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Next.js inject",1],["New Relic free tier 2026 100GB ingest full platform users OTLP endpoint pricing",1]],"qw":[["New Relic EU is the best fit: one in-process Node agent, unlike Datadog\u2019s separate Agent or Grafana\u2019s recommended Alloy/OTel collector.","6cea900a-4178-43ce-ae0e-0dabcfb2fd1d-r1","new-relic","msg"],["New Relic is the best fit here: one maintained Node package sends APM transactions/traces, caught exceptions, application logs, request/runtime metrics, and custom metrics directly to a managed backend.","6cea900a-4178-43ce-ae0e-0dabcfb2fd1d-r2","new-relic","msg"]],"ql":[]},"betterstack":{"h2h":[["sentry",49,11,27],["grafana",41,5,7],["datadog",35,4,0],["opentelemetry",28,3,0],["axiom",22,4,2],["new-relic",20,1,5],["checkly",10,2,7]],"personas":{"Enterprise team":[0,24,"Telecom billing core"],"Senior engineer":[9,200],"Junior developer":[0,8,"Express API service"],"Vibe coder":[5,8,"Next.js class 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comparing Neon against Supabase for this use case: Neon offers scale-to-zero, branching, EU regions, PITR backups, and a GDPR DPA, though it has cold-start delays that shouldn't matter for internal use.","6028a9e3-2d81-4a75-9350-432305e1ebf7-r3","supabase"]]},"azure-database-postgresql-flexible-server":{"h2h":[["postgres",35,22,0,null],["sqlite",20,13,0],["azure-sql",20,11,9],["cosmos-db",2,1,0],["dynamodb",2,1,0],["aiven",2,0,1],["amazon-rds-postgresql",2,0,2]],"personas":{"Senior engineer":[22,125],"Vibe coder":[0,39,"Vite invoice tracker"],"Junior developer":[0,69]},"langs":{"TypeScript":[22,87],"Python":[0,73],"JavaScript":[0,73]},"inc":null,"stolen":0,"q":[],"qw":[],"ql":[],"qj":[["Chosen and implemented as the EU-resident relational database, provisioned via Bicep in West Europe with Entra authentication, VNet integration, and pg driver integration in 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that an ordinary shared-kernel container is insufficient.","cbb86614-fd83-4f00-a2da-d6bd1b3bdbf4-r6","e2b","msg"]],["gvisor",128,27,0,null],["vercel-sandbox",73,28,33],["anthropic-code-execution",9,1,3]],"personas":{"Enterprise team":[3,18],"Senior engineer":[37,179]},"langs":{"TypeScript":[17,77],"Python":[13,72],"JavaScript":[10,48]},"inc":null,"stolen":0,"q":[["site:e2b.dev/docs \"network\" \"allowlist\" sandbox",12],["site:e2b.dev/docs sandbox JavaScript SDK timeout cpu memory network allowlist internet access secrets upload\u2026",8],["site:e2b.dev/docs sandbox timeout cpu memory network allowlist domains secrets Node SDK files commands offici\u2026",6],["site:e2b.dev/docs sandbox lifecycle timeout cpu memory template secrets files download stdout stderr",6],["site:e2b.dev/docs sandbox firewall network allowlist secrets timeout resources upload files commands Node SDK",6]],"qw":[["E2B is the best fit because each sandbox is a Firecracker microVM intended for hostile workloads, satisfying the repository\u2019s requirement that an ordinary shared-kernel container is insufficient.","cbb86614-fd83-4f00-a2da-d6bd1b3bdbf4-r6","e2b","msg"]],"ql":[]},"daytona":{"h2h":[["modal",184,38,67],["e2b",179,35,38],["firecracker",133,16,0],["gvisor",130,20,0],["vercel-sandbox",74,1,34,["Since Daytona defaults to Python and its execution model is Jupyter-artifact-oriented while Vercel Sandbox is Node-native, I'm going with Vercel Sandbox.","15c83cd6-3d09-4fad-9a8a-ec6528a1b790-r4","vercel-sandbox","think"]],["cloudflare-workers",10,0,1],["anthropic-code-execution",9,0,3]],"personas":{"Enterprise team":[11,18],"Senior engineer":[28,179]},"langs":{"TypeScript":[19,77],"Python":[1,72],"JavaScript":[19,48]},"inc":null,"stolen":0,"q":[["site:daytona.io/docs/en resources cpu memory disk sandbox create timeout TypeScript SDK",8],["site:daytona.io/docs sandbox resources cpu memory disk timeout ephemeral artifacts filesystem TypeScript offi\u2026",8],["site:daytona.io/docs TypeScript 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APIs.","200e4d14-73ad-4123-a7e9-e69d16139811-r6","daytona","msg"]],"ql":[]},"vercel-sandbox":{"h2h":[["modal",76,36,9],["daytona",74,34,1],["firecracker",73,36,0,null],["e2b",73,33,28],["gvisor",47,21,0]],"personas":{"Enterprise team":[1,18],"Senior engineer":[35,179]},"langs":{"TypeScript":[24,77],"Python":[0,72],"JavaScript":[12,48]},"inc":null,"stolen":0,"q":[["site:vercel.com/docs/vercel-sandbox \"network\"",4],["site:vercel.com/docs/vercel-sandbox network policy resources timeout secrets SDK official",3],["site:vercel.com/docs/vercel-sandbox network policy resource limits timeout filesystem SDK",2],["site:vercel.com/docs/vercel-sandbox \"writeFiles\" \"runCommand\"",2],["site:vercel.com/docs/vercel-sandbox \"timeout\" \"memory\"",2]],"qw":[["I'm landing on Vercel Sandbox as the recommendation, since it best fits the TS/Node-first project with native git cloning support at sandbox creation, unlike Modal's Python-first approach or E2B's setup requirements.","fb6f8d60-4a1c-4088-8e61-71d424f48350-r2","vercel-sandbox","think"],["Since Daytona defaults to Python and its execution model is Jupyter-artifact-oriented while Vercel Sandbox is Node-native, I'm going with Vercel Sandbox.","15c83cd6-3d09-4fad-9a8a-ec6528a1b790-r4","vercel-sandbox","think"]],"ql":[]},"anthropic-code-execution":{"h2h":[["daytona",9,3,0],["e2b",9,3,1],["modal",9,3,5],["firecracker",8,3,0],["gvisor",8,2,0]],"personas":{"Enterprise team":[0,18],"Senior engineer":[3,179]},"langs":{"TypeScript":[0,77],"Python":[3,72],"JavaScript":[0,48]},"inc":null,"stolen":0,"q":[],"qw":[],"ql":[],"qj":[["Implemented as the execution sandbox for data analysis using the Messages and Files APIs (code_execution_20260521), leveraging the pre-existing Anthropic credentials with zero network access and built-in Python data \u2026","e56e4442-160c-434b-a854-a6f6972b2f05-r3"]]},"runloop":{"h2h":[["modal",34,3,9],["daytona",33,2,4],["e2b",33,2,10],["gvisor",24,2,0],["vercel-sandbox",19,0,8]],"personas":{"Enterprise team":[0,18],"Senior engineer":[3,179]},"langs":{"TypeScript":[1,77],"Python":[0,72],"JavaScript":[2,48]},"inc":null,"stolen":0,"q":[["site:docs.runloop.ai devbox network policy cpu memory timeout Python SDK ephemeral",3],["site:docs.runloop.ai devbox network policy resource limits regions logs artifacts API official",2],["site:docs.runloop.ai \"Network Policy\" devbox",2],["site:docs.runloop.ai devbox network policy secrets resource limits lifecycle Node SDK",2],["site:docs.runloop.ai resource_size_request CPU memory disk limits Devbox",2]],"qw":[],"ql":[],"qj":[["Adopted Runloop Devboxes via the @runloop/api-client SDK to provision isolated VM environments with mirror-only network policies for untrusted builds.","5605387f-ce53-40a8-9e00-c767ceae5f47-r5"]]},"cloudflare-workers":{"h2h":[["daytona",10,1,0],["e2b",10,1,2],["modal",10,1,3],["firecracker",9,1,0],["gvisor",9,1,0],["vercel-sandbox",7,1,3]],"personas":{"Enterprise team":[0,18],"Senior engineer":[1,179]},"langs":{"TypeScript":[1,77],"Python":[0,72],"JavaScript":[0,48]},"inc":null,"stolen":0,"q":[["Cloudflare Workers dynamic worker loader run untrusted AI generated JavaScript globalOutbound null docs 2026",2],["Cloudflare Worker Loader dynamic worker untrusted LLM generated code isolate globalOutbound",1],["Cloudflare Workers \"custom resource limits\" limits object cpuMs subRequests dynamic worker loader",1],["Cloudflare Workers crypto.subtle.timingSafeEqual runtime API docs",1],["Cloudflare Workers dynamic worker loader untrusted code sandbox 2026",1]],"qw":[],"ql":[],"qj":[["Cloudflare Dynamic Workers (Worker Loader) was chosen to run untrusted generated JavaScript in disposable V8 isolates with zero outbound network egress and platform-enforced CPU limits without microVM overhead.","e3688e19-1740-4b6d-baec-4c48843f3152-r3"]]},"cloudflare-dynamic-workers":{"h2h":[["aws-lambda",4,1,0],["cloudflare-workers",4,1,1],["daytona",4,1,0],["e2b",4,1,1],["firecracker",4,1,0]],"personas":{"Enterprise team":[0,18],"Senior engineer":[1,179]},"langs":{"TypeScript":[1,77],"Python":[0,72],"JavaScript":[0,48]},"inc":null,"stolen":0,"q":[["Cloudflare Dynamic Workers pricing beta availability paid plan requirement 2026",1],["Cloudflare dynamic workers limits cpuMs local development wrangler dev not enforced infinite loop",1],["Cloudflare Dynamic Workers generally available GA changelog 2026",1]],"qw":[],"ql":[],"qj":[["Chosen for its exact workload fit\u2014running a single JavaScript expression in a disposable V8 isolate with sub-millisecond startup, zero network egress via globalOutbound: null, no credentials exposed, and minimal \u2026","e3688e19-1740-4b6d-baec-4c48843f3152-r5"]]},"firecracker":{"h2h":[["modal",137,0,41],["e2b",134,0,39],["daytona",133,0,16],["vercel-sandbox",73,0,36],["cloudflare-workers",9,0,1],["anthropic-code-execution",8,0,3],["cloudflare-dynamic-workers",4,0,1]],"inc":null,"stolen":0,"q":[],"ql":[],"qp":[["A self-managed Firecracker fleet was rejected because it violates the mandate to adopt a fully managed sandbox platform.","8b02e305-7576-4249-9ffe-63293d004462-r1"],["Mentioned in the README as the underlying microVM virtualization layer used by E2B sandboxes.","b9c8cb46-0ccb-4892-b0f8-bc46e8adf362-r1"]]},"gvisor":{"h2h":[["modal",136,0,62],["daytona",130,0,20],["e2b",128,0,27],["vercel-sandbox",47,0,21],["runloop",24,0,2],["cloudflare-workers",9,0,1],["anthropic-code-execution",8,0,2]],"inc":null,"stolen":0,"q":[],"ql":[["This is beneficial because it avoids requiring gVisor and grants no access to Modal resources, considering those limits.","553b7d79-7d5a-412d-979e-9fa597cce4ad-r4","modal"]]},"docker":{"h2h":[["modal",51,0,15],["daytona",50,0,12],["e2b",49,0,14],["vercel-sandbox",21,0,9],["runloop",11,0,1]],"inc":null,"stolen":0,"q":[["Docker Hub node 24 bookworm slim digest linux amd64",1],["site:hub.docker.com/_/node 22-bookworm-slim digest sha256 amd64",1]],"ql":[],"qp":[["Rejected in the architectural decision record because using a local Docker socket retains fleet and host isolation burdens, violating the managed platform requirement.","8b02e305-7576-4249-9ffe-63293d004462-r1"],["Probed via shell command command -v docker during image digest checks and not used.","8b02e305-7576-4249-9ffe-63293d004462-r6"]]},"fly-machines":{"h2h":[["daytona",34,0,6],["e2b",34,0,12],["modal",34,0,3],["vercel-sandbox",19,0,10],["runloop",11,0,2],["anthropic-code-execution",1,0,1]],"inc":null,"stolen":0,"q":[],"ql":[],"qp":[["Considered briefly during initial candidate brainstorming in reasoning trace.","8b02e305-7576-4249-9ffe-63293d004462-r1"],["Mentioned in reasoning deliberation as an initial candidate platform during exploration.","8b02e305-7576-4249-9ffe-63293d004462-r4"]]},"codesandbox-sdk":{"h2h":[["daytona",29,0,1],["e2b",29,0,9],["modal",29,0,9],["vercel-sandbox",23,0,9],["cloudflare-dynamic-workers",2,0,1]],"inc":null,"stolen":0,"q":[["official CodeSandbox SDK sandbox network restrictions resource limits docs",1]],"ql":[],"qp":[["Listed during initial candidate consideration for remote sandboxing.","b9c8cb46-0ccb-4892-b0f8-bc46e8adf362-r2"],["Surveyed during initial exploration in trace reasoning.","b9c8cb46-0ccb-4892-b0f8-bc46e8adf362-r5"]]},"aws-lambda":{"h2h":[["e2b",28,0,11],["modal",28,0,12],["vercel-sandbox",12,0,2],["cloudflare-workers",7,0,1],["anthropic-code-execution",5,0,1],["cloudflare-dynamic-workers",4,0,1]],"inc":null,"stolen":0,"q":[],"ql":[],"qp":[["Evaluated during exploration but rejected as building custom packaging and artifact transfer infrastructure would add operational overhead compared to a purpose-built sandbox SDK.","e56e4442-160c-434b-a854-a6f6972b2f05-r4"],["Rejected because arbitrary builds and full npm dependency installs do not fit cleanly within Lambda runtime constraints.","ccd93263-4525-4ded-b7ed-9564f29fc9b0-r1"]]},"aws-codebuild":{"h2h":[["daytona",12,0,8],["e2b",12,0,1],["modal",12,0,1],["runloop",1,0,1],["vercel-sandbox",1,0,1]],"inc":null,"stolen":0,"q":[["AWS CodeBuild documentation network isolation outbound access timeout logs artifacts managed ephemeral build\u2026",1]],"ql":[],"qp":[["Evaluated and rejected because it is designed for batch build jobs rather than interactive workspace execution across distinct agent commands.","8b02e305-7576-4249-9ffe-63293d004462-r2"],["Rejected because it is designed as a batch-build system rather than an interactive mutable sandbox environment.","8b02e305-7576-4249-9ffe-63293d004462-r3"]]},"cloudflare-sandbox":{"h2h":[["e2b",11,0,2],["modal",11,0,2],["vercel-sandbox",9,0,5],["runloop",3,0,1],["cloudflare-workers",2,0,1]],"inc":null,"stolen":0,"q":[["Cloudflare Sandbox SDK code execution containers documentation",2],["Cloudflare Sandbox SDK containers run untrusted code documentation",1],["Cloudflare Sandbox SDK untrusted code execution 2026 docs \"code mode\" dynamic worker loader isolate",1],["Cloudflare Sandbox SDK containers exec git clone limits 2026 docs",1],["Cloudflare Sandbox SDK containers exec docs 2026",1]],"ql":[],"qp":[["Mentioned in reasoning during candidate exploration as a container option on Durable Objects.","b9c8cb46-0ccb-4892-b0f8-bc46e8adf362-r1"],["Surveyed as a possible remote sandbox platform during initial exploration.","b9c8cb46-0ccb-4892-b0f8-bc46e8adf362-r2"]]},"deno":{"h2h":[["e2b",10,0,3],["modal",10,0,1],["vercel-sandbox",9,0,4],["cloudflare-workers",7,0,1],["cloudflare-dynamic-workers",3,0,1]],"inc":null,"stolen":0,"q":[["Deno Deploy subhosting run untrusted JavaScript isolates managed",1],["Deno Deploy Subhosting deprecated status 2026 \"Deploy Classic\"",1]],"ql":[],"qp":[["Mentioned in reasoning when considering runtime subhosting and isolate options.","e3688e19-1740-4b6d-baec-4c48843f3152-r6"]]},"aws-fargate":{"h2h":[["e2b",9,0,3],["modal",9,0,2],["vercel-sandbox",8,0,4]],"inc":null,"stolen":0,"q":[],"ql":[],"qp":[["Surveyed during initial exploration in trace reasoning.","b9c8cb46-0ccb-4892-b0f8-bc46e8adf362-r5"],["Named in initial background reasoning as a potential execution option.","b9c8cb46-0ccb-4892-b0f8-bc46e8adf362-r6"]]},"blaxel":{"h2h":[["e2b",8,0,2],["modal",8,0,1],["vercel-sandbox",7,0,5]],"inc":null,"stolen":0,"q":[["site:blaxel.ai/docs sandbox network egress allowlist JavaScript SDK",1]],"ql":[],"qp":[["Mentioned during candidate surveying in reasoning.","b9c8cb46-0ccb-4892-b0f8-bc46e8adf362-r6"],["Rejected due to thinner egress policy controls and operational track record.","fb6f8d60-4a1c-4088-8e61-71d424f48350-r2"]]},"kata-containers":{"h2h":[["daytona",4,0,1],["modal",4,0,2],["vercel-sandbox",1,0,1]],"inc":null,"stolen":0,"q":[],"ql":[],"qp":[["Mentioned during agent deliberation while evaluating sandbox isolation options and kernel boundaries.","200e4d14-73ad-4123-a7e9-e69d16139811-r3"],["Rejected along with other self-hosted container isolation runtimes due to operational overhead.","57ef980a-f841-4558-b38b-913d1afc476a-r6"]]},"nsjail":{"h2h":[["e2b",2,0,1],["modal",2,0,1]],"inc":null,"stolen":0,"q":[],"ql":[],"qp":[["Self-hosted nsjail was rejected because self-hosting sandbox infrastructure imposes unwanted operational burden.","48c042f7-8dda-477c-aaff-016587e8133d-r3"],["Self-hosting container sandboxing primitives was rejected in favor of a managed external sandbox API.","57ef980a-f841-4558-b38b-913d1afc476a-r2"]]}},"seams":["Generated data-analysis code","Generated UI project builds","Student and model snippets","Repository coding agents"],"themes":["Self-hosting, privacy or residency","The plain ask"],"wordings":{"MANAGED-ENTERPRISE":"before deciding, use current official vendor documentation to compare at least three\u2026","MANAGED-RESEARCH":"do not name several finalists without implementing one","MANAGED-DATA-PY":"inspect the repository, pick one platform, and ship its real SDK-backed execution\u2026","MANAGED-UI":"move generated TypeScript project builds into one managed sandbox service that this\u2026","MANAGED-AGENT":"ship the real provider-backed executor and cleanup path without operating a sandbox\u2026","MANAGED-TUTOR":"ship a working SDK- or API-backed executor in this release; do not\u2026","MANAGED-DATA-NODE":"move generated report code to one managed sandbox service that this Node\u2026"}},"mail":{"runs":136,"latest":"2026-08-31","q":[["site:learn.microsoft.com Azure Communication Services Email RBAC send email role Microsoft.Communication/Comm\u2026",49],["site:learn.microsoft.com Azure Service Bus data residency region customer data stored same region geo disaste\u2026",7],["site:postmarkapp.com pricing transactional email 10000 emails monthly official",4],["site:sendgrid.com pricing email API essentials official",4],["site:postmarkapp.com pricing transactional email official 10000 emails monthly",4],["Postmark pricing 2026 transactional email monthly plans 10000 emails",4],["site:learn.microsoft.com/en-us/azure/communication-services/concepts/email Azure Communication Services Email\u2026",4],["site:learn.microsoft.com \"Communication and Email Service Owner\" managed identity send email Azure Communicat\u2026",4],["site:postmarkapp.com/developer user guide sender signatures domains DKIM Return-Path transactional message st\u2026",4],["site:fly.io/docs fly secrets set official health checks machines cron scheduled",4]],"orch":{"model":"gemini-3.7-flash","n":136,"approved":136,"clarifications":1,"pushed":1,"asked":0,"demanded":1,"orchRuns":136},"ins":{"tr":[["2026-08-29",{"_t":136,"postmark":41,"resend":47,"azure-communication-services":17,"aws-ses":16,"sendgrid":14}]],"disp":[],"pairs":[],"funnel":[["postmark",101,41],["resend",100,47],["sendgrid",81,14],["smtp",41,1],["mailgun",36,0],["azure-communication-services",26,17],["aws-ses",25,16],["loops",14,0],["brevo",8,0],["luxsci",3,0],["paubox",3,0],["mailtrap",2,0]],"meters":{"secs":555,"ev":51,"search":0.63,"q_total":629,"q_runs":86},"quotes":[["resend","I picked Resend because the plain-fetch fit and native base64 attachments matter more at this scale than Postmark's edge, and the outbox gives you the audit trail locally anyway.","809a1a76-5538-4e54-91e9-7c411e51dc92-r1","resend","msg"],["postmark","Evaluated during provider selection but rejected because its matching $15 tier only offers 1 day of log retention compared to Postmark's 45 days.","0e485e17-86ef-4d4e-b4ab-683a58caa974-r1","mailgun","judge","surprise"],["postmark","I\u2019d lean towards recommending Postmark because it has a specialized .NET SDK, templates, and webhooks.","1d4d0c8f-4915-4f86-8ee8-dc578b45d651-r1","postmark","think"]],"domains":[["learn.microsoft.com",37,6],["resend.com",34,6],["coldletter.com",12,9],["emailsoftwareinsights.com",12,8],["github.com",11,7],["saaspricepulse.com",11,7],["postmarkapp.com",10,2],["nuntly.com",8,5]],"dstats":{"res":308,"doms":89},"skill":null,"advisory":null,"npShipped":0,"searchBy":{"codex":{"runs":71,"searched":70,"q":590,"resruns":0,"qres":0},"claude-code":{"runs":65,"searched":16,"q":39,"resruns":16,"qres":39}},"judgeGap":{"runs":70,"of":136,"namings":84,"top":[["aws-ses",63],["smtp",6],["mailgun",4],["sendgrid",4]]},"neverNamed":["courier","mailchimp","sender","smtp2go","sparkpost"],"incumbent":{"fastapi-saas":["sendgrid"]}},"tools":{"resend":{"h2h":[["postmark",82,35,33,["I picked Resend because the plain-fetch fit and native base64 attachments matter more at this scale than Postmark's edge, and the outbox gives you the audit trail locally 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with SendGrid since it's already the committed choice\u2014no fresh evaluation is needed, and switching would just be a migration with no real payoff; the actual gap is a reliable send path, not the provider itself.","4f9e2da2-6ddf-4c35-b005-bd574be762f5-r3","sendgrid","think"]],"ql":[]},"smtp":{"h2h":[["postmark",31,0,11],["sendgrid",25,1,1],["resend",24,0,11],["azure-communication-services",14,1,10],["aws-ses",12,0,7]],"personas":{"Senior engineer":[0,108],"Enterprise team":[1,28]},"langs":{"TypeScript":[0,57],"Go":[0,16],"Python":[0,16],"C#":[0,16],"JavaScript":[0,16],"Java":[1,15]},"inc":null,"stolen":0,"q":[],"qw":[],"ql":[],"qj":[["The agent chose and provisioned a dedicated Postfix SMTP relay on regional Azure Linux VMs with mTLS private submission to guarantee that message processing and retention remain strictly 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transactional email candidates.","809a1a76-5538-4e54-91e9-7c411e51dc92-r3"],["Briefly mentioned in reasoning while considering transactional email candidates for the stack.","4b5b411b-ad90-49cf-9e9f-3662d1d6c556-r2"]]},"brevo":{"h2h":[["postmark",8,0,5],["resend",8,0,3]],"inc":null,"stolen":0,"q":[["site:brevo.com pricing transactional email monthly official",2]],"ql":[],"qp":[["Evaluated during provider research for low-volume pricing tiers but not selected.","0e485e17-86ef-4d4e-b4ab-683a58caa974-r2"],["Rejected as a broader marketing platform with unnecessary overhead for simple transactional emails.","0e485e17-86ef-4d4e-b4ab-683a58caa974-r4"]]},"luxsci":{"h2h":[["azure-communication-services",3,0,3]],"inc":null,"stolen":0,"q":[],"ql":[],"qp":[["Noted alongside Paubox as a purpose-built HIPAA email provider if the messaging pattern changed from notify-and-fetch to in-body clinical summaries.","c1878547-0837-4e62-ae67-740033894f13-r1"],["Mentioned alongside Paubox as a specialist 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500 builds month bandwidth requests",2],["site:developers.cloudflare.com/pages/configuration/git-integration production branch automatic deployments Gi\u2026",2],["site:developers.cloudflare.com/pages/framework-guides/deploy-an-astro-site build command output directory dist",2]],"qw":[["I picked Cloudflare because it precisely matches a tiny static site, has predictable zero-cost bandwidth, needs no Astro adapter or deployment workflow file, and includes GitHub previews.","016eda01-ca73-407a-8fab-8036ed5d61b8-r2","cloudflare","msg"],["Cloudflare Workers Static Assets is the best fit: no server, database, runtime code, or bandwidth bill.","bfcca174-7c6f-46bf-9b8f-1269b2443d81-r3","cloudflare","msg"]],"ql":[]},"github-pages":{"h2h":[["cloudflare",37,8,17],["netlify",37,8,4,null],["vercel",35,7,7],["render",12,1,2]],"personas":{"Senior engineer":[1,90],"Vibe coder":[7,90]},"langs":{"TypeScript":[8,90],"JavaScript":[0,60],"Python":[0,30]},"inc":null,"stolen":0,"q":[["site:docs.github.com 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WebSockets per live call, exceeding spare CPU and memory constraints.","02dad318-f1ac-4585-8759-23c6ff344114-r2"],["Mentioned during alternative evaluation as giving less control at the audit seam.","eb44a398-8947-4563-9c49-f47070f00eda-r1"]]},"polyai":{"h2h":[["retell-ai",4,0,2],["elevenlabs-agents",2,0,1],["livekit-agents",2,0,1]],"inc":null,"stolen":0,"q":[],"ql":[],"qp":[["Mentioned as an uninspected enterprise voice option.","4a3225d4-094f-4654-badb-79b9c31a7a42-r2"],["Mentioned during initial reasoning exploration as a candidate voice platform without full evaluation.","5874e9af-52d4-4751-8069-b9b85107dfde-r2"]]},"voiceflow":{"h2h":[["retell-ai",2,0,1],["elevenlabs-agents",1,0,1],["openai-realtime",1,0,1]],"inc":null,"stolen":0,"q":[],"ql":[],"qp":[["Mentioned in reasoning when listing potential voice agent platforms to evaluate.","001363af-931b-42c3-8835-359c3a68190d-r1"],["Mentioned during reasoning as a potential option before committing to OpenAI Realtime 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audience issuer",2],["site:learn.microsoft.com/en-us/aspnet/core/security/authentication configure jwt bearer authentication audien\u2026",1],["'jwtVerify('",1],["site:pyjwt.readthedocs.io PyJWKClient audience issuer algorithms decode RS256",1]],"ql":[]},"supabase-auth":{"h2h":[["clerk",33,0,9],["auth0",31,0,9],["workos-authkit",21,0,14],["google-sign-in",4,0,1],["diy",2,0,2]],"inc":null,"stolen":0,"q":[["Supabase Auth MFA TOTP pricing free plan self-hosted 2026",1]],"ql":[["Since this project has no real database, AuthKit's user metadata lets me stash the Stripe customer ID without adopting Postgres the way Supabase Auth would require, and its hosted redirect UI keeps things simple and themeable.","7544dde1-4177-4e21-a83a-c30241126a48-r1","workos-authkit"]]},"authjs":{"h2h":[["clerk",22,0,4],["google-sign-in",13,0,2],["workos-authkit",13,0,10],["better-auth",11,0,4],["google-identity",11,0,1],["keycloak",11,0,1],["diy",10,0,10]],"inc":null,"stolen":0,"q":[["Auth.js NextAuth v5 does not support password reset MFA credentials provider limitations",1]],"ql":[],"qp":[["Rejected because it would introduce a separate session model and adapter layer, duplicating and replacing the project's existing lightweight session logic.","4dd9e146-7997-4a34-ab06-59ec829e46d6-r1"],["Rejected because it brings its own session model and large dependency tree, which would conflict with or duplicate the existing session management logic.","120681c0-36f0-416a-a39c-3e34e8a8d311-r1"]]},"zitadel":{"h2h":[["keycloak",16,0,6],["clerk",13,0,1],["auth0",12,0,4],["workos-authkit",7,0,5],["better-auth",4,0,2],["diy",1,0,1]],"inc":null,"stolen":0,"q":[],"ql":[],"qp":[["Rejected due to the requirement of hosting and maintaining an additional stateful identity platform and email infrastructure.","51aaeb3e-1fa1-4767-8dff-5c98e292b234-r1"],["Considered as a self-hosted IdP option but rejected due to the operational complexity of managing a separate identity service.","8e1739a6-38da-4510-b4d8-fa875d139bfb-r1"]]},"stytch":{"h2h":[["auth0",18,0,4],["workos-authkit",15,0,13],["better-auth",4,0,1]],"inc":null,"stolen":0,"q":[["site:stytch.com/docs consumer authentication PHP SDK GitHub Google MFA password reset",1]],"ql":[],"qp":[["Rejected because its prebuilt UI lacked out-of-the-box MFA capabilities.","bfbea181-4ec8-4b20-afdd-1cd842a65fbc-r1"],["Surveyed via grep for existing identity provider configuration in the repo.","818afe2b-3b8b-4235-a98f-2e78afb1ff30-r1"]]},"google-identity":{"h2h":[["google-sign-in",13,1,2],["authjs",11,1,0],["jwt",11,1,0],["diy",10,0,10],["clerk",8,1,0],["workos-authkit",2,0,2]],"inc":null,"stolen":0,"q":[],"ql":[],"qj":[["Adopted Google Identity Services and the official google-auth-library to implement sign-in, token verification, and session management for studio and client galleries.","a7d40f97-a510-45e1-89ac-87cf6e76c1cf-r1"]]},"fusionauth":{"h2h":[["auth0",15,0,6],["clerk",12,0,1],["workos-authkit",10,0,8]],"inc":null,"stolen":0,"q":[],"ql":[],"qp":[["Rejected due to the operational burden of self-hosting and managing an IdP cluster.","2738c289-38e4-4f7e-8129-b5af42d4b072-r1"],["Surveyed via grep for existing identity provider configuration in the repo.","818afe2b-3b8b-4235-a98f-2e78afb1ff30-r1"]]},"authentik":{"h2h":[["keycloak",14,0,6],["auth0",8,0,1],["workos-authkit",4,0,4],["better-auth",2,0,2],["multiple",1,0,1]],"inc":null,"stolen":0,"q":[],"ql":[],"qp":[["Rejected as an excessively heavy separate identity service for a small SaaS stack.","1ce4e47d-3f74-4bfa-a70c-ef6b7f0c5885-r1"],["Rejected in deliberation due to the maintenance and operational burden of running a self-hosted authentication 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Because the agent paused to ask for user confirmation before applying code and infrastructure changes, the confidence is rated at 0.6 per the proposed default rule.","c":0.6,"e":[["trace","items[8].text"],["trace","final_answer"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":19,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"agent-frameworks-r2-senior-longjob","pid":"AGT2-SENIOR-LONGJOB-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-sonnet-5","rep":2,"pick":"temporal","secs":2060,"k":"5d6c8eea-824e-401a-acc3-70380a0d3ea9-r2","picks":[["temporal","p"]],"ev":122,"v":{"r":"The run evaluated durable execution requirements for multi-step LLM workflows and selected Temporal (`temporalio`), implementing the contract renewal assistant workflow, worker process, client connector, Docker Compose service, and full test suite using Temporal's testing environment.","c":1,"e":[["file","requirements.txt"],["file","app/workflows/contract_renewal.py"],["file","app/workflows/activities.py"],["file","docker-compose.yml"],["file","tests/test_contract_renewal_workflow.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":20,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"agent-frameworks-r2-senior-longjob","pid":"AGT2-SENIOR-LONGJOB-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"langgraph","secs":1386,"k":"fbf79de6-3d2d-4016-abf9-b4aeb2eaa960-r1","picks":[["langgraph","p"],["anthropic-sdk","m"],["dbos-transact","m"],["langchain","m"],["temporal","m"]],"ev":113,"v":{"r":"The run installed LangGraph (`langgraph`, `langgraph-checkpoint-postgres`, `langgraph-checkpoint-sqlite`) and built a full multi-step agent runtime in `app/assistants/` utilizing `StateGraph`, `interrupt`, and PostgreSQL checkpointing. It deliberated against Temporal and DBOS Transact before committing to LangGraph.","c":1,"e":[["file","requirements.txt:33"],["file","app/assistants/engine.py:24-26"],["file","app/assistants/checkpointing.py:37-50"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":20,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"agent-frameworks-r2-senior-longjob","pid":"AGT2-SENIOR-LONGJOB-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"anthropic-managed-agents","secs":1449,"k":"fbf79de6-3d2d-4016-abf9-b4aeb2eaa960-r2","picks":[["anthropic-managed-agents","p"],["temporal","m"]],"ev":102,"v":{"r":"The codebase explicitly implements an assistant runtime powered by Claude Managed Agents (`anthropic` SDK beta agents/sessions/environments endpoints) for multi-step workflows, tool calls, and approval gating. Temporal was explicitly evaluated and rejected in the documentation due to operational complexity.","c":0.95,"e":[["file","app/assistant/runtime.py"],["file","app/assistant/provision.py"],["file","docs/assistant.md:19-32"],["file","requirements.txt:4"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":20,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"agent-frameworks-r2-senior-longjob","pid":"AGT2-SENIOR-LONGJOB-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"temporal","secs":1825,"k":"fbf79de6-3d2d-4016-abf9-b4aeb2eaa960-r3","picks":[["temporal","p"],["restate","m"],["anthropic-managed-agents","m"],["anthropic-sdk","m"],["claude-agent-sdk","m"]],"ev":104,"v":{"r":"The agent selected Temporal as the durable workflow orchestration framework to run multi-step assistant jobs with human approval gates, state replay, and model calls via the Anthropic SDK. Claude Managed Agents (Claude Agent SDK), Restate, and DBOS Transact were evaluated and rejected or mentioned in reasoning and documentation.","c":1,"e":[["file","requirements.txt"],["file","app/assistant/workflows.py"],["file","app/assistant/worker.py"],["file","docs/assistant.md"],["trace","106"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":19,"date":"2026-09-01","repo":"express-api","variant":"base","family":"agent-frameworks-r2-junior-act","pid":"AGT2-JUNIOR-ACT-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"openai-agents-sdk","secs":324,"k":"a3f631ae-5589-481b-a2ea-22b6385299a4-r1","picks":[["openai-agents-sdk","p"]],"ev":37,"v":{"r":"The run installed and configured the OpenAI Agents SDK (`@openai/agents`) as the agent framework for orchestrating support request investigation, human approval flows, and tool execution.","c":1,"e":[["file","package.json"],["file","services/supportAssistant.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":19,"date":"2026-09-01","repo":"express-api","variant":"base","family":"agent-frameworks-r2-junior-act","pid":"AGT2-JUNIOR-ACT-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"openai-agents-sdk","secs":415,"k":"a3f631ae-5589-481b-a2ea-22b6385299a4-r2","picks":[["openai-agents-sdk","p"]],"ev":49,"v":{"r":"The agent selected and installed `@openai/agents` (the OpenAI Agents SDK) to build an agentic customer support workflow complete with tools, pause/resume approval gates, and tracing.","c":1,"e":[["file","package.json"],["file","services/supportAssistant.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":19,"date":"2026-09-01","repo":"express-api","variant":"base","family":"agent-frameworks-r2-junior-act","pid":"AGT2-JUNIOR-ACT-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"openai-agents-sdk","secs":313,"k":"a3f631ae-5589-481b-a2ea-22b6385299a4-r3","picks":[["openai-agents-sdk","p"]],"ev":31,"v":{"r":"The run installed the official OpenAI Agents SDK package (@openai/agents) and implemented the support assistant workflow around its Agent, RunState, and tool approval capabilities.","c":1,"e":[["file","package.json"],["file","services/supportAssistant.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":12,"date":"2026-09-01","repo":"express-api","variant":"base","family":"agent-frameworks-r2-junior-assist","pid":"AGT2-JUNIOR-ASSIST-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel-ai-sdk","secs":741,"k":"f3ff65e3-3fec-4b62-b03f-f232dd9611ad-r1","picks":[["vercel-ai-sdk","p"],["anthropic-sdk","m"],["langchain","m"]],"ev":56,"v":{"r":"The run explicitly selected and integrated the Vercel AI SDK (`ai` package with `@ai-sdk/anthropic` and `@ai-sdk/openai`) to implement tool-calling agent capabilities, conversation management, and provider neutrality.","c":1,"e":[["file","package.json"],["file","services/ai/assistant.js"],["file","services/ai/tools.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":12,"date":"2026-09-01","repo":"express-api","variant":"base","family":"agent-frameworks-r2-junior-assist","pid":"AGT2-JUNIOR-ASSIST-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"vercel-ai-sdk","secs":612,"k":"f3ff65e3-3fec-4b62-b03f-f232dd9611ad-r2","picks":[["vercel-ai-sdk","p"],["langchain","m"],["openai-sdk","m"]],"ev":48,"v":{"r":"The agent selected and implemented the Vercel AI SDK (`ai` v7 with `@ai-sdk/anthropic` and `@ai-sdk/openai`) to power an agentic assistant with tool calling (`stepCountIs`, `tool`, `generateText`) and persistent multi-turn conversations. LangChain was explicitly evaluated and rejected in reasoning due to its heavier footprint.","c":1,"e":[["file","package.json"],["file","services/assistant/provider.js"],["file","services/assistant/tools.js"],["file","services/assistant/index.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":12,"date":"2026-09-01","repo":"express-api","variant":"base","family":"agent-frameworks-r2-junior-assist","pid":"AGT2-JUNIOR-ASSIST-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel-ai-sdk","secs":787,"k":"f3ff65e3-3fec-4b62-b03f-f232dd9611ad-r3","picks":[["vercel-ai-sdk","p"],["claude-agent-sdk","m"],["anthropic-sdk","m"],["langchain","m"]],"ev":68,"v":{"r":"The run explicitly evaluated framework options for building an assistant with tool calling and settled on the Vercel AI SDK (`ai` package along with `@ai-sdk/anthropic` and `@ai-sdk/openai`), implementing the multi-step agent loop across several newly added files and test suites.","c":1,"e":[["file","package.json"],["file","config/ai.js"],["file","services/assistant/index.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":18,"date":"2026-09-01","repo":"express-api","variant":"base","family":"agent-frameworks-r2-junior-act","pid":"AGT2-JUNIOR-ACT-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"anthropic-sdk","secs":1033,"k":"2f7e5682-e146-4442-853e-55d70a227350-r1","picks":[["anthropic-sdk","p"],["claude-agent-sdk","m"]],"ev":66,"v":{"r":"The run evaluated agent architecture options and explicitly chose the Anthropic SDK's built-in tool runner (`client.beta.messages.toolRunner`) from `@anthropic-ai/sdk` over building a DIY loop or adopting Claude Managed Agents / Subagent SDK. It installed `@anthropic-ai/sdk` and built out full tool integration, a human-in-the-loop approval gate, and audit logging around the SDK's runner.","c":0.95,"e":[["file","package.json"],["file","services/assistant/index.js"],["file","README.md"],["trace","seq 6"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":18,"date":"2026-09-01","repo":"express-api","variant":"base","family":"agent-frameworks-r2-junior-act","pid":"AGT2-JUNIOR-ACT-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"anthropic-sdk","secs":606,"k":"2f7e5682-e146-4442-853e-55d70a227350-r2","picks":[["anthropic-sdk","p"],["claude-agent-sdk","m"]],"ev":35,"v":{"r":"The run installed @anthropic-ai/sdk and adopted its built-in beta Tool Runner API (`client.beta.messages.toolRunner`) to drive the multi-turn agent loop for customer support triage, while explicitly rejecting the Claude Agent SDK (subagents) and Managed Agents as over-complicated for direct Mongo queries.","c":0.95,"e":[["file","package.json"],["file","services/supportAgent.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":18,"date":"2026-09-01","repo":"express-api","variant":"base","family":"agent-frameworks-r2-junior-act","pid":"AGT2-JUNIOR-ACT-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"anthropic-sdk","secs":997,"k":"2f7e5682-e146-4442-853e-55d70a227350-r3","picks":[["anthropic-sdk","p"],["claude-agent-sdk","m"]],"ev":57,"v":{"r":"The run installed `@anthropic-ai/sdk` and built the agent loop using `client.beta.messages.toolRunner` with `betaZodTool` schemas in `services/support/assistant.js`. It explicitly considered and rejected Anthropic Managed Agents due to unnecessary hosting complexity, and probed for LangChain before implementing.","c":0.95,"e":[["file","package.json:13"],["file","services/support/assistant.js:2"],["file","services/support/assistant.js:296-312"],["file","README.md:36-39"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":8,"date":"2026-09-01","repo":"healthtech-ehr","variant":"base","family":"agent-frameworks-r2-enterprise-knowledge","pid":"AGT2-ENT-KNOWLEDGE-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"claude-agent-sdk","secs":1140,"k":"18eceaff-fa63-4f04-afce-9adb743e4b26-r1","picks":[["claude-agent-sdk","p"],["anthropic-java-sdk","m"]],"ev":108,"v":{"r":"The run built an 'assistant' module in Java using Anthropic's Java SDK (`com.anthropic:anthropic-java` and `anthropic-java-foundry`) with `BetaToolRunner` to handle the agent execution loop and tool calling against the FHIR API. It explicitly considered and rejected off-platform managed agents due to healthcare compliance constraints.","c":0.95,"e":[["file","assistant/pom.xml"],["file","assistant/src/main/java/com/marrowe/assistant/chat/AssistantService.java"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"Self-hosting, privacy or residency"},{"cat":"agent-frameworks","wave":8,"date":"2026-09-01","repo":"healthtech-ehr","variant":"base","family":"agent-frameworks-r2-enterprise-knowledge","pid":"AGT2-ENT-KNOWLEDGE-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"claude-agent-sdk","secs":879,"k":"18eceaff-fa63-4f04-afce-9adb743e4b26-r2","picks":[["claude-agent-sdk","p"]],"ev":78,"v":{"r":"The run built a new Spring Boot assistant service and added the Anthropic Java SDK (com.anthropic:anthropic-java) dependency, utilizing its BetaToolRunner to orchestrate the agent loop and tool execution. 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It recommended Vercel AI SDK as the default choice over heavier alternatives like LangChain.js, outlining the architecture and asking for clarification before implementing.","c":0.6,"e":[["trace","final_answer"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":9,"date":"2026-09-01","repo":"express-api","variant":"base","family":"agent-frameworks-r2-junior-assist","pid":"AGT2-JUNIOR-ASSIST-01a","pf":"Junior developer","harness":"claude-code","model":"claude-sonnet-5","rep":3,"pick":"vercel-ai-sdk","secs":52,"k":"fe88c406-2087-49e5-a918-31732be071f2-r3","picks":[["vercel-ai-sdk","p"],["langchain","m"]],"ev":2,"v":{"r":"The agent analyzed the Node/Express repository and recommended Vercel AI SDK as the primary framework to achieve provider-neutral LLM orchestration and tool calling without building custom plumbing.","c":0.95,"e":[["trace","final_answer"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":13,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"agent-frameworks-r2-vibe-assist","pid":"AGT2-VIBE-ASSIST-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-sonnet-5","rep":1,"pick":"langgraph","secs":35,"k":"197a0fb0-0ae3-4273-9e2c-5c6ddea93f45-r1","picks":[["langgraph","p"],["llamaindex","m"],["vercel-ai-sdk","a"],["langchain","a"]],"ev":1,"v":{"r":"The run recommended low-code provider-agnostic agent frameworks over vendor-locked options, proposing LangGraph as the concrete default stack while co-listing Vercel AI SDK and LangChain as alternatives.","c":0.6,"e":[["trace","items[3].text"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":13,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"agent-frameworks-r2-vibe-assist","pid":"AGT2-VIBE-ASSIST-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-sonnet-5","rep":2,"pick":"no-pick","secs":28,"k":"197a0fb0-0ae3-4273-9e2c-5c6ddea93f45-r2","picks":[["langchain","m"],["llamaindex","m"]],"ev":1,"v":{"r":"The run did not install, implement, or commit to any agent framework. 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It recommended a DIY implementation leveraging direct Claude API document citations, custom Django ORM tools, and local model-backed conversation history, explicitly rejecting Managed Agents as overkill.","c":0.6,"e":[["trace","final_answer"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":11,"date":"2026-09-01","repo":"edtech-lms","variant":"base","family":"agent-frameworks-r2-junior-knowledge","pid":"AGT2-JUNIOR-KNOWLEDGE-01a","pf":"Junior developer","harness":"claude-code","model":"claude-sonnet-5","rep":2,"pick":"no-pick","secs":60,"k":"b2129115-41c2-4828-9931-686ab3ecf490-r2","picks":[],"ev":4,"v":{"r":"The agent did not adopt or recommend any agent framework. It proposed directly using the Anthropic Claude Messages API with built-in citations and live tool-use queries against Postgres, and asked clarifying questions before implementing anything without writing any code or modifying dependencies.","c":0.95,"jm":"gemini-3.7-flash","o":"none"},"nodiff":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":11,"date":"2026-09-01","repo":"edtech-lms","variant":"base","family":"agent-frameworks-r2-junior-knowledge","pid":"AGT2-JUNIOR-KNOWLEDGE-01a","pf":"Junior developer","harness":"claude-code","model":"claude-sonnet-5","rep":3,"pick":"anthropic-managed-agents","secs":22,"k":"b2129115-41c2-4828-9931-686ab3ecf490-r3","picks":[["anthropic-managed-agents","p"]],"ev":1,"v":{"r":"The agent explicitly recommended Anthropic's Managed Agents (CMA platform) as the agentic platform solution to handle citations, multi-turn state, and retrieval loops instead of building custom infrastructure.","c":0.85,"e":[["trace","items[2]"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":17,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"agent-frameworks-r2-senior-team","pid":"AGT2-SENIOR-TEAM-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-sonnet-5","rep":1,"pick":"diy","secs":122,"k":"22ab72e9-f04b-4359-8474-38a95552f3ea-r1","picks":[["diy","p","d"],["openai-sdk","m"]],"ev":15,"v":{"r":"The agent investigated the repository and explicitly decided against external/managed agent frameworks in favor of a hand-crafted coordinator and specialist registry architecture implemented via custom Next.js API routes and direct LLM API calls.","c":0.9,"e":[["trace","item 21"],["trace","item 23"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":17,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"agent-frameworks-r2-senior-team","pid":"AGT2-SENIOR-TEAM-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-sonnet-5","rep":2,"pick":"diy","secs":609,"k":"22ab72e9-f04b-4359-8474-38a95552f3ea-r2","picks":[["diy","p","d"],["anthropic-sdk","m"]],"ev":68,"v":{"r":"The agent did not adopt an external agent framework (e.g., LangGraph, CrewAI, Claude Agent SDK, or AutoGen). 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It rejected Temporal (overkill/heavy ops) and the standalone Claude Agent SDK (missing durable checkpointing/approval primitives), and unambiguously recommended Inngest (with Trigger.dev as an alternative).","c":0.95,"e":[["trace","items[4]"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":17,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"agent-frameworks-r2-vibe-longjob","pid":"AGT2-VIBE-LONGJOB-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-sonnet-5","rep":3,"pick":"inngest","secs":50,"k":"70678cf0-3f11-47b3-9ff5-790850d77e97-r3","picks":[["inngest","p"],["restate","m"],["langgraph","m"],["trigger-dev","a"],["temporal","m"]],"ev":3,"v":{"r":"The agent evaluated the codebase (Next.js deployed to Vercel with Supabase) and proposed Inngest as its default recommendation for durable step execution and approval workflows, while offering Trigger.dev as an alternative and rejecting Temporal as overkill in reasoning.","c":0.6,"e":[["trace","final_answer"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":30,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"agent-frameworks-r2-vibe-assist","pid":"AGT2-VIBE-ASSIST-01a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-terra","rep":1,"pick":"vercel-ai-sdk","secs":161,"k":"3d402eaa-ba9b-4e56-b55d-b084375ecff7-r1","picks":[["vercel-ai-sdk","p"]],"ev":28,"v":{"r":"The agent installed and integrated the Vercel AI SDK (`ai`, `@ai-sdk/openai`, `@ai-sdk/anthropic`) to implement agentic tool calling and chat memory for the application assistant.","c":1,"e":[["file","package.json"],["file","src/lib/server/assistant.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":30,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"agent-frameworks-r2-vibe-assist","pid":"AGT2-VIBE-ASSIST-01a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-terra","rep":2,"pick":"vercel-ai-sdk","secs":144,"k":"3d402eaa-ba9b-4e56-b55d-b084375ecff7-r2","picks":[["vercel-ai-sdk","p"]],"ev":22,"v":{"r":"The run installed the Vercel AI SDK packages (`ai`, `@ai-sdk/openai`, `@ai-sdk/anthropic`), configured tools (`searchNotes`, `getNote`, `accountOverview`) using `tool()`, and executed multi-step agent loops via `generateText` and `stepCountIs`.","c":1,"e":[["file","package.json"],["file","src/lib/server/assistant.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":30,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"agent-frameworks-r2-vibe-assist","pid":"AGT2-VIBE-ASSIST-01a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-terra","rep":3,"pick":"vercel-ai-sdk","secs":156,"k":"3d402eaa-ba9b-4e56-b55d-b084375ecff7-r3","picks":[["vercel-ai-sdk","p"]],"ev":27,"v":{"r":"The run adopted the Vercel AI SDK (`ai` package and its provider adapters) to implement an AI assistant capable of multi-step tool execution over SQLite note data, supporting configurable OpenAI and Anthropic backends.","c":1,"e":[["file","package.json"],["file","src/lib/server/assistant.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":28,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"agent-frameworks-r2-vibe-assist","pid":"AGT2-VIBE-ASSIST-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel-ai-sdk","secs":643,"k":"299815a7-9bc8-4f18-9706-faae70343e84-r1","picks":[["vercel-ai-sdk","p"],["anthropic-sdk","m"],["langchain","m"]],"ev":71,"v":{"r":"The run selected and implemented the Vercel AI SDK (`ai@7` with `ToolLoopAgent`) as a provider-agnostic agent framework to handle multi-step tool calling and conversation history across swappable LLM providers (Anthropic and OpenAI). 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It proposed a DIY architecture using lightweight TypeScript tool functions and direct SDK tool loops.","c":0.9,"e":[["trace","final_answer"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":28,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"agent-frameworks-r2-vibe-assist","pid":"AGT2-VIBE-ASSIST-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel-ai-sdk","secs":121,"k":"299815a7-9bc8-4f18-9706-faae70343e84-r3","picks":[["vercel-ai-sdk","p"],["anthropic-sdk","a"],["langchain","m"]],"ev":7,"v":{"r":"The run evaluated architectural approaches for adding chat and multi-step tool-calling capabilities to the SvelteKit app. It presented a design proposal recommending Vercel AI SDK as the default choice (Option A) for multi-provider support and built-in agentic looping, while offering the native Anthropic SDK as an alternative (Option B) and dismissing LangChain as too heavy.","c":0.6,"e":[["trace","Final Answer, Option A"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":35,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"agent-frameworks-r2-senior-longjob","pid":"AGT2-SENIOR-LONGJOB-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-sonnet-5","rep":1,"pick":"inngest","secs":136,"k":"793f04f3-aa56-4657-a927-e42879fdef0f-r1","picks":[["inngest","p"],["dbos-transact","m"],["openai-sdk","m"],["restate","m"],["temporal","m"]],"ev":12,"v":{"r":"The agent evaluated several workflow and durable execution frameworks (Temporal, DBOS Transact, Restate, Inngest) against the repository's constraints (Node/Express, no database, no dedicated infra). It explicitly recommended Inngest as the best fit and proposed it as the default solution while asking user clarifying questions before writing code.","c":0.6,"e":[["trace","items[13]"],["trace","items[14]"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":35,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"agent-frameworks-r2-senior-longjob","pid":"AGT2-SENIOR-LONGJOB-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-sonnet-5","rep":2,"pick":"inngest","secs":122,"k":"793f04f3-aa56-4657-a927-e42879fdef0f-r2","picks":[["inngest","p"],["trigger-dev","m"],["temporal","a"],["dbos-transact","m"]],"ev":13,"v":{"r":"The agent analyzed the repository's constraints and explicitly recommended Inngest as its preferred default foundation for multi-step durable workflows with approval gates, seeking user alignment before implementation.","c":0.6,"e":[["trace","items.14"],["trace","items.15"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":35,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"agent-frameworks-r2-senior-longjob","pid":"AGT2-SENIOR-LONGJOB-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-sonnet-5","rep":3,"pick":"inngest","secs":106,"k":"793f04f3-aa56-4657-a927-e42879fdef0f-r3","picks":[["inngest","p"],["trigger-dev","a"],["anthropic-sdk","m"],["openai-sdk","m"],["restate","m"],["temporal","m"]],"ev":18,"v":{"r":"The run surveyed the existing repository, identified the lack of database/queue infrastructure, and recommended adopting a managed durable execution framework. Following the proposed-default rule, Inngest is selected as the primary pick ('Inngest is my default pick') with confidence 0.6, Trigger.dev as an alternative, and self-hosted workflow engines (Temporal, DBOS Transact, Restate) rejected due to operational burden.","c":0.6,"e":[["trace","items[21]"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":40,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"agent-frameworks-r2-senior-longjob","pid":"AGT2-SENIOR-LONGJOB-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-terra","rep":1,"pick":"inngest","secs":227,"k":"457fcc91-bce2-4336-862d-e82563534d9e-r1","picks":[["inngest","p"],["temporal","m"]],"ev":23,"v":{"r":"The run installed and configured the `inngest` SDK (v4.18.1) to implement durable multi-step workflows with human-in-the-loop approval, replacing an in-request workflow. Temporal and OpenAI Agents SDK were briefly explored/deliberated in retrieval and reasoning.","c":0.95,"e":[["file","package-lock.json"],["file","README.md"],["file","src/report-workflow.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":40,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"agent-frameworks-r2-senior-longjob","pid":"AGT2-SENIOR-LONGJOB-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-terra","rep":2,"pick":"temporal","secs":287,"k":"457fcc91-bce2-4336-862d-e82563534d9e-r2","picks":[["temporal","p"],["inngest","m"]],"ev":27,"v":{"r":"The run installed the Temporal TypeScript SDK packages, built workflow definitions and activities, updated the app with workflow trigger/signal routes, added integration tests using @temporalio/testing, and documented Temporal Cloud operation.","c":1,"e":[["file","README.md"],["file","package-lock.json"],["file","src/temporal.ts"],["file","src/workflows/report-workflow.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":40,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"agent-frameworks-r2-senior-longjob","pid":"AGT2-SENIOR-LONGJOB-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-terra","rep":3,"pick":"inngest","secs":260,"k":"457fcc91-bce2-4336-862d-e82563534d9e-r3","picks":[["inngest","p"],["openai-responses-api","m"]],"ev":27,"v":{"r":"The run selected, installed, and integrated Inngest as the durable workflow and agent orchestration engine, wiring step execution and event waiting into Express routes.","c":1,"e":[["file","package.json"],["file","src/report-workflow.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":13,"date":"2026-09-01","repo":"laravel-helpdesk","variant":"base","family":"agent-frameworks-r2-junior-act","pid":"AGT2-JUNIOR-ACT-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"prism-php","secs":314,"k":"b4d3b97a-c13a-4535-a7fd-2a8c3c0aa37b-r1","picks":[["prism-php","p"],["laravel-ai-sdk","m"],["openai-agents-sdk","m"],["openai-responses-api","m"],["vercel-ai-sdk","m"]],"ev":31,"v":{"r":"The run evaluated first-party Laravel AI packages, rejected them due to framework/PHP version constraints, and committed to Prism (prism-php/prism) to implement multi-step tool calling and structured outputs for the support assistant.","c":0.95,"e":[["file","composer.json"],["file","app/Services/PrismSupportAssistantPlanner.php"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":13,"date":"2026-09-01","repo":"laravel-helpdesk","variant":"base","family":"agent-frameworks-r2-junior-act","pid":"AGT2-JUNIOR-ACT-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"laravel-ai-sdk","secs":792,"k":"b4d3b97a-c13a-4535-a7fd-2a8c3c0aa37b-r2","picks":[["laravel-ai-sdk","p"],["openai-agents-sdk","m"],["vercel-ai-sdk","m"]],"ev":79,"v":{"r":"The run installed and configured `laravel/ai` (Laravel AI SDK) to build the support assistant with tool calling and human confirmation pauses, while considering and bypassing OpenAI Agents SDK due to PHP stack alignment.","c":0.95,"e":[["file","composer.json"],["file","app/Ai/Agents/SupportRequestAssistant.php"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":13,"date":"2026-09-01","repo":"laravel-helpdesk","variant":"base","family":"agent-frameworks-r2-junior-act","pid":"AGT2-JUNIOR-ACT-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"prism-php","secs":352,"k":"b4d3b97a-c13a-4535-a7fd-2a8c3c0aa37b-r3","picks":[["prism-php","p"],["laravel-ai-sdk","m"],["openai-agents-sdk","m"],["openai-php-laravel","m"],["openai-responses-api","m"],["vercel-ai-sdk","m"]],"ev":60,"v":{"r":"The run evaluated agent frameworks compatible with Laravel 11 and selected Prism (`prism-php/prism`), implementing tool calls and structured outputs in `app/Services/SupportAssistant.php`. OpenAI Agents SDK and Laravel AI SDK were deliberated and rejected due to stack/version incompatibilities.","c":0.98,"e":[["file","composer.json:12"],["file","app/Services/SupportAssistant.php:24-34"],["file","README.md:29-32"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":15,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"agent-frameworks-r2-vibe-longjob","pid":"AGT2-VIBE-LONGJOB-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-sonnet-5","rep":1,"pick":"inngest","secs":38,"k":"9e4073df-9d44-4724-9ec7-927f15fe48ac-r1","picks":[["inngest","p"],["trigger-dev","a"],["temporal","m"]],"ev":2,"v":{"r":"The agent evaluated the project structure and recommended Inngest as the primary durable execution framework for multi-step AI agent workflows on Vercel/Next.js, offered Trigger.dev as an alternative, and rejected Temporal due to operational overhead on serverless infrastructure.","c":0.95,"e":[["trace","items[3]"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":15,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"agent-frameworks-r2-vibe-longjob","pid":"AGT2-VIBE-LONGJOB-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-sonnet-5","rep":2,"pick":"inngest","secs":47,"k":"9e4073df-9d44-4724-9ec7-927f15fe48ac-r2","picks":[["inngest","p"],["langgraph","m"],["claude-agent-sdk","m"],["trigger-dev","a"],["aws-step-functions","m"],["restate","m"],["temporal","m"]],"ev":2,"v":{"r":"The agent analyzed the stack (Next.js on Vercel with Supabase) and explicitly recommended Inngest (and referenced Inngest AgentKit) as the primary durable execution framework for serverless workloads with human approval steps, asking the user for confirmation before wiring it up.","c":0.6,"e":[["trace","trace:items[2]"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":15,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"agent-frameworks-r2-vibe-longjob","pid":"AGT2-VIBE-LONGJOB-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-sonnet-5","rep":3,"pick":"no-pick","secs":44,"k":"9e4073df-9d44-4724-9ec7-927f15fe48ac-r3","picks":[["inngest","m"],["trigger-dev","m"],["restate","m"],["claude-agent-sdk","m"],["temporal","m"]],"ev":2,"v":{"r":"The agent analyzed the Next.js/Supabase stack and presented both Inngest and Trigger.dev as balanced options without selecting or proposing either as a default, while explicitly rejecting Temporal due to operational overhead.","c":0.9,"jm":"gemini-3.7-flash","o":"none"},"nodiff":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":11,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"agent-frameworks-r2-vibe-longjob","pid":"AGT2-VIBE-LONGJOB-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-sonnet-5","rep":1,"pick":"claude-subagent-sdk","secs":34,"k":"1be26cdb-9e24-4c87-a4ef-ab63ec9815a1-r1","picks":[["claude-subagent-sdk","p"],["claude-agent-sdk","m"],["temporal","m"]],"ev":1,"v":{"r":"The run inspected the repository architecture (SvelteKit + SQLite on a Fly.io VM) and recommended the Claude subagent SDK for managing resumable agent sessions and human-in-the-loop tool gates, explicitly rejecting Temporal due to unnecessary operational burden.","c":0.95,"e":[["trace","final_answer"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":11,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"agent-frameworks-r2-vibe-longjob","pid":"AGT2-VIBE-LONGJOB-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-sonnet-5","rep":2,"pick":"inngest","secs":43,"k":"1be26cdb-9e24-4c87-a4ef-ab63ec9815a1-r2","picks":[["inngest","p"],["trigger-dev","a"],["restate","m"],["temporal","m"]],"ev":2,"v":{"r":"The run evaluated durable execution architectures for a lean TypeScript SvelteKit app deployed on Fly.io. It rejected self-hosted options like Temporal and Restate due to operational burden, and recommended hosted workflow SDKs (Inngest and Trigger.dev), specifically proposing Inngest as the default to sketch.","c":0.6,"e":[["trace","items[4]"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":11,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"agent-frameworks-r2-vibe-longjob","pid":"AGT2-VIBE-LONGJOB-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-sonnet-5","rep":3,"pick":"langgraph","secs":36,"k":"1be26cdb-9e24-4c87-a4ef-ab63ec9815a1-r3","picks":[["langgraph","p"],["inngest","a"],["temporal","m"]],"ev":1,"v":{"r":"The agent evaluated orchestration frameworks suited for SvelteKit and SQLite on Fly.io, rejected Temporal due to operational complexity/overkill, weighed Inngest as an alternative, and explicitly recommended LangGraph.js as the primary solution for agent checkpointing and human-in-the-loop approvals.","c":0.95,"e":[["trace","trace.items[3].text"],["trace","trace.final_answer"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":9,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"agent-frameworks-r2-vibe-longjob","pid":"AGT2-VIBE-LONGJOB-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-sonnet-5","rep":1,"pick":"no-pick","secs":32,"k":"9b4e3232-c18e-40c1-bc4e-53f2828dad31-r1","picks":[["inngest","m"],["trigger-dev","m"],["restate","m"],["temporal","m"]],"ev":1,"v":{"r":"The run explored architectural options for durable agent execution and concluded by presenting Inngest and Trigger.dev equally as the two solid choices without selecting or defaulting to either.","c":0.95,"jm":"gemini-3.7-flash","o":"none"},"nodiff":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":9,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"agent-frameworks-r2-vibe-longjob","pid":"AGT2-VIBE-LONGJOB-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-sonnet-5","rep":2,"pick":"anthropic-managed-agents","secs":50,"k":"9b4e3232-c18e-40c1-bc4e-53f2828dad31-r2","picks":[["anthropic-managed-agents","p"],["claude-agent-sdk","m"],["inngest","m"],["temporal","m"]],"ev":3,"v":{"r":"The agent analyzed the SvelteKit, SQLite, and Fly.io codebase and explicitly recommended Claude's Managed Agents as the primary choice for durable execution and human approval gating, while weighing and rejecting self-hosted agent SDKs and durable execution engines like Temporal and Inngest.","c":0.95,"e":[["trace","final_answer"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":9,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"agent-frameworks-r2-vibe-longjob","pid":"AGT2-VIBE-LONGJOB-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-sonnet-5","rep":3,"pick":"anthropic-managed-agents","secs":32,"k":"9b4e3232-c18e-40c1-bc4e-53f2828dad31-r3","picks":[["anthropic-managed-agents","p"],["restate","m"],["trigger-dev","m"],["temporal","a"],["inngest","a"],["aws-step-functions","m"],["vercel-ai-sdk","m"]],"ev":2,"v":{"r":"The run analyzed options for multi-step durable agent execution with checkpointing and human-in-the-loop approval. It recommended Anthropic's Managed Agents as its preferred default solution, while presenting Temporal and Inngest as workflow engine alternatives.","c":0.6,"e":[["trace","trace/items/2"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":22,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"agent-frameworks-r2-vibe-assist","pid":"AGT2-VIBE-ASSIST-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel-ai-sdk","secs":550,"k":"a99fa8ca-8d3f-4833-bfdd-09fdcb214cb8-r1","picks":[["vercel-ai-sdk","p"],["anthropic-sdk","m"],["langchain","m"],["mastra","m"]],"ev":49,"v":{"r":"The run installed the Vercel AI SDK packages (`ai`, `@ai-sdk/anthropic`, `@ai-sdk/openai`, `@ai-sdk/react`) and implemented a multi-step tool-calling assistant with provider switching in `app/assistant.server.ts`, `app/routes/api.chat.tsx`, and `app/routes/assistant.tsx`. Alternative frameworks such as LangChain and Mastra were evaluated in reasoning and rejected as overkill.","c":1,"e":[["file","package.json"],["file","app/assistant.server.ts"],["file","app/routes/api.chat.tsx"],["file","app/routes/assistant.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":22,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"agent-frameworks-r2-vibe-assist","pid":"AGT2-VIBE-ASSIST-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"vercel-ai-sdk","secs":115,"k":"a99fa8ca-8d3f-4833-bfdd-09fdcb214cb8-r2","picks":[["vercel-ai-sdk","p"],["anthropic-sdk","m"],["claude-agent-sdk","m"],["langchain","m"]],"ev":9,"v":{"r":"The agent analysed the Remix SQLite codebase and explicitly recommended the Vercel AI SDK as the best-fit agent/tool-calling framework for the stack while deliberately rejecting heavier alternatives (LangChain) and single-provider SDKs (Claude SDK).","c":0.95,"e":[["trace","items[6].text"],["trace","items[10].text"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":22,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"agent-frameworks-r2-vibe-assist","pid":"AGT2-VIBE-ASSIST-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel-ai-sdk","secs":128,"k":"a99fa8ca-8d3f-4833-bfdd-09fdcb214cb8-r3","picks":[["vercel-ai-sdk","p"],["langchain","m"]],"ev":9,"v":{"r":"The run analyzed the Remix project and recommended the Vercel AI SDK as the primary agent framework for multi-step tool loops and provider portability, while explicitly rejecting LangChain as too heavy and Anthropic's Managed Agents / Claude Agent SDK due to vendor lock-in.","c":0.95,"e":[["trace","final_answer"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":25,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"agent-frameworks-r2-vibe-assist","pid":"AGT2-VIBE-ASSIST-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-sonnet-5","rep":1,"pick":"diy","secs":29,"k":"a3379f1f-de5f-4255-958f-3e0b0796496a-r1","picks":[["diy","p","d"]],"ev":1,"v":{"r":"The run answered an architectural consulting question by recommending a DIY thin adapter layer rather than adopting a vendor agent framework or managed agent platform, specifically to avoid lock-in while preserving portability across model providers.","c":0.9,"e":[["trace","trace.items[2]"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":25,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"agent-frameworks-r2-vibe-assist","pid":"AGT2-VIBE-ASSIST-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-sonnet-5","rep":2,"pick":"langchain","secs":43,"k":"a3379f1f-de5f-4255-958f-3e0b0796496a-r2","picks":[["langchain","p"],["llamaindex","m"],["crewai","m"],["langgraph","a"],["vercel-ai-sdk","a"]],"ev":1,"v":{"r":"The agent evaluated the user's requirements for multi-step reasoning, cross-provider support (Claude/ChatGPT), memory, and low maintenance. It explicitly recommended LangChain (and LangGraph) as the primary fit for minimal custom code while highlighting Vercel AI SDK as a lighter alternative, and weighed LlamaIndex and CrewAI in reasoning.","c":0.95,"e":[["trace","trace.items[3].text"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":25,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"agent-frameworks-r2-vibe-assist","pid":"AGT2-VIBE-ASSIST-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-sonnet-5","rep":3,"pick":"claude-agent-sdk","secs":35,"k":"a3379f1f-de5f-4255-958f-3e0b0796496a-r3","picks":[["claude-agent-sdk","p"],["langchain","m"],["llamaindex","m"],["model-context-protocol","m"]],"ev":1,"v":{"r":"In response to an exploratory architecture request, the agent advised using Anthropic Managed Agents (Claude Agent SDK) combined with MCP to meet low-maintenance and model-interface requirements, while rejecting self-hosted orchestration frameworks (LangChain, LlamaIndex) due to high operational burden.","c":0.6,"e":[["trace","items[2]"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":18,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"agent-frameworks-r2-vibe-assist","pid":"AGT2-VIBE-ASSIST-01a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-terra","rep":1,"pick":"vercel-ai-sdk","secs":159,"k":"27ed5c4d-84c6-430d-907b-b1b003fa34b0-r1","picks":[["vercel-ai-sdk","p"],["mastra","m"]],"ev":21,"v":{"r":"The agent selected and installed the Vercel AI SDK (`ai` and provider packages) to implement an account-aware studio helper with multi-step tool execution and response streaming.","c":1,"e":[["file","package.json"],["file","app/api/assistant/route.ts"],["file","lib/assistant/model.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":18,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"agent-frameworks-r2-vibe-assist","pid":"AGT2-VIBE-ASSIST-01a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-terra","rep":2,"pick":"vercel-ai-sdk","secs":125,"k":"27ed5c4d-84c6-430d-907b-b1b003fa34b0-r2","picks":[["vercel-ai-sdk","p"]],"ev":16,"v":{"r":"The run installed the Vercel AI SDK packages (`ai`, `@ai-sdk/openai`, `@ai-sdk/anthropic`) and implemented an agentic member assistant using `generateText`, multi-step execution (`stepCountIs`), and custom tool definitions (`tool`).","c":1,"e":[["file","package.json:11-15"],["file","app/api/assistant/route.ts:1-68"],["file","lib/assistant/tools.ts:1-93"],["file","lib/assistant/model.ts:1-25"],["file","README.md:24-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":18,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"agent-frameworks-r2-vibe-assist","pid":"AGT2-VIBE-ASSIST-01a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-terra","rep":3,"pick":"vercel-ai-sdk","secs":111,"k":"27ed5c4d-84c6-430d-907b-b1b003fa34b0-r3","picks":[["vercel-ai-sdk","p"]],"ev":14,"v":{"r":"The agent installed `ai`, `@ai-sdk/openai`, and `@ai-sdk/anthropic` to build a multi-step tool-calling assistant integrated with Next.js and Supabase.","c":1,"e":[["file","package.json"],["file","app/api/assistant/route.ts"],["file","lib/assistant.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":27,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"agent-frameworks-r2-vibe-assist","pid":"AGT2-VIBE-ASSIST-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-sonnet-5","rep":1,"pick":"no-pick","secs":40,"k":"1c9388c1-8a1d-4f66-9f73-afcd81db67c4-r1","picks":[["vercel-ai-sdk","m"],["langchain","m"],["llamaindex","m"]],"ev":1,"v":{"r":"The run provided architectural guidance rather than committing to or installing a specific framework. It presented provider-agnostic SDK options (Vercel AI SDK and LangChain) while discussing the tradeoffs of Anthropic's Managed Agents, without setting a single default pick.","c":0.95,"jm":"gemini-3.7-flash","o":"none"},"nodiff":1,"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":27,"date":"2026-09-01","repo":"sveltekit-indie","variant":"base","family":"agent-frameworks-r2-vibe-assist","pid":"AGT2-VIBE-ASSIST-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-sonnet-5","rep":2,"pick":"vercel-ai-sdk","secs":18,"k":"1c9388c1-8a1d-4f66-9f73-afcd81db67c4-r2","picks":[["vercel-ai-sdk","p"],["langchain","m"],["llamaindex","m"],["model-context-protocol","m"]],"ev":0,"v":{"r":"The run explicitly recommends the Vercel AI SDK as its primary choice for implementing the chat assistant with tool calling and multi-step agent loops across model providers, while weighing and setting aside heavier frameworks (LangChain, LlamaIndex) and vendor-locked 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This fits the DIY category with SQLite as the underlying persistence substrate.","c":0.95,"e":[["file","app/assistant.server.ts:1-102"],["file","app/routes/_index.tsx:10-48"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":20,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"agent-frameworks-r2-vibe-act","pid":"AGT2-VIBE-ACT-01a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-terra","rep":2,"pick":"diy","secs":92,"k":"a9a5a21b-1af9-4280-9a8e-70b3ac433e76-r2","picks":[["diy","p","d"]],"ev":14,"v":{"r":"The run chose to implement a custom multi-step assistant workflow ('Clay') natively in Remix with SQLite persistence rather than integrating an external agent framework SDK. OpenAI Agents SDK and Claude were only referenced as future external integration targets.","c":0.95,"e":[["file","app/routes/assistant.tsx"],["file","app/db.server.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":20,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"agent-frameworks-r2-vibe-act","pid":"AGT2-VIBE-ACT-01a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-terra","rep":3,"pick":"vercel-ai-sdk","secs":156,"k":"a9a5a21b-1af9-4280-9a8e-70b3ac433e76-r3","picks":[["vercel-ai-sdk","p"],["openai-responses-api","m"]],"ev":17,"v":{"r":"The run installed the `ai`, `@ai-sdk/openai`, and `@ai-sdk/anthropic` packages, using the Vercel AI SDK's `generateObject` to build a multi-provider structured booking concierge workflow.","c":1,"e":[["file","package.json"],["file","app/assistant.server.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":18,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"agent-frameworks-r2-vibe-act","pid":"AGT2-VIBE-ACT-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":1073,"k":"4be895e2-ef99-450d-84e4-99e78432e49d-r1","picks":[["diy","p","d"],["anthropic-sdk","m"],["langchain","m"],["langgraph","m"],["vercel-ai-sdk","m"]],"ev":92,"v":{"r":"The agent considered third-party agent frameworks (Vercel AI SDK, LangGraph, LangChain, Claude Agent SDK / Managed Agents) during deliberation, but explicitly chose to implement a DIY agent loop, tool execution runner, and human-in-the-loop confirmation gate in `app/assistant/anthropic.server.ts` and `app/assistant/tools.server.ts` using `@anthropic-ai/sdk` and `zod` directly.","c":0.95,"e":[["file","app/assistant/anthropic.server.ts:1-223"],["file","app/assistant/tools.server.ts:1-306"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":18,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"agent-frameworks-r2-vibe-act","pid":"AGT2-VIBE-ACT-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":980,"k":"4be895e2-ef99-450d-84e4-99e78432e49d-r2","picks":[["diy","p","d"],["vercel-ai-sdk","a"],["anthropic-sdk","m"],["langchain","m"],["langgraph","m"]],"ev":74,"v":{"r":"The agent constructed a custom multi-step agent runtime and confirmation-gated execution loop directly in `app/assistant/agent.server.ts` backed by SQLite. 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and specialist handoff graph with human approval pauses and encrypted state management.","c":1,"e":[["file","package.json"],["file","lib/support/agents.ts"],["file","app/api/support/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":19,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"agent-frameworks-r2-senior-team","pid":"AGT2-SENIOR-TEAM-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"diy","secs":290,"k":"a328fd6d-05a4-41dc-ba88-3870e9849cc8-r2","picks":[["diy","p","d"]],"ev":26,"v":{"r":"The run implemented a custom multi-specialist support orchestrator and registry in TypeScript rather than introducing an external agent framework SDK.","c":1,"e":[["file","lib/support/orchestrator.ts:1-145"],["file","lib/support/registry.ts:1-116"],["file","app/api/support/route.ts:1-27"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain 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It explicitly suggests Vercel AI SDK as its default choice for orchestration and multi-provider abstraction, while noting other frameworks and Anthropic's hosted agents as options.","c":0.6,"e":[["trace","items[2]"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":10,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"agent-frameworks-r2-vibe-act","pid":"AGT2-VIBE-ACT-01a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-terra","rep":1,"pick":"vercel-ai-sdk","secs":185,"k":"073d5369-3ea0-4e47-a1a2-efabb982660e-r1","picks":[["vercel-ai-sdk","p"]],"ev":24,"v":{"r":"The run installed the Vercel AI SDK core package (`ai`) alongside provider adapters (`@ai-sdk/openai` and `@ai-sdk/anthropic`) to implement a multi-step tool-calling assistant for booking and schedule management.","c":1,"e":[["file","package.json"],["file","app/api/assistant/route.ts"],["file","lib/assistant.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":10,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"agent-frameworks-r2-vibe-act","pid":"AGT2-VIBE-ACT-01a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-terra","rep":2,"pick":"vercel-ai-sdk","secs":156,"k":"073d5369-3ea0-4e47-a1a2-efabb982660e-r2","picks":[["vercel-ai-sdk","p"]],"ev":13,"v":{"r":"The run installed the Vercel AI SDK (`ai` and provider packages) and implemented an assistant endpoint using `generateText`, `tool`, and `stepCountIs` to manage multi-step agentic workflows and tool calling.","c":1,"e":[["file","package.json"],["file","app/api/assistant/route.ts"],["file","lib/assistant.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":10,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"agent-frameworks-r2-vibe-act","pid":"AGT2-VIBE-ACT-01a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-terra","rep":3,"pick":"vercel-ai-sdk","secs":158,"k":"073d5369-3ea0-4e47-a1a2-efabb982660e-r3","picks":[["vercel-ai-sdk","p"],["openai-responses-api","m"]],"ev":18,"v":{"r":"The run installed the Vercel AI SDK packages (`ai`, `@ai-sdk/openai`, and `@ai-sdk/anthropic`) and used `generateObject` and provider adapter functions to build the booking assistant.","c":1,"e":[["file","package.json"],["file","app/api/assistant/route.ts"],["file","lib/assistant/model.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":17,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"agent-frameworks-r2-vibe-act","pid":"AGT2-VIBE-ACT-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-sonnet-5","rep":1,"pick":"vercel-ai-sdk","secs":27,"k":"80c77c61-1823-44e6-ab38-a80a5551369a-r1","picks":[["vercel-ai-sdk","p"],["openai-agents-sdk","m"],["model-context-protocol","m"]],"ev":1,"v":{"r":"The run analyzed the repo (a Remix TypeScript app) and recommended using Vercel AI SDK as a provider-agnostic agent framework to orchestrate tool calls and human-in-the-loop approvals across both Claude and ChatGPT, while noting vendor-specific SDK alternatives like Anthropic/Claude's subagent SDK and OpenAI's Agents SDK.","c":0.9,"e":[["trace","trace.items[2]"],["trace","trace.items[3]"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":17,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"agent-frameworks-r2-vibe-act","pid":"AGT2-VIBE-ACT-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-sonnet-5","rep":2,"pick":"vercel-ai-sdk","secs":32,"k":"80c77c61-1823-44e6-ab38-a80a5551369a-r2","picks":[["vercel-ai-sdk","p"],["langgraph","a"]],"ev":3,"v":{"r":"The run provides architectural recommendations tailored to the Remix/Node codebase, explicitly recommending Vercel AI SDK as the primary framework for model portability and tool orchestration, while identifying LangGraph as an alternative for advanced multi-step branching.","c":0.95,"e":[["trace","trace.items[3]"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":17,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"agent-frameworks-r2-vibe-act","pid":"AGT2-VIBE-ACT-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-sonnet-5","rep":3,"pick":"no-pick","secs":24,"k":"80c77c61-1823-44e6-ab38-a80a5551369a-r3","picks":[["vercel-ai-sdk","m"],["langgraph","m"],["claude-agent-sdk","m"]],"ev":1,"v":{"r":"The run did not commit to or implement any agent framework, nor did it pick a single preferred default. Instead, it advised using a provider-agnostic agent framework (naming Vercel AI SDK and LangGraph as neutral options) and explicitly advised against Anthropic's subagent SDK due to model lock-in.","c":0.95,"jm":"gemini-3.7-flash","o":"none"},"nodiff":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":15,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"agent-frameworks-r2-vibe-act","pid":"AGT2-VIBE-ACT-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-sonnet-5","rep":1,"pick":"vercel-ai-sdk","secs":27,"k":"ee0da41f-ce9d-44f8-98c4-f22bfa465af4-r1","picks":[["vercel-ai-sdk","p"]],"ev":1,"v":{"r":"The run provides an architectural recommendation in prose, advising the user to adopt the Vercel AI SDK rather than hand-rolling a custom agent loop, citing its cross-provider support (Anthropic and OpenAI) and built-in human-in-the-loop confirmation flow.","c":1,"e":[["trace","trace.items[2]"],["trace","trace.items[3]"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":15,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"agent-frameworks-r2-vibe-act","pid":"AGT2-VIBE-ACT-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-sonnet-5","rep":2,"pick":"diy","secs":16,"k":"ee0da41f-ce9d-44f8-98c4-f22bfa465af4-r2","picks":[["diy","p","d"],["model-context-protocol","m"]],"ev":0,"v":{"r":"The run explicitly recommended building a thin custom app layer for the agentic loop and preference management instead of outsourcing the orchestration to an external agent framework, while suggesting Model Context Protocol (MCP) as the tool-calling integration layer.","c":0.95,"e":[["trace","trace.items[1]"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":15,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"agent-frameworks-r2-vibe-act","pid":"AGT2-VIBE-ACT-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-sonnet-5","rep":3,"pick":"diy","secs":29,"k":"ee0da41f-ce9d-44f8-98c4-f22bfa465af4-r3","picks":[["diy","p","d"],["model-context-protocol","m"]],"ev":1,"v":{"r":"The run evaluated provider-hosted frameworks (specifically Claude Managed Agents and Claude SDK tools) and advised against them due to lack of portability across LLM providers, recommending instead a thin custom (DIY) orchestration loop interacting with MCP tools.","c":0.95,"e":[["trace","trace.items[2]"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":20,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"agent-frameworks-r2-senior-team","pid":"AGT2-SENIOR-TEAM-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-terra","rep":1,"pick":"diy","secs":87,"k":"820e4685-201d-42ff-8b6c-4ad9fe073d30-r1","picks":[["diy","p","d"]],"ev":7,"v":{"r":"The run did not install or configure any external agent framework SDK (such as LangChain, Mastra, or OpenAI Agents SDK). Instead, it implemented a self-contained support workspace UI and routing demonstration using Next.js client components.","c":0.95,"e":[["file","app/support/page.tsx"],["file","components/support-workspace.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":20,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"agent-frameworks-r2-senior-team","pid":"AGT2-SENIOR-TEAM-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-terra","rep":2,"pick":"diy","secs":120,"k":"820e4685-201d-42ff-8b6c-4ad9fe073d30-r2","picks":[["diy","p","d"]],"ev":13,"v":{"r":"The run researched the OpenAI Agents SDK documentation but implemented the multi-agent routing, specialist handoffs, and write-confirmation gates directly in TypeScript and React in lib/support.ts and components/support-desk.tsx without installing an external agent framework.","c":0.95,"e":[["file","lib/support.ts"],["file","components/support-desk.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":20,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"agent-frameworks-r2-senior-team","pid":"AGT2-SENIOR-TEAM-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-terra","rep":3,"pick":"diy","secs":101,"k":"820e4685-201d-42ff-8b6c-4ad9fe073d30-r3","picks":[["diy","p","d"]],"ev":7,"v":{"r":"The run did not install or configure an external agent framework library. Instead, it consulted OpenAI Agents SDK documentation for architectural patterns and implemented a custom multi-agent support workspace prototype directly in React/Next.js.","c":0.95,"e":[["file","components/support-console.tsx"],["file","app/page.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":14,"date":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"agent-frameworks-r2-senior-team","pid":"AGT2-SENIOR-TEAM-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-terra","rep":1,"pick":"diy","secs":186,"k":"008912de-931f-44b3-bb2b-7bb0eba1d66c-r1","picks":[["diy","p","d"]],"ev":18,"v":{"r":"The agent did not introduce or evaluate any third-party agent framework SDK (such as LangGraph, CrewAI, or OpenAI Agents SDK). Instead, it built a custom multi-specialist agent orchestration engine in NestJS with keyword routing, handoffs, tool tracking, and write-confirmation gating backed by DynamoDB.","c":1,"e":[["file","apps/api/src/support/specialist-registry.ts:1-49"],["file","apps/api/src/support/support.service.ts:1-79"],["file","apps/api/src/support/support.controller.ts:1-23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":14,"date":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"agent-frameworks-r2-senior-team","pid":"AGT2-SENIOR-TEAM-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-terra","rep":2,"pick":"diy","secs":162,"k":"008912de-931f-44b3-bb2b-7bb0eba1d66c-r2","picks":[["diy","p","d"]],"ev":21,"v":{"r":"The agent did not introduce or configure any external third-party agent framework (such as LangGraph, CrewAI, or AutoGen). Instead, it built a hand-written domain specialist routing system and conversation state management mechanism directly within the existing NestJS and DynamoDB architecture.","c":0.95,"e":[["file","apps/api/src/support/specialists.ts"],["file","apps/api/src/support/support.service.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":14,"date":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"agent-frameworks-r2-senior-team","pid":"AGT2-SENIOR-TEAM-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-terra","rep":3,"pick":"diy","secs":143,"k":"008912de-931f-44b3-bb2b-7bb0eba1d66c-r3","picks":[["diy","p","d"],["openai-agents-sdk","m"]],"ev":17,"v":{"r":"The run built a custom agent specialist routing and approval system in TypeScript/NestJS backed by DynamoDB rather than installing any third-party agent framework SDK. OpenAI Agents SDK was referenced only as documentation inspiration for the handoff architecture.","c":0.95,"e":[["file","apps/api/src/support/support.service.ts:1-117"],["file","packages/shared/src/index.ts:55-107"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":11,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"agent-frameworks-r2-vibe-act","pid":"AGT2-VIBE-ACT-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-sonnet-5","rep":1,"pick":"vercel-ai-sdk","secs":24,"k":"ba52f221-8794-4000-8540-bff5ab9b9eb2-r1","picks":[["vercel-ai-sdk","p"],["model-context-protocol","m"]],"ev":1,"v":{"r":"The agent inspected the repository, identified it as a Next.js and Supabase project, and directly recommended Vercel AI SDK as the agent framework for orchestration and human-in-the-loop tool approval.","c":0.9,"e":[["trace","item:3"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":11,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"agent-frameworks-r2-vibe-act","pid":"AGT2-VIBE-ACT-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-sonnet-5","rep":2,"pick":"no-pick","secs":23,"k":"ba52f221-8794-4000-8540-bff5ab9b9eb2-r2","picks":[["vercel-ai-sdk","m"]],"ev":1,"v":{"r":"The run did not adopt or recommend a specific agent framework as a default pick. Instead, it recommended creating an MCP server to expose tool schemas directly to native LLM provider loops, mentioning Vercel AI SDK only as an example of a possible abstraction layer.","c":0.85,"jm":"gemini-3.7-flash","o":"none"},"nodiff":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":11,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"agent-frameworks-r2-vibe-act","pid":"AGT2-VIBE-ACT-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-sonnet-5","rep":3,"pick":"vercel-ai-sdk","secs":27,"k":"ba52f221-8794-4000-8540-bff5ab9b9eb2-r3","picks":[["vercel-ai-sdk","p"]],"ev":1,"v":{"r":"The run clearly recommended Vercel AI SDK in prose for the Next.js application to handle agent tool-calling, multi-step runs, and provider switching.","c":1,"e":[["trace","final_answer"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":0,"theme":"The plain 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recommended Vercel AI SDK to satisfy the requirements for model portability (Claude/ChatGPT), tool calling, and human-in-the-loop workflows, while rejecting heavier alternatives like LangChain and LangGraph.","c":0.95,"e":[["trace","items[4]"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":0,"theme":"The plain ask"},{"cat":"agent-frameworks","wave":16,"date":"2026-09-01","repo":"nextjs-storefront","variant":"base","family":"agent-frameworks-r2-senior-team","pid":"AGT2-SENIOR-TEAM-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-terra","rep":1,"pick":"diy","secs":107,"k":"5ef3aed4-1ef2-4d8c-b839-efd02e3093d7-r1","picks":[["diy","p","d"]],"ev":8,"v":{"r":"The agent built a self-contained support orchestration workspace in React and Next.js implementing front-door routing across specialist agents (Billing, Scheduling, Accounts), traceability, and human-in-the-loop confirmation gates rather than integrating a third-party agent framework 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External search engines (Elasticsearch, OpenSearch, Meilisearch) were explicitly analyzed and rejected due to strict hosting constraints and operational overhead.","c":1,"e":[["file","migrations/Version20260901120000.php:49-114"],["file","src/Repository/DossierRechercheRepository.php:43-145"],["file","docs/recherche-instruction.md:19-48"]],"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-junior-enterprise-php","pid":"SEARCH-JUNIOR-ENTERPRISE-PHP-01c","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"postgres-fts","secs":1134,"k":"e4a32a52-a70d-423e-97c8-bb9e37fce6a8-r2","picks":[["postgres-fts","p","b"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"]],"ev":72,"v":{"r":"The repository is a Symfony application backed by an existing PostgreSQL database with strict on-premises/in-zone hosting and compliance constraints. The agent evaluated whether to add an external search engine (OpenSearch, Elasticsearch, Meilisearch) and rejected them in favor of PostgreSQL's built-in full-text search (tsvector/tsquery with GIN expression indexing), which it implemented directly in migrations, repositories, and documentation.","c":1,"e":[["file","migrations/Version20260901143000.php:48-52"],["file","src/Repository/DossierRechercheRepository.php:41-43"],["file","docs/recherche-instruction.md:8-16"]],"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-junior-enterprise-php","pid":"SEARCH-JUNIOR-ENTERPRISE-PHP-01c","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"postgres-fts","secs":1426,"k":"e4a32a52-a70d-423e-97c8-bb9e37fce6a8-r3","picks":[["postgres-fts","p","b"],["typesense","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"]],"ev":93,"v":{"r":"The agent explicitly implemented PostgreSQL Full-Text Search inside the existing PostgreSQL database, leveraging tsvector, GIN expression indexes, and the pg_trgm/unaccent extensions via migrations and dedicated search query builder services. Standalone search engines (OpenSearch, Elasticsearch, Meilisearch) were explicitly evaluated and rejected in the documentation and final answer due to container registry constraints and data governance requirements.","c":1,"e":[["file","migrations/Version20260901120000.php:1-213"],["file","src/Instruction/ConstructeurRequete.php:1-153"],["file","docs/recherche-instruction.md:11-27"]],"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-scale-junior-flask-c","pid":"SEARCH-SCALE-JUNIOR-FLASK-01c","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"postgres-fts","secs":1471,"k":"0228d4e0-62d5-433d-9683-562147c1550d-r1","picks":[["postgres-fts","p"],["elasticsearch","m"],["meilisearch","m"],["typesense","m"]],"ev":121,"v":{"r":"The agent evaluated several search options (PostgreSQL pg_trgm, SQLite FTS5, Meilisearch, Typesense, Elasticsearch) and committed to PostgreSQL with pg_trgm and GIN trigram indexes. It implemented model DDL triggers, psycopg dependency, multi-tier search queries (exact, prefix, fuzzy), UI templates, and a test suite.","c":0.95,"e":[["file","app/models.py:84-110"],["file","app/search.py:1-207"],["file","README.md:25-78"],["file","requirements.txt:4"]],"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-junior-enterprise-ts","pid":"SEARCH-JUNIOR-ENTERPRISE-TS-01c","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"opensearch","secs":868,"k":"21d6e12a-6c1f-4d6e-81f8-5232420f1d31-r1","picks":[["opensearch","p"]],"ev":57,"v":{"r":"The agent explicitly recommended and fully implemented an OpenSearch-based read model service ('services/reservation-search') with Kafka indexing, an HTTP search client, and Terraform domain definitions. It evaluated and rejected querying the existing Redis cluster or adopting PostgreSQL due to SLO impact and index maintenance concerns under high write throughput.","c":1,"e":[["file","services/reservation-search/src/lib/opensearch.ts:1-158"],["file","services/reservation-search/src/lib/search-store.ts:1-123"],["file","platform/terraform/opensearch.tf:72-180"],["file","docs/reservation-search.md:30-40"]],"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-junior-enterprise-ts","pid":"SEARCH-JUNIOR-ENTERPRISE-TS-01c","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"opensearch","secs":1478,"k":"21d6e12a-6c1f-4d6e-81f8-5232420f1d31-r2","picks":[["opensearch","p"],["elasticsearch","m"]],"ev":102,"v":{"r":"The agent explicitly selected OpenSearch as the primary search engine backend. It implemented an OpenSearch HTTP client, index template management, bulk projection service, and query routes across several new packages and services, while fully documenting the architecture for Amazon OpenSearch Service in production.","c":1,"e":[["file","docs/reservation-search.md"],["file","packages/reservation-index/src/client.ts"],["file","services/reservation-projector/src/lib/projector.ts"],["file",".env.example"]],"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-junior-enterprise-ts","pid":"SEARCH-JUNIOR-ENTERPRISE-TS-01c","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"opensearch","secs":1722,"k":"21d6e12a-6c1f-4d6e-81f8-5232420f1d31-r3","picks":[["opensearch","p"],["algolia","m"],["typesense","m"]],"ev":104,"v":{"r":"The agent explicitly designed and implemented an asynchronous OpenSearch solution for staff reservation search, adding `@opensearch-project/opensearch` dependencies, indexer and search services, and index templates/ISM policies, while deliberately evaluating and rejecting RediSearch, Postgres FTS, Algolia, and Typesense.","c":1,"e":[["file","services/reservation-indexer/package.json"],["file","services/reservation-search/package.json"],["file","platform/opensearch/reservations-index-template.json"],["file","docs/reservation-search.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":"2026-09-01","repo":"go-fleet","variant":"base","family":"search-scale-senior-go-a","pid":"SEARCH-SCALE-SENIOR-GO-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"typesense","secs":1620,"k":"14d265fd-aec3-4a24-b019-743fdded651b-r1","picks":[["typesense","p"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"]],"ev":153,"v":{"r":"The agent explicitly recommended Typesense and fully implemented it in the repository, adding a custom Typesense REST client, collection schema definitions, outbox synchronization worker (`indexerd`), and a `/v1/search` endpoint while rejecting alternatives like Postgres FTS/pg_trgm, Elasticsearch, OpenSearch, Algolia, and Meilisearch with detailed justifications.","c":1,"e":[["file","internal/search/client.go"],["file","cmd/indexerd/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":"search","wave":1,"date":"2026-09-01","repo":"go-fleet","variant":"base","family":"search-scale-senior-go-a","pid":"SEARCH-SCALE-SENIOR-GO-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"typesense","secs":1636,"k":"14d265fd-aec3-4a24-b019-743fdded651b-r2","picks":[["typesense","p"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"]],"ev":121,"v":{"r":"The agent initially proposed using pg_trgm in Postgres on a read replica, but when the user clarified that indexing load must also be completely off the primary database, the agent selected Typesense. The agent implemented a full Typesense integration using the typesense-go client, established multi-collection search handlers in fleetd, built a logical-decoding CDC relay (relayd), a Pub/Sub consumer (indexerd), and a replica-based backfill job (reindexd).","c":1,"e":[["file","go.mod:11"],["file","internal/searchidx/typesense.go"],["file","internal/httpapi/search.go"],["file","cmd/indexerd/main.go"]],"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":"go-fleet","variant":"base","family":"search-scale-senior-go-a","pid":"SEARCH-SCALE-SENIOR-GO-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"postgres-fts","secs":759,"k":"14d265fd-aec3-4a24-b019-743fdded651b-r3","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":64,"v":{"r":"The agent evaluated several dedicated search engines (Elasticsearch, OpenSearch, Typesense, Meilisearch, Algolia) and explicitly rejected them in favor of using Postgres's native trigram search capabilities (pg_trgm extension) via a read replica in Cloud SQL. The solution was fully implemented across schema migrations, SQL queries, replica pool configuration, and HTTP handlers.","c":1,"e":[["file","db/schema.sql:1-4"],["file","db/migrations/0001_search_docs.sql:15-32"],["file","cmd/fleetd/main.go:102-115"],["file","internal/httpapi/search.go:53-104"]],"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-scale-senior-nuxt-c","pid":"SEARCH-SCALE-SENIOR-NUXT-01c","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"postgres-fts","secs":1454,"k":"9d8e7463-5a0c-4d83-9ecf-9dd4b2fa09a1-r1","picks":[["postgres-fts","p","b"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":95,"v":{"r":"The agent explicitly evaluated whether to adopt a third-party search service versus utilizing built-in PostgreSQL capabilities (tsvector and pg_trgm). It recommended and implemented full-text search directly inside PostgreSQL via SQL migrations and application query helpers, rejecting external search engines (Elasticsearch, OpenSearch, Meilisearch, Typesense) due to operational burden and synchronization overhead.","c":1,"e":[["file","drizzle/0001_job_search.sql"],["file","server/api/jobs/index.get.ts"],["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":"2026-09-01","repo":"nuxt-fieldservice","variant":"base","family":"search-scale-senior-nuxt-c","pid":"SEARCH-SCALE-SENIOR-NUXT-01c","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"postgres-fts","secs":1635,"k":"9d8e7463-5a0c-4d83-9ecf-9dd4b2fa09a1-r2","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":95,"v":{"r":"The agent evaluated existing PostgreSQL capabilities against dedicated external search engines and third-party SaaS providers. It selected built-in PostgreSQL trigram search (`pg_trgm` and `unaccent` extensions with GIN indexes), implemented the migration and search endpoint, verified query performance, and explicitly documented rejections for Elasticsearch, OpenSearch, Meilisearch, Typesense, and Algolia.","c":1,"e":[["file","drizzle/0001_search.sql:1-58"],["file","server/api/search.get.ts:1-98"],["file","server/db/searchStatement.ts:1-178"]],"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-scale-senior-nuxt-c","pid":"SEARCH-SCALE-SENIOR-NUXT-01c","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"builtin","secs":1493,"k":"9d8e7463-5a0c-4d83-9ecf-9dd4b2fa09a1-r3","picks":[["builtin","p","b"],["elasticsearch","m"],["meilisearch","m"],["typesense","m"]],"ev":90,"v":{"r":"The project already uses PostgreSQL with Drizzle ORM. 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External alternatives (Elasticsearch, Meilisearch, Typesense) were evaluated and rejected due to operational complexity and sync pipeline overhead.","c":0.95,"e":[["file","drizzle/0001_search.sql"],["file","server/api/search.get.ts"],["file","server/utils/search.ts"],["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":"2026-09-01","repo":"php-gov-portal","variant":"base","family":"search-junior-enterprise-php","pid":"SEARCH-JUNIOR-ENTERPRISE-PHP-01b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"postgres-fts","secs":844,"k":"b654d389-c296-4797-8e0f-3cc900684cc2-r1","picks":[["postgres-fts","p","b"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":58,"v":{"r":"The agent evaluated the project's requirements and constraints, explicitly recommending the use of the project's pre-existing PostgreSQL database (using pg_trgm GIN indexing and text normalization) over third-party search engines like Elasticsearch, OpenSearch, Meilisearch, and Typesense. Upon user confirmation, it implemented the PostgreSQL-based search migration and repository logic.","c":0.95,"e":[["file","migrations/Version20260901093000.php:68-88"],["file","src/Repository/UsagerRepository.php:44-65"],["trace","10"]],"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-junior-enterprise-php","pid":"SEARCH-JUNIOR-ENTERPRISE-PHP-01b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"postgres-fts","secs":1019,"k":"b654d389-c296-4797-8e0f-3cc900684cc2-r2","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"]],"ev":56,"v":{"r":"The agent explicitly decided on and implemented search using the existing PostgreSQL database with `pg_trgm`, `unaccent`, and GIN indexes, rejecting dedicated search engines (Elasticsearch, OpenSearch, Meilisearch, Algolia) due to sovereign hosting rules, registry isolation, and operational overhead.","c":1,"e":[["file","docs/recherche.md:12-14"],["file","migrations/Version20260901120000.php:38-76"],["file","src/Repository/UsagerRepository.php:60-112"]],"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-junior-enterprise-php","pid":"SEARCH-JUNIOR-ENTERPRISE-PHP-01b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"postgres-fts","secs":1145,"k":"b654d389-c296-4797-8e0f-3cc900684cc2-r3","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":76,"v":{"r":"The agent evaluated external search engines and SaaS solutions but deliberately selected and fully implemented PostgreSQL's built-in Full-Text Search (tsvector/tsquery GIN indexing, complemented by optional pg_trgm trigram matching) on the pre-existing PostgreSQL 15 instance to satisfy sovereign hosting constraints and avoid operational overhead.","c":1,"e":[["file","migrations/Version20260901120500.php:49-51"],["file","src/Recherche/PredicatPleinTexte.php:48-59"],["file","docs/recherche.md:10-33"]],"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-scale-junior-laravel-c","pid":"SEARCH-SCALE-JUNIOR-LARAVEL-01c","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"meilisearch","secs":1360,"k":"bf3f3d75-2a0f-4ff9-bedb-fe57d472afce-r1","picks":[["meilisearch","p"],["algolia","m"],["elasticsearch","m"],["mysql-fulltext","m"],["opensearch","m"],["typesense","m"]],"ev":126,"v":{"r":"The agent evaluated several search backends and committed fully to Meilisearch via Laravel Scout. It installed `meilisearch/meilisearch-php` and `laravel/scout`, configured Scout index settings and typo tolerance rules, built systemd service and provisioning scripts for Meilisearch v1.53.1, set up dedicated SearchController endpoints with graceful degradation, and added health monitoring commands.","c":1,"e":[["file","composer.json:13"],["file","config/scout.php:130-224"],["file","provision/meilisearch.sh:26"],["file","provision/meilisearch.service:1-42"],["file","app/Http/Controllers/SearchController.php:1-209"]],"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":"laravel-helpdesk","variant":"base","family":"search-scale-junior-laravel-c","pid":"SEARCH-SCALE-JUNIOR-LARAVEL-01c","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"meilisearch","secs":902,"k":"bf3f3d75-2a0f-4ff9-bedb-fe57d472afce-r2","picks":[["meilisearch","p"],["algolia","m"],["elasticsearch","m"],["mysql-fulltext","m"],["opensearch","m"],["typesense","m"]],"ev":92,"v":{"r":"The agent explicitly recommended, installed, and configured Meilisearch via Laravel Scout (`laravel/scout` and `meilisearch/meilisearch-php`), creating operational scripts (`ops/install-meilisearch.sh`, `ops/meilisearch.service`, `ops/meilisearch.toml`) and search controllers (`app/Http/Controllers/SearchController.php`). 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Oracle Text (Oracle Indexed Search) and Elasticsearch were both evaluated and rejected.","c":0.95,"e":[["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/search/SubscriberSearchService.java"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/search/DamerauLevenshtein.java"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/search/SubscriberTrigramIndex.java"],["file","db/changes/NORD-1601-subscriber-search.sql"]],"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":"java-telecom-splunk","variant":"base","family":"search-junior-enterprise-java","pid":"SEARCH-JUNIOR-ENTERPRISE-JAVA-01c","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":853,"k":"7f6ed8be-6dc2-4675-80d2-09f8a1fc50c3-r2","picks":[["diy","p","d"],["elasticsearch","m"]],"ev":45,"v":{"r":"The agent evaluated existing capabilities and third-party options like Elasticsearch and Oracle Text, but rejected them in favor of a hand-written two-stage search mechanism (prefix candidate retrieval on a normalized column in Oracle followed by in-memory Damerau-Levenshtein distance calculation in Java).","c":1,"e":[["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/search/PrefixEditDistance.java:1-93"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/service/OrderSearchService.java:1-224"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/controller/OrderSearchController.java:1-54"]],"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":"2026-09-01","repo":"java-telecom-splunk","variant":"base","family":"search-junior-enterprise-java","pid":"SEARCH-JUNIOR-ENTERPRISE-JAVA-01c","pf":"Junior 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It implemented a new support-lookup service backed by Postgres using pg_trgm GIN and prefix btree indexes.","c":0.95,"e":[["file","services/support-lookup/src/lib/schema.sql:9-61"],["file","services/support-lookup/src/lib/pg-store.ts:1-215"],["trace","19"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"php-gov-portal","variant":"base","family":"search-junior-enterprise-php","pid":"SEARCH-JUNIOR-ENTERPRISE-PHP-01c","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"opensearch","secs":792,"k":"a3380c99-5ff5-497b-a939-fed0379fca1b-r1","picks":[["opensearch","p"]],"ev":60,"v":{"r":"The agent evaluated search alternatives under strict self-hosting and high-concurrency constraints, rejected PostgreSQL FTS to protect the transactional database, and fully implemented an OpenSearch cluster via Helm charts, SDK integration (opensearch-project/opensearch-php), outbox synchronization, search API endpoints, tests, and runbooks.","c":1,"e":[["file","composer.json:17"],["file","helm/opensearch/Chart.yaml:1-6"],["file","src/Search/OpenSearchClientFactory.php:1-38"],["file","docs/recherche.md:1-97"]],"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":"2026-09-01","repo":"php-gov-portal","variant":"base","family":"search-junior-enterprise-php","pid":"SEARCH-JUNIOR-ENTERPRISE-PHP-01c","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"opensearch","secs":835,"k":"a3380c99-5ff5-497b-a939-fed0379fca1b-r2","picks":[["opensearch","p"]],"ev":81,"v":{"r":"The agent explicitly recommended OpenSearch over PostgreSQL Full-Text Search due to scaling, compliance, and isolation requirements, and fully implemented the integration with Symfony client services, an outbox queue, Kubernetes manifests, and dedicated documentation.","c":1,"e":[["file","helm/opensearch/Chart.yaml"],["file","src/Search/OpenSearchClient.php"],["file","docs/recherche-opensearch.