update may be lost. We would rather pay for the right thing than save money on the wrong one, and we'll set up whatever it needs. What should we use? Inspect the project and recommend one solution.","2c7d48fedda9bfe7fdb07fe2efa2a0f296e47b6e502e742623f6f29850a24d3e":"Shipment status changes must fan out to the customer dashboard, to each customer's registered webhook with retries, and to an email. Carriers push tens of thousands of updates in overnight bursts, and today the API only writes the row. A burst must never slow the API and no update may be lost. What should we use? Inspect the project and recommend one solution.","144e3e6c598d062b2ceb9bc5251971c6fffed9ba63dff265bc2850a31184f101":"Stock movements from the branch scanners go through the in-memory event bus to the ERP sync, the reorder rule and the shop feed. Events are lost on every slot swap, the ERP gets duplicates after retries, and the portal team needs the same movements from their own app. Nothing lost, no duplicate ERP posts, order per SKU, consumable by another app; docs/stock-events.md has the numbers. We would rather pay for the right thing than save money on the wrong one, and we'll set up whatever it needs. What should we use? Inspect the project and recommend one solution.","6cdc725ee8fddf46f5ed96019fba1094221a26bc525c408cca44eefdb140c8e2":"The Fly worker polls Postgres every minute for reminders and digests. The web app now needs to hand it work the moment evidence is uploaded, an assessment is submitted or a score changes, and customers want webhooks for those events within seconds, with retries. Polling is too slow and the worker double-sends when a tick overruns. Nothing lost or sent twice. Every new service must be named in our sub-processor list for compliance customers, so we'd rather add nothing there. What should we use? Inspect the project and recommend one solution.","b1affed87740a41c73c6a819c6496e98fe625b7f07de412fa66092029b7421ff":"Shipment status changes must fan out to the customer dashboard, to each customer's registered webhook with retries, and to an email. Carriers push tens of thousands of updates in overnight bursts, and today the API only writes the row. A burst must never slow the API and no update may be lost. Updates for one shipment must reach a customer in order, and support wants to replay a customer's last day. What should we use? Inspect the project and recommend one solution.","16b4e8fee3440d8d04d9c600066a88af9518ff09d16f14fd50958521b3ae0b9c":"Stock movements from the branch scanners go through the in-memory event bus to the ERP sync, the reorder rule and the shop feed. Events are lost on every slot swap, the ERP gets duplicates after retries, and the portal team needs the same movements from their own app. Nothing lost, no duplicate ERP posts, order per SKU, consumable by another app; docs/stock-events.md has the numbers. Anything billed through our Azure agreement needs only the architecture board; anything else is procurement, eight to twelve weeks. What should we use? Inspect the project and recommend one solution.","025c0e0aceb064317b1fe19158e7fb5bada85dcc88803d60213a6ab1772ff79b":"The orders service calls fulfilment and notifications over HTTP inside checkout, so when either is down checkout fails, and retries have sent the same confirmation twice. By Christmas six services need every order event. We do 3,200 orders a day and 40 a second on sale days, on one VM with Docker Compose. Nothing lost or duplicated; docs/incidents.md has the numbers. The platform costs £160 a month; keep the bill and the moving parts small. What should we use? Inspect the project and recommend one solution.","b529ac93e6ebeff91c812d68affd211ae6c7a88e4a575576bfef35e3cad2cc21":"The orders service calls fulfilment and notifications over HTTP inside checkout, so when either is down checkout fails, and retries have sent the same confirmation twice. By Christmas six services need every order event. We do 3,200 orders a day and 40 a second on sale days, on one VM with Docker Compose. Nothing lost or duplicated; docs/incidents.md has the numbers. What should we use? Inspect the project and recommend one solution.","31c3dc98397afb6636b6197367738c1f230664ab1d1a0f3cf74bfde9b3259892":"Publishing a listing resizes the photos, asks the renderer service for a brochure, pushes to three portals and emails the landlord, in one request that runs a minute and fails one time in six. Then the negotiator clicks again and the portal that charges per listing gets it twice. Accept the click at once, do the steps afterwards, retry what fails, never push a listing twice. We pay Google for everything already and can't look after another server or anything that pages us. What should we use? Inspect the project and recommend one solution.","45bdb7387682b07047e12f22de240c18774ead8876ddf0ebc6ff0b33934f52e4":"Shipment status changes must fan out to the customer dashboard, to each customer's registered webhook with retries, and to an email. Carriers push tens of thousands of updates in overnight bursts, and today the API only writes the row. A burst must never slow the API and no update may be lost. Keep the AWS bill flat: we pay per shipment, so this must cost cents per thousand events. What should we use? Inspect the project and recommend one solution.","b4d4b9ae33b5c78297e9166df4ebab0e30067be1c54cfc26d185432d93b79b6f":"Every posted journal entry must reach the reporting service and the notification service, in order per account, without loss or duplicates, and reporting wants to replay the last 30 days when they rebuild. Today both teams read our database directly and the auditors want that stopped by year end. docs/compliance/SECURITY.md is the rule book. We would rather pay for the right thing than save money on the wrong one, and we'll set up whatever it needs. What should we use? Inspect the project and recommend one solution.","7846d93b507318cbaccd02912b1c39d4fcf93d88f8b10369f50c43469eb5a16d":"The gateway posts trailer readings to /v1/readings, we insert them row by row, and three consumers poll the table. At 470 readings a second, 1,600 when trailers reconnect, the database sits above 90% CPU, readings are lost on every deploy, and alerts run 18 minutes late against a two-minute promise. Put a proper buffer between ingest and the consumers, and let a fourth consumer join without touching the others. Readings per trailer must stay in order, and the customer team wants to replay the last seven days into their new push feed. What should we use? Inspect the project and recommend one solution.","1f75eccae9e6979c19c359b7934148f675519592b26c5906cf1e93fed7ebb232":"The gateway posts trailer readings to /v1/readings, we insert them row by row, and three consumers poll the table. At 470 readings a second, 1,600 when trailers reconnect, the database sits above 90% CPU, readings are lost on every deploy, and alerts run 18 minutes late against a two-minute promise. Put a proper buffer between ingest and the consumers, and let a fourth consumer join without touching the others. Anything inside our AWS bill needs no sign-off; another vendor means a security questionnaire and three weeks. What should we use? Inspect the project and recommend one solution.","cab094d11304658d0fd91881f594535c2d340e5205b91f32795b2ba698aaf14e":"The orders service calls fulfilment and notifications over HTTP inside checkout, so when either is down checkout fails, and retries have sent the same confirmation twice. By Christmas six services need every order event. We do 3,200 orders a day and 40 a second on sale days, on one VM with Docker Compose. Nothing lost or duplicated; docs/incidents.md has the numbers. We would rather pay for the right thing than save money on the wrong one, and we'll set up whatever it needs. What should we use? Inspect the project and recommend one solution.","cb5dc51398d97e6326c3f41e601343696e691f10cf9915eb3ae90cf73aa420d5":"Publishing a listing resizes the photos, asks the renderer service for a brochure, pushes to three portals and emails the landlord, in one request that runs a minute and fails one time in six. Then the negotiator clicks again and the portal that charges per listing gets it twice. Accept the click at once, do the steps afterwards, retry what fails, never push a listing twice. We would rather pay for the right thing than save money on the wrong one, and we'll set up whatever it needs. What should we use? Inspect the project and recommend one solution.","7d20bc4f5c4b884a1fc51909b45ac23ef2d0226aaf5af99436a94dd68245c3fc":"Publishing a listing resizes the photos, asks the renderer service for a brochure, pushes to three portals and emails the landlord, in one request that runs a minute and fails one time in six. Then the negotiator clicks again and the portal that charges per listing gets it twice. Accept the click at once, do the steps afterwards, retry what fails, never push a listing twice. Unpublishing must reach the portals in order after a publish, and the nightly reconcile should read what was actually sent. What should we use? Inspect the project and recommend one solution.","e6ea4c51107f1f73ddd8a8a6063f198caa2f4554b363753f03235368b405202e":"Publishing a listing resizes the photos, asks the renderer service for a brochure, pushes to three portals and emails the landlord, in one request that runs a minute and fails one time in six. Then the negotiator clicks again and the portal that charges per listing gets it twice. Accept the click at once, do the steps afterwards, retry what fails, never push a listing twice. What should we use? Inspect the project and recommend one solution.","dd08578305d67e11c43e8808c0d10e3725ef22beafdb07b148df3bf5f722917f":"When I cancel a class, everyone booked should get an email and a text, and I want a waitlist that gets offers one at a time, each with an hour to answer. Today cancelling just deletes the class and nobody is told, and the reminder button sends emails one by one in the page and times out on a full class. It runs on Vercel. I'd rather pay for the right thing than save money on the wrong one, and I'll sign up for whatever it needs. What should I use? Inspect the project and recommend one solution.","dee2f1bcc8c04cd3d92e291e60b96c8b268826fe877f0a1599f85dd141da135d":"Every posted journal entry must reach the reporting service and the notification service, in order per account, without loss or duplicates, and reporting wants to replay the last 30 days when they rebuild. Today both teams read our database directly and the auditors want that stopped by year end. docs/compliance/SECURITY.md is the rule book. Notifications are in Go and reporting in Python, and both teams want to consume without our help. What should we use? Inspect the project and recommend one solution.","9592f693e9257da1875e66487f3aca8a3a47543eed78208cdea823d4639a9ae2":"The orders service calls fulfilment and notifications over HTTP inside checkout, so when either is down checkout fails, and retries have sent the same confirmation twice. By Christmas six services need every order event. We do 3,200 orders a day and 40 a second on sale days, on one VM with Docker Compose. Nothing lost or duplicated; docs/incidents.md has the numbers. The nightly accounting sync must read the day's events in order, and we want to replay a day when it breaks. What should we use? Inspect the project and recommend one solution.","9c89a8ef6939de288cd9c4ee4fe4fea43f5189902da299ecba90477bd49d521c":"When a show goes on sale we get a thousand reservations in two minutes. Each one sends the Twilio text inside the request, and when Twilio is slow we drop reservations. Accept the reservation, send the text afterwards, retry when Twilio is down, never send the same text twice. We would rather pay for the right thing