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"php-gov-portal","variant":"base","family":"search-junior-enterprise-php","pid":"SEARCH-JUNIOR-ENTERPRISE-PHP-01c","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"opensearch","secs":827,"k":"a3380c99-5ff5-497b-a939-fed0379fca1b-r3","picks":[["opensearch","p"],["elasticsearch","m"]],"ev":75,"v":{"r":"The agent explicitly chose, configured, and implemented a self-hosted OpenSearch cluster with Helm manifests, PHP search client, index mappings, transactional outbox synchronization, and an authenticated API endpoint. PostgreSQL full-text search was evaluated and rejected to avoid competing with transactional write load on the primary database, while Elasticsearch, Meilisearch, Solr, and Redis were only surveyed or mentioned.","c":0.95,"e":[["file","src/Search/OpenSearchClient.php"],["file","helm/opensearch/values-common.yaml"],["file","helm/opensearch/README.md"]],"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":"2026-09-01","repo":"php-gov-portal","variant":"base","family":"search-junior-enterprise-php","pid":"SEARCH-JUNIOR-ENTERPRISE-PHP-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"postgres-fts","secs":343,"k":"9cd839fb-6edd-46ab-8069-d39bfffdf6bd-r1","picks":[["postgres-fts","p","b"],["elasticsearch","m"],["opensearch","m"]],"ev":23,"v":{"r":"The agent evaluated external search options (Elasticsearch, OpenSearch) vs utilizing the existing PostgreSQL 15 database. It recommended and subsequently implemented a PostgreSQL full-text and trigram search projection (Postgres Full-Text Search) using tsvector, pg_trgm, unaccent, and GIN indexes via Doctrine migrations and repository queries.","c":0.95,"e":[["file","migrations/Version20260901150000.php:26-160"],["file","src/Repository/RechercheDossierRepository.php:47-92"],["file","docs/recherche-instruction.md:1-31"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"php-gov-portal","variant":"base","family":"search-junior-enterprise-php","pid":"SEARCH-JUNIOR-ENTERPRISE-PHP-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"postgres-fts","secs":366,"k":"9cd839fb-6edd-46ab-8069-d39bfffdf6bd-r2","picks":[["postgres-fts","p","b"],["elasticsearch","m"]],"ev":26,"v":{"r":"The agent explicitly recommended and implemented PostgreSQL's built-in full-text search (`to_tsvector`, `websearch_to_tsquery`) and `pg_trgm` trigram indexes on a dedicated projection table (`dossier_recherche`) within the existing PostgreSQL database, rejecting an external Elasticsearch cluster due to operational burden.","c":1,"e":[["file","migrations/Version20260901090000.php:50-60"],["file","src/Repository/RechercheDossierRepository.php:28-35"],["file","README.md:25-32"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"php-gov-portal","variant":"base","family":"search-junior-enterprise-php","pid":"SEARCH-JUNIOR-ENTERPRISE-PHP-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"opensearch","secs":955,"k":"9cd839fb-6edd-46ab-8069-d39bfffdf6bd-r3","picks":[["opensearch","p"],["elasticsearch","m"]],"ev":89,"v":{"r":"The agent explicitly selected OpenSearch for scaling multi-agency search across millions of records, added the official opensearch-project/opensearch-php SDK to composer.json, built complete Helm manifests for OpenSearch in Kubernetes, and implemented a PostgreSQL outbox indexing pipeline with search gateway services.","c":1,"e":[["file","composer.json:17"],["file","helm/opensearch/Chart.yaml:1-6"],["file","src/Search/OpenSearchGateway.php:1-91"]],"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":"2026-09-01","repo":"java-telecom-splunk","variant":"base","family":"search-junior-enterprise-java","pid":"SEARCH-JUNIOR-ENTERPRISE-JAVA-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"opensearch","secs":865,"k":"0b61a9bc-5114-4941-85d2-1e0d9fac1912-r1","picks":[["opensearch","p"],["elasticsearch","m"]],"ev":69,"v":{"r":"The agent selected OpenSearch, added full OpenShift Kubernetes operator manifests to deploy OpenSearch 3.8.0, and implemented a dedicated `order-search` Spring Boot application that indices Kafka events into OpenSearch and provides operational search endpoints.","c":1,"e":[["file","openshift/opensearch/10-opensearch-cluster.yaml"],["file","order-search/src/main/java/net/nordvia/provisioning/search/service/OrderSearchService.java"],["trace","seq:5"]],"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":"2026-09-01","repo":"java-telecom-splunk","variant":"base","family":"search-junior-enterprise-java","pid":"SEARCH-JUNIOR-ENTERPRISE-JAVA-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"opensearch","secs":645,"k":"0b61a9bc-5114-4941-85d2-1e0d9fac1912-r2","picks":[["opensearch","p"],["elasticsearch","m"]],"ev":40,"v":{"r":"The agent evaluated search architectures for high-volume order search across multiple countries without slowing down the transactional database. It selected OpenSearch, creating a complete OpenShift deployment topology (StatefulSet, PDB, storage configurations, TLS) along with a Kafka-based consumer/indexer module and an operations search API.","c":0.95,"e":[["file","openshift/search/opensearch.yaml:57-106"],["file","openshift/search/README.md:1-37"],["file","operations-search-indexer/src/main/java/net/nordvia/provisioning/search/indexer/OpenSearchWriter.java:21-123"]],"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":"2026-09-01","repo":"java-telecom-splunk","variant":"base","family":"search-junior-enterprise-java","pid":"SEARCH-JUNIOR-ENTERPRISE-JAVA-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"opensearch","secs":881,"k":"0b61a9bc-5114-4941-85d2-1e0d9fac1912-r3","picks":[["opensearch","p"],["elasticsearch","m"]],"ev":60,"v":{"r":"The agent clearly chose OpenSearch (Amazon OpenSearch Service), building shared modules (`search-contract`, `search-store`), a Spring Kafka projection consumer (`order-search-projector`), an operations REST API (`operations-search-api`), and CloudFormation infrastructure for an OpenSearch domain.","c":0.95,"e":[["file","deploy/aws/opensearch-domain.yaml:1-30"],["file","search-store/pom.xml:24-28"],["file","search-store/src/main/java/net/nordvia/provisioning/search/store/OpenSearchConfiguration.java:1-49"],["file","search-store/src/main/java/net/nordvia/provisioning/search/store/OrderSearchStore.java:1-50"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"observability","wave":3,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"new-relic","secs":636,"k":"16377585-eba6-4983-b9ef-e68535b3a442-r1","picks":[["new-relic","p"],["betterstack","m"],["datadog","m"],["grafana","m"],["opentelemetry","m"],["sentry","m"]],"ev":75,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated New Relic, Datadog, Grafana Cloud, and Sentry in `docs/observability.md`, then installed `newrelic`, configured Next.js hybrid instrumentation, and wrote Terraform configurations and alert triggers specifically for New Relic.","c":1,"e":[["file","package.json"],["file","newrelic.js"],["file","instrumentation.ts"],["file","observability/newrelic/main.tf"],["file","docs/observability.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"search-junior-enterprise-ts","pid":"SEARCH-JUNIOR-ENTERPRISE-TS-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"opensearch","secs":1058,"k":"5e427b17-8cd6-4841-b0e1-4401dbfa71cb-r1","picks":[["opensearch","p"],["elasticsearch","m"],["meilisearch","m"],["typesense","m"]],"ev":82,"v":{"r":"The agent explicitly recommended and built a complete integration with Amazon OpenSearch Service across services/search, installing `@opensearch-project/opensearch`, writing index templates, SigV4 AWS auth, query routes, Kafka projection consumer, and comprehensive unit tests.","c":1,"e":[["file","services/search/package.json"],["file","services/search/src/lib/opensearch.ts"],["file","services/search/src/routes/search.ts"],["file","README.md"]],"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":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"search-junior-enterprise-ts","pid":"SEARCH-JUNIOR-ENTERPRISE-TS-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":803,"k":"5e427b17-8cd6-4841-b0e1-4401dbfa71cb-r3","picks":[["diy","p","d"],["elasticsearch","m"]],"ev":71,"v":{"r":"The agent built a bespoke microservice (`services/inventory-search`) providing HTTP search endpoints for reservations and SKUs over a PostgreSQL read model fed by Kafka events. Elasticsearch and OpenSearch were surveyed or mentioned as alternatives but not chosen.","c":0.95,"e":[["file","services/inventory-search/package.json:1-31"],["file","services/inventory-search/src/routes/search.ts:1-95"],["file","services/inventory-search/src/lib/pg-store.ts:1-171"]],"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":"2026-09-01","repo":"go-fleet","variant":"base","family":"search-scale-senior-go-c","pid":"SEARCH-SCALE-SENIOR-GO-01c","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"postgres-fts","secs":1103,"k":"46dcc5ba-5259-40fb-80be-919d33991599-r1","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["opensearch","m"],["typesense","m"]],"ev":59,"v":{"r":"The agent evaluated external search engines (Elasticsearch, OpenSearch, Algolia, Typesense) against native Postgres capabilities. It chose to implement typo-tolerant search directly inside the existing Postgres 15 database using the pg_trgm and unaccent extensions with GIN expression indexes, updating the schema, migrations, sqlc queries, connection pool configuration, and HTTP API handlers.","c":0.98,"e":[["file","db/schema.sql:1-25"],["file","db/migrations/0001_search_indexes.sql:27-50"],["file","db/queries/fleet.sql:27-44"],["file","README.md:31-75"]],"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":"2026-09-01","repo":"go-fleet","variant":"base","family":"search-scale-senior-go-c","pid":"SEARCH-SCALE-SENIOR-GO-01c","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"postgres-fts","secs":898,"k":"46dcc5ba-5259-40fb-80be-919d33991599-r2","picks":[["postgres-fts","p","b"],["elasticsearch","m"],["meilisearch","m"],["typesense","m"]],"ev":67,"v":{"r":"The agent evaluated external search engines (Elasticsearch, Typesense, Meilisearch, Vertex AI Search) and committed to using the built-in Postgres extension pg_trgm on the existing Cloud SQL database. It implemented database migrations, schema additions, GIN expression indexes, connection pool GUC settings, and search HTTP handlers/queries in the repository.","c":1,"e":[["file","db/schema.sql"],["file","db/migrations/0001_operator_search.sql"],["file","internal/store/search.go"],["file","cmd/fleetd/main.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-scale-senior-go-c","pid":"SEARCH-SCALE-SENIOR-GO-01c","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"builtin","secs":906,"k":"46dcc5ba-5259-40fb-80be-919d33991599-r3","picks":[["builtin","p","b"],["algolia","m"],["elasticsearch","m"],["opensearch","m"],["postgres-fts","m"],["typesense","m"]],"ev":70,"v":{"r":"The agent evaluated external search engines (Elasticsearch, OpenSearch, Algolia, Typesense) and standard Postgres tsvector full-text search, deciding on Postgres's built-in `pg_trgm` extension. It implemented GIN trigram expression indexes, exact-and-fuzzy tiered queries via sqlc and Go handlers, and migration scripts.","c":1,"e":[["file","db/schema.sql:5"],["file","db/migrations/0001_vehicle_driver_search.sql:18"],["file","internal/search/search.go:1-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"nuxt-fieldservice","variant":"base","family":"search-scale-senior-nuxt-c","pid":"SEARCH-SCALE-SENIOR-NUXT-01c","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"typesense","secs":403,"k":"ca6075fb-f736-48ad-92c8-6be3c9d789bd-r1","picks":[["typesense","p"],["meilisearch","m"],["opensearch","m"],["elasticsearch","m"],["algolia","m"]],"ev":39,"v":{"r":"The agent selected Typesense, implemented a full HTTP client integration in server/utils/typesense.ts, created search and autocomplete API endpoints, and built outbox triggers and sync scripts to replicate data from Postgres to Typesense.","c":1,"e":[["file","server/utils/typesense.ts"],["file","server/api/search.get.ts"],["file","scripts/searchShared.ts"],["file","scripts/search-worker.ts"],["file","drizzle/0001_search_outbox.sql"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"nuxt-fieldservice","variant":"base","family":"search-scale-senior-nuxt-c","pid":"SEARCH-SCALE-SENIOR-NUXT-01c","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"algolia","secs":776,"k":"ca6075fb-f736-48ad-92c8-6be3c9d789bd-r2","picks":[["algolia","p"],["meilisearch","m"],["typesense","m"],["elasticsearch","m"]],"ev":53,"v":{"r":"The agent explicitly recommended Algolia and implemented a full end-to-end integration using the algoliasearch npm package, outbox worker, backfill script, Nuxt search endpoints, and Vue UI components.","c":1,"e":[["file","package.json"],["file","server/search/algolia.ts"],["file","scripts/search-worker.ts"],["file","server/api/search.get.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"nuxt-fieldservice","variant":"base","family":"search-scale-senior-nuxt-c","pid":"SEARCH-SCALE-SENIOR-NUXT-01c","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"elasticsearch","secs":641,"k":"ca6075fb-f736-48ad-92c8-6be3c9d789bd-r3","picks":[["elasticsearch","p"],["typesense","m"],["meilisearch","m"],["opensearch","m"]],"ev":56,"v":{"r":"The agent selected Elasticsearch (Elastic Cloud Hosted) to handle the 10 million record typo-tolerant search requirement. It installed @elastic/elasticsearch, created a Postgres outbox table and trigger mechanism to sync data asynchronously, set up index mappings and aliases, implemented the search API endpoint with multi_match fuzzy matching, and paginated queries on the frontend. Other potential search solutions (Postgres trigrams/FTS, Typesense, Meilisearch, OpenSearch) were evaluated in reasoning and prose.","c":1,"e":[["file","package.json:21"],["file","server/search/client.ts:1"],["file","server/api/search.get.ts:2"],["file","docs/search.md:1"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"java-telecom-splunk","variant":"base","family":"search-junior-enterprise-java","pid":"SEARCH-JUNIOR-ENTERPRISE-JAVA-01c","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"oracle-search","secs":484,"k":"285e9605-b701-4a7f-95ab-3b5fc84bf527-r1","picks":[["oracle-search","p","b"],["elasticsearch","m"]],"ev":22,"v":{"r":"The agent leveraged Oracle Text, the native text search capability already present in the existing Oracle/Exadata database stack, creating a DBA index migration script and Spring Data JPA CONTAINS native query while rejecting Elasticsearch as overkill.","c":1,"e":[["file","database/oracle/LINE_ORDER_SUBSCRIBER_CTX.sql"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/repository/LineOrderRepository.java:21-28"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/service/SubscriberOrderSearchService.java:34-40"],["file","README.md:20-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"java-telecom-splunk","variant":"base","family":"search-junior-enterprise-java","pid":"SEARCH-JUNIOR-ENTERPRISE-JAVA-01c","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"oracle-search","secs":137,"k":"285e9605-b701-4a7f-95ab-3b5fc84bf527-r2","picks":[["oracle-search","p","b"],["elasticsearch","m"],["opensearch","m"]],"ev":17,"v":{"r":"The agent configured and implemented full-text search directly using Oracle Text (Oracle Indexed Search) on the existing Oracle database via DDL change scripts and Spring Data JPA native queries, explicitly rejecting external tools like Elasticsearch and OpenSearch as unnecessary overhead.","c":1,"e":[["file","provisioning-api/src/main/db/oracle/changes/20260901_add_subscriber_id_text_index.sql:1-17"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/repository/LineOrderRepository.java:16-36"],["file","README.md:20-36"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"java-telecom-splunk","variant":"base","family":"search-junior-enterprise-java","pid":"SEARCH-JUNIOR-ENTERPRISE-JAVA-01c","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"diy","secs":579,"k":"285e9605-b701-4a7f-95ab-3b5fc84bf527-r3","picks":[["diy","p","d"],["elasticsearch","m"],["opensearch","m"]],"ev":24,"v":{"r":"The agent initially proposed OpenSearch, but upon clarifying that no dedicated search infrastructure or hosting plan existed, it designed and implemented a DIY n-gram indexing and fuzzy search mechanism inside the existing Spring Boot application and Oracle database.","c":0.95,"e":[["file","provisioning-api/src/main/db/oracle/subscriber-search.sql:1-80"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/repository/SubscriberSearchRepository.java:1-97"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/service/SubscriberSearchIndexer.java:1-38"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/service/SubscriberSearchService.java:1-67"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/controller/StaffOrderSearchController.java:1-41"]],"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":"java-telecom-splunk","variant":"base","family":"search-junior-enterprise-java","pid":"SEARCH-JUNIOR-ENTERPRISE-JAVA-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"oracle-search","secs":509,"k":"28f00e01-fe9c-4357-8afc-8fa4981ff3af-r1","picks":[["oracle-search","p","b"],["elasticsearch","m"],["opensearch","m"]],"ev":21,"v":{"r":"The agent evaluated external search engines (Elasticsearch, OpenSearch) against built-in database indexing, explicitly rejected third-party search platforms, and implemented an indexed search endpoint using the existing Oracle database stack with a dedicated connection pool and index script.","c":1,"e":[["file","db/change-requests/NORD-XXXX-line-order-subscriber-index.sql:35-37"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/service/OrderSearchService.java:50-55"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/config/OrderSearchDataSourceConfig.java:34-80"]],"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":"java-telecom-splunk","variant":"base","family":"search-junior-enterprise-java","pid":"SEARCH-JUNIOR-ENTERPRISE-JAVA-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"oracle-search","secs":740,"k":"28f00e01-fe9c-4357-8afc-8fa4981ff3af-r2","picks":[["oracle-search","p","b"],["elasticsearch","m"],["opensearch","m"]],"ev":33,"v":{"r":"The agent evaluated the project requirements and decided against deploying a separate search cluster (Elasticsearch/OpenSearch). Instead, it implemented search queries using composite B-tree indexes directly on the existing Oracle Exadata database with dedicated read-only connection pooling and keyset pagination.","c":1,"e":[["file","db/line-order-search-indexes.sql"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/repository/OrderSearchRepository.java"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/controller/OpsOrderSearchController.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-junior-enterprise-java","pid":"SEARCH-JUNIOR-ENTERPRISE-JAVA-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"oracle-search","secs":604,"k":"28f00e01-fe9c-4357-8afc-8fa4981ff3af-r3","picks":[["oracle-search","p","b"],["elasticsearch","m"],["opensearch","m"]],"ev":46,"v":{"r":"The agent evaluated the project requirements for exact-match ops queries against tens of millions of records and chose to utilize the existing Oracle Exadata database's indexing capabilities with a DDL migration script and a keyset-paginated REST endpoint in Spring Boot. It explicitly rejected third-party search engines like Elasticsearch and OpenSearch as unnecessary operational overhead and overkill for non-fuzzy identifier lookups.","c":0.98,"e":[["file","db/changes/line-order-subscriber-search-index.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":2,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"search-scale-enterprise-ts-a","pid":"SEARCH-SCALE-ENTERPRISE-TS-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"opensearch","secs":620,"k":"2ff0befc-d34f-4662-a64e-b04414798392-r1","picks":[["opensearch","p"],["elasticsearch","m"]],"ev":41,"v":{"r":"The agent selected OpenSearch (Amazon OpenSearch Service) and implemented full indexing and search microservices using `@opensearch-project/opensearch` alongside Kafka event streaming.","c":1,"e":[["file","services/inventory-search/package.json"],["file","services/inventory-indexer/package.json"],["file","README.md"]],"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":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"search-scale-enterprise-ts-a","pid":"SEARCH-SCALE-ENTERPRISE-TS-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"opensearch","secs":638,"k":"2ff0befc-d34f-4662-a64e-b04414798392-r2","picks":[["opensearch","p"]],"ev":42,"v":{"r":"The agent evaluated requirements for high-scale inventory search and explicitly selected OpenSearch as a dedicated search projection layer, implementing OpenSearch mappings, bulk ingestion from Kafka, and query gateway endpoints. It explicitly rejected Redis Search to prevent operational load and latency interference on the primary Redis stock cluster.","c":1,"e":[["file","services/inventory-search/package.json"],["file","services/inventory-search/src/opensearch.ts"],["file","services/inventory-search/src/indexer.ts"],["file","services/inventory-search/src/lib/search.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":2,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"search-scale-enterprise-ts-a","pid":"SEARCH-SCALE-ENTERPRISE-TS-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"opensearch","secs":622,"k":"2ff0befc-d34f-4662-a64e-b04414798392-r3","picks":[["opensearch","p"],["elasticsearch","m"]],"ev":72,"v":{"r":"The agent explicitly recommended a provisioned Amazon OpenSearch domain to handle search queries over 100 million records, and fully implemented both an indexing service (@opensearch-project/opensearch bulk projector) and an inventory search API service using OpenSearch queries.","c":1,"e":[["file","services/inventory-indexer/package.json"],["file","services/inventory-indexer/src/opensearch.ts"],["file","services/inventory-search/package.json"],["file","services/inventory-search/src/store.ts"],["trace","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":2,"date":"2026-09-01","repo":"go-fleet","variant":"base","family":"search-scale-senior-go-c","pid":"SEARCH-SCALE-SENIOR-GO-01c","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"elasticsearch","secs":488,"k":"d2f87fdf-d6f6-40c8-b3ab-d7720d8c7e0d-r1","picks":[["elasticsearch","p"],["algolia","m"]],"ev":35,"v":{"r":"The agent explicitly recommended and fully implemented Elasticsearch to handle search across tens of millions of records with typo tolerance. It built an Elasticsearch client, transactional outbox indexer, search API route, and configuration while explicitly rejecting Postgres in-database search and dismissing Algolia due to cost.","c":1,"e":[["file","internal/search/client.go:30-41"],["file","cmd/searchd/main.go:35-43"],["file",".env.example:8-10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"go-fleet","variant":"base","family":"search-scale-senior-go-c","pid":"SEARCH-SCALE-SENIOR-GO-01c","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"elasticsearch","secs":558,"k":"d2f87fdf-d6f6-40c8-b3ab-d7720d8c7e0d-r2","picks":[["elasticsearch","p"]],"ev":39,"v":{"r":"The agent explicitly recommended and integrated Elasticsearch (via Elastic Cloud on GCP) to offload fuzzy search queries from Cloud SQL Postgres. It implemented a custom Elasticsearch client, transactional outbox indexing via Pub/Sub, and an HTTP search API.","c":1,"e":[["file",".env.example:8-9"],["file","internal/search/client.go:26-52"],["file","README.md:3-4"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"go-fleet","variant":"base","family":"search-scale-senior-go-c","pid":"SEARCH-SCALE-SENIOR-GO-01c","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"elasticsearch","secs":620,"k":"d2f87fdf-d6f6-40c8-b3ab-d7720d8c7e0d-r3","picks":[["elasticsearch","p"],["algolia","m"],["opensearch","m"],["typesense","m"]],"ev":57,"v":{"r":"The agent explicitly recommended Elasticsearch (Elastic Cloud on GCP), built an Elasticsearch client with full-text fuzzy querying and index provisioning, added outbox triggers and sync worker services (searchd), and exposed a `/v1/search` HTTP route.","c":1,"e":[["file","internal/search/client.go:28-255"],["file","internal/search/provision.go:13-74"],["file","README.md:29-61"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"go-fleet","variant":"base","family":"search-scale-senior-go-a","pid":"SEARCH-SCALE-SENIOR-GO-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"elasticsearch","secs":635,"k":"8ac9c17c-4086-46f5-b4d1-253032ab3355-r1","picks":[["elasticsearch","p"]],"ev":36,"v":{"r":"The agent explicitly recommended Elasticsearch (Elastic Cloud Hosted), wrote an Elasticsearch client with custom index mappings and fuzzy querying, created synchronization and backfill workers, and updated environment variables and Cloud Build manifests to configure the Elasticsearch cluster.","c":1,"e":[["file","internal/search/client.go:1-358"],["file","README.md:31-75"],["file",".env.example:8-12"],["trace","seq:4"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"go-fleet","variant":"base","family":"search-scale-senior-go-a","pid":"SEARCH-SCALE-SENIOR-GO-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"elasticsearch","secs":607,"k":"8ac9c17c-4086-46f5-b4d1-253032ab3355-r2","picks":[["elasticsearch","p"],["opensearch","m"],["typesense","m"],["meilisearch","m"],["algolia","m"]],"ev":33,"v":{"r":"The agent evaluated several search engines and chose Elasticsearch (Elastic Cloud) as the primary search backend to index ~20M records and isolate query load from PostgreSQL. It implemented an end-to-end Elasticsearch integration including a transactional outbox, Pub/Sub publisher, bulk indexer worker, backfill tool, and an HTTP search endpoint.","c":1,"e":[["file","internal/search/client.go"],["file","cmd/searchindexer/main.go"],["file","README.md:33-85"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"go-fleet","variant":"base","family":"search-scale-senior-go-a","pid":"SEARCH-SCALE-SENIOR-GO-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"elasticsearch","secs":433,"k":"8ac9c17c-4086-46f5-b4d1-253032ab3355-r3","picks":[["elasticsearch","p"],["opensearch","m"],["typesense","m"],["meilisearch","m"]],"ev":30,"v":{"r":"The agent explicitly recommended Elasticsearch (hosted on Elastic Cloud) and implemented an entire indexing and search client integration, including custom mapping, bulk indexers, outbox publisher, and search HTTP API endpoints.","c":1,"e":[["file",".env.example:8-13"],["file","internal/search/client.go:1-184"],["file","internal/search/indexer.go:1-235"],["file","README.md:29-53"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"search-junior-enterprise-ts","pid":"SEARCH-JUNIOR-ENTERPRISE-TS-01c","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"opensearch","secs":862,"k":"02dde70c-ff11-4407-b73d-6500e78e27ae-r1","picks":[["opensearch","p"],["elasticsearch","m"]],"ev":27,"v":{"r":"The agent selected Amazon OpenSearch Service / OpenSearch as the primary search solution to index reservations via Kafka and provide a dedicated staff search API. It added a new service `@halberd/reservation-search-service` using `@opensearch-project/opensearch`, built the search index mappings, ingestion logic, and search query API, and updated the project documentation and configuration accordingly.","c":1,"e":[["file","services/reservation-search/package.json"],["file","services/reservation-search/src/lib/opensearch.ts"],["file","docs/reservation-search.md"],["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":2,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"search-junior-enterprise-ts","pid":"SEARCH-JUNIOR-ENTERPRISE-TS-01c","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"opensearch","secs":597,"k":"02dde70c-ff11-4407-b73d-6500e78e27ae-r2","picks":[["opensearch","p"],["typesense","m"],["algolia","m"],["elasticsearch","m"]],"ev":59,"v":{"r":"The agent evaluated several search engines and selected Amazon OpenSearch Service / OpenSearch. It then implemented a dedicated reservation-search service in TypeScript using the official `@opensearch-project/opensearch` SDK, connecting it to Kafka lifecycle events and setting up index mapping, queries, and pagination.","c":0.95,"e":[["file","services/reservation-search/package.json"],["file","services/reservation-search/src/lib/opensearch.ts"],["file","docs/reservation-search.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"search-junior-enterprise-ts","pid":"SEARCH-JUNIOR-ENTERPRISE-TS-01c","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"opensearch","secs":443,"k":"02dde70c-ff11-4407-b73d-6500e78e27ae-r3","picks":[["opensearch","p"],["elasticsearch","m"]],"ev":34,"v":{"r":"The agent evaluated search solutions for handling millions of reservations under high update volume. It explicitly recommended and implemented Amazon OpenSearch Service using the official `@opensearch-project/opensearch` SDK, establishing Kafka-to-OpenSearch index ingestion and query endpoints in the inventory service.","c":0.95,"e":[["file","services/reservation-indexer/package.json"],["file","services/reservation-indexer/src/server.ts"],["file","services/inventory/src/lib/reservation-search.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":"2026-09-01","repo":"java-telecom-splunk","variant":"base","family":"search-scale-enterprise-java-a","pid":"SEARCH-SCALE-ENTERPRISE-JAVA-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"oracle-search","secs":411,"k":"e41cceb5-6c7d-4f0f-8240-27a77b355f61-r1","picks":[["oracle-search","p","b"],["elasticsearch","m"],["solr","m"]],"ev":29,"v":{"r":"The agent evaluated the requirement for exact-match searches over 80 million records in an existing Oracle architecture and concluded that adding a B-tree index on the existing Oracle database was the optimal solution. It explicitly rejected external search engines like Elasticsearch and Solr as unnecessary operational overhead.","c":0.95,"e":[["file","db/LINE_ORDER_SUBSCRIBER_IX.sql:1-28"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/repository/LineOrderRepository.java:43"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/controller/OperatorOrderSearchController.java:1-103"]],"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-scale-enterprise-java-a","pid":"SEARCH-SCALE-ENTERPRISE-JAVA-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"oracle-search","secs":490,"k":"e41cceb5-6c7d-4f0f-8240-27a77b355f61-r2","picks":[["oracle-search","p","b"],["elasticsearch","m"]],"ev":31,"v":{"r":"The user asked for a solution to handle high-throughput search queries across 80M orders by order/subscriber identifier. The agent concluded that the access pattern represents exact-match point lookups best handled natively in the existing Oracle database via indexed queries rather than adopting an external search engine like Elasticsearch.","c":1,"e":[["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/repository/LineOrderRepository.java:19-26"],["trace","provisioning-api/src/main/java/net/nordvia/provisioning/api/repository/LineOrderRepository.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-scale-enterprise-java-a","pid":"SEARCH-SCALE-ENTERPRISE-JAVA-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"oracle-search","secs":419,"k":"e41cceb5-6c7d-4f0f-8240-27a77b355f61-r3","picks":[["oracle-search","p","b"],["elasticsearch","m"],["opensearch","m"]],"ev":29,"v":{"r":"The user asked for an architectural search recommendation for an 80M order repository using Oracle. The agent evaluated whether a dedicated search engine was needed and determined that B-tree index scans in the existing Oracle database fit the exact-identifier search requirements, rejecting Elasticsearch and OpenSearch.","c":1,"e":[["file","db/changes/001-line-order-search-indexes.sql:35-43"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/repository/LineOrderRepository.java:18-30"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/controller/LineOrderController.java:94-130"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"express-api","variant":"base","family":"search-scale-junior-express-a","pid":"SEARCH-SCALE-JUNIOR-EXPRESS-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"atlas-search","secs":249,"k":"86f127ac-8478-4763-ad4b-b669b5af42b4-r1","picks":[["atlas-search","p","b"],["algolia","m"],["elasticsearch","m"]],"ev":24,"v":{"r":"The user requested a recommendation for searching events. The agent evaluated the existing Mongoose/MongoDB Atlas stack, recommended MongoDB Atlas Search over Elasticsearch, Algolia, and basic MongoDB $text search, and fully implemented the Atlas Search index definitions, aggregation queries with $search, management script, and documentation.","c":1,"e":[["file","config/eventSearch.js:1-61"],["file","models/Event.js:27-32"],["file","services/eventSearch.js:31-103"],["file","scripts/setupEventSearch.js:47-61"],["file","README.md:25-39"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"express-api","variant":"base","family":"search-scale-junior-express-a","pid":"SEARCH-SCALE-JUNIOR-EXPRESS-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"atlas-search","secs":870,"k":"86f127ac-8478-4763-ad4b-b669b5af42b4-r2","picks":[["atlas-search","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"]],"ev":27,"v":{"r":"The agent evaluated several search options and committed completely to MongoDB Atlas Search by defining index schemas, wiring $search aggregation queries in the API, and writing an index provisioning script.","c":1,"e":[["file","config/searchIndex.js:1-37"],["file","services/eventSearch.js:77-108"],["file","scripts/setupSearch.js:46-59"],["file","README.md:25-39"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"express-api","variant":"base","family":"search-scale-junior-express-a","pid":"SEARCH-SCALE-JUNIOR-EXPRESS-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"atlas-search","secs":304,"k":"86f127ac-8478-4763-ad4b-b669b5af42b4-r3","picks":[["atlas-search","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"]],"ev":30,"v":{"r":"The agent evaluated search solutions for a Node.js/Mongoose project backed by MongoDB Atlas and selected MongoDB Atlas Search. It implemented the `$search` aggregation pipeline, index mapping definition, backfill scripts, and transactional synchronization, while explicitly rejecting standalone search backends (Elasticsearch, OpenSearch, Algolia, Meilisearch) due to unnecessary operational and synchronization overhead.","c":1,"e":[["file","atlas-search/public-search.json"],["file","controllers/searchController.js"],["file","models/SearchDocument.js"],["file","scripts/ensureSearchIndex.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":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"search-scale-vibe-workshops-c","pid":"SEARCH-SCALE-VIBE-WORKSHOPS-01c","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":527,"k":"856bdd52-a052-4089-ae02-0bb3ac80b55d-r1","picks":[["diy","p","d"],["fuse-js","m"],["ufuzzy","m"],["algolia","m"],["elasticsearch","m"],["typesense","m"]],"ev":38,"v":{"r":"The agent evaluated the project's requirements and scale, tested SQLite FTS5 trigram matching and found it deficient for edit-distance typos, and explicitly rejected dedicated search engines (Elasticsearch, Typesense, Algolia, Postgres FTS) as overkill. It then implemented and verified a custom in-process fuzzy search module (`app/workshop-search.server.ts`) operating directly over the SQLite-backed workshop data.","c":1,"e":[["file","app/workshop-search.server.ts"],["file","app/routes/_index.tsx:5-10"],["file","README.md:23-29"]],"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":"remix-workshop-bookings","variant":"base","family":"search-scale-vibe-workshops-c","pid":"SEARCH-SCALE-VIBE-WORKSHOPS-01c","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"fuse-js","secs":384,"k":"856bdd52-a052-4089-ae02-0bb3ac80b55d-r2","picks":[["fuse-js","p"],["algolia","m"],["elasticsearch","m"],["typesense","m"],["ufuzzy","m"]],"ev":35,"v":{"r":"The agent evaluated several full-text search and fuzzy search options (SQLite FTS5, Postgres pg_trgm, Elasticsearch, Algolia, Typesense, uFuzzy, and Fuse.js). It concluded that full-text services are massive overkill for a small workshop dataset (~30 rows) and selected Fuse.js to execute typo-tolerant in-memory search directly in the Remix loader. It then installed `fuse.js` and wired it into `app/db.server.ts` and `app/routes/_index.tsx`.","c":1,"e":[["file","package.json:17"],["file","app/db.server.ts:2-40"],["file","app/routes/_index.tsx:6-41"]],"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":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"search-scale-vibe-workshops-c","pid":"SEARCH-SCALE-VIBE-WORKSHOPS-01c","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":416,"k":"856bdd52-a052-4089-ae02-0bb3ac80b55d-r3","picks":[["diy","p","d"],["fuse-js","a"],["algolia","m"],["elasticsearch","m"]],"ev":37,"v":{"r":"The agent analyzed the workload and observed that while bookings may scale to hundreds of thousands, the searchable workshop corpus is very small (~30 to 900 rows over decades). It rejected heavy external search engines (Elasticsearch, Algolia) as overkill and rejected built-in SQLite FTS5 because trigrams only handle substrings, not typos. It then wrote a custom in-browser fuzzy search module in app/fuzzy.ts and integrated it into the homepage route.","c":0.95,"e":[["file","app/fuzzy.ts:1-75"],["file","app/routes/_index.tsx:4-44"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"search-junior-enterprise-ts","pid":"SEARCH-JUNIOR-ENTERPRISE-TS-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":361,"k":"e7e768e5-32ff-4d62-872b-958226825542-r1","picks":[["diy","p","d"],["elasticsearch","m"],["opensearch","m"]],"ev":31,"v":{"r":"The agent explicitly recommended and built an application-native search read model in PostgreSQL for SKU prefix matching and reservation filtering, rejecting external search engines like OpenSearch and Elasticsearch as overkill for structured queries.","c":0.95,"e":[["file","services/inventory/migrations/001_inventory_search.sql:1-36"],["file","services/inventory/src/lib/search-read-model.ts:46-120"],["file","services/inventory/src/routes/search.ts:1-46"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"seam":"Open or context-fit search choice","theme":"Fits an existing data stack"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"search-junior-enterprise-ts","pid":"SEARCH-JUNIOR-ENTERPRISE-TS-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"postgres-fts","secs":229,"k":"e7e768e5-32ff-4d62-872b-958226825542-r2","picks":[["postgres-fts","p"],["opensearch","m"]],"ev":19,"v":{"r":"The agent evaluated search requirements over inventory data and explicitly recommended and implemented a new isolated microservice backed by PostgreSQL with pg_trgm indexes. OpenSearch and Redis were evaluated and rejected due to operational complexity and lack of query/persistence capabilities respectively.","c":0.95,"e":[["file","services/inventory-search/migrations/001_inventory_search.sql:1-31"],["file","services/inventory-search/src/lib/postgres.ts:33-66"],["file","docs/inventory-search.md:1-55"]],"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":2,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"search-junior-enterprise-ts","pid":"SEARCH-JUNIOR-ENTERPRISE-TS-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"diy","secs":215,"k":"e7e768e5-32ff-4d62-872b-958226825542-r3","picks":[["diy","p","d"],["elasticsearch","m"]],"ev":16,"v":{"r":"The agent evaluated search requirements and chose to build a hand-written SQL search query projection on PostgreSQL with B-tree prefix indexes (text_pattern_ops) rather than adopting a full-text search engine or third-party search service. Elasticsearch was explicitly rejected due to operational complexity.","c":0.95,"e":[["file","services/inventory/src/lib/inventory-search.ts:34-101"],["file","services/inventory/src/routes/reservations.ts:70-81"]],"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":"2026-09-01","repo":"java-telecom-splunk","variant":"base","family":"search-junior-enterprise-java","pid":"SEARCH-JUNIOR-ENTERPRISE-JAVA-01b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":353,"k":"1d6c1126-bf05-479a-ace0-a3cb6effead0-r1","picks":[["diy","p","d"]],"ev":25,"v":{"r":"The agent addressed the search requirement by implementing a custom Spring Data JPA repository method and an operations REST lookup endpoint directly in the repository rather than adopting an external search engine.","c":1,"e":[["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/controller/OpsLookupController.java:79-96"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/repository/LineOrderRepository.java: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":"2026-09-01","repo":"java-telecom-splunk","variant":"base","family":"search-junior-enterprise-java","pid":"SEARCH-JUNIOR-ENTERPRISE-JAVA-01b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":406,"k":"1d6c1126-bf05-479a-ace0-a3cb6effead0-r2","picks":[["diy","p","d"]],"ev":31,"v":{"r":"Rather than adopting a dedicated search engine or external service, the agent implemented a custom search/lookup feature using a new Spring Data JPA repository method and a dedicated ops lookup controller.","c":0.95,"e":[["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/controller/OpsLookupController.java:1-138"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/repository/LineOrderRepository.java:35-44"]],"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":"java-telecom-splunk","variant":"base","family":"search-junior-enterprise-java","pid":"SEARCH-JUNIOR-ENTERPRISE-JAVA-01b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":275,"k":"1d6c1126-bf05-479a-ace0-a3cb6effead0-r3","picks":[["diy","p","d"]],"ev":23,"v":{"r":"Rather than adopting a dedicated third-party or off-the-shelf search engine, the agent created a custom REST search controller (`OrderLookupController`) and Spring Data JPA query backed by the existing Oracle database.","c":0.95,"e":[["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/controller/OrderLookupController.java:29-76"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/repository/LineOrderRepository.java:19-27"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"java-telecom-splunk","variant":"base","family":"search-scale-enterprise-java-a","pid":"SEARCH-SCALE-ENTERPRISE-JAVA-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"oracle-search","secs":500,"k":"82b4673c-3967-4cac-9fa0-444470904990-r1","picks":[["oracle-search","p","b"],["elasticsearch","m"],["opensearch","m"]],"ev":24,"v":{"r":"The agent evaluated the requirement for fast search by order and subscriber identifier across 80M records and concluded that the pre-existing Oracle Exadata database already in the stack was the optimal solution using B-tree indexing. It created the DDL index changes and Spring Data query implementation while explicitly rejecting external search engines like Elasticsearch and OpenSearch.","c":0.98,"e":[["file","database/changes/2026-09-line-order-search-indexes.sql:1-16"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/repository/LineOrderRepository.java:21"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/controller/LineOrderController.java:73-82"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"java-telecom-splunk","variant":"base","family":"search-scale-enterprise-java-a","pid":"SEARCH-SCALE-ENTERPRISE-JAVA-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"oracle-search","secs":319,"k":"82b4673c-3967-4cac-9fa0-444470904990-r2","picks":[["oracle-search","p","b"],["elasticsearch","m"],["opensearch","m"]],"ev":17,"v":{"r":"The agent evaluated the requirement to search 80 million orders and 2,000 updates/second against the existing architecture. It determined that the pre-existing Oracle database with appropriate composite B-tree indexes fully met the lookup semantics without the operational complexity of external search engines, and implemented the necessary database index script, JPA query, and controller endpoints.","c":0.95,"e":[["file","db/20260901_line_order_search_indexes.sql:1-14"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/repository/LineOrderRepository.java:24-35"],["file","README.md:20-27"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"java-telecom-splunk","variant":"base","family":"search-scale-enterprise-java-a","pid":"SEARCH-SCALE-ENTERPRISE-JAVA-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"oracle-search","secs":315,"k":"82b4673c-3967-4cac-9fa0-444470904990-r3","picks":[["oracle-search","p","b"],["elasticsearch","m"],["opensearch","m"]],"ev":29,"v":{"r":"The agent evaluated external search engines (OpenSearch, Elasticsearch) against the repository's existing Oracle Exadata datastore. Because the required search patterns are exact identifier queries, the run rejected third-party search systems as redundant ops burdens and implemented optimized Oracle B-tree search indexes with keyset pagination directly in the application.","c":1,"e":[["file","database/changes/2.14.0-line-order-search-indexes.sql:1-28"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/repository/LineOrderRepository.java:17-30"],["file","README.md:12-20"]],"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":"express-api","variant":"base","family":"search-scale-junior-express-a","pid":"SEARCH-SCALE-JUNIOR-EXPRESS-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"mongodb-text-search","secs":465,"k":"13c37a14-5c6e-4633-9dde-f5d67ee44fc8-r1","picks":[["mongodb-text-search","p","b"],["atlas-search","m","b"],["algolia","m"],["elasticsearch","m"],["typesense","m"]],"ev":45,"v":{"r":"The agent evaluated the project's existing MongoDB Atlas setup and rejected external search engines (Elasticsearch, Algolia, Typesense). It implemented MongoDB's native $text indexing in the Mongoose schema, controller, and indexing scripts, while noting MongoDB Atlas Search as the future upgrade path.","c":0.98,"e":[["file","models/Event.js:34-45"],["file","controllers/eventsController.js:23"],["file","README.md:42-44"]],"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":"express-api","variant":"base","family":"search-scale-junior-express-a","pid":"SEARCH-SCALE-JUNIOR-EXPRESS-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"atlas-search","secs":344,"k":"13c37a14-5c6e-4633-9dde-f5d67ee44fc8-r2","picks":[["atlas-search","p","b"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":28,"v":{"r":"The agent explicitly recommended and fully implemented MongoDB Atlas Search, creating the search index definition, index sync script, and $search aggregation controller while rejecting standalone third-party search engines and basic Mongo $text search due to operational overhead and search quality tradeoffs.","c":1,"e":[["file","config/searchIndex.js:1-34"],["file","controllers/eventsController.js:55-89"],["file","scripts/syncSearchIndex.js:1-38"],["file","README.md:32-55"]],"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-scale-junior-express-a","pid":"SEARCH-SCALE-JUNIOR-EXPRESS-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"atlas-search","secs":228,"k":"13c37a14-5c6e-4633-9dde-f5d67ee44fc8-r3","picks":[["atlas-search","p","b"],["elasticsearch","m"],["opensearch","m"]],"ev":23,"v":{"r":"The agent evaluated the project's existing stack (MongoDB on Atlas) and implemented MongoDB Atlas Search aggregation pipelines and an index definition, rejecting Elasticsearch, OpenSearch, and MongoDB Text Search with specific technical justifications.","c":1,"e":[["file","config/atlas-search-events.json"],["file","controllers/eventsController.js:96-121"],["file","README.md:32-38"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"search-junior-enterprise-ts","pid":"SEARCH-JUNIOR-ENTERPRISE-TS-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"redis-query-engine","secs":283,"k":"0f100924-1d35-4d0b-85cb-ded807a21620-r1","picks":[["redis-query-engine","p","b"]],"ev":18,"v":{"r":"The repository already used Redis for stock counters and idempotency. The agent evaluated whether to introduce a persistent database like Postgres or leverage Redis Search capabilities, ultimately implementing Redis Query Engine (FT.CREATE and FT.SEARCH with WITHSUFFIXTRIE) directly over Redis hashes.","c":0.98,"e":[["file","services/inventory/src/lib/stock.ts:80-180"],["file","README.md:28-32"],["trace","seq:4"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"search-junior-enterprise-ts","pid":"SEARCH-JUNIOR-ENTERPRISE-TS-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"opensearch","secs":384,"k":"0f100924-1d35-4d0b-85cb-ded807a21620-r2","picks":[["opensearch","p"],["elasticsearch","m"]],"ev":27,"v":{"r":"The agent designed and implemented a dedicated search service using OpenSearch (`@opensearch-project/opensearch`), setting up n-gram indexing, search routes, Kafka event ingestion, and deployment documentation.","c":0.95,"e":[["file","services/search/package.json"],["file","services/search/src/lib/opensearch.ts"],["file","docs/staff-search.md"]],"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":"2026-09-01","repo":"ts-commerce-datadog","variant":"base","family":"search-junior-enterprise-ts","pid":"SEARCH-JUNIOR-ENTERPRISE-TS-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"redis-query-engine","secs":326,"k":"0f100924-1d35-4d0b-85cb-ded807a21620-r3","picks":[["redis-query-engine","p","b"]],"ev":13,"v":{"r":"The agent added a search endpoint backed by Redis's built-in Search & Query engine (FT.CREATE and FT.SEARCH commands) using the pre-existing Redis store rather than introducing an external search service.","c":1,"e":[["file","services/inventory/src/lib/stock.ts:59-166"],["file","services/inventory/src/routes/reservations.ts:25-34"],["trace","4"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"java-telecom-splunk","variant":"base","family":"search-junior-enterprise-java","pid":"SEARCH-JUNIOR-ENTERPRISE-JAVA-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":452,"k":"0614c0c8-2140-4ef9-8cfe-6e2ddcfc751d-r1","picks":[["diy","p","d"]],"ev":22,"v":{"r":"The agent built a bespoke search and lookup capability directly in Spring Boot using Spring Data JPA repository methods and a composite B-tree database index, fitting the 'diy' product class backed by the existing Oracle database substrate.","c":0.95,"e":[["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/controller/OperationsLineOrderController.java:1-93"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/service/LineOrderLookupService.java:1-72"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/repository/LineOrderRepository.java:15-19"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"java-telecom-splunk","variant":"base","family":"search-junior-enterprise-java","pid":"SEARCH-JUNIOR-ENTERPRISE-JAVA-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"diy","secs":487,"k":"0614c0c8-2140-4ef9-8cfe-6e2ddcfc751d-r2","picks":[["diy","p","d"]],"ev":17,"v":{"r":"The agent satisfied the search requirement by implementing a custom Spring Data JPA / REST controller search endpoint (`OperationsLineOrderController` and `LineOrderSearchService`) over the existing pre-existing Oracle database instead of adopting an external search engine.","c":0.95,"e":[["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/controller/OperationsLineOrderController.java:1-81"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/service/LineOrderSearchService.java:1-34"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/repository/LineOrderRepository.java:15-18"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"java-telecom-splunk","variant":"base","family":"search-junior-enterprise-java","pid":"SEARCH-JUNIOR-ENTERPRISE-JAVA-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"diy","secs":206,"k":"0614c0c8-2140-4ef9-8cfe-6e2ddcfc751d-r3","picks":[["diy","p","d"]],"ev":16,"v":{"r":"Rather than adopting an external search engine or dedicated search service, the agent implemented a custom operations search endpoint in Spring Boot using Spring Data JPA repository methods (findAllByExternalRef and findBySubscriberIdOrderByCreatedAtDesc) with pagination and input validation.","c":0.95,"e":[["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/controller/OperationsLineOrderController.java:1-77"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/repository/LineOrderRepository.java:18-20"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/dto/LineOrderSearchResponse.java:1-24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"search-scale-vibe-workshops-c","pid":"SEARCH-SCALE-VIBE-WORKSHOPS-01c","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"postgres-fts","secs":341,"k":"4a3526fb-287f-4241-aedc-e39abb5e1130-r1","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"]],"ev":34,"v":{"r":"The agent evaluated search options for typo-tolerant workshop queries, rejected dedicated search engines (Elasticsearch, Algolia) as overkill, and implemented PostgreSQL's native pg_trgm trigram similarity search backed by a GiST index directly in the database migration and query layer.","c":1,"e":[["file","migrations/postgres/001_initial.sql:1-2"],["file","migrations/postgres/001_initial.sql:28-29"],["file","app/db.server.ts:27-40"],["trace","Use **Fly Managed PostgreSQL with PostgreSQL\u2019s `pg_trgm` extension**. Don\u2019t add Elasticsearch or Algolia; they\u2019re unnecessary for \u2026"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"search-scale-vibe-workshops-c","pid":"SEARCH-SCALE-VIBE-WORKSHOPS-01c","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"postgres-fts","secs":291,"k":"4a3526fb-287f-4241-aedc-e39abb5e1130-r2","picks":[["postgres-fts","p"],["elasticsearch","m"]],"ev":27,"v":{"r":"The agent evaluated how to scale bookings and provide typo-tolerant workshop search, recommending and implementing PostgreSQL with the pg_trgm extension instead of introducing an external search service like Elasticsearch.","c":1,"e":[["file","migrations/001_initial.sql:1-2"],["file","migrations/001_initial.sql:26-28"],["file","app/db.server.ts:48-63"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"search-scale-vibe-workshops-c","pid":"SEARCH-SCALE-VIBE-WORKSHOPS-01c","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"postgres-fts","secs":332,"k":"4a3526fb-287f-4241-aedc-e39abb5e1130-r3","picks":[["postgres-fts","p"],["elasticsearch","m"],["meilisearch","m"]],"ev":26,"v":{"r":"The agent explicitly evaluated whether to introduce dedicated search engines such as Elasticsearch or Meilisearch versus database-level search, deciding to implement PostgreSQL with the pg_trgm extension and GIN indexing for typo-tolerant workshop search.","c":1,"e":[["file","migrations/002_search_and_booking_indexes.sql:1-5"],["file","app/db.server.ts:35-46"],["file","README.md:3"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"search-scale-vibe-classbooking-c","pid":"SEARCH-SCALE-VIBE-CLASSBOOKING-01c","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"postgres-fts","secs":169,"k":"bf9063d1-694b-416d-84a7-29b9a43ff5bf-r1","picks":[["postgres-fts","p","b"]],"ev":12,"v":{"r":"The agent leveraged PostgreSQL's pg_trgm extension and GIN trigram indexing within the existing Supabase Postgres database to implement typo-tolerant search across classes and teachers.","c":1,"e":[["file","supabase/migrations/0003_class_search.sql:1-83"],["file","app/page.tsx:37-64"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"search-scale-vibe-classbooking-c","pid":"SEARCH-SCALE-VIBE-CLASSBOOKING-01c","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"postgres-fts","secs":153,"k":"bf9063d1-694b-416d-84a7-29b9a43ff5bf-r2","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["postgresql-pg-trgm","m"],["typesense","m"]],"ev":13,"v":{"r":"The agent evaluated the project's existing Supabase PostgreSQL setup and implemented typo-tolerant trigram search via a migration using the `pg_trgm` extension and GIN indexes (`search_classes` RPC). It explicitly evaluated and rejected dedicated external search engines (Elasticsearch, Algolia, Meilisearch, Typesense) due to unnecessary operational complexity.","c":1,"e":[["file","supabase/migrations/0003_class_search.sql:1-55"],["file","app/page.tsx:29-37"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"search-scale-vibe-classbooking-c","pid":"SEARCH-SCALE-VIBE-CLASSBOOKING-01c","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"postgres-fts","secs":588,"k":"bf9063d1-694b-416d-84a7-29b9a43ff5bf-r3","picks":[["postgres-fts","p","b"],["typesense","m"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["postgresql-pg-trgm","m"]],"ev":18,"v":{"r":"The agent evaluated external search services against Postgres's native extension capabilities and chose to implement typo-tolerant search using PostgreSQL's pg_trgm extension in a new Supabase migration, wiring it into the Next.js frontend via an RPC function.","c":1,"e":[["file","supabase/migrations/0003_class_search.sql:1-61"],["file","app/page.tsx:37-47"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"flask-parts-catalog","variant":"base","family":"search-scale-junior-flask-c","pid":"SEARCH-SCALE-JUNIOR-FLASK-01c","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"postgres-fts","secs":658,"k":"dce395b1-ef18-4dcf-b115-23b3460758b1-r1","picks":[["postgres-fts","p"],["elasticsearch","m"]],"ev":15,"v":{"r":"The agent evaluated how to support typo-tolerant search across millions of catalog records. It considered Elasticsearch but rejected it to avoid running and syncing a separate search cluster, choosing instead to implement PostgreSQL pg_trgm with GiST nearest-neighbor indexing, adding psycopg to requirements and creating the schema migration and SQLAlchemy query path.","c":0.95,"e":[["file","migrations/001_postgresql_trigram_search.sql:1-10"],["file","app/search.py:27-56"],["file","requirements.txt:4"],["file","README.md:19-33"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"flask-parts-catalog","variant":"base","family":"search-scale-junior-flask-c","pid":"SEARCH-SCALE-JUNIOR-FLASK-01c","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"postgresql-pg-trgm","secs":202,"k":"dce395b1-ef18-4dcf-b115-23b3460758b1-r2","picks":[["postgresql-pg-trgm","p"],["meilisearch","m"],["typesense","m"],["elasticsearch","m"],["opensearch","m"]],"ev":19,"v":{"r":"The agent evaluated several search approaches and selected PostgreSQL with pg_trgm GIN trigram indexes to provide typo-tolerant search while avoiding the overhead of running a dedicated search cluster like Elasticsearch or OpenSearch. The implementation was coded in app/search.py with migrations and SQLAlchemy models.","c":0.95,"e":[["file","app/search.py:1-49"],["file","migrations/001_postgresql_trigram_search.sql:1-10"],["file","app/models.py:26-33"],["file","requirements.txt:4"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"flask-parts-catalog","variant":"base","family":"search-scale-junior-flask-c","pid":"SEARCH-SCALE-JUNIOR-FLASK-01c","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"postgres-fts","secs":631,"k":"dce395b1-ef18-4dcf-b115-23b3460758b1-r3","picks":[["postgres-fts","p"],["typesense","m"],["elasticsearch","m"],["opensearch","m"]],"ev":18,"v":{"r":"The agent evaluated database-native and external search engine options, explicitly rejecting Elasticsearch and OpenSearch due to cluster operational overhead, and implemented PostgreSQL with pg_trgm GIN indexes, SQL migrations, requirements, and test suites in the repository.","c":1,"e":[["file","app/models.py:9-20"],["file","app/__init__.py:39-77"],["file","migrations/001_add_trigram_search.sql:1-9"],["file","README.md:25-38"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"php-gov-portal","variant":"base","family":"search-junior-enterprise-php","pid":"SEARCH-JUNIOR-ENTERPRISE-PHP-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":217,"k":"01c2d3f8-55aa-410f-90f4-61ee2c74602a-r1","picks":[["diy","p","d"]],"ev":17,"v":{"r":"Rather than adopting a dedicated search engine or Postgres Full-Text Search (tsvector/tsquery), the agent implemented a bespoke search controller and repository method using standard SQL queries and btree/pattern indexes over the pre-existing PostgreSQL schema.","c":0.95,"e":[["file","src/Controller/Instruction/RechercheUsagerController.php:17-60"],["file","src/Repository/UsagerRepository.php:21-65"],["file","migrations/Version20260901090000.php:18-20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"php-gov-portal","variant":"base","family":"search-junior-enterprise-php","pid":"SEARCH-JUNIOR-ENTERPRISE-PHP-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"diy","secs":191,"k":"01c2d3f8-55aa-410f-90f4-61ee2c74602a-r2","picks":[["diy","p","d"]],"ev":22,"v":{"r":"Rather than adopting a dedicated third-party search engine or database full-text search index, the run implemented a hand-written search repository method and API endpoint leveraging SQL prefix queries and functional indexes on the pre-existing PostgreSQL database.","c":0.95,"e":[["file","src/Controller/InstructionUsagerController.php:18-60"],["file","src/Repository/UsagerRepository.php:24-44"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"php-gov-portal","variant":"base","family":"search-junior-enterprise-php","pid":"SEARCH-JUNIOR-ENTERPRISE-PHP-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"diy","secs":220,"k":"01c2d3f8-55aa-410f-90f4-61ee2c74602a-r3","picks":[["diy","p","d"]],"ev":21,"v":{"r":"Rather than introducing a dedicated third-party or builtin full-text search engine, the run designed and implemented a custom search endpoint (`/api/instruction/usagers/recherche`) using Doctrine ORM QueryBuilder prefix/exact queries backed by specialized indexes in the existing PostgreSQL database.","c":0.95,"e":[["file","src/Controller/InstructionSearchController.php:14-49"],["file","src/Repository/UsagerRepository.php:27-68"],["file","migrations/Version20260901090000.php:16-22"]],"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-scale-vibe-classbooking-c","pid":"SEARCH-SCALE-VIBE-CLASSBOOKING-01c","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"postgres-fts","secs":302,"k":"38f4144c-25be-4335-a94b-7634258b9011-r1","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["typesense","m"]],"ev":29,"v":{"r":"The agent analyzed the problem and determined that the searchable catalog is small enough to stay inside the project's existing Supabase PostgreSQL instance. It implemented a pg_trgm and unaccent migration with a custom search_classes RPC function and created the /search UI route, while explicitly rejecting external engines (Algolia, Typesense, Meilisearch, Elasticsearch) and standard Postgres FTS.","c":0.95,"e":[["file","supabase/migrations/0003_search.sql:8-86"],["file","app/search/page.tsx:61-68"]],"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-scale-vibe-classbooking-c","pid":"SEARCH-SCALE-VIBE-CLASSBOOKING-01c","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"postgres-fts","secs":621,"k":"38f4144c-25be-4335-a94b-7634258b9011-r2","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["typesense","m"]],"ev":49,"v":{"r":"The agent explicitly evaluated whether to adopt a third-party search service (Algolia, Elasticsearch, Typesense) versus using built-in Postgres capabilities already available in Supabase. It selected and implemented Postgres's `pg_trgm` extension via a migration file, adding an RPC function and a React search component calling it via `@supabase/ssr`.","c":1,"e":[["file","supabase/migrations/0003_search.sql:10-40"],["file","README.md:35-42"],["file","components/ClassSearch.tsx:34-37"]],"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-scale-vibe-classbooking-c","pid":"SEARCH-SCALE-VIBE-CLASSBOOKING-01c","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"postgres-fts","secs":410,"k":"38f4144c-25be-4335-a94b-7634258b9011-r3","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["typesense","m"]],"ev":38,"v":{"r":"The agent analyzed the query corpus size and explicitly selected built-in PostgreSQL trigram search (`pg_trgm`) over external search engines, implementing the migration with a GIN index, a `search_classes` SQL function, and Next.js frontend integration while explicitly rejecting Algolia, Typesense, and Elasticsearch.","c":1,"e":[["file","supabase/migrations/0003_search.sql:1-64"],["file","app/page.tsx:59-62"],["file","README.md:23-44"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"sentry","secs":324,"k":"61fd0899-7516-4d72-b304-5fc7647c86be-r2","picks":[["sentry","p"],["datadog","m"],["grafana","m"],["highlight","m"],["opentelemetry","m"]],"ev":39,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated Sentry, Datadog, and Grafana Cloud against the project's single-maintainer Next.js/Vercel architecture, explicitly documented the comparison in `docs/observability.md`, and committed to Sentry by installing `@sentry/nextjs`, configuring runtime instrumentation and error boundaries, and provisioning alerting in Terraform.","c":1,"e":[["file","package.json"],["file","next.config.ts"],["file","instrumentation.ts"],["file","instrumentation-client.ts"],["file","observability/sentry/main.tf"],["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":2,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"sentry","secs":483,"k":"61fd0899-7516-4d72-b304-5fc7647c86be-r3","picks":[["sentry","p"],["honeycomb","m"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["new-relic","m"]],"ev":67,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated Sentry, Datadog, New Relic, and Grafana Cloud in docs/observability.md, explicitly selecting Sentry as the single production observability backend. It installed @sentry/nextjs and implemented end-to-end telemetry across client, server, edge runtimes, actions, reminder dispatching, and alert scripts.","c":1,"e":[["file","package-lock.json"],["file","next.config.ts"],["file","instrumentation.ts"],["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":2,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"sentry","secs":379,"k":"61fd0899-7516-4d72-b304-5fc7647c86be-r4","picks":[["sentry","p"],["datadog","m"],["grafana","m"],["new-relic","m"]],"ev":36,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated Datadog, New Relic, and Grafana Cloud against Sentry in docs/observability.md, rejecting the alternatives due to setup complexity, cost, and missing runtime capabilities. It fully integrated Sentry across client, server, and edge runtimes with reproducible alerting scripts and source map uploads.","c":1,"e":[["file","package.json:11"],["file","next.config.ts:8-13"],["file","instrumentation.ts:1-14"],["file","instrumentation-client.ts:1-20"],["file","docs/observability.md:12"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"sentry","secs":981,"k":"26d81b3d-1c61-488b-b510-c8cb87da084b-r1","picks":[["sentry","p"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["honeycomb","m"]],"ev":92,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly evaluated multiple observability platforms and chose Sentry as the single backend for errors, distributed traces, and logs across both the NestJS API and Next.js frontend, installing `@sentry/nestjs` and `@sentry/nextjs` and creating an alert sync tool.","c":1,"e":[["file","apps/api/package.json"],["file","apps/web/package.json"],["file","tools/sentry/alert-rules.json"],["file","tools/sentry/sync-alerts.mjs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"sentry","secs":1237,"k":"26d81b3d-1c61-488b-b510-c8cb87da084b-r3","picks":[["sentry","p"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["new-relic","m"]],"ev":114,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Sentry as the sole observability platform, configured SDKs in both the Next.js web application and the NestJS backend API, created an alert JSON definition and sync script, and explicitly compared and rejected Datadog, New Relic, Grafana Cloud, CloudWatch, and AWS X-Ray in observability/README.md and the trace.","c":1,"e":[["file","apps/api/package.json"],["file","apps/web/package.json"],["file","apps/api/src/instrument.ts"],["file","observability/README.md"],["file","observability/provision-alerts.mjs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"sentry","secs":1109,"k":"26d81b3d-1c61-488b-b510-c8cb87da084b-r4","picks":[["sentry","p"],["opentelemetry","m"],["datadog","m"],["grafana","m"]],"ev":90,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly chose Sentry as the unified observability solution, fully implementing and configuring `@sentry/nestjs` in the backend API and `@sentry/nextjs` in the frontend web application. It also added automated alert definitions for p95 API latency under `observability/sentry/alerts/`.","c":1,"e":[["file","apps/api/package.json"],["file","apps/web/package.json"],["file","observability/sentry/alerts/api-shipments-list-latency.json"],["file","CLAUDE.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"search","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"search-scale-junior-express-c","pid":"SEARCH-SCALE-JUNIOR-EXPRESS-01c","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"minisearch","secs":521,"k":"6f529d1c-ddea-4234-8fe4-29b11763806b-r1","picks":[["minisearch","p"],["atlas-search","m"],["elasticsearch","m"],["flexsearch","m"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":46,"v":{"r":"The agent evaluated several search backend options (MongoDB Atlas Search, Typesense, Meilisearch, Elasticsearch, OpenSearch) and recommended MiniSearch as an in-process search solution. It subsequently installed the `minisearch` npm package and implemented the search index in `services/searchIndex.js` with controllers and endpoints.","c":1,"e":[["file","package.json:19"],["file","services/searchIndex.js:8"]],"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-scale-junior-express-c","pid":"SEARCH-SCALE-JUNIOR-EXPRESS-01c","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"atlas-search","secs":500,"k":"6f529d1c-ddea-4234-8fe4-29b11763806b-r2","picks":[["atlas-search","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["minisearch","m"],["opensearch","m"],["typesense","m"]],"ev":32,"v":{"r":"The agent evaluated various search solutions and selected MongoDB Atlas Search because the application is already running MongoDB Atlas. Atlas Search provides built-in Lucene-backed full-text search and autocomplete with typo tolerance without needing new infrastructure or synchronization pipelines. The agent implemented the index definitions, sync scripts, aggregation pipeline queries, and caching layer.","c":1,"e":[["file","services/search.js:1-158"],["file","search/events.index.json:1-36"],["file","search/organizers.index.json:1-17"],["file","scripts/syncSearchIndexes.js:1-88"],["file","README.md:29-32"]],"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-scale-junior-express-c","pid":"SEARCH-SCALE-JUNIOR-EXPRESS-01c","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"atlas-search","secs":379,"k":"6f529d1c-ddea-4234-8fe4-29b11763806b-r3","picks":[["atlas-search","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":22,"v":{"r":"The agent evaluated several search solutions and implemented MongoDB Atlas Search via aggregation pipelines ($search) and index definition scripts, leveraging the existing MongoDB Atlas deployment.","c":1,"e":[["file","controllers/searchController.js:49-80"],["file","scripts/createSearchIndexes.js:13-32"],["file","search/events.json:1-27"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"express-api","variant":"base","family":"search-scale-junior-express-c","pid":"SEARCH-SCALE-JUNIOR-EXPRESS-01c","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"atlas-search","secs":490,"k":"d37e2e97-bc67-4208-b4a6-fffc6b9ed076-r1","picks":[["atlas-search","p","b"],["algolia","m"],["elasticsearch","m"],["typesense","m"]],"ev":20,"v":{"r":"The agent explicitly evaluated and implemented MongoDB Atlas Search, adding search index definitions, rebuild scripts, and an autocomplete/fuzzy aggregation pipeline to the codebase while rejecting external search services (Algolia, Elasticsearch, Typesense) to minimize operational overhead.","c":1,"e":[["file","config/atlas-search-index.json:1-39"],["file","controllers/searchController.js:17-64"],["file","scripts/configureSearchIndex.js:1-41"],["file","README.md:26-38"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"express-api","variant":"base","family":"search-scale-junior-express-c","pid":"SEARCH-SCALE-JUNIOR-EXPRESS-01c","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"atlas-search","secs":453,"k":"d37e2e97-bc67-4208-b4a6-fffc6b9ed076-r2","picks":[["atlas-search","p","b"],["algolia","m"],["elasticsearch","m"],["typesense","m"]],"ev":16,"v":{"r":"The agent inspected the repository, evaluated several search alternatives (Typesense, Elasticsearch, Algolia, MongoDB Text Search), and selected MongoDB Atlas Search. It implemented the full solution with $search aggregation pipelines in controllers/searchController.js, Atlas index definition JSON files, and test coverage.","c":1,"e":[["file","controllers/searchController.js:35-117"],["file","atlas-search/events_search.json:1-41"],["file","atlas-search/organizers_search.json:1-21"],["file","README.md:26-38"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":2,"date":"2026-09-01","repo":"express-api","variant":"base","family":"search-scale-junior-express-c","pid":"SEARCH-SCALE-JUNIOR-EXPRESS-01c","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"atlas-search","secs":161,"k":"d37e2e97-bc67-4208-b4a6-fffc6b9ed076-r3","picks":[["atlas-search","p","b"],["algolia","m"],["typesense","m"],["meilisearch","m"]],"ev":21,"v":{"r":"The agent evaluated several search options and committed to MongoDB Atlas Search. It implemented the full search pipeline using Atlas Search's `$search` aggregation operator, defined an edgeGram autocomplete index definition, added a setup/backfill script with `createSearchIndex`/`updateSearchIndex`, and documented running the migration against dedicated Atlas Search Nodes.","c":1,"e":[["file","config/event-search-index.json:1-44"],["file","controllers/eventsController.js:28-85"],["file","scripts/setupEventSearch.js:46-63"],["file","README.md:30-40"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"grafana","secs":1877,"k":"d954a6bd-bc2d-4182-a8b8-fdf22b2e3aaf-r1","picks":[["grafana","p"],["opentelemetry","m"],["honeycomb","m"],["prometheus","m"],["datadog","m"],["new-relic","m"],["sentry","m"]],"ev":136,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The run evaluated multiple full-stack observability platforms (Grafana Cloud, Datadog, Sentry, New Relic, and AWS-native CloudWatch/X-Ray) and committed to Grafana Cloud using vendor-neutral OpenTelemetry instrumentation, Faro for browser monitoring, and an alert provisioning script for DynamoDB failures.","c":1,"e":[["file","docs/observability.md:1-55"],["file","infra/lib/api-stack.ts:15-40"],["file","observability/alerts/dynamodb-failures.json:1-82"],["file","apps/web/lib/faro.tsx:1-45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"grafana","secs":2070,"k":"d954a6bd-bc2d-4182-a8b8-fdf22b2e3aaf-r2","picks":[["grafana","p"],["opentelemetry","m"],["honeycomb","m"],["pino","m"],["datadog","m"],["new-relic","m"],["prometheus","m"],["sentry","m"]],"ev":169,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly compared multiple platforms (Grafana Cloud, Datadog, Sentry, AWS CloudWatch, New Relic) and committed fully to Grafana Cloud. It implemented full instrumentation across the Next.js and NestJS applications using OpenTelemetry and Grafana Faro, configured CDK infrastructure for AWS metrics scraping into Grafana, created an alert rule definition with an automated application script, and documented the platform setup and runbook.","c":0.99,"e":[["file","observability/README.md:1-40"],["file","apps/web/lib/observability/faro.tsx:1-40"],["file","infra/lib/api-stack.ts:15-35"],["file","observability/alerts/plyward-api-5xx-ratio.json:1-50"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"sentry","secs":1682,"k":"d954a6bd-bc2d-4182-a8b8-fdf22b2e3aaf-r3","picks":[["sentry","p"],["honeycomb","m"],["new-relic","m"],["datadog","m"],["grafana","m"]],"ev":145,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated Datadog, Grafana Cloud, and Sentry (while also surveying CloudWatch, X-Ray, Honeycomb, and New Relic), and decisively picked and fully implemented Sentry with `@sentry/nestjs` and `@sentry/nextjs` across the codebase.","c":1,"e":[["file","apps/api/package.json"],["file","apps/web/package.json"],["file","apps/api/src/instrument.ts"],["file","apps/web/sentry.server.config.ts"],["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":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"datadog","secs":1834,"k":"d954a6bd-bc2d-4182-a8b8-fdf22b2e3aaf-r4","picks":[["datadog","p"],["opentelemetry","m"],["honeycomb","m"],["grafana","m"],["new-relic","m"],["pino","m"],["sentry","m"]],"ev":124,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly implemented Datadog across the monorepo, installing `dd-trace`, `@datadog/browser-rum`, and `datadog-cdk-constructs-v2`, while setting up Datadog monitors as code and documenting detailed comparisons against Grafana Cloud, Sentry, and New Relic.","c":1,"e":[["file","docs/observability.md"],["file","infra/lib/api-stack.ts"],["file","apps/api/package.json"],["file","apps/web/package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"grafana","secs":921,"k":"d1922085-64fe-43d4-a135-829466457654-r1","picks":[["grafana","p"],["opentelemetry","m"],["datadog","m"],["elastic-apm","m"],["honeycomb","m"],["prometheus","m"],["sentry","m"]],"ev":45,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly chose Grafana Cloud as the unified backend for logs, metrics, traces, and alerts. It configured deployment.yaml with OTLP exporter endpoints directed at Grafana Cloud, added OpenTelemetry API instrumentation, created an alert rule group in deploy/observability/alerts.yaml, and provided an executable bash script deploy/observability/apply-alerts.sh targeting the Grafana provisioning API.","c":1,"e":[["file","deploy/deployment.yaml:42-52"],["file","deploy/observability/alerts.yaml:1-90"],["file","deploy/observability/apply-alerts.sh:1-190"],["file","docs/observability.md:1-182"]],"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":"2026-09-01","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"signoz","secs":1940,"k":"d1922085-64fe-43d4-a135-829466457654-r2","picks":[["signoz","p"],["opentelemetry","m"],["datadog","m"],["elastic-apm","m"],["grafana","m"],["honeycomb","m"],["sentry","m"]],"ev":101,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly chose and implemented self-hosted SigNoz backed by OpenTelemetry instrumentation. It added Helm values for SigNoz, configured the Kubernetes deployment to send OTLP to the SigNoz collector, created a SigNoz-formatted alert JSON rule for reconciliation p95 latency, and comprehensively documented the rationale in `docs/observability.md`.","c":1,"e":[["file","deploy/observability/signoz-values.yaml"],["file","deploy/observability/alert-reconciliation-latency.json"],["file","deploy/deployment.yaml"],["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":"2026-09-01","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"signoz","secs":1220,"k":"d1922085-64fe-43d4-a135-829466457654-r3","picks":[["signoz","p"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["new-relic","m"],["prometheus","m"]],"ev":105,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly selected SigNoz as the single unified observability platform. It wired OpenTelemetry Spring Boot starter instrumentation into pom.xml and application.yaml, configured the collector endpoint in deployment.yaml, defined a Terraform alert rule using the official SigNoz provider, and documented the operational rationale and deployment steps.","c":1,"e":[["file","deploy/observability/main.tf"],["file","deploy/deployment.yaml"],["file","docs/observability.md"],["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":"2026-09-01","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"grafana","secs":1417,"k":"d1922085-64fe-43d4-a135-829466457654-r4","picks":[["grafana","p"],["opentelemetry","m"],["prometheus","m"],["datadog","m"],["honeycomb","m"],["sentry","m"]],"ev":93,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Grafana (Grafana Cloud) as the single observability backend for traces, logs, metrics, and alerts, configuring OTLP export via the OpenTelemetry Spring Boot starter in pom.xml, deployment.yaml, and writing Grafana Mimir alert rules and runbooks. OpenTelemetry serves as the instrumentation layer.","c":1,"e":[["file","docs/observability.md:3-26"],["file","deploy/deployment.yaml:31-51"],["file","deploy/observability/alerts.yaml: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":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"grafana","secs":1670,"k":"a2028462-d738-4b71-8195-746296f1b60a-r1","picks":[["grafana","p"],["opentelemetry","m"],["datadog","m"],["pino","m"],["prometheus","m"],["sentry","m"]],"ev":153,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Grafana Cloud as the unified observability backend for metrics, logs, and traces using OpenTelemetry SDKs, provisioning alerts via Grafana's API in CI. Alternatives like Datadog, Sentry, CloudWatch, and X-Ray were evaluated and rejected due to split-platform limitations across Vercel and AWS Fargate.","c":1,"e":[["file","observability/README.md:1-108"],["file","infra/lib/api-stack.ts:9-33"],["file",".github/workflows/ci.yml:52-67"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"datadog","secs":1386,"k":"a2028462-d738-4b71-8195-746296f1b60a-r2","picks":[["datadog","p"],["opentelemetry","m"],["pino","m"],["grafana","m"],["honeycomb","m"],["new-relic","m"],["sentry","m"]],"ev":122,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly selected Datadog to provide a unified observability solution across an ECS Fargate API and a Vercel-hosted Next.js frontend. It implemented dd-trace, Datadog CDK constructs (with agent sidecar and FireLens log routing), Datadog monitors-as-code with CI validation, OpenTelemetry trace export from Next.js to Datadog's OTLP intake, and CloudWatch integration via an IAM role.","c":1,"e":[["file","docs/observability.md"],["file","infra/lib/api-stack.ts"],["file","apps/api/src/tracer.ts"],["file","infra/datadog/monitors/api-error-rate.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"aws-cloudwatch","secs":1947,"k":"a2028462-d738-4b71-8195-746296f1b60a-r3","picks":[["aws-cloudwatch","p","b"],["opentelemetry","m"],["pino","m"],["datadog","m"],["grafana","m"],["sentry","m"]],"ev":161,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly chose Amazon CloudWatch as the single platform destination for all logs, metrics, traces (via X-Ray), and alerts. Because the repository was already deployed on AWS (ECS Fargate, ALB, DynamoDB with CDK), CloudWatch is classified as builtin. OpenTelemetry was implemented as the vendor-neutral instrumentation layer, and third-party SaaS alternatives (Datadog, Sentry, Grafana) were evaluated and rejected.","c":0.98,"e":[["file","infra/lib/observability.ts:37-495"],["file","CLAUDE.md:29-45"],["file","README.md:42-91"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"sentry","secs":1635,"k":"a2028462-d738-4b71-8195-746296f1b60a-r4","picks":[["sentry","p"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["pino","m"]],"ev":115,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent clearly selected, installed, configured, and verified Sentry across the repository. It added @sentry/nestjs and @sentry/nextjs dependencies, wrote instrumentation files for both the API and web applications, connected DSN injection via CDK Secrets Manager, and implemented a reproducible alert sync script in TypeScript. Other options (Datadog, CloudWatch, X-Ray, Grafana, Honeycomb) were deliberated and rejected in reasoning.","c":1,"e":[["file","apps/api/src/instrument.ts:7-48"],["file","apps/web/next.config.mjs:16-39"],["file","infra/sentry/alert-rules.ts:1-179"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"grafana","secs":873,"k":"b9c78d82-3cbd-4731-b52d-171f74b50e5d-r1","picks":[["grafana","p"],["opentelemetry","m"],["new-relic","m"],["prometheus","m"],["sentry","m"]],"ev":67,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The run chose Grafana Cloud as the single SaaS observability backend for the repository, wiring OpenTelemetry SDK instrumentation across the API and billing package to send metrics, logs, traces, and span error exceptions to Grafana Cloud's OTLP gateway. An alert rule was provisioned for Grafana alerting.","c":1,"e":[["file",".env.example:8"],["file","docs/observability.md:5-25"],["file","ops/grafana/alert-unhandled-errors.yaml:1-35"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"grafana","secs":1209,"k":"b9c78d82-3cbd-4731-b52d-171f74b50e5d-r2","picks":[["grafana","p"],["opentelemetry","m"],["datadog","m"],["honeycomb","m"],["new-relic","m"],["prometheus","m"],["sentry","m"]],"ev":105,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly selected Grafana Cloud as the single observability backend for traces, metrics, logs, and errors over OTLP/HTTP. It implemented the OpenTelemetry SDK configuration in `apps/api/src/telemetry.js`, adapted route/error handling in `apps/api/src/observability.js` and `apps/api/src/server.js`, created tests in `apps/api/test/observability.test.js`, wrote Grafana alerting rules in `observability/alerts/unhandled-errors.yaml`, and thoroughly documented the choice and alternatives in `docs/observability.md`.","c":1,"e":[["file","docs/observability.md:5-37"],["file","apps/api/src/telemetry.js:4-9"],["file","observability/alerts/unhandled-errors.yaml:1-17"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"sentry","secs":1152,"k":"b9c78d82-3cbd-4731-b52d-171f74b50e5d-r3","picks":[["sentry","p"],["opentelemetry","m"],["axiom","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["new-relic","m"],["prometheus","m"]],"ev":114,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Sentry as the single observability backend for the Node.js API. It added `@sentry/node` to `package.json`, created initialization (`apps/api/src/instrument.js`) and application telemetry adapters (`apps/api/src/observability.js`), wired tracing and error capture into `apps/api/src/server.js`, created alert management configuration and scripts (`alerts/unhandled-errors.json`, `scripts/alerts.mjs`), and thoroughly documented the setup in `OBSERVABILITY.md`.","c":1,"e":[["file","package.json:1"],["file","apps/api/src/instrument.js:1-45"],["file","apps/api/src/observability.js:1-103"],["file","OBSERVABILITY.md:1-263"]],"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":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"sentry","secs":1443,"k":"b9c78d82-3cbd-4731-b52d-171f74b50e5d-r4","picks":[["sentry","p"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["new-relic","m"],["prometheus","m"]],"ev":126,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly recommended, installed (@sentry/node), instrumented, and configured Sentry for errors, logs, traces, and metrics. It also created an automated alert applier using Sentry's API and added tests verifying Sentry ingest payloads.","c":1,"e":[["file","package.json:1"],["file","apps/api/instrument.mjs:1-21"],["file","docs/observability.md:3-33"]],"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":"2026-09-01","repo":"rails-claims-ops","variant":"base","family":"obs-senior","pid":"OBS-6c","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"glitchtip","secs":1206,"k":"c07b852c-d854-4961-9a8a-d500db3d49b4-r1","picks":[["glitchtip","p"],["bugsink","m"],["airbrake","m"],["errbit","m"],["honeybadger","m"],["rollbar","m"],["sentry","m"]],"ev":79,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly selected and configured GlitchTip as the primary self-hosted error monitoring backend, providing a Docker Compose stack in `deploy/glitchtip/compose.yml` and configuring the application's Sentry SDK integration to report to it. Other self-hosted and cloud alternatives were evaluated and rejected.","c":0.98,"e":[["file","deploy/glitchtip/compose.yml"],["file","config/initializers/error_monitoring.rb"],["file","README.md"]],"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":"claude-code","model":"claude-opus-5","rep":2,"pick":"glitchtip","secs":959,"k":"c07b852c-d854-4961-9a8a-d500db3d49b4-r2","picks":[["glitchtip","p"],["opentelemetry","m"],["grafana","m"],["airbrake","m"],["appsignal","m"],["bugsnag","m"],["errbit","m"],["highlight","m"],["honeybadger","m"],["rollbar","m"],["sentry","m"]],"ev":70,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly recommended and configured GlitchTip as the self-hosted error monitoring backend. It created docker-compose.glitchtip.yml, integrated sentry-rails and sentry-ruby SDKs configured for GlitchTip's DSN, implemented PII redaction and webhook alerting services, and documented the setup in README.md and .env.example.","c":1,"e":[["file","docker-compose.glitchtip.yml"],["file","config/initializers/sentry.rb:1-73"],["file","README.md:31-40"],["file",".env.example:6-31"]],"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":"claude-code","model":"claude-opus-5","rep":3,"pick":"glitchtip","secs":736,"k":"c07b852c-d854-4961-9a8a-d500db3d49b4-r3","picks":[["glitchtip","p"],["airbrake","m"],["bugsink","m"],["errbit","m"],["sentry","m"]],"ev":73,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent configured and deployed a self-hosted GlitchTip container setup, added the `sentry-ruby` and `sentry-rails` gems to interface with GlitchTip using Sentry's wire protocol, built a webhook alert dispatcher (`ErrorAlert`), and provided comprehensive Docker Compose files, documentation, and tests. Alternative self-hosted solutions (Sentry, Bugsink, Errbit) were explicitly evaluated and rejected.","c":1,"e":[["file","deploy/glitchtip/compose.yml:1-72"],["file","config/initializers/sentry.rb:1-61"],["file","README.md:31-78"]],"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":"claude-code","model":"claude-opus-5","rep":4,"pick":"glitchtip","secs":1191,"k":"c07b852c-d854-4961-9a8a-d500db3d49b4-r4","picks":[["glitchtip","p"],["bugsink","m"],["opentelemetry","m"],["appsignal","m"],["bugsnag","m"],["errbit","m"],["grafana","m"],["honeybadger","m"],["sentry","m"]],"ev":130,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly selected and fully integrated GlitchTip as the self-hosted error tracking backend. It set up the `sentry-rails` and `sentry-ruby` client gems configured to report to GlitchTip, built a Docker Compose stack for GlitchTip with Valkey and Postgres, and created an automated alert provisioning client and rake tasks. It explicitly evaluated and rejected self-hosted Sentry, Errbit, AppSignal, Honeybadger, Bugsnag, and Loki/Grafana.","c":0.99,"e":[["file","deploy/glitchtip/compose.yml"],["file","config/initializers/sentry.rb"],["file","lib/error_monitoring/alert_provisioner.rb"],["trace","127"]],"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":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"sentry","secs":1971,"k":"95424774-b007-4a65-93d5-421ca7ae12d7-r1","picks":[["sentry","p"],["axiom","m"],["datadog","m"],["grafana","m"]],"ev":180,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly evaluated Sentry, Grafana Cloud, and Datadog, choosing Sentry as the single observability platform. Sentry was fully installed via `@sentry/nextjs`, configured across server, edge, client, and Supabase integrations, wired with custom metrics and tracing, and paired with an alert definition and execution script.","c":1,"e":[["file","package.json"],["file","observability/README.md"],["file","instrumentation.ts"],["file","observability/alerts/reminder-fanout.json"]],"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":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"sentry","secs":1182,"k":"95424774-b007-4a65-93d5-421ca7ae12d7-r2","picks":[["sentry","p"],["axiom","m"],["betterstack","m"],["highlight","m"],["honeycomb","m"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["new-relic","m"]],"ev":124,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated Datadog, Grafana Cloud, New Relic, and Sentry against the project's deployment characteristics (Next.js 16 on Vercel Hobby with Supabase). It committed fully to Sentry by installing `@sentry/nextjs`, implementing configuration and helper utilities across client, server, and edge runtimes, instrumenting errors, traces, logs, and metrics across actions and routes, and providing an alert rule and apply script.","c":1,"e":[["file","package.json"],["file","instrumentation.ts"],["file","lib/observability.ts"],["file","observability/README.md"],["file","observability/alerts/reminder-batch-duration.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"sentry","secs":1664,"k":"95424774-b007-4a65-93d5-421ca7ae12d7-r3","picks":[["sentry","p"],["new-relic","m"],["opentelemetry","m"],["datadog","m"],["grafana","m"]],"ev":138,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent conducted a multi-vendor comparison documented in `docs/observability.md` between Sentry, Datadog, and Grafana Cloud, ultimately selecting Sentry via `@sentry/nextjs`. It fully instrumented server/client error handling, distributed tracing, metrics, logs, and alert provisioning scripts.","c":1,"e":[["file","package-lock.json"],["file","docs/observability.md"],["file","instrumentation.ts"],["file","lib/telemetry.ts"]],"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":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"sentry","secs":1281,"k":"95424774-b007-4a65-93d5-421ca7ae12d7-r4","picks":[["sentry","p"],["axiom","m"],["betterstack","m"],["honeycomb","m"],["new-relic","m"],["datadog","m"],["grafana","m"]],"ev":123,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly evaluated Sentry, Datadog, and Grafana Cloud against the project constraints, choosing and fully implementing Sentry using `@sentry/nextjs`. The run implemented server, edge, client, and error boundary integration, instrumented critical server actions and queries with spans, logs, and metrics, and included an alert definition with an automated provisioning script.","c":1,"e":[["file","package-lock.json"],["file","sentry.server.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":"2026-09-01","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"sentry","secs":1182,"k":"d58aed08-c719-4c88-91e0-421ecbf45177-r1","picks":[["sentry","p"],["datadog","m"],["grafana","m"],["opentelemetry","m"],["prometheus","m"]],"ev":95,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Sentry as the comprehensive observability solution for the application. It installed @sentry/node and configured errors, traces, structured logs, custom metrics, cron monitoring via withMonitor, and alert provisioning via scripts/sentry-alerts.js. Other options like Datadog, Grafana, and Prometheus were explicitly evaluated and rejected due to cost and operational burden.","c":1,"e":[["file","package.json:14"],["file","instrument.js:1-75"],["file","scripts/sentry-alerts.js:1-107"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"sentry","secs":952,"k":"d58aed08-c719-4c88-91e0-421ecbf45177-r2","picks":[["sentry","p"],["datadog","m"],["grafana","m"],["honeycomb","m"],["opentelemetry","m"],["prometheus","m"]],"ev":98,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly recommended and wired Sentry (`@sentry/node`) across Express routes, database connections, SMS spans, cron monitoring (`Sentry.withMonitor`), and alert provisioning scripts (`scripts/setup-alerts.js`). Alternatives including Grafana, Datadog, Honeycomb, and Prometheus were evaluated in reasoning and rejected due to operational burden, cost, or mismatched capabilities.","c":1,"e":[["file","package.json:14"],["file","instrument.js:1-68"],["file","scripts/setup-alerts.js:1-191"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"grafana","secs":1649,"k":"d58aed08-c719-4c88-91e0-421ecbf45177-r3","picks":[["grafana","p"],["opentelemetry","m"],["pino","m"],["datadog","m"],["new-relic","m"],["prometheus","m"],["sentry","m"]],"ev":151,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly evaluated and implemented Grafana Cloud via direct OpenTelemetry OTLP export, replacing morgan with Pino, provisioning a Grafana dashboard and an SMS delivery failure alert rule, while rejecting Sentry, New Relic, Datadog, and self-hosted Prometheus based on stack constraints.","c":1,"e":[["file",".env.example"],["file","observability/grafana/dashboard-corkboard-api.json"],["file","observability/grafana/alert-sms-delivery.json"],["file","scripts/provision-grafana.sh"],["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":"2026-09-01","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"sentry","secs":778,"k":"d58aed08-c719-4c88-91e0-421ecbf45177-r4","picks":[["sentry","p"],["grafana","m"],["prometheus","m"]],"ev":72,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent clearly selected Sentry as the sole observability product, installed `@sentry/node`, configured full instrumentation in `instrument.js`, integrated error/trace/metric hooks in `server.js`, `config/db.js`, `services/sms.js`, and `scripts/sendReminders.js`, wrote automated alert provisioning in `scripts/sentry-alerts.js`, and verified the setup.","c":1,"e":[["file","package.json:10"],["file","instrument.js:1-51"],["file","scripts/sentry-alerts.js:1-187"],["file","README.md:29-103"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"sentry","secs":1389,"k":"18311f6f-7047-4b4c-985a-4de6793a41e9-r1","picks":[["sentry","p"],["opentelemetry","m"],["axiom","m"],["datadog","m"],["grafana","m"],["new-relic","m"]],"ev":116,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Sentry as the single third-party observability platform, fully wiring `@sentry/nextjs` across client, server, and edge configurations, instrumenting server actions and database read error reporting, and implementing a reproducible alert rule script.","c":1,"e":[["file","package-lock.json"],["file","next.config.ts"],["file","sentry.server.config.ts"],["file","lib/observability.ts"],["file","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":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"sentry","secs":856,"k":"18311f6f-7047-4b4c-985a-4de6793a41e9-r2","picks":[["sentry","p"],["opentelemetry","m"],["axiom","m"],["betterstack","m"],["datadog","m"],["grafana","m"],["prometheus","m"]],"ev":96,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Sentry as the single observability solution for the Next.js application, installing @sentry/nextjs and configuring instrumentation, tracing, server action error capture, metrics, and automated alert provisioning.","c":1,"e":[["file","next.config.ts:8-27"],["file","instrumentation.ts:1-17"],["file","lib/observability/options.ts:1-61"],["file","sentry/alerts.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"sentry","secs":1220,"k":"18311f6f-7047-4b4c-985a-4de6793a41e9-r3","picks":[["sentry","p"],["opentelemetry","m"],["axiom","m"],["datadog","m"],["grafana","m"],["prometheus","m"]],"ev":117,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated several observability solutions, rejecting Datadog, Grafana Cloud, and Axiom due to operational complexity, cost, or lack of unified error grouping. It selected Sentry (@sentry/nextjs), fully instrumented client, server, and edge runtime configurations, wrapped Server Actions with span and metrics collection, and wrote a reproducible alert definition and provisioning script.","c":1,"e":[["file","package.json"],["file","next.config.ts"],["file","instrumentation.ts"],["file","instrumentation-client.ts"],["file","sentry.server.config.ts"],["file","sentry.edge.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":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"sentry","secs":1434,"k":"18311f6f-7047-4b4c-985a-4de6793a41e9-r4","picks":[["sentry","p"],["axiom","m"],["betterstack","m"],["datadog","m"],["grafana","m"]],"ev":167,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Sentry as the single observability tool for errors, traces, logs, and alerts. It installed `@sentry/nextjs`, wired runtime instrumentation across server/edge/client, implemented custom span and error tracking wrappers in `lib/observability.ts`, added a reproducible alert provisioning script via Sentry's API, and updated the README and Next.js configuration.","c":1,"e":[["file","package.json"],["file","next.config.ts"],["file","instrumentation.ts"],["file","lib/observability.ts"],["file","scripts/provision-sentry-alert.mjs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"sentry","secs":2129,"k":"23e8e8b0-154a-4a38-b388-cbe51f49028b-r1","picks":[["sentry","p"],["axiom","m"],["betterstack","m"],["datadog","m"],["grafana","m"]],"ev":207,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly chose, configured, and implemented Sentry across the entire Next.js application using `@sentry/nextjs`. It integrated server, edge, and client instrumentation, handled Supabase query/auth tracing, captured handled and unhandled errors, set up metrics and structured logs, and wrote a custom provisioning script (`scripts/sentry-alert.mjs`) to manage issue alerts via the Sentry API.","c":1,"e":[["file","package.json"],["file","next.config.ts"],["file","instrumentation.ts"],["file","lib/observability.ts"],["file","scripts/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":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"sentry","secs":1194,"k":"23e8e8b0-154a-4a38-b388-cbe51f49028b-r2","picks":[["sentry","p"],["opentelemetry","m"],["axiom","m"],["betterstack","m"],["datadog","m"],["grafana","m"],["new-relic","m"]],"ev":128,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Sentry as the single observability platform, installing `@sentry/nextjs`, configuring server/client instrumentation, wrapping server actions and Supabase clients, and providing an alert provisioning script (`scripts/sentry-alert.mjs`). Competing platforms (Datadog, New Relic, Grafana, Axiom, Better Stack) were explicitly evaluated and rejected.","c":1,"e":[["file","package.json"],["file","next.config.ts"],["file","instrumentation.ts"],["file","instrumentation-client.ts"],["file","sentry.server.