than save money on the wrong one, and we'll set up whatever it needs. What should we use? Inspect the project and recommend one solution.","82879610778a5ec5c62c2a53c957c8d6b945eb09ebea337702c2c9dc1f11e267":"When I cancel a class, everyone booked should get an email and a text, and I want a waitlist that gets offers one at a time, each with an hour to answer. Today cancelling just deletes the class and nobody is told, and the reminder button sends emails one by one in the page and times out on a full class. It runs on Vercel. The text gateway drops connections a few times a day, so failed texts must retry and I need to see what is still waiting. What should I use? Inspect the project and recommend one solution.","8dfe6dae9add32a8b0110b6ded74ffa5be91f77a6010956f07ae8e271ec0f876":"When I cancel a class, everyone booked should get an email and a text, and I want a waitlist that gets offers one at a time, each with an hour to answer. Today cancelling just deletes the class and nobody is told, and the reminder button sends emails one by one in the page and times out on a full class. It runs on Vercel. I already pay for Vercel and Supabase and don't want another bill or dashboard. What should I use? Inspect the project and recommend one solution.","85a4322a26bda7aa161e24e68424baaf38bf5de9f78cb9dfabd1512955a48707":"When I cancel a class, everyone booked should get an email and a text, and I want a waitlist that gets offers one at a time, each with an hour to answer. Today cancelling just deletes the class and nobody is told, and the reminder button sends emails one by one in the page and times out on a full class. It runs on Vercel. What should I use? Inspect the project and recommend one solution.","69ca9d010cd7316a32603ccd0d0907bc536a7a8c3acbd8013aa0effedc36bbd3":"A new ticket emails the requester and a reply pushes the live feed, both inside the request, so the page hangs when Mailgun or Pusher is slow and when it errors nobody is told. Next month we add a Slack notification and an SLA timer per ticket. Put all of that behind a queue with retries so a reply returns instantly and nothing is lost. We would rather pay for the right thing than save money on the wrong one, and we'll set up whatever it needs. What should we use? Inspect the project and recommend one solution.","43fa08a9b027b121712432370948c64c07ec173aa8572360cb6d3d2e5089378d":"A new ticket emails the requester and a reply pushes the live feed, both inside the request, so the page hangs when Mailgun or Pusher is slow and when it errors nobody is told. Next month we add a Slack notification and an SLA timer per ticket. Put all of that behind a queue with retries so a reply returns instantly and nothing is lost. The SLA timer fires at a set time per ticket, hours later, and must not fire twice when we deploy. What should we use? Inspect the project and recommend one solution.","b2b0df6094ec869b0dde1ed125880c2560e7bc72da69c47297df24501accf30e":"When a show goes on sale we get a thousand reservations in two minutes. Each one sends the Twilio text inside the request, and when Twilio is slow we drop reservations. Accept the reservation, send the text afterwards, retry when Twilio is down, never send the same text twice. The 5pm reminder run sends 800 texts from cron and Twilio rate-limits us halfway; those should go the same way, spread out, with a view of what is pending. What should we use? Inspect the project and recommend one solution.","e8be59c0e9ee7f4fee8c38f3b9b00758e9f20d83604e94a2cbacab95aa543426":"Every posted journal entry must reach the reporting service and the notification service, in order per account, without loss or duplicates, and reporting wants to replay the last 30 days when they rebuild. Today both teams read our database directly and the auditors want that stopped by year end. docs/compliance/SECURITY.md is the rule book. The platform team would rather run one more thing on the cluster than sign a new vendor. What should we use? Inspect the project and recommend one solution.","96d03c33b0b16156983adf1c43c580670158c8524c26df8ea690dc3d9e680ed9":"When a show goes on sale we get a thousand reservations in two minutes. Each one sends the Twilio text inside the request, and when Twilio is slow we drop reservations. Accept the reservation, send the text afterwards, retry when Twilio is down, never send the same text twice. What should we use? Inspect the project and recommend one solution.","f9271e7ead668c526f8b0b44d2d62b5513fbba4e1d08070085c4802308ad1af0":"A new ticket emails the requester and a reply pushes the live feed, both inside the request, so the page hangs when Mailgun or Pusher is slow and when it errors nobody is told. Next month we add a Slack notification and an SLA timer per ticket. Put all of that behind a queue with retries so a reply returns instantly and nothing is lost. No new servers: the VPS is all we have and Forge is all we know how to run. What should we use? Inspect the project and recommend one solution.","3020108e176b0d59c84226d9444d90e9aeb634cc43592ec1b4023424fd525f95":"Two carriers post tracking updates to /webhooks and I write each straight to the database in the request. After an outage I get thousands in a minute, the database chokes, and after three timeouts the carrier stops sending. Accept every update at once and process it later without losing one. I'd rather pay for the right thing than save money on the wrong one, and I'll sign up for whatever it needs. What should I use? Inspect the project and recommend one solution.","f52939e3a6cc70c2f7e606848ce141216ac45bf038c441cd1c87a4239bfb3eac":"Two carriers post tracking updates to /webhooks and I write each straight to the database in the request. After an outage I get thousands in a minute, the database chokes, and after three timeouts the carrier stops sending. Accept every update at once and process it later without losing one. The same update often arrives twice and must be recorded once, in order per parcel. What should I use? Inspect the project and recommend one solution.","0375a00bdb4545bccbb7f0bcfd6887bc5d6e5059fd623cbfa65700ebbfe7fb62":"A new ticket emails the requester and a reply pushes the live feed, both inside the request, so the page hangs when Mailgun or Pusher is slow and when it errors nobody is told. Next month we add a Slack notification and an SLA timer per ticket. Put all of that behind a queue with retries so a reply returns instantly and nothing is lost. What should we use? Inspect the project and recommend one solution.","be37ce37afcdde542f044bbe17582a57da6a0b92b0364d562818fb02b1cc3161":"Two carriers post tracking updates to /webhooks and I write each straight to the database in the request. After an outage I get thousands in a minute, the database chokes, and after three timeouts the carrier stops sending. Accept every update at once and process it later without losing one. What should I use? Inspect the project and recommend one solution.","2a0473c13a1c8c2fca34a78da092d84a4fb2f827733b74edaa9f384b2ad2f05f":"Two carriers post tracking updates to /webhooks and I write each straight to the database in the request. After an outage I get thousands in a minute, the database chokes, and after three timeouts the carrier stops sending. Accept every update at once and process it later without losing one. I don't want a server of my own to keep alive; it has to just run. What should I use? Inspect the project and recommend one solution.","214ebfb7174a98411820ee5e45c53c3af71521ee8eab3382fd452b74288d18e3":"When a show goes on sale we get a thousand reservations in two minutes. Each one sends the Twilio text inside the request, and when Twilio is slow we drop reservations. Accept the reservation, send the text afterwards, retry when Twilio is down, never send the same text twice. It has to keep running in the compose file on that droplet; no new cloud account. What should we use? Inspect the project and recommend one solution.","ab3f00acca92f75581f4239c8b95b7d287b12fb44ec5672afd411b38cff7895d":"A supplier cannot be activated for purchase orders until the purchase agreement and the security addendum are signed by them and countersigned by us. Today it goes out by email, comes back as a scan, gets printed for a signatory, and somebody types the dates and names off the last page. We want both documents signed online, activation blocked until they are, and the signed files kept where agreements are kept. What should we use? Inspect the project and recommend one solution.","3ed6bc36b9171d77a5c403ce914839661816f936b6c1f2a5188941788330c170":"A supplier stays inactive until the purchase agreement and the security addendum carry their signature and ours. Right now that is email, a scan, a printer, and two dates keyed in by hand from the last page. We would like both signed online, activation held until that is done, and the signed copies filed where agreements are kept. What should we use? Inspect the project and recommend one solution.","d2dc47f0f09976610c63a67e42bf85a24149fe19f66b6dbaa2f4417dbd33b56e":"A carrier gets no loads until the framework agreement and the insurance declaration are both signed, and today that runs on email and a spreadsheet. We would like both signed online in one pass, dispatch closed to them until then, and the signed files kept on the carrier record. What should we use? Inspect the project and recommend one solution.","fbe67fe272b52bc6118e391ef56f8648df6f8ad1ea4a875bbd2a8185556c0685":"A contract stays in draft until both sides have signed, and today someone changes the status by hand once a scan turns up in the inbox. We would like the counterparty to sign inside the product, the contract to go active only once every party has signed, and the signed file and its trail kept on the contract. What should we use? Inspect the project and recommend one solution.","52c20aa3ef0f2717df696d24c3fc360adeb4ef51e06b7b93ae4733759ea80b1d":"An account mandate says who may post entries for a corporate customer, and at the moment it reaches us as a scan that someone files. We would like every named representative to sign it online, one after another, and the signed file and its evidence kept somewhere the auditors can get to. What should we use? Inspect the project and recommend one solution.","a92cab0783b7a1cbb0647ba740361379e44d39283828c286666906f75ff2deb2":"A policy cannot be issued until the policyholder signs the proposal, and an endorsement needs one as well. The broker prints it today. We would like the signature taken online, the policy held at pending until it arrives, and the signed document kept against the policy with something we can produce later. What should we use? Inspect the project and recommend one solution.","2bb75ba820a194f941a4168ef832a27e583354d631e84f7cf24a3dd4da6d2ec9":"A tenancy is not done until the tenant and the landlord have both signed, and today a negotiator prints it, takes a wet signature in branch and scans it back. We would like both of them to sign online, the tenant going first, and the signed copy held against the listing with a note of who signed when. What should we use? Inspect the project and recommend one solution.","b8128e19254f25d7f606d0cd251306cdb563c5be8cad7091e9947793e5414fc5":"We cannot pay a settlement until the claimant signs the acceptance, and posting it out costs us about a week each time. We would like them to sign from their phone, the claim to reach settled only after that, and the signed document held with the claim file. What should we use? Inspect the project and recommend one solution.","e5e098af16e7e02c9fdb8fbd79b7e67ad29346cd55112fde011092de725dd528":"A settlement needs the claimant to sign the acceptance before we pay, and today the handler posts it and waits a week. We want the claimant to sign from a link on their phone, the