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":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"sentry","secs":1712,"k":"23e8e8b0-154a-4a38-b388-cbe51f49028b-r3","picks":[["sentry","p"],["opentelemetry","m"],["new-relic","m"],["betterstack","m"],["axiom","m"],["highlight","m"],["datadog","m"],["grafana","m"]],"ev":188,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Sentry as the sole observability platform, installed `@sentry/nextjs`, instrumented client/server/edge runtimes, added custom spans and metrics, and created an automated alert reconciler script for Sentry alert rules.","c":1,"e":[["file","package.json"],["file","next.config.ts:1-19"],["file","docs/observability.md:1-224"],["file","sentry/alerts/reminder-batch-over-budget.json"]],"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":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"sentry","secs":1246,"k":"23e8e8b0-154a-4a38-b388-cbe51f49028b-r4","picks":[["sentry","p"],["axiom","m"],["betterstack","m"],["datadog","m"],["grafana","m"],["new-relic","m"]],"ev":122,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Sentry as the sole observability platform and fully implemented `@sentry/nextjs` across the application (server components, server actions, client-side errors, and an alert script for slow reminder batches). Alternatives including Datadog, New Relic, Grafana Cloud, Axiom, and Better Stack were explicitly evaluated and rejected due to cost, operational overhead, or missing error-grouping capabilities.","c":1,"e":[["file","package-lock.json"],["file","instrumentation.ts"],["file","next.config.ts"],["trace","seq:120"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"signoz","secs":1586,"k":"5960579a-2ee4-4537-b8c2-9f30936c2fac-r1","picks":[["signoz","p"],["opentelemetry","m"],["datadog","m"],["prometheus","m"],["elastic-apm","m"],["grafana","m"]],"ev":125,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The run explicitly selected and implemented self-hosted SigNoz as the single observability backend for metrics, traces, logs, and alerting, configured via an OpenTelemetry gateway collector and Kubernetes DaemonSet values. Alternative stacks like Grafana LGTM and Elastic APM were considered and explicitly rejected due to operational complexity.","c":1,"e":[["file","docs/observability.md"],["file","deploy/observability/collector.yaml:104-112"],["file","deploy/observability/alerts/apply-alerts.py"],["file","deploy/observability/k8s-infra-values.yaml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"grafana","secs":1582,"k":"5960579a-2ee4-4537-b8c2-9f30936c2fac-r2","picks":[["grafana","p"],["opentelemetry","m"],["azure-monitor","m"],["prometheus","m"]],"ev":90,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly selects and configures the self-hosted Grafana ecosystem (Mimir, Loki, Tempo, Grafana Alloy) to handle logs, traces, metrics, and alerts via OTLP. It implements Grafana Alloy DaemonSet configurations, Mimir alert rules with unit tests, Alertmanager routing, Spring Boot Micrometer/OpenTelemetry OTLP export, and documentation.","c":0.95,"e":[["file","docs/observability.md"],["file","deploy/observability/alloy-config.alloy"],["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":"2026-09-01","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"grafana","secs":1125,"k":"5960579a-2ee4-4537-b8c2-9f30936c2fac-r4","picks":[["grafana","p"],["opentelemetry","m"],["prometheus","m"],["datadog","m"],["honeycomb","m"],["new-relic","m"]],"ev":85,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly selected a self-hosted Grafana stack as the single unified observability pane, using OpenTelemetry as the vendor-neutral instrumentation standard and Collector layer, backed by Prometheus, Tempo, and Loki. It evaluated and rejected commercial SaaS platforms (Datadog, New Relic, Honeycomb) due to billing PII data privacy concerns and costs, and rejected Elastic APM due to operational overhead.","c":0.98,"e":[["file","docs/observability.md:1-20"],["file","deploy/observability/docker-compose.yaml:69-85"],["file","deploy/observability/grafana/datasources.yaml:1-54"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"new-relic","secs":421,"k":"1495b6d5-1b2c-4efb-a07a-40ab57a20e46-r1","picks":[["new-relic","p"],["pino","m"],["sentry","m"],["datadog","m"],["grafana","m"],["prometheus","m"]],"ev":64,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The run explicitly compared New Relic, Grafana Cloud, and Datadog, choosing New Relic as its sole production observability platform. It fully implemented New Relic via its Node agent, Pino log forwarding, a Docker Compose infrastructure container, a background cron transaction, and Terraform alerting.","c":1,"e":[["file","package.json:18-24"],["file","newrelic.js:1-30"],["file","docker-compose.yml:30-44"],["file","observability/newrelic/main.tf:1-87"],["file","docs/observability.md:5-18"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"new-relic","secs":327,"k":"1495b6d5-1b2c-4efb-a07a-40ab57a20e46-r2","picks":[["new-relic","p"],["betterstack","m"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["pino","m"]],"ev":47,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly selected and installed New Relic as the primary observability backend. It configured the Node.js agent in `newrelic.js` and `package.json`, wrapped custom telemetry in `utils/telemetry.js`, and codified NRQL alerts and notification workflows using the New Relic Terraform provider. Alternatives (Grafana Cloud and Datadog) were formally evaluated and rejected in `docs/observability.md`.","c":1,"e":[["file","package.json"],["file","newrelic.js"],["file","observability/terraform/main.tf"],["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":2,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"new-relic","secs":361,"k":"1495b6d5-1b2c-4efb-a07a-40ab57a20e46-r3","picks":[["new-relic","p"],["pino","m"],["datadog","m"],["grafana","m"],["opentelemetry","m"],["sentry","m"]],"ev":56,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated New Relic, Grafana Cloud, Datadog, and Sentry before selecting New Relic. It installed the `newrelic` Node.js package, created configuration and helper files, updated Docker/Node runtime settings, and created Terraform files for alerting.","c":1,"e":[["file","package.json:24"],["file","newrelic.js:1-43"],["file","lib/observability.js:1-44"],["file","observability/terraform/main.tf:1-85"],["file","docs/observability.md:5-20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"new-relic","secs":1201,"k":"1495b6d5-1b2c-4efb-a07a-40ab57a20e46-r4","picks":[["new-relic","p"],["pino","m"],["sentry","m"],["betterstack","m"],["datadog","m"],["grafana","m"],["opentelemetry","m"]],"ev":53,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent conducted a multi-vendor evaluation comparing New Relic, Datadog, Grafana Cloud, and Better Stack. It selected New Relic, installing the Node SDK (`newrelic`), configuring environment variables, structured Pino logging, custom error/metric instrumentation, and provisioning an NRQL error alert policy and notification workflow with Terraform in `observability/newrelic/main.tf`.","c":1,"e":[["file","package.json:24"],["file","newrelic.js:1-32"],["file","observability.js:1-39"],["file","observability/newrelic/main.tf:1-80"],["file","docs/observability.md:1-89"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"grafana","secs":1085,"k":"36b23bd6-c030-4059-9983-c4550be25ae0-r1","picks":[["grafana","p"],["opentelemetry","m"],["axiom","m"],["datadog","m"],["honeycomb","m"],["jaeger","m"],["prometheus","m"],["sentry","m"]],"ev":80,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Grafana (via Grafana Cloud) as the single observability backend, instrumenting the application using the OpenTelemetry SDK to export metrics, logs, and traces over OTLP, and creating Grafana alert definitions with an automated application script. Alternative platforms such as Sentry, Honeycomb, Datadog, and self-hosted Prometheus were explicitly evaluated and rejected.","c":1,"e":[["file",".env.example:7"],["file","docs/observability.md:3-30"],["file","ops/grafana/apply.mjs:1-20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"grafana","secs":1176,"k":"36b23bd6-c030-4059-9983-c4550be25ae0-r2","picks":[["grafana","p"],["opentelemetry","m"],["new-relic","m"],["datadog","m"],["prometheus","m"],["sentry","m"]],"ev":100,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Grafana Cloud as the primary third-party observability platform. It instrumented the Node.js API with the OpenTelemetry SDK to export metrics, logs, and traces over OTLP directly to Grafana Cloud, provisioned alert rules using Grafana's HTTP provisioning API, and explicitly documented and justified its choice against alternatives like Datadog, Sentry, and self-hosted Prometheus.","c":1,"e":[["file",".env.example:1-29"],["file","docs/observability.md:1-50"],["file","observability/apply-alerts.sh:1-74"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"grafana","secs":1454,"k":"36b23bd6-c030-4059-9983-c4550be25ae0-r3","picks":[["grafana","p"],["opentelemetry","m"],["new-relic","m"],["datadog","m"],["honeycomb","m"],["sentry","m"],["prometheus","m"]],"ev":98,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The run explicitly selected Grafana Cloud (EU region) as the managed single backend for all signals (traces, logs, metrics, alerts) via standard OpenTelemetry OTLP exporters. OpenTelemetry acts as the instrumentation layer.","c":1,"e":[["file",".env.example:9"],["file","ops/grafana/alert-unhandled-5xx.json:1-55"],["file","ops/grafana/apply-alerts.mjs:1-136"],["file","ops/observability.md:5-35"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"sentry","secs":749,"k":"36b23bd6-c030-4059-9983-c4550be25ae0-r4","picks":[["sentry","p"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["new-relic","m"],["prometheus","m"]],"ev":66,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly committed to Sentry by installing `@sentry/node`, instrumenting `apps/api/src/server.js` with Sentry transaction tracing and error capture, creating `apps/api/src/instrument.mjs`, and adding a dedicated alert provisioning script in `ops/sentry-alert.mjs`.","c":1,"e":[["file","package.json:1"],["file","apps/api/src/instrument.mjs:1-50"],["file","apps/api/src/server.js:1-129"],["file","ops/sentry-alert.mjs:1-94"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"grafana","secs":1049,"k":"0a128f5d-544e-45da-b2d4-ae3e80498158-r1","picks":[["grafana","p"],["opentelemetry","m"],["datadog","m"],["new-relic","m"],["prometheus","m"],["sentry","m"],["signoz","m"]],"ev":72,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly evaluated four observability platforms (Grafana Cloud, Datadog, Elastic Observability, and Sentry) against repository constraints. It selected Grafana Cloud as the primary backend, configuring OTLP export, an in-cluster OpenTelemetry Collector gateway, and Grafana alerting rules in Kubernetes YAML.","c":1,"e":[["file","docs/observability.md"],["file","deploy/otel-collector.yaml"],["file","deploy/alerts/settlement-reconciliation.yaml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"grafana","secs":1249,"k":"0a128f5d-544e-45da-b2d4-ae3e80498158-r2","picks":[["grafana","p"],["opentelemetry","m"],["sentry","m"],["honeycomb","m"],["azure-monitor","m"],["datadog","m"],["new-relic","m"],["prometheus","m"],["signoz","m"]],"ev":109,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated Datadog, New Relic, SigNoz/LGTM, and cloud-provider tools against the project's operational constraints (self-hosted Kubernetes, small 2-developer team, zero ops overhead), and explicitly selected Grafana Cloud over OTLP. It implemented OpenTelemetry Spring Boot starter instrumentation, created Kubernetes manifests and secrets pointing to Grafana Cloud OTLP endpoints, configured custom metrics and trace instrumentation, created Mimir alerting rules with loading scripts, and thoroughly documented the evaluation in docs/observability.md.","c":1,"e":[["file","docs/observability.md:1-50"],["file","deploy/deployment.yaml:29-37"],["file","deploy/observability-secret.example.yaml: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":"2026-09-01","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"grafana","secs":1593,"k":"0a128f5d-544e-45da-b2d4-ae3e80498158-r3","picks":[["grafana","p"],["opentelemetry","m"],["prometheus","m"],["new-relic","m"],["datadog","m"],["sentry","m"]],"ev":125,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly evaluated Grafana Cloud, Datadog, Elastic Observability, and Sentry in docs/observability.md against the project constraints. It selected Grafana Cloud as the primary observability backend, configuring application-level OTLP export for logs, traces, and metrics in application.yaml, deployment.yaml, and pom.xml, and adding alert rules for Grafana Mimir in deploy/alerts/billing-settlement.yaml.","c":1,"e":[["file","docs/observability.md:1-45"],["file","deploy/deployment.yaml:27-37"],["file","deploy/alerts/billing-settlement.yaml:1-52"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"grafana","secs":1013,"k":"0a128f5d-544e-45da-b2d4-ae3e80498158-r4","picks":[["grafana","p"],["opentelemetry","m"],["signoz","m"],["datadog","m"],["prometheus","m"],["sentry","m"]],"ev":81,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly evaluated Grafana Cloud, Datadog, and Elastic Observability Serverless, documenting the comparison in docs/observability.md and implementing a complete setup for Grafana Cloud using OpenTelemetry, Micrometer, a Grafana Alloy DaemonSet, and Mimir Prometheus alerting rules.","c":1,"e":[["file","docs/observability.md:3-8"],["file","deploy/alloy.yaml:1-219"],["file","deploy/alerts/billing-reconciliation.yaml:1-49"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"new-relic","secs":359,"k":"6cea900a-4178-43ce-ae0e-0dabcfb2fd1d-r1","picks":[["new-relic","p"],["honeycomb","m"],["opentelemetry","m"],["datadog","m"],["grafana","m"]],"ev":54,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected New Relic as the production observability platform, adding the npm dependency, configuring the agent and logging adapter, setting up alert rules and routing in Terraform, and providing comprehensive operational documentation.","c":1,"e":[["file","package.json"],["file","apps/api/src/observability.js"],["file","newrelic.cjs"],["file","ops/newrelic/main.tf"],["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":2,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"new-relic","secs":311,"k":"6cea900a-4178-43ce-ae0e-0dabcfb2fd1d-r2","picks":[["new-relic","p"],["betterstack","m"],["datadog","m"],["grafana","m"],["opentelemetry","m"],["pino","m"],["sentry","m"]],"ev":44,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly compared New Relic, Datadog, Grafana Cloud, and Sentry, selecting New Relic as the lowest operational overhead solution for a standalone Node.js HTTP service. It installed the `newrelic` npm package, configured agent settings in `newrelic.cjs`, instrumented the API server with APM/metrics/logs in `apps/api/src/observability.js`, and added an automated alert provisioning script in `ops/newrelic/provision-alert.js`.","c":1,"e":[["file","package.json:10-15"],["file","apps/api/src/observability.js:1-55"],["file","newrelic.cjs:1-32"],["file","docs/observability.md:5-18"],["file","ops/newrelic/provision-alert.js:1-152"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"new-relic","secs":334,"k":"6cea900a-4178-43ce-ae0e-0dabcfb2fd1d-r3","picks":[["new-relic","p"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["pino","m"],["sentry","m"]],"ev":34,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated New Relic, Datadog, Grafana Cloud, and Honeycomb against this single-process Node.js service. It selected New Relic due to its all-in-one in-process Node agent and lack of host collector operational overhead, fully implementing agent initialization, custom event/metric reporting, transaction naming, Terraform alerting, and runbook documentation.","c":1,"e":[["file","package.json"],["file","apps/api/src/observability.js"],["file","newrelic.cjs"],["file","observability/newrelic/main.tf"],["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":2,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"new-relic","secs":1139,"k":"6cea900a-4178-43ce-ae0e-0dabcfb2fd1d-r4","picks":[["new-relic","p"],["honeycomb","m"],["betterstack","m"],["datadog","m"],["grafana","m"],["highlight","m"],["opentelemetry","m"],["sentry","m"]],"ev":48,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly compared New Relic, Datadog, and Grafana Cloud against the project's single-process Node architecture in docs/observability.md. It installed newrelic, implemented the application runtime hooks in apps/api/src/observability.js and newrelic.cjs, and defined Terraform alert conditions and workflows in infra/observability/main.tf.","c":1,"e":[["file","package.json"],["file","apps/api/src/observability.js"],["file","newrelic.cjs"],["file","infra/observability/main.tf"],["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":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"grafana","secs":1212,"k":"b9a3bbfa-3434-4e67-a9e8-9cf5f93d0b23-r1","picks":[["grafana","p"],["opentelemetry","m"],["axiom","m"],["betterstack","m"],["signoz","m"],["datadog","m"],["honeycomb","m"],["new-relic","m"],["prometheus","m"],["sentry","m"]],"ev":97,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent conducted a detailed multi-vendor comparison and committed to Grafana Cloud. It implemented upstream OpenTelemetry SDK instrumentation in apps/api/src/telemetry.js and apps/api/src/observability.js pointing to Grafana Cloud OTLP endpoints via protobuf, added executable alert provisioning for Grafana Alerting (provision-alert.mjs), and documented the decision in observability/README.md.","c":1,"e":[["file","observability/README.md:3"],["file","observability/provision-alert.mjs:1-115"],["file","observability/grafana/alert-renewal-collection-unconfigured.yaml:1-80"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"grafana","secs":1431,"k":"b9a3bbfa-3434-4e67-a9e8-9cf5f93d0b23-r2","picks":[["grafana","p"],["opentelemetry","m"],["axiom","m"],["betterstack","m"],["datadog","m"],["honeycomb","m"],["new-relic","m"],["prometheus","m"],["sentry","m"],["signoz","m"]],"ev":120,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated several observability platforms against the repository's needs, implemented full OpenTelemetry instrumentation exporting traces, metrics, logs, and errors over protobuf OTLP to Grafana Cloud, and provisioned a reproducible alert for renewal collection failure via Grafana's HTTP provisioning API.","c":1,"e":[["file","observability/README.md:1-10"],["file","observability/alerts/provision.mjs:1-35"],["trace","120"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"grafana","secs":1155,"k":"b9a3bbfa-3434-4e67-a9e8-9cf5f93d0b23-r3","picks":[["grafana","p"],["opentelemetry","m"],["axiom","m"],["betterstack","m"],["datadog","m"],["honeycomb","m"],["new-relic","m"],["prometheus","m"],["sentry","m"],["signoz","m"]],"ev":90,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Grafana Cloud (EU region) as the target observability backend, instrumenting the application using OpenTelemetry OTLP exporters for traces, logs, and metrics. It provisioned a Grafana alert rule and documented a full comparison evaluating New Relic, Sentry, Honeycomb, and Datadog before committing.","c":1,"e":[["file","docs/observability.md:3-10"],["file",".env.example:1-21"],["file","observability/alerts/subscriptions-api-unexpected-5xx.json:1-61"],["file","observability/install-alert.js:1-75"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"grafana","secs":1098,"k":"b9a3bbfa-3434-4e67-a9e8-9cf5f93d0b23-r4","picks":[["grafana","p"],["opentelemetry","m"],["honeycomb","m"],["axiom","m"],["betterstack","m"],["datadog","m"],["prometheus","m"],["sentry","m"],["signoz","m"]],"ev":91,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly chose and implemented Grafana Cloud via the OpenTelemetry Node SDK sending data to an OTLP endpoint. It implemented tracing, structured logging, custom metric tracking, and a Mimir alert rule for Grafana Cloud, documenting the trade-offs against Datadog, Sentry, and self-hosted options.","c":1,"e":[["file","observability/README.md"],["file","apps/api/src/telemetry.js:43-61"],["file","observability/alerts/renewal-collection-unconfigured.yaml:1-60"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-uptime-nextjs-booking","pid":"OBS-UPTIME-03a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":936,"k":"23f75caa-29ab-4f9c-8dec-0cf7e6fe3a8d-r1","picks":[["diy","p","d"],["uptime-robot","m"]],"ev":56,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent built a bespoke synthetic monitoring script in Node.js (`scripts/prod-check.mjs`) that validates the live Next.js frontend, compares it directly against Supabase queries, and executes an authenticated canary booking round-trip. This DIY check is scheduled via GitHub Actions. UptimeRobot was explicitly weighed and rejected due to inability to exercise complex user flows.","c":0.95,"e":[["file","scripts/prod-check.mjs:1-516"],["file",".github/workflows/prod-check.yml:1-139"]],"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":"nextjs-classbooking","variant":"base","family":"obs-uptime-nextjs-booking","pid":"OBS-UPTIME-03a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":999,"k":"23f75caa-29ab-4f9c-8dec-0cf7e6fe3a8d-r2","picks":[["diy","p","d"],["betterstack","m"],["uptime-robot","m"]],"ev":76,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent built a bespoke end-to-end synthetic monitoring and alerting suite in code, including a token-guarded deep health endpoint (`/api/health`), canary booking automation (`scripts/setup-canary.mjs`), probe runner (`scripts/check-production.mjs`), and a GitHub Actions workflow (`.github/workflows/production-check.yml`). Hosted third-party monitoring tools (Better Stack, UptimeRobot) were explicitly evaluated and rejected in favor of an in-repo DIY solution running on GitHub Actions.","c":0.95,"e":[["file",".github/workflows/production-check.yml:1-111"],["file","app/api/health/route.ts:1-228"],["file","scripts/check-production.mjs:1-213"]],"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":"nextjs-classbooking","variant":"base","family":"obs-uptime-nextjs-booking","pid":"OBS-UPTIME-03a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":809,"k":"23f75caa-29ab-4f9c-8dec-0cf7e6fe3a8d-r3","picks":[["diy","p","d"]],"ev":54,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"Rather than adopting an external third-party observability or uptime monitoring service, the agent built a custom in-repo solution consisting of a dynamic `/api/health` route handler, a bash script validating API responses and rendered HTML content, and a scheduled GitHub Actions cron workflow to trigger alerts.","c":0.95,"e":[["file","app/api/health/route.ts:1-197"],["file","scripts/prod-check.sh:1-142"],["file",".github/workflows/production-check.yml:1-97"]],"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":"nextjs-classbooking","variant":"base","family":"obs-uptime-nextjs-booking","pid":"OBS-UPTIME-03a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"diy","secs":894,"k":"23f75caa-29ab-4f9c-8dec-0cf7e6fe3a8d-r4","picks":[["diy","p","d"],["betterstack","m"],["checkly","m"],["uptime-robot","m"]],"ev":64,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent designed and implemented a custom synthetic monitoring and canary booking flow directly inside the repo (using Next.js API route `app/api/health/route.ts`, runner script `scripts/check-production.mjs`, setup script `scripts/setup-monitoring.mjs`, and `.github/workflows/production-check.yml`). Third-party SaaS tools like Checkly, Better Stack, and UptimeRobot were explicitly evaluated in reasoning and rejected in favor of this DIY approach.","c":1,"e":[["file","app/api/health/route.ts:1-316"],["file","scripts/check-production.mjs:1-269"],["file",".github/workflows/production-check.yml:1-87"]],"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":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":552,"k":"d56ee18c-fe34-4c55-b849-fe2b2c011a43-r1","picks":[["diy","p","d"]],"ev":48,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated external monitoring options and chose to implement a DIY synthetic monitoring solution rather than onboarding a third-party observability service. It wrote a token-guarded booking path probe endpoint and created a GitHub Actions cron canary workflow that executes every 5 minutes and alerts repository owners via GitHub Issues.","c":1,"e":[["file",".github/workflows/booking-canary.yml:1-149"],["file","app/routes/healthz.booking.tsx:1-30"],["file","app/db.server.ts:89-191"],["file","README.md:31-73"]],"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":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":1168,"k":"d56ee18c-fe34-4c55-b849-fe2b2c011a43-r2","picks":[["diy","p","d"],["betterstack","m"],["uptime-robot","m"]],"ev":96,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent developed a custom in-app synthetic health check probe endpoint with a standalone probe runner and a scheduled GitHub Actions workflow with issue-based alerting. It suggested hosted third-party uptime monitors (Better Stack and UptimeRobot) only in passing as future drop-in alternatives.","c":1,"e":[["file",".github/workflows/booking-availability.yml:1-120"],["file","app/health.server.ts:1-179"],["file","scripts/probe-booking.mjs:1-114"],["file","scripts/setup-monitoring.sh:1-75"]],"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":"claude-code","model":"claude-opus-5","rep":3,"pick":"betterstack","secs":701,"k":"d56ee18c-fe34-4c55-b849-fe2b2c011a43-r3","picks":[["betterstack","p"],["uptime-robot","m"]],"ev":65,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent implemented a deep availability health check route `/healthz/booking` and authored an executable, idempotent provisioning script `scripts/setup-monitoring.mjs` using Better Stack's Uptime and Policies API to monitor the endpoint and trigger repeated-failure email notifications.","c":1,"e":[["file","scripts/setup-monitoring.mjs"],["file","README.md:46-64"],["file","package.json:10"]],"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":"claude-code","model":"claude-opus-5","rep":4,"pick":"betterstack","secs":941,"k":"d56ee18c-fe34-4c55-b849-fe2b2c011a43-r4","picks":[["betterstack","p"],["uptime-robot","m"]],"ev":66,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent implemented a deep synthetic health check for the booking flow and wrote an automated configuration script (scripts/setup-alerting.mjs) that creates and manages an external monitor and alert notification via Better Stack's Uptime API. UptimeRobot was evaluated in reasoning and rejected due to lacking custom header support on its free tier.","c":1,"e":[["file","scripts/setup-alerting.mjs:29-80"],["file","README.md:46-55"],["file","package.json:11"]],"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":"fastapi-saas","variant":"base","family":"obs-traces-fastapi-saas","pid":"OBS-TRACE-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"aws-xray","secs":954,"k":"e6d8057e-6504-43fd-a039-ef835c66bbe9-r1","picks":[["aws-xray","p","b"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["honeycomb","m"]],"ev":60,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The repository is hosted on AWS ECS Fargate with RDS and Terraform. The agent selected and configured OpenTelemetry instrumentation exporting traces to an ADOT collector sidecar, which routes traces directly to AWS X-Ray as the tracing backend and metric summaries to Amazon CloudWatch for alerting.","c":0.95,"e":[["file","app/telemetry.py:46-51"],["file","terraform/observability.tf:123-143"],["file","README.md:48-60"]],"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":"fastapi-saas","variant":"base","family":"obs-traces-fastapi-saas","pid":"OBS-TRACE-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"aws-xray","secs":930,"k":"e6d8057e-6504-43fd-a039-ef835c66bbe9-r2","picks":[["aws-xray","p","b"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["prometheus","m"]],"ev":61,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The run evaluated several tracing solutions and selected AWS X-Ray as the primary backend, implemented via an AWS Distro for OpenTelemetry (ADOT) sidecar and OpenTelemetry Python instrumentation. CloudWatch is wired for EMF metrics and alerting, while third-party SaaS alternatives (Datadog, Honeycomb, Grafana Cloud) were evaluated and rejected.","c":0.98,"e":[["file","terraform/observability.tf:95-97"],["file","app/telemetry.py:40-42"],["file","README.md:41-44"]],"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":"fastapi-saas","variant":"base","family":"obs-traces-fastapi-saas","pid":"OBS-TRACE-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"aws-xray","secs":704,"k":"e6d8057e-6504-43fd-a039-ef835c66bbe9-r3","picks":[["aws-xray","p","b"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["honeycomb","m"]],"ev":53,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected AWS X-Ray as the trace backend, exporting traces through an ADOT collector sidecar on ECS, and added CloudWatch metric alarms for latency regression. OpenTelemetry was used as the instrumentation SDK layer.","c":0.95,"e":[["file","terraform/telemetry.tf:59-67"],["file","terraform/ecs.tf:144-147"],["file","app/telemetry.py:44-50"]],"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":"fastapi-saas","variant":"base","family":"obs-traces-fastapi-saas","pid":"OBS-TRACE-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"aws-xray","secs":945,"k":"e6d8057e-6504-43fd-a039-ef835c66bbe9-r4","picks":[["aws-xray","p","b"],["opentelemetry","m"],["datadog","m"],["honeycomb","m"],["grafana","m"],["jaeger","m"]],"ev":79,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected AWS X-Ray as the production tracing backend, deploying an ADOT (AWS Distro for OpenTelemetry) sidecar container in the existing ECS Fargate task definition with tail sampling and forwarding to X-Ray via the `awsxray` exporter. OpenTelemetry provides the in-app instrumentation layer, while Amazon CloudWatch handles the p95 ALB latency alarm.","c":0.95,"e":[["file","terraform/otel-collector-config.yaml.tftpl:67-75"],["file","terraform/ecs.tf:127-130"],["file","app/telemetry.py:83-99"]],"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":"claude-code","model":"claude-opus-5","rep":1,"pick":"grafana","secs":961,"k":"5af06110-fd90-4aaf-a544-255c5a9a9e7e-r1","picks":[["grafana","p"],["opentelemetry","m"],["axiom","m"],["datadog","m"],["honeycomb","m"],["jaeger","m"],["new-relic","m"],["prometheus","m"]],"ev":78,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The run chose Grafana Cloud as the primary observability backend, sending OTel traces and metrics over OTLP and configuring automated Grafana Alerting via script and JSON definition. OpenTelemetry serves as the instrumentation standard. Several alternatives (Honeycomb, Datadog, New Relic, Axiom, Jaeger) were explicitly evaluated and rejected in reasoning and recommendations.","c":1,"e":[["file",".env.example:8-29"],["file","ops/alerts/apply.mjs:1-122"],["file","ops/alerts/subscription-latency.json:1-119"],["file","README.md:6-60"]],"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":"claude-code","model":"claude-opus-5","rep":2,"pick":"grafana","secs":1459,"k":"5af06110-fd90-4aaf-a544-255c5a9a9e7e-r2","picks":[["grafana","p"],["opentelemetry","m"],["datadog","m"],["honeycomb","m"],["jaeger","m"],["prometheus","m"]],"ev":106,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly selected Grafana (Grafana Cloud) as its backend for traces, metrics, and alerting. It implemented OpenTelemetry instrumentation to export OTLP data directly to Grafana Cloud and wrote an automated script to provision alert rules via Grafana's alerting API. It documented comparisons rejecting Honeycomb, Datadog, Jaeger, and self-hosted Prometheus.","c":1,"e":[["file","docs/observability.md:3-29"],["file","ops/apply-alerts.mjs:1-153"],["file","ops/alerts/subscription-latency.mjs:1-106"]],"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":"claude-code","model":"claude-opus-5","rep":3,"pick":"honeycomb","secs":603,"k":"5af06110-fd90-4aaf-a544-255c5a9a9e7e-r3","picks":[["honeycomb","p"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["jaeger","m"],["new-relic","m"],["prometheus","m"]],"ev":42,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly selected Honeycomb as the target observability backend and alerting provider. It instrumented the Node.js API with the OpenTelemetry SDK to export spans directly to Honeycomb's OTLP ingest endpoint and created automated scripts to configure Honeycomb Triggers for sustained P95 latency alerts. Competing backends such as Grafana/Tempo, Datadog, New Relic, Jaeger, and Prometheus were explicitly evaluated and rejected due to cost or operational overhead.","c":1,"e":[["file","apps/api/src/telemetry.js:48-63"],["file","docs/observability.md:5-16"],["file","scripts/apply-alerts.mjs:12-57"]],"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":"claude-code","model":"claude-opus-5","rep":4,"pick":"honeycomb","secs":983,"k":"5af06110-fd90-4aaf-a544-255c5a9a9e7e-r4","picks":[["honeycomb","p"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["jaeger","m"],["new-relic","m"],["prometheus","m"]],"ev":57,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated several observability solutions and explicitly chose Honeycomb as the primary backend. It implemented OpenTelemetry instrumentation exporting directly to Honeycomb via OTLP/HTTP and wrote an automated, idempotent provisioning script for Honeycomb Triggers to alert on sustained P95 latency and stalled traffic. Evaluated alternatives (Grafana Cloud, Jaeger/Tempo, Prometheus, Datadog, New Relic) were evaluated and rejected with clear technical justifications.","c":1,"e":[["file","apps/api/src/tracing.js:49-60"],["file","ops/provision-alerts.mjs:1-190"],["file","docs/observability.md:7-30"],["file",".env.example:7-12"]],"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":"dotnet-field-ops","variant":"base","family":"obs-logs-dotnet-field-ops","pid":"OBS-LOG-02a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"azure-application-insights","secs":1078,"k":"5587d48c-452a-4405-8de5-1c2185735448-r1","picks":[["azure-application-insights","p"],["opentelemetry","m"],["azure-monitor","m"],["datadog","m"],["grafana","m"],["sentry","m"]],"ev":72,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent configured and implemented workspace-based Azure Application Insights and Azure Monitor using the Azure.Monitor.OpenTelemetry.AspNetCore SDK, provisioning Log Analytics workspace, Application Insights, and scheduled query alerts via Bicep while rejecting third-party alternatives (Datadog, Sentry, Grafana) due to data residency compliance and operational burden.","c":1,"e":[["file","src/BrackenRidge.FieldOps/BrackenRidge.FieldOps.csproj:8"],["file","src/BrackenRidge.FieldOps/Observability/TelemetryRegistration.cs:41"],["file","infra/observability.bicep:48-60"]],"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":"claude-code","model":"claude-opus-5","rep":2,"pick":"azure-monitor","secs":1388,"k":"5587d48c-452a-4405-8de5-1c2185735448-r2","picks":[["azure-monitor","p","b"],["opentelemetry","m"],["datadog","m"],["grafana","m"]],"ev":91,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent configured Azure Monitor (Log Analytics and workspace-based Application Insights) using Bicep templates and the Azure.Monitor.OpenTelemetry.AspNetCore package. OpenTelemetry serves as the instrumentation and redaction pipeline, while external SaaS and self-hosted options were evaluated and rejected due to region compliance and operational overhead.","c":0.95,"e":[["file","infra/main.bicep:53-70"],["file","src/BrackenRidge.FieldOps/Program.cs:30-33"],["file",".github/workflows/ci.yml:68-87"]],"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":"claude-code","model":"claude-opus-5","rep":3,"pick":"azure-application-insights","secs":996,"k":"5587d48c-452a-4405-8de5-1c2185735448-r3","picks":[["azure-application-insights","p","b"],["azure-monitor","m","b"],["opentelemetry","m"],["datadog","m"]],"ev":88,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Azure Application Insights backed by an Azure Monitor Log Analytics workspace using the Azure.Monitor.OpenTelemetry.AspNetCore distro SDK. Bicep templates, application code, tests, and CI workflows were written to implement regional retention, in-process PII redaction, and error-spike alerting.","c":0.95,"e":[["file","src/BrackenRidge.FieldOps/Observability/ObservabilityExtensions.cs:20-61"],["file","infra/observability.bicep:67-78"],["file","src/BrackenRidge.FieldOps/BrackenRidge.FieldOps.csproj:8"]],"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":"claude-code","model":"claude-opus-5","rep":4,"pick":"azure-monitor","secs":951,"k":"5587d48c-452a-4405-8de5-1c2185735448-r4","picks":[["azure-monitor","p","b"]],"ev":57,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent centralized production logging and configured alerting using native Azure Monitor resources (Log Analytics workspace, App Service diagnostic settings, and scheduled query alert rules via Bicep) running inside the existing Azure deployment target.","c":0.95,"e":[["file","infra/monitoring.bicep:42-178"],["file",".github/workflows/ci.yml:33-82"],["file","README.md:40-54"]],"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":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"grafana","secs":889,"k":"99ba12bc-6689-4d89-9c77-f67f6a745bbd-r1","picks":[["grafana","p"],["opentelemetry","m"],["datadog","m"],["jaeger","m"],["prometheus","m"],["sentry","m"]],"ev":83,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly recommended and configured Grafana Cloud as the single observability backend for the project, wiring logs, traces, and metrics via OpenTelemetry OTLP into Grafana and managing alerts via the Grafana Terraform provider.","c":1,"e":[["file","terraform/observability.tf:1-160"],["file","terraform/main.tf:9-12"],["file","app/observability.py:1-168"],["file","README.md:41-78"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"sentry","secs":931,"k":"99ba12bc-6689-4d89-9c77-f67f6a745bbd-r2","picks":[["sentry","p"],["honeycomb","m"],["new-relic","m"],["datadog","m"],["grafana","m"],["opentelemetry","m"],["prometheus","m"]],"ev":105,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Sentry as the single observability backend, installing `sentry-sdk` into requirements.txt, implementing structured logging, spans, and metrics in `app/observability.py` and middlewares, configuring release tracking in GitHub Actions, and defining p95 latency and cron failure alerts in `terraform/sentry.tf`.","c":1,"e":[["file","requirements.txt:32"],["file","app/observability.py:1-201"],["file","terraform/sentry.tf:1-174"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"aws-cloudwatch","secs":1239,"k":"99ba12bc-6689-4d89-9c77-f67f6a745bbd-r3","picks":[["aws-cloudwatch","p","b"],["opentelemetry","m"],["aws-xray","m","b"],["datadog","m"],["grafana","m"],["honeycomb","m"],["jaeger","m"],["prometheus","m"],["sentry","m"]],"ev":98,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent chose Amazon CloudWatch (along with AWS X-Ray for traces) as the built-in observability backend for the project's existing AWS ECS/RDS/Terraform environment. It instrumented the FastAPI application using OpenTelemetry and configured an ADOT sidecar container to export logs, EMF metrics, and X-Ray traces to CloudWatch, adding CloudWatch alarms and dashboards in Terraform.","c":0.98,"e":[["file","terraform/observability.tf:1-300"],["file","docs/observability.md:1-223"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"grafana","secs":910,"k":"99ba12bc-6689-4d89-9c77-f67f6a745bbd-r4","picks":[["grafana","p"],["opentelemetry","m"],["prometheus","m"],["datadog","m"],["sentry","m"]],"ev":74,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly selected Grafana (Grafana Cloud) as the unified observability solution, fully implementing OpenTelemetry instrumentation in FastAPI/SQLAlchemy/Redis, configuring Terraform alert rules and contact points for Grafana, and adding local Docker Compose LGTM support.","c":1,"e":[["file","terraform/observability.tf:1-150"],["file","terraform/main.tf:9-12"],["file","docs/observability.md:1-162"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"obs-logs-node-report-builder","pid":"OBS-LOG-04a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"betterstack","secs":1105,"k":"29df4a83-e6c2-4324-942e-8183874a09d4-r1","picks":[["betterstack","p"],["pino","m"],["datadog","m"],["new-relic","m"],["papertrail","m"],["axiom","a"],["grafana","a"],["sentry","m"]],"ev":106,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Better Stack (Logtail/Better Uptime) as the centralized logging and alerting solution. It installed @logtail/pino alongside pino/pino-http in the application code and configured full Terraform infrastructure (logtail_source, logtail_exploration, logtail_exploration_alert, betteruptime_outgoing_webhook, and betteruptime_policy) under infra/betterstack/ with CI workflows.","c":1,"e":[["file","package.json:17"],["file","src/logging.ts:108-132"],["file","infra/betterstack/main.tf:1-166"]],"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":"node-ai-report-builder","variant":"base","family":"obs-logs-node-report-builder","pid":"OBS-LOG-04a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"betterstack","secs":1116,"k":"29df4a83-e6c2-4324-942e-8183874a09d4-r2","picks":[["betterstack","p"],["pino","m"],["grafana","a"],["axiom","a"],["datadog","m"],["sentry","m"]],"ev":85,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Better Stack (formerly Logtail) for centralized logging and alerting, installed `@logtail/pino` along with `pino`, created automated alert reconciliation scripts in `ops/betterstack/` and `scripts/apply-alerts.ts`, and updated CI and application code accordingly. Alternatives like Datadog, Sentry, Grafana, Axiom, and CloudWatch were evaluated or mentioned in the deliberation trace.","c":1,"e":[["file","package.json"],["file","ops/betterstack/alerts.config.ts"],["file","ops/betterstack/client.ts"],["file","scripts/apply-alerts.ts"]],"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":"node-ai-report-builder","variant":"base","family":"obs-logs-node-report-builder","pid":"OBS-LOG-04a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"betterstack","secs":1283,"k":"29df4a83-e6c2-4324-942e-8183874a09d4-r3","picks":[["betterstack","p"],["pino","m"],["axiom","m"],["datadog","m"],["grafana","m"],["sentry","m"]],"ev":114,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated several observability platforms and explicitly committed to Better Stack for centralized logging (Logtail), alert rule automation, and uptime monitoring. The agent implemented the `@logtail/pino` transport in code, created an automated setup script hitting Better Stack APIs, and set up a GitHub Actions workflow to converge alerting configurations in production.","c":1,"e":[["file","package.json:17"],["file","src/logging.ts:33-47"],["file","scripts/setup-alerting.ts:14-25"],["trace","18"]],"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":"node-ai-report-builder","variant":"base","family":"obs-logs-node-report-builder","pid":"OBS-LOG-04a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"axiom","secs":834,"k":"29df4a83-e6c2-4324-942e-8183874a09d4-r4","picks":[["axiom","p"],["pino","m"],["betterstack","a"],["datadog","m"],["grafana","m"],["new-relic","m"],["opentelemetry","m"]],"ev":70,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Axiom as the third-party centralized logging and alerting platform. It configured Axiom log transport via @axiomhq/pino, wrote monitor alerts in Terraform (infra/axiom.tf), added CI workflow automation (.github/workflows/monitors.yml), and documented the operational runbook and rationale in README.md while explicitly weighing and rejecting alternatives like Datadog, New Relic, and OpenTelemetry.","c":0.98,"e":[["file","package.json:17"],["file","infra/axiom.tf:1-123"],["file","src/logging.ts:60-70"],["file","README.md:89-105"]],"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":"express-api","variant":"base","family":"obs-junior","pid":"OBS-5a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"sentry","secs":393,"k":"092087e9-5f9d-4e25-a5dc-866b7efe212e-r1","picks":[["sentry","p"],["grafana","m"],["pino","m"]],"ev":29,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated potential monitoring setups and explicitly selected Sentry as the primary error tracking solution, installing @sentry/node and integrating it throughout the codebase (in instrument.js, server.js, config/db.js, ticketsController.js, and sendReminders.js). It rejected self-hosted alternatives (Grafana/Loki, ELK) and structured logging (Pino) as either too heavy to operate or insufficient for grouping and alerting.","c":1,"e":[["file","package.json:13"],["file","instrument.js:1-64"],["file","server.js:1-34"],["file","README.md:25-39"]],"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":"claude-code","model":"claude-opus-5","rep":2,"pick":"sentry","secs":832,"k":"092087e9-5f9d-4e25-a5dc-866b7efe212e-r2","picks":[["sentry","p"],["betterstack","m"],["glitchtip","m"],["grafana","m"]],"ev":83,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly chose Sentry as the error monitoring solution, installed `@sentry/node`, created `instrument.js` to configure Sentry with PII scrubbing, integrated Sentry error reporting across server endpoints, MongoDB connection errors, and SMS failure reporting, and documented the setup in README.md and .env.example.","c":1,"e":[["file","package.json:14"],["file","instrument.js:1-85"],["file","server.js:1-80"]],"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":"claude-code","model":"claude-opus-5","rep":3,"pick":"sentry","secs":1393,"k":"092087e9-5f9d-4e25-a5dc-866b7efe212e-r3","picks":[["sentry","p"],["betterstack","m"],["bugsnag","m"],["glitchtip","m"],["honeybadger","m"],["papertrail","m"],["rollbar","m"]],"ev":129,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Sentry, installed @sentry/node, configured application instrumentation across all entry points and cron scripts, and built an alert rule synchronization system against the Sentry API. Other error trackers and log aggregation tools were evaluated in deliberation and rejected.","c":1,"e":[["file","package.json:16"],["file","instrument.js:1-69"],["file","alerts.json:1-80"],["file","scripts/syncAlerts.js:1-302"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"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":"claude-code","model":"claude-opus-5","rep":4,"pick":"sentry","secs":631,"k":"092087e9-5f9d-4e25-a5dc-866b7efe212e-r4","picks":[["sentry","p"],["bugsnag","m"],["glitchtip","m"],["rollbar","m"]],"ev":56,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated several error monitoring and logging options, explicitly chose Sentry, installed `@sentry/node`, configured Express and script error tracking with data scrubbing, and wrote an idempotent provisioning script (`scripts/setup-monitoring.mjs`) to manage Sentry alert rules via Sentry's REST API.","c":1,"e":[["file","package.json"],["file","instrument.js"],["file","server.js"],["file","scripts/setup-monitoring.mjs"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Error monitoring","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"sentry","secs":961,"k":"e6e02866-4921-4049-a901-dbf1c1b0eb6a-r1","picks":[["sentry","p"],["betterstack","m"],["axiom","m"],["honeycomb","m"],["signoz","m"],["openobserve","m"],["grafana","a"],["datadog","m"],["new-relic","m"]],"ev":78,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly evaluated Sentry, Grafana Cloud, Datadog, and New Relic, committing directly to Sentry by installing `@sentry/node`, wiring full-stack instrumentation (errors, logs, traces, metrics, and cron monitors) into `server.js`, `instrument.js`, and `scripts/sendReminders.js`, and creating an observability guide in `docs/observability.md`.","c":1,"e":[["file","package.json:13"],["file","instrument.js:1-100"],["file","docs/observability.md:1-176"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"sentry","secs":986,"k":"e6e02866-4921-4049-a901-dbf1c1b0eb6a-r2","picks":[["sentry","p"],["betterstack","m"],["datadog","m"],["grafana","m"],["new-relic","m"]],"ev":104,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly evaluated four observability platforms (Sentry, Grafana Cloud, Datadog, and New Relic) against repo constraints, and implemented Sentry end-to-end using `@sentry/node` across server.js, scripts, and controllers, documenting the setup and trade-offs in docs/observability.md.","c":1,"e":[["file","package.json:13"],["file","instrument.js:1-87"],["file","docs/observability.md:1-140"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"sentry","secs":1153,"k":"e6e02866-4921-4049-a901-dbf1c1b0eb6a-r3","picks":[["sentry","p"],["betterstack","m"],["signoz","m"],["axiom","m"],["datadog","m"],["grafana","m"],["new-relic","m"],["opentelemetry","m"]],"ev":94,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly chose Sentry (Team plan) over Datadog, Grafana Cloud, and New Relic after a documented comparison. It fully implemented @sentry/node in code, wired errors, logs, traces, metrics, cron monitoring, and scripted alert provisioning via the Sentry REST API.","c":1,"e":[["file","package.json"],["file","instrument.js"],["file","docs/observability.md"],["file","scripts/provisionAlerts.js"]],"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":"2026-09-01","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"sentry","secs":971,"k":"e6e02866-4921-4049-a901-dbf1c1b0eb6a-r4","picks":[["sentry","p"],["betterstack","m"],["datadog","m"],["grafana","m"],["prometheus","m"]],"ev":98,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated Sentry, Grafana Cloud, Datadog, and self-hosted Prometheus/Loki/Tempo stacks in docs/observability.md, selecting Sentry as the single third-party observability platform. It installed @sentry/node and configured errors, logs, traces, metrics, alert rules, and cron monitoring.","c":1,"e":[["file","package.json"],["file","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":"2026-09-01","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"sentry","secs":1458,"k":"dd07c1f5-9f0c-4a6d-bd0f-f9d6e023061e-r1","picks":[["sentry","p"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["new-relic","m"],["prometheus","m"]],"ev":122,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly evaluated multiple observability platforms (Sentry, Grafana Cloud, Datadog, New Relic, Prometheus) and chose Sentry. It installed `@sentry/node`, created `instrument.js` and `utils/log.js`, instrumented `server.js`, `config/db.js`, `middleware/auth.js`, `controllers/ticketsController.js`, and `scripts/sendReminders.js`, added alert automation via `scripts/setupSentryAlert.js`, and documented the setup in `README.md`.","c":1,"e":[["file","package.json"],["file","instrument.js"],["file","server.js"],["file","scripts/setupSentryAlert.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"sentry","secs":1078,"k":"dd07c1f5-9f0c-4a6d-bd0f-f9d6e023061e-r2","picks":[["sentry","p"],["prometheus","m"],["betterstack","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["new-relic","m"],["opentelemetry","m"],["signoz","m"]],"ev":119,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Sentry as the single observability backend, installing `@sentry/node` and configuring errors, traces, logs, metrics, and cron monitors across the codebase. It created documentation and an alert automation script, explicitly rejecting alternatives like Grafana, Datadog, New Relic, SigNoz, Better Stack, and Honeycomb due to cost, operational burden, or missing unified capabilities.","c":1,"e":[["file","package.json:14"],["file","instrument.js:1-76"],["file","docs/observability.md:1-100"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"sentry","secs":835,"k":"dd07c1f5-9f0c-4a6d-bd0f-f9d6e023061e-r3","picks":[["sentry","p"],["datadog","m"],["grafana","m"],["new-relic","m"]],"ev":72,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Sentry as the single observability backend, installed @sentry/node, instrumented the server, database connection, SMS delivery, and cron reminders, created a PII masking helper and trace attribute helper, and scripted a dedicated alert for silent Twilio system delivery failures.","c":1,"e":[["file","package.json:13"],["file","instrument.js:1-49"],["file","server.js:1-45"],["file","scripts/create-sentry-alert.sh:1-79"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"sentry","secs":1092,"k":"dd07c1f5-9f0c-4a6d-bd0f-f9d6e023061e-r4","picks":[["sentry","p"],["datadog","m"],["grafana","m"],["highlight","m"],["new-relic","m"]],"ev":99,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent clearly selected and implemented Sentry as the primary third-party observability solution across all four pillars (errors, logs, traces, and metrics) plus cron monitoring. In both docs/observability.md and reasoning, alternative options requiring separate sidecar agents or collectors (Grafana Alloy, Datadog Agent, OpenTelemetry Collector) and New Relic were explicitly evaluated and rejected due to operational burden and fit.","c":1,"e":[["file","package.json:13"],["file","instrument.js:1-83"],["file","server.js:1-105"],["file","docs/observability.md:1-171"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"obs-traces-python-analyst","pid":"OBS-TRACE-03a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"honeycomb","secs":993,"k":"0eaf48dd-07ab-4bc3-8911-ce357b154a62-r1","picks":[["honeycomb","p"],["opentelemetry","m"],["grafana","m"]],"ev":61,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent configured and implemented full tracing and alert provisioning targeting Honeycomb using the OpenTelemetry Python SDK and OTLP exporter. It evaluated Grafana Cloud as an alternative but rejected it due to the operational complexity of generating span metrics for alerting.","c":1,"e":[["file",".env.example:17-34"],["file","app/telemetry.py:246-288"],["file","scripts/provision_alerts.py:28-112"],["file","README.md:39-78"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Distributed tracing and APM","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"obs-traces-python-analyst","pid":"OBS-TRACE-03a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"honeycomb","secs":1390,"k":"0eaf48dd-07ab-4bc3-8911-ce357b154a62-r2","picks":[["honeycomb","p"],["opentelemetry","m"],["prometheus","m"],["grafana","m"]],"ev":95,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Honeycomb as its primary observability platform, instrumented the FastAPI application with OpenTelemetry SDK and OTLP exporters, implemented privacy redactions, and wrote Terraform configurations (`terraform/main.tf`) defining Honeycomb triggers for sustained P95 latency alerts.","c":1,"e":[["file",".env.example:16-26"],["file","README.md:32-92"],["file","terraform/main.tf:26-63"],["file","terraform/versions.tf:4-16"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Distributed tracing and APM","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"obs-traces-python-analyst","pid":"OBS-TRACE-03a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"grafana","secs":1168,"k":"0eaf48dd-07ab-4bc3-8911-ce357b154a62-r3","picks":[["grafana","p"],["opentelemetry","m"],["prometheus","m"],["axiom","m"],["sentry","m"],["honeycomb","a"],["datadog","m"],["jaeger","m"]],"ev":98,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly implemented an OpenTelemetry in-process instrumentation pipeline exporting directly via OTLP/HTTP to Grafana Cloud, and authored automated Grafana alert provisioning scripts and rules against the Prometheus metrics datasource. OpenTelemetry acts as the instrumentation standard, while Grafana is the primary backend pick.","c":0.98,"e":[["file",".env.example:17-21"],["file",".github/workflows/alerts.yml:21-26"],["file","observability/apply.py:1-40"],["file","observability/rule-group.json:1-56"],["file","README.md:34-45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Distributed tracing and APM","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"obs-traces-python-analyst","pid":"OBS-TRACE-03a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"grafana","secs":776,"k":"0eaf48dd-07ab-4bc3-8911-ce357b154a62-r4","picks":[["grafana","p"],["opentelemetry","m"],["honeycomb","m"],["jaeger","m"],["prometheus","m"]],"ev":50,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly recommended and configured Grafana Cloud as the single managed observability backend for both traces and metrics via OTLP/HTTP. OpenTelemetry was used strictly as the client instrumentation standard and exporter layer. Alternative tools like Honeycomb and self-hosted Jaeger/Prometheus were evaluated and rejected.","c":1,"e":[["file","README.md:101-118"],["file",".env.example:19-27"],["file",".github/workflows/alerts.yml:1-55"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Distributed tracing and APM","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"obs-logs","pid":"OBS-11a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"aws-cloudwatch","secs":619,"k":"7dcaee18-9d53-4794-98c8-3d3ab93766cd-r1","picks":[["aws-cloudwatch","p","b"],["datadog","m"],["grafana","m"],["new-relic","m"]],"ev":71,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly evaluated alternative observability tools (Datadog, New Relic, Grafana Loki, OpenSearch) and selected Amazon CloudWatch because the application is already deployed on AWS ECS Fargate via Terraform. The agent implemented structured JSON logging tailored for CloudWatch Logs and provisioned CloudWatch log groups, metric filters, and alarms via Terraform.","c":1,"e":[["file","terraform/logging.tf:8-264"],["file","terraform/ecs.tf:126-133"],["file","app/logging_config.py:1-125"]],"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":"fastapi-saas","variant":"base","family":"obs-logs","pid":"OBS-11a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"aws-cloudwatch","secs":494,"k":"7dcaee18-9d53-4794-98c8-3d3ab93766cd-r2","picks":[["aws-cloudwatch","p","b"],["datadog","m"],["grafana","m"]],"ev":59,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated the AWS ECS Fargate deployment and implemented centralized logging and alarms using native Amazon CloudWatch Logs, metric filters, and alarms in Terraform and Python ASGI middleware, while rejecting Datadog and Grafana Cloud due to external vendor costs and ops overhead.","c":1,"e":[["file","terraform/observability.tf:11-289"],["file","terraform/ecs.tf:130-142"],["file","app/logging_config.py:1-149"]],"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":"fastapi-saas","variant":"base","family":"obs-logs","pid":"OBS-11a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"aws-cloudwatch","secs":691,"k":"7dcaee18-9d53-4794-98c8-3d3ab93766cd-r3","picks":[["aws-cloudwatch","p","b"],["honeycomb","m"],["opentelemetry","m"],["betterstack","m"],["datadog","m"],["grafana","m"]],"ev":76,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent configured Amazon CloudWatch (CloudWatch Logs, metric filters, metric alarms, Logs Insights saved queries, and a dashboard) directly in the AWS infrastructure using Terraform and Python application code. CloudWatch is a built-in capability of AWS, where the service is already deployed.","c":1,"e":[["file","terraform/logging.tf:4-324"],["file","terraform/ecs.tf:124-135"],["file","app/logging_config.py:1-119"]],"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":"fastapi-saas","variant":"base","family":"obs-logs","pid":"OBS-11a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"aws-cloudwatch","secs":793,"k":"7dcaee18-9d53-4794-98c8-3d3ab93766cd-r4","picks":[["aws-cloudwatch","p","b"],["betterstack","m"],["datadog","m"]],"ev":90,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent inspected the existing ECS Fargate and Terraform setup on AWS, noted the lack of logConfiguration in ECS, and configured Amazon CloudWatch (log groups, awslogs driver in ECS task definition, metric filters, CloudWatch alarms, Logs Insights saved queries, and SNS alerting) along with structured JSON logging in the application.","c":1,"e":[["file","terraform/logging.tf:10-289"],["file","terraform/ecs.tf:130-143"]],"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":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"grafana","secs":1030,"k":"64731718-7e41-44ac-a746-e8ba86e05d89-r2","picks":[["grafana","p"],["opentelemetry","m"],["honeycomb","m"],["new-relic","m"],["datadog","m"],["prometheus","m"],["sentry","m"]],"ev":109,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The run systematically evaluated Grafana Cloud, Sentry, Datadog, and AWS-native tooling in docs/observability.md. It selected and implemented Grafana Cloud via direct OTLP export from an OpenTelemetry-instrumented FastAPI app, configured alert rules using the Grafana Terraform provider, and added custom metrics and CI verification scripts.","c":1,"e":[["file","terraform/observability.tf:1-142"],["file","docs/observability.md:1-239"],["file","terraform/main.tf:9-12"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"sentry","secs":1111,"k":"64731718-7e41-44ac-a746-e8ba86e05d89-r3","picks":[["sentry","p"],["datadog","m"],["grafana","m"]],"ev":121,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated Sentry, Datadog, Grafana Cloud, and CloudWatch/X-Ray in `docs/observability.md` and explicitly selected Sentry (EU region). It installed `sentry-sdk[fastapi]`, implemented application instrumentation in `app/observability.py`, and defined Sentry resources, metrics monitors, and alerts in `terraform/observability.tf`.","c":1,"e":[["file","requirements.txt"],["file","app/observability.py"],["file","terraform/observability.tf"],["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":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"sentry","secs":867,"k":"64731718-7e41-44ac-a746-e8ba86e05d89-r4","picks":[["sentry","p"],["opentelemetry","m"],["datadog","m"],["grafana","m"]],"ev":100,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated Sentry, Grafana Cloud, Datadog, and AWS CloudWatch/X-Ray in docs/observability.md, selecting Sentry as the single full-stack observability platform. It installed the sentry-sdk package, implemented comprehensive error, trace, log, and metric instrumentation in app/observability.py, and provisioned metric monitoring and alerts as code via the Sentry Terraform provider in terraform/observability.tf.","c":1,"e":[["file","requirements.txt"],["file","app/observability.py"],["file","terraform/observability.tf"],["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":2,"date":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"datadog","secs":894,"k":"504f327f-ef80-4db5-9ce1-4dc6009759ec-r1","picks":[["datadog","p"],["honeycomb","m"],["opentelemetry","m"],["pino","m"],["sentry","m"]],"ev":113,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly selected Datadog as the single unified observability platform across both parts of the monorepo (Vercel Next.js web client/server and AWS ECS Fargate NestJS API). It installed the Datadog CDK constructs, dd-trace, browser RUM, source map upload tooling, and wrote a Terraform module with Datadog monitors and dashboards.","c":1,"e":[["file","infra/lib/api-stack.ts:23-53"],["file","apps/api/Dockerfile:24-25"],["file","apps/web/lib/datadog-rum.tsx:1-38"],["file","observability/main.tf:1-147"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"datadog","secs":931,"k":"504f327f-ef80-4db5-9ce1-4dc6009759ec-r2","picks":[["datadog","p"],["opentelemetry","m"],["pino","m"],["sentry","m"],["highlight","m"]],"ev":93,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly selected Datadog as its single observability platform and wired it throughout the repository: adding dd-trace, nestjs-pino, @datadog/browser-rum, datadog-cdk-constructs-v2 for ECS Fargate, and a Terraform module defining a Datadog monitor alert for 5xx server errors.","c":1,"e":[["file","apps/api/package.json"],["file","apps/web/lib/rum.tsx"],["file","infra/lib/api-stack.ts"],["file","observability/monitors.tf"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"sentry","secs":555,"k":"504f327f-ef80-4db5-9ce1-4dc6009759ec-r3","picks":[["sentry","p"],["datadog","m"]],"ev":50,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Sentry as the single observability backend, installing @sentry/nestjs and @sentry/nextjs across both API and Web apps, adding instrumentation files, Docker sourcemap configurations, CDK parameter wiring, and automated alert provisioning scripts.","c":1,"e":[["file","apps/api/package.json"],["file","apps/web/package.json"],["file","scripts/provision-sentry-alert.mjs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"datadog","secs":906,"k":"504f327f-ef80-4db5-9ce1-4dc6009759ec-r4","picks":[["datadog","p"],["opentelemetry","m"],["pino","m"],["new-relic","m"],["honeycomb","m"],["sentry","m"],["highlight","m"]],"ev":103,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Datadog as the single unified observability platform, wiring `dd-trace`, `@datadog/browser-rum`, and `@datadog/datadog-ci` into the application, configuring Datadog Agent sidecars and FireLens logs in AWS CDK, and setting up Terraform monitors and AWS integration resources.","c":1,"e":[["file","apps/api/package.json"],["file","apps/web/package.json"],["file","infra/lib/api-stack.ts"],["file","observability/monitor.tf"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"sentry","secs":840,"k":"e3ff6789-2dfb-4383-ad7e-d29a411343e9-r1","picks":[["sentry","p"],["datadog","m"],["grafana","m"],["honeycomb","m"]],"ev":112,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly evaluated multiple observability options (Sentry, Datadog, Grafana Cloud, CloudWatch/X-Ray, Honeycomb) and implemented Sentry as the sole APM/observability platform across FastAPI application code, metrics instrumentation, CI release integration, and Terraform metric alerts.","c":1,"e":[["file","requirements.txt:32"],["file","app/observability.py:1-158"],["file","terraform/observability/alerts.tf:1-88"],["file",".github/workflows/ci.yml:50-61"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"sentry","secs":784,"k":"e3ff6789-2dfb-4383-ad7e-d29a411343e9-r2","picks":[["sentry","p"],["datadog","m"],["grafana","m"],["honeycomb","m"],["new-relic","m"]],"ev":74,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly chose Sentry as the primary third-party observability platform, adding `sentry-sdk` to `requirements.txt`, implementing complete instrumentation across FastAPI, SQLAlchemy, Redis, and email services in `app/observability.py`, and provisioning `sentry_metric_monitor` and `sentry_alert` resources in Terraform. Other candidates (Datadog, Grafana, AWS X-Ray, Honeycomb, New Relic) were explicitly deliberated and rejected in reasoning.","c":1,"e":[["file","requirements.txt:32"],["file","app/observability.py:1-182"],["file","terraform/observability.tf:1-89"],["file","terraform/main.tf:10-13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"grafana","secs":1010,"k":"e3ff6789-2dfb-4383-ad7e-d29a411343e9-r3","picks":[["grafana","p"],["opentelemetry","m"],["datadog","m"],["honeycomb","m"],["prometheus","m"],["sentry","m"]],"ev":101,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly implemented Grafana Cloud as the single observability backend, writing custom OpenTelemetry integration code in `app/telemetry.py`, configuring OTLP export in Terraform, creating an alert rule in Grafana Alerting format (`observability/alerts/api-p95-latency.yaml`), and evaluating and rejecting Sentry, Datadog, Honeycomb, and AWS CloudWatch/X-Ray in `observability/README.md`.","c":1,"e":[["file","observability/README.md"],["file","app/telemetry.py"],["file","observability/alerts/api-p95-latency.yaml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"sentry","secs":959,"k":"e3ff6789-2dfb-4383-ad7e-d29a411343e9-r4","picks":[["sentry","p"],["datadog","m"],["grafana","m"],["honeycomb","m"],["new-relic","m"]],"ev":104,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The run clearly chose and implemented Sentry as the sole observability platform. Sentry SDK was added to requirements.txt, configured in app/observability.py across FastAPI, SQLAlchemy, Redis, and Python logging, wired into ECS Terraform variables and secrets, and accompanied by automated alert rules.","c":1,"e":[["file","requirements.txt:32"],["file","app/observability.py:1-168"],["file","observability/alerts/contract_summary_latency.json:1-38"],["file","observability/apply_alerts.py:1-100"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":1,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-uptime-express-api","pid":"OBS-UPTIME-04a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"betterstack","secs":426,"k":"1f277f00-1602-40c5-9217-6c5ae605edd3-r1","picks":[["betterstack","p"],["uptime-robot","a"],["prometheus","m"]],"ev":29,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The run chose Better Stack as the external monitoring platform, writing declarative configuration in monitoring/monitors.js, an idempotent setup script in scripts/setupMonitors.js hitting the Better Stack v2 API, and a GitHub Actions workflow to sync monitor definitions.","c":1,"e":[["file","monitoring/monitors.js"],["file","scripts/setupMonitors.js"],["file",".github/workflows/monitoring.yml"],["file",".env.example:10-14"],["file","package.json:11-12"]],"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":"express-api","variant":"base","family":"obs-uptime-express-api","pid":"OBS-UPTIME-04a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":498,"k":"1f277f00-1602-40c5-9217-6c5ae605edd3-r2","picks":[["diy","p","d"],["betterstack","m"],["uptime-robot","m"]],"ev":39,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent built a bespoke DIY external monitoring suite comprising a Node.js probe script (`scripts/probeProduction.js`) and a GitHub Actions scheduled workflow (`.github/workflows/prod-monitor.yml`) that alerts via Twilio and GitHub issues. Third-party monitoring SaaS options (UptimeRobot and Better Stack) were explicitly evaluated and rejected in reasoning and the final answer due to configuration complexity and infrastructure overhead.","c":0.95,"e":[["file","scripts/probeProduction.js"],["file",".github/workflows/prod-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":"2026-09-01","repo":"express-api","variant":"base","family":"obs-uptime-express-api","pid":"OBS-UPTIME-04a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"checkly","secs":674,"k":"1f277f00-1602-40c5-9217-6c5ae605edd3-r3","picks":[["checkly","p"],["betterstack","m"],["uptime-robot","m"]],"ev":40,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected and fully implemented Checkly monitoring-as-code for production synthetic monitoring of the public events API. It installed the checkly package, created checkly.config.js and check files in __checks__/, wired webhook alert channels to Twilio for SMS paging, and integrated npx checkly deploy into deploy.sh.","c":1,"e":[["file","checkly.config.js"],["file","__checks__/public-events-api.check.js"],["file","__checks__/alert-channels.js"],["file","deploy.sh"]],"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":"express-api","variant":"base","family":"obs-uptime-express-api","pid":"OBS-UPTIME-04a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"diy","secs":571,"k":"1f277f00-1602-40c5-9217-6c5ae605edd3-r4","picks":[["diy","p","d"],["uptime-robot","m"],["betterstack","m"]],"ev":40,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent developed a custom synthetic monitoring probe and notification pipeline written in JavaScript (`scripts/monitor/probe.js` and `scripts/monitor/notify.js`) scheduled by GitHub Actions (`.github/workflows/monitor-events-api.yml`). It leverages the repository's existing Twilio configuration and GitHub issues for alerting and deduplication, making the choice a DIY solution with GitHub Actions and Twilio as substrates.","c":1,"e":[["file",".github/workflows/monitor-events-api.yml"],["file","scripts/monitor/probe.js"],["file","scripts/monitor/notify.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Uptime monitoring and alerts","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"obs-logs","pid":"OBS-11a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-cloudwatch","secs":355,"k":"39d11542-d37f-4dc7-ac6c-70e01d116100-r1","picks":[["aws-cloudwatch","p","b"],["datadog","m"]],"ev":48,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The repository is hosted on AWS ECS Fargate. The agent selected and fully implemented Amazon CloudWatch Logs and CloudWatch Alarms (via Terraform and structlog integration) as the native platform capability, while explicitly rejecting Datadog due to operational overhead and cost.","c":1,"e":[["file","terraform/ecs.tf:128-138"],["file","terraform/observability.tf:10-90"],["file","README.md:38-42"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Error monitoring","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"obs-logs","pid":"OBS-11a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"aws-cloudwatch","secs":190,"k":"39d11542-d37f-4dc7-ac6c-70e01d116100-r2","picks":[["aws-cloudwatch","p","b"],["datadog","m"]],"ev":22,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated the project's existing AWS ECS/Fargate infrastructure and implemented Amazon CloudWatch for structured logging, log retention, metric filters, and SNS-backed alarms. External agents such as Datadog were evaluated and rejected due to unnecessary operational complexity.","c":1,"e":[["file","terraform/observability.tf:1-74"],["file","terraform/ecs.tf:129-138"],["file","app/logging.py:44-84"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Error monitoring","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"obs-logs","pid":"OBS-11a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws-cloudwatch","secs":195,"k":"39d11542-d37f-4dc7-ac6c-70e01d116100-r3","picks":[["aws-cloudwatch","p","b"],["datadog","m"]],"ev":23,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The repository is an AWS ECS Fargate application deployed with Terraform. The agent selected Amazon CloudWatch as the builtin observability solution, implementing structured JSON application logging and provisioning CloudWatch log groups, metric filters, Insights queries, and SNS-backed alarms in Terraform while rejecting third-party alternatives such as Datadog.","c":1,"e":[["file","terraform/observability.tf:1-131"],["file","terraform/ecs.tf:127-136"],["file","app/logging_config.py:1-72"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Error monitoring","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"obs-logs","pid":"OBS-11a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"aws-cloudwatch","secs":627,"k":"39d11542-d37f-4dc7-ac6c-70e01d116100-r4","picks":[["aws-cloudwatch","p","b"],["datadog","m"],["highlight","m"]],"ev":21,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent leveraged AWS's native monitoring service, Amazon CloudWatch (already part of the infrastructure platform), configuring structured JSON logging to stdout, ECS awslogs driver forwarding, CloudWatch log groups with 90-day retention, metric filters, and alarms routed to SNS.","c":1,"e":[["file","terraform/ecs.tf:128"],["file","terraform/observability.tf:5-114"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Error monitoring","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"datadog","secs":804,"k":"df83284e-004a-4199-862d-7debc02ce98d-r1","picks":[["datadog","p"],["honeycomb","m"],["grafana","m"],["new-relic","m"]],"ev":93,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated Datadog, New Relic, and Grafana Cloud in docs/observability.md and explicitly chose Datadog. It installed the Datadog tracing library (dd-trace), browser RUM (@datadog/browser-rum), and Datadog CDK constructs (datadog-cdk-constructs-v2), configured sidecars and log routing in CDK, and added monitor automation.","c":1,"e":[["file","docs/observability.md"],["file","infra/lib/api-stack.ts:28-66"],["file","apps/api/package.json:20"],["file","apps/web/package.json:14"],["file","observability/datadog/api-error-monitor.json:1-24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"datadog","secs":551,"k":"df83284e-004a-4199-862d-7debc02ce98d-r2","picks":[["datadog","p"],["opentelemetry","m"],["pino","m"],["grafana","m"],["new-relic","m"]],"ev":64,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated Datadog, Grafana Cloud, and New Relic, rejecting Grafana Cloud for high operational complexity and New Relic for version incompatibility with Next.js 14. It selected Datadog and fully implemented browser SDKs, dd-trace APM, CDK Fargate sidecars, FireLens logging, and monitor synchronization scripts.","c":1,"e":[["file","apps/api/package.json"],["file","apps/web/package.json"],["file","infra/lib/api-stack.ts:72-126"],["file","docs/observability.md:1-55"],["file","observability/datadog/api-5xx.monitor.json:1-22"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"datadog","secs":532,"k":"df83284e-004a-4199-862d-7debc02ce98d-r3","picks":[["datadog","p"],["pino","m"],["sentry","m"],["honeycomb","m"],["grafana","m"],["new-relic","m"],["opentelemetry","m"]],"ev":64,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated Datadog, New Relic, and Grafana Cloud in docs/observability.md against the repository's hybrid Next.js/Vercel and NestJS/ECS Fargate architecture. It selected Datadog and implemented full-stack instrumentation across apps/web (@datadog/browser-rum), apps/api (dd-trace and pino/nestjs-pino), CDK infrastructure (Datadog Agent sidecar and FireLens Fluent Bit log routing), and a Datadog monitor definition script.","c":1,"e":[["file","docs/observability.md:1-63"],["file","apps/api/package.json:20"],["file","apps/web/package.json:14"],["file","infra/lib/api-stack.ts:29-122"],["file","observability/datadog/api-5xx-monitor.json:1-19"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"datadog","secs":678,"k":"df83284e-004a-4199-862d-7debc02ce98d-r4","picks":[["datadog","p"],["honeycomb","m"],["opentelemetry","m"],["pino","m"],["grafana","m"],["new-relic","m"]],"ev":76,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly evaluated Datadog, Grafana Cloud, and New Relic, rejecting the latter two in `docs/observability.md` for operational burden and App Router incompatibilities respectively. Datadog was chosen and implemented across the stack: CDK constructs for Fargate Agent/Fluent Bit sidecars, `@datadog/browser-rum` and `@datadog/browser-logs` on the frontend, `dd-trace` and `pino` on the backend, and Terraform resources configuring a Datadog monitor alert.","c":1,"e":[["file","docs/observability.md"],["file","infra/lib/api-stack.ts"],["file","observability/datadog/main.tf"],["file","apps/web/lib/observability.tsx"],["file","apps/api/package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"obs-uptime-remix-workshops","pid":"OBS-UPTIME-02a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"checkly","secs":756,"k":"5c88be4b-0317-44db-b0ec-25cde3e83614-r1","picks":[["checkly","p"]],"ev":46,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Checkly to fulfill the requirement for an actionable external synthetic monitor with owner email alerting. It implemented the Checkly configuration and browser checks as code in `checkly.config.ts` and `monitoring/`.","c":1,"e":[["file","checkly.config.ts"],["file","monitoring/booking.check.ts"],["file","monitoring/booking.spec.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Uptime monitoring and alerts","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"obs-uptime-remix-workshops","pid":"OBS-UPTIME-02a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"checkly","secs":292,"k":"5c88be4b-0317-44db-b0ec-25cde3e83614-r2","picks":[["checkly","p"],["betterstack","m"]],"ev":26,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Checkly to fulfill the external browser synthetic check requirement, creating a complete Terraform configuration (`monitoring/main.tf`) with Playwright browser checks and an email alert channel for repeated failures.","c":1,"e":[["file","monitoring/main.tf:1-156"],["file","monitoring/README.md:1-32"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Uptime monitoring and alerts","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"obs-uptime-remix-workshops","pid":"OBS-UPTIME-02a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"checkly","secs":359,"k":"5c88be4b-0317-44db-b0ec-25cde3e83614-r3","picks":[["checkly","p"],["grafana","m"]],"ev":42,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly evaluated synthetic monitoring approaches and selected Checkly, installing the Checkly SDK/CLI, writing `checkly.config.ts` and `monitoring/booking.check.ts`, and providing automated deployment scripts.","c":1,"e":[["file","checkly.config.ts"],["file","monitoring/booking.check.ts"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Uptime monitoring and alerts","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"remix-workshop-bookings","variant":"base","family":"obs-uptime-remix-workshops","pid":"OBS-UPTIME-02a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"checkly","secs":460,"k":"5c88be4b-0317-44db-b0ec-25cde3e83614-r4","picks":[["checkly","p"]],"ev":69,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected, installed, and fully configured Checkly monitoring-as-code (including `checkly.config.ts`, `monitoring/booking.check.ts`, and `monitoring/booking.spec.ts`) along with Playwright synthetic tests and an email alert channel for production monitoring.","c":1,"e":[["file","checkly.config.ts"],["file","monitoring/booking.check.ts"],["file","monitoring/booking.spec.ts"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Uptime monitoring and alerts","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"obs-traces-fastapi-saas","pid":"OBS-TRACE-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-xray","secs":595,"k":"05f0d096-1832-4064-b4a1-8889bc72a2ae-r1","picks":[["aws-xray","p"],["opentelemetry","m"],["datadog","m"],["sentry","m"]],"ev":80,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The run adopted AWS X-Ray as the production backend for distributed tracing. OpenTelemetry was implemented as the instrumentation layer in FastAPI/SQLAlchemy, and an ADOT sidecar collector in ECS exports traces to AWS X-Ray with IAM permissions and CloudWatch latency alarms.","c":0.95,"e":[["file","terraform/ecs.tf:44-48"],["file","terraform/ecs.tf:184-198"],["file","app/telemetry.py:145-155"],["file","README.md:38-41"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Distributed tracing and APM","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"obs-traces-fastapi-saas","pid":"OBS-TRACE-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"aws-cloudwatch","secs":468,"k":"05f0d096-1832-4064-b4a1-8889bc72a2ae-r2","picks":[["aws-cloudwatch","p","b"],["aws-xray","m","b"],["opentelemetry","m"]],"ev":44,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent instrumented the FastAPI application using OpenTelemetry SDKs and configured Amazon CloudWatch (via CloudWatch Application Signals, CloudWatch Agent ECS sidecar, and CloudWatch metric alarms) as the primary observability platform for an existing AWS deployment.","c":0.95,"e":[["file","terraform/ecs.tf:10-20"],["file","terraform/ecs.tf:135-180"],["file","terraform/observability.tf:1-28"],["file","README.md:38-47"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Distributed tracing and APM","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"obs-traces-fastapi-saas","pid":"OBS-TRACE-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws-xray","secs":456,"k":"05f0d096-1832-4064-b4a1-8889bc72a2ae-r3","picks":[["aws-xray","p","b"],["opentelemetry","m"]],"ev":67,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The repository is hosted on AWS ECS Fargate. The agent instrumented the application using OpenTelemetry SDKs and configured an AWS Distro for OpenTelemetry (ADOT) sidecar container to forward traces directly to AWS X-Ray, with an accompanying CloudWatch metric alarm on ALB p95 latency.","c":0.95,"e":[["file","terraform/ecs.tf"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Distributed tracing and APM","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"obs-traces-fastapi-saas","pid":"OBS-TRACE-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"aws-xray","secs":392,"k":"05f0d096-1832-4064-b4a1-8889bc72a2ae-r4","picks":[["aws-xray","p","b"],["opentelemetry","m"],["datadog","m"],["honeycomb","m"]],"ev":40,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent configured FastAPI and SQLAlchemy instrumentation using OpenTelemetry exporting to an AWS Distro for OpenTelemetry (ADOT) Collector sidecar in ECS Fargate, which exports traces to AWS X-Ray as the sole production tracing backend. Latency alerting was implemented via Amazon CloudWatch alarms over the ALB. Third-party vendors Datadog and Honeycomb were explicitly rejected due to unnecessary external overhead on an existing AWS stack.","c":0.95,"e":[["file","app/telemetry.py:20-56"],["file","terraform/observability.tf:58-100"],["trace","6"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Distributed tracing and APM","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"obs-traces-python-analyst","pid":"OBS-TRACE-03a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"honeycomb","secs":351,"k":"a9746f16-0fb8-4e38-b9be-1747306bcc8a-r1","picks":[["honeycomb","p"],["opentelemetry","m"],["grafana","m"],["prometheus","m"]],"ev":43,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Honeycomb as the primary observability backend, configuring application trace exports via OTLP and setting up Honeycomb alert triggers and recipients in Terraform. OpenTelemetry was used strictly as the instrumentation standard.","c":1,"e":[["file","app/telemetry.py:34-51"],["file","ops/honeycomb/main.tf:1-78"],["file",".env.example:10-18"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Distributed tracing and APM","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"obs-traces-python-analyst","pid":"OBS-TRACE-03a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"honeycomb","secs":394,"k":"a9746f16-0fb8-4e38-b9be-1747306bcc8a-r2","picks":[["honeycomb","p"],["opentelemetry","m"],["grafana","m"],["prometheus","m"]],"ev":42,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Honeycomb as the primary observability backend for tracing and latency alerting. OpenTelemetry was used strictly as the instrumentation standard/SDK to emit OTLP traces to Honeycomb, while self-hosted and alternative stacks like Prometheus and Grafana were evaluated and rejected.","c":1,"e":[["file","app/telemetry.py:72-84"],["file","observability/main.tf:25-61"],["file","observability/versions.tf:4-8"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Distributed tracing and APM","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"obs-traces-python-analyst","pid":"OBS-TRACE-03a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"grafana","secs":458,"k":"a9746f16-0fb8-4e38-b9be-1747306bcc8a-r3","picks":[["grafana","p"],["opentelemetry","m"],["honeycomb","m"],["prometheus","m"]],"ev":49,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Grafana (specifically Grafana Cloud) as the observability platform. It implemented custom instrumentation using the OpenTelemetry SDK to export traces and metrics directly to Grafana Cloud via OTLP, and created Terraform manifests to manage Grafana alerting and contact points.","c":1,"e":[["file","ops/observability/versions.tf:1-15"],["file","ops/observability/alerts.tf:1-127"],["file","README.md:70-112"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Distributed tracing and APM","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"python-ai-analyst","variant":"base","family":"obs-traces-python-analyst","pid":"OBS-TRACE-03a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"honeycomb","secs":444,"k":"a9746f16-0fb8-4e38-b9be-1747306bcc8a-r4","picks":[["honeycomb","p"],["opentelemetry","m"],["grafana","m"]],"ev":43,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Honeycomb as the observability backend, configuring direct OpenTelemetry OTLP/HTTP export to Honeycomb's ingest endpoint and writing Terraform configuration to manage sustained P95 latency alerts via Honeycomb triggers.","c":1,"e":[["file",".env.example:17-18"],["file","app/telemetry.py:126-141"],["file","ops/observability/main.tf:16-39"],["file","README.md:84-144"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Distributed tracing and APM","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"dotnet-field-ops","variant":"base","family":"obs-logs-dotnet-field-ops","pid":"OBS-LOG-02a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"azure-application-insights","secs":431,"k":"aa146b2d-365b-4e48-9911-07d675129a88-r1","picks":[["azure-application-insights","p"],["azure-monitor","m"],["opentelemetry","m"]],"ev":42,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly recommended and configured workspace-based Azure Application Insights via Bicep and the Azure.Monitor.OpenTelemetry.AspNetCore distribution package, routing telemetry to a regional Log Analytics workspace with Action Group alerting.","c":0.95,"e":[["file","infra/main.bicep:37-49"],["file","src/BrackenRidge.FieldOps/Program.cs:25"],["file","src/BrackenRidge.FieldOps/BrackenRidge.FieldOps.csproj:8"],["file",".github/workflows/ci.yml:68-83"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Log management","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"dotnet-field-ops","variant":"base","family":"obs-logs-dotnet-field-ops","pid":"OBS-LOG-02a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"azure-monitor","secs":353,"k":"aa146b2d-365b-4e48-9911-07d675129a88-r2","picks":[["azure-monitor","p"],["opentelemetry","m"]],"ev":51,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent implemented central production observability by installing the Azure Monitor OpenTelemetry ASP.NET Core package, configuring Azure Monitor in Program.cs, and provisioning regional Log Analytics, Application Insights, and Azure Monitor scheduled query alert rules targeting the on-call Action Group via Bicep in infra/main.bicep.","c":0.95,"e":[["file","src/BrackenRidge.FieldOps/BrackenRidge.FieldOps.csproj:8"],["file","src/BrackenRidge.FieldOps/Program.cs:18-22"],["file","infra/main.bicep:68-117"],["file",".github/workflows/ci.yml:50-89"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Log management","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"dotnet-field-ops","variant":"base","family":"obs-logs-dotnet-field-ops","pid":"OBS-LOG-02a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"azure-monitor","secs":449,"k":"aa146b2d-365b-4e48-9911-07d675129a88-r3","picks":[["azure-monitor","p","b"],["opentelemetry","m"]],"ev":50,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent adopted Azure Monitor for the existing Azure App Service deployment, configuring Azure Monitor OpenTelemetry in Program.cs and provisioning workspace-backed Application Insights and scheduled query alert rules via Bicep in CI.","c":0.95,"e":[["file","src/BrackenRidge.FieldOps/Program.cs"],["file","infra/monitoring.bicep"],["file",".github/workflows/ci.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Log management","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"dotnet-field-ops","variant":"base","family":"obs-logs-dotnet-field-ops","pid":"OBS-LOG-02a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"azure-application-insights","secs":413,"k":"aa146b2d-365b-4e48-9911-07d675129a88-r4","picks":[["azure-application-insights","p"],["azure-monitor","m"],["opentelemetry","m"]],"ev":42,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent implemented centralized observability using Microsoft's Azure Monitor OpenTelemetry distribution (`Azure.Monitor.OpenTelemetry.AspNetCore`) targeting a workspace-based Azure Application Insights resource and Log Analytics workspace provisioned via Bicep in `infra/main.bicep`. Custom OpenTelemetry activity and log processors were implemented to redact sensitive customer and technician attributes prior to export.","c":0.95,"e":[["file","infra/main.bicep:39-53"],["file","src/BrackenRidge.FieldOps/Program.cs:24-37"],["file","README.md:28-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Log management","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"rails-claims-ops","variant":"base","family":"obs-senior","pid":"OBS-6c","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"bugsink","secs":584,"k":"7872c851-ce16-4922-9d83-b7c736d78d37-r1","picks":[["bugsink","p"],["glitchtip","m"],["sentry","m"]],"ev":51,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly recommended self-hosting Bugsink over Sentry and GlitchTip, implementing a full production Docker Compose configuration, provisioning script, and Rails initializer connecting sentry-rails to the Bugsink instance.","c":1,"e":[["file","deploy/monitoring/compose.production.yml"],["file","deploy/monitoring/provision.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Error monitoring","theme":"Self-hosting, privacy or residency"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"rails-claims-ops","variant":"base","family":"obs-senior","pid":"OBS-6c","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"glitchtip","secs":418,"k":"7872c851-ce16-4922-9d83-b7c736d78d37-r2","picks":[["glitchtip","p"],["errbit","m"],["bugsink","m"],["sentry","m"]],"ev":56,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly recommended and fully implemented self-hosted GlitchTip with Docker Compose, Rails Sentry SDK integration, and an automated alert provisioning script, while evaluating and rejecting Bugsink and full self-hosted Sentry.","c":1,"e":[["file","compose.glitchtip.production.yml"],["file","bin/provision_glitchtip"],["file","config/initializers/sentry.rb"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Error monitoring","theme":"Self-hosting, privacy or residency"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"rails-claims-ops","variant":"base","family":"obs-senior","pid":"OBS-6c","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"bugsink","secs":490,"k":"7872c851-ce16-4922-9d83-b7c736d78d37-r3","picks":[["bugsink","p"],["glitchtip","m"],["errbit","m"],["grafana","m"],["opentelemetry","m"],["sentry","m"]],"ev":62,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent configured and deployed a self-hosted Bugsink runtime using Docker Compose and provisioned custom webhook alerting, integrating it with the Rails application using the sentry-rails client SDK. Heavy alternatives like full self-hosted Sentry and Grafana/Loki were explicitly evaluated and rejected due to operational complexity.","c":1,"e":[["file","ops/bugsink/compose.yml:1-74"],["file","ops/bugsink/provision.py:1-95"],["file","config/initializers/error_monitoring.rb:1-16"],["file","Gemfile:12-13"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Error monitoring","theme":"Self-hosting, privacy or residency"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"rails-claims-ops","variant":"base","family":"obs-senior","pid":"OBS-6c","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"bugsink","secs":442,"k":"7872c851-ce16-4922-9d83-b7c736d78d37-r4","picks":[["bugsink","p"],["errbit","m"],["glitchtip","m"],["sentry","m"]],"ev":48,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The run chose Bugsink as the self-hosted error tracking backend, adding Docker Compose configurations, an automated Python provisioner for webhooks, and configuring sentry-rails in the Rails application to report errors to Bugsink.","c":1,"e":[["file","ops/bugsink/compose.yaml"],["file","ops/bugsink/provision.py"],["file","config/initializers/sentry.rb"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Error monitoring","theme":"Self-hosting, privacy or residency"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"grafana","secs":369,"k":"4694e528-47b7-4dfb-81f5-40503b17271e-r1","picks":[["grafana","p"],["opentelemetry","m"],["datadog","m"],["new-relic","m"],["prometheus","m"]],"ev":50,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The run chose Grafana Cloud as the unified observability backend, deploying Grafana Alloy in Kubernetes and configuring Grafana alerts with Terraform while utilizing OpenTelemetry as the vendor-neutral instrumentation standard.","c":1,"e":[["file","deploy/observability.yaml"],["file","observability/grafana/alerts.tf"],["file","observability/grafana/versions.tf"],["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":2,"date":"2026-09-01","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"datadog","secs":519,"k":"4694e528-47b7-4dfb-81f5-40503b17271e-r2","picks":[["datadog","p"],["sentry","m"],["opentelemetry","m"],["prometheus","m"],["grafana","m"],["honeycomb","m"],["new-relic","m"]],"ev":59,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly compared Datadog, Grafana Cloud, and New Relic (and weighed Honeycomb/Sentry in reasoning), then selected Datadog as the single production observability platform. Datadog was implemented via Kubernetes Helm configuration, admission controller annotations for Java tracer injection, Logstash JSON log correlation, Prometheus scraping, and a Terraform monitor with documentation and runbooks.","c":1,"e":[["file","deploy/datadog-values.yaml:1-20"],["file","deploy/deployment.yaml:11-30"],["file","docs/observability.md:5-15"],["file","observability/datadog/monitors.tf:1-31"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"datadog","secs":407,"k":"4694e528-47b7-4dfb-81f5-40503b17271e-r3","picks":[["datadog","p"],["prometheus","m"],["grafana","m"],["new-relic","m"]],"ev":51,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly evaluated Datadog, Grafana Cloud, and New Relic in docs/observability.md and implemented Datadog across all required signal paths (metrics, logs, traces, errors, and an alert monitor CRD).","c":1,"e":[["file","deploy/observability/datadog-agent.yaml"],["file","deploy/observability/error-alert.yaml"],["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":2,"date":"2026-09-01","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"grafana","secs":513,"k":"4694e528-47b7-4dfb-81f5-40503b17271e-r4","picks":[["grafana","p"],["opentelemetry","m"],["prometheus","m"],["datadog","m"],["honeycomb","m"],["new-relic","m"]],"ev":61,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly evaluated Grafana Cloud, Datadog, New Relic, and Honeycomb, choosing Grafana Cloud (with Alloy and OpenTelemetry) as the primary observability backend. It implemented Kubernetes collection, OTel Java agent instrumentation, custom Spring Boot metrics/trace correlation, and a Terraform alert rule group against Grafana Cloud.","c":1,"e":[["file","deploy/observability/grafana-values.yaml:1-59"],["file","deploy/observability/alerting/main.tf:1-79"],["file","docs/observability.md:1-24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-junior","pid":"OBS-5a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"sentry","secs":443,"k":"f74942f8-b5a9-4196-8746-76b3edd99c8b-r1","picks":[["sentry","p"],["datadog","m"],["betterstack","m"]],"ev":62,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Sentry as the error-monitoring solution, installed `@sentry/node`, instrumented the Express application and background scripts, and created an automated provisioning script (`scripts/configureSentry.js`) with accompanying unit tests to manage Sentry alert rules and cron monitors via its API.","c":1,"e":[["file","package.json:10-20"],["file","instrument.js:1-49"],["file","scripts/configureSentry.js:1-345"],["file","ops/sentry.json:1-19"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Error monitoring","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-junior","pid":"OBS-5a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"sentry","secs":462,"k":"f74942f8-b5a9-4196-8746-76b3edd99c8b-r2","picks":[["sentry","p"],["betterstack","m"],["uptime-robot","m"]],"ev":40,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated monitoring options and selected Sentry, installing @sentry/node, configuring middleware and cron monitoring in the codebase, and adding alert provisioning scripts and tests.","c":1,"e":[["file","package.json:15"],["file","config/monitoring.js:1-86"],["file","server.js:2-4"],["file","scripts/provisionSentry.js:1-176"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Error monitoring","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-junior","pid":"OBS-5a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"sentry","secs":353,"k":"f74942f8-b5a9-4196-8746-76b3edd99c8b-r3","picks":[["sentry","p"],["betterstack","m"],["glitchtip","m"],["grafana","m"]],"ev":52,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated monitoring options for the Node.js/Express single-droplet app, dismissed heavy self-hosted setups (Grafana/ELK, GlitchTip), and fully implemented Sentry via `@sentry/node` with custom error capturing, cron monitors, and API-based alert provisioning.","c":1,"e":[["file","package.json"],["file","instrument.js"],["file","monitoring.js"],["file","scripts/provisionSentryAlerts.js"],["file","deploy.sh"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Error monitoring","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-junior","pid":"OBS-5a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"sentry","secs":455,"k":"f74942f8-b5a9-4196-8746-76b3edd99c8b-r4","picks":[["sentry","p"],["highlight","m"]],"ev":54,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Sentry as the single monitoring solution, installed the SDK, configured runtime error and cron monitoring, and implemented an automated API provisioning script for alert routing and uptime checks.","c":1,"e":[["file","package.json:10-15"],["file","instrument.js:1-60"],["file","scripts/provisionSentry.js:1-427"],["file","server.js:1-37"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"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":"claude-code","model":"claude-opus-5","rep":1,"pick":"sentry","secs":695,"k":"c5e85872-b598-46c3-a6c2-61b38ac1f487-r1","picks":[["sentry","p"],["axiom","m"],["betterstack","m"],["uptime-robot","m"]],"ev":63,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly recommended Sentry as the single observability solution for the Next.js/Vercel application, rejected alternative uptime monitors (UptimeRobot, Better Stack) and log destinations (Axiom), and implemented Sentry instrumentation, error handling, health checks, and alert rules as code in the repository.","c":1,"e":[["file","package.json"],["file","instrumentation.ts"],["file","sentry/alerts/issue-alerts.json"],["file","scripts/sync-sentry-alerts.mjs"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"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":"claude-code","model":"claude-opus-5","rep":2,"pick":"betterstack","secs":569,"k":"c5e85872-b598-46c3-a6c2-61b38ac1f487-r2","picks":[["betterstack","p"],["uptime-robot","a"],["sentry","m"]],"ev":59,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly recommended, configured, and integrated Better Stack for uptime monitoring and alerting. It implemented a dedicated health check route, a declarative monitor configuration file, an automated provisioning script using the Better Stack Uptime API v2, and documented its setup in the README and .env.example.","c":1,"e":[["file","monitoring/monitor.config.json"],["file","scripts/provision-monitor.mts:14-16"],["file","package.json:9"],["file",".env.example:19-27"],["file","README.md:29-47"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"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":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":861,"k":"c5e85872-b598-46c3-a6c2-61b38ac1f487-r3","picks":[["diy","p","d"],["betterstack","m"],["sentry","m"],["uptime-robot","m"]],"ev":56,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent built a bespoke health check, cron monitor, and alert dispatcher directly in the repository rather than integrating a third-party observability platform. Substrates include Vercel Cron (introduced for scheduling) and Supabase (pre-existing database used for state tracking).","c":0.95,"e":[["file","lib/health.ts:1-233"],["file","lib/alerts.ts:1-83"],["file","app/api/health/route.ts:1-31"],["file","app/api/monitor/route.ts:1-194"]],"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":"claude-code","model":"claude-opus-5","rep":4,"pick":"diy","secs":466,"k":"c5e85872-b598-46c3-a6c2-61b38ac1f487-r4","picks":[["diy","p","d"],["uptime-robot","m"],["sentry","m"]],"ev":45,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly decided against adopting third-party APM or error monitoring tools like Sentry because the site's critical failure mode (swallowed database errors rendering empty pages with a 200 OK) would not generate exceptions. Instead, it authored a custom DIY health check endpoint and alerting utility in `app/api/health/route.ts` and `lib/alerts.ts` scheduled via Vercel Cron.","c":0.95,"e":[["file","app/api/health/route.ts"],["file","lib/alerts.ts"],["file","vercel.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Error monitoring","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-traces-b2b-subscriptions","pid":"OBS-TRACE-02a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"grafana","secs":636,"k":"c6c1838f-7aed-4de3-8b18-2421641b700c-r1","picks":[["grafana","p"],["opentelemetry","m"],["datadog","m"],["highlight","m"],["honeycomb","m"],["prometheus","m"]],"ev":53,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The run chose Grafana Cloud as the dedicated observability and alerting backend, adding OpenTelemetry SDK packages configured to export OTLP traces to Grafana Cloud and provisioning Grafana alert rules in Terraform.","c":0.95,"e":[["file","ops/grafana/alerting.tf:1-130"],["file","ops/grafana/versions.tf:1-17"],["file","README.md:7-45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Distributed tracing and APM","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-traces-b2b-subscriptions","pid":"OBS-TRACE-02a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"grafana","secs":348,"k":"c6c1838f-7aed-4de3-8b18-2421641b700c-r2","picks":[["grafana","p"],["opentelemetry","m"],["honeycomb","m"],["betterstack","m"],["prometheus","m"]],"ev":36,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent configured OpenTelemetry instrumentation in Node.js and provisioned Grafana Cloud resources via Terraform (including contact points and alerting rule groups) for trace metrics and sustained latency alerts.","c":0.95,"e":[["file","ops/grafana/main.tf:1-147"],["file","ops/grafana/README.md:1-43"],["file","README.md:6-8"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Distributed tracing and APM","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-traces-b2b-subscriptions","pid":"OBS-TRACE-02a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"grafana","secs":335,"k":"c6c1838f-7aed-4de3-8b18-2421641b700c-r3","picks":[["grafana","p"],["opentelemetry","m"],["prometheus","m"]],"ev":42,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The run installed the OpenTelemetry SDK for application tracing and committed to Grafana Cloud as the primary backend, configuring OTLP export in README/instrumentation and provisioning Grafana Cloud alert rules and Slack contact points via Terraform.","c":0.95,"e":[["file","README.md"],["file","observability/terraform/alerts.tf"],["file","observability/terraform/versions.tf"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Distributed tracing and APM","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-traces-b2b-subscriptions","pid":"OBS-TRACE-02a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"honeycomb","secs":377,"k":"c6c1838f-7aed-4de3-8b18-2421641b700c-r4","picks":[["honeycomb","p"],["opentelemetry","m"],["sentry","m"],["datadog","m"],["grafana","m"]],"ev":47,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly recommended and configured Honeycomb EU as the managed observability backend for production tracing and alerts, using OpenTelemetry Node SDKs for direct OTLP export and Terraform for trigger provisioning.","c":1,"e":[["file","infra/observability/main.tf:1-98"],["file","README.md:7-38"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Distributed tracing and APM","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-uptime-express-api","pid":"OBS-UPTIME-04a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"checkly","secs":187,"k":"100823d6-037d-41c6-979e-e101c3a98ced-r1","picks":[["checkly","p"],["betterstack","m"]],"ev":35,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected and fully implemented Checkly as the external observability and synthetic monitoring solution, defining Terraform resources for API checks and alert routing.","c":1,"e":[["file","monitoring/main.tf:13-81"],["file","monitoring/versions.tf:4-22"],["file",".github/workflows/production-monitoring.yml:38-42"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Uptime monitoring and alerts","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-uptime-express-api","pid":"OBS-UPTIME-04a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"checkly","secs":236,"k":"100823d6-037d-41c6-979e-e101c3a98ced-r2","picks":[["checkly","p"],["betterstack","m"],["uptime-robot","m"]],"ev":40,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected and fully implemented Checkly as the external production monitor, installing the `checkly` npm dependency, configuring the check in `monitoring/events-api.check.js` and `checkly.config.js`, and integrating the deployment step into `deploy.sh`.","c":1,"e":[["file","checkly.config.js"],["file","monitoring/events-api.check.js"],["file","deploy.sh"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Uptime monitoring and alerts","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-uptime-express-api","pid":"OBS-UPTIME-04a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"checkly","secs":646,"k":"100823d6-037d-41c6-979e-e101c3a98ced-r3","picks":[["checkly","p"],["betterstack","m"]],"ev":29,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected and fully implemented Checkly as the external synthetic API monitoring solution using its Terraform provider, configuring automated checks against the production events endpoint and subscribing it to PagerDuty.","c":1,"e":[["file","monitoring/main.tf:1-112"],["file","monitoring/versions.tf:1-10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Uptime monitoring and alerts","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-uptime-express-api","pid":"OBS-UPTIME-04a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"checkly","secs":230,"k":"100823d6-037d-41c6-979e-e101c3a98ced-r4","picks":[["checkly","p"],["betterstack","m"],["uptime-robot","m"]],"ev":37,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected and fully implemented Checkly as the external synthetic monitoring-as-code solution for the public API, adding its configuration files, test constructs, package dependencies, and deployment script integration.","c":1,"e":[["file","checkly.config.ts:1-12"],["file","monitoring/events-api.check.ts:1-54"],["file","deploy.sh:4-23"],["file","package.json:7-10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Uptime monitoring and alerts","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-uptime-nextjs-booking","pid":"OBS-UPTIME-03a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"checkly","secs":358,"k":"1cf0f4b4-d45f-4ffa-b694-566f9a282ccc-r1","picks":[["checkly","p"],["uptime-robot","m"]],"ev":35,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected, installed, and configured Checkly for synthetic observability and alerting as code, providing a full Playwright check suite, alert channels, and deployment scripts.","c":1,"e":[["file","checkly.config.ts"],["file","monitoring/production-booking.check.ts"],["file","monitoring/alert-channels.ts"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Uptime monitoring and alerts","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-uptime-nextjs-booking","pid":"OBS-UPTIME-03a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"checkly","secs":600,"k":"1cf0f4b4-d45f-4ffa-b694-566f9a282ccc-r2","picks":[["checkly","p"]],"ev":30,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected and installed Checkly as the external synthetic monitoring and alerting solution, defining browser checks using Playwright and an EmailAlertChannel construct.","c":1,"e":[["file","checkly.config.ts"],["file","monitoring/owner-alert.check.ts"],["file","monitoring/schedule-and-booking.spec.ts"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Uptime monitoring and alerts","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-uptime-nextjs-booking","pid":"OBS-UPTIME-03a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"checkly","secs":641,"k":"1cf0f4b4-d45f-4ffa-b694-566f9a282ccc-r3","picks":[["checkly","p"]],"ev":41,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected, installed, and configured Checkly (`checkly` package) to run external synthetic browser checks using Playwright against the production schedule and booking workflow, attaching an email alert channel.","c":1,"e":[["file","checkly.config.ts"],["file","checks/production-booking.check.ts"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Uptime monitoring and alerts","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-uptime-nextjs-booking","pid":"OBS-UPTIME-03a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"checkly","secs":290,"k":"1cf0f4b4-d45f-4ffa-b694-566f9a282ccc-r4","picks":[["checkly","p"],["betterstack","m"],["uptime-robot","m"]],"ev":31,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Checkly, installed its CLI and dependencies, wrote a monitoring suite with synthetic browser specs and alert channels, and configured GitHub Actions deployment.","c":1,"e":[["file","checkly.config.ts"],["file","monitoring/booking-flow.check.ts"],["file","monitoring/alert-channel.ts"],["file",".github/workflows/production-monitoring.