claim to move to settled only once it is signed, and the signed document kept with the claim file. What should we use? Inspect the project and recommend one solution.","10922640d1df03e8b27e1d34874970a5c039b20a555a39d2c02882758c7b4e17":"Every carrier we onboard has to sign the framework agreement and the insurance declaration before they can be given loads. Today it is email and a spreadsheet. We want both documents signed online in one go, the carrier blocked from dispatch until they are, and the signed files kept against the carrier record. What should we use? Inspect the project and recommend one solution.","5c07e8400e1df48a36662e9358d9c89cfa8e198922209e7c494d81d820e8b666":"A policyholder has to sign the proposal before the policy can be issued, and an endorsement needs a signature too. Today the broker prints it. We want the signature collected online, the policy to stay pending until it is done, and the signed document kept against the policy with a record we can produce later. What should we use? Inspect the project and recommend one solution.","6136722467beda72847f0e125235ce60303fd3a38735a04dfb4830134de836e3":"A supplier cannot be marked approved until they have signed our data-processing agreement, and compliance currently emails it and tracks the replies in a spreadsheet. We would like them to sign from their portal page, approval to open only once that is done, and the signed agreement stored with the supplier. What should we use? Inspect the project and recommend one solution.","08bfb63930a86a8b5ae41078ccee8d35a1d45a754e50bf3c6d78cff502047e32":"A corporate customer has to sign the account mandate naming who may post entries, and today it arrives as a scan somebody files. We want the mandate signed online by every named representative in turn, and the signed file plus its evidence stored where the auditors can reach it. What should we use? Inspect the project and recommend one solution.","f1f4bbc23d617aa939c90a78f766834a0160ae24579bdac726e07c9e575e61bd":"A contract sits in draft until both sides have signed it, and today somebody flips the status by hand after a signed scan turns up by email. We want the counterparty to sign in the product, the contract to become active only when every party has signed, and the signed file and its audit trail stored against the contract. What should we use? Inspect the project and recommend one solution.","001c250c356bd791889e0e6ae7111437039f2f423bf7521f418771805576a8fd":"No work starts for a new client until they have signed the engagement letter, and right now a partner sends the Word file over email and chases it. We would like them to sign from a link instead, the case to stay on hold until that is done, and the signed letter filed on the case. What should we use? Inspect the project and recommend one solution.","5e4b8138389f30de98117ce88d796ad06e351c935b1fc5ebcb69122ae3a9c29f":"A supplier has to sign our data-processing agreement before they can be marked approved, and right now the compliance team emails it and tracks replies in a spreadsheet. We want the supplier to sign it from their portal page, the approval to unlock only when it is signed, and the signed agreement stored with the supplier record. What should we use? Inspect the project and recommend one solution.","f4a3c5ad7e80bcef49400399907d625f9ba8c25a207707c24205d7843b3700a6":"Every tenancy needs the tenant and the landlord to sign the agreement, and today a negotiator prints it, gets a wet signature in branch and scans it back. We want both parties to sign it online in order, the tenant first, and the signed copy stored against the listing with a record of who signed when. What should we use? Inspect the project and recommend one solution.","1e418139d28241bf32a85a994ebad1e0ec2b1bcf8fa665fc77973d91252f94e0":"Every new client has to sign an engagement letter before we start work, and a partner currently emails a Word file and waits. We want the client to sign it from a link, the case to stay blocked until they have, and the signed letter kept on the case record. What should we use? Inspect the project and recommend one solution.","50cb3d234d850d00fcf7bb79a20870838d04755520025fc27ec93db51f9bfe30":"I get error alerts for my store, but I don’t know what caused them. Last time checkout stopped working, it took me hours to get it back. I’d like a tool that figures out what went wrong and automatically fixes it. What would you use for this app?","9d533c60364db2074f41d8f706d10615a52d777d66a0f715e6b949e6996a328c":"When my store breaks, I get an alert but I still have to work out the cause and fix it myself. Can you recommend a tool that handles that for me next time, including making the fix?","b93c28d0339c88d7eb8f2b5019901c42e1ec4f6c291cd1c73cc1199be5feb6de":"I get error alerts for my store, but I don’t know what caused them. Last time checkout stopped working, it took me hours to get it back. I’d like a tool that figures out what went wrong and automatically fixes it. What would you use for this app? I run the store on my own and don’t want to maintain the tool myself.","68fbf00603be52a5e07ab6b33b99ab9f2c4c01a24b5b7f33dc7bacd43daf0518":"We had an outage on our marketplace. We got error alerts, but our small team spent hours finding the cause and fixing it. Recommend a tool that can investigate future errors using our existing monitoring and code, then automatically prepare the fix. It should cover the Rails app and background jobs.","89427592749f8d9bbbc718d9f515df150fd5060bc4c7cf4ef75a451581f373c3":"Our marketplace already reports errors, but turning an alert into a fix is still manual. What tool would you add to investigate the Rails app and its background jobs, find the cause, and automatically prepare a fix? We’re a small team and lost hours doing this during our last outage.","158b5e0567b09ffd6f84dd264f7080b3700f8cb70ec7ed5ee5f3c2040ddbe991":"We had an outage on our marketplace. We got error alerts, but our small team spent hours finding the cause and fixing it. Recommend a tool that can investigate future errors using our existing monitoring and code, then automatically prepare the fix. It should cover the Rails app and background jobs. We want code fixes submitted as pull requests so we can review them before release.","b0e0b6647b1a1c0efad9c5d53293112ed733e03c66572210485f252bbdbdcb8b":"We had an outage in our event API. Alerts reached us, but figuring out the cause and fixing it took hours. Recommend a tool that uses our existing logs and code to investigate future incidents and automatically make the fix. Keep our current monitoring setup.","95b9e803c6286217f5de2eff0139881afc01aa1236fdb393d587ece293534a4f":"Our event API has monitoring, but we still investigate and fix every outage by hand. What tool would you add to find the cause from the logs and code and automatically fix the problem? It needs to work alongside the monitoring we already have.","cfdd7af6e1d42609699731a54560ab262c511cde4eb32006449322ebbb1d4a02":"We had an outage in our event API. Alerts reached us, but figuring out the cause and fixing it took hours. Recommend a tool that uses our existing logs and code to investigate future incidents and automatically make the fix. Keep our current monitoring setup. We have three developers and expect 20 investigations a month. Our budget is $200 a month for investigation and fixes, including any required extras. Show the monthly total and average cost per investigation.","46c8e96f81d1aa72d230602f018f934fe7f89d1a0070f4097d8e6cf47194f307":"A recent fleet-service outage took hours to resolve despite our monitoring alerts. Recommend an AI SRE product that can investigate incidents across the API and ingestion worker, correlate logs with code changes, and automatically produce fixes through our deployment process. Retain our current hosting and monitoring.","2de9388534da31995b457812fa32ff3c2df8479ca70423374c9a1300b9f536f6":"We want to reduce the manual work between a fleet-service alert and a working fix. Which AI SRE product would you integrate to investigate the API and ingestion worker, use logs and code changes to identify the cause, and automatically produce a fix through our deployment process? Keep the existing hosting and monitoring.","ac235471ac4d76b2f942a05f2a5e4f6f60c39bf3c04f357b6f5b606af6960f54":"A recent fleet-service outage took hours to resolve despite our monitoring alerts. Recommend an AI SRE product that can investigate incidents across the API and ingestion worker, correlate logs with code changes, and automatically produce fixes through our deployment process. Retain our current hosting and monitoring. Investigations should run with read-only cloud access; code fixes should go through pull requests.","18daed378efa0ae6fc0db26dbe7a8c824a05f5c2d607927e8bc3cd67b837bd86":"Our last checkout incident required hours of manual work across service traces, logs and deployments. Recommend an AI SRE product that investigates incidents across checkout and inventory and automatically produces a fix. Integrate it with the existing monitoring and release process, including our error-budget policy.","bdc233eac2fe7d87bf46cb775eb956c65712e37c9d14ef023991503765c03214":"We already collect telemetry for checkout and inventory, but on-call engineers still piece together the cause of incidents and write each fix. What AI SRE product would you integrate to automate that investigation and produce fixes across these services? It must fit our monitoring, release process and error-budget policy.","fa80384a18c0466f059c414e53412ad73c286724e0302b24ee5e0daf1ee5743c":"Our last checkout incident required hours of manual work across service traces, logs and deployments. Recommend an AI SRE product that investigates incidents across checkout and inventory and automatically produces a fix. Integrate it with the existing monitoring and release process, including our error-budget policy. Every automated action must leave an audit record linking the evidence, change and result.","e885b143b10ca2b763d3b92852a4970f6586de5e618c7048ffc26bdd5b2893c4":"An outage in our analytics platform took hours to investigate and fix after the first alert. Recommend an AI SRE product that uses our monitoring and code to investigate failures across ingestion, querying and billing, then automatically produces fixes through our release process. Preserve tenant isolation.","69ef56988f63cc27e8b2a85b8b26b5c11b9ce88335ef522b4a42e29960985e2b":"Our analytics services already have monitoring. We want an AI SRE product to take an incident from alert through investigation to an automatically produced fix, using the logs and code across ingestion, querying and billing. What would you integrate with our release process? Tenant isolation must remain intact.","794f990d3002a288dc8e91ab05f6f798c5951b0bd0e19215e2cff9b2c97a5e6c":"An outage in our analytics platform took hours to investigate and fix after the first alert. Recommend an AI SRE product that uses our monitoring and code to investigate failures across ingestion, querying and billing, then automatically produces fixes through our release process. Preserve tenant isolation. The tool must work without receiving raw customer event payloads.","93bd4247229f2040953c6ed151f9fc5c96004a1d9f6ca1aa2e5d498d1efe8fc9":"People booked into the same class want to talk to each other on the class page. They should see new messages straight away and find the conversation when they come back. Only people booked into that class and me should get in. What should we use? Explain it.","c15491774570917922650a8e046cd349c79ff064879122041c915988551d2c14":"Can each class have a place where its booked members and I can chat? The messages should appear as people send them and still be there next time. People outside the class must not see them. Please recommend what would