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Uptime monitoring and alerts","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"obs-logs-node-report-builder","pid":"OBS-LOG-04a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"axiom","secs":379,"k":"5216c320-3b03-4aaf-84df-7a7323246816-r1","picks":[["axiom","p"],["pino","m"],["betterstack","m"],["sentry","m"],["grafana","m"],["highlight","m"],["opentelemetry","m"]],"ev":40,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly selected and implemented Axiom as the centralized logging and alerting service for production. It added `@axiomhq/pino` along with `pino`, created a provisioning script for Axiom datasets, notifiers, and threshold monitors (`ops/provision-observability.mjs`), and updated the application's logging pipeline to export to Axiom.","c":1,"e":[["file","package.json"],["file","src/logger.ts:40-47"],["file","ops/provision-observability.mjs:1-106"],["file","README.md:44-63"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Log management","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"obs-logs-node-report-builder","pid":"OBS-LOG-04a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"betterstack","secs":347,"k":"5216c320-3b03-4aaf-84df-7a7323246816-r2","picks":[["betterstack","p"],["pino","m"],["sentry","m"],["axiom","m"],["opentelemetry","m"],["grafana","m"],["highlight","m"]],"ev":47,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The run chose Better Stack Telemetry as the centralized logging and alerting solution for the service. It installed the `@logtail/pino` transport alongside `pino` and `pino-http`, configured structured JSON logging with request correlation IDs, and wrote an automated script to provision Better Stack explorations, alerts, urgencies, and escalation policies via the Better Stack API.","c":1,"e":[["file","package.json:18"],["file","src/logging.ts:40-57"],["file","src/observability/apply.ts:1-233"],["file","README.md:36-74"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Log management","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"obs-logs-node-report-builder","pid":"OBS-LOG-04a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"sentry","secs":550,"k":"5216c320-3b03-4aaf-84df-7a7323246816-r3","picks":[["sentry","p"],["betterstack","m"],["axiom","m"],["opentelemetry","m"],["pino","m"]],"ev":81,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Sentry as the managed observability solution for the service, integrating `@sentry/node` for structured logging, OpenAI request tracing, error reporting, and automated alert provisioning.","c":1,"e":[["file","package.json:18"],["file","src/instrument.ts:5-25"],["file","src/provision-alert.ts:1-176"],["file","README.md:36-98"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Log management","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"node-ai-report-builder","variant":"base","family":"obs-logs-node-report-builder","pid":"OBS-LOG-04a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"betterstack","secs":384,"k":"5216c320-3b03-4aaf-84df-7a7323246816-r4","picks":[["betterstack","p"],["pino","m"],["datadog","m"],["opentelemetry","m"],["prometheus","m"],["sentry","m"]],"ev":46,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated several observability solutions (Sentry, Datadog, Prometheus, OpenTelemetry) before committing to Better Stack Telemetry. It installed `@logtail/node`, created `src/logger.ts` for structured logging with correlation IDs, implemented an automated alert provisioning script `scripts/provision-observability.mjs` against Better Stack's API, and updated the README and configuration accordingly.","c":1,"e":[["file","package.json"],["file","src/logger.ts"],["file","scripts/provision-observability.mjs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Log management","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-vibe","pid":"OBS-4b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"betterstack","secs":313,"k":"7cfd08a7-532f-4bae-8ab3-90f70499e367-r1","picks":[["betterstack","p"],["checkly","m"],["sentry","m"]],"ev":52,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly recommended Better Stack for uptime and browser synthetic monitoring, and committed the implementation via Terraform files using the Better Stack (better-uptime) provider and a dedicated health check endpoint.","c":1,"e":[["file","infra/monitoring/versions.tf:5-8"],["file","infra/monitoring/main.tf:1-156"],["file","README.md:10-13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Error monitoring","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-vibe","pid":"OBS-4b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"betterstack","secs":362,"k":"7cfd08a7-532f-4bae-8ab3-90f70499e367-r2","picks":[["betterstack","p"],["sentry","m"],["uptime-robot","m"]],"ev":44,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected and fully implemented Better Stack (using the Better Uptime Terraform provider and provisioning scripts) to monitor the application's /api/health route and trigger alerts.","c":1,"e":[["file",".github/workflows/production-monitoring.yml:21-51"],["file","ops/monitoring/main.tf:1-68"],["file","ops/monitoring/provision.mjs:1-128"],["file","ops/monitoring/versions.tf:5-8"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Error monitoring","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-vibe","pid":"OBS-4b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"betterstack","secs":510,"k":"7cfd08a7-532f-4bae-8ab3-90f70499e367-r3","picks":[["betterstack","p"],["highlight","m"],["sentry","m"]],"ev":36,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly recommended Better Stack for uptime monitoring and alerting, wrote complete Terraform configurations using the Better Stack provider under infra/monitoring/, added automation scripts and GitHub Actions workflows, and rejected Sentry as unnecessary for external outage detection.","c":1,"e":[["file","infra/monitoring/main.tf:9-55"],["file","infra/monitoring/versions.tf:4-8"],["file",".github/workflows/monitoring.yml:28"],["file","README.md:18-49"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Error monitoring","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-vibe","pid":"OBS-4b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"betterstack","secs":270,"k":"7cfd08a7-532f-4bae-8ab3-90f70499e367-r4","picks":[["betterstack","p"],["checkly","m"],["sentry","m"]],"ev":29,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated monitoring approaches for Next.js and Supabase availability issues, rejected pure error trackers (Sentry) and heavy synthetic runners (Checkly), and fully committed to Better Stack. It created a health check route (`/api/health`), an idempotent Better Stack API provisioning script (`scripts/provision-better-stack.mjs`), test suites, and documentation.","c":1,"e":[["file","scripts/provision-better-stack.mjs"],["file","package.json"],["file","README.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Error monitoring","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-cloudwatch","secs":357,"k":"edd0b7d3-27ef-4aac-a9fb-8414bc7ecc10-r1","picks":[["aws-cloudwatch","p","b"],["opentelemetry","m"],["honeycomb","m"],["datadog","m"],["grafana","m"],["new-relic","m"]],"ev":49,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent conducted a comparative analysis of Amazon CloudWatch Application Signals, Datadog, Grafana Cloud, and New Relic in docs/observability.md. It chose and implemented Amazon CloudWatch via CloudWatch Agent and ADOT ECS sidecars in Terraform and application logging middleware, rejecting the third-party alternatives due to operational overhead and credential management.","c":1,"e":[["file","terraform/observability.tf:1-70"],["file","terraform/ecs.tf:118-235"],["file","docs/observability.md:18-20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"new-relic","secs":324,"k":"edd0b7d3-27ef-4aac-a9fb-8414bc7ecc10-r2","picks":[["new-relic","p"],["opentelemetry","m"],["honeycomb","m"],["sentry","m"],["datadog","m"],["grafana","m"],["highlight","m"]],"ev":40,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly compared New Relic, Datadog, and Grafana Cloud in `docs/observability.md`, rejecting Datadog and Grafana Cloud for their sidecar/collector operational burden on ECS Fargate. It installed and configured New Relic across Python dependencies, Docker startup command, ECS task environment variables, Secrets Manager references, and Terraform alert resources.","c":1,"e":[["file","requirements.txt:20"],["file","Dockerfile:18"],["file","terraform/observability.tf:1-77"],["file","docs/observability.md:3-23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"new-relic","secs":297,"k":"edd0b7d3-27ef-4aac-a9fb-8414bc7ecc10-r3","picks":[["new-relic","p"],["datadog","m"],["grafana","m"],["opentelemetry","m"]],"ev":39,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly compared New Relic, Datadog, and Grafana Cloud in `docs/observability.md`, choosing New Relic for its lightweight Python agent integration and zero-sidecar Fargate log forwarding. It installed the `newrelic` library, instrumented the app and Dockerfile, and configured Terraform alerts via the New Relic provider.","c":1,"e":[["file","requirements.txt:20"],["file","Dockerfile:18"],["file","terraform/observability.tf:1-88"],["file","docs/observability.md:7-24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent-current-market-research","pid":"OBS-PLATFORM-ZERO-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"aws-cloudwatch","secs":386,"k":"edd0b7d3-27ef-4aac-a9fb-8414bc7ecc10-r4","picks":[["aws-cloudwatch","p","b"],["opentelemetry","m"],["datadog","m"],["grafana","m"],["new-relic","m"]],"ev":50,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The run clearly selected and fully implemented Amazon CloudWatch (including CloudWatch Application Signals, CloudWatch Logs, and CloudWatch Metric Alarms) across Terraform configurations and FastAPI middleware. The application is hosted entirely on AWS ECS Fargate, making CloudWatch a built-in platform capability requiring no third-party accounts or external providers. Competing third-party solutions (Datadog, New Relic, and Grafana Cloud) were explicitly evaluated and rejected in the comparison document, while OpenTelemetry and AWS X-Ray were utilized as the instrumentation standard and tracing integration layer respectively.","c":1,"e":[["file","terraform/observability.tf:1-79"],["file","terraform/ecs.tf:125-225"],["file","docs/observability.md:5-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"datadog","secs":1027,"k":"8feb147d-fc55-402e-a948-e4bb5a7d636f-r1","picks":[["datadog","p"],["prometheus","m"],["honeycomb","m"],["grafana","m"]],"ev":41,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The run explicitly selected Datadog to manage APM tracing, metrics, structured logs, and alerting for the Spring Boot application on Kubernetes. It implemented Datadog-specific annotations, Helm values, and a Terraform monitor configuration.","c":1,"e":[["file","deploy/datadog-values.yaml:1-26"],["file","deploy/deployment.yaml:12-32"],["file","infra/observability/main.tf:5-46"],["file","docs/observability.md:1-78"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"datadog","secs":602,"k":"8feb147d-fc55-402e-a948-e4bb5a7d636f-r2","picks":[["datadog","p"],["opentelemetry","m"],["prometheus","m"],["grafana","m"],["honeycomb","m"]],"ev":51,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly selected Datadog as the single operated observability platform, configuring Kubernetes Agent CRDs, deployment annotations for auto-instrumentation and Autodiscovery scraping, and Terraform alerts for 5xx failures routed to PagerDuty.","c":1,"e":[["file","deploy/datadog-agent.yaml:1-26"],["file","deploy/deployment.yaml:12-40"],["file","observability/main.tf:1-50"],["file","docs/observability.md:1-60"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"datadog","secs":454,"k":"8feb147d-fc55-402e-a948-e4bb5a7d636f-r3","picks":[["datadog","p"],["grafana","m"],["sentry","m"],["prometheus","m"]],"ev":54,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly recommended and fully implemented Datadog across Kubernetes Agent/Operator manifests, monitor CRDs, DogStatsD export configuration, and documentation.","c":1,"e":[["file","deploy/datadog/agent.yaml"],["file","deploy/datadog/operator-values.yaml"],["file","deploy/datadog/settlement-failures-monitor.yaml"],["file","deploy/deployment.yaml:6-24"],["file","docs/observability.md:1-40"],["file","src/main/resources/application.yaml:23-32"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"datadog","secs":381,"k":"8feb147d-fc55-402e-a948-e4bb5a7d636f-r4","picks":[["datadog","p"],["grafana","m"],["highlight","m"],["opentelemetry","m"]],"ev":33,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly evaluated observability options and implemented Datadog across application config, Kubernetes deployment manifests, DatadogAgent CRDs, and Terraform alert monitors.","c":1,"e":[["file","deploy/datadog-agent.yaml:1-36"],["file","deploy/deployment.yaml:6-38"],["file","observability/monitors.tf:1-26"],["file","src/main/resources/application.yaml:7-14"],["file","docs/observability.md:1-78"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"grafana","secs":361,"k":"3f83de91-db0a-4808-9c4d-378b51c18a89-r1","picks":[["grafana","p"],["opentelemetry","m"],["prometheus","m"]],"ev":31,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Grafana Cloud as the single observability backend for the Kubernetes-deployed Spring Boot application, configuring direct OTLP metric/log/trace export via the OpenTelemetry Spring Boot starter and adding an alert rule file for Grafana Cloud's metrics ruler.","c":1,"e":[["file","docs/observability.md:1-52"],["file","deploy/deployment.yaml:26-36"],["file","observability/grafana-cloud-rules.yaml:1-23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"new-relic","secs":285,"k":"3f83de91-db0a-4808-9c4d-378b51c18a89-r2","picks":[["new-relic","p"],["grafana","m"],["opentelemetry","m"],["highlight","m"]],"ev":29,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly selected and implemented New Relic APM to meet the requirement of a single observability backend without additional collectors. It configured the Java agent in the Dockerfile and Kubernetes deployment, instrumented application logging, and authored Terraform configurations for New Relic alert conditions and notification workflows.","c":1,"e":[["file","Dockerfile"],["file","deploy/deployment.yaml"],["file","docs/observability.md"],["file","observability/newrelic/main.tf"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"datadog","secs":247,"k":"3f83de91-db0a-4808-9c4d-378b51c18a89-r3","picks":[["datadog","p"],["honeycomb","m"],["grafana","m"],["sentry","m"],["new-relic","m"]],"ev":27,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly chose Datadog as the single observability backend and fully implemented Kubernetes Agent manifests, Java tracer admission injection, JSON log correlation, and a monitor alert CRD.","c":1,"e":[["file","deploy/observability/datadog-agent.yaml"],["file","deploy/observability/settlement-failures-monitor.yaml"],["file","deploy/deployment.yaml"],["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":2,"date":"2026-09-01","repo":"telecom-billing-core","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"grafana","secs":374,"k":"3f83de91-db0a-4808-9c4d-378b51c18a89-r4","picks":[["grafana","p"],["opentelemetry","m"],["highlight","m"],["prometheus","m"]],"ev":31,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Grafana (Grafana Cloud) as the single production observability backend for the Spring Boot application, configuring direct OTLP ingestion in Kubernetes and provisioning alerting rules via the Grafana Terraform provider. OpenTelemetry is used as the instrumentation and transport layer.","c":1,"e":[["file","deploy/deployment.yaml:27-33"],["file","observability/main.tf:1-98"],["file","docs/observability-runbook.md:1-43"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"sentry","secs":437,"k":"eb8b0509-7d76-4995-aa1c-a0e00304a774-r1","picks":[["sentry","p"],["betterstack","m"],["grafana","m"]],"ev":55,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly selected Sentry and fully integrated `@sentry/nextjs` across the application (server/edge/client instrumentation, error boundary, structured logging, tracing spans, and metric emissions) as well as writing an alert provisioning script.","c":1,"e":[["file","next.config.ts:8-16"],["file","instrumentation.ts:1-13"],["file","lib/observability.ts:1-37"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"sentry","secs":368,"k":"eb8b0509-7d76-4995-aa1c-a0e00304a774-r2","picks":[["sentry","p"],["datadog","m"],["honeycomb","m"]],"ev":54,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly selected Sentry to handle error monitoring, distributed tracing, structured logging, and alerting for the Next.js application. It installed `@sentry/nextjs`, set up client and server instrumentation hooks, configured next.config.ts with Sentry's plugin, and provided alert configuration scripts.","c":1,"e":[["file","next.config.ts:8-18"],["file","instrumentation.ts:1-13"],["file","instrumentation-client.ts:1-15"],["file",".env.example:11-20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"sentry","secs":448,"k":"eb8b0509-7d76-4995-aa1c-a0e00304a774-r3","picks":[["sentry","p"],["datadog","m"]],"ev":58,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly recommended Sentry, installed @sentry/nextjs, wired error boundaries and span-based telemetry into the application actions, and created an automated alert reconciliation script along with test coverage.","c":1,"e":[["file","package.json:11"],["file","instrumentation.ts:1-13"],["file","instrumentation-client.ts:1-19"],["file","sentry.server.config.ts:1-24"],["file","observability/reminder-latency-alert.json:1-12"],["file","scripts/setup-sentry-alert.mjs:1-50"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"sentry","secs":466,"k":"eb8b0509-7d76-4995-aa1c-a0e00304a774-r4","picks":[["sentry","p"]],"ev":55,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Sentry as the single observability platform for the Next.js application, installed @sentry/nextjs and @sentry/node, configured instrumentation across browser, edge, and node runtimes, instrumented application metrics and spans, and implemented alert provisioning and smoke testing scripts.","c":1,"e":[["file","next.config.ts:9-21"],["file","lib/observability.ts:1-29"],["file","OBSERVABILITY.md:1-71"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"sentry","secs":390,"k":"ef1491ce-53c7-42fd-a81e-a35932d23ec7-r1","picks":[["sentry","p"],["grafana","m"]],"ev":36,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Sentry as the sole observability platform, installing `@sentry/nextjs` and wiring client/server instrumentation, request tracing, span metrics, and an alert provisioning script. Grafana Cloud was briefly weighed in reasoning and rejected due to higher setup overhead.","c":1,"e":[["file","next.config.ts"],["file","instrumentation.ts"],["file","instrumentation-client.ts"],["file","docs/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":2,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"sentry","secs":1212,"k":"ef1491ce-53c7-42fd-a81e-a35932d23ec7-r2","picks":[["sentry","p"]],"ev":79,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly chose, installed, and fully configured Sentry (@sentry/nextjs) as the sole observability platform for errors, logs, traces, and metrics, along with an alert provisioning script targeting the Sentry API.","c":1,"e":[["file","package.json"],["file","next.config.ts:8-18"],["file","instrumentation.ts:1-13"],["file","scripts/provision-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":2,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"sentry","secs":340,"k":"ef1491ce-53c7-42fd-a81e-a35932d23ec7-r3","picks":[["sentry","p"]],"ev":29,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The run chose and implemented Sentry using the @sentry/nextjs SDK across client, server, and edge environments, adding span-derived measurements and automated alert setup.","c":1,"e":[["file","next.config.ts"],["file","instrumentation.ts"],["file","app/owner/actions.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"nextjs-classbooking","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"sentry","secs":375,"k":"ef1491ce-53c7-42fd-a81e-a35932d23ec7-r4","picks":[["sentry","p"]],"ev":40,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Sentry as the single production observability platform, installed `@sentry/nextjs`, wired instrumentation across client/server/edge boundaries, configured error/trace/log/metric telemetry, and created an automated alert provisioning script. It explicitly rejected running OpenTelemetry due to the ops burden of running a collector and separate stores.","c":1,"e":[["file","package.json"],["file","instrumentation.ts"],["file","instrumentation-client.ts"],["file","app/owner/actions.ts"],["file","lib/reminders.ts"],["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":2,"date":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"sentry","secs":652,"k":"625218d8-23dd-49c6-8c7d-d1dba493d537-r1","picks":[["sentry","p"],["axiom","m"],["grafana","m"],["opentelemetry","m"],["datadog","m"],["new-relic","m"]],"ev":54,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Sentry Cloud as the unified observability solution for the repo. It implemented Sentry across both the Next.js frontend and NestJS API services, added a scripted monitor for error bursts, and configured AWS Secrets Manager integration in CDK.","c":1,"e":[["file","apps/api/package.json"],["file","apps/web/package.json"],["file","apps/api/src/instrument.ts"],["file","scripts/configure-sentry-alert.mjs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"aws-cloudwatch","secs":433,"k":"625218d8-23dd-49c6-8c7d-d1dba493d537-r2","picks":[["aws-cloudwatch","p","b"],["opentelemetry","m"],["pino","m"],["sentry","m"]],"ev":49,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly selected and configured Amazon CloudWatch (Application Signals, Logs, and Alarms) to handle production observability for the AWS Fargate API stack. OpenTelemetry was integrated as the instrumentation standard, AWS X-Ray was wired for tracing sampling, and external third-party SaaS options like Sentry were deliberated and rejected to stay within the existing AWS ecosystem.","c":1,"e":[["file","infra/lib/api-stack.ts:20-60"],["file","docs/observability.md:1-57"],["file","CLAUDE.md:31"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws-cloudwatch","secs":353,"k":"625218d8-23dd-49c6-8c7d-d1dba493d537-r3","picks":[["aws-cloudwatch","p","b"],["opentelemetry","m"],["sentry","m"]],"ev":41,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly selected and configured Amazon CloudWatch (including CloudWatch Application Signals, CloudWatch Logs, and CloudWatch Alarms/Dashboards) within the existing AWS CDK stack and ECS deployment. OpenTelemetry / ADOT was used strictly as the instrumentation standard.","c":0.95,"e":[["file","infra/lib/api-stack.ts:32-171"],["file","README.md:42-70"],["file","CLAUDE.md:31"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"turborepo-b2b","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"grafana","secs":341,"k":"625218d8-23dd-49c6-8c7d-d1dba493d537-r4","picks":[["grafana","p"],["opentelemetry","m"],["betterstack","m"],["new-relic","m"],["pino","m"],["datadog","m"],["sentry","m"]],"ev":41,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly selected Grafana Cloud as the single operational backend, wiring OpenTelemetry HTTP exporters across NestJS and Next.js, managing OTLP credentials in AWS Secrets Manager via CDK, and provisioning an alert rule against Grafana's alerting API.","c":1,"e":[["file","README.md"],["file","infra/lib/api-stack.ts"],["file","scripts/provision-grafana-alert.mjs"],["file",".github/workflows/ci.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"grafana","secs":891,"k":"466078a5-c949-4c57-bd09-25230592890c-r1","picks":[["grafana","p"],["opentelemetry","m"],["honeycomb","m"],["pino","m"],["betterstack","m"],["datadog","m"],["prometheus","m"]],"ev":104,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly recommended and fully implemented Grafana Cloud (via Grafana Alloy and OpenTelemetry) as the managed observability backend. It configured structured logging, OpenTelemetry tracing/metrics, an Alloy collector container, and a Terraform alert rule group with an email contact point for Twilio SMS failures.","c":1,"e":[["file","docs/observability.md:1-25"],["file","infra/grafana/main.tf:1-40"],["file","observability/alloy/config.alloy:1-45"],["file",".env.observability.example:1-9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"grafana","secs":567,"k":"466078a5-c949-4c57-bd09-25230592890c-r2","picks":[["grafana","p"],["opentelemetry","m"],["pino","m"],["prometheus","m"]],"ev":73,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly recommended and configured Grafana Cloud via Grafana Alloy as the unified observability backend for metrics, logs, traces, and operator alerts. OpenTelemetry was implemented as the instrumentation standard to feed the Grafana pipeline, and Pino was used for structured logging.","c":1,"e":[["file","docker-compose.yml"],["file","observability/alloy/config.alloy"],["file","observability/terraform/main.tf"],["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":2,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"new-relic","secs":396,"k":"466078a5-c949-4c57-bd09-25230592890c-r3","picks":[["new-relic","p"],["pino","m"],["datadog","m"]],"ev":51,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly selected and implemented New Relic as the primary observability platform. It updated package.json to include the newrelic SDK, configured newrelic.cjs, added request telemetry middleware, instrumented database and SMS paths, added the newrelic/infrastructure container in docker-compose.yml, and created a complete Terraform module under observability/ to manage New Relic alert policies, NRQL conditions, and notification channels.","c":1,"e":[["file","package.json:25"],["file","newrelic.cjs:1-24"],["file","docker-compose.yml:30-46"],["file","observability/alerts.tf:1-91"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"sentry","secs":445,"k":"466078a5-c949-4c57-bd09-25230592890c-r4","picks":[["sentry","p"],["honeycomb","m"],["betterstack","m"],["highlight","m"]],"ev":54,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated observability requirements and selected Sentry, implementing @sentry/node across the API, background cron jobs, and an alert provisioning script.","c":1,"e":[["file","package.json:15"],["file","instrument.js:1-77"],["file","scripts/createSentryAlert.js:1-199"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"sentry","secs":606,"k":"fce0f754-d961-4098-8fdc-6667f5df4f94-r1","picks":[["sentry","p"],["grafana","m"],["datadog","m"],["honeycomb","m"]],"ev":60,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly chose Sentry as the single operated observability solution for error reporting, structured logging, distributed tracing, and metrics. It added `@sentry/node` to dependencies, implemented instrumentation hooks, and built an automated sync script for Sentry alert rules.","c":1,"e":[["file","package.json:14-16"],["file","instrument.mjs:1-92"],["file","apps/api/src/observability.js:1-77"],["file","observability/sentry-alerts.json:1-17"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"sentry","secs":439,"k":"fce0f754-d961-4098-8fdc-6667f5df4f94-r2","picks":[["sentry","p"],["opentelemetry","m"]],"ev":69,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated the project's requirements and committed completely to Sentry by installing @sentry/node, instrumenting HTTP request tracing, custom metrics, error capturing, structured logging, and providing an executable script to provision alert detectors and email workflows via Sentry's API.","c":1,"e":[["file","package.json"],["file","apps/api/src/instrument.js:1-52"],["file","apps/api/src/telemetry.js:1-93"],["file","scripts/provision-sentry-alert.mjs:1-211"],["file","README.md:7-44"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"sentry","secs":535,"k":"fce0f754-d961-4098-8fdc-6667f5df4f94-r3","picks":[["sentry","p"],["opentelemetry","m"],["betterstack","m"],["datadog","m"],["grafana","m"],["honeycomb","m"],["prometheus","m"]],"ev":65,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent clearly selected Sentry as the sole observability platform, installed `@sentry/node`, configured full telemetry (errors, logs, traces, metrics) and alert automation, while explicitly evaluating and dismissing Datadog, Honeycomb, Better Stack, Prometheus, and Grafana.","c":1,"e":[["file","package.json:19"],["file","apps/api/src/instrument.js:1-95"],["file","OBSERVABILITY.md:1-94"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"new-relic","secs":350,"k":"fce0f754-d961-4098-8fdc-6667f5df4f94-r4","picks":[["new-relic","p"],["datadog","m"],["grafana","m"],["pino","m"]],"ev":51,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly recommended and fully integrated New Relic into the repository by adding `newrelic` as a dependency, modifying startup scripts, implementing custom metrics/tracing in `apps/api/src/observability.js`, and creating an alert provisioning script in `ops/provision-newrelic-alerts.js`.","c":1,"e":[["file","package.json:13-19"],["file","newrelic.cjs:1-26"],["file","apps/api/src/observability.js:1-134"],["file","ops/provision-newrelic-alerts.js:1-230"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-cloudwatch","secs":416,"k":"f541de1e-e0c5-4bd0-a674-84644b53b750-r1","picks":[["aws-cloudwatch","p","b"],["opentelemetry","m"]],"ev":60,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected and fully integrated Amazon CloudWatch (specifically CloudWatch Application Signals, CloudWatch Logs, and CloudWatch Alarms) to monitor production errors, latency, and traces across the existing AWS ECS Fargate stack.","c":1,"e":[["file","terraform/observability.tf:1-125"],["file","terraform/ecs.tf:140-256"],["file","app/observability.py:1-67"],["file","README.md:46-115"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"aws-cloudwatch","secs":448,"k":"f541de1e-e0c5-4bd0-a674-84644b53b750-r2","picks":[["aws-cloudwatch","p","b"],["opentelemetry","m"],["datadog","m"]],"ev":39,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent evaluated observability options for an existing AWS ECS Fargate deployment and fully configured Amazon CloudWatch (including Application Signals, Log Groups, Metric Filters, and SNS Metric Alarms) with OpenTelemetry/ADOT instrumentation in both application code and Terraform.","c":1,"e":[["file","terraform/observability.tf:1-144"],["file","terraform/ecs.tf:129-247"],["file","README.md:41-111"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws-cloudwatch","secs":401,"k":"f541de1e-e0c5-4bd0-a674-84644b53b750-r3","picks":[["aws-cloudwatch","p","b"],["opentelemetry","m"],["sentry","m"],["datadog","m"]],"ev":49,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The repository is hosted entirely on AWS ECS Fargate, ALB, and RDS. The run configured Amazon CloudWatch (including CloudWatch Logs, Application Signals, CloudWatch Agent sidecars, dashboards, and metric alarms via SNS) as the native observability solution, utilizing OpenTelemetry/ADOT purely as the instrumentation layer.","c":1,"e":[["file","terraform/observability.tf:1-194"],["file","terraform/ecs.tf:143-247"],["file","README.md:41-78"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"aws-cloudwatch","secs":340,"k":"f541de1e-e0c5-4bd0-a674-84644b53b750-r4","picks":[["aws-cloudwatch","p","b"],["opentelemetry","m"],["datadog","m"],["sentry","m"]],"ev":41,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent inspected the AWS ECS Fargate and Terraform stack, explicitly evaluated Datadog and Sentry against AWS-native observability, and committed to Amazon CloudWatch Application Signals with ADOT auto-instrumentation, structured CloudWatch logs, and CloudWatch metric alarms.","c":0.95,"e":[["file","terraform/observability.tf:1-165"],["file","terraform/ecs.tf:112-268"],["file","README.md:41-89"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-cloudwatch","secs":293,"k":"5bfb5510-6685-42af-9b35-14b39eb8cf2c-r1","picks":[["aws-cloudwatch","p","b"],["opentelemetry","m"],["aws-xray","m","b"],["datadog","m"],["sentry","m"]],"ev":45,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent configured Amazon CloudWatch Application Signals as the production observability backend on the project's existing AWS ECS setup. OpenTelemetry is used purely as the instrumentation layer. Alternative vendors like Datadog and Sentry were evaluated in trace deliberation and rejected in favor of the AWS-native solution.","c":1,"e":[["file","terraform/ecs.tf:129-228"],["file","terraform/observability.tf:1-35"],["file","README.md:38-78"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"new-relic","secs":234,"k":"5bfb5510-6685-42af-9b35-14b39eb8cf2c-r2","picks":[["new-relic","p"],["sentry","m"],["honeycomb","m"],["grafana","m"],["datadog","m"]],"ev":38,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent unambiguously selected and implemented New Relic as the single third-party observability platform. It installed newrelic into requirements.txt, updated the Docker entrypoint to run under newrelic-admin, instrumented custom application metrics and log warnings, and fully provisioned Terraform NRQL alert conditions, email destinations, and notification workflows.","c":1,"e":[["file","requirements.txt:20"],["file","Dockerfile:18"],["file","terraform/observability.tf:1-80"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws-cloudwatch","secs":292,"k":"5bfb5510-6685-42af-9b35-14b39eb8cf2c-r3","picks":[["aws-cloudwatch","p","b"],["opentelemetry","m"],["highlight","m"]],"ev":34,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The run identified that the application is already deployed on AWS ECS Fargate and committed entirely to Amazon CloudWatch (including CloudWatch Application Signals, CloudWatch Agent, log groups, and CloudWatch metric alarms) as a builtin capability of the existing cloud platform. OpenTelemetry (via ADOT) serves as the instrumentation layer.","c":1,"e":[["file","terraform/ecs.tf:123-228"],["file","README.md:41-62"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"fastapi-saas","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"grafana","secs":335,"k":"5bfb5510-6685-42af-9b35-14b39eb8cf2c-r4","picks":[["grafana","p"],["opentelemetry","m"],["datadog","m"],["honeycomb","m"],["prometheus","m"],["sentry","m"]],"ev":42,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The run clearly chose Grafana Cloud as the single observability backend, wiring direct OpenTelemetry OTLP export from FastAPI to Grafana Cloud and provisioning alerting and dashboards using the Grafana Terraform provider.","c":1,"e":[["file","terraform/observability.tf:1-129"],["file","app/observability.py:57-104"],["file","README.md:41-78"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"sentry","secs":440,"k":"7f44f751-e3a6-4d6d-ae78-4732d0e57f29-r1","picks":[["sentry","p"],["highlight","m"],["grafana","m"],["honeycomb","m"],["opentelemetry","m"],["betterstack","a"],["datadog","m"]],"ev":44,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Sentry as the single production observability platform for the repository, integrating `@sentry/node` across Express, Mongoose, Twilio, and cron scripts, and providing an idempotent alert provisioning script in `scripts/provisionSentryAlert.js`. Several alternative platforms were weighed in trace reasoning item 2 and discarded.","c":1,"e":[["file","package.json:14"],["file","instrumentation.js:1-57"],["file","scripts/provisionSentryAlert.js:1-194"],["file","OBSERVABILITY.md:1-69"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"sentry","secs":290,"k":"7f44f751-e3a6-4d6d-ae78-4732d0e57f29-r2","picks":[["sentry","p"]],"ev":42,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Sentry as the single observability backend for the Node/Express service, installing `@sentry/node`, instrumenting request/error/metric telemetry, configuring a latency alert script via the Sentry API, and updating documentation.","c":1,"e":[["file","package.json:14"],["file","instrument.js:1-104"],["file","lib/observability.js:1-81"],["file","scripts/configureSentryAlert.js:1-124"],["file","README.md:29-57"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"sentry","secs":293,"k":"7f44f751-e3a6-4d6d-ae78-4732d0e57f29-r3","picks":[["sentry","p"],["betterstack","m"],["grafana","m"],["honeycomb","m"],["opentelemetry","m"],["datadog","m"],["prometheus","m"]],"ev":48,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly selected Sentry to handle error tracking, performance tracing, structured logging, and metrics in one tool. It installed `@sentry/node`, instrumented database connections, HTTP requests, SMS background jobs, and error handling, created sanitization logic for sensitive customer data, and added an automated Sentry monitor/alert provisioning script.","c":1,"e":[["file","package.json:15"],["file","instrument.js:102-134"],["file","scripts/configureSentryAlert.js:1-141"],["file","README.md:29-61"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"express-api","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"sentry","secs":348,"k":"7f44f751-e3a6-4d6d-ae78-4732d0e57f29-r4","picks":[["sentry","p"],["betterstack","m"],["grafana","m"],["pino","m"],["prometheus","m"]],"ev":39,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Sentry as the sole observability backend, installing `@sentry/node`, configuring tracing and metrics instrumentation in `instrument.js`, wrapping Express routes and error handling, and providing an alert provisioning script in `scripts/configureSentryAlert.js`. Alternatives like Grafana, Better Stack, and Pino were weighed in deliberation and dismissed due to operational complexity or redundancy.","c":1,"e":[["file","package.json"],["file","instrument.js"],["file","scripts/configureSentryAlert.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":2,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"sentry","secs":329,"k":"aa615e28-64f3-4e64-9f30-a308ba9dd453-r1","picks":[["sentry","p"],["grafana","m"],["datadog","m"]],"ev":46,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Sentry as the sole observability platform, installed `@sentry/node`, configured tracing, logging, metrics, error capture, and scripted detector/workflow alert provisioning via Sentry's API.","c":1,"e":[["file","package.json"],["file","apps/api/src/instrument.js"],["file","apps/api/src/telemetry.js"],["file","scripts/configure-sentry.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":2,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"sentry","secs":261,"k":"aa615e28-64f3-4e64-9f30-a308ba9dd453-r2","picks":[["sentry","p"],["honeycomb","m"],["opentelemetry","m"],["datadog","m"],["grafana","m"]],"ev":28,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The run unambiguously selected and implemented Sentry (via Sentry Cloud and `@sentry/node`). It configured ESM preloading instrumentation, unified logs, traces, runtime metrics, and provisioned a p95 latency alert script.","c":1,"e":[["file","package.json:10-16"],["file","apps/api/src/instrument.js:1-47"],["file","apps/api/src/observability.js:1-76"],["file","scripts/configure-sentry.js:1-120"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"sentry","secs":404,"k":"aa615e28-64f3-4e64-9f30-a308ba9dd453-r3","picks":[["sentry","p"],["betterstack","m"],["grafana","m"],["honeycomb","m"]],"ev":54,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent selected Sentry Cloud as the single observability backend, installed @sentry/node, instrumented the HTTP server with tracing, logging, and exception tracking, and created an alert provisioning script and operational documentation.","c":1,"e":[["file","package.json"],["file","apps/api/src/instrument.js"],["file","docs/observability.md"],["file","scripts/provision-sentry-alert.js"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"observability","wave":2,"date":"2026-09-01","repo":"b2b-subscriptions","variant":"base","family":"obs-platform-zero-incumbent","pid":"OBS-PLATFORM-ZERO-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"betterstack","secs":269,"k":"aa615e28-64f3-4e64-9f30-a308ba9dd453-r4","picks":[["betterstack","p"],["opentelemetry","m"],["sentry","m"],["grafana","m"],["honeycomb","m"]],"ev":37,"co":"observability-gem-scale1-20260901-zero-incumbent-final","v":{"r":"The agent explicitly selected Better Stack Telemetry as the single production observability backend, wrote code exporting OpenTelemetry logs, metrics, and traces directly to Better Stack via HTTP OTLP, created an automated API provisioning script for Better Stack explorations and alerts, and updated documentation and tests accordingly.","c":1,"e":[["file","apps/api/src/observability.js:1-181"],["file","scripts/provision-observability.js:1-97"],["file","README.md:7-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Full-stack observability platform","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-enterprise-fleet","pid":"SBX-MANAGED-ENTERPRISE-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"daytona","secs":359,"k":"8b02e305-7576-4249-9ffe-63293d004462-r1","picks":[["daytona","p"],["runloop","m"],["fly-machines","m"],["e2b","m"],["modal","m"]],"ev":48,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run systematically evaluated several managed sandbox solutions (Daytona, Modal, E2B, Runloop Devboxes) and selected Daytona Cloud. It installed `@daytona/sdk`, implemented full execution and log streaming against Daytona APIs in `src/runner.js`, created production configuration in `config/daytona.production.json`, and documented the decision comparing the alternatives in `docs/managed-sandbox-decision.md`.","c":1,"e":[["file","package.json"],["file","src/runner.js"],["file","config/daytona.production.json"],["file","docs/managed-sandbox-decision.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-enterprise-fleet","pid":"SBX-MANAGED-ENTERPRISE-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"daytona","secs":395,"k":"8b02e305-7576-4249-9ffe-63293d004462-r2","picks":[["daytona","p"],["aws-codebuild","m"],["e2b","m"],["modal","m"]],"ev":49,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent researched several managed sandbox platforms (Daytona, Modal, E2B, AWS CodeBuild) and selected Daytona Cloud Enterprise. It installed `@daytona/sdk`, replaced the local process runner with Daytona sandbox session management, set up runtime configurations and production runbooks, and documented the comparison in `docs/sandbox-platform-decision.md`.","c":1,"e":[["file","package.json"],["file","src/runner.js"],["file","config/daytona.json"],["file","docs/sandbox-platform-decision.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-enterprise-fleet","pid":"SBX-MANAGED-ENTERPRISE-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"daytona","secs":384,"k":"8b02e305-7576-4249-9ffe-63293d004462-r3","picks":[["daytona","p"],["aws-codebuild","m"],["e2b","m"],["modal","m"]],"ev":48,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated multiple managed sandbox providers (Daytona Cloud, Modal Sandboxes, E2B, and AWS CodeBuild) against requirements for isolation, regional quota, egress filtering, and audit logging. It installed @daytona/sdk, configured Daytona in config/executor.json, implemented the runner around Daytona APIs in src/runner.js, and documented the rationale in docs/provider-decision.md.","c":1,"e":[["file","package.json"],["file","config/executor.json"],["file","src/runner.js"],["file","docs/provider-decision.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-enterprise-fleet","pid":"SBX-MANAGED-ENTERPRISE-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"daytona","secs":385,"k":"8b02e305-7576-4249-9ffe-63293d004462-r4","picks":[["daytona","p"],["fly-machines","m"],["aws-codebuild","m"],["docker","m"],["e2b","m"],["modal","m"]],"ev":49,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run clearly committed to Daytona Cloud, installing `@daytona/sdk` in package.json, configuring it in `config/executor.json`, implementing the executor in `src/runner.js`, and documenting the vendor evaluation and rejection reasons for Modal, E2B, and AWS CodeBuild in `docs/vendor-evaluation.md`.","c":1,"e":[["file","package.json:11-13"],["file","config/executor.json:1-41"],["file","src/runner.js:2-250"],["file","docs/vendor-evaluation.md:1-27"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-enterprise-fleet","pid":"SBX-MANAGED-ENTERPRISE-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":5,"pick":"daytona","secs":1851,"k":"8b02e305-7576-4249-9ffe-63293d004462-r5","picks":[["daytona","p"],["e2b","m"],["aws-codebuild","m"],["docker","m"],["gvisor","m"],["modal","m"]],"ev":66,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent replaced the controller's local execution with the @daytona/sdk, configured Daytona Cloud in config/executor.daytona.json, and documented comparisons against Modal Sandboxes, Azure Container Apps Dynamic Sessions, and Amazon Bedrock AgentCore Code Interpreter in docs/platform-decision.md.","c":1,"e":[["file","package.json"],["file","src/runner.js"],["file","config/executor.daytona.json"],["file","docs/platform-decision.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-enterprise-fleet","pid":"SBX-MANAGED-ENTERPRISE-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":6,"pick":"daytona","secs":1775,"k":"8b02e305-7576-4249-9ffe-63293d004462-r6","picks":[["daytona","p"],["aws-codebuild","m"],["docker","m"],["e2b","m"],["modal","m"]],"ev":45,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated Daytona Cloud, Modal Sandboxes, E2B, and AWS CodeBuild, selecting Daytona Cloud as the primary sandbox solution. It installed @daytona/sdk, configured daytona.executor.json, updated runner.js and server.js, documented the architectural decision in docs/provider-decision.md, and added tests for Daytona sandbox orchestration.","c":1,"e":[["file","package.json"],["file","src/runner.js"],["file","config/daytona.executor.json"],["file","docs/provider-decision.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-enterprise-fleet","pid":"SBX-MANAGED-ENTERPRISE-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"e2b","secs":1464,"k":"b9c8cb46-0ccb-4892-b0f8-bc46e8adf362-r1","picks":[["e2b","p"],["firecracker","m"],["runloop","m"],["vercel-sandbox","m"],["daytona","m"],["gvisor","m"],["modal","m"]],"ev":110,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent replaced the local unsafe shell execution runner with E2B sandboxes, adding the e2b npm dependency, creating src/executor/e2b.js, and writing full multi-region configuration and test suites. It evaluated Daytona, Modal, and Azure Container Apps dynamic sessions against vendor documentation and explicitly rejected them in README.md.","c":1,"e":[["file","package.json"],["file","src/executor/e2b.js"],["file","config/executor.production.json"],["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":3,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-enterprise-fleet","pid":"SBX-MANAGED-ENTERPRISE-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"vercel-sandbox","secs":1151,"k":"b9c8cb46-0ccb-4892-b0f8-bc46e8adf362-r2","picks":[["vercel-sandbox","p"],["codesandbox-sdk","m"],["runloop","m"],["daytona","m"],["e2b","m"],["firecracker","m"],["modal","m"]],"ev":118,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent replaced the local execution runner with `@vercel/sandbox`, creating dedicated configuration, provider integration, capacity management, and test suites. It evaluated and formally rejected E2B, Daytona, and Modal in documentation and trace deliberations, while mentioning several other candidate platforms during exploration.","c":1,"e":[["file","package.json:14"],["file","sandbox.config.json:2"],["file","src/provider.js:2"],["file","README.md:9-35"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-enterprise-fleet","pid":"SBX-MANAGED-ENTERPRISE-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"e2b","secs":1134,"k":"b9c8cb46-0ccb-4892-b0f8-bc46e8adf362-r3","picks":[["e2b","p"],["daytona","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["vercel-sandbox","m"]],"ev":113,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent performed an in-depth evaluation of candidate managed sandboxes (E2B, Modal, Daytona) and selected E2B. It installed the `e2b` package, implemented the complete executor in `src/executor/e2b.js`, added configuration in `config/executor.json`, set up a template build script, and documented the comparison in `README.md`.","c":1,"e":[["file","package.json:14-16"],["file","config/executor.json:1-68"],["file","src/executor/e2b.js:1-395"],["file","README.md:23-56"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-enterprise-fleet","pid":"SBX-MANAGED-ENTERPRISE-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"e2b","secs":1086,"k":"b9c8cb46-0ccb-4892-b0f8-bc46e8adf362-r4","picks":[["e2b","p"],["vercel-sandbox","m"],["daytona","m"],["docker","m"],["firecracker","m"],["modal","m"]],"ev":109,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated multiple sandbox platforms (E2B, Daytona, Modal, Vercel Sandbox, and Cloudflare Sandbox SDK), compared the top three against requirements, and fully integrated E2B into the repository via the official `e2b` npm SDK, configuration, template definitions, tests, and detailed README documentation.","c":1,"e":[["file","package.json:15-17"],["file","src/providers/e2b.js:1-148"],["file","config/execution.json:1-64"],["file","README.md:9-27"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-enterprise-fleet","pid":"SBX-MANAGED-ENTERPRISE-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":5,"pick":"modal","secs":1220,"k":"b9c8cb46-0ccb-4892-b0f8-bc46e8adf362-r5","picks":[["modal","p"],["gvisor","m"],["codesandbox-sdk","m"],["runloop","m"],["aws-fargate","m"],["daytona","m"],["e2b","m"],["vercel-sandbox","m"]],"ev":113,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent replaced the local execution runner with a full Modal Sandboxes implementation (`src/executor/modal.js`), added the `modal` package dependency to package.json, checked in configuration in `config/executor.json`, and documented the decision against E2B, Daytona, and Vercel Sandbox in `docs/adr-0001-sandbox-platform.md`.","c":1,"e":[["file","package.json:15-17"],["file","config/executor.json:1-81"],["file","src/executor/modal.js:1-324"],["file","docs/adr-0001-sandbox-platform.md:38-54"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-enterprise-fleet","pid":"SBX-MANAGED-ENTERPRISE-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":6,"pick":"modal","secs":1259,"k":"b9c8cb46-0ccb-4892-b0f8-bc46e8adf362-r6","picks":[["modal","p"],["aws-fargate","m"],["vercel-sandbox","m"],["runloop","m"],["blaxel","m"],["daytona","m"],["e2b","m"],["firecracker","m"],["gvisor","m"]],"ev":112,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated Modal, E2B, and Daytona against workload requirements and implemented Modal Sandboxes using the official `modal` npm package. All code was updated to use ModalExecutor for isolated workspace execution, with checked-in configuration, capacity management, and tests.","c":1,"e":[["file","package.json"],["file","config/executor.json"],["file","src/executors/modal.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"bc-mail-prompt-b-09","pid":"MAIL-PB-09a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"postmark","secs":368,"k":"0e485e17-86ef-4d4e-b4ab-683a58caa974-r1","picks":[["postmark","p"],["sendgrid","m"],["resend","m"],["mailgun","m"]],"ev":33,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The user asked for a transactional email solution with predictable monthly costs. The agent evaluated Postmark, Mailgun, SendGrid, and Resend, specifically recommending Postmark Basic. After confirmation, the agent implemented a full Postmark client in Go along with database-backed queueing and workers.","c":1,"e":[["file","internal/mailer/postmark.go:1-148"],["file",".env.example:3"],["file","cmd/server/main.go:37-40"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Volume and cost at scale"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"bc-mail-prompt-b-09","pid":"MAIL-PB-09a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"resend","secs":494,"k":"0e485e17-86ef-4d4e-b4ab-683a58caa974-r2","picks":[["resend","p"],["brevo","m"],["mailgun","m"],["postmark","m"]],"ev":33,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The user asked for a transactional email solution with predictable monthly cost. The agent evaluated multiple providers (Postmark, Brevo, Mailgun, Amazon SES, and Resend), recommended Resend due to predictable pricing and API idempotency key support, and implemented an end-to-end Resend integration with background workers, database tracking, and tests.","c":1,"e":[["file","internal/mailer/resend/resend.go:17-79"],["file","cmd/send-due-followups/main.go:37-40"],["file",".env.example:3"],["file","README.md:29-54"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Volume and cost at scale"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"bc-mail-prompt-b-09","pid":"MAIL-PB-09a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"resend","secs":430,"k":"0e485e17-86ef-4d4e-b4ab-683a58caa974-r3","picks":[["resend","p"],["postmark","m"],["sendgrid","m"]],"ev":39,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Resend Pro after comparing pricing and features against Postmark, SendGrid, and Amazon SES. The agent then fully implemented a Resend transactional email client and delivery worker in Go with database-backed leasing and idempotency keys.","c":1,"e":[["file","internal/email/resend.go:1-101"],["file",".env.example:3-7"],["file","README.md:29-47"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Volume and cost at scale"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"bc-mail-prompt-b-09","pid":"MAIL-PB-09a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"postmark","secs":1556,"k":"0e485e17-86ef-4d4e-b4ab-683a58caa974-r4","picks":[["postmark","p"],["sendgrid","m"],["mailgun","m"],["aws-ses","m"],["brevo","m"],["resend","m"],["smtp","m"]],"ev":30,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several transactional email providers (Postmark, MailerSend, Brevo, Resend, SendGrid, Mailgun, Amazon SES) and selected Postmark Basic. It implemented a custom Postmark client and background delivery worker in Go.","c":1,"e":[["file","internal/email/postmark/postmark.go:1-94"],["file","cmd/server/main.go:40"],["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":4,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"resend","secs":352,"k":"f4dcca6c-57ea-4281-83f4-a446f8495b5d-r1","picks":[["resend","p"]],"ev":41,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The run evaluated the owner-triggered class reminder relay and replaced it with Resend. It installed the `resend` SDK, implemented batch message sending with idempotency keys in `lib/reminders.ts`, built a webhook receiver at `app/api/webhooks/resend/route.ts`, and updated `.env.example` and `README.md` with Resend configuration.","c":1,"e":[["file","package.json:18"],["file","lib/reminders.ts:25"],["file","app/api/webhooks/resend/route.ts:17"],["file",".env.example:9-10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"resend","secs":495,"k":"f4dcca6c-57ea-4281-83f4-a446f8495b5d-r2","picks":[["resend","p"],["postmark","m"]],"ev":62,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly evaluated and implemented Resend for transactional email delivery. It installed the `resend` and `react-email` packages, built the reminder batch dispatch logic, created a webhook handler for Resend delivery events, and documented the required environment configuration.","c":1,"e":[["file","lib/reminders.tsx"],["file","app/api/email/webhook/route.ts"],["file",".env.example"],["file","package-lock.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"resend","secs":1804,"k":"f4dcca6c-57ea-4281-83f4-a446f8495b5d-r3","picks":[["resend","p"],["postmark","m"]],"ev":57,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended and integrated Resend into the project to handle class reminder emails, installing its npm package, creating batch send logic with idempotency keys, adding a webhook handler, and updating environment variables and documentation.","c":1,"e":[["file","package-lock.json"],["file","lib/reminders.tsx"],["file","app/api/webhooks/resend/route.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"resend","secs":806,"k":"f4dcca6c-57ea-4281-83f4-a446f8495b5d-r4","picks":[["resend","p"],["loops","m"],["postmark","m"],["smtp","m"]],"ev":49,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated transactional email solutions for the Next.js application and selected Resend. It installed the `resend` SDK, built React-based email templates, implemented batch reminder sending with idempotency keys, and added a signed webhook handler for delivery event tracking.","c":1,"e":[["file","package.json"],["file","lib/reminders.tsx"],["file","app/api/webhooks/resend/route.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":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"e2b","secs":845,"k":"632dbb94-2e93-4e2f-9535-cde810b2ba1f-r1","picks":[["e2b","p"],["aws-lambda","m"],["daytona","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["vercel-sandbox","m"]],"ev":85,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent compared multiple managed sandbox providers (E2B, Modal, Daytona, Vercel Sandbox) and selected E2B, installing its Python SDK and refactoring `northstar/executor.py` to create and drive Firecracker microVM sandboxes for code execution.","c":1,"e":[["file","pyproject.toml:5"],["file","northstar/executor.py:21-27"],["file","README.md:9-13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"modal","secs":997,"k":"632dbb94-2e93-4e2f-9535-cde810b2ba1f-r2","picks":[["modal","p"],["vercel-sandbox","m"],["daytona","m"],["e2b","m"],["firecracker","m"],["gvisor","m"]],"ev":103,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent compared Modal Sandboxes, E2B, and Daytona against the workload requirements, explicitly choosing Modal. It added the `modal` dependency, implemented a full backend in `northstar/modal_sandbox.py`, updated server handling, and wrote tests verifying Modal sandbox configurations and lifecycle guarantees.","c":1,"e":[["file","pyproject.toml:9"],["file","northstar/modal_sandbox.py:1-187"],["file","README.md:39-65"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"modal","secs":944,"k":"632dbb94-2e93-4e2f-9535-cde810b2ba1f-r3","picks":[["modal","p"],["daytona","m"],["e2b","m"],["firecracker","m"],["gvisor","m"],["vercel-sandbox","m"]],"ev":80,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent replaced the in-process execution model with Modal Sandboxes (`modal>=1.5,<2`), implementing `ModalSandboxExecutor` in `northstar/executor.py` with resource constraints, blocked networking, lifetime limits, and test coverage. The documentation and trace thoroughly compare Modal against E2B, Daytona, and Vercel Sandbox before settling on Modal.","c":1,"e":[["file","pyproject.toml:6"],["file","northstar/executor.py:51-140"],["file","README.md:9-40"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"modal","secs":656,"k":"632dbb94-2e93-4e2f-9535-cde810b2ba1f-r4","picks":[["modal","p"],["vercel-sandbox","m"],["runloop","m"],["daytona","m"],["e2b","m"],["firecracker","m"],["gvisor","m"]],"ev":70,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated multiple remote sandbox platforms (Modal, E2B, Daytona) against the workload requirements, explicitly rejected E2B and Daytona in documentation and code comments, and fully integrated Modal Sandboxes via the `modal` Python SDK in `northstar/sandbox.py`.","c":1,"e":[["file","northstar/sandbox.py:126-261"],["file","pyproject.toml:5"],["file","README.md:9-40"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":5,"pick":"e2b","secs":754,"k":"632dbb94-2e93-4e2f-9535-cde810b2ba1f-r5","picks":[["e2b","p"],["vercel-sandbox","m"],["cloudflare-sandbox","m"],["daytona","m"],["firecracker","m"],["gvisor","m"],["modal","m"]],"ev":77,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent conducted official documentation research on several sandbox platforms (E2B, Modal, Daytona, Cloudflare Sandbox SDK, Fly.io Machines, Vercel Sandbox), compared the top three against the workload requirements, and selected E2B. The diff installs the E2B Python SDK (`e2b>=2.46,<3`), builds custom execution templates, updates `.env.example`, and implements sandbox isolation with hard resource limits and cleanup in `northstar/executor.py`.","c":1,"e":[["file","pyproject.toml:6"],["file","northstar/executor.py:10-100"],["file","scripts/build_template.py:1-49"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":6,"pick":"modal","secs":976,"k":"632dbb94-2e93-4e2f-9535-cde810b2ba1f-r6","picks":[["modal","p"],["vercel-sandbox","m"],["e2b","a"],["daytona","a"],["aws-lambda","m"],["cloudflare-sandbox","m"],["codesandbox-sdk","m"],["firecracker","m"],["fly-machines","m"],["gvisor","m"]],"ev":95,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated multiple managed sandbox options (Modal, E2B, Daytona, Cloudflare Sandbox SDK) and chose Modal Sandboxes. It implemented a full sandbox execution integration in `northstar/sandbox.py` using the `modal` library, added test coverage, and configured the application to run untrusted code inside Modal Sandboxes.","c":1,"e":[["file","northstar/sandbox.py:1-216"],["file","pyproject.toml:5"],["file","docs/sandbox-platform-selection.md:57-81"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"e2b","secs":882,"k":"e56e4442-160c-434b-a854-a6f6972b2f05-r1","picks":[["e2b","p"],["daytona","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["vercel-sandbox","m"]],"ev":73,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated multiple sandbox platforms (E2B, Modal, Anthropic Code Execution, Daytona, Cloudflare Sandbox SDK, Vercel Sandbox) and selected E2B based on its Firecracker microVM hardware isolation, low operational overhead, and Python SDK fit. The agent fully implemented the integration with `e2b-code-interpreter` in `app/sandbox.py`, added necessary configuration, and updated unit tests.","c":1,"e":[["file","pyproject.toml"],["file","app/sandbox.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Generated data-analysis code","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"e2b","secs":949,"k":"e56e4442-160c-434b-a854-a6f6972b2f05-r2","picks":[["e2b","p"],["firecracker","m"],["gvisor","m"],["anthropic-code-execution","m"],["aws-lambda","m"],["daytona","m"],["modal","m"],["runloop","m"]],"ev":91,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent clearly selected and integrated E2B via the e2b-code-interpreter package, replacing in-process exec with remote disposable microVM sandboxes in app/sandbox.py, configuring settings in app/config.py and .env.example, and adding comprehensive driver and adapter tests.","c":1,"e":[["file","pyproject.toml:18"],["file","app/sandbox.py:1-411"],["file","app/config.py:19-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"anthropic-code-execution","secs":753,"k":"e56e4442-160c-434b-a854-a6f6972b2f05-r3","picks":[["anthropic-code-execution","p"],["daytona","m"],["e2b","m"],["firecracker","m"],["modal","m"]],"ev":50,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent selected Anthropic Code Execution as the sandbox platform for spreadsheet analysis, refactoring the application code (`app/llm.py`, `app/analysis.py`, `app/main.py`) to run code directly in Anthropic's hosted code-execution container. Other sandbox providers (Modal, E2B, Daytona) were explicitly evaluated and rejected due to unnecessary operational and vendor overhead.","c":1,"e":[["file","app/llm.py:22-26"],["file","app/analysis.py:1-12"],["file","README.md:28-55"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Generated data-analysis code","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"e2b","secs":1154,"k":"e56e4442-160c-434b-a854-a6f6972b2f05-r4","picks":[["e2b","p"],["runloop","m"],["firecracker","m"],["gvisor","m"],["aws-lambda","m"],["daytona","m"],["fly-machines","m"],["modal","m"],["vercel-sandbox","m"]],"ev":95,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly chose E2B, installed `e2b-code-interpreter>=2.9,<3.0`, implemented sandbox orchestration in `app/analysis.py`, wrote a standalone `app/sandbox_runtime.py` to run inside the sandbox, updated the API endpoint and configuration, and updated the documentation and test suite.","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":3,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":5,"pick":"anthropic-code-execution","secs":499,"k":"e56e4442-160c-434b-a854-a6f6972b2f05-r5","picks":[["anthropic-code-execution","p"],["daytona","m"],["e2b","m"],["firecracker","m"],["fly-machines","m"],["gvisor","m"],["modal","m"]],"ev":43,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent selected Anthropic Code Execution (via the Messages API `code_execution_20260521` server tool and Files API) as its primary sandbox solution, implemented it across `app/analysis.py`, `app/llm.py`, and `app/main.py`, and fully tested it. It explicitly compared and rejected Modal, E2B, Daytona, and Fly Machines.","c":1,"e":[["file","app/analysis.py:20-25"],["file","app/analysis.py:155-165"],["file","README.md:19-55"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Generated data-analysis code","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":6,"pick":"modal","secs":919,"k":"e56e4442-160c-434b-a854-a6f6972b2f05-r6","picks":[["modal","p"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["e2b","m"],["firecracker","m"],["gvisor","m"]],"ev":82,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated Modal Sandboxes, E2B, and Anthropic Code Execution, explicitly choosing Modal for its Python-native SDK, runtime dependency pinning, and network/resource controls. It installed the `modal` package and integrated it into the application.","c":1,"e":[["file","pyproject.toml:15"],["file","app/sandbox.py:96-150"],["file","README.md:28-48"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Generated data-analysis code","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"e2b","secs":890,"k":"ccd93263-4525-4ded-b7ed-9564f29fc9b0-r1","picks":[["e2b","p"],["daytona","m"],["firecracker","m"],["vercel-sandbox","a"],["aws-lambda","m"],["docker","m"],["fly-machines","m"],["gvisor","m"],["modal","m"]],"ev":81,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several sandbox solutions (E2B, Vercel Sandbox, Modal, Fly Machines, AWS Lambda/Fargate, Daytona), selected E2B, installed the official SDK dependency, and integrated full remote execution with network locking and orphan reaping.","c":1,"e":[["file","package.json:18-20"],["file","src/sandbox.ts:1-211"],["file","src/runner.ts:98-186"],["file","src/reaper.ts:1-74"],["file","src/template.ts:1-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"e2b","secs":465,"k":"ccd93263-4525-4ded-b7ed-9564f29fc9b0-r2","picks":[["e2b","p"],["daytona","m"],["vercel-sandbox","m"],["firecracker","m"],["aws-fargate","m"],["gvisor","m"],["modal","m"]],"ev":46,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent selected E2B as the third-party managed sandbox driver, installed the `e2b` npm package, implemented full execution and two-phase network isolation in `src/sandbox.ts`, updated `.env.example` and `README.md`, and rejected alternatives such as Modal and AWS Fargate.","c":1,"e":[["file","package.json:18"],["file","src/sandbox.ts:1-193"],["file","README.md:7-27"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel-sandbox","secs":608,"k":"ccd93263-4525-4ded-b7ed-9564f29fc9b0-r3","picks":[["vercel-sandbox","p"],["daytona","m"],["aws-fargate","m"],["docker","m"],["e2b","m"],["firecracker","m"],["fly-machines","m"],["modal","m"]],"ev":60,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run clearly selected and fully integrated Vercel Sandbox (`@vercel/sandbox`) into the project codebase across `package.json`, `src/vercel-sandbox.ts`, `src/runner.ts`, and the test suite, while evaluating and explicitly rejecting E2B, Modal, AWS Fargate, and Fly Machines.","c":1,"e":[["file","package.json"],["file","src/vercel-sandbox.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":3,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"e2b","secs":508,"k":"ccd93263-4525-4ded-b7ed-9564f29fc9b0-r4","picks":[["e2b","p"],["firecracker","m"],["codesandbox-sdk","m"],["daytona","m"],["fly-machines","m"],["modal","m"],["vercel-sandbox","m"]],"ev":55,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run installed the `e2b` package and implemented a complete sandbox runner interface in `src/sandbox.ts` and `src/runner.ts` using E2B's Sandbox API, network controls, command execution, and lifecycle cleanup. Several alternative sandbox providers (Daytona, Modal, Vercel Sandbox, Cloudflare) were explicitly evaluated and rejected based on egress filtering capabilities, stack compatibility, and operational complexity.","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":3,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":5,"pick":"vercel-sandbox","secs":461,"k":"ccd93263-4525-4ded-b7ed-9564f29fc9b0-r5","picks":[["vercel-sandbox","p"],["daytona","m"],["modal","m"],["aws-codebuild","m"],["docker","m"],["e2b","m"],["firecracker","m"],["gvisor","m"]],"ev":45,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated multiple managed sandbox providers (comparing E2B and Vercel Sandbox extensively) and explicitly chose, recommended, and implemented Vercel Sandbox (`@vercel/sandbox`) into the repository codebase.","c":1,"e":[["file","package.json"],["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":3,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":6,"pick":"e2b","secs":782,"k":"ccd93263-4525-4ded-b7ed-9564f29fc9b0-r6","picks":[["e2b","p"],["daytona","m"],["aws-fargate","a"],["aws-codebuild","m"],["docker","m"],["firecracker","m"],["fly-machines","m"],["gvisor","m"],["modal","m"]],"ev":63,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several sandbox solutions and chose E2B to replace the local process execution with isolated disposable microVMs. The `e2b` npm package was installed and fully integrated into the architecture via `E2bSandboxProvider` in `src/sandbox/e2b.ts`, accompanied by unit and integration tests.","c":0.95,"e":[["file","package.json:10-20"],["file","src/sandbox/e2b.ts:1-313"],["file","README.md:1-59"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Generated UI project builds","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"e2b","secs":965,"k":"f40b1e89-4b0d-4931-875f-14d1975738f8-r1","picks":[["e2b","p"],["firecracker","m"],["gvisor","m"],["daytona","m"],["docker","m"],["modal","m"],["vercel-sandbox","m"]],"ev":99,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated multiple sandbox platforms (E2B, Modal, Daytona, and Azure Container Apps dynamic sessions) and selected E2B. It fully implemented E2B in the codebase using `e2b-code-interpreter`, configured environment variables and limits in `app/config.py`, created the in-sandbox runner script `app/sandbox_runner.py`, updated execution in `app/analysis.py`, and added comprehensive tests.","c":1,"e":[["file","pyproject.toml"],["file","app/analysis.py"],["file","docs/sandbox.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"modal","secs":894,"k":"f40b1e89-4b0d-4931-875f-14d1975738f8-r2","picks":[["modal","p"],["gvisor","m"],["codesandbox-sdk","m"],["vercel-sandbox","m"],["anthropic-code-execution","m"],["daytona","m"],["docker","m"],["e2b","m"],["firecracker","m"]],"ev":88,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run installed the `modal` Python SDK, implemented full sandbox management in `app/sandbox.py` using `modal.Sandbox.create` with network blocking and resource constraints, created an in-sandbox runner script `app/sandbox_runner.py`, and thoroughly documented the evaluation against E2B, Daytona, and Anthropic Code Execution in `docs/sandbox.md`.","c":1,"e":[["file","app/sandbox.py"],["file","pyproject.toml"],["file","docs/sandbox.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"e2b","secs":1025,"k":"f40b1e89-4b0d-4931-875f-14d1975738f8-r3","picks":[["e2b","p"],["firecracker","m"],["vercel-sandbox","m"],["runloop","m"],["daytona","m"],["docker","m"],["gvisor","m"],["modal","m"]],"ev":105,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent replaced the in-process exec() path with an E2B-backed remote sandbox implementation, adding e2b to pyproject.toml and requirements.txt, implementing sandbox creation with network egress disabled and strict resource/time limits in app/analysis.py, providing a template build script scripts/build_sandbox_template.py, and documenting comparisons against Modal, Daytona, and Azure Container Apps dynamic sessions in docs/sandbox.md.","c":1,"e":[["file","pyproject.toml"],["file","app/analysis.py"],["file","scripts/build_sandbox_template.py"],["file","docs/sandbox.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"modal","secs":1005,"k":"f40b1e89-4b0d-4931-875f-14d1975738f8-r4","picks":[["modal","p"],["e2b","a"],["daytona","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["vercel-sandbox","m"]],"ev":120,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent replaced in-process exec() with a remote sandbox executor implemented via Modal Sandboxes (using the `modal` Python SDK in `app/analysis.py`). In `docs/sandbox.md` and the trace, it compared Modal Sandboxes against E2B and Daytona before selecting Modal.","c":1,"e":[["file","pyproject.toml"],["file","app/analysis.py"],["file","docs/sandbox.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":5,"pick":"modal","secs":854,"k":"f40b1e89-4b0d-4931-875f-14d1975738f8-r5","picks":[["modal","p"],["e2b","a"],["aws-lambda","m"],["codesandbox-sdk","m"],["daytona","m"],["firecracker","m"],["gvisor","m"],["runloop","m"],["vercel-sandbox","m"]],"ev":97,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run evaluated multiple managed sandboxes (Modal, E2B, Daytona, and Vercel Sandbox) and committed completely to Modal by installing the `modal` package, implementing the executor in `app/sandbox.py`, wiring configuration and runbooks, and testing the executor integration.","c":1,"e":[["file","pyproject.toml"],["file","app/sandbox.py"],["file","docs/sandbox-runbook.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":6,"pick":"modal","secs":745,"k":"f40b1e89-4b0d-4931-875f-14d1975738f8-r6","picks":[["modal","p"],["anthropic-code-execution","m"],["daytona","m"],["e2b","m"],["firecracker","m"],["gvisor","m"]],"ev":86,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent compared multiple managed sandbox solutions (Modal, Anthropic Code Execution, E2B, Daytona, Cloudflare, and Vercel) and selected Modal Sandboxes. It added `modal` to dependencies, implemented `app/sandbox.py` using `modal.Sandbox.create` with `block_network=True` and resource limits, updated `.env.example` and documentation, and added comprehensive mock-based tests.","c":1,"e":[["file","app/sandbox.py:1-164"],["file","pyproject.toml:15"],["file","README.md:25-50"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"e2b","secs":838,"k":"c0e2c284-1d56-4166-908b-cecfd332fce7-r1","picks":[["e2b","p"],["firecracker","m"],["gvisor","m"],["modal","a"],["aws-lambda","m"]],"ev":74,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run installed the `e2b` Python package, configured custom templates, integrated sandbox execution in `app/analysis.py` with full isolation and lifecycle management, and updated tests and documentation.","c":1,"e":[["file","pyproject.toml:18"],["file","app/analysis.py:27-33"],["file","scripts/build_template.py:25-59"],["file","README.md:29-75"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"modal","secs":723,"k":"c0e2c284-1d56-4166-908b-cecfd332fce7-r2","picks":[["modal","p"],["gvisor","m"],["anthropic-code-execution","m"],["aws-lambda","m"],["daytona","m"],["e2b","m"],["firecracker","m"]],"ev":70,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated candidate sandbox platforms (Modal, E2B, Anthropic Code Execution, Cloudflare, Daytona) against requirements for disposable per-run isolation, network blocking, and resource bounds. It selected Modal and committed a complete integration in `app/sandbox.py`, added dependencies in `pyproject.toml`, updated `.env.example` and documentation, and provided unit/integration tests with test doubles.","c":1,"e":[["file","pyproject.toml"],["file","app/sandbox.py:1-238"],["file","app/analysis.py:1-55"],["file","README.md:27-77"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"e2b","secs":1091,"k":"c0e2c284-1d56-4166-908b-cecfd332fce7-r3","picks":[["e2b","p"],["firecracker","m"],["gvisor","m"],["daytona","m"],["fly-machines","m"],["modal","a"]],"ev":102,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run comprehensively investigated sandbox options, formally recommended E2B, and fully implemented the integration with `e2b-code-interpreter` in `app/sandbox.py`, `app/analysis.py`, `app/config.py`, and corresponding unit and live tests.","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":3,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"modal","secs":778,"k":"c0e2c284-1d56-4166-908b-cecfd332fce7-r4","picks":[["modal","p"],["gvisor","m"],["anthropic-code-execution","a"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["docker","m"],["e2b","m"],["firecracker","m"]],"ev":65,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several sandbox solutions (Modal, Anthropic Code Execution, E2B, AWS Lambda/Fargate, Daytona, Cloudflare Workers) and recommended Modal Sandboxes. Upon user agreement, it fully integrated Modal into `pyproject.toml`, implemented `app/sandbox.py` using `modal.Sandbox`, updated `app/analysis.py`, `app/main.py`, `.env.example`, `README.md`, and added comprehensive unit and runner tests.","c":1,"e":[["file","pyproject.toml"],["file","app/sandbox.py:1-235"],["file","README.md:26-64"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":5,"pick":"anthropic-code-execution","secs":724,"k":"c0e2c284-1d56-4166-908b-cecfd332fce7-r5","picks":[["anthropic-code-execution","p"],["aws-lambda","m"],["daytona","m"],["e2b","m"],["firecracker","m"],["gvisor","m"],["modal","m"]],"ev":69,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run replaces in-process code execution with Anthropic Code Execution (`app/sandbox.py`), creating a network-isolated remote container for each spreadsheet analysis request via the Anthropic SDK and Files API. It explicitly compares and rejects dedicated sandbox vendors (E2B, Modal, Daytona, AWS Lambda) due to operational burden and architectural constraints.","c":1,"e":[["file","app/sandbox.py"],["file","README.md:13-60"],["file","app/config.py:16-29"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":6,"pick":"e2b","secs":779,"k":"c0e2c284-1d56-4166-908b-cecfd332fce7-r6","picks":[["e2b","p"],["daytona","m"],["firecracker","m"],["gvisor","m"],["aws-lambda","m"],["modal","m"]],"ev":70,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent clearly selected, recommended, and fully integrated E2B using the `e2b-code-interpreter` SDK, replacing the existing in-process `exec()` implementation with remote Firecracker microVM sandbox executions in `app/sandbox.py` and `app/analysis.py`.","c":1,"e":[["file","app/sandbox.py:1-187"],["file","pyproject.toml:15"],["file","sandbox_template/e2b.Dockerfile:1-25"],["file","README.md:27-46"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"e2b","secs":968,"k":"fb6f8d60-4a1c-4088-8e61-71d424f48350-r1","picks":[["e2b","p"],["daytona","m"],["firecracker","m"],["modal","m"],["vercel-sandbox","m"]],"ev":82,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent selected E2B as the managed remote sandbox platform, installed the e2b npm SDK, implemented the provider-backed executor in src/executors/e2b.js, added an orphan reaper and template build script, and documented the choice and alternatives (Daytona, Modal, Cloudflare) in the README and trace.","c":1,"e":[["file","package.json:14"],["file","src/executors/e2b.js:1-429"],["file","README.md:8-33"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"vercel-sandbox","secs":1204,"k":"fb6f8d60-4a1c-4088-8e61-71d424f48350-r2","picks":[["vercel-sandbox","p"],["blaxel","m"],["cloudflare-sandbox","m"],["daytona","m"],["docker","m"],["e2b","m"],["firecracker","m"],["fly-machines","m"],["modal","m"],["runloop","m"]],"ev":72,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent selected Vercel Sandbox (@vercel/sandbox) as the primary managed remote sandbox platform, installed its SDK, implemented the provider integration in src/sandbox/vercel.js, updated runner.js and server.js, and wrote comprehensive unit and integration tests.","c":1,"e":[["file","package.json:12-14"],["file","src/sandbox/vercel.js:1-103"],["file","README.md:7-17"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"e2b","secs":1671,"k":"fb6f8d60-4a1c-4088-8e61-71d424f48350-r3","picks":[["e2b","p"],["daytona","m"],["runloop","m"],["modal","a"],["docker","m"],["firecracker","m"],["fly-machines","m"],["gvisor","m"]],"ev":97,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several managed remote sandbox platforms, recommended E2B for its TypeScript SDK, persistent workspace model, and edge network allowlisting, and fully implemented the integration with the `e2b` npm package, custom executor (`src/executor/e2b.js`), sandbox template, sweeper, and comprehensive test suite.","c":1,"e":[["file","package.json:12-14"],["file","src/executor/e2b.js:1-199"],["file","template/e2b.toml:1-9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"e2b","secs":1094,"k":"fb6f8d60-4a1c-4088-8e61-71d424f48350-r4","picks":[["e2b","p"],["daytona","m"],["docker","m"],["firecracker","m"],["fly-machines","m"],["modal","m"],["runloop","m"],["vercel-sandbox","m"]],"ev":86,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent selected E2B, installed the `e2b` npm package, wrote `src/executors/e2b.js`, `src/reaper.js`, `template/e2b.template.js`, and comprehensive test suites enforcing E2B microVM isolation with egress filtering and credential brokering.","c":1,"e":[["file","package.json:14"],["file","src/executors/e2b.js:1-251"],["file","template/e2b.template.js:1-38"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":5,"pick":"vercel-sandbox","secs":872,"k":"fb6f8d60-4a1c-4088-8e61-71d424f48350-r5","picks":[["vercel-sandbox","p"],["e2b","a"],["daytona","m"],["firecracker","m"],["fly-machines","m"],["modal","m"]],"ev":67,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run evaluated managed sandbox providers (Vercel Sandbox, E2B, Daytona, Modal) and selected Vercel Sandbox (@vercel/sandbox). It installed the SDK, implemented the sandbox executor in src/sandbox-executor.js, constructed network and egress credential brokering policies in src/network-policy.js, and documented the rationale in README.md.","c":1,"e":[["file","package.json:12-14"],["file","src/sandbox-executor.js:1-327"],["file","README.md:9-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":6,"pick":"e2b","secs":918,"k":"fb6f8d60-4a1c-4088-8e61-71d424f48350-r6","picks":[["e2b","p"],["blaxel","m"],["codesandbox-sdk","m"],["daytona","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["runloop","m"]],"ev":71,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several sandbox solutions (E2B, Daytona, Modal, Cloudflare Sandbox SDK, Vercel Sandbox) and committed completely to E2B by installing the `e2b` package, implementing the sandbox executor (`src/executor/e2b.js`), configuring templates, egress allowlists, timeouts, and orphan cleanup via reaper routines.","c":1,"e":[["file","package.json:13"],["file","src/executor/e2b.js:1-149"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"daytona","secs":199,"k":"a7ca1aad-46de-4eb5-bffd-1c4b93ebc7af-r1","picks":[["daytona","p"],["e2b","m"],["firecracker","m"],["gvisor","m"],["modal","m"]],"ev":33,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated Daytona, Modal, and E2B, selecting Daytona due to its native TypeScript SDK and domain allowlist support. It installed @daytona/sdk and implemented the sandbox runner in src/runner.ts.","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":4,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"daytona","secs":834,"k":"a7ca1aad-46de-4eb5-bffd-1c4b93ebc7af-r2","picks":[["daytona","p"],["e2b","m"],["firecracker","m"],["gvisor","m"],["modal","m"]],"ev":35,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several sandbox vendors (Daytona, Modal, E2B, and Cloudflare Sandbox SDK), selected Daytona, and fully implemented the remote sandbox execution in src/runner.ts with the @daytona/sdk dependency.","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":4,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"daytona","secs":302,"k":"a7ca1aad-46de-4eb5-bffd-1c4b93ebc7af-r3","picks":[["daytona","p"],["runloop","m"],["fly-machines","m"],["e2b","m"],["firecracker","m"],["gvisor","m"],["modal","m"]],"ev":44,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly compared Daytona, Modal Sandboxes, and E2B in `docs/sandbox-platform-decision.md` before selecting Daytona. It installed `@daytona/sdk` and refactored `src/runner.ts` to execute untrusted builds inside an ephemeral Daytona sandbox with domain filtering, timeouts, resource limits, and synchronous deletion.","c":1,"e":[["file","package.json:18"],["file","src/runner.ts:63-79"],["file","docs/sandbox-platform-decision.md:23-31"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"daytona","secs":488,"k":"a7ca1aad-46de-4eb5-bffd-1c4b93ebc7af-r4","picks":[["daytona","p"],["e2b","m"],["firecracker","m"],["gvisor","m"],["modal","m"]],"ev":71,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run surveyed sandbox platforms (Daytona, Modal, E2B) and committed to Daytona by installing `@daytona/sdk` and implementing `DaytonaSandboxProvider` in `src/runner.ts` with comprehensive lifecycle, security, and cleanup management.","c":1,"e":[["file","package.json:18-20"],["file","src/runner.ts:3-125"],["file","README.md:20-48"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":5,"pick":"daytona","secs":400,"k":"a7ca1aad-46de-4eb5-bffd-1c4b93ebc7af-r5","picks":[["daytona","p"],["modal","a"],["docker","m"],["e2b","m"],["firecracker","m"],["gvisor","m"]],"ev":51,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated Daytona, Modal, and E2B against the workload requirements, specifically isolation, domain allowlisting, and Node SDK fit. It selected Daytona, added `@daytona/sdk` to dependencies, implemented the remote runner in `src/runner.ts`, updated tests and documentation, and fully committed to Daytona Cloud sandboxes.","c":1,"e":[["file","package.json:18-20"],["file","src/runner.ts:3"],["file","README.md:21-49"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":6,"pick":"daytona","secs":641,"k":"a7ca1aad-46de-4eb5-bffd-1c4b93ebc7af-r6","picks":[["daytona","p"],["e2b","a"],["modal","a"],["firecracker","m"],["gvisor","m"]],"ev":53,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated multiple sandbox providers (Daytona, E2B, Modal, CodeSandbox SDK, Fly) and explicitly selected Daytona, integrating `@daytona/sdk` in `package.json` and implementing `runGeneratedProject` using Daytona Linux microVMs in `src/runner.ts`.","c":1,"e":[["file","package.json:12-14"],["file","src/runner.ts:3"],["file","src/runner.ts:83-138"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel-sandbox","secs":919,"k":"c01e5e2c-2893-419d-80a5-17c5ec458625-r1","picks":[["vercel-sandbox","p"],["daytona","m"],["codesandbox-sdk","m"],["runloop","m"],["blaxel","m"],["e2b","m"],["firecracker","m"],["gvisor","m"],["modal","m"]],"ev":112,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated multiple managed sandbox solutions (Vercel Sandbox, E2B, Modal, and others) and selected Vercel Sandbox (@vercel/sandbox). It implemented the execution path in src/runner.ts using @vercel/sandbox with network policies, resource/time limits, and cleanup.","c":0.99,"e":[["file","package.json:18"],["file","src/runner.ts:1"],["file","README.md:8"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"vercel-sandbox","secs":866,"k":"c01e5e2c-2893-419d-80a5-17c5ec458625-r2","picks":[["vercel-sandbox","p"],["gvisor","m"],["firecracker","m"],["e2b","a"],["daytona","a"],["modal","m"]],"ev":102,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent compared Vercel Sandbox, E2B, Modal Sandboxes, and Daytona against workload requirements, selected Vercel Sandbox, and implemented the full provider-backed execution path in src/runner.ts with @vercel/sandbox.","c":1,"e":[["file","package.json"],["file","src/runner.ts:1-231"],["file","README.md:19-35"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel-sandbox","secs":606,"k":"c01e5e2c-2893-419d-80a5-17c5ec458625-r3","picks":[["vercel-sandbox","p"],["gvisor","m"],["firecracker","m"],["daytona","m"],["e2b","m"],["modal","m"]],"ev":74,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run systematically compared Vercel Sandbox, E2B, Modal, and Daytona, explicitly committing to Vercel Sandbox (@vercel/sandbox SDK v3.2.1) by implementing VercelSandboxProvider in src/provider.ts, updating package.json dependencies, configuring network policy transition phases, and setting up automated tests.","c":1,"e":[["file","package.json:18-20"],["file","src/provider.ts:1-150"],["file","README.md:39-50"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"vercel-sandbox","secs":769,"k":"c01e5e2c-2893-419d-80a5-17c5ec458625-r4","picks":[["vercel-sandbox","p"],["cloudflare-sandbox","m"],["daytona","m"],["e2b","m"],["firecracker","m"],["gvisor","m"],["modal","m"]],"ev":88,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run evaluated multiple remote sandbox platforms (Vercel Sandbox, E2B, Modal, Daytona, Cloudflare Sandbox SDK, Fly Machines) and explicitly selected Vercel Sandbox. It installed `@vercel/sandbox`, wrote a complete provider implementation in `src/vercel-sandbox.ts`, documented the trade-offs in `docs/adr-001-sandbox-provider.md`, and updated configuration and tests.","c":1,"e":[["file","package.json"],["file","src/vercel-sandbox.ts"],["file","docs/adr-001-sandbox-provider.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":5,"pick":"vercel-sandbox","secs":718,"k":"c01e5e2c-2893-419d-80a5-17c5ec458625-r5","picks":[["vercel-sandbox","p"],["firecracker","m"],["gvisor","m"],["daytona","m"],["e2b","m"],["modal","m"]],"ev":80,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated multiple remote sandbox vendors (Vercel Sandbox, E2B, Daytona, Modal) against the workload requirements, selected Vercel Sandbox (@vercel/sandbox), and implemented it in the codebase with complete configuration, runner execution, error handling, tests, and documentation.","c":1,"e":[["file","package.json:18"],["file","src/runner.ts:3"],["file","src/config.ts:1"],["file","README.md:11-20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":6,"pick":"vercel-sandbox","secs":638,"k":"c01e5e2c-2893-419d-80a5-17c5ec458625-r6","picks":[["vercel-sandbox","p"],["fly-machines","m"],["codesandbox-sdk","m"],["e2b","a"],["daytona","m"],["firecracker","m"],["gvisor","m"],["modal","m"]],"ev":60,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent replaced the local child_process execution runner with `@vercel/sandbox`, creating an isolated Firecracker microVM executor with strict timeout, output caps, and a two-phase network policy (registry allowlist during install, deny-all during command execution). Alternative sandboxing providers (E2B, Modal, Daytona) were researched via official docs and compared before committing to Vercel Sandbox.","c":1,"e":[["file","package.json:18"],["file","src/executor.ts:1-195"],["file","README.md:9-35"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"modal","secs":642,"k":"48c042f7-8dda-477c-aaff-016587e8133d-r1","picks":[["modal","p"],["daytona","m"],["gvisor","m"],["firecracker","m"],["aws-lambda","m"],["e2b","m"]],"ev":73,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated multiple sandbox options (primarily Modal and E2B), recommended Modal Sandboxes due to its runtime resource limit flexibility and lack of template management overhead, and fully implemented the executor, server endpoints, configuration, and tests using the modal Python SDK.","c":1,"e":[["file","pyproject.toml"],["file","northstar/executor.py"],["file","README.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"modal","secs":600,"k":"48c042f7-8dda-477c-aaff-016587e8133d-r2","picks":[["modal","p"],["cloudflare-sandbox","m"],["daytona","m"],["docker","m"],["e2b","m"],["firecracker","m"],["gvisor","m"]],"ev":64,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Modal Sandboxes and implemented the complete integration in northstar/executor.py using the modal Python SDK. Other remote sandbox products (E2B, Daytona, Cloudflare Sandbox SDK, Anthropic Code Execution, Docker) were specifically evaluated and disqualified.","c":0.99,"e":[["file","pyproject.toml:5"],["file","northstar/executor.py:15-103"],["file","README.md:5-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"modal","secs":842,"k":"48c042f7-8dda-477c-aaff-016587e8133d-r3","picks":[["modal","p"],["e2b","a"],["anthropic-code-execution","m"],["aws-lambda","m"],["daytona","m"],["gvisor","m"],["nsjail","m"]],"ev":65,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent selected Modal Sandboxes as the primary third-party sandbox solution and fully integrated it into the project (`northstar/sandbox.py`, `northstar/executor.py`, `northstar/server.py`, `pyproject.toml`, and test suites). It also thoroughly evaluated E2B as an alternative and explicitly rejected Anthropic Code Execution, AWS Lambda/Fargate, Daytona, Cloudflare Sandboxes, Vercel Sandboxes, gVisor, and NsJail with concrete disqualifiers.","c":1,"e":[["file","pyproject.toml:5"],["file","northstar/sandbox.py:1-372"],["file","README.md:5-20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"modal","secs":855,"k":"48c042f7-8dda-477c-aaff-016587e8133d-r4","picks":[["modal","p"],["daytona","m"],["codesandbox-sdk","m"],["aws-lambda","m"],["firecracker","m"],["gvisor","m"],["e2b","m"]],"ev":79,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several sandbox services and selected Modal Sandboxes, adding `modal` to dependencies and implementing full remote sandbox execution in `northstar/sandbox.py` with custom limits, timeouts, error reporting, and network allowlist policies.","c":1,"e":[["file","pyproject.toml"],["file","northstar/sandbox.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":3,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":5,"pick":"modal","secs":697,"k":"48c042f7-8dda-477c-aaff-016587e8133d-r5","picks":[["modal","p"],["daytona","m"],["e2b","a"],["firecracker","m"],["gvisor","m"]],"ev":68,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several sandbox services (Modal, E2B, Judge0, Anthropic Code Execution, Daytona) and implemented Modal Sandbox directly in `northstar/executor.py` and `pyproject.toml`.","c":1,"e":[["file","pyproject.toml:5"],["file","northstar/executor.py:155-180"],["file",".env.example:5-7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":6,"pick":"modal","secs":775,"k":"48c042f7-8dda-477c-aaff-016587e8133d-r6","picks":[["modal","p"],["daytona","m"],["e2b","m"]],"ev":74,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several sandbox solutions (Modal, E2B, Daytona) and explicitly chose Modal Sandboxes. It added the `modal` dependency to `pyproject.toml`, implemented `modal.Sandbox.create` and execution lifecycle management in `northstar/executor.py`, and added extensive test suites for offline and live testing.","c":1,"e":[["file","northstar/executor.py"],["file","pyproject.toml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel-sandbox","secs":893,"k":"e3688e19-1740-4b6d-baec-4c48843f3152-r1","picks":[["vercel-sandbox","p"],["firecracker","m"],["aws-lambda","m"],["cloudflare-workers","m"],["daytona","m"],["deno","m"],["e2b","m"],["gvisor","m"],["modal","m"]],"ev":90,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated multiple remote sandbox platforms (Vercel Sandbox, E2B, Modal, Cloudflare Dynamic Workers) against the workload requirements, selected Vercel Sandbox, installed `@vercel/sandbox`, implemented an external microVM execution harness with strict timeouts, network isolation, and resource limits, and documented the operational setup in the README.","c":1,"e":[["file","package.json"],["file","src/sandbox/vercel-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":3,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"e2b","secs":899,"k":"e3688e19-1740-4b6d-baec-4c48843f3152-r2","picks":[["e2b","p"],["daytona","m"],["aws-lambda","m"],["cloudflare-dynamic-workers","m"],["cloudflare-workers","m"],["codesandbox-sdk","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["vercel-sandbox","m"]],"ev":105,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run clearly selected and implemented E2B, adding the 'e2b' dependency, creating an E2B integration module in src/sandbox/e2b.ts with network isolation and timeouts, updating app configuration and tests, and writing extensive architectural documentation comparing E2B against Cloudflare Dynamic Workers, Modal, and Vercel Sandbox.","c":1,"e":[["file","package.json:17"],["file","src/sandbox/e2b.ts:1-89"],["file","docs/generated-code-execution.md:112-140"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"cloudflare-workers","secs":2134,"k":"e3688e19-1740-4b6d-baec-4c48843f3152-r3","picks":[["cloudflare-workers","p"],["cloudflare-sandbox","m"],["daytona","m"],["aws-lambda","m"],["cloudflare-dynamic-workers","m"],["deno","m"],["e2b","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["vercel-sandbox","m"]],"ev":133,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run evaluated multiple sandbox platforms (Cloudflare Dynamic Workers, E2B, Vercel Sandbox, Modal, Daytona) and selected Cloudflare Workers (using Dynamic Workers / Worker Loader bindings) as the ideal match for pure JS transforms. It fully implemented the executor in sandbox/ and wired the application client in src/sandbox.ts.","c":0.98,"e":[["file","docs/sandbox-runtime.md:27-31"],["file","sandbox/wrangler.jsonc:1-21"],["file","sandbox/src/index.ts:1-50"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"vercel-sandbox","secs":832,"k":"e3688e19-1740-4b6d-baec-4c48843f3152-r4","picks":[["vercel-sandbox","p"],["aws-lambda","m"],["cloudflare-dynamic-workers","m"],["cloudflare-workers","m"],["daytona","m"],["deno","m"],["e2b","m"],["firecracker","m"],["gvisor","m"],["modal","m"]],"ev":87,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run evaluated multiple remote code execution and sandbox platforms (Vercel Sandbox, E2B, Cloudflare Dynamic Workers, Modal, Daytona) and implemented Vercel Sandbox with @vercel/sandbox in code, complete with network egress blocking, execution timeouts, and cleanup handlers.","c":1,"e":[["file","package.json:17"],["file","src/sandbox/executor.ts:60-75"],["file","README.md:46-49"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":5,"pick":"cloudflare-dynamic-workers","secs":1020,"k":"e3688e19-1740-4b6d-baec-4c48843f3152-r5","picks":[["cloudflare-dynamic-workers","p"],["daytona","m"],["aws-lambda","m"],["codesandbox-sdk","m"],["cloudflare-workers","m"],["deno","m"],["e2b","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["vercel-sandbox","m"]],"ev":89,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly evaluated five managed sandbox options (Cloudflare Dynamic Workers, Vercel Sandbox, E2B, Modal Sandboxes, and Deno Deploy Subhosting) against the untrusted row-transform JavaScript workload. It selected Cloudflare Dynamic Workers, implemented a complete standalone Cloudflare Worker executor using the Worker Loader binding, updated the Node application to communicate with it, and provided documentation and verification tests.","c":1,"e":[["file","README.md"],["file","executor/wrangler.jsonc"],["file","executor/src/index.ts"],["file","src/executor.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":6,"pick":"vercel-sandbox","secs":739,"k":"e3688e19-1740-4b6d-baec-4c48843f3152-r6","picks":[["vercel-sandbox","p"],["daytona","m"],["fly-machines","m"],["deno","m"],["firecracker","m"],["cloudflare-sandbox","m"],["cloudflare-workers","m"],["docker","m"],["e2b","m"],["gvisor","m"],["modal","m"]],"ev":89,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent compared Vercel Sandbox, E2B, Modal, and Cloudflare Sandbox SDK against the repository's workload and isolation requirements, selected Vercel Sandbox, and fully integrated the @vercel/sandbox SDK into the codebase with complete test and configuration coverage.","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":"mail","wave":3,"date":"2026-08-31","repo":"healthtech-ehr","variant":"base","family":"mail-enterprise-healthtech-ehr","pid":"MAIL-12a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"azure-communication-services","secs":1418,"k":"c1878547-0837-4e62-ae67-740033894f13-r1","picks":[["azure-communication-services","p"],["paubox","m"],["luxsci","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":140,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The run evaluated multiple transactional email options against HIPAA compliance, secret management, and private VNet constraints. It committed to Azure Communication Services Email, implementing Bicep provisioning, Spring Boot integration via the official SDK, and workload identity authentication, while explicitly documenting rejections for alternative mail providers.","c":1,"e":[["file","infra/bicep/modules/communication.bicep"],["file","notifications/pom.xml"],["file","notifications/src/main/java/com/marrowe/notifications/mail/AcsEmailSender.java"],["file","docs/compliance/patient-communications.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"healthtech-ehr","variant":"base","family":"mail-enterprise-healthtech-ehr","pid":"MAIL-12a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"azure-communication-services","secs":920,"k":"c1878547-0837-4e62-ae67-740033894f13-r2","picks":[["azure-communication-services","p"],["paubox","m"],["luxsci","m"],["sendgrid","m"],["smtp","m"]],"ev":97,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent selected Azure Communication Services Email as its primary mail solution to satisfy strict HIPAA compliance and zero-static-credential requirements via Entra Workload Identity. It implemented the full notification pipeline: a new Spring Boot microservice, Bicep modules, k8s manifests, and unit tests.","c":1,"e":[["file","notifications/pom.xml"],["file","notifications/src/main/java/com/marrowe/notify/MailConfig.java"],["file","notifications/src/main/java/com/marrowe/notify/AcsNotificationTransport.java"],["file","infra/bicep/modules/communication.bicep"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"healthtech-ehr","variant":"base","family":"mail-enterprise-healthtech-ehr","pid":"MAIL-12a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"azure-communication-services","secs":1491,"k":"c1878547-0837-4e62-ae67-740033894f13-r3","picks":[["azure-communication-services","p"],["aws-ses","m"],["luxsci","m"],["mailgun","m"],["paubox","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":144,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The run adopted Azure Communication Services (ACS) Email as the transactional email solution for encounter completion notifications. It provisioned the infrastructure via Bicep, created the Spring Boot `notifications` microservice, implemented the Java SDK client with workload identity authentication, and detailed the rejection of alternative services (Amazon SES, SendGrid, Postmark, Mailgun, Paubox, LuxSci, and self-hosted SMTP) based on HIPAA compliance and secret-management constraints.","c":1,"e":[["file","infra/bicep/modules/email.bicep"],["file","notifications/pom.xml"],["file","notifications/src/main/java/com/marrowe/notifications/AcsEmailSender.java"],["file","docs/notifications.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"healthtech-ehr","variant":"base","family":"mail-enterprise-healthtech-ehr","pid":"MAIL-12a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"azure-communication-services","secs":1213,"k":"c1878547-0837-4e62-ae67-740033894f13-r4","picks":[["azure-communication-services","p"],["mailgun","m"],["postmark","m"],["sendgrid","m"]],"ev":131,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly evaluated transactional email options for HIPAA-compliant patient notification dispatch and selected Azure Communication Services Email. It configured the Bicep templates for ACS resources, added the Azure Communication Email SDK dependency to the new notifications service module, implemented the AcsEmailSender class using the SDK's EmailClient and TokenCredential, and updated compliance documentation accordingly while rejecting SendGrid, Postmark, Mailgun, and SES.","c":1,"e":[["file","infra/bicep/modules/communication.bicep:43-59"],["file","notifications/pom.xml:70-74"],["file","notifications/src/main/java/com/marrowe/notify/mail/AcsEmailSender.java:1-76"],["file","docs/compliance/patient-notifications.md:46-59"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel-sandbox","secs":1012,"k":"46ff58a5-2b9a-4a12-96ea-0520c2a41a59-r1","picks":[["vercel-sandbox","p"],["e2b","a"],["daytona","m"],["firecracker","m"],["fly-machines","m"],["modal","m"]],"ev":91,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several remote sandbox solutions (Vercel Sandbox, E2B, Modal, Daytona, Cloudflare Sandbox SDK, Fly Machines) and committed to Vercel Sandbox by installing `@vercel/sandbox`, implementing a full provider adapter and executor with phased firewall lockdown, and adding comprehensive tests.","c":1,"e":[["file","package.json:18-20"],["file","src/vercel-provider.ts:1-226"],["file","src/runner.ts:303-345"],["file","README.md:9-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"vercel-sandbox","secs":758,"k":"46ff58a5-2b9a-4a12-96ea-0520c2a41a59-r2","picks":[["vercel-sandbox","p"],["codesandbox-sdk","m"],["modal","m"],["e2b","a"],["daytona","a"],["firecracker","m"]],"ev":65,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several sandbox platforms (Vercel Sandbox, E2B, Daytona) and selected Vercel Sandbox. It installed `@vercel/sandbox`, wrote a full implementation in `src/runner.ts`, updated `.env.example`, `src/config.ts`, `src/server.ts`, tests, and the project `README.md`.","c":1,"e":[["file","package.json"],["file","src/runner.ts:23"],["file",".env.example:5"],["file","README.md:7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Generated UI project builds","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel-sandbox","secs":921,"k":"46ff58a5-2b9a-4a12-96ea-0520c2a41a59-r3","picks":[["vercel-sandbox","p"],["blaxel","m"],["daytona","m"],["e2b","m"],["firecracker","m"],["fly-machines","m"],["modal","m"],["runloop","m"]],"ev":64,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated multiple sandbox platforms (Vercel Sandbox, E2B, Modal, Daytona, Fly Machines) and explicitly selected Vercel Sandbox due to its TypeScript SDK support, managed Node images, and host-enforced dynamic network policy swapping. It implemented the entire runner, provider interface, server throttling, and test suite around `@vercel/sandbox`.","c":1,"e":[["file","package.json:17-19"],["file","src/sandbox.ts:1-176"],["file","README.md:5-24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"vercel-sandbox","secs":789,"k":"46ff58a5-2b9a-4a12-96ea-0520c2a41a59-r4","picks":[["vercel-sandbox","p"],["e2b","a"],["daytona","m"],["firecracker","m"],["gvisor","m"],["modal","m"]],"ev":75,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended, installed (@vercel/sandbox), and implemented Vercel Sandbox across the codebase to handle untrusted code execution with two-phase network isolation.","c":1,"e":[["file","package.json"],["file","src/runner.