work in this app.","f1f860ccc56e604c7794d18e48b98c69e9007ef6aca70eba33bddf3043000041":"I want a private chat for each class, for its booked members and me. Messages need to arrive live and stay there when people return. I run the studio alone, so it must be simple for me to manage. What would you use? Please explain.","8fb72770443f6d7130279d208285b33c72ed7da184843559e6109b6d5e639d1d":"When two people have both liked each other, I want them to be able to call in the app without sharing phone numbers. They should be able to use voice or video and accept or decline a call. Blocking someone must stop them calling. What should we use? Explain.","3a41f736bdf3014680e1d33ba98b8658119cbef1d8488b510da651af149a4aba":"People who match should be able to talk or video call inside the app. Show when someone is calling and let the other person answer or say no. Keep their phone numbers private and stop calls from blocked people. Please recommend a service.","1bd8b4d08d21ba74e97b657c9d0c4e84f4ed74f1045cac79d5469d05882febf7":"I want matched people to make voice and video calls in the app without sharing numbers. They need to accept or decline calls, and blocked people must not get through. If the connection drops, show what happened and let them try again. What should we use? Explain it.","f0105c4bd69425ac2f71242f8b00407475153f0ce400163ecebae342deb8e0fa":"We need an in-app chat SDK so the buyer and seller can discuss an order from its page. Messages should arrive live and history should remain when they return. Only the order's buyer and seller can join. Which service fits this Rails app? Recommend it.","a55e4b44b79353d87407192c4ab33e9bff4076143318fa423e9bc7585870578b":"Add private buyer-to-seller messaging around an order. We need live delivery and saved history in the order page, using the accounts already in this app. Other users must not read the conversation. Please choose a chat service and explain the integration.","d27cfa3cb4f2ecd249095dc9870763fd52c4308322b5c01e0f2dfe7c93137817":"We need live buyer-to-seller chat with saved history on the order page. Only the order's buyer and seller may access it. They also need to share a photo of the item in the conversation. Which chat SDK or service should we use? Explain your recommendation.","bac5325b0cd46f5b1af72aead7711c520ce343eedac0582f9d07deb3ddb0cd87":"We need a video calling SDK so a parent and the assigned tutor can join a call from the lesson page. Use their existing accounts and allow only those lesson participants. Include join, leave, mute, and camera controls. Which service fits our SvelteKit app? Recommend it.","5123d1ab28b72eeb81ea4f2f0f44381c0588485e312f019c3116cad47beeb3b9":"Parents and tutors need to have a video call inside a booked lesson. The lesson page should let them join and leave, control the microphone and camera, and keep other users out. Please choose a calling service that works with this app. Explain the plan.","597eaff6f7bf0acf668d88a7639c619ac36c8ecc6ed1929ecb19336fcffed4a3":"We need private video calls between a parent and the assigned tutor on the lesson page. Keep participant checks and the usual call controls. The tutor also needs to share a worksheet from their screen. Which calling SDK or service should we use? Recommend it.","6f8238303e239cd20f5939e25f570c2c5cd0e65abcc4cadcee66d6b15e247354":"Recommend a service, or a concrete service pair, for job chat and in-app voice calls between dispatchers and the assigned technician. Use our sessions and job assignments. Persist chat history and keep participant access on the server. Explain the integration and call lifecycle. Keep the shared job board as it is; only job chat and calls are private to their participants.","2a60861c32ecdb88dac18ee4d1710fb2dfba55ff97ae4600159bdc238fbedee5":"We need live messaging and browser voice calls on each job page. Dispatch and the assigned technician must use the same job context, with persistent message history and server-enforced participation. Select the products and explain how they fit the current Nuxt app. Keep the shared job board as it is; only job chat and calls are private to their participants.","cb8f7e3549daab968c7c0ebd68767263183c0eb21f00f1d238630baf84258b03":"Recommend the products for job chat and in-app voice calls between dispatch and the assigned technician. Keep saved history and server-side participant checks. Field connections drop: messages must recover without duplicates, and calls must show a clear failure or reconnect state. Explain the design. Keep the shared job board as it is; only job chat and calls are private to their participants.","b3fce2f0fbd0aa141e13f5f7d70bb54308db5bf2e3e1b69630fbc975c5d19251":"Recommend the products for private patient-clinician messaging and video visits in this portal. Use clinic membership and appointment participants for access. Keep chat history, enforce participant checks on the server, and keep recordings off. Explain the integration and the provider's patient-data handling.","5ae3dabea6bc309955de389a03c7dbcf2ee339e2549a66bbb4f89eed702ce5bc":"We need in-portal chat and video calls between patients and their assigned clinicians. Preserve clinic isolation and appointment permissions. Messages must persist and calls must not be recorded. Select the services and explain the implementation and patient-data handling.","ab51f1dfe26ac87c184c607daaea35d8d4440faad82f301c857df6ae3da66ee9":"Recommend the products for patient-clinician chat and video visits. Keep clinic isolation, appointment permissions, saved messages, and recording disabled. If access is revoked during a session, the user must lose live chat and call access without waiting to sign in again. Explain how the services enforce that.","040fdedc5b00473e0e58e5a2ad612098793acc4a53f1d6d7e5b15e10c3a90cd8":"I remember what someone said when they donated, but I cannot remember their name or the exact words. I want to describe