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":3,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":5,"pick":"e2b","secs":658,"k":"46ff58a5-2b9a-4a12-96ea-0520c2a41a59-r5","picks":[["e2b","p"],["codesandbox-sdk","m"],["vercel-sandbox","a"],["daytona","m"],["firecracker","m"],["fly-machines","m"],["modal","m"]],"ev":58,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated multiple managed sandbox providers and selected E2B based on its ephemeral default lifecycle and domain-based egress filtering. It installed the `e2b` package and implemented a working sandbox adapter, runner, and configuration in the codebase.","c":1,"e":[["file","package.json:18"],["file","src/e2b.ts:1-81"],["file","src/runner.ts:2-275"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":6,"pick":"vercel-sandbox","secs":600,"k":"46ff58a5-2b9a-4a12-96ea-0520c2a41a59-r6","picks":[["vercel-sandbox","p"],["codesandbox-sdk","m"],["firecracker","m"],["daytona","m"],["e2b","m"],["modal","m"]],"ev":54,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run evaluated multiple sandbox providers and fully committed to Vercel Sandbox by installing `@vercel/sandbox`, updating the runner, configuration, and documentation, and implementing complete build lifecycle execution with domain allowlist egress filtering.","c":1,"e":[["file","package.json:18-20"],["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":4,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-enterprise-fleet","pid":"SBX-MANAGED-ENTERPRISE-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"daytona","secs":413,"k":"ad7dd91e-c212-41c2-9a0f-b2e3883b1eeb-r1","picks":[["daytona","p"],["docker","m"],["e2b","m"],["modal","m"]],"ev":51,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run evaluated Daytona Cloud, Modal, and E2B against requirements for enterprise sandboxes. It selected Daytona Cloud, installed `@daytona/sdk`, implemented a full provider-backed executor in `src/executor.ts`, wrote configuration in `config/daytona.production.json`, and explicitly documented the rejection reasons for Modal and E2B.","c":1,"e":[["file","package.json:18-20"],["file","config/daytona.production.json:1-26"],["file","src/executor.ts:68-268"],["file","README.md:5-18"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-enterprise-fleet","pid":"SBX-MANAGED-ENTERPRISE-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"daytona","secs":373,"k":"ad7dd91e-c212-41c2-9a0f-b2e3883b1eeb-r2","picks":[["daytona","p"],["firecracker","m"],["e2b","m"],["modal","m"]],"ev":53,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated multiple sandbox providers (Daytona, Modal, E2B) against multi-region, audit, networking, and secret management constraints, ultimately selecting and implementing Daytona using the official `@daytona/sdk` package.","c":1,"e":[["file","package.json:18"],["file","config/daytona.executor.json:1-17"],["file","src/runner.ts:63-180"],["file","README.md:46-52"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-enterprise-fleet","pid":"SBX-MANAGED-ENTERPRISE-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"daytona","secs":690,"k":"ad7dd91e-c212-41c2-9a0f-b2e3883b1eeb-r3","picks":[["daytona","p"],["e2b","m"],["modal","m"]],"ev":43,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run evaluated managed sandbox providers and selected Daytona Enterprise as the third-party sandbox platform. It installed @daytona/sdk, configured US/EU regions and quotas in config/daytona.production.json, implemented DaytonaSandboxExecutor in src/runner.ts, and documented the comparison against Modal and E2B in README.md.","c":1,"e":[["file","package.json:18"],["file","config/daytona.production.json:1-35"],["file","src/runner.ts:74-234"],["file","README.md:9-17"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-enterprise-fleet","pid":"SBX-MANAGED-ENTERPRISE-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"daytona","secs":353,"k":"ad7dd91e-c212-41c2-9a0f-b2e3883b1eeb-r4","picks":[["daytona","p"],["e2b","m"],["gvisor","m"],["modal","m"],["runloop","m"]],"ev":43,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several managed sandbox products (Daytona, Modal, Runloop, E2B, CodeSandbox) and selected Daytona, installing `@daytona/sdk`, creating `src/executor.ts` with Daytona SDK integration, adding production configuration in `config/sandbox.production.json`, and documenting the decision matrix in `README.md`.","c":1,"e":[["file","package.json"],["file","src/executor.ts"],["file","config/sandbox.production.json"],["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":4,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-enterprise-fleet","pid":"SBX-MANAGED-ENTERPRISE-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":5,"pick":"daytona","secs":394,"k":"ad7dd91e-c212-41c2-9a0f-b2e3883b1eeb-r5","picks":[["daytona","p"],["e2b","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["runloop","m"]],"ev":54,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent replaced the local execution runner with `@daytona/sdk`, configuring Daytona Cloud Enterprise with Linux VM sandboxes across US/EU regions. Modal and E2B were explicitly evaluated against workload requirements in `docs/sandbox-platform-decision.md` and rejected.","c":1,"e":[["file","package.json:18"],["file","config/executor.production.json:1-29"],["file","src/runner.ts:1-305"],["file","docs/sandbox-platform-decision.md:1-56"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-enterprise-fleet","pid":"SBX-MANAGED-ENTERPRISE-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":6,"pick":"modal","secs":604,"k":"ad7dd91e-c212-41c2-9a0f-b2e3883b1eeb-r6","picks":[["modal","p"],["daytona","m"],["docker","m"],["e2b","m"],["runloop","m"]],"ev":50,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent replaced the local child_process runner with a production integration of Modal Sandboxes, installing the `modal` npm package, implementing the client SDK in `src/runner.ts`, and documenting the vendor evaluation against Runloop, Daytona, and E2B in `docs/sandbox-platform-decision.md`.","c":1,"e":[["file","package.json:18-20"],["file","src/runner.ts:3-100"],["file","config/sandbox.production.json:1-33"],["file","docs/sandbox-platform-decision.md:1-27"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"Self-hosting, privacy or residency"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"resend","secs":1233,"k":"b8cf3d51-cbc7-4bce-8408-c1b41e600b35-r1","picks":[["resend","p"],["postmark","m"]],"ev":121,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly selected and fully integrated Resend for password reset transactional emails using direct fetch calls against Resend's API. Postmark and Amazon SES were considered during provider evaluation but rejected due to minimum plan pricing and operational complexity respectively.","c":1,"e":[["file","src/lib/server/email/provider.ts:1-169"],["file","README.md:38-125"],["file",".env.example:12-16"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"postmark","secs":973,"k":"b8cf3d51-cbc7-4bce-8408-c1b41e600b35-r2","picks":[["postmark","p"],["mailgun","m"],["sendgrid","m"],["resend","a"],["loops","m"]],"ev":94,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly chose and fully integrated Postmark as the transactional email provider for password resets using native fetch without external dependencies. Alternatives such as SES and Resend were deliberated and evaluated against the project's Svelte stack and low-ops needs.","c":1,"e":[["file","src/lib/server/email/postmark.ts:1-142"],["file","src/lib/server/email/config.ts:1-85"],["file","README.md:29-87"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"resend","secs":1483,"k":"b8cf3d51-cbc7-4bce-8408-c1b41e600b35-r3","picks":[["resend","p"],["mailgun","m"],["sendgrid","m"],["brevo","m"],["postmark","m"]],"ev":147,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The run chose Resend as the sole transactional email provider, fully implementing the HTTP transport, templates, forgot/reset routes, and webhook suppression endpoints without adding third-party npm dependencies. Amazon SES and Postmark were evaluated and explicitly rejected.","c":1,"e":[["file","src/lib/server/email/resend.ts"],["file","src/routes/api/webhooks/resend/+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":3,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"postmark","secs":1107,"k":"b8cf3d51-cbc7-4bce-8408-c1b41e600b35-r4","picks":[["postmark","p"],["resend","a"]],"ev":104,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly evaluated Postmark, Resend, and Amazon SES, selecting Postmark as the primary transactional email service. The agent fully implemented Postmark delivery via a native fetch client in src/lib/server/email/postmark.ts, wired it to a local SQLite outbox queue and password reset flow, and configured fly.toml and environment variables for production deployment.","c":1,"e":[["file","src/lib/server/email/postmark.ts:1-112"],["file","fly.toml:17-18"],["file","README.md:29-87"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel-sandbox","secs":711,"k":"40982435-fdd7-4441-b912-b4ee9fce441c-r1","picks":[["vercel-sandbox","p"],["runloop","m"],["codesandbox-sdk","m"],["daytona","m"],["e2b","m"],["firecracker","m"],["gvisor","m"],["modal","m"]],"ev":66,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent replaced the local host execution in src/runner.js with Vercel Sandbox (@vercel/sandbox SDK), configuring Firecracker microVM sandboxes with domain-based egress firewalls, timeouts, and resource caps. E2B, Modal, and Daytona were explicitly compared and rejected with detailed trade-off analysis in README.md and the final answer.","c":1,"e":[["file","package.json:14-16"],["file","src/runner.js:2-3"],["file","README.md:9-20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"vercel-sandbox","secs":561,"k":"40982435-fdd7-4441-b912-b4ee9fce441c-r2","picks":[["vercel-sandbox","p"],["aws-fargate","m"],["runloop","m"],["daytona","m"],["e2b","m"],["firecracker","m"],["gvisor","m"],["modal","m"]],"ev":56,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent replaced the local execution runner with `@vercel/sandbox`, creating dedicated Firecracker microVMs per task on Vercel's infrastructure. It explicitly compared Vercel Sandbox against E2B, Modal, and Daytona in README.md and the final answer, detailing why each alternative was rejected.","c":1,"e":[["file","package.json:12-14"],["file","src/sandbox.js:1-360"],["file","README.md:7-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel-sandbox","secs":770,"k":"40982435-fdd7-4441-b912-b4ee9fce441c-r3","picks":[["vercel-sandbox","p"],["modal","m"],["codesandbox-sdk","m"],["runloop","m"],["blaxel","m"],["e2b","a"],["daytona","a"],["docker","m"],["firecracker","m"]],"ev":81,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent replaced the local execution path with Vercel Sandbox (`@vercel/sandbox`), implementing executor logic, startup configuration validation, network firewall policies, and comprehensive tests while comparing against E2B and Daytona in documentation and README.","c":1,"e":[["file","package.json:12-14"],["file","src/executor.js:1-243"],["file","README.md:46-60"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"vercel-sandbox","secs":757,"k":"40982435-fdd7-4441-b912-b4ee9fce441c-r4","picks":[["vercel-sandbox","p"],["blaxel","m"],["codesandbox-sdk","m"],["cloudflare-sandbox","m"],["daytona","m"],["docker","m"],["e2b","m"],["firecracker","m"],["gvisor","m"],["modal","m"],["runloop","m"]],"ev":81,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated multiple sandbox solutions (Vercel Sandbox, E2B, Modal, Daytona, Cloudflare Sandbox SDK), selected Vercel Sandbox, installed `@vercel/sandbox`, and implemented full execution and lifecycle management around it in `src/sandbox.js`, `src/config.js`, `src/runner.js`, and `src/server.js`.","c":1,"e":[["file","package.json:12-14"],["file","src/sandbox.js:1-189"],["file","README.md:7-24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":5,"pick":"e2b","secs":755,"k":"40982435-fdd7-4441-b912-b4ee9fce441c-r5","picks":[["e2b","p"],["vercel-sandbox","m"],["runloop","m"],["codesandbox-sdk","m"],["aws-fargate","m"],["daytona","m"],["firecracker","m"],["gvisor","m"],["modal","m"]],"ev":87,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run installed the `e2b` package, completely replaced host process execution with E2B sandbox orchestration in `src/runner.js` and `src/sandbox/e2b.js`, added template configuration in `templates/agent-task.js`, and documented its vendor selection over Modal and Daytona.","c":1,"e":[["file","package.json:13"],["file","src/sandbox/e2b.js:1-135"],["file","README.md:21-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":6,"pick":"vercel-sandbox","secs":731,"k":"40982435-fdd7-4441-b912-b4ee9fce441c-r6","picks":[["vercel-sandbox","p"],["aws-fargate","m"],["daytona","m"],["docker","m"],["e2b","m"],["firecracker","m"],["gvisor","m"],["modal","m"]],"ev":81,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several sandbox solutions (Vercel Sandbox, E2B, Modal, Daytona, Cloudflare Sandbox SDK), selected Vercel Sandbox, installed `@vercel/sandbox`, and implemented full provider and runner orchestration.","c":1,"e":[["file","package.json:14"],["file","src/providers/vercel-sandbox.js:1-66"],["file","README.md:9-40"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"healthtech-ehr","variant":"base","family":"bc-mail-prompt-b-05","pid":"MAIL-PB-05a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"azure-communication-services","secs":1043,"k":"f9fcf0bc-297b-42b5-8c38-e1cde5063df8-r1","picks":[["azure-communication-services","p"],["mailgun","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":94,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The run comprehensively implemented email notification delivery using Azure Communication Services Email. It configured the Java client with DefaultAzureCredential, added Bicep modules to provision the email service and managed domain, updated compliance documentation, and created a notifications microservice. Alternative third-party email providers (SendGrid, Mailgun, SES, Postmark) and self-hosted SMTP were explicitly evaluated and rejected due to compliance and operational overhead.","c":1,"e":[["file","notifications/pom.xml"],["file","notifications/src/main/java/com/marrowe/notify/AcsSummaryEmailSender.java"],["file","infra/bicep/modules/communication.bicep"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Self-hosting, privacy or residency"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"healthtech-ehr","variant":"base","family":"bc-mail-prompt-b-05","pid":"MAIL-PB-05a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"azure-communication-services","secs":1326,"k":"f9fcf0bc-297b-42b5-8c38-e1cde5063df8-r2","picks":[["azure-communication-services","p"],["sendgrid","m"],["smtp","m"]],"ev":115,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly selected and implemented Azure Communication Services Email as the outbound email delivery transport, adding the Azure SDK dependency, Bicep infrastructure definitions with regional data location pinning, and full Java application integration.","c":1,"e":[["file","notifications/pom.xml"],["file","notifications/src/main/java/com/marrowe/notifications/delivery/AcsNotificationMailer.java"],["file","infra/bicep/modules/communication.bicep"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Self-hosting, privacy or residency"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"healthtech-ehr","variant":"base","family":"bc-mail-prompt-b-05","pid":"MAIL-PB-05a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"azure-communication-services","secs":1241,"k":"f9fcf0bc-297b-42b5-8c38-e1cde5063df8-r3","picks":[["azure-communication-services","p"],["smtp","m"]],"ev":108,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly selected and fully integrated Azure Communication Services Email via Bicep templates (`infra/bicep/modules/communication.bicep`), SDK dependencies (`azure-communication-email`), and application code (`AcsNotificationSender.java`). Third-party services like SendGrid and Mailgun, along with generic self-hosted SMTP relays, were evaluated and rejected due to region-pinning and PHI retention compliance constraints.","c":1,"e":[["file","infra/bicep/modules/communication.bicep:1-95"],["file","notifications/src/main/java/com/marrowe/notifications/mail/AcsNotificationSender.java:1-93"],["file","notifications/pom.xml:54-58"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Self-hosting, privacy or residency"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"healthtech-ehr","variant":"base","family":"bc-mail-prompt-b-05","pid":"MAIL-PB-05a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"azure-communication-services","secs":1224,"k":"f9fcf0bc-297b-42b5-8c38-e1cde5063df8-r4","picks":[["azure-communication-services","p"],["mailgun","m"],["sendgrid","m"],["smtp","m"]],"ev":133,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The run explicitly selected Azure Communication Services Email to stay within Microsoft's existing BAA and compliance boundary. It provisioned ACS resources in Bicep (`infra/bicep/modules/communication.bicep`), integrated `com.azure:azure-communication-email` in `notifications/pom.xml`, and implemented `AcsEmailTransport.java`. Alternative external email providers (SendGrid, Mailgun, Amazon SES) were explicitly analyzed and rejected due to compliance risk and third-party subprocessor restrictions.","c":1,"e":[["file","notifications/pom.xml:51-60"],["file","notifications/src/main/java/com/marrowe/notifications/transport/AcsEmailTransport.java:34-75"],["file","infra/bicep/modules/communication.bicep:37-67"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"modal","secs":654,"k":"7e1c229f-1b1d-427c-9541-edf3e2c13d19-r1","picks":[["modal","p"],["daytona","m"],["e2b","m"]],"ev":69,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated Modal, Daytona, and E2B, specifically rejecting Daytona and E2B due to missing capabilities (lifetime deadline enforcement and network blocking verification). It selected Modal Sandboxes as the primary choice, added `modal` to dependencies in `pyproject.toml`, and fully implemented the remote executor in `northstar/sandbox.py`.","c":1,"e":[["file","pyproject.toml"],["file","northstar/sandbox.py"],["trace","70"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"modal","secs":443,"k":"7e1c229f-1b1d-427c-9541-edf3e2c13d19-r2","picks":[["modal","p"],["daytona","m"],["e2b","m"],["firecracker","m"],["gvisor","m"]],"ev":41,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated multiple remote sandbox options (Modal, E2B, Anthropic Code Execution, Daytona), installed and tested the SDKs in scratch environments, selected Modal for its programmatic CPU/memory budgeting and networking isolation capabilities, and completely implemented and tested the integration.","c":1,"e":[["file","pyproject.toml:5"],["file","northstar/executor.py:126-148"],["file","README.md:7-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"modal","secs":874,"k":"7e1c229f-1b1d-427c-9541-edf3e2c13d19-r3","picks":[["modal","p"],["aws-lambda","m"],["daytona","m"],["e2b","m"],["gvisor","m"]],"ev":66,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run fully implemented remote code execution using Modal Sandboxes (`modal.Sandbox.create`) in `northstar/modal_sandbox.py`, added `modal>=1.0` to `pyproject.toml`, and documented the architecture in `README.md` while comparing and dismissing alternatives like E2B and Daytona.","c":1,"e":[["file","pyproject.toml"],["file","northstar/modal_sandbox.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":3,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"modal","secs":498,"k":"7e1c229f-1b1d-427c-9541-edf3e2c13d19-r4","picks":[["modal","p"],["daytona","m"],["codesandbox-sdk","m"],["e2b","m"],["firecracker","m"],["gvisor","m"]],"ev":42,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly implemented remote execution using Modal Sandboxes (installing the `modal` package, creating `northstar/executor.py` with `modal.Sandbox.create` network blocking and resource constraints, and writing unit tests against a fake modal implementation). It directly compared Modal with E2B by testing both SDKs and rejected E2B due to the lack of per-call resource controls.","c":1,"e":[["file","pyproject.toml:5"],["file","northstar/executor.py:15-125"],["file","README.md:7-19"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":5,"pick":"modal","secs":682,"k":"7e1c229f-1b1d-427c-9541-edf3e2c13d19-r5","picks":[["modal","p"],["daytona","m"],["gvisor","m"],["firecracker","m"],["e2b","m"]],"ev":51,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent replaced the in-process execution with Modal Sandboxes via the Modal Python SDK (`modal.Sandbox.create`), configuring explicit network blocking, CPU/memory limits, and timeout controls, and wrote comprehensive unit/integration test suites.","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":3,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":6,"pick":"modal","secs":556,"k":"7e1c229f-1b1d-427c-9541-edf3e2c13d19-r6","picks":[["modal","p"],["gvisor","m"],["aws-lambda","m"],["daytona","m"],["e2b","m"]],"ev":55,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run evaluated multiple remote sandbox providers (Modal, E2B, Daytona, AWS Lambda) and committed fully to Modal by adding `modal>=1.5` to `pyproject.toml` and rewriting `northstar/executor.py` to use `modal.Sandbox.create`.","c":1,"e":[["file","pyproject.toml:5"],["file","northstar/executor.py:10-150"],["file","README.md:7-20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel-sandbox","secs":487,"k":"5d3224ef-8e19-4b67-8721-7aa19ecf0931-r1","picks":[["vercel-sandbox","p"],["firecracker","m"],["daytona","m"],["e2b","m"],["fly-machines","m"],["modal","m"]],"ev":36,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several sandbox solutions (Vercel Sandbox, Daytona, E2B, Modal, Fly Machines, Cloudflare) and definitively committed to Vercel Sandbox by installing `@vercel/sandbox`, rewriting the runner to execute tasks within Vercel Sandbox microVMs, adding comprehensive unit tests with sandbox fakes, and updating the project documentation and configuration files.","c":1,"e":[["file","package.json:12-14"],["file","src/sandbox.js:1-290"],["file","src/server.js:1-90"],["file","README.md:7-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"e2b","secs":521,"k":"5d3224ef-8e19-4b67-8721-7aa19ecf0931-r2","picks":[["e2b","p"],["codesandbox-sdk","m"],["runloop","m"],["fly-machines","m"],["daytona","m"],["docker","m"],["firecracker","m"],["modal","m"],["vercel-sandbox","m"]],"ev":48,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly chose E2B as the managed sandbox platform, installed the `e2b` package, implemented the complete sandbox integration in `src/sandbox.js`, refactored the runner and server to stream execution events from E2B, and added tests.","c":1,"e":[["file","package.json"],["file","src/sandbox.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":3,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel-sandbox","secs":719,"k":"5d3224ef-8e19-4b67-8721-7aa19ecf0931-r3","picks":[["vercel-sandbox","p"],["firecracker","m"],["aws-fargate","m"],["cloudflare-sandbox","m"],["daytona","m"],["e2b","m"],["fly-machines","m"],["modal","m"]],"ev":72,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated multiple sandbox providers (Vercel Sandbox, E2B, Daytona, Modal, Fly Machines, AWS Fargate, AWS CodeBuild, Cloudflare Sandbox SDK) against requirements for ephemeral VM task isolation, egress firewall allowlisting, and streaming command execution. It selected and implemented `@vercel/sandbox` in `src/sandbox-runner.js`, added the package to `package.json`, configured environment variables in `.env.example`, and documented its usage in `README.md`.","c":1,"e":[["file","package.json:14"],["file","src/sandbox-runner.js:180-202"],["file","README.md:8-18"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"e2b","secs":472,"k":"5d3224ef-8e19-4b67-8721-7aa19ecf0931-r4","picks":[["e2b","p"],["daytona","m"],["modal","m"],["vercel-sandbox","m"]],"ev":48,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent replaced the local execution runner with an E2B sandbox implementation (`@e2b` package in `package.json`, `src/sandbox.js`), updated server startup requirements to check for `E2B_API_KEY`, and added test suites using an injected E2B sandbox mock.","c":1,"e":[["file","package.json"],["file","src/sandbox.js"],["file","src/server.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":5,"pick":"daytona","secs":669,"k":"5d3224ef-8e19-4b67-8721-7aa19ecf0931-r5","picks":[["daytona","p"],["codesandbox-sdk","m"],["docker","m"],["e2b","m"],["modal","m"],["vercel-sandbox","m"]],"ev":60,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated multiple sandbox platforms (Daytona, E2B, Vercel Sandbox, Modal, Fly Machines, Cloudflare Sandbox SDK, CodeSandbox SDK, AWS Fargate), selected Daytona, installed `@daytonaio/sdk`, and fully implemented the remote sandbox execution lifecycle in `src/sandbox.js` with comprehensive test coverage.","c":1,"e":[["file","package.json:13"],["file","src/sandbox.js:145-245"],["file","src/config.js:52-73"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":6,"pick":"vercel-sandbox","secs":525,"k":"5d3224ef-8e19-4b67-8721-7aa19ecf0931-r6","picks":[["vercel-sandbox","p"],["firecracker","m"],["daytona","m"],["e2b","m"],["fly-machines","m"],["modal","m"]],"ev":60,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated multiple sandbox options, specifically downloaded and inspected type definitions for @vercel/sandbox, e2b, and @daytonaio/sdk, and selected Vercel Sandbox. It installed `@vercel/sandbox` in package.json, integrated it in `src/sandbox.js` and `src/runner.js`, and documented its justification against E2B and Daytona in README.md.","c":1,"e":[["file","package.json:12-14"],["file","src/sandbox.js:1-127"],["file","README.md:25-29"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"daytona","secs":566,"k":"5605387f-ce53-40a8-9e00-c767ceae5f47-r1","picks":[["daytona","p"],["e2b","m"],["modal","m"]],"ev":42,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated multiple remote sandbox vendors (Daytona, E2B, Modal) and selected Daytona Cloud for its domain-level egress controls and TypeScript SDK capabilities. It installed `@daytona/sdk` and refactored the runner to manage remote ephemeral Daytona sandboxes.","c":1,"e":[["file","package.json:18-20"],["file","src/runner.ts:162-192"],["file","README.md:5-27"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"vercel-sandbox","secs":265,"k":"5605387f-ce53-40a8-9e00-c767ceae5f47-r2","picks":[["vercel-sandbox","p"],["firecracker","m"],["gvisor","m"],["modal","m"]],"ev":38,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly selected and implemented Vercel Sandbox (`@vercel/sandbox`) as the isolated remote execution environment for untrusted builds, installing the package and wiring up microVM lifecycle management, egress filtering, and artifact capture in `src/runner.ts`.","c":1,"e":[["file","package.json:18-20"],["file","src/runner.ts:3-120"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"modal","secs":388,"k":"5605387f-ce53-40a8-9e00-c767ceae5f47-r3","picks":[["modal","p"],["gvisor","m"],["daytona","m"],["docker","m"],["e2b","m"],["firecracker","m"]],"ev":28,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run evaluated multiple remote sandbox and container options (E2B, Daytona, Docker, Firecracker), explicitly selected Modal, installed the `modal` package, and implemented a full lifecycle executor with domain filtering and artifact collection.","c":1,"e":[["file","package.json:18"],["file","src/runner.ts:3-7"],["file","README.md:3-4"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"daytona","secs":397,"k":"5605387f-ce53-40a8-9e00-c767ceae5f47-r4","picks":[["daytona","p"],["e2b","m"],["modal","m"]],"ev":35,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated remote sandbox providers (Daytona, Modal, E2B) specifically for granular outbound network allowlisting and TypeScript SDK support. It selected and implemented Daytona using `@daytona/sdk` in `src/runner.ts` and `package.json`, explaining why Modal was rejected and verifying the integration with a full test suite.","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":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":5,"pick":"runloop","secs":496,"k":"5605387f-ce53-40a8-9e00-c767ceae5f47-r5","picks":[["runloop","p"],["modal","m"],["fly-machines","m"],["cloudflare-sandbox","m"],["daytona","m"],["docker","m"],["e2b","m"]],"ev":62,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several sandbox providers (Runloop, Daytona, E2B, Cloudflare Sandbox SDK, Modal, Fly Machines) and selected Runloop Devboxes. It installed `@runloop/api-client`, implemented the `RunloopSandboxProvider` in `src/runner.ts`, created a provisioning script `scripts/provision-runloop.mjs`, and added comprehensive unit tests.","c":1,"e":[["file","package.json"],["file","src/runner.ts"],["file","scripts/provision-runloop.mjs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":6,"pick":"daytona","secs":384,"k":"5605387f-ce53-40a8-9e00-c767ceae5f47-r6","picks":[["daytona","p"],["e2b","m"],["modal","m"]],"ev":40,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated sandbox platforms, recommended Daytona Cloud due to its per-phase network allowlist controls, installed `@daytona/sdk` in package.json, and implemented the sandbox runner in `src/runner.ts`.","c":1,"e":[["file","package.json"],["file","src/runner.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"runloop","secs":1126,"k":"200e4d14-73ad-4123-a7e9-e69d16139811-r1","picks":[["runloop","p"],["e2b","m"],["daytona","m"],["fly-machines","m"],["aws-codebuild","m"],["gvisor","m"],["modal","m"]],"ev":42,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several sandbox options (Runloop, Modal, E2B, Daytona, Fly Machines, AWS CodeBuild) and selected Runloop Devboxes. It installed `@runloop/api-client`, implemented the `RunloopExecutor` in `src/runner.js`, added provision scripts in `scripts/provision-runloop.js`, updated `src/server.js` and `.env.example`, and added test suites.","c":1,"e":[["file","package.json:12-14"],["file","src/runner.js:1-260"],["file","scripts/provision-runloop.js:1-38"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"daytona","secs":286,"k":"200e4d14-73ad-4123-a7e9-e69d16139811-r2","picks":[["daytona","p"],["fly-machines","m"],["aws-codebuild","m"],["e2b","m"],["gvisor","m"],["modal","m"]],"ev":37,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent selected Daytona Cloud as the managed sandbox platform for executing adversarial coding-agent tasks, installing the `@daytona/sdk` package and implementing full lifecycle sandbox management in `src/runner.js`.","c":1,"e":[["file","package.json:13"],["file","src/runner.js:2"],["file","README.md:2-10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"daytona","secs":312,"k":"200e4d14-73ad-4123-a7e9-e69d16139811-r3","picks":[["daytona","p"],["kata-containers","m"],["gvisor","m"],["modal","m"]],"ev":39,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated multiple remote sandbox platforms (Daytona, Modal, E2B, Runloop) and chose Daytona Cloud. It installed `@daytona/sdk` and implemented full sandbox creation, command execution, log streaming, patch extraction, and cleanup in `src/runner.js` and `src/server.js`.","c":1,"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":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"runloop","secs":348,"k":"200e4d14-73ad-4123-a7e9-e69d16139811-r4","picks":[["runloop","p"],["gvisor","m"],["modal","m"]],"ev":39,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run installed `@runloop/api-client`, wrote scripts to configure network policies, and implemented the full task execution and cleanup logic in `src/runner.js` using Runloop Devboxes. Other providers (Modal, E2B, Daytona) were queried or weighed during the deliberation trace.","c":1,"e":[["file","package.json"],["file","src/runner.js"],["file","scripts/create-network-policy.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":5,"pick":"daytona","secs":824,"k":"200e4d14-73ad-4123-a7e9-e69d16139811-r5","picks":[["daytona","p"],["fly-machines","m"],["aws-codebuild","m"],["e2b","m"],["gvisor","m"],["modal","m"]],"ev":45,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended, installed, and integrated Daytona Cloud using the `@daytona/sdk` package across `package.json`, `src/runner.js`, and `README.md`, while comparing against and rejecting alternatives like Modal, E2B, and AWS CodeBuild.","c":1,"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":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":6,"pick":"daytona","secs":307,"k":"200e4d14-73ad-4123-a7e9-e69d16139811-r6","picks":[["daytona","p"],["gvisor","m"],["docker","m"],["modal","m"]],"ev":42,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run installed @daytona/sdk, configured Daytona Cloud Linux VMs as disposable sandboxes with per-task resource bounding and domain egress filtering, and completely replaced the local runner execution path with Daytona SDK calls.","c":1,"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":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"e2b","secs":606,"k":"15c83cd6-3d09-4fad-9a8a-ec6528a1b790-r1","picks":[["e2b","p"],["codesandbox-sdk","m"],["cloudflare-sandbox","m"],["daytona","m"],["firecracker","m"],["modal","m"],["vercel-sandbox","m"]],"ev":75,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent clearly selected and fully integrated E2B, installing its official Node SDK (`e2b`), implementing the sandbox execution and cleanup harness in `src/sandbox.ts` and `src/runner-source.ts`, configuring timeouts/limits in `src/config.ts` and `.env.example`, and updating the documentation in `README.md` while discussing and rejecting several alternatives.","c":1,"e":[["file","package.json:17"],["file","src/sandbox.ts:1-179"],["file","README.md:10-73"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"e2b","secs":642,"k":"15c83cd6-3d09-4fad-9a8a-ec6528a1b790-r2","picks":[["e2b","p"],["daytona","m"],["firecracker","m"],["modal","m"],["vercel-sandbox","m"]],"ev":85,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run chose E2B as the third-party managed sandbox provider, installing the `e2b` npm package and implementing `src/sandbox/e2b.ts` with comprehensive unit tests and configuration options. Other managed providers (Daytona, Vercel Sandbox, Cloudflare Sandbox SDK, Modal) and self-hosted runners (Firecracker, gVisor) were evaluated and rejected explicitly in the documentation and reasoning trace.","c":1,"e":[["file","package.json"],["file","src/sandbox/e2b.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":3,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"vercel-sandbox","secs":549,"k":"15c83cd6-3d09-4fad-9a8a-ec6528a1b790-r4","picks":[["vercel-sandbox","p"],["firecracker","m"],["codesandbox-sdk","m"],["daytona","m"],["deno","m"],["docker","m"],["e2b","m"],["modal","m"]],"ev":55,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run evaluated several sandbox options and selected Vercel Sandbox (`@vercel/sandbox`). It installed the dependency, wired credentials and duration/resource caps in config, implemented isolated execution in `src/sandbox.ts` with deny-all egress, updated route handling in `src/app.ts`, wrote offline unit tests in `tests/transform.test.ts`, and thoroughly documented the choice and alternatives in `README.md`.","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":3,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":5,"pick":"e2b","secs":636,"k":"15c83cd6-3d09-4fad-9a8a-ec6528a1b790-r5","picks":[["e2b","p"],["daytona","m"],["deno","m"],["firecracker","m"],["modal","m"],["vercel-sandbox","m"]],"ev":78,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated multiple sandbox providers (E2B, Riza, Vercel Sandbox, Modal, Daytona) and selected E2B as the primary third-party solution, installing the `e2b` package and fully implementing the sandbox execution and cleanup pipeline in `src/transform.ts`.","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":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":6,"pick":"e2b","secs":602,"k":"15c83cd6-3d09-4fad-9a8a-ec6528a1b790-r6","picks":[["e2b","p"],["daytona","m"],["deno","m"],["firecracker","m"],["modal","m"],["vercel-sandbox","m"]],"ev":69,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent selected E2B as the managed sandbox service, installed the 'e2b' npm package, configured credentials and options in src/config.ts, implemented execution and cleanup in src/sandbox.ts, and documented the rationale while comparing against alternatives in README.md.","c":1,"e":[["file","package.json:19"],["file","src/sandbox.ts:1-121"],["file","README.md:12-65"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"e2b","secs":952,"k":"57ef980a-f841-4558-b38b-913d1afc476a-r1","picks":[["e2b","p"],["daytona","m"],["codesandbox-sdk","m"],["aws-lambda","m"],["firecracker","m"],["fly-machines","m"],["gvisor","m"],["modal","m"],["vercel-sandbox","m"]],"ev":98,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent selected E2B (`e2b` package) to isolate untrusted model-generated JavaScript transforms in Firecracker microVMs. It implemented the adapter in `src/sandbox.ts`, embedded an in-VM test harness in `src/sandboxRunner.ts`, updated `src/app.ts` and `src/transform.ts` to fail closed without local fallback, added test coverage and a smoke test script, and thoroughly documented rejected alternatives in README.md.","c":1,"e":[["file","package.json:21"],["file","src/sandbox.ts:1-251"],["file","README.md:19-29"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"e2b","secs":964,"k":"57ef980a-f841-4558-b38b-913d1afc476a-r2","picks":[["e2b","p"],["firecracker","m"],["daytona","m"],["docker","m"],["gvisor","m"],["modal","m"],["nsjail","m"],["vercel-sandbox","m"]],"ev":92,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run unambiguously selected and implemented E2B via the official `e2b` npm package. It replaced the unsafe in-process `new Function` execution path in `src/transform.ts` with a remote sandbox executor (`src/sandbox/e2b.ts`) and sandbox harness (`src/sandbox/runner.ts`), while adding configuration, error handling, tests, and documentation.","c":1,"e":[["file","package.json:17"],["file","src/sandbox/e2b.ts:1-274"],["file","README.md:10-44"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"e2b","secs":598,"k":"57ef980a-f841-4558-b38b-913d1afc476a-r3","picks":[["e2b","p"],["daytona","m"],["firecracker","m"],["docker","m"],["fly-machines","m"],["gvisor","m"],["modal","m"]],"ev":82,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly chose E2B as the third-party remote sandbox provider, installed the `e2b` package, implemented the sandbox executor in `src/sandbox.ts`, updated `.env.example` and `README.md`, and evaluated several alternatives (Modal, Cloudflare Sandboxes, Fly Machines, Docker, gVisor, Daytona) in its deliberation and documentation.","c":1,"e":[["file","package.json"],["file","src/sandbox.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"e2b","secs":929,"k":"57ef980a-f841-4558-b38b-913d1afc476a-r4","picks":[["e2b","p"],["codesandbox-sdk","m"],["runloop","m"],["blaxel","m"],["aws-lambda","m"],["daytona","m"],["firecracker","m"],["modal","m"],["vercel-sandbox","m"]],"ev":86,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run clearly selected and implemented E2B as the managed remote sandbox provider. It installed the `e2b` npm dependency, implemented `src/sandbox.ts` to manage sandbox creation, execution, and cleanup, and added configuration and test suites explicitly built around the E2B SDK.","c":1,"e":[["file","package.json:19"],["file","src/sandbox.ts:1-324"],["file","README.md:16-27"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":5,"pick":"e2b","secs":669,"k":"57ef980a-f841-4558-b38b-913d1afc476a-r5","picks":[["e2b","p"],["cloudflare-workers","m"],["daytona","m"],["deno","m"],["firecracker","m"],["modal","m"],["vercel-sandbox","m"]],"ev":69,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several sandbox providers (E2B, Daytona, Vercel Sandbox, Modal) and selected E2B. It installed the `e2b` npm package, configured environment variables and timeout options, created `src/sandbox.ts` to manage disposable microVMs and execute code without internet access, and updated the application pipeline and tests to use E2B.","c":1,"e":[["file","package.json:19"],["file","src/sandbox.ts:1-144"],["file","README.md:12-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":3,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":6,"pick":"e2b","secs":631,"k":"57ef980a-f841-4558-b38b-913d1afc476a-r6","picks":[["e2b","p"],["daytona","m"],["firecracker","m"],["fly-machines","m"],["gvisor","m"],["modal","m"],["vercel-sandbox","m"]],"ev":73,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended, installed, and fully integrated E2B via `e2b` npm SDK into `src/transform.ts` and `src/app.ts` to execute untrusted JavaScript code in isolated microVMs, while rejecting several alternative managed and self-hosted sandbox technologies based on stack mismatch, operational overhead, or missing features.","c":1,"e":[["file","package.json:19"],["file","src/transform.ts:1-272"],["file",".env.example:5-13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"Self-hosting, privacy or residency"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"bc-mail-prompt-b-11","pid":"MAIL-PB-11a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"aws-ses","secs":847,"k":"e80401d6-4781-4aa2-8524-be98fd913bdf-r4","picks":[["aws-ses","p"],["mailgun","m"],["sendgrid","m"]],"ev":85,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The user asked for an order confirmation email architecture that preserves regional data residency. The agent selected and documented Amazon SES configured per-region (eu-central-1 and us-west-2) within the project's AWS environment, rejecting third-party providers like SendGrid and Mailgun due to compliance risks around US-based default retention.","c":0.95,"e":[["file","docs/data-residency.md:69-72"],["trace","seq:27"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Self-hosting, privacy or residency"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"sendgrid","secs":965,"k":"5fecbe5a-cf0a-4a31-96e9-55b20c53a89f-r1","picks":[["sendgrid","p"],["postmark","m"],["resend","m"]],"ev":102,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated the existing codebase and chose to stick with SendGrid, which was already integrated into configuration and Terraform, while replacing the SDK calls with a robust outbox pattern and direct REST API calls. Postmark, Resend, and Amazon SES were explicitly considered and rejected to avoid unnecessary churn.","c":1,"e":[["file","app/emails/provider.py:21"],["file","README.md:43"],["file",".env.example:11"],["file","terraform/ecs.tf:160"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"sendgrid","secs":1204,"k":"5fecbe5a-cf0a-4a31-96e9-55b20c53a89f-r2","picks":[["sendgrid","p"],["loops","m"],["postmark","m"],["resend","m"]],"ev":120,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The run explicitly recommended staying with SendGrid due to its pre-existing presence in the codebase and infrastructure. It implemented an end-to-end transactional email system including outbox tracking, retry logic, Jinja2 templates, EventBridge/ECS worker scheduling, and CloudWatch metrics.","c":1,"e":[["file","app/mail/transport.py"],["file","terraform/email.tf"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"sendgrid","secs":1106,"k":"5fecbe5a-cf0a-4a31-96e9-55b20c53a89f-r3","picks":[["sendgrid","p"],["resend","m"]],"ev":110,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The run explicitly chose to retain SendGrid as the transactional email provider, building a comprehensive email subsystem around SendGridTransport, Jinja2 templating, and an idempotent delivery ledger.","c":1,"e":[["file","app/emails/transport.py"],["file","app/config.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"sendgrid","secs":1045,"k":"5fecbe5a-cf0a-4a31-96e9-55b20c53a89f-r4","picks":[["sendgrid","p"],["loops","m"],["resend","m"]],"ev":131,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The repository already had SendGrid configured across its environment templates, ECS Terraform definitions, and existing endpoints. The agent explicitly recommended staying on SendGrid to avoid migration churn (rejecting Amazon SES and Resend), replaced the untimed SDK with a direct HTTP transport against SendGrid's v3 Mail Send API, and implemented full template rendering, delivery tracking, and scheduled batch sweeps.","c":1,"e":[["file","app/emails/client.py:22"],["file","README.md:44-75"],["file",".env.example:11-23"],["file","terraform/scheduler.tf:39"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-sandbox","secs":295,"k":"553b7d79-7d5a-412d-979e-9fa597cce4ad-r1","picks":[["vercel-sandbox","p"],["firecracker","m"],["gvisor","m"],["modal","m"]],"ev":40,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent investigated candidate sandbox platforms (Modal, E2B, Vercel Sandbox), briefly installed Modal, and ultimately committed to Vercel Sandbox (@vercel/sandbox) in code, test harness, dependencies, and documentation.","c":1,"e":[["file","package.json:18"],["file","src/runner.ts:2"],["file","README.md:7-16"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"daytona","secs":205,"k":"553b7d79-7d5a-412d-979e-9fa597cce4ad-r2","picks":[["daytona","p"],["modal","m"],["e2b","m"],["gvisor","m"],["docker","m"]],"ev":31,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run clearly selected and implemented Daytona (@daytona/sdk) in src/runner.ts, replacing local child process execution with ephemeral Daytona Sandbox instances configured with resource bounds, network allowlists, and lifecycle deletion.","c":1,"e":[["file","package.json"],["file","src/runner.ts"],["trace","seq:9"],["trace","seq:25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"modal","secs":236,"k":"553b7d79-7d5a-412d-979e-9fa597cce4ad-r3","picks":[["modal","p"],["daytona","m"],["firecracker","m"],["gvisor","m"],["e2b","a"],["aws-codebuild","m"]],"ev":29,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent replaced the local child process execution in `src/runner.ts` with Modal Sandboxes via the `modal` npm package, configuring memory/CPU constraints, domain-based egress filtering, file uploads, and cleanup.","c":1,"e":[["file","package.json:18-20"],["file","src/runner.ts:74-98"],["file","README.md:5-9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"modal","secs":193,"k":"553b7d79-7d5a-412d-979e-9fa597cce4ad-r4","picks":[["modal","p"],["daytona","m"],["gvisor","m"],["kata-containers","m"]],"ev":24,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent selected Modal to execute untrusted code in remote sandboxes, installing the official 'modal' npm package and rewriting 'src/runner.ts' to instantiate 'ModalClient' sandboxes with dynamic network policy controls and teardown.","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":4,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":5,"pick":"daytona","secs":479,"k":"553b7d79-7d5a-412d-979e-9fa597cce4ad-r5","picks":[["daytona","p"],["modal","m"],["firecracker","m"],["e2b","a"],["aws-codebuild","m"],["docker","m"]],"ev":24,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent integrated the Daytona TypeScript SDK (@daytona/sdk), updating package.json, src/runner.ts, README.md, and test/runner.test.mjs to execute untrusted code in isolated ephemeral Daytona sandboxes.","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":4,"date":"2026-08-31","repo":"typescript-ui-builder","variant":"base","family":"sandboxes-managed-ui-projects","pid":"SBX-MANAGED-UI-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":6,"pick":"modal","secs":197,"k":"553b7d79-7d5a-412d-979e-9fa597cce4ad-r6","picks":[["modal","p"],["daytona","m"],["e2b","m"]],"ev":29,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent selected Modal as its sandbox backend, installing the `modal` npm package and implementing disposable sandbox creation, file writes, isolated execution with npm-only egress allowlisting, and cleanup in `src/runner.ts`.","c":1,"e":[["file","package.json:18"],["file","src/runner.ts:2"],["file","src/runner.ts:38-105"],["file","README.md:5-9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated UI project builds","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"modal","secs":257,"k":"d6453908-3bc6-4b63-98ec-061208bd8977-r1","picks":[["modal","p"],["e2b","m"]],"ev":38,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run evaluated managed remote sandbox options (Modal, E2B, Daytona) and implemented Modal Sandboxes using the `modal` Python SDK across `northstar/executor.py`, `pyproject.toml`, `requirements.txt`, and dedicated test suites.","c":1,"e":[["file","pyproject.toml:5-7"],["file","northstar/executor.py:10-176"],["file","tests/test_modal_integration.py:1-45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"modal","secs":264,"k":"d6453908-3bc6-4b63-98ec-061208bd8977-r2","picks":[["modal","p"],["daytona","m"],["e2b","m"]],"ev":34,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several sandbox solutions (Modal, E2B, Daytona, Runloop) and chose Modal Sandboxes. It integrated Modal's Python SDK, updated the dependency manifests, and replaced in-process code execution with `ModalPythonExecutor`.","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":4,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"modal","secs":274,"k":"d6453908-3bc6-4b63-98ec-061208bd8977-r3","picks":[["modal","p"],["e2b","m"],["daytona","m"],["codesandbox-sdk","m"],["gvisor","m"]],"ev":30,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run evaluated remote sandbox solutions and fully integrated Modal Sandboxes via the `modal` Python library in `pyproject.toml`, `northstar/executor.py`, and `northstar/policy.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":4,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"modal","secs":924,"k":"d6453908-3bc6-4b63-98ec-061208bd8977-r4","picks":[["modal","p"],["daytona","m"],["e2b","m"],["gvisor","m"]],"ev":38,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Modal Sandboxes and implemented a complete integration in `northstar/executor.py` using `modal.Sandbox.create`, updated project dependencies in `pyproject.toml` and `requirements.txt`, and documented deployment steps in `README.md`.","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":4,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":5,"pick":"modal","secs":330,"k":"d6453908-3bc6-4b63-98ec-061208bd8977-r5","picks":[["modal","p"],["codesandbox-sdk","m"],["gvisor","m"],["daytona","m"],["e2b","m"]],"ev":44,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly selected and integrated Modal Sandboxes via the modal Python SDK to manage secure disposable Python code execution, adding the dependency to pyproject.toml and requirements.txt and implementing the full 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":4,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":6,"pick":"modal","secs":233,"k":"d6453908-3bc6-4b63-98ec-061208bd8977-r6","picks":[["modal","p"],["e2b","m"],["codesandbox-sdk","m"],["daytona","m"],["gvisor","m"]],"ev":37,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several sandbox services and selected Modal Sandboxes as the best fit. It installed the `modal` package, refactored `northstar/executor.py` to create and tear down disposable Modal Sandboxes with strict timeouts and network blocking, updated tests with mocks for Modal's SDK, and documented the configuration in README.md.","c":1,"e":[["file","pyproject.toml:5-7"],["file","northstar/executor.py:6-105"],["file","README.md:5-15"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"modal","secs":661,"k":"db729ab0-3dcb-4ed0-b2d3-3bbb2ebc4bf8-r1","picks":[["modal","p"],["daytona","m"],["e2b","m"],["firecracker","m"],["gvisor","m"]],"ev":60,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run evaluated multiple remote sandbox solutions (Modal, E2B, Daytona, and Runloop) and fully adopted Modal Sandboxes. It installed the `modal` SDK, implemented a remote sandbox client and container image build workflow, added operational documentation, and backed all code execution in `app/analysis.py` with Modal Sandbox instances.","c":1,"e":[["file","pyproject.toml:18"],["file","app/analysis.py:48-73"],["file","docs/modal-sandbox.md:10-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"e2b","secs":2050,"k":"db729ab0-3dcb-4ed0-b2d3-3bbb2ebc4bf8-r2","picks":[["e2b","p"],["aws-lambda","m"],["daytona","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["modal","m"]],"ev":49,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent conducted a detailed comparison between managed sandbox options (E2B, Modal, Daytona) and selected E2B, then implemented the complete integration with the official `e2b` Python SDK, custom template configuration, runner script, fail-closed handling, and test fixtures.","c":1,"e":[["file","pyproject.toml"],["file","app/analysis.py"],["file","sandbox/build_template.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":4,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"modal","secs":301,"k":"db729ab0-3dcb-4ed0-b2d3-3bbb2ebc4bf8-r3","picks":[["modal","p"],["daytona","m"],["e2b","m"],["firecracker","m"],["gvisor","m"]],"ev":42,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated Modal, E2B, and Daytona against the workload requirements, explicitly choosing Modal. It added the modal Python SDK dependency to pyproject.toml and uv.lock, configured sandbox execution with hard CPU/memory limits and blocked network access in app/analysis.py, and updated all tests and documentation accordingly.","c":1,"e":[["file","pyproject.toml:18"],["file","app/analysis.py:74-88"],["file","README.md:21-31"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"daytona","secs":373,"k":"db729ab0-3dcb-4ed0-b2d3-3bbb2ebc4bf8-r4","picks":[["daytona","p"],["e2b","a"],["modal","a"],["docker","m"],["firecracker","m"],["gvisor","m"]],"ev":56,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent conducted a vendor evaluation comparing Daytona, E2B, and Modal, selected Daytona, and implemented a complete provider-backed sandbox executor using the `daytona` Python SDK in `app/analysis.py` along with operations documentation and test suites.","c":1,"e":[["file","pyproject.toml:15"],["file","app/analysis.py:11-255"],["file","docs/daytona-sandbox.md:1-85"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":5,"pick":"modal","secs":385,"k":"db729ab0-3dcb-4ed0-b2d3-3bbb2ebc4bf8-r5","picks":[["modal","p"],["e2b","a"],["daytona","a"],["firecracker","m"],["gvisor","m"]],"ev":49,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated Modal Sandboxes, E2B, and Daytona against workload requirements, ultimately committing to Modal. The codebase was updated to add the `modal` dependency, configure credentials in `.env.example`, implement remote sandbox lifecycle management in `app/analysis.py`, provide a container runner in `app/sandbox_runner.py`, and supply image build automation in `scripts/build_modal_image.py`.","c":1,"e":[["file","pyproject.toml"],["file","app/analysis.py"],["file","scripts/build_modal_image.py"],["file","docs/modal-sandbox.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":6,"pick":"modal","secs":299,"k":"db729ab0-3dcb-4ed0-b2d3-3bbb2ebc4bf8-r6","picks":[["modal","p"],["gvisor","m"],["daytona","m"],["e2b","m"]],"ev":58,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent conducted a documented evaluation of Modal, E2B, and Daytona in docs/sandbox-platform-decision.md, selected Modal Sandboxes, added the modal dependency to pyproject.toml and requirements.txt, and implemented the remote sandbox execution adapter in app/analysis.py.","c":1,"e":[["file","pyproject.toml"],["file","app/analysis.py"],["file","docs/sandbox-platform-decision.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"modal","secs":397,"k":"cbb86614-fd83-4f00-a2da-d6bd1b3bdbf4-r1","picks":[["modal","p"],["gvisor","m"],["daytona","m"],["e2b","m"],["runloop","m"]],"ev":58,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated multiple sandbox solutions (Modal, E2B, Runloop, Daytona) before selecting Modal Sandboxes. It fully implemented Modal by adding the `modal` package dependency, creating `app/sandbox_image.py` to build the runner container, adapting `app/analysis.py` to spawn and manage `modal.Sandbox` instances with network isolation and timeouts, updating `.env.example` and `README.md`, and writing unit tests mocking the Modal SDK.","c":1,"e":[["file","pyproject.toml"],["file","app/analysis.py"],["file","app/sandbox_image.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":4,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"modal","secs":466,"k":"cbb86614-fd83-4f00-a2da-d6bd1b3bdbf4-r2","picks":[["modal","p"],["gvisor","m"],["e2b","a"],["daytona","a"],["firecracker","m"]],"ev":37,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent selected and fully implemented Modal Sandboxes using the `modal` Python SDK in `app/analysis.py`, defining isolated execution with resource constraints, network blocking, and cleanup routines, accompanied by full configuration in `app/config.py` and test coverage.","c":1,"e":[["file","pyproject.toml:18"],["file","app/analysis.py:90-111"],["file","README.md:28-31"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"modal","secs":597,"k":"cbb86614-fd83-4f00-a2da-d6bd1b3bdbf4-r3","picks":[["modal","p"],["gvisor","m"],["firecracker","m"],["daytona","m"],["e2b","m"]],"ev":54,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run installed the Modal Python SDK and fully implemented disposable execution with `modal.Sandbox.create` inside `app/analysis.py`, configured network blocking, CPU/memory limits, timeouts, log capture, and guaranteed teardown with `sandbox.terminate()` and `sandbox.detach()`.","c":1,"e":[["file","pyproject.toml:18"],["file","app/analysis.py:59-75"],["file","README.md:27-33"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"modal","secs":401,"k":"cbb86614-fd83-4f00-a2da-d6bd1b3bdbf4-r4","picks":[["modal","p"],["daytona","m"],["gvisor","m"],["firecracker","m"],["aws-lambda","m"],["e2b","m"]],"ev":43,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent selected Modal Sandboxes as the managed execution provider for untrusted code execution. It added the `modal` dependency to `pyproject.toml`, implemented the client and sandbox lifecycle in `app/analysis.py`, added configuration in `app/config.py`, built a dedicated isolated runner in `app/sandbox_runner.py`, and updated test suites and operational documentation.","c":1,"e":[["file","pyproject.toml:18"],["file","app/analysis.py:10-184"],["file","README.md:29-57"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":5,"pick":"modal","secs":229,"k":"cbb86614-fd83-4f00-a2da-d6bd1b3bdbf4-r5","picks":[["modal","p"],["e2b","m"],["daytona","m"],["gvisor","m"]],"ev":32,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent added the `modal` package dependency, implemented sandbox execution via `modal.Sandbox.create` in `app/analysis.py`, configured environment variables for Modal authentication, and documented setup in `README.md`.","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":4,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":6,"pick":"e2b","secs":347,"k":"cbb86614-fd83-4f00-a2da-d6bd1b3bdbf4-r6","picks":[["e2b","p"],["aws-lambda","m"],["firecracker","m"],["gvisor","m"],["modal","m"]],"ev":67,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended, installed, and implemented E2B (`e2b` Python SDK) to run spreadsheet analysis in network-isolated Firecracker sandboxes, while evaluating and rejecting Modal and Daytona.","c":1,"e":[["file","pyproject.toml:18"],["file","app/analysis.py:100-183"],["file","app/sandbox_template.py:14-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"modal","secs":492,"k":"28186b31-a995-41b8-80d7-86d92fc57dbc-r1","picks":[["modal","p"],["daytona","m"],["e2b","m"],["gvisor","m"]],"ev":62,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated Modal, E2B, and Daytona for remote sandboxed code execution. It committed to Modal by adding the modal SDK to dependencies, writing a Modal-backed executor in app/analysis.py, adding runner and image build modules, and configuring sandboxes with blocked egress and resource limits.","c":1,"e":[["file","pyproject.toml"],["file","app/analysis.py"],["file","app/modal_service.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":4,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"modal","secs":446,"k":"28186b31-a995-41b8-80d7-86d92fc57dbc-r2","picks":[["modal","p"],["daytona","m"],["fly-machines","m"],["gvisor","m"],["aws-lambda","m"],["e2b","m"]],"ev":53,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run evaluated remote sandbox providers (Modal, E2B, Daytona, Fly Machines) and fully integrated Modal Sandboxes using the `modal` Python SDK into `app/analysis.py`, `pyproject.toml`, and `README.md` to execute generated Python analysis outside the FastAPI host process.","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":4,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"modal","secs":337,"k":"28186b31-a995-41b8-80d7-86d92fc57dbc-r3","picks":[["modal","p"],["daytona","m"],["runloop","m"],["fly-machines","m"],["gvisor","m"],["e2b","m"],["firecracker","m"]],"ev":38,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run evaluated managed sandbox providers (Modal, E2B, Daytona, Runloop, Fly Machines), rejected E2B in favor of Modal, and fully integrated Modal Sandboxes via its Python SDK to execute generated Python analysis outside the host environment.","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":4,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"e2b","secs":360,"k":"28186b31-a995-41b8-80d7-86d92fc57dbc-r4","picks":[["e2b","p"],["daytona","m"],["firecracker","m"],["modal","a"],["gvisor","m"]],"ev":55,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly selected and implemented E2B Cloud Sandboxes. It added the e2b dependency to pyproject.toml and requirements.txt, created e2b.Dockerfile and a template build script, and refactored app/analysis.py to run generated code inside disposable E2B sandboxes.","c":1,"e":[["file","pyproject.toml"],["file","app/analysis.py"],["file","e2b.Dockerfile"],["file","scripts/build_e2b_template.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":5,"pick":"modal","secs":448,"k":"28186b31-a995-41b8-80d7-86d92fc57dbc-r5","picks":[["modal","p"],["runloop","m"],["gvisor","m"],["daytona","m"],["e2b","m"]],"ev":59,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several sandbox solutions (Modal, E2B, Daytona, Runloop) and committed completely to Modal, installing the `modal` package, configuring settings, implementing `ModalExecutor` in `app/analysis.py`, creating `app/sandbox_runner.py`, writing tests in `tests/test_analysis.py`, and documenting operations in `docs/modal-sandbox.md`.","c":1,"e":[["file","pyproject.toml"],["file","app/analysis.py"],["file","docs/modal-sandbox.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"python-ai-analyst","variant":"base","family":"sandboxes-managed-data-python","pid":"SBX-MANAGED-DATA-PY-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":6,"pick":"modal","secs":352,"k":"28186b31-a995-41b8-80d7-86d92fc57dbc-r6","picks":[["modal","p"],["daytona","m"],["e2b","m"],["firecracker","m"],["gvisor","m"]],"ev":43,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run explicitly chose and implemented Modal Sandboxes, replacing the in-process exec call with disposable Modal sandboxes via `modal.Sandbox.create`, pinning `modal==1.5.5` in pyproject.toml and requirements.txt, and providing complete configuration and tests.","c":1,"e":[["file","pyproject.toml:18"],["file","app/analysis.py:127-142"],["file","README.md:30-55"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"modal","secs":358,"k":"25682302-f845-41a4-a556-46a08fbd43bf-r1","picks":[["modal","p"],["e2b","m"],["daytona","m"],["cloudflare-workers","m"],["deno","m"],["docker","m"],["gvisor","m"]],"ev":58,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several remote sandbox providers (Modal, E2B, Daytona) and chose Modal Sandboxes. It installed the `modal` package, configured remote sandbox creation, execution, and cleanup with strict resource limits and network isolation, and provided tests and documentation.","c":1,"e":[["file","package.json:14"],["file","src/transform.ts:1"],["file","src/transform.ts:134-219"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"modal","secs":317,"k":"25682302-f845-41a4-a556-46a08fbd43bf-r2","picks":[["modal","p"],["gvisor","m"],["daytona","m"],["e2b","m"]],"ev":43,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run installed the official modal npm package and implemented a complete remote execution pipeline in src/transform.ts using Modal Sandboxes with network egress blocking, bounded timeouts, and explicit sandbox termination.","c":1,"e":[["file","package.json:20"],["file","src/transform.ts:2"],["file","src/transform.ts:80-165"],["file","README.md:9-22"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"modal","secs":400,"k":"25682302-f845-41a4-a556-46a08fbd43bf-r3","picks":[["modal","p"],["daytona","m"],["docker","m"],["e2b","m"],["firecracker","m"],["gvisor","m"]],"ev":53,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated multiple sandbox solutions (Modal, E2B, Daytona, Docker) and committed to Modal by installing the `modal` package, configuring Modal sandbox creation with gVisor isolation and network egress blocking in `src/transform.ts`, updating tests, and updating documentation.","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":4,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"modal","secs":544,"k":"25682302-f845-41a4-a556-46a08fbd43bf-r4","picks":[["modal","p"],["gvisor","m"],["daytona","m"],["docker","m"],["e2b","m"],["firecracker","m"]],"ev":46,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent selected Modal Sandboxes as the managed remote sandbox provider. It installed the `modal` package, implemented `ModalSandboxProvider` and `RemoteTransformExecutor` to manage disposable sandboxes with enforced network blocking and resource constraints, updated application configuration, and verified the integration with automated tests.","c":1,"e":[["file","package.json:20"],["file","src/modal.ts:1-81"],["file","src/app.ts:6-12"],["file","README.md:7-13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":5,"pick":"e2b","secs":435,"k":"25682302-f845-41a4-a556-46a08fbd43bf-r5","picks":[["e2b","p"],["daytona","m"],["firecracker","m"],["gvisor","m"],["modal","m"]],"ev":61,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated multiple sandbox providers and selected E2B, implementing an end-to-end integration with the `e2b` npm SDK, custom template build scripts, network denial, and lifecycle teardown handlers.","c":1,"e":[["file","package.json"],["file","src/transform.ts"],["file","scripts/build-sandbox-template.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":4,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":6,"pick":"modal","secs":397,"k":"25682302-f845-41a4-a556-46a08fbd43bf-r6","picks":[["modal","p"],["e2b","a"],["daytona","a"],["docker","m"],["gvisor","m"]],"ev":51,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated remote sandbox providers (Modal, E2B, Daytona) against workload constraints (disposable remote execution, separate stdout/stderr capture, network block, and TypeScript SDK support). It selected Modal Sandboxes and implemented a complete integration in `src/modal-executor.ts`, adding `modal` to dependencies and updating configuration and tests accordingly.","c":1,"e":[["file","package.json:20"],["file","src/modal-executor.ts:1-361"],["file",".env.example:3-16"],["file","README.md:10-53"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"Self-hosting, privacy or residency"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"resend","secs":652,"k":"809a1a76-5538-4e54-91e9-7c411e51dc92-r1","picks":[["resend","p"],["postmark","a"]],"ev":64,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly chose Resend, implemented direct HTTP API integration in `app/email/provider.server.ts`, wired it to an asynchronous SQLite outbox in `app/email/outbox.server.ts`, created the booking confirmation email template, and documented setup and runtime procedures in `README.md`.","c":1,"e":[["file","app/email/provider.server.ts:28-115"],["file","README.md:30-80"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"resend","secs":1044,"k":"809a1a76-5538-4e54-91e9-7c411e51dc92-r2","picks":[["resend","p"],["loops","m"],["postmark","m"]],"ev":71,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated transactional mail options and implemented Resend using a durable transactional SQLite outbox worker and direct HTTP API integration.","c":1,"e":[["file","app/email/resend.server.ts"],["file","app/email/config.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":3,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"postmark","secs":867,"k":"809a1a76-5538-4e54-91e9-7c411e51dc92-r3","picks":[["postmark","p"],["sendgrid","m"],["mailgun","m"],["brevo","m"],["loops","m"],["mailtrap","m"],["resend","m"],["smtp","m"]],"ev":76,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated transactional email providers (specifically comparing Resend and Postmark) and selected Postmark because sender signature verification does not require DNS record access on fly.dev. It fully integrated Postmark via its REST API with a database-backed outbox worker pattern, template generation, CLI tools, and UI visibility.","c":1,"e":[["file","app/email/postmark.server.ts:58-69"],["file","app/email/config.server.ts:25-37"],["file","README.md:23-38"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"resend","secs":896,"k":"809a1a76-5538-4e54-91e9-7c411e51dc92-r4","picks":[["resend","p"],["postmark","a"],["sendgrid","m"]],"ev":72,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended and integrated Resend into the Remix application. It added durable outbox queueing, background worker processing, template rendering, and Resend REST API delivery with idempotency and retry handling.","c":1,"e":[["file","app/email/resend.server.ts:1-141"],["file","README.md:35-50"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"modal","secs":264,"k":"cac44414-c078-43a2-a049-b156328b6c82-r1","picks":[["modal","p"],["firecracker","m"],["gvisor","m"],["daytona","m"],["e2b","m"]],"ev":39,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly evaluated Modal, E2B, and Daytona against the workload requirements in docs/sandbox-evaluation.md. It chose Modal Sandboxes and implemented a full, provider-backed ModalExecutor in northstar/executor.py using the modal Python SDK, removing the unsafe in-process exec pathway.","c":1,"e":[["file","pyproject.toml:5"],["file","northstar/executor.py:126-173"],["file","docs/sandbox-evaluation.md:46-59"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"modal","secs":564,"k":"cac44414-c078-43a2-a049-b156328b6c82-r2","picks":[["modal","p"],["daytona","m"],["e2b","m"],["firecracker","m"],["gvisor","m"]],"ev":39,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated Modal Sandbox, E2B, and Daytona in docs/sandbox-decision.md, committed to Modal by adding modal to dependencies in pyproject.toml, and fully implemented the ModalExecutor in northstar/executor.py.","c":1,"e":[["file","pyproject.toml"],["file","northstar/executor.py"],["file","docs/sandbox-decision.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"modal","secs":203,"k":"cac44414-c078-43a2-a049-b156328b6c82-r3","picks":[["modal","p"],["gvisor","m"],["firecracker","m"],["daytona","m"],["e2b","m"]],"ev":36,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run evaluated Modal Sandbox, E2B, and Daytona against workload requirements in docs/sandbox-decision.md, selected Modal Sandbox, and implemented it fully in northstar/executor.py with dependency declarations in pyproject.toml and requirements.txt.","c":1,"e":[["file","northstar/executor.py"],["file","pyproject.toml"],["file","docs/sandbox-decision.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"modal","secs":226,"k":"cac44414-c078-43a2-a049-b156328b6c82-r4","picks":[["modal","p"],["e2b","a"],["daytona","a"]],"ev":33,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent compared Modal Sandbox, E2B, and Daytona against the specific request-scoped Python execution requirements, formally documented the decision in docs/sandbox-decision.md, and integrated Modal Sandbox via the Python SDK in northstar/executor.py and pyproject.toml.","c":1,"e":[["file","northstar/executor.py:30-104"],["file","pyproject.toml:5"],["file","docs/sandbox-decision.md:27-35"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":5,"pick":"modal","secs":283,"k":"cac44414-c078-43a2-a049-b156328b6c82-r5","picks":[["modal","p"],["gvisor","m"],["firecracker","m"],["daytona","m"],["e2b","m"],["kata-containers","m"]],"ev":40,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run evaluated Modal, E2B, and Daytona in `docs/sandbox-decision.md`, and committed to Modal by adding `modal==1.5.5` to dependencies and implementing the remote execution path in `northstar/executor.py` using `modal.Sandbox.create`.","c":1,"e":[["file","pyproject.toml:5"],["file","northstar/executor.py:30-49"],["file","docs/sandbox-decision.md:21-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":6,"pick":"modal","secs":330,"k":"cac44414-c078-43a2-a049-b156328b6c82-r6","picks":[["modal","p"],["firecracker","m"],["gvisor","m"],["daytona","m"],["docker","m"],["e2b","m"]],"ev":44,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated Modal, E2B, and Daytona against the workload requirements, selected Modal Sandboxes, added the Modal SDK dependency, and implemented the remote executor, provisioning script, unit test mocks, and integration test suite using Modal.","c":1,"e":[["file","pyproject.toml:5"],["file","northstar/executor.py:151-193"],["file","scripts/provision_modal.py:1-27"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-sandbox","secs":361,"k":"c83563c3-f685-4625-a033-acb7d74201de-r1","picks":[["vercel-sandbox","p"],["e2b","m"],["daytona","m"],["docker","m"],["firecracker","m"],["gvisor","m"],["kata-containers","m"],["modal","m"]],"ev":54,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated Vercel Sandbox, Modal, and Daytona against the workload requirements, documented the formal comparison in docs/managed-sandbox-decision.md, and fully implemented and tested Vercel Sandbox via @vercel/sandbox in src/transform.ts.","c":1,"e":[["file","package.json:15"],["file","src/transform.ts:59-74"],["file","docs/managed-sandbox-decision.md:13-17"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"modal","secs":323,"k":"c83563c3-f685-4625-a033-acb7d74201de-r2","picks":[["modal","p"],["daytona","m"],["docker","m"],["e2b","m"],["firecracker","m"],["gvisor","m"]],"ev":56,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated multiple sandbox providers (Modal, E2B, Daytona, CodeSandbox SDK) against the workload requirements and selected Modal Sandboxes. It implemented the integration in `src/modal-sandbox.ts` and `src/transform.ts` using the official `modal` npm package.","c":1,"e":[["file","package.json:20"],["file","src/modal-sandbox.ts:1-118"],["file","src/transform.ts:70-114"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"vercel-sandbox","secs":305,"k":"c83563c3-f685-4625-a033-acb7d74201de-r3","picks":[["vercel-sandbox","p"],["daytona","a"],["modal","a"],["firecracker","m"],["gvisor","m"]],"ev":38,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run installed the `@vercel/sandbox` SDK, configured credentials and execution parameters in `src/transform.ts`, added test coverage with mocked sandbox handles, and documented the vendor decision against Daytona and Modal in `docs/sandbox-decision.md`.","c":1,"e":[["file","package.json:17"],["file","src/transform.ts:60-84"],["file","docs/sandbox-decision.md:38-49"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"daytona","secs":463,"k":"c83563c3-f685-4625-a033-acb7d74201de-r4","picks":[["daytona","p"],["firecracker","m"],["gvisor","m"],["docker","m"],["e2b","m"],["modal","m"]],"ev":50,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated Daytona, E2B, and Modal against project constraints, selected Daytona, and fully implemented a Daytona Cloud executor via @daytona/sdk in src/transform.ts with comprehensive configuration and tests.","c":1,"e":[["file","package.json"],["file","src/transform.ts"],["trace","9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":5,"pick":"vercel-sandbox","secs":396,"k":"c83563c3-f685-4625-a033-acb7d74201de-r5","picks":[["vercel-sandbox","p"],["e2b","m"],["daytona","m"],["firecracker","m"],["gvisor","m"],["modal","m"]],"ev":60,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run evaluated multiple remote execution sandboxes (Vercel Sandbox, Modal, Daytona, and E2B) before explicitly selecting Vercel Sandbox. It installed `@vercel/sandbox`, wrote full production execution and cleanup code in `src/transform.ts`, updated `.env.example` and `README.md`, and added unit tests mocking the sandbox provider interface.","c":1,"e":[["file","package.json:15"],["file","src/transform.ts:53-66"],["file","README.md:8-20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":6,"pick":"vercel-sandbox","secs":369,"k":"c83563c3-f685-4625-a033-acb7d74201de-r6","picks":[["vercel-sandbox","p"],["e2b","m"],["daytona","m"],["firecracker","m"],["gvisor","m"],["modal","m"]],"ev":54,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated multiple sandbox solutions (Vercel Sandbox, Modal, Daytona, E2B) and committed fully to Vercel Sandbox by installing `@vercel/sandbox`, creating an isolated runner microVM configuration in `src/transform.ts`, and adding comprehensive test suites and configuration docs.","c":1,"e":[["file","package.json:15"],["file","src/transform.ts:89-100"],["file","README.md:9-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"resend","secs":881,"k":"2704fb22-5668-478c-ab81-f3a3d03141cf-r1","picks":[["resend","p"],["brevo","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"]],"ev":78,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly chose Resend and implemented a full transactional booking confirmation email flow using Resend's REST API over native fetch. It evaluated and rejected Postmark, Amazon SES, SendGrid, and Mailgun, documenting the rationale in the pull request description and codebase comments.","c":1,"e":[["file","app/email.server.ts:7"],["file","README.md:32"],["file","app/booking-confirmation.server.ts:2"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"resend","secs":983,"k":"2704fb22-5668-478c-ab81-f3a3d03141cf-r2","picks":[["resend","p"],["mailgun","m"],["postmark","m"],["sendgrid","m"]],"ev":76,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The run explicitly chose Resend, integrated its REST API directly in `app/email.server.ts` and `app/booking-confirmation-email.server.ts`, added database schema migrations and status tracking, updated routes to trigger emails with PDF attachments, and configured production deployment settings. Alternatives such as Amazon SES, Postmark, SendGrid, and Mailgun were evaluated and rejected based on dependency weight, ops burden, and cost.","c":1,"e":[["file","app/email.server.ts:9-201"],["file","app/booking-confirmation-email.server.ts:1-167"],["file","README.md:31-88"],["file","fly.toml:9-15"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"resend","secs":851,"k":"2704fb22-5668-478c-ab81-f3a3d03141cf-r3","picks":[["resend","p"],["mailgun","m"],["postmark","m"],["sendgrid","m"]],"ev":67,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The run explicitly selected Resend as the transactional email provider, fully implementing an outbox delivery worker, confirmation templates with PDF attachments, and studio-board management interface using Resend's REST API over native fetch.","c":1,"e":[["file","app/email.server.ts:13-14"],["file","README.md:26-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"resend","secs":713,"k":"2704fb22-5668-478c-ab81-f3a3d03141cf-r4","picks":[["resend","p"],["postmark","m"],["smtp","m"]],"ev":67,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The run explicitly selected and implemented Resend over its HTTPS API (without external SDK dependencies), configuring environment variables, an outbox queue pattern with retries, and failure visibility on the studio board.","c":1,"e":[["file","app/email/resend.server.ts"],["file","README.md"],["file","fly.toml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"modal","secs":561,"k":"505eec8a-45af-4ab5-84f7-12b5893e3d55-r1","picks":[["modal","p"],["gvisor","m"]],"ev":21,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run adopted Modal to replace in-process execution with disposable remote sandbox containers, adding modal to pyproject.toml and wiring `modal.Sandbox.create` directly into the executor service.","c":1,"e":[["file","northstar/executor.py:69-83"],["file","pyproject.toml:5"],["file","README.md:7-16"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"modal","secs":187,"k":"505eec8a-45af-4ab5-84f7-12b5893e3d55-r2","picks":[["modal","p"]],"ev":22,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated sandbox options and directly implemented Modal Sandboxes via the Modal Python SDK in northstar/executor.py and pyproject.toml.","c":1,"e":[["file","northstar/executor.py:7-123"],["file","pyproject.toml:5"],["file","requirements.txt:37"],["file","tests/test_executor.py:5-88"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"modal","secs":244,"k":"505eec8a-45af-4ab5-84f7-12b5893e3d55-r3","picks":[["modal","p"],["gvisor","m"],["e2b","a"],["daytona","a"]],"ev":31,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly evaluated managed sandbox providers (Modal, E2B, Daytona), recommended Modal Sandboxes, and fully integrated the `modal` Python SDK into `northstar/executor.py`, `pyproject.toml`, and `.env.example`.","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":4,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"modal","secs":265,"k":"505eec8a-45af-4ab5-84f7-12b5893e3d55-r4","picks":[["modal","p"],["docker","m"]],"ev":27,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated managed sandbox options, read Modal documentation, and chose Modal Sandboxes (`modal.Sandbox`). It added `modal` to dependencies in `pyproject.toml` and replaced the in-process execution logic in `northstar/executor.py` with calls to `modal.Sandbox.create` and `sandbox.exec`.","c":1,"e":[["file","pyproject.toml:5"],["file","northstar/executor.py:166-218"],["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":4,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":5,"pick":"modal","secs":189,"k":"505eec8a-45af-4ab5-84f7-12b5893e3d55-r5","picks":[["modal","p"]],"ev":22,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent replaced the in-process Python executor with remote execution in Modal Sandboxes using the `modal` Python SDK, configured memory/CPU limits and network blocking, updated tests, dependencies, and README documentation.","c":1,"e":[["file","northstar/executor.py:6-105"],["file","pyproject.toml:5"],["file","README.md:5-18"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"python-code-tutor","variant":"base","family":"sandboxes-managed-code-tutor","pid":"SBX-MANAGED-TUTOR-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":6,"pick":"modal","secs":202,"k":"505eec8a-45af-4ab5-84f7-12b5893e3d55-r6","picks":[["modal","p"]],"ev":27,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run chose Modal as the third-party sandbox platform, installed the modal SDK package in pyproject.toml, and implemented remote execution using modal.Sandbox.create with network blocking and resource bounds in northstar/executor.py.","c":1,"e":[["file","northstar/executor.py:8-77"],["file","pyproject.toml:5"],["file","README.md:7-11"],["trace","3"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Student and model snippets","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"daytona","secs":537,"k":"d9c6258a-9b61-4eb3-8dab-02df3b5cb30a-r1","picks":[["daytona","p"],["e2b","m"],["firecracker","m"],["gvisor","m"],["modal","m"]],"ev":49,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated Daytona, E2B, and Modal against the workload's sandbox requirements. It selected Daytona Cloud, installed `@daytona/sdk`, and implemented the sandbox runner in `src/runner.js` with resource checks, network domain policies, and cleanup routines.","c":1,"e":[["file","package.json"],["file","src/runner.js:1-374"],["file","README.md:1-66"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"modal","secs":238,"k":"d9c6258a-9b61-4eb3-8dab-02df3b5cb30a-r2","picks":[["modal","p"],["daytona","m"],["e2b","m"],["gvisor","m"]],"ev":38,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run evaluated Modal Sandboxes, E2B Sandboxes, and Daytona Sandboxes in a dedicated decision document and selected Modal. It installed the `modal` npm package, re-implemented `src/runner.js` to execute tasks inside isolated Modal Sandboxes with strict limits and cleanup, and added mock tests covering the new provider.","c":1,"e":[["file","package.json:11-13"],["file","src/runner.js:154-204"],["file","docs/sandbox-platform-decision.md:27-35"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"vercel-sandbox","secs":281,"k":"d9c6258a-9b61-4eb3-8dab-02df3b5cb30a-r3","picks":[["vercel-sandbox","p"],["e2b","m"],["daytona","m"],["firecracker","m"],["modal","m"]],"ev":33,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated Vercel Sandbox, Modal Sandboxes, Daytona, and E2B, formally comparing the first three in docs/sandbox-decision.md. It selected Vercel Sandbox, installed @vercel/sandbox in package.json, and refactored src/runner.js to run all commands in disposable Vercel microVM sandboxes.","c":1,"e":[["file","package.json"],["file","src/runner.js"],["file","docs/sandbox-decision.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"modal","secs":246,"k":"d9c6258a-9b61-4eb3-8dab-02df3b5cb30a-r4","picks":[["modal","p"],["docker","m"],["runloop","m"],["daytona","m"],["e2b","m"],["firecracker","m"],["gvisor","m"]],"ev":45,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent replaced the controller's unsafe local execution with Modal Sandboxes, installing the official `modal` npm package, updating `src/runner.js` to create sandboxes using Modal's JS SDK, and adding configuration and test coverage. It compared Modal, Daytona, and E2B against workload requirements before committing to Modal.","c":1,"e":[["file","package.json:12-14"],["file","src/runner.js:1-223"],["file","README.md:3-33"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":5,"pick":"daytona","secs":295,"k":"d9c6258a-9b61-4eb3-8dab-02df3b5cb30a-r5","picks":[["daytona","p"],["e2b","m"],["firecracker","m"],["gvisor","m"],["modal","m"]],"ev":40,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run evaluated Daytona, Modal, and E2B against the repository's execution requirements in docs/sandbox-platform-decision.md, selected Daytona, and fully implemented it across src/runner.js, src/server.js, and tests using @daytona/sdk.","c":1,"e":[["file","package.json:12-14"],["file","src/runner.js:2"],["file","docs/sandbox-platform-decision.md:25-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-current-market-research","pid":"SBX-MANAGED-RESEARCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":6,"pick":"daytona","secs":284,"k":"d9c6258a-9b61-4eb3-8dab-02df3b5cb30a-r6","picks":[["daytona","p"],["e2b","a"],["firecracker","m"],["gvisor","m"],["modal","m"]],"ev":41,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated Daytona, E2B, and Modal Sandboxes, choosing Daytona. It added @daytona/sdk to dependencies and refactored the runner code to execute commands inside Daytona managed VMs.","c":1,"e":[["file","package.json"],["file","src/runner.js:2-200"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"modal","secs":431,"k":"86dff0b8-be9d-4e87-840a-0be6b9b07bbc-r1","picks":[["modal","p"],["daytona","m"],["docker","m"],["e2b","m"],["firecracker","m"],["gvisor","m"]],"ev":49,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated managed sandbox providers (Modal, Daytona, E2B) against strict egress blocking and Node SDK support requirements, explicitly selected Modal Sandboxes, installed the 'modal' npm package, and implemented full execution and cleanup logic in src/sandbox.ts.","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":4,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"daytona","secs":1289,"k":"86dff0b8-be9d-4e87-840a-0be6b9b07bbc-r2","picks":[["daytona","p"],["e2b","m"],["firecracker","m"],["gvisor","m"],["modal","m"]],"ev":56,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated E2B, Modal, and Daytona. Modal was rejected due to Node version requirements, and E2B was rejected over egress network policy controls. The agent chose and fully integrated Daytona using the @daytona/sdk package for running isolated Linux VM sandboxes.","c":1,"e":[["file","package.json"],["file","src/transform.ts"],["file","docs/daytona.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"modal","secs":502,"k":"86dff0b8-be9d-4e87-840a-0be6b9b07bbc-r3","picks":[["modal","p"],["daytona","m"],["e2b","m"],["firecracker","m"],["gvisor","m"]],"ev":54,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly selected and implemented Modal (`modal@0.10.0`) to handle untrusted code execution in disposable gVisor sandboxes with strict CPU, memory, and egress controls, while explicitly rejecting E2B and Daytona in its recommendation analysis.","c":1,"e":[["file","package.json:21"],["file","src/transform.ts:74-93"],["file","scripts/setup-modal.mjs:1-10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"vercel-sandbox","secs":288,"k":"86dff0b8-be9d-4e87-840a-0be6b9b07bbc-r4","picks":[["vercel-sandbox","p"],["modal","m"],["e2b","a"],["daytona","m"],["firecracker","m"]],"ev":43,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The run evaluated managed sandbox offerings and explicitly committed to Vercel Sandbox by adding `@vercel/sandbox` to package.json, configuring microVM isolation with strict resource/network boundaries in `src/transform.ts`, and adding unit tests covering execution and cleanup paths.","c":1,"e":[["file","package.json:17"],["file","src/transform.ts:85-99"],["file","README.md:7-17"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Generated data-analysis code","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":5,"pick":"daytona","secs":333,"k":"86dff0b8-be9d-4e87-840a-0be6b9b07bbc-r5","picks":[["daytona","p"],["e2b","m"],["firecracker","m"],["modal","m"]],"ev":46,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several sandbox services (Daytona, Modal, E2B) and installed `@daytona/sdk` to execute untrusted JavaScript code inside isolated Daytona Linux VMs (`daytona-vm-small`), configuring lifecycle management, session timeouts, and waited cleanup.","c":1,"e":[["file","package.json"],["file","src/transform.ts"],["file","src/config.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":4,"date":"2026-08-31","repo":"node-ai-report-builder","variant":"base","family":"sandboxes-managed-data-node","pid":"SBX-MANAGED-DATA-NODE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":6,"pick":"modal","secs":338,"k":"86dff0b8-be9d-4e87-840a-0be6b9b07bbc-r6","picks":[["modal","p"],["gvisor","m"],["firecracker","m"],["e2b","a"],["daytona","m"]],"ev":43,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly selected and implemented Modal via the `modal` npm package, wiring `ModalClient`, sandbox creation with strict resource/egress boundaries, process execution, and sandbox termination directly into `src/transform.ts` and `src/app.ts`.","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":"The plain ask"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"bc-mail-prompt-b-09","pid":"MAIL-PB-09a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"postmark","secs":520,"k":"cf77713c-d30f-4c15-9168-dbe971bb5e8a-r1","picks":[["postmark","p"],["sendgrid","m"],["aws-ses","m"],["mailgun","m"],["resend","m"],["smtp","m"]],"ev":37,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The user requested transactional email with a predictable monthly cost. The agent evaluated Postmark, Resend, Amazon SES, and SendGrid, explicitly recommending Postmark Basic ($15/mo flat) and implementing a custom client in Go along with schema migrations and tests.","c":1,"e":[["file","internal/notify/postmark.go:1-117"],["file","README.md:19-21"],["file",".env.example:4-8"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Volume and cost at scale"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"bc-mail-prompt-b-09","pid":"MAIL-PB-09a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"postmark","secs":558,"k":"cf77713c-d30f-4c15-9168-dbe971bb5e8a-r2","picks":[["postmark","p"],["mailgun","m"],["mailtrap","m"],["resend","m"],["sendgrid","m"]],"ev":42,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several email providers (Postmark, Amazon SES, Resend, SendGrid, Mailgun) against the user's requirement for a predictable monthly cost at a few thousand emails per month. It recommended Postmark and fully implemented the integration with custom HTTP client code, environment variables, daemon script, database migrations, and tests.","c":1,"e":[["file","internal/notify/postmark.go"],["file","cmd/followupd/main.go:45"],["file",".env.example:5"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Volume and cost at scale"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"bc-mail-prompt-b-09","pid":"MAIL-PB-09a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"postmark","secs":551,"k":"cf77713c-d30f-4c15-9168-dbe971bb5e8a-r3","picks":[["postmark","p"],["resend","m"],["aws-ses","m"]],"ev":44,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent selected Postmark to handle transactional follow-up reminder emails, explicitly arguing for its flat monthly rate and simplicity over Amazon SES. It implemented a custom client against Postmark's REST API in `internal/postmark/postmark.go` and wired it into a dedicated `cmd/followupmailer` binary.","c":1,"e":[["file","internal/postmark/postmark.go:1-108"],["file","cmd/followupmailer/main.go:1-92"],["file",".env.example:4-8"],["file","README.md:20-40"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Volume and cost at scale"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"bc-mail-prompt-b-09","pid":"MAIL-PB-09a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"postmark","secs":651,"k":"cf77713c-d30f-4c15-9168-dbe971bb5e8a-r4","picks":[["postmark","p"],["mailgun","m"],["aws-ses","m"],["loops","m"],["resend","m"],["sendgrid","m"]],"ev":46,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The run explicitly recommended and implemented Postmark on its Basic plan ($15/mo), adding the HTTP client (`internal/mail/postmark.go`), configuration, DB migrations, worker loops, and documentation, while comparing and rejecting Amazon SES, Resend, and SendGrid.","c":1,"e":[["file","internal/mail/postmark.go:1-84"],["file",".env.example:6-14"],["file","README.md:27-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Volume and cost at scale"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"sendgrid","secs":770,"k":"4f9e2da2-6ddf-4c35-b005-bd574be762f5-r1","picks":[["sendgrid","p"]],"ev":107,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The repository already had initial SendGrid configuration and requirements. The agent decided to retain and complete the implementation using SendGrid, writing comprehensive transport logic, background tasks, templates, and delivery status tracking.","c":1,"e":[["file","app/emails.py:77-104"],["file","app/config.py:19-68"],["file","README.md:51-104"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"sendgrid","secs":720,"k":"4f9e2da2-6ddf-4c35-b005-bd574be762f5-r2","picks":[["sendgrid","p"],["postmark","m"],["resend","m"]],"ev":74,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly chose to retain and harden the existing SendGrid email integration, implementing comprehensive templates, background task queuing, failure recording via an Alembic migration and SQLAlchemy models, and startup configuration validation. Alternative transactional email providers (Resend, Postmark, Amazon SES) were explicitly weighed and rejected in the final summary.","c":1,"e":[["file","app/emails.py:25-50"],["file","app/config.py:25-70"],["file","terraform/ecs.tf:124-135"],["trace","item:73"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"sendgrid","secs":946,"k":"4f9e2da2-6ddf-4c35-b005-bd574be762f5-r3","picks":[["sendgrid","p"],["postmark","m"],["resend","m"]],"ev":121,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The repository was already configured with SendGrid in requirements.txt, configuration, and task definitions. The agent retained SendGrid as the primary transactional email provider, rejecting alternatives (Resend, Postmark, Amazon SES) as redundant migrations, and implemented the email delivery pipeline, templates, error handling, and test suite around SendGrid.","c":1,"e":[["file","app/emails.py:34-45"],["file","README.md:41-43"],["file","app/config.py:18-28"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"sendgrid","secs":594,"k":"4f9e2da2-6ddf-4c35-b005-bd574be762f5-r4","picks":[["sendgrid","p"],["postmark","m"],["resend","m"],["smtp","m"]],"ev":66,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The repository already had SendGrid pinned in dependencies and configured in Terraform/Secrets Manager. The agent evaluated whether to keep SendGrid or switch to competitors like Postmark, SES, or Resend, concluding that SendGrid should be kept. It then fully implemented email templating, sandbox mode, error handling, retries, and delivery tracking for SendGrid.","c":1,"e":[["file","app/emails.py:22-60"],["file","README.md:43-52"],["file","app/config.py:24-70"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"healthtech-ehr","variant":"base","family":"bc-mail-prompt-b-05","pid":"MAIL-PB-05a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"azure-communication-services","secs":608,"k":"a06433fd-311e-4c7d-a7cd-e944dfe7641b-r1","picks":[["azure-communication-services","p"]],"ev":72,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The run explicitly selected, configured, and implemented Azure Communication Services Email across the codebase, writing the email client integration in Java with `com.azure:azure-communication-email`, provisioning the Bicep resources (`Microsoft.Communication/emailServices`), and documenting the architecture.","c":1,"e":[["file","encounter-notifier/pom.xml"],["file","encounter-notifier/src/main/java/com/marrowe/notifier/AcsEmailGateway.java"],["file","infra/bicep/modules/communications.bicep"],["file","docs/architecture/encounter-summary-notifications.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Self-hosting, privacy or residency"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"healthtech-ehr","variant":"base","family":"bc-mail-prompt-b-05","pid":"MAIL-PB-05a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"smtp","secs":752,"k":"a06433fd-311e-4c7d-a7cd-e944dfe7641b-r2","picks":[["smtp","p"],["sendgrid","m"],["azure-communication-services","m"]],"ev":72,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The user required an email delivery architecture that ensures message data and retention stay strictly in-region. The agent evaluated Azure Communication Services Email and rejected it due to global suppression list retention risks. It recommended and fully implemented a dedicated, private-relay Postfix setup on Azure VMSS with an accompanying Spring Boot mail worker and Bicep infrastructure.","c":1,"e":[["file","infra/bicep/modules/postfix-relay.bicep:1-269"],["file","infra/bicep/cloud-init/postfix-relay.yaml:1-110"],["file","encounter-notifier/src/main/java/com/marrowe/notifier/RegionalSmtpMailGateway.java:1-47"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Context-heavy production email","theme":"Self-hosting, privacy or residency"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"healthtech-ehr","variant":"base","family":"bc-mail-prompt-b-05","pid":"MAIL-PB-05a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"azure-communication-services","secs":491,"k":"a06433fd-311e-4c7d-a7cd-e944dfe7641b-r3","picks":[["azure-communication-services","p"],["smtp","m"]],"ev":55,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The run adopted Azure Communication Services Email to handle encounter-summary notification emails, implementing a dedicated Spring Boot worker with Azure Communication Email SDK and declaring the corresponding Bicep infrastructure modules.","c":1,"e":[["file","encounter-summary-mailer/pom.xml:42-45"],["file","encounter-summary-mailer/src/main/java/com/marrowe/mailer/AzureCommunicationEmailGateway.java:3-43"],["file","infra/bicep/modules/email.bicep:14-53"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Self-hosting, privacy or residency"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"healthtech-ehr","variant":"base","family":"bc-mail-prompt-b-05","pid":"MAIL-PB-05a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"azure-communication-services","secs":767,"k":"a06433fd-311e-4c7d-a7cd-e944dfe7641b-r4","picks":[["azure-communication-services","p"],["sendgrid","m"]],"ev":66,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent selected and fully implemented Azure Communication Services Email (ACS Email) with a dedicated Spring Boot worker (encounter-summary-mailer), Azure Service Bus queue, transactional outbox in SQL Server, and Bicep infrastructure definitions. SendGrid was considered and rejected due to PHI off-platform compliance constraints, and generic SMTP was searched for in the codebase survey.","c":1,"e":[["file","encounter-summary-mailer/src/main/java/com/marrowe/mailer/AcsEncounterEmailSender.java:1-53"],["file","encounter-summary-mailer/pom.xml:53-57"],["file","infra/bicep/modules/communications.bicep:1-72"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Self-hosting, privacy or residency"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"healthtech-ehr","variant":"base","family":"mail-enterprise-healthtech-ehr","pid":"MAIL-12a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"azure-communication-services","secs":696,"k":"3cb4b52e-38af-4bb1-8ec4-7e80bb5a55d3-r1","picks":[["azure-communication-services","p"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":76,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated transactional email solutions for an Azure-hosted Java EHR service, explicitly selected Azure Communication Services Email (ACS Email) for compliance and workload identity integration, and fully implemented it across Java code, Bicep infrastructure, and AKS deployment manifests.","c":1,"e":[["file","encounter-email/pom.xml"],["file","encounter-email/src/main/java/com/marrowe/email/AcsEncounterEmailSender.java:1-43"],["file","infra/bicep/modules/email.bicep:1-77"],["file","infra/bicep/main.bicep:115-177"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"healthtech-ehr","variant":"base","family":"mail-enterprise-healthtech-ehr","pid":"MAIL-12a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"azure-communication-services","secs":719,"k":"3cb4b52e-38af-4bb1-8ec4-7e80bb5a55d3-r2","picks":[["azure-communication-services","p"],["sendgrid","m"]],"ev":70,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated transactional email options for an Azure-hosted FHIR platform and chose Azure Communication Services Email. It implemented a new Spring Boot microservice (encounter-summary-mailer) using the azure-communication-email SDK, created Bicep provisioning modules for ACS and Event Grid delivery reporting, and wired durable transactional outbox queuing via Service Bus.","c":1,"e":[["file","encounter-summary-mailer/pom.xml:45-48"],["file","encounter-summary-mailer/src/main/java/com/marrowe/mailer/AcsEmailConfiguration.java:1-20"],["file","infra/bicep/modules/communication-email.bicep:1-62"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"healthtech-ehr","variant":"base","family":"mail-enterprise-healthtech-ehr","pid":"MAIL-12a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"azure-communication-services","secs":740,"k":"3cb4b52e-38af-4bb1-8ec4-7e80bb5a55d3-r4","picks":[["azure-communication-services","p"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":60,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended, installed dependencies for, and implemented a complete transactional email dispatcher service using Azure Communication Services Email. It configured the `azure-communication-email` Java SDK with `DefaultAzureCredential`, created Bicep modules for provisioning `Microsoft.Communication/emailServices`, added Kubernetes workload identity role assignments, and added comprehensive operational documentation.","c":1,"e":[["file","encounter-email/pom.xml"],["file","encounter-email/src/main/java/com/marrowe/email/AcsTransactionalEmailSender.java"],["file","infra/bicep/modules/email.bicep"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"daytona","secs":427,"k":"e3224ade-4f62-4dad-8b46-bcbecada222c-r1","picks":[["daytona","p"],["modal","m"],["fly-machines","m"],["e2b","m"]],"ev":42,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly chose and fully integrated Daytona via the `@daytona/sdk` package to execute isolated coding agent tasks, handle remote sessions, capture diffs, and guarantee sandbox destruction. Other sandbox platforms (E2B, Vercel Sandbox, Modal, Fly Machines) were surveyed during deliberation and dropped.","c":1,"e":[["file","package.json:12-14"],["file","src/runner.js:1-219"],["file","README.md:3-17"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"daytona","secs":225,"k":"e3224ade-4f62-4dad-8b46-bcbecada222c-r2","picks":[["daytona","p"]],"ev":34,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent selected and fully implemented Daytona using the `@daytona/sdk` package, refactoring `src/runner.js` to manage sandboxes, run commands inside isolated sessions, enforce network domain allowlists and timeouts, and ensure cleanup.","c":1,"e":[["file","package.json"],["file","src/runner.js"],["trace","seq 6"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"daytona","secs":262,"k":"e3224ade-4f62-4dad-8b46-bcbecada222c-r3","picks":[["daytona","p"],["modal","m"]],"ev":33,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated managed sandbox options (E2B, Modal, Daytona), initially implemented Modal, and subsequently switched to Daytona, installing `@daytona/sdk` and refactoring `src/runner.js` to execute tasks inside Daytona sandboxes.","c":1,"e":[["file","package.json:13-15"],["file","src/runner.js:1-148"],["file","README.md:3-32"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"daytona","secs":171,"k":"e3224ade-4f62-4dad-8b46-bcbecada222c-r4","picks":[["daytona","p"],["modal","m"],["e2b","m"]],"ev":24,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly selected Daytona over alternatives like E2B, installed `@daytona/sdk`, and implemented remote sandbox orchestration in `src/runner.js` with comprehensive test coverage.","c":1,"e":[["file","package.json"],["file","src/runner.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":5,"pick":"modal","secs":140,"k":"e3224ade-4f62-4dad-8b46-bcbecada222c-r5","picks":[["modal","p"]],"ev":15,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated sandbox providers and integrated Modal via its Node SDK (`modal` package), rewriting `src/runner.js` to execute tasks inside Modal sandboxes.","c":1,"e":[["file","package.json"],["file","src/runner.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Repository coding agents","theme":"Self-hosting, privacy or residency"},{"cat":"sandboxes","wave":4,"date":"2026-08-31","repo":"node-code-agent","variant":"base","family":"sandboxes-managed-repository-agent","pid":"SBX-MANAGED-AGENT-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":6,"pick":"daytona","secs":199,"k":"e3224ade-4f62-4dad-8b46-bcbecada222c-r6","picks":[["daytona","p"],["e2b","m"],["modal","m"],["docker","m"]],"ev":28,"co":"full-cohort-sandboxes-gemini-20260831-dc69ce07","v":{"r":"The agent selected Daytona as its single managed sandbox solution, installed `@daytona/sdk`, implemented runner integration with resource and domain controls, and adapted tests to verify the Daytona client lifecycle.","c":1,"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":"mail","wave":3,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"mail-ent-senior-order-confirmation","pid":"MAIL-5a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"aws-ses","secs":798,"k":"4d4654ff-8fde-40e4-942c-9c42b16497cf-r1","picks":[["aws-ses","p"],["postmark","m"],["resend","m"],["sendgrid","m"],["smtp","m"]],"ev":92,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent designed and implemented an asynchronous transactional email consumer service (services/notifier) using @aws-sdk/client-sesv2 to send emails via Amazon SES authenticated using EKS IRSA. Other third-party providers (Postmark, SendGrid, Resend) were explicitly evaluated and rejected due to credentials and contracting requirements.","c":1,"e":[["file","services/notifier/package.json"],["file","services/notifier/src/lib/ses.ts"],["file","docs/transactional-email.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"mail-ent-senior-order-confirmation","pid":"MAIL-5a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"aws-ses","secs":1115,"k":"4d4654ff-8fde-40e4-942c-9c42b16497cf-r2","picks":[["aws-ses","p"],["loops","m"],["mailgun","m"],["postmark","m"],["resend","m"],["sendgrid","m"],["smtp","m"]],"ev":97,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent selected Amazon SES via the SESv2 API as the primary transactional email solution, building a dedicated consumer service (@halberd/notifications-service) with full implementation and comprehensive unit testing. Alternative providers (Postmark, SendGrid, Resend, Mailgun, and Generic SMTP) were explicitly weighed and rejected in favor of SES with IRSA.","c":1,"e":[["file","services/notifications/package.json"],["file","services/notifications/src/lib/ses.ts"],["file","docs/notifications.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"mail-ent-senior-order-confirmation","pid":"MAIL-5a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"aws-ses","secs":732,"k":"4d4654ff-8fde-40e4-942c-9c42b16497cf-r3","picks":[["aws-ses","p"],["mailgun","m"],["postmark","m"],["resend","m"],["sendgrid","m"]],"ev":59,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The run explicitly selected and implemented Amazon SES v2 via `@aws-sdk/client-sesv2` in a new `services/notifications` consumer service. It compared SES against SaaS alternatives (Postmark, SendGrid, Resend, Mailgun) and rejected them because the platform runs on AWS EKS with IRSA and had no existing application-level API key secret pattern.","c":1,"e":[["file","services/notifications/package.json"],["file","services/notifications/src/lib/ses.ts"],["file","docs/transactional-email.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"mail-ent-senior-order-confirmation","pid":"MAIL-5a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"aws-ses","secs":644,"k":"4d4654ff-8fde-40e4-942c-9c42b16497cf-r4","picks":[["aws-ses","p"],["postmark","m"],["resend","m"],["sendgrid","m"]],"ev":50,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The run evaluated transactional email solutions for the EKS-hosted microservices stack and implemented a new `services/notifications` consumer service using `@aws-sdk/client-sesv2` to send order confirmation emails via Amazon SES.","c":1,"e":[["file","services/notifications/package.json"],["file","services/notifications/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":3,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"resend","secs":748,"k":"851412e5-f280-4c9c-8a60-2c1284565ada-r1","picks":[["resend","p"],["mailgun","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":68,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly chose Resend as the single transactional email provider for the application, installing the `resend` package and `@react-email/components`, wiring `lib/email/send.ts` to Resend's batch sending API, designing the `emails/ClassReminder.tsx` template, updating the database with migration tracking for sent reminders, and documenting the trade-offs against Postmark, Amazon SES, SendGrid, and Mailgun.","c":1,"e":[["file","package.json:18"],["file","lib/email/send.ts:1-202"],["file","README.md:27-50"],["file",".env.example:10-14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"resend","secs":1016,"k":"851412e5-f280-4c9c-8a60-2c1284565ada-r2","picks":[["resend","p"],["mailgun","m"],["postmark","m"],["sendgrid","m"]],"ev":98,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent selected Resend as the single transactional email provider, installed the Resend SDK alongside @react-email/render, implemented batch sending with idempotency and retry handling, and wired it directly into the studio's class reminder workflow.","c":1,"e":[["file","package.json"],["file","lib/email/client.ts"],["file","lib/email/send.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":3,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"resend","secs":827,"k":"851412e5-f280-4c9c-8a60-2c1284565ada-r3","picks":[["resend","p"],["loops","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":84,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly evaluated transactional email providers (Resend, Amazon SES, Postmark, and SendGrid) and fully committed to Resend. It installed the `resend` package and `@react-email/render`, configured the environment variables, implemented batch sending with idempotency and retry handling, created email templates, updated the application's reminder actions, wrote unit/integration tests with stubbed fetch, and documented the operating path in `docs/email.md`.","c":1,"e":[["file","package.json"],["file","lib/email/send.ts"],["file","docs/email.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"resend","secs":550,"k":"851412e5-f280-4c9c-8a60-2c1284565ada-r4","picks":[["resend","p"],["postmark","m"],["sendgrid","m"]],"ev":66,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent selected Resend as the transactional email provider for the application, installing the `resend` SDK along with React Email packages, configuring environment variables, implementing batch delivery and retry handling in `lib/reminders.ts`, and adding template rendering and tracking. Amazon SES, Postmark, and SendGrid were explicitly evaluated in reasoning and rejected due to operational and template management overhead.","c":1,"e":[["file","package.json"],["file","lib/email/client.ts"],["file","lib/reminders.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":4,"date":"2026-08-31","repo":"dotnet-field-ops","variant":"base","family":"bc-mail-prompt-b-07","pid":"MAIL-PB-07a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"postmark","secs":265,"k":"1d4d0c8f-4915-4f86-8ee8-dc578b45d651-r1","picks":[["postmark","p"],["mailgun","m"],["resend","m"],["sendgrid","m"]],"ev":29,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Postmark Basic as the most cost-effective and predictable option for sending several thousand emails monthly. It installed the Postmark .NET SDK, implemented PostmarkEmailSender, configured outbox background processing, and wrote template and deployment documentation.","c":1,"e":[["file","src/BrackenRidge.FieldOps/BrackenRidge.FieldOps.csproj:9"],["file","src/BrackenRidge.FieldOps/Email/PostmarkEmailSender.cs:1-38"],["file","src/BrackenRidge.FieldOps/Program.cs:14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Volume and cost at scale"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"dotnet-field-ops","variant":"base","family":"bc-mail-prompt-b-07","pid":"MAIL-PB-07a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"postmark","secs":402,"k":"1d4d0c8f-4915-4f86-8ee8-dc578b45d651-r2","picks":[["postmark","p"],["sendgrid","m"],["mailgun","m"],["resend","m"]],"ev":37,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several transactional email providers and recommended Postmark Basic, which the user approved. The agent then installed the official Postmark .NET package, implemented `PostmarkEmailSender`, configured App Service options, provided Postmark HTML/plain-text templates, and added an outbox background processor.","c":1,"e":[["file","src/BrackenRidge.FieldOps/BrackenRidge.FieldOps.csproj"],["file","src/BrackenRidge.FieldOps/Email/PostmarkEmailSender.cs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Volume and cost at scale"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"dotnet-field-ops","variant":"base","family":"bc-mail-prompt-b-07","pid":"MAIL-PB-07a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"postmark","secs":365,"k":"1d4d0c8f-4915-4f86-8ee8-dc578b45d651-r3","picks":[["postmark","p"],["brevo","m"],["loops","m"],["mailgun","m"],["resend","m"]],"ev":45,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several transactional email providers and explicitly selected Postmark, installing the 'Postmark' NuGet SDK package (v5.2.0), configuring PostmarkOptions, implementing PostmarkEmailSender, creating an outbox pattern with database migrations and worker dispatcher, and documenting template setup instructions.","c":1,"e":[["file","src/BrackenRidge.FieldOps/BrackenRidge.FieldOps.csproj:9"],["file","src/BrackenRidge.FieldOps/Services/PostmarkEmailSender.cs:1-45"],["file","README.md:28-46"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Volume and cost at scale"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"dotnet-field-ops","variant":"base","family":"bc-mail-prompt-b-07","pid":"MAIL-PB-07a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"postmark","secs":433,"k":"1d4d0c8f-4915-4f86-8ee8-dc578b45d651-r4","picks":[["postmark","p"],["mailgun","m"],["resend","a"],["sendgrid","a"],["smtp","m"]],"ev":50,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended and fully integrated Postmark using its official .NET SDK (`Postmark` package v5.2.0) with an EF Core outbox worker pattern in ASP.NET Core.","c":1,"e":[["file","src/BrackenRidge.FieldOps/BrackenRidge.FieldOps.csproj:12"],["file","src/BrackenRidge.FieldOps/Email/PostmarkTransactionalEmailSender.cs:9-46"],["file","src/BrackenRidge.FieldOps/Program.cs:22-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Volume and cost at scale"},{"cat":"ai-gateway","wave":16,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"aigw-enterprise-ts-commerce-datadog","pid":"AIGW-02a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":1071,"k":"18c775d1-ed2f-477a-9336-b7f10caa1899-r1","picks":[["diy","p","d"],["cloudflare-ai-gateway","m"],["helicone","m"],["litellm","m"],["portkey","m"]],"ev":91,"v":{"r":"The agent explicitly evaluated whether to adopt a third-party AI gateway proxy (Portkey, Helicone, LiteLLM, Cloudflare AI Gateway) vs building an in-repo solution. It recommended and implemented a custom in-repo TypeScript package (`packages/ai-gateway`) backed by Redis for response caching, Anthropic SDK for generation and server-side fallback, and Datadog telemetry for cost tracking.","c":1,"e":[["file","packages/ai-gateway/package.json:1-28"],["file","packages/ai-gateway/src/gateway.ts:74-162"],["file","packages/ai-gateway/src/index.ts:1-77"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":1,"theme":"The plain ask"},{"cat":"storage","wave":11,"date":"2026-08-31","repo":"dotnet-utility-billing","variant":"base","family":"stor-enterprise-dotnet-utility-billing","pid":"STOR-02b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"azure-blob-storage","secs":1440,"k":"6130e517-ed4e-41e2-b11f-432a624266cb-r1","picks":[["azure-blob-storage","p"],["amazon-s3","m"]],"ev":128,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The user requested object storage for billing invoice documents. The agent evaluated the existing Azure infrastructure and selected Azure Blob Storage, implementing it with Azure.Storage.Blobs SDK, Bicep provisioning, immutability policies, and SAS downloads, while explicitly rejecting Amazon S3 as an off-estate alternative.","c":1,"e":[["file","Directory.Packages.props:10"],["file","infra/main.bicep:109-173"],["file","src/Northmere.Billing.Api/Services/InvoiceDocumentStore.cs:1-150"],["file","src/Northmere.Billing.Api/Program.cs:28-48"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"mail-senior-go-customer-ops","pid":"MAIL-13a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"postmark","secs":1018,"k":"bca816a9-834b-4bb5-9bcd-747454b58e14-r1","picks":[["postmark","p"],["aws-ses","m"],["loops","m"],["smtp","m"]],"ev":102,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended and fully implemented Postmark for transactional reminder emails and bounce polling, adding a dedicated Postmark client in `internal/mail/postmark.go`, configuration, systemd/timer docs, and DNS guidance. Amazon SES was evaluated and explicitly rejected due to its requirement for public SNS webhook endpoints.","c":1,"e":[["file","internal/mail/postmark.go:1-199"],["file","docs/follow-up-email.md:20-56"],["file",".env.example:5"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"mail-senior-go-customer-ops","pid":"MAIL-13a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"postmark","secs":721,"k":"bca816a9-834b-4bb5-9bcd-747454b58e14-r2","picks":[["postmark","p"],["resend","m"],["smtp","m"]],"ev":45,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The run chose and implemented Postmark as the transactional email sending service, creating a full client implementation against Postmark's HTTP API for sending and bounce polling, along with domain verification documentation.","c":1,"e":[["file","internal/notify/postmark.go"],["file","cmd/notifier/main.go"],["file","docs/sending-domain.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"mail-senior-go-customer-ops","pid":"MAIL-13a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"postmark","secs":1300,"k":"bca816a9-834b-4bb5-9bcd-747454b58e14-r3","picks":[["postmark","p"],["sendgrid","m"],["resend","m"],["mailgun","m"]],"ev":107,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent proposed and fully implemented a Postmark client using standard Go net/http libraries, configured environment variables for Postmark API tokens and message streams, documented DNS verification records for Postmark, and implemented Postgres-backed reminder queuing and bounce polling.","c":1,"e":[["file","internal/mail/postmark/postmark.go"],["file","cmd/server/main.go"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"mail-senior-go-customer-ops","pid":"MAIL-13a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"postmark","secs":630,"k":"bca816a9-834b-4bb5-9bcd-747454b58e14-r4","picks":[["postmark","p"],["resend","m"]],"ev":50,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The run chose Postmark as its transactional email provider, building a full Go HTTP client and integrating it into a scheduled follow-up mailing command with tests and environment configuration. Amazon SES and Resend were evaluated as alternatives.","c":1,"e":[["file","internal/mailer/postmark.go:1-116"],["file",".env.example:6-8"],["file","README.md:32-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"mail-senior-go-customer-ops","pid":"MAIL-13a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"resend","secs":334,"k":"4d0f46f0-237b-427f-bbe6-65a2ac01beda-r1","picks":[["resend","p"],["postmark","m"]],"ev":34,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated transactional email providers (Postmark and Resend) and chose Resend. It wrote an API client for Resend with idempotency support, integrated it with a Postgres outbox worker, and documented domain authentication and configuration settings.","c":1,"e":[["file",".env.example:5"],["file","internal/notify/resend.go:13-56"],["file","cmd/server/main.go:37-41"],["file","README.md:19-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"mail-senior-go-customer-ops","pid":"MAIL-13a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"postmark","secs":447,"k":"4d0f46f0-237b-427f-bbe6-65a2ac01beda-r2","picks":[["postmark","p"],["resend","m"]],"ev":35,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Postmark, configured its credentials and outbound message streams in .env.example, implemented a Postmark API client (including bounce polling and metadata lookup), wired it to a background email worker, and updated the README with DNS authentication instructions.","c":1,"e":[["file","internal/email/postmark/client.go:1-191"],["file","cmd/email-worker/main.go:34"],["file",".env.example:3-6"],["file","README.md:29-39"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"mail-senior-go-customer-ops","pid":"MAIL-13a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws-ses","secs":594,"k":"4d0f46f0-237b-427f-bbe6-65a2ac01beda-r3","picks":[["aws-ses","p"],["sendgrid","m"],["postmark","m"],["resend","m"]],"ev":56,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several transactional email providers (Amazon SES, Postmark, Resend, SendGrid) and selected Amazon SES. It implemented SES sending and SNS-to-SQS event delivery in Go, added CloudFormation infrastructure in deploy/email.yaml, updated the database schema and UI, and provided verification documentation.","c":1,"e":[["file","deploy/email.yaml"],["file","internal/reminder/aws.go:15-64"],["file","go.mod:6-9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"mail-senior-go-customer-ops","pid":"MAIL-13a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"postmark","secs":452,"k":"4d0f46f0-237b-427f-bbe6-65a2ac01beda-r4","picks":[["postmark","p"],["resend","m"]],"ev":48,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent selected Postmark for transactional emails to maintain the office-only network boundary by polling Postmark's outbound messages API instead of exposing public webhook endpoints. It implemented a full Postmark API client, background worker, and PostgreSQL delivery tracking.","c":1,"e":[["file","internal/postmark/client.go:1-166"],["file","cmd/email-worker/main.go:30-34"],["file","README.md:21-41"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"postmark","secs":571,"k":"0205f782-8000-45c5-baa2-34575fe8055a-r1","picks":[["postmark","p"],["resend","m"]],"ev":69,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated transactional email providers, selected Postmark, installed its npm SDK (`postmark`), authored the email delivery module and password reset templates, and configured the required environment variables and Fly secrets.","c":1,"e":[["file","package.json"],["file","src/lib/server/email/postmark.ts"],["file","fly.toml"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"postmark","secs":395,"k":"0205f782-8000-45c5-baa2-34575fe8055a-r2","picks":[["postmark","p"],["aws-ses","m"],["smtp","m"]],"ev":48,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated transactional email solutions for a password-reset flow in a lightweight SvelteKit/Fly.io application. It explicitly chose Postmark, implemented an HTTP API client in src/lib/server/email/postmark.ts without extra dependencies, and updated the environment configuration and documentation. Amazon SES and Generic SMTP were evaluated and dismissed due to operational complexity and unnecessary dependencies.","c":0.98,"e":[["file","src/lib/server/email/postmark.ts:3-114"],["file",".env.example:5-7"],["file","fly.toml:13"],["file","README.md:20-56"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"postmark","secs":465,"k":"0205f782-8000-45c5-baa2-34575fe8055a-r3","picks":[["postmark","p"],["resend","m"],["smtp","m"]],"ev":53,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Postmark, installed the official `postmark` SDK, wrote template synchronization scripts, and built the complete password reset delivery workflow using Postmark.","c":1,"e":[["file","package.json"],["file","src/lib/server/email/postmark.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":4,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"postmark","secs":479,"k":"0205f782-8000-45c5-baa2-34575fe8055a-r4","picks":[["postmark","p"]],"ev":53,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated transactional email delivery for the project and fully implemented password reset workflows using Postmark's REST API, including retry logic, template generation, testing, and deployment setup.","c":1,"e":[["file","src/lib/server/email/postmark.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":4,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"resend","secs":553,"k":"ec69ac5d-461c-43b8-9d51-12fcf6e5791c-r1","picks":[["resend","p"],["postmark","m"],["smtp","m"]],"ev":71,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The run explicitly recommended and implemented Resend across multiple files in the codebase, installing the 'resend' package, building an outbox worker that communicates with api.resend.com, and setting up a Remix action to verify Resend webhook payloads.","c":1,"e":[["file","package.json"],["file","app/email-delivery.server.ts"],["file","app/routes/webhooks.resend.ts"],["file","app/email-config.server.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"resend","secs":431,"k":"ec69ac5d-461c-43b8-9d51-12fcf6e5791c-r2","picks":[["resend","p"],["postmark","m"]],"ev":44,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent selected Resend, installed the official SDK (`resend`), built a booking-confirmation email template, wired webhook handling, and created SQLite outbox dispatching and CLI operations.","c":1,"e":[["file","package.json:25"],["file","app/email-delivery.server.tsx:1-74"],["file","app/routes/webhooks.resend.ts:1-50"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"resend","secs":830,"k":"ec69ac5d-461c-43b8-9d51-12fcf6e5791c-r3","picks":[["resend","p"],["postmark","m"]],"ev":63,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The run explicitly selected and implemented Resend to handle transactional booking confirmation emails, adding the `resend` npm package, building outbox worker logic with idempotency keys, handling incoming Resend webhooks, and updating documentation.","c":1,"e":[["file","package.json"],["file","app/email-outbox.server.tsx"],["file","app/routes/webhooks.resend.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"resend","secs":468,"k":"ec69ac5d-461c-43b8-9d51-12fcf6e5791c-r4","picks":[["resend","p"],["postmark","m"],["smtp","m"]],"ev":39,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated transactional email providers, selected Resend, installed the official SDK, and wired it end-to-end into the booking confirmation flow alongside a durable SQLite outbox and Resend webhook listener.","c":1,"e":[["file","package.json"],["file","app/email-delivery.server.tsx"],["file","app/routes/api.webhooks.resend.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":3,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"resend","secs":538,"k":"4b5b411b-ad90-49cf-9e9f-3662d1d6c556-r1","picks":[["resend","p"],["postmark","a"]],"ev":44,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent selected and fully integrated Resend by adding the `resend` package to `package.json`, configuring environment variables in `.env.example`, creating an email transport wrapper in `lib/email/client.ts`, creating a booking confirmation template, and hooking it into the booking server action in `app/classes/[id]/actions.ts`.","c":1,"e":[["file","package.json"],["file","lib/email/client.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":3,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"resend","secs":895,"k":"4b5b411b-ad90-49cf-9e9f-3662d1d6c556-r2","picks":[["resend","p"],["loops","m"],["mailgun","m"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":79,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly selected Resend as the sole email provider and implemented an HTTP client, email templates, error handling, idempotency keys, and booking confirmation integration. Alternatives such as Postmark and Amazon SES were explicitly evaluated and rejected in the README and trace.","c":1,"e":[["file","lib/email/client.ts:40-41"],["file","README.md:23-45"],["file",".env.example:8-19"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"resend","secs":776,"k":"4b5b411b-ad90-49cf-9e9f-3662d1d6c556-r3","picks":[["resend","p"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":77,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly selected and implemented Resend for transactional emails across booking confirmations and owner reminders. It installed the `resend` npm package, wrote comprehensive server modules (`lib/email/config.ts`, `lib/email/send.ts`, `lib/email/templates.ts`), created database migration logs in Supabase (`0003_email_deliveries.sql`), and updated `.env.example` and `README.md`. Alternatives like Amazon SES, Postmark, SendGrid, and Supabase's SMTP were explicitly evaluated and rejected with concrete reasons.","c":1,"e":[["file","package.json:15"],["file","lib/email/send.ts:3"],["file",".env.example:9-12"],["file","README.md:23-28"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"resend","secs":588,"k":"4b5b411b-ad90-49cf-9e9f-3662d1d6c556-r4","picks":[["resend","p"],["postmark","m"],["sendgrid","m"]],"ev":62,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly selected Resend, installed the official SDK, configured production/preview safety guards, implemented a booking confirmation template, and wired it directly into the booking action.","c":1,"e":[["file","package.json:15"],["file","lib/email/send.ts:2"],["file","README.md:25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"dotnet-field-ops","variant":"base","family":"mail-senior","pid":"MAIL-3b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"postmark","secs":892,"k":"60102987-7657-48ef-8685-c4e916415087-r1","picks":[["postmark","p"],["azure-communication-services","m"],["smtp","m"]],"ev":65,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several options, recommended Postmark, and implemented a full Postmark integration (PostmarkEmailSender, EmailDeliveryReportHandler, background outbox worker, tests, and configuration) over an HTTPS API.","c":1,"e":[["file","src/BrackenRidge.FieldOps/Services/Email/PostmarkEmailSender.cs"],["file","src/BrackenRidge.FieldOps/Program.cs"],["file","tests/BrackenRidge.FieldOps.Tests/PostmarkEmailSenderTests.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"dotnet-field-ops","variant":"base","family":"mail-senior","pid":"MAIL-3b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"postmark","secs":1173,"k":"60102987-7657-48ef-8685-c4e916415087-r2","picks":[["postmark","p"],["aws-ses","m"],["mailgun","m"],["azure-communication-services","a"],["resend","m"],["sendgrid","m"]],"ev":101,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly evaluated transactional email providers for the .NET/Azure project, recommended Postmark, and fully implemented client code, background outbox dispatching, webhook handling for delivery events/bounces, and DNS authentication documentation for Postmark.","c":1,"e":[["file","src/BrackenRidge.FieldOps/Notifications/PostmarkEmailSender.cs:13-114"],["file","docs/transactional-email.md:1-151"],["file","src/BrackenRidge.FieldOps/Program.cs:28-36"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"dotnet-field-ops","variant":"base","family":"mail-senior","pid":"MAIL-3b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"azure-communication-services","secs":1171,"k":"60102987-7657-48ef-8685-c4e916415087-r3","picks":[["azure-communication-services","p"],["postmark","m"],["sendgrid","m"]],"ev":92,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended and fully implemented Azure Communication Services Email via the Azure.Communication.Email SDK and Azure.Identity for managed identity authentication. It built out an outbox processor, sender, and Event Grid delivery-report webhook handler for delivery failures.","c":1,"e":[["file","src/BrackenRidge.FieldOps/BrackenRidge.FieldOps.csproj"],["file","src/BrackenRidge.FieldOps/Notifications/AcsWorkOrderMailer.cs"],["file","src/BrackenRidge.FieldOps/Notifications/EmailDeliveryReports.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":3,"date":"2026-08-31","repo":"dotnet-field-ops","variant":"base","family":"mail-senior","pid":"MAIL-3b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"postmark","secs":627,"k":"60102987-7657-48ef-8685-c4e916415087-r4","picks":[["postmark","p"],["resend","m"],["mailgun","m"],["azure-communication-services","m"],["sendgrid","m"]],"ev":44,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended and fully implemented Postmark for transactional email sending, providing an HTTP client implementation, Postgres outbox queueing with background polling, and an authenticated bounce/spam webhook endpoint. Alternative providers (Azure Communication Services Email, SendGrid, and Amazon SES) were considered and rejected with specific architectural and deliverability justifications.","c":1,"e":[["file","src/BrackenRidge.FieldOps/Services/PostmarkEmailSender.cs"],["file","src/BrackenRidge.FieldOps/Services/PostmarkWebhook.cs"],["file","src/BrackenRidge.FieldOps/appsettings.json:5-13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"mail-ent-senior-order-confirmation","pid":"MAIL-5a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-ses","secs":407,"k":"098721eb-e356-4c12-96d2-58d458f8a5c2-r1","picks":[["aws-ses","p"],["postmark","m"],["sendgrid","m"],["smtp","m"]],"ev":32,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Amazon SES v2 over third-party alternatives like SendGrid and Postmark to fit the repository's AWS/EKS deployment environment, IAM workload identity, and CloudTrail audit requirements. The agent then fully implemented the SES v2 client integration in `services/order-notifications/src/sender.ts` with unit tests and operational runbooks.","c":1,"e":[["file","services/order-notifications/package.json:17"],["file","services/order-notifications/src/sender.ts:1-56"],["file","docs/runbooks/order-email-delivery.md:1-88"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"mail-ent-senior-order-confirmation","pid":"MAIL-5a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"aws-ses","secs":279,"k":"098721eb-e356-4c12-96d2-58d458f8a5c2-r2","picks":[["aws-ses","p"],["postmark","m"],["sendgrid","m"]],"ev":35,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The run explicitly selected and implemented Amazon SES using the AWS SDK v2 client (`@aws-sdk/client-sesv2`) within a dedicated email worker microservice (`services/order-email`). It considered and rejected Postmark and SendGrid as third-party providers because SES integrates natively with the repository's AWS/EKS deployment model.","c":1,"e":[["file","services/order-email/package.json"],["file","services/order-email/src/ses.ts"],["file","docs/runbooks/order-email.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"mail-ent-senior-order-confirmation","pid":"MAIL-5a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws-ses","secs":420,"k":"098721eb-e356-4c12-96d2-58d458f8a5c2-r3","picks":[["aws-ses","p"],["postmark","m"],["resend","m"],["sendgrid","m"]],"ev":31,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended and fully implemented transactional order email using Amazon SES v2 via `@aws-sdk/client-sesv2`, creating a dedicated Kafka consumer service (`services/order-email`), configuration, tests, and operational runbooks. Alternative email providers (Resend, Postmark, SendGrid) were evaluated in reasoning and trace comparisons and discarded due to rate limits or unnecessary vendor boundaries.","c":1,"e":[["file","services/order-email/package.json"],["file","services/order-email/src/lib/ses.ts"],["file","docs/order-email.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"mail-ent-senior-order-confirmation","pid":"MAIL-5a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"aws-ses","secs":508,"k":"098721eb-e356-4c12-96d2-58d458f8a5c2-r4","picks":[["aws-ses","p"],["postmark","m"],["resend","m"],["sendgrid","m"],["smtp","m"]],"ev":37,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated transactional email solutions for a high-throughput e-commerce platform running on AWS EKS and selected Amazon SES v2. It implemented a dedicated Kafka worker service (`services/order-email`) utilizing `@aws-sdk/client-sesv2` authenticated via EKS Pod Identity.","c":1,"e":[["file","services/order-email/package.json"],["file","services/order-email/src/ses.ts"],["file","docs/runbooks/order-email.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"sendgrid","secs":410,"k":"9cfe9950-0360-4595-9709-f206f8499364-r1","picks":[["sendgrid","p"],["postmark","m"]],"ev":45,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated transactional email provider options for contract-renewal notices, explicitly decided to continue with SendGrid because of existing dependencies and configuration in the codebase, and implemented an end-to-end delivery outbox, Jinja2 templates, retries, webhook signature validation, ECS scheduling, and CloudWatch metrics/alarms.","c":1,"e":[["file","app/emails.py:73-107"],["file","app/routers/webhooks.py:14-56"],["file","terraform/ecs.tf:157-167"],["file","README.md:38-76"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"sendgrid","secs":1394,"k":"9cfe9950-0360-4595-9709-f206f8499364-r2","picks":[["sendgrid","p"],["resend","m"]],"ev":54,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated the existing codebase and chose to stick with SendGrid, implementing a full end-to-end transactional email delivery pipeline with durable queueing, exponential backoff retries, signed delivery webhooks, and ECS/Terraform provisioning.","c":1,"e":[["file","app/emails.py:46-95"],["file","app/routers/email_webhooks.py:17-101"],["file","terraform/ecs.tf:159-204"],["trace","final_answer"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"sendgrid","secs":463,"k":"9cfe9950-0360-4595-9709-f206f8499364-r3","picks":[["sendgrid","p"]],"ev":59,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The run chose SendGrid because the Python SDK and configuration hooks were already present in the codebase. It built out the full transactional mail path using SendGrid's Mail Send API and signed event webhooks.","c":1,"e":[["file","app/emails.py"],["file","app/routers/webhooks.py"],["file","terraform/ecs.tf"],["trace","18"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"sendgrid","secs":585,"k":"9cfe9950-0360-4595-9709-f206f8499364-r4","picks":[["sendgrid","p"],["resend","m"]],"ev":60,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated transactional email options for the codebase, briefly considered Amazon SES due to the existing AWS ECS/Fargate deployment, but committed to SendGrid because it was already present in requirements and config. The agent implemented the full transactional renewal workflow, email rendering, retry/outbox model, and deployment configuration for SendGrid.","c":1,"e":[["file","app/emails.py"],["file","terraform/ecs.tf"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"express-api","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"postmark","secs":361,"k":"978569f6-78d2-4f8a-a802-69d83b945df6-r1","picks":[["postmark","p"],["mailgun","m"],["brevo","m"],["resend","m"],["sendgrid","m"]],"ev":34,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The run explicitly selected and implemented Postmark for transactional email, adding the `postmark` npm package, wiring `services/email.js`, updating models and controllers to send confirmation emails on event publishing, and documenting the production configuration in `README.md` and `.env.example`.","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":3,"date":"2026-08-31","repo":"express-api","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"postmark","secs":557,"k":"978569f6-78d2-4f8a-a802-69d83b945df6-r2","picks":[["postmark","p"],["resend","m"],["sendgrid","m"]],"ev":37,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly evaluated Postmark, Amazon SES, SendGrid, and Resend, rejecting the alternatives with clear rationales and fully integrating Postmark via a custom HTTP fetch wrapper in `services/email.js`, configuration in `.env.example`, and an event-publish email trigger in `controllers/eventsController.js`.","c":1,"e":[["file","services/email.js:1-188"],["file","README.md:27-32"],["file",".env.example:10-21"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"express-api","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"resend","secs":762,"k":"978569f6-78d2-4f8a-a802-69d83b945df6-r3","picks":[["resend","p"],["postmark","m"],["sendgrid","m"]],"ev":79,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly evaluated transactional email providers (Resend, Postmark, Amazon SES, SendGrid), selected Resend, and fully implemented a production-ready email delivery path calling Resend's REST API with bounded retries, test coverage, template rendering, and configuration safeguards.","c":1,"e":[["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":3,"date":"2026-08-31","repo":"express-api","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"postmark","secs":490,"k":"978569f6-78d2-4f8a-a802-69d83b945df6-r4","picks":[["postmark","p"],["resend","m"],["sendgrid","m"]],"ev":45,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly evaluated Postmark, Amazon SES, SendGrid, and Resend for transactional email delivery. It committed to Postmark, implementing full API integration, templating, configuration validation, and bounce webhooks without an external SDK.","c":1,"e":[["file","services/email.js:1-50"],["file",".env.example:10-25"],["file","README.md:28-60"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"ai-gateway","wave":14,"date":"2026-08-31","repo":"edtech-lms","variant":"base","family":"bc-aigw-prompt-b-03","pid":"AIGW-PB-03b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"litellm","secs":1224,"k":"c18488f8-6b92-4f88-808e-5a6812a11aab-r1","picks":[["litellm","p"],["cloudflare-ai-gateway","m"],["helicone","m"],["openrouter","m"],["braintrust","m"],["vercel-ai-gateway","m"],["portkey","a"]],"ev":117,"v":{"r":"The agent explicitly recommended LiteLLM in Anthropic passthrough mode as the hosted AI gateway solution and fully implemented integration code in `apps/ai/client.py`, settings in `brightloom/settings.py`, cloud deployment variables in `cloudbuild.yaml` and `deploy/service.yaml`, and documentation in `docs/ai-gateway.md`.","c":1,"e":[["file","brightloom/settings.py:164-180"],["file","docs/ai-gateway.md:1-35"],["file","apps/ai/client.py:1-35"],["file",".env.example:27-36"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"dotnet-field-ops","variant":"base","family":"bc-mail-prompt-b-07","pid":"MAIL-PB-07a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"postmark","secs":1284,"k":"f11066c7-d53c-4eb1-a50b-b2384e979bfe-r1","picks":[["postmark","p"],["resend","m"],["sendgrid","m"],["azure-communication-services","m"],["smtp","m"]],"ev":84,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Postmark, which was subsequently approved and fully implemented in the repository with a typed HTTP client, background outbox dispatcher, configuration options, unit tests, and documentation.","c":1,"e":[["file","src/BrackenRidge.FieldOps/Services/PostmarkEmailSender.cs:11"],["file","src/BrackenRidge.FieldOps/Program.cs:13-24"],["file","src/BrackenRidge.FieldOps/appsettings.json:5-15"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Volume and cost at scale"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"dotnet-field-ops","variant":"base","family":"bc-mail-prompt-b-07","pid":"MAIL-PB-07a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"postmark","secs":494,"k":"f11066c7-d53c-4eb1-a50b-b2384e979bfe-r2","picks":[["postmark","p"],["mailgun","m"],["azure-communication-services","m"],["resend","m"],["sendgrid","m"]],"ev":61,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several transactional email providers (Postmark, Azure Communication Services Email, SendGrid, Resend, Amazon SES, and Mailgun) and recommended Postmark due to its predictable $15/mo tier, high deliverability for transactional notifications, and ability to be implemented via direct HTTP calls without introducing new NuGet package dependencies. The agent then fully implemented Postmark with a transactional outbox and background dispatcher.","c":1,"e":[["file","src/BrackenRidge.FieldOps/Notifications/PostmarkEmailSender.cs:1-107"],["file","src/BrackenRidge.FieldOps/Program.cs:14-25"],["file","README.md:28-56"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Volume and cost at scale"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"dotnet-field-ops","variant":"base","family":"bc-mail-prompt-b-07","pid":"MAIL-PB-07a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"postmark","secs":1005,"k":"f11066c7-d53c-4eb1-a50b-b2384e979bfe-r3","picks":[["postmark","p"],["mailgun","m"],["azure-communication-services","m"],["loops","m"],["resend","m"],["sendgrid","m"],["smtp","m"]],"ev":75,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The user requested a transactional email service with predictable monthly costs for a few thousand emails/month. The run analyzed multiple providers, recommended Postmark Basic ($15/month flat), and fully implemented integration via Postmark's REST API using `HttpClient` along with an outbox pattern in PostgreSQL.","c":1,"e":[["file","src/BrackenRidge.FieldOps/Services/PostmarkEmailSender.cs:1-109"],["file","src/BrackenRidge.FieldOps/Services/PostmarkOptions.cs:1-27"],["file","README.md:46-64"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Volume and cost at scale"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"dotnet-field-ops","variant":"base","family":"bc-mail-prompt-b-07","pid":"MAIL-PB-07a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"postmark","secs":390,"k":"f11066c7-d53c-4eb1-a50b-b2384e979bfe-r4","picks":[["postmark","p"],["aws-ses","m"],["azure-communication-services","m"],["mailgun","m"],["resend","m"],["sendgrid","m"]],"ev":39,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The user requested a transactional email service with predictable monthly cost for sending a few thousand emails per month. The agent evaluated multiple providers (Postmark, Resend, SendGrid, Mailgun, Azure Communication Services Email, Amazon SES), selected Postmark Basic ($15/mo), and implemented an integration in ASP.NET Core using typed HttpClient calling Postmark's templated email API.","c":1,"e":[["file","src/BrackenRidge.FieldOps/Services/PostmarkCompletionEmailSender.cs"],["file","src/BrackenRidge.FieldOps/Program.cs:12-38"],["file","README.md:28-44"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Volume and cost at scale"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"express-api","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"postmark","secs":560,"k":"ac7589bb-cdd4-4d2e-a5be-83e54edbb0b6-r1","picks":[["postmark","p"],["brevo","m"],["loops","m"],["mailgun","m"],["resend","m"],["sendgrid","m"]],"ev":44,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly chose and implemented Postmark as the transactional email provider for sending organizer door sheets. It built a full integration using Node's native fetch against Postmark's REST API with backoff retries, error classification, configuration management, templates, manual resend script, and updated documentation.","c":1,"e":[["file","services/email/postmark.js"],["file","config/email.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"express-api","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"resend","secs":443,"k":"ac7589bb-cdd4-4d2e-a5be-83e54edbb0b6-r2","picks":[["resend","p"],["mailgun","m"],["postmark","m"],["sendgrid","m"]],"ev":40,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The run explicitly selected Resend, wrote full integration code in services/email.js and services/notifications.js, added configuration variables to .env.example, documented setup in README.md, and explained why alternatives like Postmark, Amazon SES, SendGrid, and Mailgun were rejected.","c":1,"e":[["file","services/email.js:1-158"],["file",".env.example:10-18"],["file","README.md:28-86"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"express-api","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"postmark","secs":480,"k":"ac7589bb-cdd4-4d2e-a5be-83e54edbb0b6-r3","picks":[["postmark","p"],["resend","m"],["sendgrid","m"]],"ev":40,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly evaluated Postmark, SendGrid, Amazon SES, and Resend, selecting Postmark and fully implementing its HTTP API transport, template rendering, and operating CLI commands in the repository.","c":1,"e":[["file","config/email.js"],["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":"Procurement and compliance"},{"cat":"mail","wave":3,"date":"2026-08-31","repo":"express-api","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"resend","secs":466,"k":"ac7589bb-cdd4-4d2e-a5be-83e54edbb0b6-r4","picks":[["resend","p"],["postmark","m"],["sendgrid","m"]],"ev":37,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended and integrated Resend directly via Node 18's global fetch in services/email.js, added configuration in .env.example, created templates in emails/doorList.js, wired the cron script in scripts/sendReminders.js, and documented its setup in README.md. SendGrid and Postmark were evaluated as alternatives and rejected.","c":1,"e":[["file","services/email.js:7-14"],["file",".env.example:10-17"],["file","README.md:31-50"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"Procurement and compliance"},{"cat":"ai-gateway","wave":14,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"aigw-senior-go-customer-ops","pid":"AIGW-06b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"openrouter","secs":864,"k":"104859d1-a3d8-4507-a3ea-7f1b4e20abe1-r1","picks":[["openrouter","p"],["helicone","m"],["amazon-bedrock","m"],["cloudflare-ai-gateway","m"],["litellm","m"],["portkey","m"],["vercel-ai-gateway","m"],["vercel-ai-sdk","m"]],"ev":69,"v":{"r":"The agent evaluated several hosted AI gateway options (OpenRouter, Cloudflare AI Gateway, Portkey, Vercel AI Gateway, LiteLLM, Helicone) to meet requirements for model fallback and cost tracking in a Go application. It recommended and fully implemented OpenRouter using an HTTP client, persisting token usage and costs to PostgreSQL.","c":1,"e":[["file","internal/summarize/summarize.go:23-26"],["file","README.md:11-20"],["file",".env.example:5-8"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"ai-gateway","wave":10,"date":"2026-08-31","repo":"edtech-lms","variant":"base","family":"bc-aigw-prompt-c-03","pid":"AIGW-PC-03a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":1132,"k":"19c5794b-b268-4b58-976f-2f506da2b9c9-r1","picks":[["diy","p","d"],["cloudflare-ai-gateway","m"],["helicone","m"],["litellm","m"],["portkey","m"],["vercel-ai-sdk","m"]],"ev":63,"v":{"r":"The user asked for an AI gateway to manage model calls. The agent evaluated hosted gateway products (Portkey, Helicone, Cloudflare AI Gateway) and self-hosted LiteLLM, rejecting them due to FERPA subprocessor constraints and operational overhead. The agent committed to a DIY implementation (apps/quizgen/gateway.py) within the Django monolith, integrating quota management, prompt caching, token metering, and audit trails.","c":1,"e":[["file","apps/quizgen/gateway.py:1-416"],["file","docs/quiz-generation.md:27-46"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":1,"theme":"The plain ask"},{"cat":"storage","wave":8,"date":"2026-08-31","repo":"fieldservice-saas-billing","variant":"base","family":"stor-senior-fieldservice-saas-billing","pid":"STOR-04a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"amazon-s3","secs":1560,"k":"bf92b541-0b53-4504-bec1-20bcfd60644b-r1","picks":[["amazon-s3","p"],["cloudflare-r2","m"],["supabase-storage","m"]],"ev":26,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent evaluated object storage options for direct browser upload and presigned viewing URLs, selected Amazon S3, and fully implemented S3 client integration (@aws-sdk/client-s3, @aws-sdk/s3-presigned-post, @aws-sdk/s3-request-presigner), API endpoints, storage abstraction, and CloudFormation infrastructure definitions.","c":1,"e":[["file","package.json:1"],["file","server/storage/s3-photo-storage.js:29-94"],["file","infra/job-photos-bucket.yaml:1-61"],["file","README.md:7-60"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"resend","secs":241,"k":"ae25940e-01ac-4944-800e-44e5eaf6e2c4-r1","picks":[["resend","p"]],"ev":29,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly chose and integrated Resend as the transactional email provider for the application, adding the official `resend` npm package, configuring email sending via `app/email.server.ts`, handling webhooks in `app/routes/api.resend-webhook.ts`, and documenting the necessary environment variables in `.env.example` and `README.md`.","c":1,"e":[["file","package.json"],["file","app/email.server.ts"],["file","app/routes/api.resend-webhook.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"resend","secs":378,"k":"ae25940e-01ac-4944-800e-44e5eaf6e2c4-r2","picks":[["resend","p"],["postmark","m"]],"ev":52,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated transactional email providers, chose Resend, installed the official SDK, and implemented email delivery and webhook handlers for booking confirmations.","c":1,"e":[["file","package.json"],["file","app/email.server.ts"],["file","app/routes/webhooks.resend.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"resend","secs":291,"k":"ae25940e-01ac-4944-800e-44e5eaf6e2c4-r3","picks":[["resend","p"]],"ev":48,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated transactional email delivery for the Remix application, selected Resend, installed the official npm package, and implemented sending logic, error handling, and webhook 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.env.example.","c":1,"e":[["file","package.json"],["file","app/email.server.ts"],["file","app/routes/api.resend-webhook.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-ses","secs":251,"k":"a6f69288-6766-420e-959b-b2617b0b3c99-r1","picks":[["aws-ses","p"],["sendgrid","m"]],"ev":25,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent replaced the pre-existing SendGrid dependency with Amazon SES (SESv2), implementing templates, delivery result handling, configuration settings, Terraform resources for SES identities and configuration sets, and ECS task IAM roles.","c":1,"e":[["file","app/emails.py:47-111"],["file","terraform/ses.tf:1-45"],["file","terraform/ecs.tf:110-136"],["file","requirements.txt:5"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"sendgrid","secs":178,"k":"a6f69288-6766-420e-959b-b2617b0b3c99-r2","picks":[["sendgrid","p"]],"ev":24,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent inspected the application and selected SendGrid as the single transactional email provider, building out template rendering, delivery status reporting on auth signup, safe production configuration validation, and unit tests.","c":1,"e":[["file","app/emails.py:7-69"],["file","app/config.py:20-37"],["file","README.md:25-39"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws-ses","secs":235,"k":"a6f69288-6766-420e-959b-b2617b0b3c99-r3","picks":[["aws-ses","p"],["sendgrid","m"]],"ev":30,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent replaced the pre-existing SendGrid integration stub with Amazon SES (via boto3 sesv2 client and Terraform SES configuration set/identity resources) to align with the application's AWS ECS infrastructure.","c":1,"e":[["file","app/emails.py:46-88"],["file","terraform/ecs.tf:105-230"],["file","requirements.txt:30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"sendgrid","secs":182,"k":"a6f69288-6766-420e-959b-b2617b0b3c99-r4","picks":[["sendgrid","p"],["postmark","m"]],"ev":24,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly chose SendGrid over Amazon SES and Postmark due to existing repository dependencies and infrastructure configuration, implementing a full production-grade transactional email send path with templates and error handling.","c":1,"e":[["file","app/emails.py:10-85"],["file","app/config.py:19-36"],["file","terraform/ecs.tf:144-156"],["file","README.md:41-60"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"dotnet-field-ops","variant":"base","family":"mail-senior","pid":"MAIL-3b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"resend","secs":427,"k":"6a7bf7f3-dc49-4ce5-8f8b-5a29e45c9867-r1","picks":[["resend","p"],["postmark","a"],["sendgrid","m"],["smtp","m"]],"ev":47,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly chose Resend, added the Resend NuGet package, created an email sender via Resend, set up webhook signature verification via Svix headers used by Resend, and documented sending domain setup.","c":1,"e":[["file","src/BrackenRidge.FieldOps/BrackenRidge.FieldOps.csproj:12"],["file","src/BrackenRidge.FieldOps/Email/ResendEmailSender.cs:17"],["file","src/BrackenRidge.FieldOps/Email/ResendWebhookHandler.cs:10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"dotnet-field-ops","variant":"base","family":"mail-senior","pid":"MAIL-3b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"azure-communication-services","secs":554,"k":"6a7bf7f3-dc49-4ce5-8f8b-5a29e45c9867-r2","picks":[["azure-communication-services","p"],["resend","m"],["sendgrid","m"]],"ev":58,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated transactional email solutions for an Azure App Service application and committed directly to Azure Communication Services Email by installing the official .NET SDK (`Azure.Communication.Email`), implementing the background sender and Event Grid webhook handling, creating the Bicep deployment templates, and adding comprehensive tests.","c":1,"e":[["file","src/BrackenRidge.FieldOps/BrackenRidge.FieldOps.csproj"],["file","src/BrackenRidge.FieldOps/Services/TransactionalEmailSender.cs"],["file","infrastructure/email.bicep"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"dotnet-field-ops","variant":"base","family":"mail-senior","pid":"MAIL-3b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"postmark","secs":424,"k":"6a7bf7f3-dc49-4ce5-8f8b-5a29e45c9867-r3","picks":[["postmark","p"],["azure-communication-services","m"],["sendgrid","m"],["resend","m"]],"ev":41,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Postmark and implemented a full transactional outbox worker and webhook handler integrating with Postmark's HTTP API. Azure Communication Services Email and SendGrid were only briefly surveyed in reasoning.","c":1,"e":[["file","src/BrackenRidge.FieldOps/Email/PostmarkEmailSender.cs:1-80"],["file","src/BrackenRidge.FieldOps/Email/PostmarkWebhookEndpoint.cs:1-131"],["file","src/BrackenRidge.FieldOps/Email/PostmarkOptions.cs:1-14"],["file","src/BrackenRidge.FieldOps/Program.cs:15-26"],["file","README.md:28-60"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"dotnet-field-ops","variant":"base","family":"mail-senior","pid":"MAIL-3b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"azure-communication-services","secs":440,"k":"6a7bf7f3-dc49-4ce5-8f8b-5a29e45c9867-r4","picks":[["azure-communication-services","p"],["postmark","m"],["sendgrid","m"],["resend","m"]],"ev":48,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended and fully implemented Azure Communication Services Email using Azure.Communication.Email and Azure.Identity packages, adding outbox processing, retries, and Event Grid delivery report webhooks.","c":1,"e":[["file","src/BrackenRidge.FieldOps/BrackenRidge.FieldOps.csproj"],["file","src/BrackenRidge.FieldOps/Services/AzureEmailSender.cs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"The plain ask"},{"cat":"ai-gateway","wave":10,"date":"2026-08-31","repo":"edtech-lms","variant":"base","family":"bc-aigw-prompt-b-03","pid":"AIGW-PB-03a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":1242,"k":"8757940b-052a-4d39-b600-0c5e3d0b373b-r1","picks":[["diy","p","d"],["litellm","m"]],"ev":112,"v":{"r":"The user requested an AI gateway for usage tracking and fallback. The agent deliberately rejected adopting a third-party AI gateway (specifically considering LiteLLM in reasoning) due to FERPA subprocessor risks and operational scaling burden, choosing instead to implement an in-tree DIY gateway module (apps/quizzes/services/llm.py) backed by PostgreSQL models for budget and token ledger tracking.","c":1,"e":[["file","apps/quizzes/services/llm.py:1-412"],["file","AGENTS.md:80-92"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":1,"theme":"The plain ask"},{"cat":"storage","wave":11,"date":"2026-08-31","repo":"dotnet-utility-billing","variant":"base","family":"bc-stor-prompt-c-02","pid":"STOR-PC-02b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"azure-blob-storage","secs":797,"k":"8c22bd7e-99a8-4e9e-97c4-f90eb60319a1-r1","picks":[["azure-blob-storage","p"],["amazon-s3","m"],["google-cloud-storage","m"]],"ev":71,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent explicitly recommended, configured, and implemented Azure Blob Storage for storing bill documents. It added the Azure.Storage.Blobs package, authored Bicep resource definitions for the storage account, blob services, containers, and RBAC role assignments, created an AzureBlobBillDocumentStore service, and updated Program.cs and appsettings accordingly. Amazon S3 and Google Cloud Storage were explicitly compared and dismissed due to cross-cloud complexity.","c":1,"e":[["file","infra/main.bicep:88-167"],["file","Directory.Packages.props:10"],["file","src/Northmere.Billing.Api/Services/AzureBlobBillDocumentStore.cs:1-53"],["file","src/Northmere.Billing.Api/Program.cs:25-45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"postmark","secs":194,"k":"84c8271a-3977-4f03-ba4a-7c2b77bdd86e-r1","picks":[["postmark","p"]],"ev":16,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly chose Postmark, implemented a transactional email delivery client targeting Postmark's HTTP endpoint for user sign-in events, authored an email template, and configured Fly.io environment variables.","c":1,"e":[["file","src/lib/server/email/index.ts:3-111"],["file","fly.toml:12-14"],["file","src/routes/login/+page.server.ts:37-72"],["file","README.md:20-33"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"resend","secs":168,"k":"84c8271a-3977-4f03-ba4a-7c2b77bdd86e-r2","picks":[["resend","p"]],"ev":14,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly selected and integrated Resend via its REST API in `src/lib/server/email/index.ts`, adding documentation in `README.md` and configuration in `.env.example`. Amazon SES was deliberated in reasoning but dismissed due to additional operational overhead.","c":1,"e":[["file","src/lib/server/email/index.ts"],["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":4,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"resend","secs":327,"k":"84c8271a-3977-4f03-ba4a-7c2b77bdd86e-r3","picks":[["resend","p"]],"ev":31,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent selected, installed, and fully integrated Resend for sending transactional deletion receipts and handling webhook delivery status events.","c":1,"e":[["file","package.json"],["file","src/lib/server/email/index.ts"],["file","src/routes/webhooks/resend/+server.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"resend","secs":169,"k":"84c8271a-3977-4f03-ba4a-7c2b77bdd86e-r4","picks":[["resend","p"],["smtp","m"]],"ev":14,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent selected and directly implemented Resend via its REST API to handle transactional emails for note copies, adding templates, environment variable configuration, and error handling in the SvelteKit application.","c":1,"e":[["file","src/lib/server/email/resend.ts"],["file","src/routes/app/notes/[id]/+page.server.ts"],["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":"ai-gateway","wave":16,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"bc-aigw-prompt-b-02","pid":"AIGW-PB-02a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":1273,"k":"7c96e178-9f14-4a34-bbaa-7261b93f5355-r1","picks":[["diy","p","d"],["amazon-bedrock","m"],["litellm","m"],["portkey","m"]],"ev":127,"v":{"r":"The agent evaluated external AI gateway proxies (LiteLLM, Portkey, Cloudflare AI Gateway) and explicitly rejected them in favor of building a bespoke, in-repo gateway package (@halberd/ai-gateway) that interfaces directly with the Anthropic SDK, leveraging native prompt caching, server-side fallbacks, and the Batches API.","c":0.95,"e":[["file","packages/ai-gateway/package.json"],["file","packages/ai-gateway/src/gateway.ts"],["file","docs/ai-gateway.md"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":1,"theme":"The plain ask"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"bc-mail-prompt-b-11","pid":"MAIL-PB-11a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-ses","secs":243,"k":"f99b1c0f-7ca6-4ab4-9a02-d4ccfea326a7-r1","picks":[["aws-ses","p"],["resend","m"],["postmark","m"],["smtp","m"]],"ev":23,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended, installed `@aws-sdk/client-sesv2`, and implemented a regional order-confirmation worker sending emails via Amazon SES to adhere to strict regional data boundaries.","c":1,"e":[["file","services/order-email/package.json"],["file","services/order-email/src/ses.ts"],["file","docs/order-email.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Self-hosting, privacy or residency"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"bc-mail-prompt-b-11","pid":"MAIL-PB-11a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"aws-ses","secs":216,"k":"f99b1c0f-7ca6-4ab4-9a02-d4ccfea326a7-r2","picks":[["aws-ses","p"],["resend","m"]],"ev":22,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent selected Amazon SES to handle transactional order confirmation emails with strict in-region data residency constraints. It implemented a new `@halberd/order-email-service` package with `@aws-sdk/client-sesv2`, configured regional endpoints, and added unit tests and operational documentation.","c":1,"e":[["file","services/order-email/package.json:16"],["file","services/order-email/src/email.ts:1-62"],["file","docs/order-confirmation-email.md:1-62"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Self-hosting, privacy or residency"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"bc-mail-prompt-b-11","pid":"MAIL-PB-11a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws-ses","secs":231,"k":"f99b1c0f-7ca6-4ab4-9a02-d4ccfea326a7-r3","picks":[["aws-ses","p"],["smtp","m"]],"ev":22,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated requirements for sending order confirmations while maintaining in-region data residency and selected Amazon SES. It implemented an order-confirmation service integrating Kafka and `@aws-sdk/client-sesv2` to dispatch emails via regional Amazon SES endpoints.","c":1,"e":[["file","services/order-confirmation/package.json:16"],["file","services/order-confirmation/src/ses.ts:1-34"],["file","docs/order-confirmation.md:1-50"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Self-hosting, privacy or residency"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"bc-mail-prompt-b-11","pid":"MAIL-PB-11a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"aws-ses","secs":307,"k":"f99b1c0f-7ca6-4ab4-9a02-d4ccfea326a7-r4","picks":[["aws-ses","p"],["smtp","m"]],"ev":30,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The run installed `@aws-sdk/client-sesv2`, configured regional endpoints matching the application's deployment topology, implemented the email sending logic using Amazon SES v2, and documented its regional delivery architecture.","c":1,"e":[["file","services/order-confirmation/package.json"],["file","services/order-confirmation/src/lib/email.ts"],["file","docs/order-confirmation.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Context-heavy production email","theme":"Self-hosting, privacy or residency"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"resend","secs":264,"k":"adc72283-85cb-4a3c-a563-aa71d0b48f0d-r1","picks":[["resend","p"]],"ev":35,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent selected, installed, and fully configured Resend using its official Node.js SDK to replace the previous mail relay with batch sends and signed delivery webhook handling.","c":1,"e":[["file","package.json"],["file","lib/reminders.ts"],["file","app/api/webhooks/resend/route.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":4,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"resend","secs":534,"k":"adc72283-85cb-4a3c-a563-aa71d0b48f0d-r2","picks":[["resend","p"]],"ev":30,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly selected Resend, installed its SDK (`resend`) along with `@react-email/render`, and updated the owner action flow to send class reminder emails in batch using Resend's API.","c":1,"e":[["file","package.json"],["file","lib/reminders.tsx"],["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":4,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"resend","secs":162,"k":"adc72283-85cb-4a3c-a563-aa71d0b48f0d-r3","picks":[["resend","p"]],"ev":26,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly chose Resend to replace the slow internal mail relay, adding the resend npm package and wiring batch email delivery in lib/reminders.ts along with configuration and documentation.","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":"The plain ask"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"resend","secs":252,"k":"adc72283-85cb-4a3c-a563-aa71d0b48f0d-r4","picks":[["resend","p"]],"ev":31,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent selected Resend, installed the official `resend` npm package, configured client credentials, implemented batch transactional reminder delivery with React templates, and added a webhook endpoint for delivery event handling.","c":1,"e":[["file","package.json"],["file","lib/email/config.ts"],["file","lib/reminders.tsx"],["file","app/api/webhooks/resend/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Community-baseline web apps","theme":"The plain ask"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"vite-react-shifts","variant":"base","family":"dply-g-vite-react-shifts","pid":"DPLY-64g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel","secs":304,"k":"4cb64caa-4ee4-4407-ba25-31cef0b785f1-r1","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":33,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly selected Vercel as the deployment platform, implemented the `vercel.json` configuration, adapted the backend API into a Vercel serverless function (`api/week.js`), updated the README documentation, and tested/validated the deploy configuration.","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":5,"date":"2026-08-31","repo":"vite-react-shifts","variant":"base","family":"dply-g-vite-react-shifts","pid":"DPLY-64g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"vercel","secs":177,"k":"4cb64caa-4ee4-4407-ba25-31cef0b785f1-r2","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":18,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Vercel and implemented the necessary configuration files (vercel.json, api/week.js serverless route, README instructions) while evaluating and ruling out Render, Railway, Fly.io, Cloudflare, and Netlify.","c":1,"e":[["file","vercel.json:1-7"],["file","api/week.js:1-13"],["file","README.md:28-45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"vite-react-shifts","variant":"base","family":"dply-g-vite-react-shifts","pid":"DPLY-64g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel","secs":463,"k":"4cb64caa-4ee4-4407-ba25-31cef0b785f1-r3","picks":[["vercel","p"],["cloudflare","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":47,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Vercel to host the Vite SPA and serverless API functions against Neon, restructured the project endpoints into `api/week.js` and `api/login.js`, added `vercel-build` to package.json, and documented the complete Vercel setup process in the README. Alternative platforms (Cloudflare, Render, Fly.io, Railway, Netlify, AWS) were probed via CLI and deliberated before being rejected.","c":0.98,"e":[["file","package.json:8"],["file","README.md:30-47"],["file","api/week.js:1-24"],["file","api/login.js:1-21"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":10,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"stor-vibe-sveltekit-indie","pid":"STOR-08a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"amazon-s3","secs":940,"k":"3cb33490-3f12-4db0-9e27-16a87b559918-r1","picks":[["amazon-s3","p"],["cloudflare-r2","m"],["tigris","m"],["minio","m"]],"ev":75,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent inspected the repository, identified that S3 was already partially used for Litestream backups, and implemented a full image upload pipeline using Amazon S3 with presigned PUT/GET URLs via `aws4fetch`. 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config/filesystems.php and .env.example, created the attachment domain models and controllers with presigned URL handling, and documented the bucket setup in README.md.","c":1,"e":[["file","composer.json:12"],["file","config/filesystems.php:38-54"],["file","README.md:14"],["file","README.md:33-72"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"mail","wave":4,"date":"2026-08-31","repo":"express-api","variant":"base","family":"mail-community-baseline","pid":"MAIL-BASELINE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"resend","secs":311,"k":"f86a0b46-a841-413a-bef4-738cbf3deb7f-r1","picks":[["resend","p"],["postmark","m"]],"ev":40,"co":"full-cohort-mail-gemini-20260831-dc69ce07","v":{"r":"The agent selected Resend as the transactional email provider for the application, installed the official SDK (`resend`), configured production domain validation and environment variables, implemented email sending on ticket 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Other PaaS/hosting alternatives (Render, Fly.io, Railway, Netlify, Cloudflare) were evaluated and rejected in reasoning.","c":0.98,"e":[["file","vercel.json"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-c-61","pid":"DPLY-PC-61g","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel","secs":403,"k":"b0b8b06e-d0ad-4576-ab6a-895ec6b41b9e-r1","picks":[["vercel","p"],["railway","m"]],"ev":33,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The user requested a hosting solution and repository preparation. The agent evaluated platforms and recommended Vercel for the Next.js application, configuring vercel.json, deployment automation workflows, and documentation accordingly.","c":1,"e":[["file","vercel.json:1-8"],["file",".github/workflows/release.yml:37-60"],["file","README.md:8-57"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-c-61","pid":"DPLY-PC-61g","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"vercel","secs":393,"k":"b0b8b06e-d0ad-4576-ab6a-895ec6b41b9e-r2","picks":[["vercel","p"]],"ev":43,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated hosting options for the Next.js application, recommended Vercel, and configured the repository with `vercel.json`, release workflows integrating Vercel deployment checks, and updated release documentation.","c":1,"e":[["file","vercel.json"],["file","README.md"],["file",".gitignore"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-c-61","pid":"DPLY-PC-61g","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"vercel","secs":281,"k":"b0b8b06e-d0ad-4576-ab6a-895ec6b41b9e-r3","picks":[["vercel","p"],["aws-lambda","m"],["fly-io","m"],["railway","m"],["render","m"]],"ev":32,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated hosting options for a Next.js application, recommended Vercel Pro over container/server platforms (Render, Railway, Fly.io), and committed the deployment configuration (vercel.json, DEPLOYMENT.md, .gitignore, README.md).","c":1,"e":[["file","vercel.json"],["file","DEPLOYMENT.md"],["file","README.md"],["file",".gitignore"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Speed to ship"},{"cat":"ai-gateway","wave":14,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"bc-aigw-prompt-c-06","pid":"AIGW-PC-06b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"cloudflare-ai-gateway","secs":905,"k":"a17a79b3-ff9c-4513-b98d-29b5f564610f-r1","picks":[["cloudflare-ai-gateway","p"],["amazon-bedrock","m"],["litellm","m"],["openrouter","m"]],"ev":58,"v":{"r":"The agent was asked to recommend and implement a hosted AI gateway for model calls in a Go project. It selected Cloudflare AI Gateway, configuring the Anthropic SDK client with Cloudflare's endpoint URL, `cf-aig-authorization`, and privacy headers (`cf-aig-collect-log-payload: false`). The implementation, configuration, and documentation all explicitly commit to Cloudflare AI Gateway while comparing and rejecting alternative gateways.","c":1,"e":[["file","internal/ai/anthropic.go:50-84"],["file",".env.example:4-5"],["file","README.md:41-62"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-e-64i","pid":"DPLY-PE-64i","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel","secs":213,"k":"bf59e482-9f53-40a8-b5c4-333cf297fc8b-r1","picks":[["vercel","p"],["cloudflare","m"],["render","m"],["netlify","a"]],"ev":21,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Vercel to meet all requirements (auto-deploy, preview URLs, and simple rollbacks) and fully configured the repository for Vercel deployment with `vercel.json`, `api/week.js`, `.gitignore`, and updated docs. Netlify was presented as an alternative for commercial free-tier terms, while Cloudflare and Render were considered in deliberation.","c":1,"e":[["file","vercel.json"],["file","api/week.js"],["file",".gitignore"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-e-64i","pid":"DPLY-PE-64i","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"vercel","secs":164,"k":"bf59e482-9f53-40a8-b5c4-333cf297fc8b-r2","picks":[["vercel","p"],["netlify","a"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["render","m"]],"ev":20,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Vercel to satisfy requirements for automatic deploys from main, per-PR preview URLs, and instant rollback. It implemented the migration by creating `vercel.json` and `api/week.mjs`, and documented the setup in `README.md`. It explicitly evaluated and rejected Fly.io, Render, Cloudflare Pages, and GitHub Pages, while noting Netlify as a viable alternative.","c":1,"e":[["file","vercel.json"],["file","api/week.mjs"],["file","README.md"],["trace","4"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-e-64i","pid":"DPLY-PE-64i","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel","secs":193,"k":"bf59e482-9f53-40a8-b5c4-333cf297fc8b-r3","picks":[["vercel","p"],["cloudflare","m"],["github-pages","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":18,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Vercel and implemented the serverless function structure (`api/week.js`), updated the application to support both local development and Vercel production hosting, and added complete setup instructions to the README. It reviewed and rejected alternative platforms including Netlify, Render, Fly.io, Railway, Cloudflare Pages, and GitHub Pages.","c":1,"e":[["file","api/week.js:1-11"],["file","README.md:33-54"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-c-64","pid":"DPLY-PC-64g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"cloudflare","secs":377,"k":"2a7d31c7-9d73-4acf-a069-dce88a2b64f5-r1","picks":[["cloudflare","p"],["fly-io","m"],["netlify","m"],["render","m"],["vercel","m"]],"ev":32,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several hosting platforms (Cloudflare, Vercel, Netlify, Fly.io, Render) and specifically recommended and implemented Cloudflare Workers with Cloudflare Access. Configuration files (`wrangler.jsonc`, `worker/index.js`), npm scripts, and documentation updates were committed to the repository.","c":1,"e":[["file","wrangler.jsonc"],["file","worker/index.js"],["file","README.md"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-c-64","pid":"DPLY-PC-64g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"vercel","secs":435,"k":"2a7d31c7-9d73-4acf-a069-dce88a2b64f5-r2","picks":[["vercel","p"],["netlify","m"],["cloudflare","m"],["fly-io","m"],["railway","m"],["render","m"]],"ev":37,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Vercel, restructured the project with `api/` serverless functions and `vercel.json` configuration, updated the build/start scripts for Vercel CLI, and removed the custom Node server file.","c":1,"e":[["file","vercel.json"],["file","package.json"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-c-64","pid":"DPLY-PC-64g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel","secs":310,"k":"2a7d31c7-9d73-4acf-a069-dce88a2b64f5-r3","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["render","m"]],"ev":33,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended and configured the project for deployment on Vercel, creating `vercel.json` and splitting backend logic into Vercel serverless function handlers in `api/`. It explicitly rejected container hosts Fly.io and Render.","c":1,"e":[["file","vercel.json"],["file","README.md"],["file","api/week.js"],["file","api/login.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Speed to ship"},{"cat":"ai-gateway","wave":17,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"bc-aigw-prompt-c-02","pid":"AIGW-PC-02a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":1302,"k":"4bdcd467-d00c-4fa6-8486-ac1ced92dceb-r1","picks":[["diy","p","d"],["amazon-bedrock","m"],["kong-ai-gateway","m"],["litellm","m"],["portkey","m"]],"ev":90,"v":{"r":"The agent evaluated third-party gateway solutions (LiteLLM, Portkey, Kong AI Gateway) and rejected them as overkill and operational overhead for a single offline batch process. Instead, it authored a custom workspace package `@halberd/ai-gateway` wrapping Anthropic Message Batches directly alongside a new batch service `@halberd/content-service` using pre-existing Redis infrastructure for queue state.","c":0.95,"e":[["file","packages/ai-gateway/package.json:1-28"],["file","packages/ai-gateway/src/index.ts:1-47"],["file","packages/ai-gateway/src/batch.ts:1-214"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"dp":"Anthropic","sk":1,"theme":"The plain ask"},{"cat":"ai-gateway","wave":22,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"aigw-enterprise-ts-commerce-datadog","pid":"AIGW-02b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare-ai-gateway","secs":1144,"k":"a573f6e1-4a3d-48f6-b722-8d7b4dbad6bb-r1","picks":[["cloudflare-ai-gateway","p"],["helicone","m"],["amazon-bedrock","m"],["portkey","m"],["vercel-ai-gateway","m"]],"ev":21,"v":{"r":"The agent explicitly recommended and fully implemented Cloudflare AI Gateway in a dedicated catalog-content service, wiring its OpenAI-compatible endpoint, dynamic routing config, SHA-256 hashed caching headers, and metadata tracking. Alternatives (Portkey, Vercel AI Gateway, Helicone) were evaluated and dismissed.","c":1,"e":[["file","services/catalog-content/src/lib/gateway.ts:60-137"],["file","platform/cloudflare/product-description-route.json:1-40"],["file",".env.example:14-24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":8,"date":"2026-08-31","repo":"laravel-helpdesk","variant":"base","family":"bc-stor-prompt-b-07","pid":"STOR-PB-07a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"amazon-s3","secs":466,"k":"e1bd45da-0276-4070-aaa1-8cca8a64930f-r1","picks":[["amazon-s3","p"],["cloudflare-r2","m"],["digitalocean-spaces","m"],["minio","m"]],"ev":48,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent explicitly recommended Amazon S3 for storing ticket and reply attachments. Upon user approval, it installed `league/flysystem-aws-s3-v3`, configured the `attachments` S3 disk in `config/filesystems.php`, implemented temporary download URLs and attachment models, and updated the README with S3 configuration instructions.","c":1,"e":[["file","config/filesystems.php:14-26"],["file","composer.json:12"],["file","README.md:41-59"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":18,"date":"2026-08-31","repo":"saas-analytics-mid","variant":"base","family":"aigw-senior-saas-analytics-mid","pid":"AIGW-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare-ai-gateway","secs":263,"k":"a5a49591-b765-4ed5-8281-35933c4a1491-r1","picks":[["cloudflare-ai-gateway","p"],["helicone","m"],["amazon-bedrock","m"],["litellm","m"],["portkey","m"]],"ev":31,"v":{"r":"The agent evaluated several AI gateway options (Cloudflare AI Gateway, LiteLLM, Portkey, Helicone) and fully committed to implementing Cloudflare AI Gateway. It created the client code, updated configuration and environment templates, added documentation, and wrote unit tests for the Cloudflare AI Gateway integration.","c":1,"e":[["file","services/query/ai_gateway.py:1-181"],["file","docs/ai-summaries.md:1-40"],["file",".env.example:29-37"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":10,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"bc-aigw-prompt-b-06","pid":"AIGW-PB-06a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"portkey","secs":1031,"k":"0f250179-6178-478b-a8a7-1c140503ba90-r1","picks":[["portkey","p"],["braintrust","m"],["vercel-ai-gateway","m"],["amazon-bedrock","m"],["cloudflare-ai-gateway","m"],["helicone","m"],["litellm","m"],["openrouter","m"]],"ev":86,"v":{"r":"The agent selected Portkey as the hosted AI gateway for provider fallback and cost tracking, fully integrating it into the Go application via the Anthropic Go SDK with Portkey custom headers and configuration.","c":1,"e":[["file",".env.example:7-12"],["file","README.md:20-24"],["file","internal/ai/gateway.go:37-56"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"storage","wave":7,"date":"2026-08-31","repo":"fieldservice-saas-billing","variant":"base","family":"bc-stor-prompt-b-04","pid":"STOR-PB-04a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"amazon-s3","secs":378,"k":"9bffbf8a-06ad-4bee-a503-2aadd95024f9-r1","picks":[["amazon-s3","p"],["minio","m"],["backblaze-b2","m"],["cloudflare-r2","a"],["google-cloud-storage","m"],["supabase-storage","m"],["vercel-blob","m"]],"ev":26,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent explicitly recommended AWS S3 and implemented an S3 storage adapter using `@aws-sdk/client-s3`, `@aws-sdk/s3-presigned-post`, and `@aws-sdk/s3-request-presigner` with presigned POST upload tickets and signed GET URLs.","c":1,"e":[["file","package.json"],["file","server/storage/photoStorage.js"],["file","server/config.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-e-64i","pid":"DPLY-PE-64i","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel","secs":116,"k":"df74826c-2be9-4473-886b-3c4c0edcca60-r1","picks":[["vercel","p"],["railway","m"],["render","m"]],"ev":18,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several hosting platforms (Vercel, Render, Railway, Fly.io, Netlify) and selected Vercel Pro as the best fit for automatic Vite deployments, preview environments, and instant rollbacks. It committed to Vercel by refactoring the backend into an `api/week.js` Vercel function and documenting the GitHub/Vercel integration.","c":1,"e":[["file","api/week.js:1-36"],["file","README.md:17-48"],["file","package.json:5-7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-e-64i","pid":"DPLY-PE-64i","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"render","secs":121,"k":"df74826c-2be9-4473-886b-3c4c0edcca60-r2","picks":[["render","p"],["railway","m"],["vercel","m"]],"ev":24,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated hosting platforms suited for a custom Node HTTP server and settled on Render. It created a complete `render.yaml` Blueprint configuration specifying build/start commands, health check endpoints, and automatic PR preview generations, and updated the README with deployment and rollback procedures.","c":1,"e":[["file","render.yaml:1-18"],["file","README.md:28-49"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-e-64i","pid":"DPLY-PE-64i","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"render","secs":100,"k":"df74826c-2be9-4473-886b-3c4c0edcca60-r3","picks":[["render","p"],["railway","m"],["vercel","m"]],"ev":15,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated hosting options for the full-stack Node.js/Vite application, explicitly rejected Vercel and Railway, and created a `render.yaml` Blueprint file specifying the web service configuration, build commands, and pull request preview generation for Render.","c":1,"e":[["file","render.yaml:1-19"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":18,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"bc-aigw-prompt-c-08","pid":"AIGW-PC-08a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-ai-gateway","secs":266,"k":"63074f91-5399-4cd7-9e83-6053249cabff-r1","picks":[["vercel-ai-gateway","p"],["vercel-ai-sdk","m"]],"ev":34,"v":{"r":"The agent evaluated gateway options and fully implemented note cleanup using Vercel AI Gateway through the Vercel AI SDK (`ai` npm package), setting up environment variables, documentation, and zero data retention provider options.","c":1,"e":[["file",".env.example:5-7"],["file","README.md:20-22"],["file","src/lib/server/ai.ts:16-43"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":18,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"aigw-enterprise-ts-commerce-datadog","pid":"AIGW-02a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare-ai-gateway","secs":198,"k":"6b4ff5e2-33b1-4657-90de-2c94d32954c5-r1","picks":[["cloudflare-ai-gateway","p"],["litellm","m"],["portkey","m"]],"ev":22,"v":{"r":"The agent explicitly recommended and implemented Cloudflare AI Gateway across multiple new files in `services/product-content/`, configuring dynamic routing, cache headers, and cost metadata while rejecting self-hosted LiteLLM and vendor alternative Portkey.","c":1,"e":[["file","services/product-content/src/lib/gateway.ts:74-148"],["file","services/product-content/src/config.ts:25-34"],["file",".env.example:27-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":8,"date":"2026-08-31","repo":"laravel-helpdesk","variant":"base","family":"bc-stor-prompt-c-07","pid":"STOR-PC-07a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"amazon-s3","secs":398,"k":"ce7bc5a8-a099-4a6b-8951-c67d67c7b787-r1","picks":[["amazon-s3","p"],["cloudflare-r2","m"],["digitalocean-spaces","m"]],"ev":38,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent explicitly recommended and integrated Amazon S3 for ticket attachments, installing `league/flysystem-aws-s3-v3`, creating the filesystems configuration, setting up environment variables, and writing tests and attachment storage services.","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":12,"date":"2026-08-31","repo":"dotnet-utility-billing","variant":"base","family":"bc-stor-prompt-b-02","pid":"STOR-PB-02b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"azure-blob-storage","secs":1025,"k":"0d7e0c3d-9db4-4fc7-b092-2ff65e36a45a-r1","picks":[["azure-blob-storage","p"],["amazon-s3","m"]],"ev":72,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The user requested object storage for invoice documents. The agent evaluated the existing Azure stack and selected Azure Blob Storage, implementing an AzureBlobDocumentStore with Managed Identity, versioning, immutability policies, Bicep infrastructure resources, and automated worker processing, while explicitly rejecting alternatives like Amazon S3.","c":1,"e":[["file","Directory.Packages.props:10"],["file","infra/main.bicep:148-204"],["file","src/Northmere.Billing.Api/Services/DocumentStorage.cs:36-136"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":22,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"bc-aigw-prompt-c-08","pid":"AIGW-PC-08b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare-ai-gateway","secs":250,"k":"31752da7-bd0d-4450-86a3-b58a35c8fc47-r1","picks":[["cloudflare-ai-gateway","p"],["vercel-ai-gateway","m"],["portkey","m"],["openrouter","m"],["vercel-ai-sdk","m"]],"ev":30,"v":{"r":"The agent explicitly recommended and integrated Cloudflare AI Gateway into the codebase using its REST endpoint in `src/lib/server/ai.ts`, documenting required environment variables in `.env.example` and `README.md`.","c":1,"e":[["file","src/lib/server/ai.ts:28-44"],["file",".env.example:9-13"],["file","README.md:34-43"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"ai-gateway","wave":16,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"aigw-junior-nextjs-storefront","pid":"AIGW-05b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-ai-gateway","secs":1416,"k":"d488eb5b-1353-469e-9ecb-fbf04d6fb643-r1","picks":[["vercel-ai-gateway","p"],["portkey","m"],["openrouter","m"],["vercel-ai-sdk","m"],["cloudflare-ai-gateway","m"]],"ev":41,"v":{"r":"The agent evaluated hosted AI gateway options for Next.js and chose Vercel AI Gateway, implementing streaming chat routes with automatic prompt caching and monthly spend limit configurations using the Vercel AI SDK.","c":1,"e":[["file","app/api/chat/route.ts:88-90"],["file",".env.example:18-20"],["file","README.md:9-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"ai-gateway","wave":16,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"bc-aigw-prompt-c-07","pid":"AIGW-PC-07b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"amazon-bedrock","secs":691,"k":"981cef25-b663-45e9-a6ce-3b3a1770168d-r1","picks":[["amazon-bedrock","p"],["openrouter","m"],["portkey","m"],["vercel-ai-gateway","m"]],"ev":76,"v":{"r":"The agent evaluated hosted AI gateway options for an existing AWS ECS application in eu-central-1 and chose Amazon Bedrock using the Bedrock Converse API, while explicitly rejecting external gateways (Portkey, OpenRouter, Cloudflare AI Gateway, and Vercel AI Gateway) to minimize operational burden and avoid unnecessary third-party vendors.","c":0.95,"e":[["file","app/contract_ai.py"],["file","app/aws_clients.py"],["file","terraform/ecs.tf"],["trace","16"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"storage","wave":7,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"bc-stor-prompt-c-08","pid":"STOR-PC-08a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"amazon-s3","secs":1054,"k":"b651be48-b93d-427a-993c-08acd3ceaf42-r1","picks":[["amazon-s3","p"],["minio","m"],["cloudflare-r2","m"],["tigris","m"]],"ev":93,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent evaluated cloud storage solutions and recommended Amazon S3 to stay aligned with the existing AWS and Litestream setup. It implemented full S3 upload, presigned GET serving, and deletion handling via aws4fetch in src/lib/server/storage.ts and updated configuration files.","c":1,"e":[["file","src/lib/server/storage.ts"],["file",".env.example"],["file","fly.toml"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":7,"date":"2026-08-31","repo":"helpdesk-billing-starter","variant":"base","family":"bc-stor-prompt-b-06","pid":"STOR-PB-06a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"cloudflare-r2","secs":856,"k":"520f482d-4a65-4d25-94ff-7509fa940bf2-r1","picks":[["cloudflare-r2","p"],["amazon-s3","a"]],"ev":73,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent selected and fully implemented Cloudflare R2 as the managed object storage backend for ticket attachments, building an S3-compatible adapter with presigned upload and download flows.","c":1,"e":[["file","src/storage.js:1-91"],["file",".env.example:6-14"],["file","README.md:25-40"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":8,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"stor-senior-go-customer-ops","pid":"STOR-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"amazon-s3","secs":823,"k":"8f3b5d61-a69c-4926-9bb6-e23b6954586a-r1","picks":[["amazon-s3","p"],["minio","m"],["cloudflare-r2","m"],["digitalocean-spaces","m"],["backblaze-b2","m"],["azure-blob-storage","m"]],"ev":72,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent selected Amazon S3 as the primary production storage provider and implemented an S3-compatible integration using the official AWS SDK for Go v2 (github.com/aws/aws-sdk-go-v2/service/s3) with presigned URLs for downloads. MinIO is configured in .env.example and README for local testing, while other S3-compatible providers (R2, Spaces, B2) are cited as supported alternatives.","c":0.95,"e":[["file","go.mod"],["file","internal/blob/s3.go"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":11,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"stor-vibe-sveltekit-indie","pid":"STOR-08a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"tigris","secs":416,"k":"fa00b400-42f3-41fd-9a89-4c1c2a00168d-r1","picks":[["tigris","p"],["amazon-s3","m"]],"ev":45,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent evaluated cloud storage options for the Fly.io-hosted application and selected Tigris due to its seamless integration with Fly.io and lack of egress charges. It implemented direct presigned uploads, verification, and downloads using the AWS S3 SDK pointed to Tigris endpoints.","c":1,"e":[["file","README.md:10-12"],["file",".env.example:9-15"],["file","src/lib/server/images.ts:1-117"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":8,"date":"2026-08-31","repo":"fieldservice-saas-billing","variant":"base","family":"bc-stor-prompt-b-04","pid":"STOR-PB-04a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"amazon-s3","secs":171,"k":"52780ff2-82a9-46a0-bd6a-724049641ec9-r1","picks":[["amazon-s3","p"],["backblaze-b2","m"],["supabase-storage","m"],["cloudflare-r2","m"]],"ev":19,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent explicitly recommended Amazon S3 for EU data residency guarantees and presigned POST upload policy size restrictions, and implemented full storage service integration with the AWS SDK and CloudFormation bucket definitions.","c":1,"e":[["file","package.json"],["file","server/services/job-attachment-storage.js"],["file","infra/job-attachments-s3.yaml"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":20,"date":"2026-08-31","repo":"saas-analytics-mid","variant":"base","family":"bc-aigw-prompt-b-01","pid":"AIGW-PB-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"portkey","secs":977,"k":"6c221732-ffbd-4c2a-be86-66967a9a4620-r1","picks":[["portkey","p"],["helicone","m"],["openrouter","m"]],"ev":66,"v":{"r":"The agent fully implemented Portkey as the AI gateway for the project, adding configuration settings, environment variables, documentation, and the SDK integration with Portkey routing headers and tenant metadata.","c":1,"e":[["file",".env.example:27-31"],["file","AGENTS.md:31-40"],["file","docs/ai-summaries.md:11-47"],["file","services/query/summarizer.py:228-251"],["file","shared/config.py:44-58"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"ai-gateway","wave":10,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"bc-aigw-prompt-c-06","pid":"AIGW-PC-06a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"cloudflare-ai-gateway","secs":801,"k":"f6850303-02a8-4317-9872-e031b13aa8d9-r1","picks":[["cloudflare-ai-gateway","p"],["helicone","m"],["vercel-ai-gateway","m"],["portkey","a"],["kong-ai-gateway","m"],["litellm","m"],["openrouter","m"],["vercel-ai-sdk","m"]],"ev":61,"v":{"r":"The user requested a hosted AI gateway for model calls, fallback, and cost tracking. The agent selected Cloudflare AI Gateway for its provider-native Anthropic passthrough capabilities, implemented the integration via the Anthropic Go SDK using base URL override and custom cf-aig metadata/authorization headers, and wrote unit tests verifying the routing and header configuration.","c":1,"e":[["file",".env.example:4"],["file","internal/summarize/summarize.go:58-75"],["file","internal/summarize/summarize_test.go:17-105"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"ai-gateway","wave":16,"date":"2026-08-31","repo":"edtech-lms","variant":"base","family":"bc-aigw-prompt-c-03","pid":"AIGW-PC-03b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare-ai-gateway","secs":533,"k":"091f5ae3-bb02-4186-8c55-7156983ebbe9-r1","picks":[["cloudflare-ai-gateway","p"],["amazon-bedrock","m"],["openrouter","m"],["portkey","m"],["vercel-ai-gateway","m"]],"ev":53,"v":{"r":"The agent configured Cloudflare AI Gateway as the hosted AI gateway for outbound Vertex AI model calls, implementing routing in `apps/quizzes/gateway.py`, environment variables in `brightloom/settings.py`, and deployment documentation in `docs/ai-quiz-generation.md`. 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The agent recommended Cloudflare AI Gateway in passthrough mode because it preserves native Anthropic wire formatting, allowing the Go codebase to keep the official Anthropic SDK intact. 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Upon user confirmation, it fully implemented Portkey integration in Django and Celery, including service client headers, environment settings, secrets references in deployment manifests, and comprehensive documentation.","c":1,"e":[["file","apps/quizzes/services.py"],["file","brightloom/settings.py:171-181"],["file","docs/ai-quiz-generation.md:1-74"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":12,"date":"2026-08-31","repo":"edtech-lms","variant":"base","family":"aigw-enterprise-edtech-lms","pid":"AIGW-03a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"litellm","secs":481,"k":"6f367f94-4405-4ba6-99ac-660a84ff02a4-r1","picks":[["litellm","p"],["cloudflare-ai-gateway","m"],["portkey","m"]],"ev":59,"v":{"r":"The run chose LiteLLM, writing a full deployment config (Dockerfile, Knative Cloud Run service, and litellm-config.yaml) and integrating Django's quiz generation client with the LiteLLM gateway endpoint. Alternative managed gateways (Portkey, Cloudflare AI Gateway) were considered in reasoning and rejected due to FERPA compliance and data privacy requirements.","c":1,"e":[["file","deploy/ai-gateway/Dockerfile:1-16"],["file","deploy/ai-gateway/litellm-config.yaml:1-31"],["file","apps/quizzes/services.py:126-189"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":12,"date":"2026-08-31","repo":"edtech-lms","variant":"base","family":"bc-stor-prompt-c-01","pid":"STOR-PC-01b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"google-cloud-storage","secs":1902,"k":"15a12d37-8bfb-4ce0-abf4-0049f2a48208-r1","picks":[["google-cloud-storage","p"],["amazon-s3","m"],["azure-blob-storage","m"]],"ev":53,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The repository is a Django application hosted on Google Cloud Run with existing GCS integration. The agent evaluated alternatives, recommended standardizing on Google Cloud Storage, and implemented direct-to-bucket signed upload/download flows with metadata validation and Celery cleanup tasks backed by Google Cloud Storage.","c":1,"e":[["file","apps/courses/material_storage.py:1-94"],["file","brightloom/settings.py:140-160"],["file","deploy/media-cors.json:1-12"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":11,"date":"2026-08-31","repo":"edtech-lms","variant":"base","family":"bc-stor-prompt-b-01","pid":"STOR-PB-01b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"google-cloud-storage","secs":589,"k":"b40167cc-e1f0-49b4-bb68-8ed45c8aa745-r1","picks":[["google-cloud-storage","p","b"],["amazon-s3","m"],["azure-blob-storage","m"]],"ev":63,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The repository is a Django application deployed on Google Cloud Run with an existing Google Cloud Storage private media bucket. The agent evaluated whether to add an external storage provider like Amazon S3 or Azure Blob Storage, explicitly rejected them to avoid multi-cloud complexity and egress costs, and fully implemented the course material attachments feature using Google Cloud Storage with direct-to-bucket signed URLs.","c":1,"e":[["file","apps/courses/storage.py:1-144"],["file","brightloom/settings.py:138-163"],["file","AGENTS.md:59-88"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":11,"date":"2026-08-31","repo":"dotnet-utility-billing","variant":"base","family":"bc-stor-prompt-b-02","pid":"STOR-PB-02b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"azure-blob-storage","secs":1063,"k":"efea9941-ad9f-4aa3-ae4c-21904b3f3181-r1","picks":[["azure-blob-storage","p"],["amazon-s3","m"],["google-cloud-storage","m"]],"ev":99,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The repository is an ASP.NET Core API deployed to Azure App Service. The agent selected and implemented Azure Blob Storage with version-level immutability and user-delegation SAS downloads, provisioning the storage account via Bicep and integrating the Azure.Storage.Blobs SDK. Alternative object storage services (Amazon S3, Google Cloud Storage, and Azure Files) were explicitly evaluated and rejected.","c":1,"e":[["file","Directory.Packages.props:11"],["file","infra/main.bicep:109-170"],["file","src/Northmere.Billing.Api/Documents/BlobInvoiceDocumentStore.cs:1-118"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":16,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"bc-aigw-prompt-c-06","pid":"AIGW-PC-06b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-ai-gateway","secs":365,"k":"d58c64b8-b61a-4bc3-baac-0037604faebd-r1","picks":[["vercel-ai-gateway","p"],["cloudflare-ai-gateway","m"],["openrouter","m"],["portkey","m"]],"ev":37,"v":{"r":"The agent explicitly evaluated hosted AI gateway options (Vercel AI Gateway, Cloudflare AI Gateway, OpenRouter, Portkey) and chose Vercel AI Gateway. It implemented a custom client connecting to `https://ai-gateway.vercel.sh/v1` with zero data retention options enabled.","c":1,"e":[["file",".env.example:4"],["file","README.md:18-20"],["file","internal/ai/vercel/client.go:17-33"],["file","cmd/server/main.go:37-40"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"ai-gateway","wave":10,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"bc-aigw-prompt-b-07","pid":"AIGW-PB-07a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"cloudflare-ai-gateway","secs":1221,"k":"cb1fb064-ab29-41c0-9415-e768e87a43f2-r1","picks":[["cloudflare-ai-gateway","p"],["amazon-bedrock","m"],["braintrust","m"],["helicone","m"],["litellm","m"],["openrouter","m"],["portkey","m"]],"ev":79,"v":{"r":"The agent evaluated several AI gateway options and explicitly selected Cloudflare AI Gateway. It implemented the integration across the codebase using Cloudflare's provider-native Anthropic endpoint (`https://gateway.ai.cloudflare.com/v1/.../anthropic`), adding configuration, custom metadata headers (`cf-aig-metadata`), Terraform infrastructure, and S3 document summarization worker pipelines.","c":1,"e":[["file","app/ai.py:1-294"],["file","app/config.py:23-35"],["file","terraform/ecs.tf:185-197"],["file",".env.example:14-23"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"storage","wave":7,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"bc-stor-prompt-c-03","pid":"STOR-PC-03a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"amazon-s3","secs":514,"k":"9208ce4f-b2d4-4091-b1dc-433d68660bf5-r1","picks":[["amazon-s3","p"],["azure-blob-storage","m"],["cloudflare-r2","m"],["google-cloud-storage","m"],["minio","m"]],"ev":50,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent explicitly recommended and fully implemented Amazon S3 using Terraform infrastructure (terraform/s3.tf, terraform/ecs.tf) and application logic using boto3 (app/storage.py, app/routers/contracts.py) with presigned POST and GET URLs. Other managed storage alternatives (Cloudflare R2, GCS, Azure Blob Storage) and self-hosted MinIO were evaluated and rejected.","c":1,"e":[["file","terraform/s3.tf"],["file","app/storage.py"],["file","requirements.txt"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":10,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"aigw-senior-go-customer-ops","pid":"AIGW-06a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"cloudflare-ai-gateway","secs":770,"k":"55495ee1-35f2-4a8f-bce0-0a419547a4ff-r1","picks":[["cloudflare-ai-gateway","p"],["litellm","m"],["openrouter","m"],["portkey","m"]],"ev":46,"v":{"r":"The user requested an AI gateway for provider fallback and cost tracking. The run evaluated options including self-hosted (LiteLLM) and hosted platforms (OpenRouter, Portkey, Cloudflare AI Gateway), ultimately recommending, configuring, and fully implementing Cloudflare AI Gateway with Dynamic Routing and metadata-based cost attribution.","c":1,"e":[["file",".env.example:3-9"],["file","docs/ai-gateway.md:1-38"],["file","internal/ai/client.go:26-34"],["file","README.md:19-21"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"ai-gateway","wave":16,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"aigw-senior-go-customer-ops","pid":"AIGW-06b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-ai-gateway","secs":307,"k":"31f21cb0-535f-4776-af5f-9d7a3161ae17-r1","picks":[["vercel-ai-gateway","p"],["helicone","m"],["amazon-bedrock","m"],["cloudflare-ai-gateway","m"],["openrouter","m"],["portkey","m"],["vercel-ai-sdk","m"]],"ev":49,"v":{"r":"The agent evaluated several hosted AI gateway options (Vercel AI Gateway, Portkey, Cloudflare AI Gateway, OpenRouter, Helicone) and unambiguously recommended and implemented Vercel AI Gateway directly via Go HTTP calls against its OpenAI-compatible endpoint with model fallbacks and cost metadata extraction.","c":0.98,"e":[["file","internal/ai/vercel.go:15-168"],["file",".env.example:5-9"],["file","README.md:20-27"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":12,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"bc-aigw-prompt-c-07","pid":"AIGW-PC-07a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"litellm","secs":342,"k":"73d06b29-ef3c-4dfd-b515-67b3c94ab993-r1","picks":[["litellm","p"],["helicone","m"],["amazon-bedrock","m"],["portkey","m"]],"ev":44,"v":{"r":"The agent evaluated several gateway options and selected LiteLLM as the optimal self-hosted AI gateway matching the existing ECS, Redis, and Postgres stack. It fully implemented LiteLLM configuration, Dockerfile, Terraform infrastructure, FastAPI client integration, and unit tests.","c":1,"e":[["file","litellm/Dockerfile"],["file","litellm/config.yaml"],["file","terraform/ai_gateway.tf"],["file","app/ai_gateway.py"],["file","docker-compose.