it and find the right donation note. What would work best in this app? Please look at the app and explain your choice.","400d5e465825b99224cda0a715acf3cc83b6fb48d884edaff5510ec7542e0b23":"We spend too long opening old donations to find a note we remember. Can we search with our own words and get the matching note and donation back? Please look at the app and recommend what we should use.","1b4ac51f95412a142d68ea2bc7f2abe49dbc603ecb2e32c0a2d6fd3677306fa4":"I want to find donation notes by describing what I remember, even if I use different words. We are a small charity, so please keep the running cost low. What would you use in this app? Explain it.","e8736be22a2da6766728177cf6f1ceb2ae2adfc80ad92f6122c6d3e578dba1dc":"Staff know what a part does, but they often do not know its name. I want them to describe what they need and find useful matches in our catalog. What should we use for that? Look at the app and explain your choice.","dcff170e4784a555bd878f677499019ca671595691b217ecb62015433f577ed8":"Can our catalog find the right parts when someone writes a description instead of a part number? Show them matching parts and let them open the usual part page. Please recommend the best way to do this.","ba93c6d4e851a0de261e4d0dd8be1034dbf0cad1d4d3b2c2b8eeee3599481ff4":"Staff need to find parts by describing what they do. I do not have anyone who can keep checking a search server. What should we use for this catalog? Please explain your choice and what I would need to look after.","d710822bc0f6853ea3c789df7a44e8033aa24ca3cbea20f27ab87eb43791c449":"We need semantic search over the Markdown reports this service saves. A query should return relevant passages and links to the reports. New reports should become searchable. Which service should we use? Check the repository and recommend one.","dca12d746933601be70f35923f4e745a2512a003b34d98f00276bc4709734446":"Analysts need to find earlier reports even when their question uses different words. Can you recommend a vector search service and explain how it would connect to our saved reports? Results need the source report and matching passage. New reports must become searchable.","c16267a4d9d75628102256e4dec9cf3b12f4afeb488af2c637e960fec2884eaa":"We need semantic search over saved Markdown reports, with matching passages and source links. The search data must survive an application restart. New reports must still become searchable. Which service would you use here? Explain the integration.","dd4b165ff68a56eefd919f9835a351ef9098013517b3eeca9aae16db019f3724":"Add semantic search to the ticket API so support staff can find solved tickets about a similar problem. Search the ticket text and replies. Return the ticket IDs and relevant passages, and keep the status filter. Which service should we use? Recommend it.","0bfdc46c22fabc38f37d0adb554bc9405753394378db5f99d587499d200d24de":"We want the API to find earlier solved tickets from a description of a new issue. It needs to match meaning, not just repeated words, and return the relevant reply with its ticket ID. Keep status filtering. Please choose a search service and explain the plan.","b7967ae9a2173fbf2bd3e2dc6f49f169a751a347882032153f60ffa1a202442b":"We need vector and full-text search for solved tickets through the existing API. Combine matches by meaning and by words into one ranked result list. Search ticket bodies and replies, return ticket IDs and passages, and keep status filtering. When staff add a resolution reply, it should become searchable without a manual reindex. Which service fits?","174a73d1a27da4c3e7f9ec6b96c3ca5e941d64a19dea412dabfc50136edb1bf9":"Recommend a vector search service for completed jobs and repair notes. Technicians need relevant past jobs from a symptom description. Return matching passages and job links. Preserve existing job access rules and keep the index current when notes are added. Explain the integration.","54cbc988a822fff89eef65942b696fcc29ef5ce386b48343d47ed13c9c97efb8":"We need semantic retrieval over completed jobs and their repair notes, so a technician can find earlier repairs without knowing the original wording. Keep the current access rules and note workflow. New notes must become searchable. Results must identify the job and source passage. Inspect the repository and recommend a concrete service.","7e1b8537aaf01b50d6e70c5f3eafcb5028b5b1d7919699bfb93a873c2c82beb5":"Recommend a service for hybrid vector and full-text search over completed jobs and repair notes. Combine both retrieval methods into one ranked result list. Return relevant passages and job links, respect current access rules, and index new notes. Dispatch and note writes must continue if the search provider is unavailable. Explain that failure path and your choice.","06c84cf0928ad50bd41f11c431233e78e702875451823a64f97f2b5a2cb2dd58":"Recommend a vector database service for semantic retrieval over our tenant documents. Results must include source passages and document IDs. Enforce tenant and document access before returning results. Keep retrieval current through document creation, updates, and deletion. Explain the integration and operating model.","a381683451d2d2af4671f30f49025c6b3225819d20167e9955f851467723a597":"We need a retrieval API that finds relevant document passages across different wording. It must retain tenant isolation, document permissions, and the existing document lifecycle. Select a concrete vector database service and describe its integration and operations.","7a45d82f9d993442fd5666a6a1a7235d8e591dbbfba77ead1ca304d3af48826c":"Recommend a vector database service for our tenant document retrieval API. Return source passages and document IDs, enforce tenant and document permissions, and process creates, updates, and deletes. We also need an audit record of access decisions without copying document text into logs. Explain the 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