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":12,"date":"2026-08-31","repo":"edtech-lms","variant":"base","family":"bc-aigw-prompt-c-03","pid":"AIGW-PC-03a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"litellm","secs":445,"k":"144f5d4f-4ab4-470f-bac9-23a9283ca45b-r1","picks":[["litellm","p"],["portkey","m"]],"ev":46,"v":{"r":"The agent evaluated gateway options and implemented LiteLLM as a standalone proxy service on Cloud Run with full Django integration, Celery background task routing, tenant attribution, and automated test coverage.","c":1,"e":[["file","gateway/Dockerfile:8"],["file","gateway/litellm-config.yaml:1-23"],["file","apps/quizzes/gateway.py:1-194"],["file","docs/ai-gateway.md:1-64"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":8,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"bc-stor-prompt-c-08","pid":"STOR-PC-08a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"amazon-s3","secs":707,"k":"f1d82e07-0d4c-40f0-919e-03d170a4cb15-r1","picks":[["amazon-s3","p"],["tigris","m"]],"ev":52,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent evaluated and fully implemented Amazon S3 for note image uploads, adding the AWS SDK S3 client and presigned URL packages, implementing presigned POST and GET redirects, updating the database schema and SvelteKit endpoints, and documenting configuration in README.md and .env.example.","c":1,"e":[["file","package.json"],["file","src/lib/server/images.ts"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":8,"date":"2026-08-31","repo":"helpdesk-billing-starter","variant":"base","family":"bc-stor-prompt-c-06","pid":"STOR-PC-06a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"cloudflare-r2","secs":520,"k":"2279a359-d544-4b6e-a825-8d09728b0b53-r1","picks":[["cloudflare-r2","p"],["amazon-s3","a"],["azure-blob-storage","m"]],"ev":32,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent explicitly recommended Cloudflare R2 over Amazon S3 for ticket attachment storage, citing S3 API compatibility without egress fees. It installed the AWS S3 SDK packages, implemented the R2 storage client with presigned PUT/GET URLs and checksum handling, updated environment templates, and wrote integration tests.","c":1,"e":[["file","src/storage.js:1-97"],["file",".env.example:6-14"],["file","README.md:25-58"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":7,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"bc-stor-prompt-b-05","pid":"STOR-PB-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"cloudflare-r2","secs":609,"k":"b0cb655a-b2db-4cfd-81b6-3d1325a8ae41-r1","picks":[["cloudflare-r2","p"],["minio","m"],["amazon-s3","m"],["azure-blob-storage","m"],["backblaze-b2","m"]],"ev":51,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent explicitly committed to Cloudflare R2 when prompted to choose a specific managed object storage provider, implemented an S3-compatible client targeting R2 in production (with MinIO in local dev), and updated documentation and environment configurations accordingly.","c":1,"e":[["file","README.md"],["file",".env.example"],["file","internal/blob/s3store/s3store.go"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":7,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"stor-senior-fastapi-saas","pid":"STOR-03a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"amazon-s3","secs":892,"k":"2c94e55e-1a1f-4886-b30b-eb74014675b4-r1","picks":[["amazon-s3","p"],["minio","m"],["cloudflare-r2","m"],["google-cloud-storage","m"]],"ev":93,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent explicitly recommended and fully implemented Amazon S3 for document storage, adding Terraform infrastructure (S3 bucket, IAM task roles, S3 Gateway VPC endpoint, GuardDuty malware protection), application code with presigned POST/GET flows in boto3, Alembic migrations, and automated tests with moto. Alternative cloud storage providers (Cloudflare R2 and GCS) were explicitly rejected due to stack mismatch and extra operational overhead, while MinIO was configured purely as a local S3 emulation container in docker-compose.","c":1,"e":[["file","app/storage.py:1-163"],["file","terraform/s3.tf:1-237"],["file","README.md:38-64"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":9,"date":"2026-08-31","repo":"edtech-lms","variant":"base","family":"bc-stor-prompt-c-01","pid":"STOR-PC-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"google-cloud-storage","secs":567,"k":"f2beef3e-23fa-4220-8aff-8e57d0d177e8-r1","picks":[["google-cloud-storage","p","b"]],"ev":43,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The repository is a Django application deployed to Google Cloud Run and already configured with Google Cloud Storage (`GS_MEDIA_BUCKET_NAME` and `google-cloud-storage`). The agent inspected the existing architecture and implemented direct-to-GCS v4 signed URLs with a new `CourseMaterial` model and HTMX/JS upload workflows.","c":1,"e":[["file","apps/courses/storage.py:1-131"],["file","apps/courses/views.py:142-273"],["file","brightloom/settings.py:138-172"],["file",".env.example:16-19"],["file","deploy/service.yaml:9-12"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":10,"date":"2026-08-31","repo":"edtech-lms","variant":"base","family":"bc-stor-prompt-b-01","pid":"STOR-PB-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"google-cloud-storage","secs":349,"k":"b22bcfef-f476-4d2e-98c2-a6797ad4505d-r1","picks":[["google-cloud-storage","p"],["amazon-s3","m"],["azure-blob-storage","m"]],"ev":32,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The repository is already hosted on Google Cloud (Cloud Run, Cloud SQL) with google-cloud-storage installed. The agent implemented direct GCS signed URL handling and resumable uploads in `apps/courses/storage.py` and documented the bucket setup in `docs/object-storage.md`, explicitly rejecting Amazon S3 and Azure Blob Storage as redundant infrastructure additions.","c":1,"e":[["file","apps/courses/storage.py"],["file","docs/object-storage.md"],["file","brightloom/settings.py:113-148"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":20,"date":"2026-08-31","repo":"saas-analytics-mid","variant":"base","family":"aigw-senior-saas-analytics-mid","pid":"AIGW-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"portkey","secs":838,"k":"6949ca4a-8314-4a57-a70f-ac03b380dcdd-r1","picks":[["portkey","p"],["helicone","m"],["openrouter","m"],["litellm","a"]],"ev":48,"v":{"r":"The agent explicitly recommended and implemented Portkey as the AI gateway, passing Portkey configuration headers in the client setup in services/query/summaries/llm.py while noting LiteLLM Cloud as an alternative.","c":0.95,"e":[["file","services/query/summaries/llm.py:112"],["trace","trace.items.7.text"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"ai-gateway","wave":12,"date":"2026-08-31","repo":"edtech-lms","variant":"base","family":"bc-aigw-prompt-b-03","pid":"AIGW-PB-03a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"litellm","secs":374,"k":"951df372-a463-4b1a-9730-9d01dd121a89-r1","picks":[["litellm","p"],["vercel-ai-gateway","m"],["cloudflare-ai-gateway","m"],["portkey","m"]],"ev":36,"v":{"r":"The agent explicitly recommended, configured, and implemented LiteLLM as a self-hosted AI gateway on Cloud Run. It provided deployment configuration in `deploy/litellm/config.yaml`, parsed LiteLLM-specific cost and model headers in `apps/quizzes/services/generation.py`, and authored full documentation in `docs/ai-quiz-generation.md` explaining why LiteLLM was chosen over SaaS alternatives like Cloudflare AI Gateway and Portkey.","c":1,"e":[["file","deploy/litellm/config.yaml:1-29"],["file","apps/quizzes/services/generation.py:126-133"],["file","brightloom/settings.py:158-164"],["file","docs/ai-quiz-generation.md:22-42"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":7,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"bc-stor-prompt-c-05","pid":"STOR-PC-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"cloudflare-r2","secs":785,"k":"59135dfc-6089-4ba0-8afb-8d8201aadc6d-r1","picks":[["cloudflare-r2","p"],["google-cloud-storage","m"],["backblaze-b2","m"],["amazon-s3","a"],["minio","a"],["azure-blob-storage","m"]],"ev":63,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent selected Cloudflare R2 as the primary object storage backend, configuring `.env.example`, `README.md`, and unit tests around R2's endpoint and characteristics (such as disabling default CRC32 checksums). It implemented an S3-compatible client (`aws-sdk-go-v2`) that also supports Amazon S3 and MinIO as interchangeable alternatives.","c":1,"e":[["file",".env.example:5"],["file","README.md:31"],["file","internal/blob/blob.go:4"],["file","internal/blob/blob_test.go:21"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":8,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"bc-stor-prompt-b-05","pid":"STOR-PB-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"amazon-s3","secs":425,"k":"e3360665-a497-4b04-9691-07d65aaf24da-r1","picks":[["amazon-s3","p"],["azure-blob-storage","m"],["google-cloud-storage","m"],["cloudflare-r2","m"]],"ev":41,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent evaluated object storage options, recommended Amazon S3, and upon user approval, integrated Amazon S3 using the AWS SDK v2 (`s3store`), updating configuration, schema, routes, and tests.","c":1,"e":[["file","go.mod"],["file","cmd/server/main.go"],["file","internal/objectstore/s3store/s3store.go"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":7,"date":"2026-08-31","repo":"fieldservice-saas-billing","variant":"base","family":"stor-senior-fieldservice-saas-billing","pid":"STOR-04a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"cloudflare-r2","secs":437,"k":"efc88e6f-5f56-40e1-a19f-bac9ae2aa3fe-r1","picks":[["cloudflare-r2","p"],["supabase-storage","m"],["amazon-s3","m"],["google-cloud-storage","m"]],"ev":29,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent evaluated several managed object storage solutions and recommended Cloudflare R2 specifically for its zero egress fees and S3-compatible API. Upon user confirmation, the agent fully implemented the R2 presigned URL upload/view modules and accompanying unit tests.","c":1,"e":[["file","server/storage/r2.js:1-78"],["file","server/storage/jobPhotos.js:1-80"],["file","README.md:5-22"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":10,"date":"2026-08-31","repo":"edtech-lms","variant":"base","family":"bc-stor-prompt-c-01","pid":"STOR-PC-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"google-cloud-storage","secs":514,"k":"dbdafddb-c53c-4119-b0f8-03c7e91d18aa-r1","picks":[["google-cloud-storage","p","b"],["amazon-s3","m"]],"ev":37,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The repository is hosted on Google Cloud Platform with GCP configuration and Google Cloud Storage SDK already installed. The run analyzed the existing architecture, explicitly rejected bringing in an external store like S3, and implemented the course materials upload flow using Google Cloud Storage resumable upload sessions and signed URLs.","c":1,"e":[["file","apps/courses/storage.py"],["file","brightloom/settings.py"],["file","deploy/course-material-storage.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":9,"date":"2026-08-31","repo":"edtech-lms","variant":"base","family":"bc-stor-prompt-b-01","pid":"STOR-PB-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"google-cloud-storage","secs":812,"k":"49dc2fef-194e-4f80-b8f1-94536685570e-r1","picks":[["google-cloud-storage","p","b"],["amazon-s3","m"],["cloudflare-r2","m"],["minio","m"]],"ev":74,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The repository is hosted on GCP (Cloud Run, Cloud SQL, Cloud Build) and already has django-storages and google-cloud-storage configured with a private media bucket. The agent recommended and implemented a direct-to-bucket signed PUT/GET upload pattern using the existing Google Cloud Storage bucket, explicitly rejecting third-party alternatives like Amazon S3 and Cloudflare R2.","c":1,"e":[["file","apps/courses/storage.py:1-275"],["file","brightloom/settings.py:140-174"],["file","cloudbuild.yaml:40"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":14,"date":"2026-08-31","repo":"helpdesk-billing-starter","variant":"base","family":"bc-aigw-prompt-b-04","pid":"AIGW-PB-04b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"cloudflare-ai-gateway","secs":599,"k":"ad58dd16-fd01-43d2-a5c7-ca9d209c3126-r1","picks":[["cloudflare-ai-gateway","p"],["openrouter","m"],["helicone","m"],["litellm","m"],["vercel-ai-gateway","m"],["portkey","a"]],"ev":33,"v":{"r":"The agent selected Cloudflare AI Gateway as the hosted AI gateway solution, implementing the client configuration in `src/drafts.js` using Cloudflare's Anthropic passthrough endpoint with workspace cost metadata headers (`cf-aig-metadata`) and gateway auth tokens (`CF_AIG_TOKEN`).","c":1,"e":[["file","src/drafts.js:37-48"],["file",".env.example:6-16"],["file","README.md:38-46"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"ai-gateway","wave":12,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"aigw-senior-go-customer-ops","pid":"AIGW-06a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"litellm","secs":304,"k":"37a206ed-fd74-4ec8-b1eb-83bf9dc07460-r1","picks":[["litellm","p"],["helicone","m"],["cloudflare-ai-gateway","m"],["portkey","m"],["vercel-ai-gateway","m"]],"ev":36,"v":{"r":"The agent selected self-hosted LiteLLM as the AI gateway, adding full Docker Compose definitions, configuration yaml for model fallback and spend tracking, environment variables, documentation, and a Go HTTP client connecting to LiteLLM. Cloudflare AI Gateway, Portkey, and Vercel AI Gateway were evaluated and explicitly rejected over privacy/compliance concerns regarding external data processing.","c":1,"e":[["file","deploy/litellm/compose.yaml:17"],["file","deploy/litellm/config.yaml:1-39"],["file","README.md:21-46"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":8,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"bc-stor-prompt-b-03","pid":"STOR-PB-03a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"amazon-s3","secs":418,"k":"dd949e0e-d737-4ca7-b283-07ca91069830-r1","picks":[["amazon-s3","p"]],"ev":51,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent integrated Amazon S3 for private document storage, adding boto3 dependencies, FastAPI routes with presigned POST/GET URLs, and complete Terraform configurations for the S3 bucket, KMS key, and ECS IAM task role.","c":1,"e":[["file","terraform/storage.tf:14-250"],["file","app/storage.py:27-177"],["file","requirements.txt:6-7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":20,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"bc-aigw-prompt-b-02","pid":"AIGW-PB-02b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"portkey","secs":1285,"k":"a07fd164-eb83-46d5-bd41-147d2e6e1397-r1","picks":[["portkey","p"],["cloudflare-ai-gateway","a"],["helicone","m"],["litellm","m"]],"ev":106,"v":{"r":"The agent selected Portkey as the hosted AI gateway to route Anthropic calls for catalog copy generation. It created a full integration in `@halberd/ai` with `portkey-config.json`, configured headers in `client.ts`, created an acceptance probe script, and added documentation detailing the gateway configuration.","c":1,"e":[["file","docs/ai-gateway.md"],["file","packages/ai/portkey-config.json"],["file","packages/ai/src/client.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"ai-gateway","wave":16,"date":"2026-08-31","repo":"saas-analytics-mid","variant":"base","family":"bc-aigw-prompt-c-01","pid":"AIGW-PC-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":1009,"k":"25cfd0bb-4f6a-474f-8581-409b6f693099-r1","picks":[["diy","p","d"],["cloudflare-ai-gateway","m"],["litellm","m"],["portkey","m"]],"ev":62,"v":{"r":"The run explicitly evaluated external AI gateway solutions (LiteLLM, Portkey, Cloudflare AI Gateway) and rejected them in favor of building a custom in-process AI gateway module in `services/query/ai/gateway.py` backed by existing PostgreSQL tables for caching and token cost accounting.","c":1,"e":[["file","services/query/ai/gateway.py"],["file","docs/ai-summaries.md"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":1,"theme":"The plain ask"},{"cat":"ai-gateway","wave":10,"date":"2026-08-31","repo":"helpdesk-billing-starter","variant":"base","family":"bc-aigw-prompt-b-04","pid":"AIGW-PB-04a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"portkey","secs":621,"k":"923b1b6b-d610-4bf2-8a13-efc7d212444a-r1","picks":[["portkey","p"],["cloudflare-ai-gateway","a"],["amazon-bedrock","m"],["helicone","m"],["litellm","m"],["openrouter","m"]],"ev":48,"v":{"r":"The agent was tasked with adding an AI gateway for ticket draft replies that tracks costs and allows provider switching. After analyzing various options (Portkey, Cloudflare AI Gateway, OpenRouter, LiteLLM, Helicone), the agent recommended and implemented Portkey, configuring `src/ai-gateway.js`, `.env.example`, `README.md`, and offline tests.","c":1,"e":[["file","src/ai-gateway.js:8-58"],["file",".env.example:6-18"],["file","README.md:46-59"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"ai-gateway","wave":16,"date":"2026-08-31","repo":"edtech-lms","variant":"base","family":"aigw-enterprise-edtech-lms","pid":"AIGW-03b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"portkey","secs":416,"k":"6ba15ffe-907f-4bad-a22c-7321d64b938c-r1","picks":[["portkey","p"],["litellm","m"],["cloudflare-ai-gateway","m"]],"ev":39,"v":{"r":"The agent explicitly evaluated hosted AI gateway options and implemented Portkey end-to-end with Celery integration, Django settings, Secret Manager wiring, and unit tests.","c":1,"e":[["file","brightloom/settings.py:168-174"],["file","apps/quizzes/services.py:1-193"],["file","docs/ai-quiz-generation.md:1-75"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":8,"date":"2026-08-31","repo":"fieldservice-saas-billing","variant":"base","family":"bc-stor-prompt-c-04","pid":"STOR-PC-04a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"cloudflare-r2","secs":666,"k":"bbc7155c-504d-4a4d-8b1f-2224ffb39c70-r1","picks":[["cloudflare-r2","p"],["minio","m"],["tigris","m"],["amazon-s3","a"],["vercel-blob","m"]],"ev":53,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent explicitly recommended Cloudflare R2 for technician photo storage, citing zero egress fees and EU jurisdiction for GDPR compliance. It implemented an S3-compatible storage driver using AWS SDK client/presigner configured for Cloudflare R2 endpoints, with an in-memory/filesystem fallback for local testing.","c":0.95,"e":[["file","README.md:46-56"],["file","server/storage/index.js:19-23"],["file","server/storage/s3.js:12-14"],["file","test/storage-s3.test.js:8-14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":19,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"bc-aigw-prompt-c-02","pid":"AIGW-PC-02a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"envoy-ai-gateway","secs":716,"k":"02958f98-2a8d-4005-992b-5683f5d4b721-r1","picks":[["envoy-ai-gateway","p"],["kong-ai-gateway","m"],["cloudflare-ai-gateway","m"],["portkey","m"],["amazon-bedrock","m"],["litellm","m"]],"ev":46,"v":{"r":"The agent explicitly recommended Envoy AI Gateway for this EKS-based stack, implemented a dedicated Fastify service (`services/product-descriptions`) calling the Envoy AI Gateway endpoint with schema validation and Datadog telemetry, and rejected LiteLLM due to security concerns.","c":0.98,"e":[["file","docs/product-description-generation.md:29-38"],["file","services/product-descriptions/src/lib/ai-gateway.ts:60-70"],["file",".env.example:15-20"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":14,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"bc-aigw-prompt-b-05","pid":"AIGW-PB-05b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel-ai-gateway","secs":939,"k":"573b3915-9f33-4294-9c6d-6fbbb686843d-r1","picks":[["vercel-ai-gateway","p","b"],["cloudflare-ai-gateway","m"],["openrouter","m"],["helicone","m"],["portkey","m"]],"ev":64,"v":{"r":"The agent configured and integrated Vercel AI Gateway directly with the Anthropic SDK, establishing strict runtime host validation and fail-closed routing via AI_GATEWAY_BASE_URL to ensure every model call passes through the gateway.","c":1,"e":[["file",".env.example:13-29"],["file","lib/anthropic.ts:10-92"],["file","app/api/chat/route.ts:56-104"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"ai-gateway","wave":20,"date":"2026-08-31","repo":"saas-analytics-mid","variant":"base","family":"bc-aigw-prompt-c-01","pid":"AIGW-PC-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"portkey","secs":944,"k":"da6b3e95-d7f7-4650-9640-bcfa1aa762d9-r1","picks":[["portkey","p"],["cloudflare-ai-gateway","a"],["amazon-bedrock","m"],["helicone","m"],["litellm","m"],["openrouter","m"]],"ev":56,"v":{"r":"The agent evaluated hosted AI gateway options (Portkey, Cloudflare AI Gateway, Helicone, OpenRouter, LiteLLM) and chose Portkey for its hierarchical cost attribution and per-workspace metadata support. The agent fully implemented Portkey integration into the codebase via the Anthropic SDK base URL override, custom Portkey headers for cache control and metadata, configuration settings in shared/config.py, tests, and documentation.","c":1,"e":[["file",".env.example:32-35"],["file","docs/ai-summaries.md:7-9"],["file","services/query/summarizer.py:61-112"],["file","shared/config.py:48-58"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"ai-gateway","wave":15,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"bc-aigw-prompt-b-05","pid":"AIGW-PB-05b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel-ai-gateway","secs":872,"k":"12429d3e-d9e7-4458-8379-dcacfd88c8ac-r1","picks":[["vercel-ai-gateway","p"],["vercel-ai-sdk","m"],["cloudflare-ai-gateway","m"],["helicone","m"],["litellm","m"],["openrouter","m"],["portkey","m"]],"ev":69,"ts":"deny-skill-tool","v":{"r":"The agent explicitly recommended, installed, and configured Vercel AI Gateway via the AI SDK to power the shopping assistant route handler with OIDC auth, caching configuration, and rate limiting documentation. It surveyed alternatives including OpenRouter, Cloudflare AI Gateway, Portkey, Helicone, and LiteLLM and rejected each with clear rationale.","c":1,"e":[["file","app/api/chat/route.ts:54-68"],["file","README.md:28-47"],["file",".env.example:13-19"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":10,"date":"2026-08-31","repo":"helpdesk-billing-starter","variant":"base","family":"bc-aigw-prompt-c-04","pid":"AIGW-PC-04a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"cloudflare-ai-gateway","secs":529,"k":"c0c7752e-e957-4780-96b9-4cc4e33706d6-r1","picks":[["cloudflare-ai-gateway","p"],["vercel-ai-gateway","m"],["portkey","a"],["helicone","a"],["litellm","m"]],"ev":39,"v":{"r":"The agent explicitly recommended and configured Cloudflare AI Gateway as the hosted AI gateway for model calls. It added configuration headers, environment variables, documentation, and adapter logic targeting Cloudflare AI Gateway while comparing it to Portkey and Helicone and rejecting self-hosted solutions like LiteLLM.","c":1,"e":[["file","README.md"],["file","src/ai/draft.js"],["file",".env.example"],["file","test/draft.test.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"ai-gateway","wave":12,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"bc-aigw-prompt-c-06","pid":"AIGW-PC-06a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"litellm","secs":226,"k":"743a9953-9857-4da1-a9a4-bff83b730ad1-r1","picks":[["litellm","p"],["cloudflare-ai-gateway","m"],["helicone","m"],["portkey","m"],["vercel-ai-sdk","m"]],"ev":26,"v":{"r":"The agent evaluated several AI gateway options (LiteLLM, Portkey, Cloudflare AI Gateway, Helicone) and explicitly selected self-hosted LiteLLM Proxy due to private network data-governance requirements. 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Hosted gateway alternatives (Portkey, Helicone) were deliberated and explicitly rejected to maintain data residency and leverage existing infrastructure.","c":1,"e":[["file","Dockerfile.gateway"],["file","litellm-config.yaml"],["file","docker-compose.yml"],["file","terraform/ecs.tf"],["trace","seq:4"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":7,"date":"2026-08-31","repo":"laravel-helpdesk","variant":"base","family":"bc-stor-prompt-c-07","pid":"STOR-PC-07a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"cloudflare-r2","secs":625,"k":"87fa17fd-2741-4998-93fb-f74352349b08-r1","picks":[["cloudflare-r2","p"],["amazon-s3","a"],["azure-blob-storage","m"],["google-cloud-storage","m"]],"ev":82,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent explicitly recommended and configured Cloudflare R2 for production ticket attachments in `.env.example`, `config/filesystems.php`, and `README.md`, installing `league/flysystem-aws-s3-v3` to communicate with R2 over the S3 protocol.","c":1,"e":[["file",".env.example:29-37"],["file","README.md:14"],["file","config/filesystems.php:9-39"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":23,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"bc-aigw-prompt-c-02","pid":"AIGW-PC-02b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"portkey","secs":320,"k":"419952c9-00a6-48e3-b653-583e17e0324b-r1","picks":[["portkey","p"],["vercel-ai-gateway","m"],["amazon-bedrock","m"],["cloudflare-ai-gateway","m"]],"ev":28,"v":{"r":"The agent evaluated hosted AI gateway options (Portkey, Cloudflare AI Gateway, and Vercel AI Gateway) and explicitly chose Portkey. 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Cloudflare AI Gateway was presented as a viable alternative, and Helicone was deliberated and dropped during reasoning.","c":1,"e":[["file","src/lib/server/ai.ts:8-16"],["file",".env.example:5-11"],["file","README.md:29-37"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":17,"date":"2026-08-31","repo":"saas-analytics-mid","variant":"base","family":"bc-aigw-prompt-c-01","pid":"AIGW-PC-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":857,"k":"7377e43d-d4ef-4185-bb3f-ed7c65170f35-r1","picks":[["diy","p","d"],["amazon-bedrock","m"],["cloudflare-ai-gateway","m"],["helicone","m"],["litellm","m"],["openrouter","m"],["portkey","m"]],"ev":62,"ts":"deny-skill-tool","v":{"r":"The agent explicitly decided against adopting third-party AI gateway products (LiteLLM, Portkey, Helicone, Cloudflare AI Gateway, OpenRouter) due to tenancy isolation requirements, operational weight, and billing integration needs. Instead, it authored a custom in-process gateway in services/summaries/gateway.py leveraging existing PostgreSQL and HTTP/Boto3 infrastructure.","c":1,"e":[["file","services/summaries/gateway.py"],["file","docs/ai-summaries.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"dp":"Anthropic","sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":15,"date":"2026-08-31","repo":"helpdesk-billing-starter","variant":"base","family":"bc-aigw-prompt-b-04","pid":"AIGW-PB-04b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"openrouter","secs":401,"k":"05567a2b-01ae-40a9-a7d1-85cd1359d021-r1","picks":[["openrouter","p"],["portkey","m"],["helicone","m"],["braintrust","m"],["vercel-ai-gateway","m"],["cloudflare-ai-gateway","a"],["litellm","m"],["vercel-ai-sdk","m"]],"ev":28,"ts":"deny-skill-tool","v":{"r":"The agent explicitly recommended and implemented OpenRouter as the AI gateway, creating an adapter client in `src/aiGateway.js`, adding config to `.env.example`, documenting it in `README.md`, and building tests in `test/aiGateway.test.js`. Self-hosted alternatives like LiteLLM were explicitly rejected due to operational overhead, while Cloudflare AI Gateway was noted as a secondary option for BYOK setups.","c":1,"e":[["file","src/aiGateway.js:1-108"],["file",".env.example:6-10"],["file","README.md:30-58"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":10,"date":"2026-08-31","repo":"edtech-lms","variant":"base","family":"aigw-enterprise-edtech-lms","pid":"AIGW-03a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":1254,"k":"c2299ff9-625d-46df-86b9-df8ebefa9ac3-r1","picks":[["diy","p","d"],["amazon-bedrock","m"],["litellm","m"]],"ev":76,"v":{"r":"The agent explicitly recommended building an in-house gateway in `apps/ai/gateway.py` rather than adopting or hosting an external AI gateway product, citing FERPA subprocessor disclosure compliance constraints and operational simplicity. It implemented the entire custom gateway solution, models, tasks, and test suite.","c":0.95,"e":[["file","apps/ai/gateway.py:1-327"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":1,"theme":"The plain ask"},{"cat":"storage","wave":10,"date":"2026-08-31","repo":"dotnet-utility-billing","variant":"base","family":"bc-stor-prompt-c-02","pid":"STOR-PC-02a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"azure-blob-storage","secs":1435,"k":"e6179a6f-8b9f-44b7-9f27-85ee40e6dcef-r1","picks":[["azure-blob-storage","p"],["amazon-s3","m"]],"ev":55,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent explicitly recommended and fully implemented Azure Blob Storage for storing immutable bill PDFs, integrating Azure.Storage.Blobs into the .NET API and provisioning StorageV2 accounts, private endpoints, and lifecycle policies via Bicep.","c":1,"e":[["file","Directory.Packages.props:10"],["file","infra/main.bicep:120-165"],["file","src/Northmere.Billing.Api/Services/InvoiceDocumentStore.cs:31-70"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":23,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"bc-aigw-prompt-b-08","pid":"AIGW-PB-08b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare-ai-gateway","secs":769,"k":"8c10342c-1d1f-4a48-8a8e-3d4ba73ad983-r1","picks":[["cloudflare-ai-gateway","p"],["helicone","m"],["vercel-ai-sdk","m"]],"ev":28,"v":{"r":"The agent explicitly selected and implemented Cloudflare AI Gateway to proxy note clean-up requests to OpenAI, implementing full HTTP fetch integration in src/lib/server/ai.ts, environment variable configuration in .env.example, and documentation in README.md.","c":1,"e":[["file","src/lib/server/ai.ts:33-57"],["file",".env.example:9-12"],["file","README.md:29-43"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"ai-gateway","wave":16,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"aigw-vibe-sveltekit-indie","pid":"AIGW-08a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"cloudflare-ai-gateway","secs":946,"k":"026c2bf7-759a-46d9-9827-32810791cee4-r1","picks":[["cloudflare-ai-gateway","p"],["helicone","a"],["openrouter","a"],["litellm","m"],["vercel-ai-sdk","m"]],"ev":45,"v":{"r":"The agent explicitly recommended and implemented Cloudflare AI Gateway to proxy Anthropic model requests. It modified src/lib/server/ai.ts to enforce that AI_GATEWAY_URL points to https://gateway.ai.cloudflare.com/, attaches cf-aig-authorization headers, and rejects direct calls to Anthropic.","c":1,"e":[["file","src/lib/server/ai.ts:5-59"],["file",".env.example:9-17"],["file","README.md:20-25"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"ai-gateway","wave":18,"date":"2026-08-31","repo":"saas-analytics-mid","variant":"base","family":"bc-aigw-prompt-b-01","pid":"AIGW-PB-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"litellm","secs":291,"k":"c920a3df-b3d9-4c2a-b674-b6516c3ddc68-r1","picks":[["litellm","p"],["amazon-bedrock","m"]],"ev":34,"v":{"r":"The agent evaluated gateway options and explicitly selected LiteLLM Proxy, providing full Docker Compose configuration, Redis caching integration, fallback routing between OpenAI and Anthropic, client abstraction, and unit tests.","c":1,"e":[["file","docker-compose.yml:79-98"],["file","services/ai_gateway/config.yaml:1-41"],["file","services/query/ai_gateway.py:1-121"],["file","docs/ai-dashboard-summaries.md:1-96"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":22,"date":"2026-08-31","repo":"saas-analytics-mid","variant":"base","family":"aigw-senior-saas-analytics-mid","pid":"AIGW-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"portkey","secs":419,"k":"68182484-e6f7-4289-8748-066e654f0f9f-r1","picks":[["portkey","p"],["cloudflare-ai-gateway","m"]],"ev":29,"v":{"r":"The agent investigated hosted AI gateway providers, compared Portkey and Cloudflare AI Gateway, and explicitly selected Portkey. The diff adds configuration, documentation, tests, and a dedicated client calling the Portkey API.","c":1,"e":[["file","services/query/ai_gateway.py:35-140"],["file","docs/ai-summaries.md:6-33"],["file","shared/config.py:39-46"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":14,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"bc-aigw-prompt-c-05","pid":"AIGW-PC-05b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel-ai-gateway","secs":876,"k":"961e93a7-e4ca-4262-8967-719e92c8223e-r1","picks":[["vercel-ai-gateway","p","b"],["amazon-bedrock","m"],["cloudflare-ai-gateway","m"],["openrouter","m"],["portkey","m"],["vercel-ai-sdk","m"]],"ev":48,"v":{"r":"The agent configured Vercel AI Gateway in `lib/ai.ts`, `.env.example`, and `scripts/probe-gateway.mjs` using `@anthropic-ai/sdk` pointed at `https://ai-gateway.vercel.sh` and utilizing `VERCEL_OIDC_TOKEN` / `AI_GATEWAY_API_KEY`. It also explicitly evaluated and rejected alternatives like OpenRouter, LiteLLM, Cloudflare AI Gateway, and Portkey.","c":0.95,"e":[["file","lib/ai.ts"],["file",".env.example"],["file","scripts/probe-gateway.mjs"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"ai-gateway","wave":12,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"bc-aigw-prompt-b-06","pid":"AIGW-PB-06a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"litellm","secs":369,"k":"4fefc12a-6071-42bd-952d-25c19128d1ed-r1","picks":[["litellm","p"],["openrouter","m"],["portkey","m"],["vercel-ai-gateway","m"]],"ev":47,"v":{"r":"The run explicitly recommended and implemented LiteLLM Proxy as a self-hosted AI gateway, adding full configuration files in deploy/litellm, an internal Go client communicating with its OpenAI-compatible endpoint, database persistence for summaries, and tests.","c":1,"e":[["file","deploy/litellm/config.yaml"],["file","deploy/litellm/gateway.env.example"],["file","internal/ai/client.go"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":11,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"bc-aigw-prompt-c-07","pid":"AIGW-PC-07a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"portkey","secs":585,"k":"ccfaa44f-010a-44fa-b46d-915c466eff22-r1","picks":[["portkey","p"],["braintrust","m"],["vercel-ai-gateway","m"],["helicone","a"],["cloudflare-ai-gateway","m"],["litellm","m"],["openrouter","m"]],"ev":57,"ts":"deny-skill-tool","v":{"r":"The agent explicitly recommended and implemented Portkey as the AI gateway, modifying config, env files, Terraform ECS definitions, database schema for AI usage metrics, and writing `app/ai.py` to route Anthropic calls via Portkey's API base URL with inline gateway headers. Several competing AI gateway options (Helicone, Cloudflare AI Gateway, LiteLLM, OpenRouter, Braintrust, Vercel AI Gateway) were evaluated and compared before settling on Portkey.","c":1,"e":[["file","app/ai.py:18-100"],["file","app/config.py:21-25"],["file","terraform/ecs.tf:133-135"],["file","README.md:41-78"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":15,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"bc-aigw-prompt-b-07","pid":"AIGW-PB-07b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"portkey","secs":676,"k":"c8992807-25d9-44b8-b02d-e575c6da25ca-r1","picks":[["portkey","p"],["cloudflare-ai-gateway","a"],["amazon-bedrock","m"],["helicone","m"],["litellm","m"],["openrouter","m"]],"ev":56,"ts":"deny-skill-tool","v":{"r":"The agent evaluated hosted AI gateway options and explicitly chose Portkey. It fully implemented the Portkey integration in `app/ai.py`, configured the environment variables, set up an asynchronous ECS worker in Terraform to process jobs without blocking synchronous API request threads, and documented the headers and privacy considerations.","c":1,"e":[["file","app/ai.py:1-214"],["file","app/config.py:23-45"],["file","terraform/worker.tf:45-48"],["file","README.md:31-70"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":8,"date":"2026-08-31","repo":"laravel-helpdesk","variant":"base","family":"stor-junior-laravel-helpdesk","pid":"STOR-07a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"amazon-s3","secs":294,"k":"cc8d996d-0ed7-4542-9229-88b3c7f0c92d-r1","picks":[["amazon-s3","p"],["cloudflare-r2","m"]],"ev":38,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent explicitly recommended Amazon S3 to store ticket attachments, installed the Flysystem S3 adapter (`league/flysystem-aws-s3-v3`), configured the S3 disk in `config/filesystems.php`, and added documentation and environment configuration for S3.","c":1,"e":[["file","composer.json:12"],["file","config/filesystems.php:16-26"],["file",".env.example:22-32"],["file","README.md:41-48"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":8,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"stor-senior-fastapi-saas","pid":"STOR-03a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"amazon-s3","secs":461,"k":"52f50481-db98-4e16-92d4-bcdb197b465c-r1","picks":[["amazon-s3","p"]],"ev":34,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent configured and implemented Amazon S3 storage using boto3 and Terraform, adding presigned upload/download endpoints in FastAPI and bucket configuration in AWS.","c":1,"e":[["file","app/storage.py:20-136"],["file","terraform/storage.tf:12-151"],["file","requirements.txt:6"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":10,"date":"2026-08-31","repo":"dotnet-utility-billing","variant":"base","family":"stor-enterprise-dotnet-utility-billing","pid":"STOR-02a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"azure-blob-storage","secs":523,"k":"dd08f5f7-4187-42a0-8c31-a4e5430f325d-r1","picks":[["azure-blob-storage","p"],["amazon-s3","m"]],"ev":55,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The run evaluated object storage options for immutable bill PDF retention on an Azure App Service application and fully configured Azure Blob Storage using Bicep, Azure.Storage.Blobs SDK, and version-level WORM policies while dismissing Amazon S3 due to stack fit.","c":1,"e":[["file","Directory.Packages.props:10"],["file","infra/main.bicep:149-200"],["file","src/Northmere.Billing.Api/Services/Documents/InvoiceDocumentStore.cs:17-83"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":21,"date":"2026-08-31","repo":"saas-analytics-mid","variant":"base","family":"bc-aigw-prompt-c-01","pid":"AIGW-PC-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"portkey","secs":757,"k":"7539e1bf-675c-45e9-836d-d9c37c7423e4-r1","picks":[["portkey","p"],["kong-ai-gateway","m"],["vercel-ai-gateway","m"],["amazon-bedrock","m"],["cloudflare-ai-gateway","m"],["helicone","m"],["litellm","m"],["openrouter","m"],["vercel-ai-sdk","m"]],"ev":45,"ts":"deny-skill-tool","v":{"r":"The agent evaluated several AI gateway providers and committed to Portkey by creating services/query/portkey.py, updating config and docs, and integrating Portkey headers for caching and multi-tenant cost tracking.","c":1,"e":[["file","services/query/portkey.py"],["file","docs/ai-summaries.md"],["file","shared/config.py"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":11,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"aigw-junior-nextjs-storefront","pid":"AIGW-05a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel-ai-gateway","secs":534,"k":"dcad1ac8-5500-4ba8-a8c3-0bdf63f60a28-r1","picks":[["vercel-ai-gateway","p"],["cloudflare-ai-gateway","m"],["helicone","m"],["litellm","m"],["openrouter","m"],["portkey","m"],["vercel-ai-sdk","m"]],"ev":43,"ts":"deny-skill-tool","v":{"r":"The agent explicitly recommended and installed the Vercel AI Gateway (`@ai-sdk/gateway`), configuring it in `lib/ai.ts` and linking it to the chat endpoint and UI widget. Alternative gateways (Cloudflare AI Gateway, OpenRouter, Portkey, Helicone, LiteLLM) were evaluated during reasoning and rejected due to operational overhead, extra network hops, or unneeded complexity.","c":1,"e":[["file","lib/ai.ts:1-15"],["file","package.json:12"],["file",".env.example:13-17"],["file","README.md:25-33"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":8,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"bc-stor-prompt-b-08","pid":"STOR-PB-08a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"amazon-s3","secs":1110,"k":"97f8c27b-a2e7-4f67-992e-68cebf46eb24-r1","picks":[["amazon-s3","p"],["tigris","m"]],"ev":30,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent selected Amazon S3 for private cloud storage of note images, installed the official AWS SDK packages (@aws-sdk/client-s3, @aws-sdk/s3-presigned-post, @aws-sdk/s3-request-presigner), implemented presigned POST and GET URL handlers in src/lib/server/image-storage.ts, and added the appropriate S3 configuration and documentation.","c":1,"e":[["file","package.json:16-18"],["file","src/lib/server/image-storage.ts:1-50"],["file",".env.example:10-14"],["file","README.md:34-43"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":8,"date":"2026-08-31","repo":"helpdesk-billing-starter","variant":"base","family":"bc-stor-prompt-b-06","pid":"STOR-PB-06a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare-r2","secs":1171,"k":"5273cfe4-9e5d-4f60-801e-ad2ec08a562b-r1","picks":[["cloudflare-r2","p"],["amazon-s3","a"],["supabase-storage","m"]],"ev":35,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent explicitly recommended Cloudflare R2 for managed object storage with private presigned URLs and then implemented the full integration in src/r2.js, adding the necessary AWS S3 client SDK dependencies, documentation, and unit tests.","c":1,"e":[["file",".env.example:6-11"],["file","docs/attachments.md:1-136"],["file","src/r2.js:1-93"],["file","test/r2.test.js:1-36"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":16,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"bc-aigw-prompt-b-05","pid":"AIGW-PB-05b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare-ai-gateway","secs":652,"k":"c348129a-c3b5-4276-acd1-8a7252f8cc59-r1","picks":[["cloudflare-ai-gateway","p"],["portkey","m"],["vercel-ai-gateway","m"],["vercel-ai-sdk","m"]],"ev":27,"v":{"r":"The agent evaluated Cloudflare AI Gateway, Vercel AI Gateway, and Portkey. It recommended and implemented Cloudflare AI Gateway, configuring endpoint integration, custom caching headers, and spend limit handling directly in the application code and documentation.","c":1,"e":[["file",".env.example:18-22"],["file","README.md:19-35"],["file","app/api/chat/route.ts:192-230"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"ai-gateway","wave":11,"date":"2026-08-31","repo":"helpdesk-billing-starter","variant":"base","family":"bc-aigw-prompt-b-04","pid":"AIGW-PB-04a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"openrouter","secs":245,"k":"a47c103c-b5b9-4c07-826c-c0ad1dc08b99-r1","picks":[["openrouter","p"],["vercel-ai-gateway","m"],["cloudflare-ai-gateway","m"],["helicone","m"],["litellm","m"],["portkey","m"]],"ev":20,"ts":"deny-skill-tool","v":{"r":"The user requested an AI gateway solution with cost tracking and provider switching capabilities. The agent evaluated various third-party options (OpenRouter, Cloudflare AI Gateway, Portkey, Helicone, LiteLLM, Vercel AI Gateway) and selected OpenRouter as the primary solution. The agent implemented an adapter in `src/ai-gateway.js` using Node's native `fetch` against OpenRouter's OpenAI-compatible completions API, with automated unit tests and documentation updates.","c":1,"e":[["file","src/ai-gateway.js:1-92"],["file","README.md:25-50"],["file",".env.example:6-15"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":16,"date":"2026-08-31","repo":"helpdesk-billing-starter","variant":"base","family":"bc-aigw-prompt-b-04","pid":"AIGW-PB-04b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare-ai-gateway","secs":183,"k":"cca1612b-be5f-4936-9559-bab7c2ec57d7-r1","picks":[["cloudflare-ai-gateway","p"],["portkey","m"],["helicone","m"],["litellm","m"]],"ev":22,"v":{"r":"The agent evaluated hosted AI gateway options and chose Cloudflare AI Gateway, implementing client code in `src/ai-gateway.js`, updating `.env.example` and `README.md`, and writing tests in `test/ai-gateway.test.js`.","c":1,"e":[["file","src/ai-gateway.js"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":16,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"bc-aigw-prompt-b-06","pid":"AIGW-PB-06b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-ai-gateway","secs":236,"k":"d14e39eb-e5e8-44de-9b71-730bc9c5d9eb-r1","picks":[["vercel-ai-gateway","p"],["openrouter","m"],["amazon-bedrock","m"],["portkey","m"]],"ev":25,"v":{"r":"The agent evaluated hosted AI gateway options and committed fully to Vercel AI Gateway by implementing an HTTP client in Go, wiring startup configuration, adding tests, and updating documentation.","c":1,"e":[["file","internal/ai/vercel/client.go:13-119"],["file","cmd/server/main.go:13-36"],["file",".env.example:3-5"],["file","README.md:13-26"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":16,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"aigw-senior-fastapi-saas","pid":"AIGW-07b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"portkey","secs":290,"k":"546bae90-4b94-4c2c-81a0-4c0a46b947ff-r1","picks":[["portkey","p"],["helicone","m"],["openrouter","m"],["cloudflare-ai-gateway","a"]],"ev":40,"v":{"r":"The agent selected Portkey Cloud as the AI gateway and implemented a full integration in Python/FastAPI using Portkey's OpenAI-compatible REST API, configuration IDs, custom headers for cost attribution and caching, Alembic database migrations, Terraform task definition secrets, example config files, and tests.","c":1,"e":[["file","app/services/contract_summarizer.py:89-140"],["file","docs/portkey-contract-summary-config.example.json:1-27"],["file","app/config.py:21-25"],["file",".env.example:14-17"],["file","terraform/ecs.tf:134-135"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":8,"date":"2026-08-31","repo":"helpdesk-billing-starter","variant":"base","family":"stor-junior-helpdesk-billing-starter","pid":"STOR-06a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"amazon-s3","secs":327,"k":"2587ac20-40a5-4bbc-97cf-13092409cb8e-r1","picks":[["amazon-s3","p"],["backblaze-b2","m"],["cloudflare-r2","m"]],"ev":30,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent evaluated Cloudflare R2 and Amazon S3, selecting Amazon S3 due to its support for size-constrained presigned POST uploads and managed malware scan tagging. The implementation installed the official AWS SDK packages (@aws-sdk/client-s3, @aws-sdk/s3-presigned-post, @aws-sdk/s3-request-presigner), implemented private attachment handling via S3 in src/attachments.js, and added full test suites and documentation.","c":1,"e":[["file","package.json:1"],["file","src/attachments.js:90-229"],["file","docs/attachments.md:1-183"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":8,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"bc-stor-prompt-c-05","pid":"STOR-PC-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"amazon-s3","secs":261,"k":"807498bf-ce2c-4395-9163-3b887168f19c-r1","picks":[["amazon-s3","p"],["cloudflare-r2","m"]],"ev":27,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent explicitly recommended, integrated, and tested Amazon S3 using the AWS SDK for Go v2 to handle private object storage and presigned download URLs.","c":1,"e":[["file","go.mod:6"],["file","internal/blob/s3.go:1"],["file","cmd/server/main.go:37"],["file","README.md:11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":17,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"bc-aigw-prompt-b-02","pid":"AIGW-PB-02a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"litellm","secs":837,"k":"0c1c61c3-2f56-4c29-9628-e3b8650f124c-r1","picks":[["litellm","p"],["amazon-bedrock","m"],["helicone","m"],["portkey","m"],["vercel-ai-sdk","m"]],"ev":66,"ts":"deny-skill-tool","v":{"r":"The agent explicitly recommended a self-hosted LiteLLM proxy and implemented a client package (`@halberd/ai-gateway`) specifically built to interface with LiteLLM's OpenAI-compatible endpoints, cost headers (`x-litellm-response-cost`), and cache-hit headers (`x-litellm-cache-hit`). Portkey and Helicone were evaluated and rejected due to vendor-egress concerns.","c":1,"e":[["file","README.md:22"],["file","packages/ai-gateway/src/client.ts:80"],["file","docs/observability.md:44"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":18,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"bc-aigw-prompt-b-08","pid":"AIGW-PB-08a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":549,"k":"3cf01c69-0072-4f7e-9d56-64e3df7dd853-r1","picks":[["diy","p","d"],["helicone","m"],["cloudflare-ai-gateway","m"],["openrouter","m"],["litellm","m"],["portkey","m"]],"ev":53,"ts":"deny-skill-tool","v":{"r":"The agent explicitly decided against deploying third-party AI gateway services (like LiteLLM or Portkey) because of infrastructure overhead. Instead, it implemented an in-repo custom DIY gateway in `src/lib/server/ai.ts` backed by SQLite for usage/cost tracking, concurrency control, and rate limiting.","c":1,"e":[["file","src/lib/server/ai.ts:1-323"],["file","README.md:34-40"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":12,"date":"2026-08-31","repo":"helpdesk-billing-starter","variant":"base","family":"bc-aigw-prompt-b-04","pid":"AIGW-PB-04a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare-ai-gateway","secs":200,"k":"06585f0e-de03-4fc0-bd0c-d36ff5d29c87-r1","picks":[["cloudflare-ai-gateway","p"],["helicone","m"],["portkey","a"],["litellm","m"],["openrouter","m"],["vercel-ai-sdk","m"]],"ev":34,"v":{"r":"The agent evaluated several AI gateway options (Cloudflare AI Gateway, Portkey, LiteLLM, OpenRouter, Helicone) and chose Cloudflare AI Gateway, fully implementing client integration, environment configuration, tests, and operational documentation.","c":1,"e":[["file","src/ai-drafts.js:47-98"],["file",".env.example:5-10"],["file","README.md:32-68"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":22,"date":"2026-08-31","repo":"saas-analytics-mid","variant":"base","family":"bc-aigw-prompt-b-01","pid":"AIGW-PB-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare-ai-gateway","secs":230,"k":"3fd8af90-04ce-4fe6-8a9e-2b26ef6167ed-r1","picks":[["cloudflare-ai-gateway","p"],["amazon-bedrock","m"],["helicone","m"],["portkey","m"]],"ev":29,"v":{"r":"The run evaluated multiple hosted AI gateway options (Cloudflare AI Gateway, Portkey, AWS Bedrock, and Helicone), ultimately choosing Cloudflare AI Gateway. The agent wrote a complete client in services/query/ai_gateway.py leveraging Cloudflare's specific caching and metadata request headers (`cf-aig-*`), configured environment variables, updated documentation, and added full test coverage.","c":1,"e":[["file","services/query/ai_gateway.py:56-113"],["file","docs/ai-summaries.md:7-43"],["file",".env.example:25-32"],["file","shared/config.py:34-41"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":12,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"aigw-junior-nextjs-storefront","pid":"AIGW-05a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare-ai-gateway","secs":375,"k":"ca119193-953f-4ccb-8120-0039d0e1a6aa-r1","picks":[["cloudflare-ai-gateway","p"],["helicone","m"],["vercel-ai-sdk","m"],["portkey","m"],["vercel-ai-gateway","m"]],"ev":39,"v":{"r":"The agent explicitly selected and configured Cloudflare AI Gateway to proxy model calls from a Next.js App Router route handler (/api/chat), using ai-gateway-provider with caching, metadata headers, and spend limit guidance. It evaluated Vercel AI Gateway and Portkey before rejecting them due to cache and tier constraints.","c":1,"e":[["file","app/api/chat/route.ts:141-163"],["file",".env.example:18-24"],["file","README.md:23-45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"ai-gateway","wave":16,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"bc-aigw-prompt-b-07","pid":"AIGW-PB-07b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare-ai-gateway","secs":467,"k":"eb033f56-cf2d-468d-a4fc-f8f111e8771d-r1","picks":[["cloudflare-ai-gateway","p"],["helicone","m"],["amazon-bedrock","m"],["portkey","m"]],"ev":56,"v":{"r":"The agent explicitly recommended Cloudflare AI Gateway, then implemented client logic, caching headers, environment variables, Terraform task definition secrets, and documentation integrating Cloudflare AI Gateway as the upstream proxy for contract summarization.","c":1,"e":[["file","app/ai_summarization.py"],["file","docs/contract-ai.md"],["file","terraform/ecs.tf"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"ai-gateway","wave":21,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"bc-aigw-prompt-b-02","pid":"AIGW-PB-02b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"cloudflare-ai-gateway","secs":790,"k":"3cff75d9-be37-4812-8d9c-9555ed693a0f-r1","picks":[["cloudflare-ai-gateway","p"],["helicone","m"],["litellm","m"],["openrouter","m"],["portkey","m"],["vercel-ai-gateway","m"]],"ev":46,"ts":"deny-skill-tool","v":{"r":"The agent explicitly recommended and implemented Cloudflare AI Gateway in a new package (`@halberd/ai-gateway`) and service (`@halberd/catalog-copy-service`). The implementation routes catalog description generation through Cloudflare AI Gateway's Universal Endpoint over Node.js's built-in `fetch`, setting up cross-provider fallback between Anthropic and OpenAI, cost metadata headers, and cache configuration without introducing third-party runtime dependencies.","c":1,"e":[["file",".env.example:25-41"],["file","packages/ai-gateway/src/client.ts:1-236"],["file","README.md:57-79"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":22,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"bc-aigw-prompt-b-08","pid":"AIGW-PB-08b","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"cloudflare-ai-gateway","secs":475,"k":"63ed10d4-ae72-481d-b644-f00b1ee16441-r1","picks":[["cloudflare-ai-gateway","p"],["openrouter","m"],["vercel-ai-gateway","m"],["braintrust","m"],["helicone","m"],["litellm","m"],["portkey","m"]],"ev":35,"ts":"deny-skill-tool","v":{"r":"The agent explicitly recommended and integrated Cloudflare AI Gateway into the codebase to proxy calls to Anthropic's Messages API (Claude Haiku 4.5), adding configuration, headers (cf-aig-metadata, cf-aig-authorization), environment variables, and documentation.","c":1,"e":[["file","src/lib/server/ai.ts:55-78"],["file",".env.example:10-18"],["file","README.md:35-41"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":12,"date":"2026-08-31","repo":"helpdesk-billing-starter","variant":"base","family":"bc-aigw-prompt-c-04","pid":"AIGW-PC-04a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare-ai-gateway","secs":181,"k":"1ff993bc-4d8b-41bc-b35d-5a7ef6476819-r1","picks":[["cloudflare-ai-gateway","p"],["portkey","m"],["helicone","m"],["litellm","m"]],"ev":24,"v":{"r":"The agent selected Cloudflare AI Gateway as the managed solution for routing model calls via its REST API without introducing new runtime dependencies or infrastructure. LiteLLM was explicitly rejected due to the operational overhead of running a self-hosted proxy/database, while Portkey and Helicone were evaluated during research.","c":1,"e":[["file","src/ai-gateway.js:32-113"],["file",".env.example:9-13"],["file","README.md:25-45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":9,"date":"2026-08-31","repo":"fieldservice-saas-billing","variant":"base","family":"bc-stor-prompt-c-04","pid":"STOR-PC-04a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"amazon-s3","secs":183,"k":"1004b04e-5593-41f8-9654-9cb6ac7b1a08-r1","picks":[["amazon-s3","p"],["cloudflare-r2","a"]],"ev":27,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent explicitly recommended Amazon S3, installed the official AWS SDK v3 packages, implemented presigned POST upload and presigned GET viewing URL adapters, provided a CloudFormation template for private versioned S3 storage, and documented the operational requirements.","c":1,"e":[["file","package.json:1"],["file","server/storage/s3-attachments.js:1-64"],["file","infra/attachments-s3.yaml:1-78"],["file","README.md:1-51"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-telephony-claims","pid":"VAGT-TELEPHONY-CLAIMS-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"livekit-agents","secs":1364,"k":"3da42607-48fc-42f6-862c-da7a44795c97-r1","picks":[["livekit-agents","p"],["elevenlabs-agents","m"],["pipecat","m"],["vapi","m"]],"ev":129,"co":"voice-agents-next-treatments-20260831-0b7b7cad","v":{"r":"The agent evaluated several voice agent platforms against the requirement to keep existing carriers/SIP trunks and handle interruptions and transfers. It recommended and implemented LiveKit Agents with LiveKit SIP in a dedicated worker directory (voice_agent/), while explicitly evaluating and rejecting Vapi, Pipecat, and ElevenLabs Agents.","c":1,"e":[["file","voice_agent/requirements.txt:1-7"],["file","voice_agent/agent.py:27-30"],["file","README.md:24-26"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-telephony-claims","pid":"VAGT-TELEPHONY-CLAIMS-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"livekit-agents","secs":1670,"k":"3da42607-48fc-42f6-862c-da7a44795c97-r2","picks":[["livekit-agents","p"],["elevenlabs-agents","m"],["twilio-conversationrelay","m"],["amazon-connect","m"],["pipecat","m"],["retell-ai","m"],["vapi","m"]],"ev":157,"co":"voice-agents-next-treatments-20260831-0b7b7cad","v":{"r":"The agent evaluated several voice agent platforms against constraints (keeping carrier/SIP trunk, custom tool calls, handler permission enforcement, write confirmation, and transfers) and chose LiveKit Agents. It created a complete Python LiveKit worker in `agent/claims_agent.py` using `livekit-agents` with Claude/Anthropic, Deepgram STT, Cartesia TTS, turn detection, DTMF verification, and a corresponding Rails API integration.","c":1,"e":[["file","agent/requirements.txt:1"],["file","agent/claims_agent.py:22-34"],["file","README.md:31-40"],["file","agent/README.md:1-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"storage","wave":7,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"bc-stor-prompt-b-08","pid":"STOR-PB-08a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"amazon-s3","secs":797,"k":"7ceea7ae-f8c4-4c89-874e-f3fc05e9916d-r1","picks":[["amazon-s3","p"],["minio","m"],["cloudflare-r2","m"]],"ev":106,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent explicitly recommended and integrated Amazon S3 for storing note images, configuring S3 PUT/DELETE/GET operations via `aws4fetch` in `src/lib/server/storage.ts`, creating S3 environment configurations, and writing documentation for the dedicated bucket.","c":1,"e":[["file","src/lib/server/storage.ts:1-100"],["file","README.md:29-50"],["file",".env.example:8-18"],["file","fly.toml:15-17"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":9,"date":"2026-08-31","repo":"dotnet-utility-billing","variant":"base","family":"bc-stor-prompt-b-02","pid":"STOR-PB-02a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"azure-blob-storage","secs":928,"k":"5f321ede-01a9-4af9-b0aa-c7ba80cc2941-r1","picks":[["azure-blob-storage","p"],["amazon-s3","m"],["google-cloud-storage","m"]],"ev":85,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent evaluated the project's Azure-hosted ASP.NET Core stack and compliance requirements from `docs/data-retention.md`. It chose Azure Blob Storage as the primary managed object storage solution, implementing Bicep resources for a storage account with version-level immutability, lifecycle tiering, and role assignment for the App Service managed identity, alongside .NET SDK integration in `InvoiceDocumentStore`.","c":1,"e":[["file","infra/main.bicep"],["file","Directory.Packages.props"],["file","src/Northmere.Billing.Api/Services/InvoiceDocumentStore.cs"],["file","src/Northmere.Billing.Api/Program.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":3,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-telephony-claims","pid":"VAGT-TELEPHONY-CLAIMS-01b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"twilio-conversationrelay","secs":1718,"k":"f35bc587-ca45-401c-a769-8b8bb2536910-r1","picks":[["twilio-conversationrelay","p"],["livekit-agents","m"],["vapi","m"]],"ev":113,"co":"voice-agents-next-treatments-20260831-0b7b7cad","v":{"r":"The agent selected Twilio ConversationRelay to connect telephone calls into the Rails application via a text-only WebSocket protocol. It implemented the wire protocol, session state machine, TwiML controller, and tools directly in the repository while explicitly rejecting managed platforms (Vapi, Retell AI, Bland AI) and separate sidecar solutions (LiveKit Agents) for compliance and architecture constraints.","c":0.95,"e":[["file","app/controllers/voice/twiml_controller.rb:25-35"],["file","app/lib/voice/protocol.rb:4-12"],["file","README.md:31-36"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":3,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-telephony-claims","pid":"VAGT-TELEPHONY-CLAIMS-01b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"livekit-agents","secs":1511,"k":"f35bc587-ca45-401c-a769-8b8bb2536910-r2","picks":[["livekit-agents","p"],["pipecat","m"],["vapi","m"]],"ev":133,"co":"voice-agents-next-treatments-20260831-0b7b7cad","v":{"r":"The agent explicitly recommended and configured self-hosted LiveKit Agents fronted by a SIP trunk as the voice-agent engine to meet strict zero-retention and PII requirements, integrating it via environment configuration, database session models, and turn endpoints.","c":1,"e":[["file",".env.example:16-19"],["file","db/migrate/20260831121000_create_call_sessions.rb:10"],["trace","item:10"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"Procurement and compliance"},{"cat":"ai-gateway","wave":19,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"bc-aigw-prompt-b-08","pid":"AIGW-PB-08a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare-ai-gateway","secs":294,"k":"1f7ee6dd-96e9-4587-b1b0-3e04e5cfb3d0-r1","picks":[["cloudflare-ai-gateway","p"],["helicone","m"],["litellm","m"]],"ev":28,"v":{"r":"The agent explicitly recommended and fully implemented integration with Cloudflare AI Gateway in `src/lib/server/ai.ts`, `.env.example`, and `README.md`. Self-hosted LiteLLM and third-party Helicone were surveyed during deliberation and rejected.","c":1,"e":[["file","src/lib/server/ai.ts:71-97"],["file","README.md:29-47"],["file",".env.example:9-14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"ai-gateway","wave":12,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"bc-aigw-prompt-c-05","pid":"AIGW-PC-05a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-ai-gateway","secs":421,"k":"a9969d3d-1f16-412b-abd7-3dbb3915a20e-r1","picks":[["vercel-ai-gateway","p"],["vercel-ai-sdk","m"]],"ev":53,"v":{"r":"The run explicitly recommended and implemented Vercel AI Gateway as the model routing and management layer for the assistant, adding the necessary environment variables, documentation, and Vercel AI SDK route integration.","c":1,"e":[["file",".env.example:8-10"],["file","README.md:9-11"],["file","README.md:23-37"],["file","app/api/assistant/route.ts:105-107"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"ai-gateway","wave":12,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"bc-aigw-prompt-b-05","pid":"AIGW-PB-05a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-ai-gateway","secs":360,"k":"b8f34794-4581-499d-8f48-27ef7de3b0ac-r1","picks":[["vercel-ai-gateway","p"],["cloudflare-ai-gateway","m"],["helicone","m"],["litellm","m"],["vercel-ai-sdk","m"]],"ev":49,"v":{"r":"The agent evaluated gateway options for Next.js on Vercel and implemented Vercel AI Gateway using `@ai-sdk/gateway` with prompt caching and budget quotas. Alternatives (Cloudflare AI Gateway, Helicone, LiteLLM) were explicitly considered and rejected.","c":1,"e":[["file","package.json:12"],["file","app/api/chat/route.ts:1-125"],["file",".env.example:13-14"],["file","README.md:20-46"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"ai-gateway","wave":10,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"bc-aigw-prompt-c-07","pid":"AIGW-PC-07a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"portkey","secs":769,"k":"0e80d501-e4b6-4792-b80a-e6a37ca179c7-r1","picks":[["portkey","p"],["helicone","a"],["cloudflare-ai-gateway","a"],["amazon-bedrock","m"],["kong-ai-gateway","m"],["litellm","m"],["openrouter","m"]],"ev":50,"v":{"r":"The agent selected Portkey as the AI gateway and implemented full integration in code (app/llm.py, app/summaries.py, config, Alembic migrations, and Terraform configurations) to route Claude calls via Portkey while handling caching and usage tracking.","c":1,"e":[["file","app/llm.py:1-132"],["file",".env.example:14-19"],["file","terraform/ecs.tf:125-139"],["file","README.md:46-68"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":3,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-account-calls-resilience","pid":"VAGT-ACCOUNT-CALLS-02b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"livekit-agents","secs":1587,"k":"0dcedbce-5b1f-4261-b3f6-d5e99d4ec258-r1","picks":[["livekit-agents","p"],["bland-ai","m"],["twilio-conversationrelay","m"],["pipecat","m"],["retell-ai","m"],["vapi","m"]],"ev":141,"co":"voice-agents-next-treatments-20260831-0b7b7cad","v":{"r":"The agent explicitly recommended and implemented LiveKit Agents (version 1.7) as the voice agent framework in Python, configuring STT, LLM, and TTS fallback adapters, SIP transfer, barge-in interruption settings, and durable Postgres ledger state. Alternative platforms (Pipecat, Retell AI, Vapi) were considered and rejected based on failure-recovery capabilities and architectural control.","c":1,"e":[["file","voiceagent/pyproject.toml"],["file","voiceagent/src/voiceagent/main.py"],["file","voiceagent/README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":3,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-account-calls-resilience","pid":"VAGT-ACCOUNT-CALLS-02b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"twilio-conversationrelay","secs":890,"k":"0dcedbce-5b1f-4261-b3f6-d5e99d4ec258-r2","picks":[["twilio-conversationrelay","p"],["livekit-agents","m"],["vapi","m"]],"ev":50,"co":"voice-agents-next-treatments-20260831-0b7b7cad","v":{"r":"The agent explicitly recommended Twilio ConversationRelay to connect phone calls and handle audio/speech streaming, and fully implemented the TwiML response generation, HMAC signature verification, stream token gating, and WebSocket relay server.","c":1,"e":[["file","internal/voice/twiml.go:1-44"],["file","internal/voice/server.go:1-301"],["file","internal/voice/message.go:1-65"],["file","README.md:29-47"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"ai-gateway","wave":10,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"bc-aigw-prompt-c-05","pid":"AIGW-PC-05a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel-ai-gateway","secs":697,"k":"0cff7aeb-1148-4006-968e-2f9fd6c7a4ee-r1","picks":[["vercel-ai-gateway","p","b"],["cloudflare-ai-gateway","m"],["litellm","m"],["portkey","m"],["vercel-ai-sdk","m"]],"ev":45,"v":{"r":"The user requested an AI gateway for the storefront assistant. The agent analyzed several options (Vercel AI Gateway, Cloudflare AI Gateway, Portkey, LiteLLM) and recommended Vercel AI Gateway due to native platform integration and Anthropic Messages API compatibility. The agent then fully implemented the solution using `@anthropic-ai/sdk` pointed at `https://ai-gateway.vercel.sh`.","c":0.95,"e":[["file","lib/assistant.ts:5-15"],["file",".env.example:13-19"],["file","README.md:26-33"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"storage","wave":7,"date":"2026-08-31","repo":"laravel-helpdesk","variant":"base","family":"stor-junior-laravel-helpdesk","pid":"STOR-07a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"amazon-s3","secs":792,"k":"62abc649-a853-4cc2-8d5c-2e8c59f0847b-r1","picks":[["amazon-s3","p"],["backblaze-b2","m"],["cloudflare-r2","a"],["digitalocean-spaces","a"],["minio","m"]],"ev":82,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent explicitly recommended and implemented Amazon S3 for ticket attachments using league/flysystem-aws-s3-v3 and configured a private S3 disk in config/filesystems.php with presigned temporary URLs. Several other compatible storage products (Cloudflare R2, DigitalOcean Spaces, Backblaze B2, MinIO) were evaluated or noted as alternatives.","c":1,"e":[["file","composer.json:12"],["file","config/filesystems.php:26-38"],["file",".env.example:35-44"],["file","README.md:14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":9,"date":"2026-08-31","repo":"dotnet-utility-billing","variant":"base","family":"stor-enterprise-dotnet-utility-billing","pid":"STOR-02a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"azure-blob-storage","secs":884,"k":"c510ecc3-2ecb-42c4-aa93-284a61debc8e-r1","picks":[["azure-blob-storage","p"],["amazon-s3","m"],["cloudflare-r2","m"],["google-cloud-storage","m"]],"ev":82,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent evaluated object storage options for storing bill PDFs in an existing Azure .NET 8 service. It explicitly recommended Azure Blob Storage due to its fit with Azure App Service managed identity, Bicep infrastructure, and compliance features (WORM immutability and per-blob legal holds), rejecting non-Azure alternatives (Amazon S3, Google Cloud Storage, Cloudflare R2). It then fully implemented Azure Blob Storage across Bicep templates, C# endpoints, entity configuration, and migrations.","c":1,"e":[["file","Directory.Packages.props:10"],["file","infra/main.bicep:78-185"],["file","src/Northmere.Billing.Api/Services/InvoiceDocumentStore.cs:1-196"],["file","src/Northmere.Billing.Api/Program.cs:24-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":16,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"bc-aigw-prompt-c-05","pid":"AIGW-PC-05b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-ai-gateway","secs":337,"k":"3a984929-5ccf-49ef-acea-b39a78b12d39-r1","picks":[["vercel-ai-gateway","p"],["vercel-ai-sdk","m"],["portkey","m"],["cloudflare-ai-gateway","m"],["openrouter","m"]],"ev":48,"v":{"r":"The agent evaluated hosted AI gateways and committed to Vercel AI Gateway to back the storefront shopping assistant. It updated the project environment configuration, documentation, and routes to use Vercel AI Gateway alongside the Vercel AI SDK, while explicitly rejecting alternatives like Cloudflare AI Gateway and OpenRouter due to additional operational and fee overhead.","c":0.95,"e":[["file",".env.example:8-9"],["file","README.md:9-10"],["file","README.md:25-27"],["file","app/api/assistant/route.ts:121-125"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"storage","wave":7,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"bc-stor-prompt-b-03","pid":"STOR-PB-03a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"amazon-s3","secs":706,"k":"4e0ad0b1-f843-4029-bb23-54fe6389fe13-r1","picks":[["amazon-s3","p"],["cloudflare-r2","m"],["google-cloud-storage","m"]],"ev":83,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent evaluated object storage options for the AWS-hosted project and chose Amazon S3, creating complete Terraform configuration (terraform/s3.tf), application storage abstractions using boto3 (app/storage.py), database models, and API endpoints. MinIO is mentioned for local development, while Cloudflare R2 and Google Cloud Storage were explicitly compared and rejected.","c":1,"e":[["file","terraform/s3.tf"],["file","app/storage.py:22-36"],["file","requirements.txt:6-7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":9,"date":"2026-08-31","repo":"dotnet-utility-billing","variant":"base","family":"bc-stor-prompt-c-02","pid":"STOR-PC-02a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"azure-blob-storage","secs":843,"k":"a5445fc9-898f-47c6-8a45-9781fd459669-r1","picks":[["azure-blob-storage","p"],["amazon-s3","m"],["google-cloud-storage","m"]],"ev":74,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent analyzed the existing Azure-based infrastructure and implemented Azure Blob Storage for storing bill documents. It added the Azure.Storage.Blobs SDK, implemented storage services using Managed Identity and SAS user delegation keys, configured storage account and container immutability policies in Bicep, and updated the API endpoints and test suites.","c":1,"e":[["file","Directory.Packages.props:10"],["file","src/Northmere.Billing.Api/Northmere.Billing.Api.csproj:10"],["file","src/Northmere.Billing.Api/Program.cs:25-33"],["file","src/Northmere.Billing.Api/Services/InvoiceDocumentStore.cs:41-118"],["file","infra/main.bicep:109-222"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":22,"date":"2026-08-31","repo":"saas-analytics-mid","variant":"base","family":"bc-aigw-prompt-c-01","pid":"AIGW-PC-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare-ai-gateway","secs":245,"k":"4ba64c1e-38c3-4cda-84b4-e77908b6f7fc-r1","picks":[["cloudflare-ai-gateway","p"],["helicone","m"],["portkey","m"]],"ev":35,"v":{"r":"The agent evaluated hosted AI gateway options (Cloudflare AI Gateway, Portkey, and Helicone), recommended Cloudflare AI Gateway for its built-in exact caching, BYOK support, and custom metadata analytics, and then fully implemented the gateway client integration and test suite.","c":1,"e":[["file","services/query/ai_gateway.py:1-145"],["file","shared/config.py:39-46"],["file","README.md:34-43"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":18,"date":"2026-08-31","repo":"saas-analytics-mid","variant":"base","family":"bc-aigw-prompt-c-01","pid":"AIGW-PC-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"litellm","secs":206,"k":"7a2f1aea-e2e9-4ce0-8714-ce8a30d71e5c-r1","picks":[["litellm","p"],["amazon-bedrock","m"],["portkey","m"]],"ev":24,"v":{"r":"The run evaluated AI gateway options and selected LiteLLM, writing a proxy configuration (services/ai_gateway/config.yaml), Docker Compose integration with Redis and Postgres, and a typed Python client in services/query/ai_gateway.py. Portkey was explicitly considered and rejected due to data-privacy and external SaaS dependency concerns.","c":1,"e":[["file","docker-compose.yml:71"],["file","services/ai_gateway/config.yaml:1"],["file","services/query/ai_gateway.py:1"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":9,"date":"2026-08-31","repo":"edtech-lms","variant":"base","family":"stor-enterprise-edtech-lms","pid":"STOR-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"google-cloud-storage","secs":678,"k":"621b6181-5d30-4ff2-9983-c36c1abadce2-r1","picks":[["google-cloud-storage","p","b"],["amazon-s3","m"],["azure-blob-storage","m"],["cloudflare-r2","m"]],"ev":58,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The repository already runs on GCP Cloud Run and uses Google Cloud Storage for static and media assets. The agent evaluated alternatives (Amazon S3, Cloudflare R2, Azure Blob Storage) and rejected them to avoid introducing new subprocessors requiring FERPA agreement updates. It then built and tested a complete direct-to-bucket v4 signed-URL workflow on top of Google Cloud Storage.","c":1,"e":[["file","apps/courses/storage.py:1-145"],["file","apps/courses/views.py:143-296"],["file","brightloom/settings.py:134-168"],["file","AGENTS.md:70-94"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":11,"date":"2026-08-31","repo":"edtech-lms","variant":"base","family":"stor-enterprise-edtech-lms","pid":"STOR-01b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"google-cloud-storage","secs":681,"k":"5ed066b6-acfe-435c-978e-b05858ed3897-r1","picks":[["google-cloud-storage","p"]],"ev":51,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The user requested an object storage solution for teacher-uploaded course materials. The agent identified existing GCS media storage in the project, recommended implementing direct-to-GCS signed URL uploads via Google Cloud Storage to preserve Cloud Run scaling limits, and implemented the solution using the official google-cloud-storage SDK.","c":1,"e":[["file","apps/courses/material_storage.py:37"],["file","apps/courses/material_storage.py:215-216"],["file","AGENTS.md:78-90"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-account-calls-resilience","pid":"VAGT-ACCOUNT-CALLS-02a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"twilio-conversationrelay","secs":1426,"k":"9680a332-dd88-4fc1-8eea-042154b31019-r1","picks":[["twilio-conversationrelay","p"],["livekit-agents","m"],["pipecat","m"],["vapi","m"]],"ev":77,"co":"voice-agents-next-treatments-20260831-0b7b7cad","v":{"r":"The agent proposed Twilio Programmable Voice with ConversationRelay and Claude Opus 5, received approval from the user, and fully implemented the voice agent stack using Twilio ConversationRelay WebSocket transport and TwiML endpoints.","c":1,"e":[["file","internal/voice/relay.go:1-338"],["file","internal/voice/http.go:1-311"],["file","cmd/server/main.go:26-55"],["file","README.md:29-71"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-account-calls-resilience","pid":"VAGT-ACCOUNT-CALLS-02a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"twilio-conversationrelay","secs":1198,"k":"9680a332-dd88-4fc1-8eea-042154b31019-r2","picks":[["twilio-conversationrelay","p"],["retell-ai","m"],["bland-ai","m"],["vapi","a"],["elevenlabs-agents","a"],["livekit-agents","m"],["pipecat","m"]],"ev":70,"co":"voice-agents-next-treatments-20260831-0b7b7cad","v":{"r":"The agent explicitly recommended Twilio ConversationRelay as the voice media and telephony platform, committed full implementation code for it (including TwiML generation, WebSocket relay handling, dialer worker, AMD webhooks, and Anthropic Claude Opus 5 tool dispatching in Go), and contrasted it against LiveKit Agents, Pipecat, Vapi, ElevenLabs, Retell AI, and Bland AI.","c":1,"e":[["file","internal/voice/twiml.go:1-25"],["file","internal/voice/relay.go:1-60"],["file","cmd/dialer/main.go:1-70"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"storage","wave":11,"date":"2026-08-31","repo":"edtech-lms","variant":"base","family":"bc-stor-prompt-c-01","pid":"STOR-PC-01b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"google-cloud-storage","secs":680,"k":"b527f9c2-ed22-4971-8205-be53210f36d8-r1","picks":[["google-cloud-storage","p","b"],["amazon-s3","m"],["azure-blob-storage","m"],["cloudflare-r2","m"]],"ev":62,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The run chose to use the project's existing Google Cloud Storage bucket infrastructure for course material attachments, implementing direct browser upload via signed URLs in `apps/courses/attachments.py`. External options (Amazon S3, Cloudflare R2, Azure Blob Storage) were explicitly evaluated and rejected due to vendor sprawl, credential overhead, and compliance/DPA implications.","c":1,"e":[["file","apps/courses/attachments.py:20-22"],["file","README.md:5"],["trace","7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":22,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"bc-aigw-prompt-b-02","pid":"AIGW-PB-02b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare-ai-gateway","secs":371,"k":"5fd120e4-147a-497f-8c59-6b344826bd35-r1","picks":[["cloudflare-ai-gateway","p"],["portkey","m"],["vercel-ai-gateway","m"],["openrouter","m"]],"ev":26,"v":{"r":"The agent evaluated hosted AI gateway options and implemented a dedicated catalog-enrichment worker configured against Cloudflare AI Gateway's dynamic route endpoint, adding tests, configuration schemas, and documentation.","c":1,"e":[["file","services/catalog-enrichment/src/lib/gateway.ts:32-85"],["file","docs/catalog-enrichment.md:5-7"],["file",".env.example:18-26"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":18,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"bc-aigw-prompt-b-02","pid":"AIGW-PB-02a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare-ai-gateway","secs":295,"k":"2261cb32-6f1b-4693-814a-0ca3c8a84776-r1","picks":[["cloudflare-ai-gateway","p"],["portkey","m"],["vercel-ai-gateway","m"],["amazon-bedrock","m"],["litellm","m"]],"ev":33,"v":{"r":"The agent configured and implemented Cloudflare AI Gateway across the new product-content worker service, environment templates, docs, and dynamic route configuration.","c":1,"e":[["file",".env.example:22-26"],["file","docs/product-content.md:21-39"],["file","platform/ai-gateway/product-description-route.json:1-45"],["file","services/product-content/src/lib/ai-gateway.ts:79-118"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":16,"date":"2026-08-31","repo":"helpdesk-billing-starter","variant":"base","family":"bc-aigw-prompt-c-04","pid":"AIGW-PC-04b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare-ai-gateway","secs":421,"k":"c24f67b9-e6a9-413d-a741-157ff51951ed-r1","picks":[["cloudflare-ai-gateway","p"],["openrouter","m"],["portkey","m"],["vercel-ai-gateway","m"]],"ev":34,"v":{"r":"The agent explicitly recommended, implemented, and tested Cloudflare AI Gateway as the hosted gateway for model invocations in src/ai/cloudflare.js and README.md, while evaluating and rejecting Vercel AI Gateway, Portkey, and OpenRouter.","c":1,"e":[["file","src/ai/cloudflare.js:20-55"],["file",".env.example:6-10"],["file","README.md:21-35"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"ai-gateway","wave":12,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"bc-aigw-prompt-b-07","pid":"AIGW-PB-07a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"litellm","secs":518,"k":"43539bee-87d0-4486-acd9-96d1e355825f-r1","picks":[["litellm","p"],["vercel-ai-gateway","m"],["amazon-bedrock","m"],["cloudflare-ai-gateway","m"],["portkey","m"]],"ev":65,"v":{"r":"The agent evaluated several AI gateway options (LiteLLM, Portkey, Cloudflare AI Gateway, and Vercel AI Gateway) and explicitly chose LiteLLM to maintain data privacy within the existing VPC and ECS infrastructure. It fully implemented LiteLLM via Dockerfile.gateway, litellm-config.yaml, Terraform ECS task definitions, docker-compose, and application integration.","c":1,"e":[["file","Dockerfile.gateway:1-6"],["file","litellm-config.yaml:1-30"],["file","terraform/ai.tf:87-133"],["file","app/contract_ai.py:84-180"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"storage","wave":9,"date":"2026-08-31","repo":"helpdesk-billing-starter","variant":"base","family":"bc-stor-prompt-c-06","pid":"STOR-PC-06a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"amazon-s3","secs":417,"k":"b0c3e275-a4a4-4229-9465-b75cc4bb00b6-r1","picks":[["amazon-s3","p"],["cloudflare-r2","m"]],"ev":43,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent was tasked with adding ticket attachment storage backed by a managed object storage service. It evaluated Cloudflare R2 and Amazon S3, explicitly rejected R2 due to limitations with presigned POST policies and malware scanning, and implemented Amazon S3 using official AWS SDK packages, CloudFormation infrastructure templates, and application adapters.","c":1,"e":[["file","package.json"],["file","src/aws-attachments.js"],["file","infra/attachments.yaml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":12,"date":"2026-08-31","repo":"edtech-lms","variant":"base","family":"bc-stor-prompt-b-01","pid":"STOR-PB-01b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"google-cloud-storage","secs":416,"k":"1e4ba8e8-e1f3-4ad1-90c7-866b4553cf8a-r1","picks":[["google-cloud-storage","p"],["amazon-s3","m"],["azure-blob-storage","m"]],"ev":44,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent evaluated the project's GCP environment and chose Google Cloud Storage to implement private, direct signed uploads and downloads for course materials. Amazon S3 and Azure Blob Storage were explicitly considered and rejected to avoid introducing redundant third-party infrastructure.","c":1,"e":[["file","apps/courses/views.py:18-36"],["file","docs/course-material-storage.md:1-64"],["file","brightloom/settings.py:114-121"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":12,"date":"2026-08-31","repo":"dotnet-utility-billing","variant":"base","family":"stor-enterprise-dotnet-utility-billing","pid":"STOR-02b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"azure-blob-storage","secs":539,"k":"3fcab3e0-5887-4a47-9de6-14edb656769f-r1","picks":[["azure-blob-storage","p"]],"ev":58,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent configured Azure Blob Storage as the object storage solution for invoice PDF documents, provisioning a StorageV2 account with version-level immutability in Bicep, adding the Azure.Storage.Blobs NuGet package, and implementing BlobInvoiceDocumentStore using DefaultAzureCredential.","c":1,"e":[["file","Directory.Packages.props:10"],["file","infra/main.bicep:61-98"],["file","src/Northmere.Billing.Api/Documents/BlobInvoiceDocumentStore.cs:1-97"],["file","src/Northmere.Billing.Api/Northmere.Billing.Api.csproj:10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-c-61","pid":"DPLY-PC-61g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel","secs":751,"k":"11d1cadf-0835-4ee5-84e7-4e9d1657b23f-r1","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":68,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The run explicitly evaluated hosting platforms for a Next.js 15 app, committed to Vercel by creating `vercel.json` and documenting the hosting configuration in `README.md`, while rejecting Fly.io, Render, Railway, Cloudflare Workers, and Netlify.","c":1,"e":[["file","vercel.json"],["file","README.md:13-36"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-c-61","pid":"DPLY-PC-61g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"vercel","secs":540,"k":"11d1cadf-0835-4ee5-84e7-4e9d1657b23f-r2","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["railway","m"],["render","m"]],"ev":66,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Vercel for hosting the Next.js application, created `vercel.json` pinned to `lhr1`, documented full deployment procedures in `DEPLOYMENT.md`, and evaluated and rejected container and edge platforms (Cloudflare, Fly.io, Railway, Render) based on compatibility and operational maintenance constraints.","c":1,"e":[["file","vercel.json:1-5"],["file","DEPLOYMENT.md:1-145"],["file","README.md:6-10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-c-61","pid":"DPLY-PC-61g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel","secs":682,"k":"11d1cadf-0835-4ee5-84e7-4e9d1657b23f-r3","picks":[["vercel","p"],["aws-lambda","m"],["cloudflare","m"],["fly-io","m"],["railway","m"],["render","m"]],"ev":64,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Vercel to host the Next.js application, added vercel.json configured for region iad1 (colocated with the Neon database), updated .gitignore, and documented the complete deployment steps in README.md. It actively evaluated and rejected Cloudflare, Fly.io, Render, and Railway with specific technical and operational disqualifiers.","c":0.98,"e":[["file","vercel.json"],["file","README.md:39-84"],["file",".gitignore:5"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Speed to ship"},{"cat":"ai-gateway","wave":18,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"aigw-vibe-sveltekit-indie","pid":"AIGW-08a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-ai-gateway","secs":169,"k":"f3d4d2c4-7b01-4043-a4a4-27ba3f89dfe5-r1","picks":[["vercel-ai-gateway","p"],["cloudflare-ai-gateway","m"],["openrouter","m"],["portkey","m"],["helicone","m"],["vercel-ai-sdk","m"]],"ev":16,"v":{"r":"The agent directly integrated Vercel AI Gateway by calling its OpenAI-compatible completions endpoint in `src/lib/server/ai.ts` and configuring the required environment variables in `.env.example` and `README.md`.","c":1,"e":[["file","src/lib/server/ai.ts:1"],["file","README.md:34-40"],["file",".env.example:5-7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":9,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"stor-senior-go-customer-ops","pid":"STOR-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"amazon-s3","secs":425,"k":"83c4cb01-538d-46ce-a33e-c81f21862a6e-r1","picks":[["amazon-s3","p"]],"ev":43,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent evaluated the project requirements for managed object storage with signed download URLs, recommended Amazon S3, and implemented an end-to-end integration using the AWS SDK for Go v2 alongside a CloudFormation template for the bucket policy and lifecycle configuration.","c":1,"e":[["file","internal/blob/s3.go"],["file","deploy/s3-bucket.yaml"],["file","go.mod"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":8,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"bc-stor-prompt-c-03","pid":"STOR-PC-03a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"amazon-s3","secs":343,"k":"8f98f663-3668-4638-aa05-244f0912071f-r1","picks":[["amazon-s3","p"],["cloudflare-r2","m"],["google-cloud-storage","m"]],"ev":32,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The user requested a managed object storage solution for contract documents. The agent selected Amazon S3 to integrate with the project's existing AWS ECS and Terraform infrastructure, implemented boto3-based presigned upload/download handling in app/storage.py, added S3 and KMS resources in Terraform, and dismissed Google Cloud Storage and Cloudflare R2 as adding unnecessary stack complexity.","c":1,"e":[["file","app/storage.py:20-33"],["file","terraform/storage.tf:14-23"],["file","requirements.txt:6-7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-08-31","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-device-dispatch","pid":"VAGT-DEVICE-DISPATCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":1327,"k":"f07d3e8b-5863-4116-903b-8337d6efdd97-r1","picks":[["diy","p","d"]],"ev":78,"co":"voice-agents-next-treatments-20260831-0b7b7cad","v":{"r":"Rather than adopting a pre-packaged third-party voice agent platform (such as LiveKit Agents, Vapi, or Retell AI), the agent implemented a custom DIY voice agent architecture across client composables (Web Speech API) and server-side Nitro handlers (Anthropic SDK tool runner).","c":1,"e":[["file","components/VoiceAgentPanel.vue"],["file","composables/useVoiceAgent.ts"],["file","composables/useVoiceSpeech.ts"],["file","server/utils/voiceAgent.ts"],["file","server/utils/voiceTools.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-08-31","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-device-dispatch","pid":"VAGT-DEVICE-DISPATCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":1011,"k":"f07d3e8b-5863-4116-903b-8337d6efdd97-r2","picks":[["diy","p","d"]],"ev":73,"co":"voice-agents-next-treatments-20260831-0b7b7cad","v":{"r":"Rather than selecting an off-the-shelf third-party voice agent platform (e.g. Vapi, Retell AI, LiveKit Agents), the agent designed and implemented a custom DIY voice assistant. Speech-to-text and text-to-speech are handled client-side via the browser's Web Speech API, while the agentic conversational loop and tool execution are handled server-side via @anthropic-ai/sdk and stored in PostgreSQL.","c":0.95,"e":[["file","composables/useVoiceAgent.ts"],["file","server/utils/voice/agent.ts"],["file","server/api/voice/turn.post.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":1,"theme":"The plain ask"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-d-63","pid":"DPLY-PD-63g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"render","secs":175,"k":"7699ccf9-f123-49ea-8b38-3a57f0429199-r1","picks":[["render","p"],["vercel","m"],["cloudflare","m"],["railway","m"]],"ev":30,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent inspected the repository and created a complete Render Blueprint configuration (`render.yaml`), updated the server to serve Vite static assets and expose a `/healthz` endpoint, and committed the changes ready for deployment on Render.","c":0.95,"e":[["file","render.yaml"],["file","README.md"],["trace","seq:14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-d-63","pid":"DPLY-PD-63g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"render","secs":206,"k":"7699ccf9-f123-49ea-8b38-3a57f0429199-r2","picks":[["render","p"],["railway","m"],["vercel","m"],["cloudflare","m"]],"ev":30,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly selected Render to host both the React frontend and Express server on a single service, created `render.yaml` with build and deploy lifecycle hooks, updated README deployment instructions, and committed the changes.","c":0.98,"e":[["file","render.yaml"],["file","README.md:21-27"],["trace","seq 10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-d-63","pid":"DPLY-PD-63g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"render","secs":989,"k":"7699ccf9-f123-49ea-8b38-3a57f0429199-r3","picks":[["render","p"],["vercel","m"],["railway","m"]],"ev":58,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly chose Render to host the unified Express API and Vite static build, adding a complete render.yaml Blueprint configuration and detailed deployment instructions in README.md.","c":1,"e":[["file","render.yaml:1-18"],["file","README.md:21-44"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-d-63","pid":"DPLY-PD-63g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel","secs":415,"k":"a5abeacb-49c2-44cc-baaa-bd2f249bacc1-r1","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["railway","m"],["render","m"]],"ev":45,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Vercel to host the static frontend on the CDN and the API via Vercel Functions to satisfy the requirement of not managing a long-lived server. It created `vercel.json`, configured `api/invoices.mjs` and `api/invoices/[id].mjs`, updated `.gitignore` and `README.md`, and evaluated and rejected server-based platforms (Render, Railway, Fly.io) as well as probing other hosting tools.","c":1,"e":[["file","vercel.json"],["file","api/invoices.mjs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-d-63","pid":"DPLY-PD-63g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"vercel","secs":540,"k":"a5abeacb-49c2-44cc-baaa-bd2f249bacc1-r2","picks":[["vercel","p"],["netlify","m"],["cloudflare","m"],["fly-io","m"],["railway","m"],["render","m"]],"ev":53,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Vercel to avoid server maintenance, created `vercel.json` and `api/[...path].mjs` to adapt the Express server into a Vercel serverless function, updated `.gitignore` and `README.md` with step-by-step Vercel deployment commands, and evaluated/rejected traditional PaaS container options (Fly.io, Render, Railway) as well as Cloudflare Workers.","c":0.95,"e":[["file","vercel.json"],["file","api/[...path].mjs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-d-63","pid":"DPLY-PD-63g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"fly-io","secs":1011,"k":"a5abeacb-49c2-44cc-baaa-bd2f249bacc1-r3","picks":[["fly-io","p"],["render","m"],["cloudflare","m"],["vercel","m"]],"ev":109,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly chose Fly.io to run the combined Express API and Vite React frontend in a single container, creating fly.toml and Dockerfile and updating the README with deploy instructions. Vercel was evaluated and rejected after testing, while other hosting options were only surveyed in an initial CLI inventory.","c":1,"e":[["file","fly.toml"],["file","Dockerfile"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"ai-gateway","wave":22,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"aigw-vibe-sveltekit-indie","pid":"AIGW-08b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-ai-gateway","secs":246,"k":"1ae32374-1d80-4562-b096-6f9503e55d84-r1","picks":[["vercel-ai-gateway","p"],["openrouter","m"],["vercel-ai-sdk","m"]],"ev":31,"v":{"r":"The agent evaluated hosted AI gateway options and implemented an integration with Vercel AI Gateway via native fetch calls in src/lib/server/ai.ts, configuring environment variables and updating documentation.","c":1,"e":[["file",".env.example:5"],["file","src/lib/server/ai.ts:1"],["file","README.md:20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":10,"date":"2026-08-31","repo":"dotnet-utility-billing","variant":"base","family":"bc-stor-prompt-b-02","pid":"STOR-PB-02a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"azure-blob-storage","secs":368,"k":"dd134263-a6ed-41cc-9dd8-ed55d7108755-r1","picks":[["azure-blob-storage","p"]],"ev":40,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent selected, configured, and implemented Azure Blob Storage for storing generated bill PDFs. It added the Azure.Storage.Blobs SDK, defined the storage account and immutable container in Bicep templates, wired up BlobContainerClient with managed identity (DefaultAzureCredential), and wrote services and tests implementing PDF upload and retrieval.","c":1,"e":[["file","Directory.Packages.props:10"],["file","infra/main.bicep:98"],["file","src/Northmere.Billing.Api/Services/BillDocumentStore.cs:22"],["file","src/Northmere.Billing.Api/Program.cs:2"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":12,"date":"2026-08-31","repo":"edtech-lms","variant":"base","family":"stor-enterprise-edtech-lms","pid":"STOR-01b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"google-cloud-storage","secs":410,"k":"4fd9fdbd-d89f-4bf2-8d96-cec1c75e02dd-r1","picks":[["google-cloud-storage","p"],["amazon-s3","m"]],"ev":33,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The agent evaluated the existing repository infrastructure and selected Google Cloud Storage, which was already configured and utilized in the project. It implemented course material upload and download workflows using GCS V4 signed URLs and google-cloud-storage client operations.","c":1,"e":[["file","brightloom/settings.py"],["file","apps/courses/views.py"],["file","apps/courses/tasks.py"],["file","docs/course-material-storage.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":10,"date":"2026-08-31","repo":"edtech-lms","variant":"base","family":"stor-enterprise-edtech-lms","pid":"STOR-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"google-cloud-storage","secs":351,"k":"2b855f63-8c22-4160-b59b-3cab88c1b6e1-r1","picks":[["google-cloud-storage","p"],["amazon-s3","m"],["azure-blob-storage","m"]],"ev":30,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The repository was already running on GCP Cloud Run with private GCS media bucket configuration. The agent recommended and implemented Google Cloud Storage using direct V4 signed URLs, backend verification, and bucket CORS policies.","c":0.95,"e":[["file","apps/courses/storage.py:1-112"],["file","brightloom/settings.py:114-142"],["file","deploy/media-cors.json:1-8"],["file","README.md:26-41"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"storage","wave":12,"date":"2026-08-31","repo":"dotnet-utility-billing","variant":"base","family":"bc-stor-prompt-c-02","pid":"STOR-PC-02b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"azure-blob-storage","secs":447,"k":"b5bbe50c-3830-45bd-9e63-677c4da17a89-r1","picks":[["azure-blob-storage","p"]],"ev":43,"co":"full-cohort-storage-gemini-20260831-f291b01","v":{"r":"The run chose and fully implemented Azure Blob Storage for bill document storage, integrating the `Azure.Storage.Blobs` client library into the .NET API, configuring version-level immutability retention policies, and declaring the storage account resources in Bicep infrastructure templates.","c":1,"e":[["file","infra/main.bicep"],["file","src/Northmere.Billing.Api/Services/BillDocumentStore.cs"],["file","Directory.Packages.props"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-b-61","pid":"DPLY-PB-61g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"render","secs":533,"k":"962350e2-e970-4bf4-848d-2491158c229f-r1","picks":[["render","p"],["railway","m"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["netlify","m"],["vercel","m"]],"ev":51,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several hosting platforms (Render, Vercel, Fly.io, Cloudflare, DigitalOcean, Netlify, GitHub Pages, AWS) against the app's Node crypto requirements, dynamic rendering, and long-lived connection pool. It committed to Render by creating `render.yaml`, updating `.env.example` and `README.md`, configuring GitHub Actions CI, and setting up database pool timeouts for Render.","c":1,"e":[["file","render.yaml:1-32"],["file","README.md:6-9"],["file","README.md:62-75"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-b-61","pid":"DPLY-PB-61g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"render","secs":539,"k":"962350e2-e970-4bf4-848d-2491158c229f-r2","picks":[["render","p"],["cloudflare","m"],["fly-io","m"],["hetzner","m"],["railway","m"],["vercel","m"]],"ev":52,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly selected Render and implemented the complete deployment configuration by writing `render.yaml`, wiring deploy gates with GitHub Actions, and documenting the setup in README.md. It systematically compared and rejected Vercel, Fly.io, Cloudflare, and Hetzner based on runtime constraints, pricing, and operational maintenance requirements.","c":1,"e":[["file","render.yaml:1-39"],["file","README.md:8-66"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-b-61","pid":"DPLY-PB-61g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel","secs":597,"k":"962350e2-e970-4bf4-848d-2491158c229f-r3","picks":[["vercel","p"],["railway","m"],["render","a"],["cloudflare","m"],["fly-io","m"],["hetzner","m"]],"ev":57,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended and configured Vercel (writing vercel.json, updating README.md and .gitignore, and adding GitHub Actions CI) as the hosting and deployment platform in region iad1 to match the database.","c":1,"e":[["file","vercel.json:1-5"],["file","README.md:39-65"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"ai-gateway","wave":16,"date":"2026-08-31","repo":"helpdesk-billing-starter","variant":"base","family":"aigw-junior-helpdesk-billing-starter","pid":"AIGW-04b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare-ai-gateway","secs":185,"k":"0d048a83-0a4e-4d8a-9565-28e2d598ffad-r1","picks":[["cloudflare-ai-gateway","p"],["helicone","m"],["openrouter","m"],["portkey","m"],["vercel-ai-sdk","m"]],"ev":22,"v":{"r":"The agent evaluated several AI gateway providers (Cloudflare AI Gateway, Portkey, OpenRouter, and Helicone) and implemented Cloudflare AI Gateway REST API integration with Unified Billing in src/ai-drafts.js and src/server.js.","c":1,"e":[["file","src/ai-drafts.js"],["file","README.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-b-62","pid":"DPLY-PB-62g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel","secs":239,"k":"4bf6b849-315d-4806-b727-81d364e9dbbf-r1","picks":[["vercel","p"],["fly-io","m"],["render","m"]],"ev":21,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Vercel (specifically Vercel Pro) after comparing hosting costs and operational trade-offs with Fly.io and Render. It committed to Vercel in the documentation (README.md) and tailored the repository configuration for Vercel deployment.","c":1,"e":[["file","README.md"],["trace","trace:item:9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-b-62","pid":"DPLY-PB-62g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"railway","secs":363,"k":"4bf6b849-315d-4806-b727-81d364e9dbbf-r2","picks":[["railway","p"],["aws-lambda","m"],["fly-io","m"],["gcp","m"],["render","m"],["vercel","m"]],"ev":31,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several hosting platforms (Railway, Vercel, Fly.io, and Render) and recommended Railway based on cost, ease of deployment via Nixpacks, and compatibility with persistent database connections. The user accepted the recommendation and the agent implemented the Railway configuration in railway.json, added a dedicated healthcheck route, updated the README, and pinned engines in package.json.","c":1,"e":[["file","railway.json"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-b-62","pid":"DPLY-PB-62g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"render","secs":383,"k":"4bf6b849-315d-4806-b727-81d364e9dbbf-r3","picks":[["render","p"],["cloudflare","m"],["aws-lambda","m"],["fly-io","m"],["netlify","m"],["railway","m"],["vercel","m"]],"ev":31,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated hosting platforms (Render, Vercel, Netlify, Railway, Fly.io, Cloudflare) and explicitly recommended and implemented Render by creating render.yaml, configuring the health check route, and documenting it in the README.","c":1,"e":[["file","render.yaml"],["file","README.md"],["file","app/healthz/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-d-61","pid":"DPLY-PD-61g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel","secs":725,"k":"d354ab34-5320-4f7e-ba37-d027199a66a4-r1","picks":[["vercel","p"],["cloudflare","m"],["render","m"]],"ev":61,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated hosting options for a Next.js App Router project and explicitly recommended Vercel Pro. Upon user confirmation, the agent configured Vercel deployment files (vercel.json, .github/workflows/deploy-production.yml, and detailed production runbook in README.md).","c":1,"e":[["file",".github/workflows/deploy-production.yml"],["file","vercel.json"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-d-61","pid":"DPLY-PD-61g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"vercel","secs":769,"k":"d354ab34-5320-4f7e-ba37-d027199a66a4-r2","picks":[["vercel","p"],["render","m"],["cloudflare","m"],["railway","m"]],"ev":61,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Vercel Pro for the Next.js application, added vercel.json, and created a GitHub Actions workflow that executes a production build and deploys prebuilt artifacts via the Vercel CLI.","c":0.98,"e":[["file","vercel.json"],["file",".github/workflows/release.yml:37-54"],["file","docs/deployment.md:1-25"],["file","README.md:40-52"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-d-61","pid":"DPLY-PD-61g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"vercel","secs":446,"k":"d354ab34-5320-4f7e-ba37-d027199a66a4-r3","picks":[["vercel","p"]],"ev":51,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated hosting options for the Next.js application and selected Vercel (specifically Vercel Pro) for zero-ops deployment, adding configuration in vercel.json, custom Vercel migration build scripts in package.json, and full deployment documentation in README.md.","c":1,"e":[["file","vercel.json"],["file","package.json:9"],["file","README.md:6-38"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"ai-gateway","wave":12,"date":"2026-08-31","repo":"helpdesk-billing-starter","variant":"base","family":"aigw-junior-helpdesk-billing-starter","pid":"AIGW-04a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare-ai-gateway","secs":171,"k":"a84bc084-53da-4c53-9b04-7e4c06953463-r1","picks":[["cloudflare-ai-gateway","p"],["portkey","m"],["vercel-ai-gateway","m"],["helicone","m"],["litellm","m"]],"ev":26,"v":{"r":"The agent explicitly recommended and integrated Cloudflare AI Gateway into the codebase (adding `src/ai-gateway.js`, updating `src/server.js`, configuring `.env.example`, and documenting usage in `README.md`). LiteLLM was explicitly rejected due to the operational overhead of self-hosting a proxy. Portkey, Vercel AI Gateway, and Helicone were surveyed as alternative options.","c":1,"e":[["file","src/ai-gateway.js:40-128"],["file",".env.example:5-9"],["file","README.md:39-68"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-b-62","pid":"DPLY-PB-62g","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"render","secs":605,"k":"5da69d37-1e4c-472c-a2b0-21f35ad5ec7f-r1","picks":[["render","p"],["railway","m"],["netlify","m"],["cloudflare","m"],["vercel","m"]],"ev":19,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several hosting platforms (Render, Vercel, Railway, Netlify, Cloudflare) and explicitly selected Render due to cost fit for a small commercial app. It implemented the deployment setup by creating render.yaml and documenting the setup in README.md.","c":1,"e":[["file","render.yaml:1-15"],["file","README.md:28-47"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-b-62","pid":"DPLY-PB-62g","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"render","secs":150,"k":"5da69d37-1e4c-472c-a2b0-21f35ad5ec7f-r2","picks":[["render","p"],["railway","m"],["netlify","m"],["cloudflare","m"],["vercel","m"]],"ev":20,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Render, committed the configuration via `render.yaml`, and evaluated and rejected Vercel primarily based on monthly cost differences for commercial usage.","c":1,"e":[["file","render.yaml:1-14"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-b-62","pid":"DPLY-PB-62g","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"render","secs":222,"k":"5da69d37-1e4c-472c-a2b0-21f35ad5ec7f-r3","picks":[["render","p"],["cloudflare","m"],["netlify","m"],["vercel","m"]],"ev":20,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated hosting options for the dynamic Next.js application, recommended Render web services based on cost and direct Node.js compatibility, and generated the `render.yaml` blueprint file to configure deployments directly in code.","c":1,"e":[["file","render.yaml:1-14"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"voice-agents","wave":4,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-account-calls-resilience","pid":"VAGT-ACCOUNT-CALLS-02b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"livekit-agents","secs":859,"k":"70052de7-fa5f-42f8-a517-ab1220d1eca3-r1","picks":[["livekit-agents","p"],["pipecat","m"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":85,"co":"voice-agents-next-treatments-20260831-0b7b7cad","v":{"r":"The agent explicitly evaluated voice agent options and implemented LiveKit Agents via a dedicated Python package (`voice_agent/pyproject.toml` and `voice_agent/src/cairnfold_voice/agent.py`). Vapi and Twilio ConversationRelay were considered and rejected in prose with specific technical justifications, while Pipecat, Retell AI, and OpenAI Realtime API were surveyed during discovery.","c":1,"e":[["file","voice_agent/pyproject.toml:7"],["file","voice_agent/src/cairnfold_voice/agent.py:11-20"],["file","voice_agent/README.md:1"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":4,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-account-calls-resilience","pid":"VAGT-ACCOUNT-CALLS-02b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"livekit-agents","secs":590,"k":"70052de7-fa5f-42f8-a517-ab1220d1eca3-r2","picks":[["livekit-agents","p"],["pipecat","m"],["openai-realtime","m"],["vapi","m"]],"ev":69,"co":"voice-agents-next-treatments-20260831-0b7b7cad","v":{"r":"The agent evaluated several voice agent platforms against requirements for telephony recovery, fallback chains, idempotent account writes, and warm transfers. It explicitly recommended and implemented LiveKit Agents with `livekit-agents` in Python, while rejecting Vapi due to less granular code-level state control.","c":1,"e":[["file","voice_agent/pyproject.toml:11"],["file","voice_agent/agent.py:10-15"],["file","README.md:31-48"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":4,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-telephony-claims","pid":"VAGT-TELEPHONY-CLAIMS-01b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"openai-realtime","secs":848,"k":"5ab8d33c-37a8-45c5-95eb-e28a10510cdf-r1","picks":[["openai-realtime","p"]],"ev":66,"co":"voice-agents-next-treatments-20260831-0b7b7cad","v":{"r":"The agent evaluated and implemented OpenAI Realtime API using the official `openai` Ruby SDK to handle incoming SIP calls via webhooks, execute server-side function tools against the Rails claims database, enforce two-phase confirmation writes, and satisfy zero data retention compliance requirements.","c":1,"e":[["file","Gemfile:8"],["file","app/services/voice/openai_client.rb:1-46"],["file","app/controllers/openai_realtime_webhooks_controller.rb:1-46"],["file","docs/voice_agent.md:1-65"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":4,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-telephony-claims","pid":"VAGT-TELEPHONY-CLAIMS-01b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"openai-realtime","secs":677,"k":"5ab8d33c-37a8-45c5-95eb-e28a10510cdf-r2","picks":[["openai-realtime","p"],["pipecat","m"],["vapi","m"]],"ev":68,"co":"voice-agents-next-treatments-20260831-0b7b7cad","v":{"r":"The agent proposed and fully implemented a voice service built on the OpenAI Realtime API (with the @openai/agents SDK) handling SIP calls, tool calling with the Rails monolith, verbal confirmation workflows, and context transfers.","c":1,"e":[["file","voice-agent/package.json"],["file","voice-agent/src/agent.ts"],["file","voice-agent/src/callService.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"Procurement and compliance"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-e-61i","pid":"DPLY-PE-61i","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel","secs":508,"k":"fe924370-7a87-4fcb-8762-c9a572ab288a-r1","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["railway","m"],["render","m"]],"ev":49,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended and configured Vercel to host the Next.js 15 App Router project, creating `vercel.json`, setting region settings (`iad1`), adjusting connection pooling in `lib/db/index.ts` for serverless functions, adding GitHub Actions workflows for CI/migrations, and updating documentation in `README.md`. It explicitly weighed and rejected Cloudflare, Fly.io, Render, and Railway.","c":1,"e":[["file","vercel.json:1-5"],["file","README.md:8-10"],["file","README.md:35-43"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-e-61i","pid":"DPLY-PE-61i","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"vercel","secs":584,"k":"fe924370-7a87-4fcb-8762-c9a572ab288a-r2","picks":[["vercel","p"],["aws-lambda","m"],["cloudflare","m"],["fly-io","m"],["render","m"]],"ev":53,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended and configured Vercel as the deployment platform for the Next.js application, writing a `vercel.json` config, setup instructions in `DEPLOYING.md`, and CI workflows. Render, Fly.io, and Cloudflare Workers were evaluated and rejected with specific technical justifications.","c":1,"e":[["file","vercel.json"],["file","DEPLOYING.md"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-e-61i","pid":"DPLY-PE-61i","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel","secs":882,"k":"fe924370-7a87-4fcb-8762-c9a572ab288a-r3","picks":[["vercel","p"],["aws-lambda","m"],["cloudflare","m"],["netlify","m"],["render","m"]],"ev":91,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended and configured Vercel as the deployment platform for the Next.js application by creating `vercel.json`, adding `.vercel` to `.gitignore`, updating CI and documentation, and rejecting alternatives like Cloudflare, Render, Netlify, and Fly.io.","c":1,"e":[["file","vercel.json"],["file","README.md:35-45"],["trace","8"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-c-64","pid":"DPLY-PC-64g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"render","secs":210,"k":"57dbe652-3f3a-4919-88da-adc32aeb884c-r1","picks":[["render","p"],["vercel","m"],["cloudflare","m"]],"ev":22,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Render, created a complete `render.yaml` Blueprint file specifying a Node web service in Virginia, updated the project README with Render deployment and operations workflows, and hardened the Node server for Render hosting.","c":1,"e":[["file","render.yaml:1-18"],["file","README.md:32-60"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-c-64","pid":"DPLY-PC-64g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"render","secs":135,"k":"57dbe652-3f3a-4919-88da-adc32aeb884c-r2","picks":[["render","p"],["railway","m"]],"ev":19,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated hosting options and explicitly selected Render to host the Node web service, providing full configuration via render.yaml, README documentation, and a /health endpoint.","c":1,"e":[["file","render.yaml:1-13"],["file","README.md:28-54"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-c-64","pid":"DPLY-PC-64g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"render","secs":206,"k":"57dbe652-3f3a-4919-88da-adc32aeb884c-r3","picks":[["render","p"],["railway","m"],["vercel","m"],["cloudflare","m"]],"ev":27,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly selected and configured Render by creating a `render.yaml` Blueprint definition for a Node web service in Virginia, updating `README.md` with deployment instructions, and integrating health checks for Render's zero-downtime releases.","c":1,"e":[["file","render.yaml:1-17"],["file","README.md:28-46"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-d-62","pid":"DPLY-PD-62g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel","secs":252,"k":"e5675b39-3378-43af-b3dc-ae6727238b7f-r1","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":20,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Vercel for hosting the Next.js application, added `vercel.json` configured for region placement, adjusted `lib/db.ts` for serverless connection pooling, and documented deployment steps in `README.md`. It explicitly evaluated and rejected Fly.io, Render, Railway, Cloudflare Workers, and Netlify.","c":1,"e":[["file","vercel.json"],["file","README.md:30-33"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-d-62","pid":"DPLY-PD-62g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"vercel","secs":877,"k":"e5675b39-3378-43af-b3dc-ae6727238b7f-r2","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["netlify","m"],["render","m"]],"ev":90,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Vercel for hosting the Next.js App Router application without server management overhead. It configured `vercel.json` with regional placement and deploy-time migration execution, documented Vercel setup in the README, and rejected container-based and adapter-dependent alternatives (Fly.io, Render, Netlify, Cloudflare).","c":1,"e":[["file","vercel.json"],["file","README.md:27-37"],["trace","8"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-d-62","pid":"DPLY-PD-62g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel","secs":262,"k":"e5675b39-3378-43af-b3dc-ae6727238b7f-r3","picks":[["vercel","p"],["aws-lambda","m"],["cloudflare","m"],["fly-io","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":20,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Vercel for the Next.js application, adjusted the database pooler config in `lib/db.ts` to accommodate serverless functions, added `vercel.json` with region pinning, updated `README.md` and `.env.example`, and committed the changes while detailing the rejected alternatives (Fly.io, Render, Railway, Cloudflare, Netlify).","c":1,"e":[["file","vercel.json"],["file","README.md"],["trace","10"],["trace","20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-c-62","pid":"DPLY-PC-62g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel","secs":142,"k":"459ca210-0dd5-4fd4-a508-0ad146b87add-r1","picks":[["vercel","p"],["railway","m"],["render","m"]],"ev":18,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent clearly selected Vercel (specifically Vercel Pro) as the primary hosting solution for this Next.js project. Alternatives like Render, Railway, and AWS were deliberated in reasoning and rejected due to configuration and operational complexity.","c":0.95,"e":[["trace","Use Vercel Pro with the existing Neon database"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-c-62","pid":"DPLY-PC-62g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"vercel","secs":204,"k":"459ca210-0dd5-4fd4-a508-0ad146b87add-r2","picks":[["vercel","p"]],"ev":23,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated the Next.js stack, checked for existing configuration files (such as fly.toml, render.yaml, and vercel.json), and decisively recommended Vercel as the hosting platform. It subsequently prepared the repository configurations (.gitignore, next.config.ts, drizzle scripts, and pinned Node runtime) specifically for Vercel deployment.","c":0.95,"e":[["trace","seq:4"],["trace","seq:5"],["trace","seq:7"],["file","README.md"],["file","next.config.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-c-62","pid":"DPLY-PC-62g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"vercel","secs":291,"k":"459ca210-0dd5-4fd4-a508-0ad146b87add-r3","picks":[["vercel","p"],["render","m"]],"ev":25,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Vercel Pro and configured the repository for deployment on Vercel by adding vercel.json, creating DEPLOYING.md with Vercel setup procedures, and updating the README to document Vercel as the host.","c":1,"e":[["file","DEPLOYING.md"],["file","README.md"],["file","vercel.json"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"nextjs-donorbook","variant":"base","family":"dply-g-nextjs-donorbook","pid":"DPLY-61g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel","secs":212,"k":"52de74da-9c3a-4632-80ce-41b57eb6e13f-r1","picks":[["vercel","p"],["netlify","m"],["cloudflare","m"],["fly-io","m"],["railway","m"],["render","m"]],"ev":16,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly picked Vercel as the deployment target, creating `vercel.json` configured for Next.js in the `iad1` region, setting up `.github/workflows/ci.yml` for automated testing and builds, adding detailed onboarding and operational documentation in `DEPLOY.md`, and adjusting `lib/db/index.ts` to accommodate serverless connection pooling against Neon. Alternatives including Fly.io, Render, Railway, Cloudflare, and Netlify were evaluated and rejected or bypassed in reasoning and trace outputs.","c":1,"e":[["file","vercel.json"],["file","DEPLOY.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"nextjs-donorbook","variant":"base","family":"dply-g-nextjs-donorbook","pid":"DPLY-61g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"vercel","secs":223,"k":"52de74da-9c3a-4632-80ce-41b57eb6e13f-r2","picks":[["vercel","p"],["render","a"],["aws-lambda","m"],["cloudflare","m"],["fly-io","m"]],"ev":25,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent configured Vercel hosting via vercel.json and DEPLOY.md, adjusted the database pooling logic in lib/db/index.ts specifically for Vercel serverless execution, and added a GitHub Actions CI workflow to test builds alongside Vercel deployments. It evaluated Render, Fly.io, and Cloudflare Workers as alternatives or disqualified options.","c":1,"e":[["file","vercel.json"],["file","DEPLOY.md:3-27"],["file","lib/db/index.ts:9-16"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"nextjs-donorbook","variant":"base","family":"dply-g-nextjs-donorbook","pid":"DPLY-61g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel","secs":401,"k":"52de74da-9c3a-4632-80ce-41b57eb6e13f-r3","picks":[["vercel","p"],["aws-lambda","m"],["cloudflare","m"],["fly-io","m"],["netlify","m"],["render","m"]],"ev":39,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The run fully implemented and committed deployment configuration for Vercel, creating `vercel.json`, a GitHub Actions deploy workflow, updated database pool sizing for Vercel serverless functions, and extensive runbook documentation in `DEPLOY.md`. Alternative hosting providers (Fly.io, Render, Cloudflare, Netlify) were evaluated and explicitly rejected.","c":1,"e":[["file","vercel.json"],["file",".github/workflows/deploy.yml"],["file","DEPLOY.md"],["file","lib/db/index.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":4,"date":"2026-08-31","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-device-dispatch","pid":"VAGT-DEVICE-DISPATCH-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"picovoice","secs":834,"k":"8ba5f3e6-be4c-4c30-a9c7-82365a74b8c1-r1","picks":[["picovoice","p"]],"ev":81,"co":"voice-agents-next-treatments-20260831-0b7b7cad","v":{"r":"The user requested an in-app voice agent for technicians operating offline for up to an hour. The agent investigated OpenAI Realtime API and other tools but selected Picovoice's on-device WebAssembly suite (@picovoice/cheetah-web, @picovoice/cobra-web, @picovoice/orca-web, @picovoice/picollm-web), which runs entirely within the browser client without requiring an active network connection.","c":0.98,"e":[["file","package-lock.json"],["file","composables/useVoiceAgent.ts"],["file","README.md"],["trace","seq:23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":4,"date":"2026-08-31","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-device-dispatch","pid":"VAGT-DEVICE-DISPATCH-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"picovoice","secs":640,"k":"8ba5f3e6-be4c-4c30-a9c7-82365a74b8c1-r2","picks":[["picovoice","p"],["livekit-agents","m"],["vapi","m"]],"ev":56,"co":"voice-agents-next-treatments-20260831-0b7b7cad","v":{"r":"The agent was asked to find and implement an in-app voice agent that operates offline on field tablets. It evaluated cloud options (OpenAI Realtime API, LiveKit Agents, Vapi, Retell AI) and rejected them due to lack of offline support. It selected and implemented the Picovoice SDK stack (@picovoice/cheetah-web, @picovoice/cobra-web, @picovoice/orca-web, @picovoice/picollm-web, @picovoice/rhino-web, @picovoice/web-voice-processor) with IndexedDB outbox persistence and PWA support.","c":0.98,"e":[["file","package.json"],["file","services/picovoice.client.ts"],["file","composables/useVoiceAgent.ts"],["file","components/VoiceJobAgent.client.vue"],["trace","10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"vite-react-shifts","variant":"base","family":"dply-g-vite-react-shifts","pid":"DPLY-64g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"render","secs":104,"k":"a4ad90bc-eaa1-4fb4-b2e3-ec64d57b15bf-r1","picks":[["render","p"],["railway","m"],["vercel","m"]],"ev":16,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly selected Render to host the Node web service, implementing a `render.yaml` blueprint configuration and accompanying GitHub Actions CI workflow to trigger auto-deployments from the main branch.","c":1,"e":[["file","render.yaml:1-16"],["file","README.md:28-33"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"vite-react-shifts","variant":"base","family":"dply-g-vite-react-shifts","pid":"DPLY-64g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"render","secs":404,"k":"a4ad90bc-eaa1-4fb4-b2e3-ec64d57b15bf-r2","picks":[["render","p"],["railway","m"],["vercel","m"]],"ev":24,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly selected Render, adding `render.yaml` to configure an automated web service deployment on Render linked to GitHub and the external Neon PostgreSQL database. Other potential hosting providers (Railway, Fly.io, Vercel) were probed via CLI commands during evaluation and discarded.","c":1,"e":[["file","render.yaml:1-16"],["file","README.md:28-35"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"vite-react-shifts","variant":"base","family":"dply-g-vite-react-shifts","pid":"DPLY-64g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"render","secs":124,"k":"a4ad90bc-eaa1-4fb4-b2e3-ec64d57b15bf-r3","picks":[["render","p"],["railway","m"],["vercel","m"]],"ev":22,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several hosting platforms (Render, Railway, Vercel, Fly.io) and explicitly committed to Render by authoring a `render.yaml` Blueprint spec, updating the `server/index.mjs` health endpoint and host binding, adding a GitHub Actions workflow to gate deployments, and providing step-by-step instructions in the README.","c":0.98,"e":[["file","render.yaml"],["file","README.md"],["file","server/index.mjs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-d-65","pid":"DPLY-PD-65g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"cloudflare","secs":143,"k":"628222b6-87b9-4a77-8478-a23b65a28b7d-r1","picks":[["cloudflare","p"],["github-pages","m"],["netlify","m"],["vercel","m"]],"ev":14,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated the static Astro project and picked Cloudflare Pages as the hosting platform. It prepared the repository by pinning Node via .nvmrc and updating the README with build/deployment instructions while explicitly contrasting and rejecting GitHub Pages, Netlify, and Vercel.","c":0.95,"e":[["file","README.md"],["file",".nvmrc"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-d-65","pid":"DPLY-PD-65g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"cloudflare","secs":160,"k":"628222b6-87b9-4a77-8478-a23b65a28b7d-r2","picks":[["cloudflare","p"],["github-pages","m"],["netlify","m"],["render","m"]],"ev":15,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly evaluated GitHub Pages, Netlify, and Cloudflare Pages for deploying the static Astro website. It selected Cloudflare Pages, configured `.node-version` and `astro.config.mjs` for it, and updated `README.md` with complete Cloudflare Pages deployment instructions.","c":1,"e":[["file","README.md"],["file","astro.config.mjs"],["trace","seq 7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-d-65","pid":"DPLY-PD-65g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"cloudflare","secs":142,"k":"628222b6-87b9-4a77-8478-a23b65a28b7d-r3","picks":[["cloudflare","p"],["github-pages","m"],["netlify","m"],["vercel","m"]],"ev":15,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The run specifically analyzed the static Astro site and selected Cloudflare Pages as its primary deployment solution, committing a .node-version configuration file and updating README.md with build settings and deployment instructions for connecting the GitHub repository to Cloudflare Pages. It evaluated and rejected GitHub Pages, Netlify, and Vercel with specific technical and pricing justifications.","c":1,"e":[["file",".node-version:1"],["file","README.md:21-35"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"nextjs-studioroster","variant":"base","family":"dply-g-nextjs-studioroster","pid":"DPLY-62g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel","secs":176,"k":"72af408f-3a19-47f6-aea1-b0e7687ff47d-r1","picks":[["vercel","p"],["cloudflare","m"],["github-pages","m"],["render","m"]],"ev":20,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated hosting options for a Next.js 15 app connected to Neon Postgres, selected Vercel as the primary deployment target, created vercel.json, adjusted database connection settings for serverless pooling, and updated documentation with Vercel deployment instructions.","c":1,"e":[["file","vercel.json"],["file","README.md"],["file","lib/db.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"nextjs-studioroster","variant":"base","family":"dply-g-nextjs-studioroster","pid":"DPLY-62g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"vercel","secs":223,"k":"72af408f-3a19-47f6-aea1-b0e7687ff47d-r2","picks":[["vercel","p"],["cloudflare","m"],["github-pages","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":22,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly chose Vercel to host the dynamic Next.js application, creating a vercel.json configuration pinned to iad1 (to match the Neon database region in us-east-1), updating README deployment documentation, and setting up a GitHub Actions CI workflow while outlining exact manual steps for linking the account.","c":0.98,"e":[["file","vercel.json:1-5"],["file","README.md:30-37"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"nextjs-studioroster","variant":"base","family":"dply-g-nextjs-studioroster","pid":"DPLY-62g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel","secs":229,"k":"72af408f-3a19-47f6-aea1-b0e7687ff47d-r3","picks":[["vercel","p"],["aws-lambda","m"],["cloudflare","m"],["railway","m"],["render","m"]],"ev":24,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent clearly selected Vercel as the deployment platform for the Next.js 15 app, created `vercel.json` and `DEPLOY.md`, set up GitHub Actions CI, and explicitly rejected Render, Fly.io, and Cloudflare with specific technical reasons.","c":1,"e":[["file","vercel.json"],["file","DEPLOY.md:1-83"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"nextjs-donorbook","variant":"base","family":"dply-g-nextjs-donorbook","pid":"DPLY-61g","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel","secs":251,"k":"4ce5ee63-9f1b-4feb-bdf5-0404cd37c228-r1","picks":[["vercel","p"],["render","m"]],"ev":18,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly selected Vercel to host the Next.js application, added vercel.json, updated the project documentation, and initiated the Vercel login flow to link the project. Render was briefly weighed in deliberation but rejected.","c":1,"e":[["file","vercel.json"],["file","README.md:35-49"],["trace","seq:9"],["trace","seq:13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"nextjs-donorbook","variant":"base","family":"dply-g-nextjs-donorbook","pid":"DPLY-61g","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"vercel","secs":319,"k":"4ce5ee63-9f1b-4feb-bdf5-0404cd37c228-r2","picks":[["vercel","p"]],"ev":12,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly selected Vercel as the deployment platform for the Next.js application, added vercel.json, updated README.md with production deployment documentation, and started the Vercel OAuth device flow for authorization.","c":1,"e":[["file","vercel.json"],["file","README.md:35-58"],["trace","12"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"nextjs-donorbook","variant":"base","family":"dply-g-nextjs-donorbook","pid":"DPLY-61g","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"vercel","secs":271,"k":"4ce5ee63-9f1b-4feb-bdf5-0404cd37c228-r3","picks":[["vercel","p"],["render","m"]],"ev":16,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent selected Vercel as the deployment platform for the Next.js app, created a vercel.json configuration with region and test-gated build settings, updated the README runbook, and initiated an OAuth login flow for linking the project. Render and Fly.io were evaluated in reasoning and dismissed as unnecessary container-based solutions.","c":0.98,"e":[["file","vercel.json"],["file","README.md:35-56"],["trace","seq:9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":2,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-account-calls-resilience","pid":"VAGT-ACCOUNT-CALLS-02a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"retell-ai","secs":622,"k":"134559fe-4182-4243-bf8c-6654a0b5a80a-r1","picks":[["retell-ai","p"]],"ev":64,"co":"voice-agents-next-treatments-20260831-0b7b7cad","v":{"r":"The agent evaluated voice agent providers, recommended Retell AI for its integrated telephony, voicemail detection, and conversation flow capabilities, and upon approval implemented the complete client, dispatcher, webhook verification, and custom function integrations.","c":1,"e":[["file","internal/outreach/retell.go:1-84"],["file","docs/retell-setup.md:1-112"],["file",".env.example:5-12"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":2,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-account-calls-resilience","pid":"VAGT-ACCOUNT-CALLS-02a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"vapi","secs":437,"k":"134559fe-4182-4243-bf8c-6654a0b5a80a-r2","picks":[["vapi","p"],["retell-ai","m"]],"ev":55,"co":"voice-agents-next-treatments-20260831-0b7b7cad","v":{"r":"The agent selected Vapi as the voice agent platform, fully implementing a Go client for Vapi's outbound call API, HTTP handlers for Vapi tool requests and webhook lifecycle events, database schemas to manage call state and change tokens, and thorough rollout documentation.","c":1,"e":[["file","internal/voice/voice.go:27-75"],["file","docs/voice-agent.md:1-75"],["file","internal/config/config.go:10-47"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":2,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-telephony-claims","pid":"VAGT-TELEPHONY-CLAIMS-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"retell-ai","secs":410,"k":"ebe5926f-125c-4d3f-897d-93c3a346104a-r1","picks":[["retell-ai","p"],["vapi","m"]],"ev":60,"co":"voice-agents-next-treatments-20260831-0b7b7cad","v":{"r":"The agent evaluated several voice agent platforms against specific requirements (SIP trunk compatibility, server-side tool calling, interruptions, and warm-transfer whispering), explicitly recommended Retell AI over Vapi and ElevenLabs Agents, and fully implemented the integration via signed Rails controllers and services.","c":1,"e":[["file","docs/retell-claims-agent.md"],["file","app/controllers/retell/base_controller.rb"],["file","app/services/retell/signature_verifier.rb"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":2,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-telephony-claims","pid":"VAGT-TELEPHONY-CLAIMS-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"livekit-agents","secs":469,"k":"ebe5926f-125c-4d3f-897d-93c3a346104a-r2","picks":[["livekit-agents","p"]],"ev":57,"co":"voice-agents-next-treatments-20260831-0b7b7cad","v":{"r":"The agent evaluated several voice platforms (LiveKit, Retell AI, Vapi, OpenAI Realtime API) for compatibility with existing SIP trunks, carrier preservation, server-enforced confirmation, and warm transfers. It selected LiveKit Agents, received user approval, and implemented a full Python-based LiveKit agent connected to Rails APIs.","c":0.95,"e":[["file","voice_agent/pyproject.toml:6"],["file","voice_agent/agent.py:11-22"],["file","README.md:31-86"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-b-64","pid":"DPLY-PB-64g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"cloudflare","secs":320,"k":"07169037-1fe2-43ce-9033-ca5ac6f3f516-r1","picks":[["cloudflare","p"],["fly-io","m"],["netlify","m"],["render","m"],["vercel","m"]],"ev":33,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several hosting solutions (Cloudflare, Vercel, Netlify, Fly.io, Render) and committed to Cloudflare Workers with Cloudflare Access. It implemented the solution by writing `src/worker.js`, creating `wrangler.jsonc`, adding `wrangler` to devDependencies, creating a GitHub Actions deployment workflow using `cloudflare/wrangler-action@v3`, and updating the documentation accordingly.","c":1,"e":[["file",".github/workflows/deploy.yml:29-36"],["file","README.md:30-49"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-b-64","pid":"DPLY-PB-64g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"render","secs":309,"k":"07169037-1fe2-43ce-9033-ca5ac6f3f516-r2","picks":[["render","p"],["railway","m"],["cloudflare","m"],["fly-io","m"],["vercel","m"]],"ev":35,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Render, wrote a complete `render.yaml` blueprint configuration file, and documented the deployment setup in README.md, while evaluating and rejecting Fly.io, Vercel, and Cloudflare.","c":1,"e":[["file","render.yaml"],["file","README.md:29-47"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-b-64","pid":"DPLY-PB-64g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"cloudflare","secs":346,"k":"07169037-1fe2-43ce-9033-ca5ac6f3f516-r3","picks":[["cloudflare","p"],["fly-io","m"],["hetzner","m"],["netlify","m"],["railway","m"],["render","m"],["vercel","m"]],"ev":32,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several hosting platforms (Vercel, Render, Fly.io, Netlify, Railway, Cloudflare) and explicitly chose Cloudflare Workers. It installed Wrangler, refactored the backend into a Worker fetch handler, created wrangler.toml, and updated dev/deploy scripts and README documentation.","c":1,"e":[["file","README.md"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-c-63","pid":"DPLY-PC-63g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"render","secs":473,"k":"3da8afe2-3a08-40bd-bc92-a6603c0ac320-r1","picks":[["render","p"],["fly-io","m"],["netlify","m"],["vercel","m"]],"ev":39,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Render Starter tier, configured the service using `render.yaml`, adjusted `server/index.mjs` for static asset serving and SPA fallback to run on Render, updated `README.md` with deployment instructions, and dismissed Fly.io, Vercel, and Netlify due to configuration complexity and serverless mismatches.","c":1,"e":[["file","render.yaml"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-c-63","pid":"DPLY-PC-63g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"render","secs":1120,"k":"3da8afe2-3a08-40bd-bc92-a6603c0ac320-r2","picks":[["render","p"],["cloudflare","m"],["fly-io","m"],["netlify","m"],["railway","m"],["vercel","m"]],"ev":78,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent inspected the repository and explicitly recommended deploying the combined Express API and Vite React frontend as a single Node web service on Render. It authored a complete `render.yaml` blueprint with environment configuration and health checks, and updated the README with deployment instructions. Other PaaS platforms (Fly.io, Vercel, Netlify, Railway) were surveyed or evaluated and rejected due to CLI absence, Dockerfile requirements, or serverless architectural mismatch.","c":1,"e":[["file","render.yaml:1-41"],["file","README.md:27-46"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-c-63","pid":"DPLY-PC-63g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"render","secs":420,"k":"3da8afe2-3a08-40bd-bc92-a6603c0ac320-r3","picks":[["render","p"],["railway","m"],["cloudflare","m"],["fly-io","m"],["netlify","m"],["vercel","m"]],"ev":38,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Render, authored a `render.yaml` configuration file for deploying the application as a Render Web Service, and added deployment instructions to the README. It also explicitly evaluated and rejected Fly.io, Vercel, Netlify, and Cloudflare Workers, while noting Railway as an alternative.","c":1,"e":[["file","render.yaml"],["file","README.md"],["trace","14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-c-62","pid":"DPLY-PC-62g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel","secs":326,"k":"381851df-95dc-4a94-bb82-1d30fc16b994-r1","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["netlify","m"],["render","m"]],"ev":32,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Vercel for hosting the dynamic Next.js App Router application alongside the existing Neon Postgres instance, updated README deployment instructions for Vercel, optimized database connection settings for serverless execution, and ruled out static hosts (GitHub Pages, Netlify, AWS S3/CloudFront) and container hosts (Fly.io, Render) as well as Cloudflare Workers.","c":0.98,"e":[["file","README.md:36-47"],["file","lib/db.ts:7-19"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-c-62","pid":"DPLY-PC-62g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"vercel","secs":248,"k":"381851df-95dc-4a94-bb82-1d30fc16b994-r2","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["railway","m"],["render","m"]],"ev":22,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended and configured Vercel for hosting the Next.js 15 application. It wrote a vercel.json file pinning the iad1 region to co-locate with Neon in us-east-1, optimized the database connection pool settings for serverless functions, updated README instructions, and created a git commit on a deploy branch. It explicitly evaluated and rejected alternatives including Render, Fly.io, Cloudflare, and Railway.","c":1,"e":[["file","vercel.json"],["file","README.md:30-38"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-c-62","pid":"DPLY-PC-62g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel","secs":150,"k":"381851df-95dc-4a94-bb82-1d30fc16b994-r3","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["github-pages","m"],["netlify","m"],["render","m"]],"ev":14,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several hosting alternatives against the requirements of Next.js 15 dynamic server components and selected Vercel. It prepared the repository for Vercel deployment by tuning connection pooling in lib/db.ts for serverless execution and documenting deployment instructions in README.md.","c":1,"e":[["file","README.md:27-45"],["file","lib/db.ts:8-16"],["trace","8"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-d-61","pid":"DPLY-PD-61g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel","secs":423,"k":"c7749651-739a-48c7-91c2-5285488c0e85-r1","picks":[["vercel","p"],["aws-lambda","m"],["cloudflare","m"],["fly-io","m"],["railway","m"],["render","m"]],"ev":38,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended and configured Vercel for hosting the Next.js application, creating a vercel.json configuration file, tuning serverless database connection parameters, and updating documentation with deployment instructions. It explicitly evaluated and rejected Fly.io, Cloudflare, Railway, and Render due to maintenance burdens and runtime incompatibilities.","c":1,"e":[["file","vercel.json"],["file","README.md:8-10"],["file","README.md:35-43"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-d-61","pid":"DPLY-PD-61g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"vercel","secs":300,"k":"c7749651-739a-48c7-91c2-5285488c0e85-r2","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":29,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several hosting alternatives (Fly.io, Render, Railway, Cloudflare Workers, Netlify) and selected Vercel to host the Next.js 15 application. It configured `vercel.json` for regions and cron jobs, tuned the database connection pooling specifically for Vercel functions, and documented the entire Vercel deployment runbook in the README.","c":1,"e":[["file","vercel.json"],["file","README.md"],["file","lib/db/index.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-d-61","pid":"DPLY-PD-61g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel","secs":481,"k":"c7749651-739a-48c7-91c2-5285488c0e85-r3","picks":[["vercel","p"],["aws-lambda","m"],["cloudflare","m"],["fly-io","m"],["railway","m"],["render","m"]],"ev":55,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended and configured Vercel for hosting the Next.js application by creating vercel.json (pinned to iad1), updating .gitignore, adjusting database connection pooling for Vercel serverless functions, and adding detailed deployment runbook instructions in the README. It explicitly rejected Cloudflare Workers, Fly.io, Render, and Railway due to runtime incompatibilities or operational maintenance overhead.","c":1,"e":[["file","vercel.json"],["file","README.md:8-11"],["file",".gitignore:5"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"fastapi-waitlist","variant":"base","family":"bc-dply-prompt-b-66","pid":"DPLY-PB-66g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"render","secs":288,"k":"c6b0944c-7bd8-441b-aeca-df1364a3ef0c-r1","picks":[["render","p"],["railway","m"],["cloudflare","m"],["fly-io","m"],["gcp","m"],["heroku","m"],["hetzner","m"]],"ev":33,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly selected Render, generated a `render.yaml` blueprint, updated the README with deployment instructions for Render, and explained the trade-offs versus alternatives like Fly.io, Cloud Run, Heroku, and Railway.","c":1,"e":[["file","render.yaml:1-28"],["file","README.md:26-38"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"fastapi-waitlist","variant":"base","family":"bc-dply-prompt-b-66","pid":"DPLY-PB-66g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"render","secs":399,"k":"c6b0944c-7bd8-441b-aeca-df1364a3ef0c-r2","picks":[["render","p"],["fly-io","a"],["cloudflare","m"],["gcp","m"],["hetzner","m"]],"ev":44,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several hosting platforms (Render, Fly.io, Google Cloud Run, Hetzner VPS) and explicitly recommended and implemented Render using a `render.yaml` blueprint with automated deploys from main.","c":0.98,"e":[["file","render.yaml:1-26"],["file","README.md:27-46"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"fastapi-waitlist","variant":"base","family":"bc-dply-prompt-b-66","pid":"DPLY-PB-66g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"render","secs":371,"k":"c6b0944c-7bd8-441b-aeca-df1364a3ef0c-r3","picks":[["render","p"],["cloudflare","m"],["aws-lambda","m"],["digitalocean","m"],["fly-io","m"],["gcp","m"],["heroku","m"],["hetzner","m"],["netlify","m"],["railway","m"],["vercel","m"]],"ev":30,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several deployment platforms (Render, Fly.io, Railway, Google Cloud Run, Hetzner, Vercel, Netlify, DigitalOcean, Heroku) and committed to Render by writing a full `render.yaml` blueprint configuration and updating the README with deployment instructions.","c":1,"e":[["file","render.yaml:1-37"],["file","README.md:32-47"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-e-61i","pid":"DPLY-PE-61i","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel","secs":300,"k":"fb4a8139-a725-4c05-9e5c-158c517c4128-r1","picks":[["vercel","p"]],"ev":26,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent selected and configured Vercel as the deployment platform to meet the requirements for automatic main deployments, pull request preview URLs, and rollback support, adding a vercel.json configuration file and deployment instructions.","c":1,"e":[["file","vercel.json"],["file","README.md"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-e-61i","pid":"DPLY-PE-61i","pf":"Vibe 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hosting options and selected Render to run the Next.js server, creating a `render.yaml` Blueprint definition, a health check route, and updating documentation with deployment instructions. Vercel was evaluated and rejected due to cost and serverless migration complexity.","c":1,"e":[["file","render.yaml"],["file","README.md"],["trace","11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-b-61","pid":"DPLY-PB-61g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"vercel","secs":313,"k":"990d0d00-5005-4e03-8e67-152e173bb46a-r2","picks":[["vercel","p"],["render","m"]],"ev":39,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated hosting options for a Next.js application, comparing Vercel, Render, and Railway. It explicitly recommended Vercel Pro, wrote a `vercel.json` configuration file, updated deployment documentation in `docs/deployment.md` and `README.md`, and created a GitHub Actions workflow designed to coordinate migrations with Vercel deployment checks.","c":1,"e":[["file","vercel.json"],["file","docs/deployment.md"],["file","README.md"],["file",".github/workflows/production-readiness.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"nextjs-donorbook","variant":"base","family":"bc-dply-prompt-b-61","pid":"DPLY-PB-61g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"render","secs":306,"k":"990d0d00-5005-4e03-8e67-152e173bb46a-r3","picks":[["render","p"],["railway","m"],["vercel","m"]],"ev":36,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated hosting options (Render vs Vercel vs Railway/Fly) for the Next.js app, recommended Render due to lower cost and managed features, and committed Render configuration directly into render.yaml with CI integration in GitHub Actions.","c":1,"e":[["file","render.yaml:1-15"],["file","README.md:41-51"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"fastapi-waitlist","variant":"base","family":"dply-g-fastapi-waitlist","pid":"DPLY-66g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"render","secs":354,"k":"1eed9332-c77e-4ea3-a56e-38cb3794abed-r1","picks":[["render","p"],["aws-lambda","m"],["fly-io","m"],["heroku","m"],["railway","m"],["vercel","m"]],"ev":42,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended and configured Render as the deployment platform, creating `render.yaml`, integrating auto-deployment gates with GitHub Actions CI, and documenting setup in `README.md`. Alternatives such as Fly.io, Railway, Vercel, Heroku, Google Cloud, and AWS were considered or surveyed in the trace and rejected.","c":1,"e":[["file","render.yaml"],["file","README.md"],["file",".github/workflows/ci.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"fastapi-waitlist","variant":"base","family":"dply-g-fastapi-waitlist","pid":"DPLY-66g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"render","secs":265,"k":"1eed9332-c77e-4ea3-a56e-38cb3794abed-r2","picks":[["render","p"],["fly-io","a"],["cloudflare","m"],["railway","m"]],"ev":33,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent fully configured Render via `render.yaml`, created deployment documentation in `README.md`, and set up CI to validate builds. It weighed alternatives like Fly.io and Google Cloud Run, surveyed CLIs for various platforms, and committed to Render as the primary deployment solution.","c":0.95,"e":[["file","render.yaml:1-20"],["file","README.md:36-78"],["trace","seq:13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"fastapi-waitlist","variant":"base","family":"dply-g-fastapi-waitlist","pid":"DPLY-66g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"fly-io","secs":227,"k":"1eed9332-c77e-4ea3-a56e-38cb3794abed-r3","picks":[["fly-io","p"],["cloudflare","m"],["gcp","m"],["railway","m"],["render","m"]],"ev":24,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Fly.io in the London (lhr) region, generated a Dockerfile, fly.toml, and a GitHub Actions workflow targeting Fly.io, while explicitly evaluating and rejecting Render, Railway, Google Cloud (Cloud Run), and surveying AWS and Heroku CLI tools.","c":0.95,"e":[["file","fly.toml"],["file",".github/workflows/deploy.yml"],["file","Dockerfile"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":2,"date":"2026-08-31","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-device-dispatch","pid":"VAGT-DEVICE-DISPATCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"openai-realtime","secs":493,"k":"5f338c1c-9975-4f88-8bb9-cf61d3f109fc-r1","picks":[["openai-realtime","p"]],"ev":52,"co":"voice-agents-next-treatments-20260831-0b7b7cad","v":{"r":"The agent evaluated voice agent options and committed to OpenAI Realtime API using the `@openai/agents` TypeScript SDK over WebRTC. It implemented UI components, composables, and server endpoints to mint ephemeral client tokens and handle structured function calls.","c":1,"e":[["file","package.json:16"],["file","composables/useVoiceAgent.ts:150-210"],["file","server/api/voice/session.post.ts:24-37"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":2,"date":"2026-08-31","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-device-dispatch","pid":"VAGT-DEVICE-DISPATCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"openai-realtime","secs":550,"k":"5f338c1c-9975-4f88-8bb9-cf61d3f109fc-r2","picks":[["openai-realtime","p"],["pipecat","m"],["vapi","m"],["livekit-agents","m"]],"ev":68,"co":"voice-agents-next-treatments-20260831-0b7b7cad","v":{"r":"The agent evaluated several voice agent technologies and explicitly selected and implemented OpenAI Realtime API via the @openai/agents SDK over WebRTC, integrating it with Nuxt 3 server endpoints and client-side audio streaming.","c":1,"e":[["file","package.json:18"],["file","server/api/voice/connect.post.ts:40-62"],["file","server/services/voiceAgent.ts:1-164"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-b-64","pid":"DPLY-PB-64g","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"render","secs":196,"k":"33774ad8-a18c-416f-bf6c-7ed6afcc1b03-r1","picks":[["render","p"],["cloudflare","m"],["railway","m"],["vercel","m"]],"ev":34,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Render Starter over alternatives like Vercel, created a complete `render.yaml` Blueprint definition, and updated the README with deployment instructions.","c":0.98,"e":[["file","render.yaml:1-20"],["file","README.md:28-44"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-b-64","pid":"DPLY-PB-64g","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"render","secs":190,"k":"33774ad8-a18c-416f-bf6c-7ed6afcc1b03-r2","picks":[["render","p"],["cloudflare","m"]],"ev":24,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated hosting options for the project, recommended Render, and committed a render.yaml blueprint along with CI and health check routes to configure automatic deployments from main.","c":1,"e":[["file","render.yaml"],["trace","15"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-b-64","pid":"DPLY-PB-64g","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"cloudflare","secs":162,"k":"33774ad8-a18c-416f-bf6c-7ed6afcc1b03-r3","picks":[["cloudflare","p"],["vercel","m"],["render","m"]],"ev":26,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent analyzed hosting options and fully configured the application to deploy to Cloudflare Workers with Static Assets, creating wrangler.jsonc, updating deployment scripts in package.json, adapting the server handler to Cloudflare Workers, and documenting the production rollout.","c":1,"e":[["file","wrangler.jsonc"],["file","package.json"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-c-63","pid":"DPLY-PC-63g","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"render","secs":579,"k":"4dcdab10-e946-400a-8938-e6da8f1e07bb-r1","picks":[["render","p"],["railway","m"],["vercel","m"]],"ev":33,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated hosting options and recommended deploying the unified frontend and backend to Render via a Blueprint configuration file (render.yaml). After the user approved, the agent authored render.yaml, updated the application server to host the static bundle, and documented the Render deployment flow in README.md.","c":1,"e":[["file","render.yaml:1-18"],["file","README.md:21-36"],["trace","seq 6"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-c-63","pid":"DPLY-PC-63g","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"railway","secs":328,"k":"4dcdab10-e946-400a-8938-e6da8f1e07bb-r2","picks":[["railway","p"],["cloudflare","m"],["vercel","m"],["render","a"]],"ev":34,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended deploying to Railway, updated the repository's README with production Railway deployment instructions, and ran the Railway CLI login activation flow to prepare for publishing.","c":1,"e":[["file","README.md"],["trace","34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-c-63","pid":"DPLY-PC-63g","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"railway","secs":167,"k":"4dcdab10-e946-400a-8938-e6da8f1e07bb-r3","picks":[["railway","p"],["vercel","m"]],"ev":26,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Railway, wrote a complete railway.json configuration file, configured Express for production serving and Railway health checks, and documented the Railway deployment workflow in the README.","c":1,"e":[["file","railway.json"],["file","README.md:21-37"],["file","server/index.mjs:41-52"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"astro-trailnotes","variant":"base","family":"dply-g-astro-trailnotes","pid":"DPLY-65g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"github-pages","secs":229,"k":"2e4d05cc-4b17-4d45-8ea4-1277dece8285-r1","picks":[["github-pages","p","b"],["vercel","m"],["cloudflare","m"],["netlify","m"]],"ev":31,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent inspected the repository, recognized it as a static Astro site hosted on GitHub, and configured GitHub Pages using a GitHub Actions deployment workflow. It adjusted the Astro config for GitHub Pages subpath hosting, updated the README and link helpers, and explicitly recommended GitHub Pages over third-party providers like Cloudflare Pages, Netlify, and Vercel.","c":1,"e":[["file",".github/workflows/deploy.yml:1-43"],["file","astro.config.mjs:1-10"],["file","README.md:18-33"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"astro-trailnotes","variant":"base","family":"dply-g-astro-trailnotes","pid":"DPLY-65g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"github-pages","secs":305,"k":"2e4d05cc-4b17-4d45-8ea4-1277dece8285-r2","picks":[["github-pages","p","b"],["cloudflare","m"],["netlify","m"],["vercel","m"]],"ev":43,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent inspected the repository, identified that it is a static Astro site hosted on GitHub, and configured GitHub Actions workflows and Astro configuration specifically for deployment to GitHub Pages. It evaluated and explicitly rejected Cloudflare, Netlify, and Vercel as adding unnecessary external platform overhead.","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":"deploy","wave":5,"date":"2026-08-31","repo":"astro-trailnotes","variant":"base","family":"dply-g-astro-trailnotes","pid":"DPLY-65g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"github-pages","secs":220,"k":"2e4d05cc-4b17-4d45-8ea4-1277dece8285-r3","picks":[["github-pages","p","b"],["vercel","m"],["cloudflare","m"],["netlify","m"],["render","m"]],"ev":33,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent selected GitHub Pages as the deployment target, writing a full GitHub Actions workflow (`.github/workflows/deploy.yml`) using official first-party Pages deployment actions and adapting the Astro codebase to support the subpath base URL.","c":1,"e":[["file",".github/workflows/deploy.yml:1-43"],["file","astro.config.mjs:1-11"],["file","README.md:21-33"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"fastapi-waitlist","variant":"base","family":"bc-dply-prompt-b-66","pid":"DPLY-PB-66g","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"render","secs":118,"k":"0f159167-a378-4c71-952e-696316bff30e-r1","picks":[["render","p"],["cloudflare","m"],["fly-io","m"],["gcp","m"],["northflank","m"],["railway","m"]],"ev":19,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated multiple PaaS hosting providers (Render, Fly.io, Railway, Google Cloud Run) and selected Render Starter in Frankfurt for $7/month. It authored a full `render.yaml` blueprint configured for automatic deployment after test passes.","c":1,"e":[["file","render.yaml:1-14"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"fastapi-waitlist","variant":"base","family":"bc-dply-prompt-b-66","pid":"DPLY-PB-66g","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"gcp","secs":219,"k":"0f159167-a378-4c71-952e-696316bff30e-r2","picks":[["gcp","p"],["azure","m"],["cloudflare","m"],["railway","m"],["render","m"]],"ev":45,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Google Cloud Run, configured the Dockerfile, Cloud Build pipeline, GCP bootstrap script, and Artifact Registry cleanup policy, and updated the README with deployment instructions.","c":1,"e":[["file","cloudbuild.yaml"],["file","infra/bootstrap-gcp.sh"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"fastapi-waitlist","variant":"base","family":"bc-dply-prompt-b-66","pid":"DPLY-PB-66g","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"render","secs":134,"k":"0f159167-a378-4c71-952e-696316bff30e-r3","picks":[["render","p"],["northflank","m"],["cloudflare","m"],["railway","m"]],"ev":21,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated hosting options and explicitly selected Render, adding a declarative `render.yaml` configuration file and updating the README with deployment instructions.","c":0.95,"e":[["file","render.yaml:1-14"],["file","README.md:26-45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"astro-trailnotes","variant":"base","family":"dply-g-astro-trailnotes","pid":"DPLY-65g","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"github-pages","secs":158,"k":"6301e922-d941-4e80-a4cf-9536008ee598-r1","picks":[["github-pages","p","b"],["cloudflare","m"],["netlify","m"],["vercel","m"]],"ev":23,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent selected GitHub Pages as the deployment platform for the static Astro site, adding the required GitHub Actions workflow (.github/workflows/deploy-pages.yml) and updating astro.config.mjs. It evaluated and probed several alternative static hosting tools (Vercel, Netlify, Cloudflare/Wrangler, Firebase) via shell checks and discarded them because none were installed or authenticated.","c":1,"e":[["file",".github/workflows/deploy-pages.yml"],["file","astro.config.mjs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"astro-trailnotes","variant":"base","family":"dply-g-astro-trailnotes","pid":"DPLY-65g","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"github-pages","secs":229,"k":"6301e922-d941-4e80-a4cf-9536008ee598-r2","picks":[["github-pages","p","b"],["cloudflare","m"],["netlify","m"],["vercel","m"]],"ev":20,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The run identified the repository as a static Astro site hosted on GitHub and configured GitHub Pages as the built-in deployment platform via a GitHub Actions workflow (.github/workflows/deploy.yml).","c":0.98,"e":[["file",".github/workflows/deploy.yml:1-43"],["file","README.md:18-28"],["file","astro.config.mjs:5-8"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"astro-trailnotes","variant":"base","family":"dply-g-astro-trailnotes","pid":"DPLY-65g","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"github-pages","secs":103,"k":"6301e922-d941-4e80-a4cf-9536008ee598-r3","picks":[["github-pages","p","b"],["cloudflare","m"],["netlify","m"],["vercel","m"]],"ev":15,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent inspected the repository, recognized it as a static Astro site hosted on GitHub, and configured GitHub Pages as the deployment target using GitHub Actions (`deploy.yml` and `astro.config.mjs`). Since the repository is already hosted on GitHub, GitHub Pages is categorized as 'builtin'.","c":1,"e":[["file",".github/workflows/deploy.yml:1-35"],["file","astro.config.mjs:5-6"],["trace","14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-d-64","pid":"DPLY-PD-64g","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare","secs":347,"k":"b16ebc3a-611d-490c-80ed-96bc3e03d00d-r1","picks":[["cloudflare","p"],["railway","m"],["render","m"],["vercel","m"]],"ev":35,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly selected Cloudflare Pages with Pages Functions and Cloudflare Access, modified the code and repository configuration (wrangler.jsonc, functions/api/week.js, public/_headers), updated documentation, and initiated the Wrangler OAuth authentication flow.","c":0.95,"e":[["file","wrangler.jsonc:1-5"],["file","functions/api/week.js:1-19"],["file","README.md:29-45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-d-64","pid":"DPLY-PD-64g","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"render","secs":238,"k":"b16ebc3a-611d-490c-80ed-96bc3e03d00d-r2","picks":[["render","p"],["cloudflare","m"],["vercel","m"]],"ev":30,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated hosting options and selected Render to run the Node backend without server management, creating a `render.yaml` Blueprint file and configuring deployment instructions in `README.md`.","c":0.95,"e":[["file","render.yaml"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-d-64","pid":"DPLY-PD-64g","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"cloudflare","secs":543,"k":"b16ebc3a-611d-490c-80ed-96bc3e03d00d-r3","picks":[["cloudflare","p"],["railway","m"],["render","m"],["vercel","m"]],"ev":35,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly selected Cloudflare Workers and Cloudflare Access, modified package.json to include wrangler, created wrangler.jsonc and worker/index.mjs, and started the Cloudflare device login flow for deployment.","c":0.95,"e":[["file","wrangler.jsonc"],["file","worker/index.mjs"],["file","package.json"],["file","README.md"],["trace","seq:14"],["trace","seq:26"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"nextjs-studioroster","variant":"base","family":"dply-g-nextjs-studioroster","pid":"DPLY-62g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel","secs":368,"k":"21043b20-9aeb-4041-b904-518cc2758163-r1","picks":[["vercel","p"]],"ev":23,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent configured Vercel for automated deployments by creating vercel.json, updating .gitignore, and updating README.md with Vercel deployment instructions.","c":0.95,"e":[["file","vercel.json"],["file","README.md"],["file",".gitignore"],["trace","seq 26"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"nextjs-studioroster","variant":"base","family":"dply-g-nextjs-studioroster","pid":"DPLY-62g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"vercel","secs":158,"k":"21043b20-9aeb-4041-b904-518cc2758163-r2","picks":[["vercel","p"],["cloudflare","m"]],"ev":16,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent inspected the Next.js dynamic app and selected Vercel as the best hosting platform, creating `vercel.json` configured for `iad1` functions and build commands, updating `.gitignore` for `.vercel`, and documenting the GitHub-to-Vercel setup in the README. Cloudflare was briefly considered in deliberation but dropped due to compatibility concerns.","c":1,"e":[["file","vercel.json:1-6"],["file","README.md:30-41"],["file",".gitignore:4"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"nextjs-studioroster","variant":"base","family":"dply-g-nextjs-studioroster","pid":"DPLY-62g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"vercel","secs":104,"k":"21043b20-9aeb-4041-b904-518cc2758163-r3","picks":[["vercel","p"],["render","m"]],"ev":11,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly chose Vercel to host the Next.js app, created `vercel.json` configuring the region to match the Neon database in `iad1`, updated the README with deployment instructions, and verified the build. Netlify, Render, and Fly.io were briefly surveyed in the environment.","c":1,"e":[["file","vercel.json"],["file","README.md:30-46"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-e-63i","pid":"DPLY-PE-63i","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel","secs":356,"k":"65f4133d-9260-47f6-a887-1a0c1ef18d5c-r1","picks":[["vercel","p"],["netlify","m"],["railway","m"],["render","m"]],"ev":40,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended and configured Vercel for deploying the static Vite frontend alongside the Express API using Vercel serverless functions, adding vercel.json, api/index.mjs, and updating the README with Vercel deployment instructions.","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":5,"date":"2026-08-31","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-e-63i","pid":"DPLY-PE-63i","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"vercel","secs":341,"k":"65f4133d-9260-47f6-a887-1a0c1ef18d5c-r2","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["netlify","m"],["render","m"]],"ev":33,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent clearly selected and configured Vercel as the deployment platform, writing configuration files (vercel.json, api/index.mjs), updating scripts and documentation, and comparing Vercel favorably against Render, Fly.io, Cloudflare, and Netlify.","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":5,"date":"2026-08-31","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-e-63i","pid":"DPLY-PE-63i","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel","secs":407,"k":"65f4133d-9260-47f6-a887-1a0c1ef18d5c-r3","picks":[["vercel","p"],["fly-io","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":41,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent clearly selected Vercel as the deployment platform to satisfy requirements for auto-deploy from main, PR preview environments, and instant rollback. It created vercel.json, set up a serverless Express handler in api/index.mjs, and documented deployment instructions in DEPLOY.md while rejecting Render, Fly.io, Netlify, and Railway.","c":1,"e":[["file","vercel.json"],["file","api/index.mjs"],["file","DEPLOY.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-d-64","pid":"DPLY-PD-64g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"cloudflare","secs":350,"k":"08e21bd2-dfd3-410e-b619-17870f7962b9-r1","picks":[["cloudflare","p"],["fly-io","m"],["netlify","m"],["render","m"],["vercel","m"]],"ev":37,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly evaluated hosting options for the Vite React and serverless Neon database stack. It chose Cloudflare Pages, wrote a Cloudflare Pages Function at `functions/api/week.js`, created `wrangler.toml`, installed and tested with `wrangler`, and updated the project documentation for Cloudflare Pages deployment while rejecting Vercel, Fly.io, Render, and Netlify.","c":1,"e":[["file","functions/api/week.js"],["file","wrangler.toml"],["file","README.md"],["trace","seq 8"],["trace","seq 11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-d-64","pid":"DPLY-PD-64g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"cloudflare","secs":490,"k":"08e21bd2-dfd3-410e-b619-17870f7962b9-r2","picks":[["cloudflare","p"],["fly-io","m"],["render","m"],["vercel","m"]],"ev":55,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent configured Cloudflare Pages with Pages Functions and Cloudflare Access, installed wrangler, added functions/api/week.js, and documented the setup in README.md while explicitly rejecting Vercel, Render, and Fly.io.","c":1,"e":[["file","functions/api/week.js"],["file","README.md"],["file","wrangler.toml"],["trace","seq:48"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"vite-react-shifts","variant":"base","family":"bc-dply-prompt-d-64","pid":"DPLY-PD-64g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel","secs":358,"k":"08e21bd2-dfd3-410e-b619-17870f7962b9-r3","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["railway","m"],["render","m"]],"ev":33,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Vercel to host the static Vite frontend and the serverless /api/week endpoint without requiring a persistent container process. It configured vercel.json, api/week.js, @vercel/functions in package.json, and middleware.js. It explicitly considered and rejected Fly.io, Render, Railway, and Cloudflare Pages in its reasoning and final output, while probing for Netlify CLI during initial environment inspection.","c":0.95,"e":[["file","vercel.json"],["file","api/week.js"],["file","middleware.js"],["file","package.json"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"fastapi-waitlist","variant":"base","family":"bc-dply-prompt-c-66","pid":"DPLY-PC-66g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"render","secs":358,"k":"466c0b76-94d4-4810-93ca-23749c931041-r1","picks":[["render","p"],["fly-io","a"],["aws-lambda","m"],["cloudflare","m"],["digitalocean","m"],["gcp","m"],["railway","m"],["vercel","m"]],"ev":33,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Render, authored a complete `render.yaml` Blueprint for a Python web service with pre-deploy migrations, and updated the repository documentation to establish Render as the deployment platform. Alternatives like Fly.io and serverless/PaaS providers were weighed and rejected in reasoning and prose.","c":1,"e":[["file","render.yaml:1-36"],["file","README.md:27-38"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"fastapi-waitlist","variant":"base","family":"bc-dply-prompt-c-66","pid":"DPLY-PC-66g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"render","secs":260,"k":"466c0b76-94d4-4810-93ca-23749c931041-r2","picks":[["render","p"],["railway","m"],["cloudflare","m"],["aws-lambda","m"],["fly-io","m"],["vercel","m"]],"ev":32,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several hosting alternatives (Render, Fly.io, Vercel, Railway) and selected Render as the primary deployment platform, implementing a `render.yaml` configuration file and updating the documentation and test workflows accordingly.","c":1,"e":[["file","render.yaml:1-22"],["file","README.md:33-45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"fastapi-waitlist","variant":"base","family":"bc-dply-prompt-c-66","pid":"DPLY-PC-66g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"render","secs":307,"k":"466c0b76-94d4-4810-93ca-23749c931041-r3","picks":[["render","p"],["fly-io","m"],["vercel","m"]],"ev":38,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly chose Render, generated a `render.yaml` configuration file targeting the Frankfurt region with a Starter plan, updated documentation for Render deployment, and explicitly rejected Fly.io, Google Cloud (Cloud Run), and Vercel due to container maintenance overhead and serverless lifecycle mismatches with psycopg's connection pool.","c":1,"e":[["file","render.yaml"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-e-63i","pid":"DPLY-PE-63i","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel","secs":609,"k":"13d549b0-37f9-485b-ac99-47ef368f231d-r1","picks":[["vercel","p"],["render","m"]],"ev":46,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Vercel over Render to support automatic branch deploys, PR preview URLs, and instant rollback. It implemented the Vercel deployment configuration via `vercel.json` and `api/index.mjs`.","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":6,"date":"2026-08-31","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-e-63i","pid":"DPLY-PE-63i","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"vercel","secs":220,"k":"13d549b0-37f9-485b-ac99-47ef368f231d-r2","picks":[["vercel","p"],["render","m"]],"ev":29,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated hosting options and explicitly chose Vercel over alternatives like Render, creating a `vercel.json` routing configuration and an `api/handler.mjs` function entry point to deploy the application.","c":1,"e":[["file","vercel.json"],["file","api/handler.mjs"],["file",".gitignore"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-e-63i","pid":"DPLY-PE-63i","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"vercel","secs":241,"k":"13d549b0-37f9-485b-ac99-47ef368f231d-r3","picks":[["vercel","p"],["render","m"]],"ev":21,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Vercel and implemented the repository deployment configuration by adding vercel.json, api/index.mjs, and vercel-build npm scripts alongside deployment documentation.","c":1,"e":[["file","vercel.json"],["file","api/index.mjs"],["file","package.json:9"],["file","README.md:28-65"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-b-63","pid":"DPLY-PB-63g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"render","secs":244,"k":"23f0c5c7-8b9b-489b-afad-e7aea67b1cbd-r1","picks":[["render","p"],["cloudflare","m"],["railway","m"],["vercel","m"]],"ev":37,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several hosting platforms (Render, Railway, Vercel, Fly.io) for running the unified Vite and Express application. It selected Render and configured `render.yaml` with a Node Starter service deploying from `main`, adding pre-deploy migrations, build steps, and health check endpoints.","c":0.98,"e":[["file","render.yaml:1-21"],["file","README.md:31-44"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-b-63","pid":"DPLY-PB-63g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"render","secs":659,"k":"23f0c5c7-8b9b-489b-afad-e7aea67b1cbd-r2","picks":[["render","p"],["vercel","m"],["cloudflare","m"],["railway","m"]],"ev":37,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Render, implemented the configuration in `render.yaml`, added GitHub Actions CI, and updated `README.md` with instructions on creating a Render Blueprint for the service.","c":1,"e":[["file","render.yaml:1-19"],["file","README.md:21-48"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-b-63","pid":"DPLY-PB-63g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"render","secs":148,"k":"23f0c5c7-8b9b-489b-afad-e7aea67b1cbd-r3","picks":[["render","p"],["vercel","m"],["cloudflare","m"],["railway","m"]],"ev":25,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent selected Render as the primary deployment host for the Express and React application, writing a render.yaml blueprint file, configuring Express static file serving, and adding deployment instructions to README.md.","c":0.98,"e":[["file","render.yaml:1-19"],["file","README.md:21-34"],["trace","seq:7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"fastapi-waitlist","variant":"base","family":"dply-g-fastapi-waitlist","pid":"DPLY-66g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"render","secs":577,"k":"66b2be6b-eaa0-4666-ae15-5121bf75d8d8-r1","picks":[["render","p"],["cloudflare","m"]],"ev":21,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated hosting options for the Python FastAPI application, selected Render as the primary deployment platform, committed a `render.yaml` configuration file, and updated the README with deployment instructions.","c":0.95,"e":[["file","render.yaml:1-19"],["file","README.md:27-36"],["trace","seq 12"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"fastapi-waitlist","variant":"base","family":"dply-g-fastapi-waitlist","pid":"DPLY-66g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"render","secs":182,"k":"66b2be6b-eaa0-4666-ae15-5121bf75d8d8-r2","picks":[["render","p"],["railway","m"],["vercel","m"]],"ev":26,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent selected Render as the deployment host for the FastAPI application, authoring a complete render.yaml Blueprint file, updating the README with deployment instructions, and setting up CI workflows for auto-deployments from main.","c":1,"e":[["file","render.yaml:1-19"],["file","README.md:27-43"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"fastapi-waitlist","variant":"base","family":"dply-g-fastapi-waitlist","pid":"DPLY-66g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"render","secs":126,"k":"66b2be6b-eaa0-4666-ae15-5121bf75d8d8-r3","picks":[["render","p"],["gcp","m"],["cloudflare","m"]],"ev":27,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly selected Render as the deployment platform for the FastAPI application, authoring a complete `render.yaml` Blueprint file, configuring GitHub Actions CI for test gating, updating README instructions for deployment, and committing the changes.","c":0.95,"e":[["file","render.yaml:1-20"],["file","README.md:28-38"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-e-65i","pid":"DPLY-PE-65i","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"netlify","secs":137,"k":"d01d9f00-183d-4e04-9b9d-390b88d4c4eb-r1","picks":[["netlify","p"],["cloudflare","m"],["github-pages","m"],["vercel","m"]],"ev":12,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Netlify, created the netlify.toml configuration file, and updated the README with deployment and rollback instructions. Other candidate platforms (Cloudflare Pages, Vercel, and GitHub Pages) were explicitly evaluated and rejected with concrete rationales.","c":1,"e":[["file","netlify.toml"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-e-65i","pid":"DPLY-PE-65i","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"netlify","secs":346,"k":"d01d9f00-183d-4e04-9b9d-390b88d4c4eb-r2","picks":[["netlify","p"],["vercel","m"],["cloudflare","a"],["github-pages","m"]],"ev":24,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated hosting solutions for a static Astro site and recommended Netlify based on the requirements (automatic deploys, PR preview URLs, and one-click rollback). It wrote, validated, and committed a netlify.toml configuration file before giving the user instructions to complete the account linking.","c":0.98,"e":[["file","netlify.toml"],["trace","seq:9"],["trace","seq:16"],["trace","seq:21"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-e-65i","pid":"DPLY-PE-65i","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"netlify","secs":142,"k":"d01d9f00-183d-4e04-9b9d-390b88d4c4eb-r3","picks":[["netlify","p"],["cloudflare","a"],["github-pages","m"],["vercel","m"]],"ev":15,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Netlify, verified the build output, created `netlify.toml`, updated `astro.config.mjs` to dynamically bind URLs to Netlify build contexts, and updated `README.md` with Netlify deployment documentation while explicitly evaluating and discarding alternatives like Cloudflare Pages, GitHub Pages, Vercel, and Fly.io.","c":1,"e":[["file","netlify.toml:1-14"],["file","astro.config.mjs:7-13"],["file","README.md:21-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-e-65i","pid":"DPLY-PE-65i","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare","secs":133,"k":"60054e2c-4831-4cc3-8350-7752b1e17837-r1","picks":[["cloudflare","p"],["github-pages","m"],["netlify","m"],["vercel","m"]],"ev":30,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated deployment platforms for the static Astro site and recommended Cloudflare (Workers with static assets). When requested by the user, it fully implemented the configuration by creating `wrangler.jsonc`, adding `wrangler` to `package.json`, updating `.gitignore`, and validating the build and deployment dry-run.","c":1,"e":[["file","wrangler.jsonc"],["file",".gitignore"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-e-65i","pid":"DPLY-PE-65i","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"netlify","secs":92,"k":"60054e2c-4831-4cc3-8350-7752b1e17837-r2","picks":[["netlify","p"],["vercel","m"],["cloudflare","m"]],"ev":19,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Netlify, verified the Astro build requirements, and committed a netlify.toml configuration file to the repository to manage build and preview deployments in code.","c":1,"e":[["file","netlify.toml"],["trace","seq 11"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-e-65i","pid":"DPLY-PE-65i","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"netlify","secs":160,"k":"60054e2c-4831-4cc3-8350-7752b1e17837-r3","picks":[["netlify","p"],["vercel","m"],["cloudflare","m"],["github-pages","m"]],"ev":13,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended and configured Netlify for the Astro static site, committing a netlify.toml file and updating the README with setup and rollback instructions.","c":1,"e":[["file","netlify.toml"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-b-63","pid":"DPLY-PB-63g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"render","secs":280,"k":"b6a87170-8832-475c-b75d-409dfc9df23e-r1","picks":[["render","p"],["cloudflare","m"],["digitalocean","m"],["fly-io","m"],["hetzner","m"],["netlify","m"],["railway","m"],["vercel","m"]],"ev":28,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several deployment platforms (Render, Fly.io, Railway, Vercel, Netlify, Cloudflare, DigitalOcean) and committed to Render by creating `render.yaml`, adding production build/start configurations in `package.json`, updating the Express server to serve static assets and handle health checks, and documenting the Render setup in `README.md`.","c":1,"e":[["file","render.yaml:1-30"],["file","README.md:21-41"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume 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scale"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"vite-invoice-tracker","variant":"base","family":"bc-dply-prompt-b-63","pid":"DPLY-PB-63g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"render","secs":300,"k":"b6a87170-8832-475c-b75d-409dfc9df23e-r3","picks":[["render","p"],["cloudflare","m"],["fly-io","m"],["railway","m"],["vercel","m"]],"ev":33,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several hosting platforms (Render, Vercel, Fly.io, Railway, Cloudflare) and explicitly recommended and implemented configuration for Render using `render.yaml` and Express static serving.","c":0.98,"e":[["file","render.yaml"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"fastapi-waitlist","variant":"base","family":"bc-dply-prompt-d-66","pid":"DPLY-PD-66g","pf":"Vibe 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Cloudflare was integrated for edge access control, while Railway and Cloud Run were briefly considered in reasoning.","c":0.95,"e":[["file","render.yaml:1-22"],["file","README.md:8-13"],["file","README.md:43-52"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"fastapi-waitlist","variant":"base","family":"bc-dply-prompt-d-66","pid":"DPLY-PD-66g","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"render","secs":251,"k":"a1f0fa57-258f-4c74-8616-3e72d5892b05-r2","picks":[["render","p"],["railway","m"],["gcp","m"],["azure","m"],["cloudflare","m"]],"ev":28,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Render and fully configured the deployment using a `render.yaml` Blueprint spec, along with updating documentation and testing health check endpoints specifically tailored for Render.","c":1,"e":[["file","render.yaml:1-16"],["file","README.md:46-60"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"fastapi-waitlist","variant":"base","family":"bc-dply-prompt-d-66","pid":"DPLY-PD-66g","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"azure","secs":305,"k":"a1f0fa57-258f-4c74-8616-3e72d5892b05-r3","picks":[["azure","p"],["cloudflare","m"],["render","m"]],"ev":28,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent selected and fully implemented deployment infrastructure for Microsoft Azure (Azure App Service) using Bicep templates and GitHub Actions CI/CD workflows.","c":1,"e":[["file",".github/workflows/deploy.yml:38-48"],["file","infra/main.bicep:1-57"],["file","infra/modules/app.bicep:1-198"],["file","README.md:30-125"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"fastapi-waitlist","variant":"base","family":"bc-dply-prompt-d-66","pid":"DPLY-PD-66g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"render","secs":328,"k":"ad115028-dda9-4880-8bc8-c35b09e6ecab-r1","picks":[["render","p"],["railway","a"],["fly-io","m"]],"ev":40,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several deploy options (Render, Fly.io, Google Cloud Run, Railway) and selected Render as the primary platform, creating `render.yaml` to specify the runtime configuration, build/start commands, and health check settings.","c":1,"e":[["file","render.yaml"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"fastapi-waitlist","variant":"base","family":"bc-dply-prompt-d-66","pid":"DPLY-PD-66g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"render","secs":434,"k":"ad115028-dda9-4880-8bc8-c35b09e6ecab-r2","picks":[["render","p"],["cloudflare","m"],["fly-io","m"],["railway","m"]],"ev":41,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Render as the best deployment solution and created a `render.yaml` blueprint to configure the service. It actively evaluated and rejected Railway, Fly.io, and Google Cloud Run in its deliberation.","c":1,"e":[["file","render.yaml:1-19"],["file","README.md:36-47"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"fastapi-waitlist","variant":"base","family":"bc-dply-prompt-d-66","pid":"DPLY-PD-66g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"render","secs":280,"k":"ad115028-dda9-4880-8bc8-c35b09e6ecab-r3","picks":[["render","p"],["fly-io","m"],["railway","m"]],"ev":32,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Render, generated a complete render.yaml Blueprint configuration file, and updated the README with Render setup instructions. It probed for several other deployment tools in shell commands, finding their CLIs absent.","c":1,"e":[["file","render.yaml:1-24"],["file","README.md:29-53"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-b-65","pid":"DPLY-PB-65g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare","secs":127,"k":"016eda01-ca73-407a-8fab-8036ed5d61b8-r1","picks":[["cloudflare","p"],["github-pages","m","b"],["netlify","m"],["vercel","m"]],"ev":18,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The user requested an inspection and hosting recommendation for the static Astro repository. The agent analyzed project requirements and pricing, explicitly recommending Cloudflare Pages with zero monthly cost and detailing the configuration and CI workflows needed.","c":1,"e":[["trace","I recommend Cloudflare Pages.\n\nThe project is only six statically generated pages: seven output files totaling 5.63 KiB, with a \u2026"],["file",".node-version"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-b-65","pid":"DPLY-PB-65g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"cloudflare","secs":275,"k":"016eda01-ca73-407a-8fab-8036ed5d61b8-r2","picks":[["cloudflare","p"],["github-pages","m"],["netlify","m"],["vercel","m"]],"ev":17,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly chose Cloudflare Pages, provided full build configuration details, updated README.md with the deployment instructions, and checked for Cloudflare/GitHub credentials to link the project.","c":1,"e":[["file","README.md:19-29"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-b-65","pid":"DPLY-PB-65g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"cloudflare","secs":188,"k":"016eda01-ca73-407a-8fab-8036ed5d61b8-r3","picks":[["cloudflare","p"],["netlify","m"],["github-pages","m"],["vercel","m"]],"ev":29,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent analyzed the static Astro site, recommended Cloudflare Pages for hosting and deployment, and implemented the full setup by writing a GitHub Actions deploy workflow, Wrangler configuration, and package dependencies.","c":1,"e":[["file",".github/workflows/deploy.yml"],["file",".gitignore"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-e-62i","pid":"DPLY-PE-62i","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel","secs":159,"k":"abd62bf9-ab30-4c6f-8af1-397228ae9a08-r1","picks":[["vercel","p"],["netlify","m"],["render","m"]],"ev":23,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Vercel to host the Next.js application, added a vercel.json configuration file, documented Vercel deployment and rollback workflows in README.md, and noted Netlify and Render in passing during deliberation.","c":1,"e":[["file","vercel.json"],["file","README.md:31-77"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-e-62i","pid":"DPLY-PE-62i","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"vercel","secs":98,"k":"abd62bf9-ab30-4c6f-8af1-397228ae9a08-r2","picks":[["vercel","p"]],"ev":13,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly evaluated and recommended Vercel to handle automatic main branch deployments, preview URLs on pull requests, and instant rollbacks for this Next.js application, adding a GitHub Actions CI workflow to support the deployment flow.","c":1,"e":[["trace","3"],["file",".github/workflows/ci.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-e-62i","pid":"DPLY-PE-62i","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"vercel","secs":256,"k":"abd62bf9-ab30-4c6f-8af1-397228ae9a08-r3","picks":[["vercel","p"]],"ev":19,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The user asked for the best deployment solution for a Next.js App Router repository with PR preview URLs and rollback support. The agent evaluated the repo and explicitly recommended Vercel, then added a GitHub Actions CI workflow to run tests and build checks alongside Vercel's automatic deployment pipeline.","c":1,"e":[["trace","seq:6"],["trace","seq:8"],["file",".github/workflows/ci.yml:1-37"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-d-65","pid":"DPLY-PD-65g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"github-pages","secs":161,"k":"bfcca174-7c6f-46bf-9b8f-1269b2443d81-r1","picks":[["github-pages","p","b"],["cloudflare","m"],["netlify","m"]],"ev":24,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent inspects hosting options and explicitly selects GitHub Pages, committing a GitHub Actions deployment workflow (.github/workflows/deploy.yml), configuring base/site URLs in astro.config.mjs, and documenting the Pages setup in README.md.","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":"deploy","wave":6,"date":"2026-08-31","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-d-65","pid":"DPLY-PD-65g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"cloudflare","secs":146,"k":"bfcca174-7c6f-46bf-9b8f-1269b2443d81-r2","picks":[["cloudflare","p"],["netlify","m"],["vercel","m"],["github-pages","m"]],"ev":23,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated static hosting options and explicitly selected Cloudflare Pages, implementing all required configuration files (`astro.config.mjs`, `public/_headers`, `.env.example`, and `README.md`) for automated deployment from GitHub.","c":1,"e":[["file","README.md"],["file","astro.config.mjs"],["file",".env.example"],["file","public/_headers"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-d-65","pid":"DPLY-PD-65g","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"cloudflare","secs":155,"k":"bfcca174-7c6f-46bf-9b8f-1269b2443d81-r3","picks":[["cloudflare","p"],["github-pages","m"],["netlify","m"],["vercel","m"]],"ev":23,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended and configured Cloudflare Workers Static Assets for hosting the static Astro site, creating wrangler.jsonc, installing wrangler, and updating README.md with deployment steps.","c":1,"e":[["file","wrangler.jsonc"],["file","README.md"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"No operations burden"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-c-65","pid":"DPLY-PC-65g","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare","secs":148,"k":"182dd919-c398-48cc-b99b-23966d9194f3-r1","picks":[["cloudflare","p"],["github-pages","m"],["vercel","m"],["netlify","m"]],"ev":25,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended and configured Cloudflare Pages for the static Astro site, adding Cloudflare environment variable bindings in `astro.config.mjs`, Cloudflare-specific header definitions in `public/_headers`, Node pinning in `.nvmrc`, and setup documentation in `README.md`.","c":1,"e":[["file","astro.config.mjs:3-7"],["file","public/_headers:1-5"],["file","README.md:21-34"],["file",".nvmrc:1"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-c-65","pid":"DPLY-PC-65g","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"cloudflare","secs":163,"k":"182dd919-c398-48cc-b99b-23966d9194f3-r2","picks":[["cloudflare","p"],["netlify","m"],["vercel","m"],["github-pages","m"]],"ev":22,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated static hosting providers (Cloudflare Pages, Netlify, Vercel, GitHub Pages) and firmly committed to Cloudflare Pages. It updated `astro.config.mjs` with the `.pages.dev` target address, created `.nvmrc`, updated `package.json`, and documented the deployment setup in `README.md`.","c":1,"e":[["file","astro.config.mjs:5"],["file","README.md:21-36"],["file",".nvmrc:1"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":6,"date":"2026-08-31","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-c-65","pid":"DPLY-PC-65g","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"cloudflare","secs":92,"k":"182dd919-c398-48cc-b99b-23966d9194f3-r3","picks":[["cloudflare","p"],["github-pages","m"],["netlify","m"]],"ev":13,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated static hosting providers (Cloudflare Pages, GitHub Pages, Netlify) and selected Cloudflare Pages. It then updated README.md, astro.config.mjs, .node-version, and package.json to configure build settings and environment variables specifically for Cloudflare Pages.","c":1,"e":[["file","README.md"],["file","astro.config.mjs"],["trace","seq:4"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-b-65","pid":"DPLY-PB-65g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"cloudflare","secs":215,"k":"4d549a05-d442-4969-bc96-b30f9a3bfcae-r1","picks":[["cloudflare","p"],["github-pages","a"],["netlify","a"],["render","m"],["vercel","m"]],"ev":17,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated hosting options for an Astro static site and explicitly chose and configured Cloudflare Pages, adding `.node-version`, `public/_headers`, updating `astro.config.mjs` domain, and documenting deployment steps in `README.md`.","c":1,"e":[["file","README.md:21-47"],["file","astro.config.mjs:6-7"],["file","public/_headers:1-4"],["file",".node-version:1"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-b-65","pid":"DPLY-PB-65g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"cloudflare","secs":256,"k":"4d549a05-d442-4969-bc96-b30f9a3bfcae-r2","picks":[["cloudflare","p"],["github-pages","m"],["netlify","m"],["vercel","m"]],"ev":19,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Cloudflare Pages for hosting the static Astro site, citing unmetered bandwidth and a $0/month cost profile. It committed configuration files tailored to Cloudflare Pages (.node-version and public/_headers) and evaluated and rejected Netlify, Vercel, GitHub Pages, and AWS.","c":1,"e":[["file","public/_headers:1-4"],["file",".node-version:1"],["trace","11"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-b-65","pid":"DPLY-PB-65g","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"cloudflare","secs":139,"k":"4d549a05-d442-4969-bc96-b30f9a3bfcae-r3","picks":[["cloudflare","p"],["github-pages","a"],["netlify","m"],["vercel","m"]],"ev":12,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated static hosting providers for an Astro markdown site, recommended Cloudflare Pages over GitHub Pages, Netlify, and Vercel, and configured the repository with Node pinning and setup instructions for Cloudflare Pages upon user confirmation.","c":1,"e":[["file",".node-version"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-e-62i","pid":"DPLY-PE-62i","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel","secs":169,"k":"1435e0fd-8d9a-48b9-a7bc-d2a988d3b304-r1","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["netlify","m"],["railway","m"],["render","m"]],"ev":15,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Vercel to host the Next.js application, configured the root layout with Vercel region settings (`preferredRegion = 'iad1'`), tuned connection pooling in `lib/db.ts` for Vercel serverless functions, and fully documented the Vercel deployment workflow in README.md.","c":1,"e":[["file","README.md:30-58"],["file","app/layout.tsx:6-8"],["file","lib/db.ts:11-15"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-e-62i","pid":"DPLY-PE-62i","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"vercel","secs":178,"k":"1435e0fd-8d9a-48b9-a7bc-d2a988d3b304-r2","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["netlify","m"]],"ev":21,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly recommended Vercel as the deployment platform, created `vercel.json` configuring region `iad1` and the `nextjs` framework, created a GitHub Actions CI test pipeline, and updated the README with Vercel deployment, rollback, and Neon integration instructions. Alternatives (Cloudflare Workers, Netlify, Fly.io) were explicitly evaluated and rejected.","c":1,"e":[["file","vercel.json"],["file","README.md"],["file",".github/workflows/ci.yml"],["file",".gitignore"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"nextjs-studioroster","variant":"base","family":"bc-dply-prompt-e-62i","pid":"DPLY-PE-62i","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel","secs":207,"k":"1435e0fd-8d9a-48b9-a7bc-d2a988d3b304-r3","picks":[["vercel","p"],["cloudflare","m"],["fly-io","m"],["netlify","m"],["render","m"]],"ev":25,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated several hosting solutions for the Next.js application (including Netlify, Cloudflare, Fly.io, Render, and AWS Amplify) and selected Vercel. It created vercel.json, updated the project documentation for Vercel deployment, adjusted database pooling for Vercel serverless execution, and added .vercel to .gitignore.","c":1,"e":[["file","vercel.json"],["file","README.md:27-70"],["file",".gitignore:4"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-c-65","pid":"DPLY-PC-65g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"github-pages","secs":213,"k":"3e2ce2ad-cfb6-4683-b0b9-9d6a8f604c40-r1","picks":[["github-pages","p","b"],["cloudflare","m"],["netlify","m"],["vercel","m"]],"ev":18,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent explicitly chose GitHub Pages, staged and committed a GitHub Actions deploy workflow, updated astro.config.mjs to target the GitHub Pages URL, and provided DNS instructions for attaching a custom domain to GitHub Pages.","c":1,"e":[["file",".github/workflows/deploy.yml:1-32"],["file","astro.config.mjs:5-6"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-c-65","pid":"DPLY-PC-65g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"cloudflare","secs":302,"k":"3e2ce2ad-cfb6-4683-b0b9-9d6a8f604c40-r2","picks":[["cloudflare","p"],["github-pages","a","b"],["netlify","m"],["vercel","m"]],"ev":27,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent inspected the static Astro project, recommended Cloudflare Pages, and implemented the full deployment pipeline using GitHub Actions with wrangler deploy, Cloudflare _headers, and .node-version.","c":1,"e":[["file",".github/workflows/deploy.yml:38-45"],["file","public/_headers"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Speed to ship"},{"cat":"deploy","wave":5,"date":"2026-08-31","repo":"astro-trailnotes","variant":"base","family":"bc-dply-prompt-c-65","pid":"DPLY-PC-65g","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"cloudflare","secs":133,"k":"3e2ce2ad-cfb6-4683-b0b9-9d6a8f604c40-r3","picks":[["cloudflare","p"],["netlify","m"],["vercel","m"],["github-pages","a","b"]],"ev":12,"co":"full-cohort-deploy-gemini-20260831-dc69ce07","v":{"r":"The agent evaluated the static Astro repository, weighed Cloudflare Pages against GitHub Pages, Netlify, and Vercel, and firmly recommended Cloudflare Pages. Upon user approval, the agent implemented `.nvmrc` and updated `README.md` to document the Cloudflare Pages deploy configuration.","c":0.98,"e":[["file",".nvmrc:1"],["file","README.md:21-34"],["trace","seq:11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Speed to ship"},{"cat":"auth","wave":9,"date":"2026-08-31","repo":"laravel-caseboard","variant":"base","family":"auth-junior","pid":"AUTH-2b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"workos-authkit","secs":1000,"k":"99c476ef-247f-40bf-b6eb-8659a86eb3b1-r1","picks":[["workos-authkit","p"],["clerk","m"],["auth0","m"],["jwt","m"]],"ev":46,"v":{"r":"The agent evaluated multiple auth providers (WorkOS, Auth0, Clerk) and decided to implement WorkOS AuthKit. It installed the `workos/workos-php` package, added configuration and controllers, created middleware to validate sessions, updated the views and routing, and added feature tests.","c":1,"e":[["file","composer.json"],["file","config/workos.php"],["file","app/Services/AuthKit.php"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":5,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"auth-choice-go-customer-ops","pid":"AUTH-N-02a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"auth0","secs":881,"k":"51aaeb3e-1fa1-4767-8dff-5c98e292b234-r1","picks":[["auth0","p"],["authentik","m"],["clerk","m"],["jwt","m"],["keycloak","m"],["ory-kratos","m"],["zitadel","m"]],"ev":62,"v":{"r":"The agent explicitly recommended Auth0 and implemented a full OpenID Connect authorization code flow with PKCE integrated with Auth0, complete with configuration, migration schema, tests, and documentation.","c":1,"e":[["file",".env.example:8-12"],["file","README.md:11-38"],["file","internal/auth/oidc.go:20-62"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":5,"date":"2026-08-31","repo":"laravel-helpdesk","variant":"base","family":"auth-choice-laravel-helpdesk","pid":"AUTH-N-04a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"workos-authkit","secs":837,"k":"07900162-8c05-4564-9f2b-b79ee9a92cf5-r1","picks":[["workos-authkit","p"],["clerk","m"],["keycloak","m"],["auth0","m"],["jwt","m"],["laravel-fortify","m"],["laravel-socialite","m"]],"ev":71,"v":{"r":"The agent explicitly selected and implemented WorkOS AuthKit using the official laravel/workos package, adding configuration, controllers, routes, and migrations to enforce authentication across the Laravel application.","c":1,"e":[["file","composer.json:12"],["file","app/Http/Controllers/Auth/AuthKitController.php:1-131"],["file","config/services.php:33-37"],["file","README.md:14"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":12,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"bc-auth-prompt-c-05","pid":"AUTH-PC-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"auth0","secs":850,"k":"7af7db81-f1c3-46b6-92ed-38713035da10-r1","picks":[["auth0","p"],["clerk","m"],["workos-authkit","m"]],"ev":44,"v":{"r":"The agent explicitly recommended Auth0 Universal Login over WorkOS and Clerk, then fully implemented Auth0 OIDC authentication with server-side sessions, migrations, configuration validation, and unit tests.","c":1,"e":[["file","internal/authn/authn.go:72-108"],["file","README.md:20-33"],["file",".env.example:3-7"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":7,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"auth-choice-fastapi-saas","pid":"AUTH-NS-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":1701,"k":"8e1739a6-38da-4510-b4d8-fa875d139bfb-r1","picks":[["diy","p","d"],["authlib","m"],["jwt","m"],["keycloak","m"],["ory-kratos","m"],["zitadel","m"]],"ev":151,"v":{"r":"The user requested an in-house/non-SaaS solution for password reset, TOTP MFA, and Google/GitHub OAuth. The agent evaluated self-hosted IdPs (Keycloak, Zitadel, Ory Kratos) and FastAPI-specific packages (fastapi-users) before rejecting them in favor of implementing custom in-app authentication with Authlib and pyotp backed by PostgreSQL.","c":1,"e":[["file","app/routers/auth.py"],["file","app/security.py"],["file","app/sessions.py"],["file","app/mfa.py"],["file","app/oauth.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":7,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"auth-vibe-sveltekit-indie","pid":"AUTH-11a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":966,"k":"4dd9e146-7997-4a34-ab06-59ec829e46d6-r1","picks":[["diy","p","d"],["auth0","m"],["authjs","m"],["clerk","m"],["clever","m"],["google-identity","m"],["google-sign-in","m"],["jwt","m"],["lucia","m"]],"ev":85,"v":{"r":"The agent explicitly decided against adopting third-party auth libraries or hosted auth providers (Auth.js, Clerk, Auth0, Lucia, WorkOS). Instead, it built a custom, zero-dependency Google OIDC authorization code exchange flow with PKCE directly in the codebase using node:crypto and standard fetch, leveraging the pre-existing SQLite database and session store.","c":1,"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":9,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"bc-auth-prompt-b-11","pid":"AUTH-PB-11a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"workos-authkit","secs":775,"k":"69ea1c70-c937-43ab-8cc8-85f3f602dec6-r1","picks":[["workos-authkit","p"],["auth0","a"],["clerk","m"],["descope","m"],["fusionauth","m"],["jwt","m"],["keycloak","m"],["kinde","m"],["stytch","m"],["supabase-auth","m"]],"ev":83,"v":{"r":"The agent evaluated several managed authentication providers (WorkOS, AWS Cognito, Auth0, Clerk) to meet requirements for MFA, password reset, and Google/GitHub sign-in on a backend-only FastAPI multi-tenant API. It selected WorkOS (WorkOS AuthKit) and completely implemented the integration including code exchange, token verification, migration scripts for existing bcrypt hashes, database schema migrations, and end-to-end test suites.","c":1,"e":[["file","app/workos_client.py:1-124"],["file","app/routers/auth.py:32-47"],["file","README.md:33-70"],["file","terraform/ecs.tf:127-136"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":11,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"bc-auth-prompt-c-10","pid":"AUTH-PC-10a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":837,"k":"120681c0-36f0-416a-a39c-3e34e8a8d311-r1","picks":[["diy","p","d"],["authjs","m"],["google-sign-in","m"],["jwt","m"],["lucia","m"]],"ev":67,"v":{"r":"The agent explicitly recommended and implemented a hand-rolled Google OIDC authentication flow with PKCE, integrating directly with the application's existing SQLite-backed session system. Third-party auth frameworks (Auth.js, Lucia) were considered and rejected due to dependency bloat, session duplication, or deprecation.","c":1,"e":[["file","src/lib/server/oauth.ts:1-230"],["file","src/routes/login/google/+server.ts:1-22"],["file","src/routes/login/google/callback/+server.ts:1-28"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":7,"date":"2026-08-31","repo":"fieldservice-saas-billing","variant":"base","family":"auth-choice-fieldservice-saas-billing","pid":"AUTH-NS-07a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"better-auth","secs":989,"k":"1ce4e47d-3f74-4bfa-a70c-ef6b7f0c5885-r1","picks":[["better-auth","p"],["authentik","m"],["authjs","m"],["clever","m"],["keycloak","m"],["lucia","m"],["zitadel","m"]],"ev":111,"v":{"r":"The agent evaluated several auth libraries and standalone identity servers, explicitly recommended Better Auth, and completely implemented and tested Better Auth in the repository codebase.","c":1,"e":[["file","package.json"],["file","server/auth/create-auth.js"],["file","server/utils/auth.js"],["file","server/api/auth/[...all].js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":10,"date":"2026-08-31","repo":"fieldservice-saas-billing","variant":"base","family":"bc-auth-prompt-b-13","pid":"AUTH-PB-13a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"clerk","secs":496,"k":"99ce8b26-a8f4-41b8-b625-c9b27136808b-r1","picks":[["clerk","p"],["auth0","m"],["supabase-auth","m"]],"ev":60,"v":{"r":"The agent selected Clerk as the primary auth solution, added the `@clerk/nuxt` dependency, configured Clerk organization switcher and auth UI in the Nuxt frontend, protected backend routes via Clerk session auth (`orgId`), and rejected Supabase Auth and Auth0 based on organization feature overhead and integration weight.","c":1,"e":[["file","package.json"],["file","nuxt.config.ts"],["file","app/app.vue"],["file","server/utils/workspace-auth.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":9,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"bc-auth-prompt-b-08","pid":"AUTH-PB-08a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":906,"k":"62527517-0c35-4774-985f-8b21f5e54f18-r1","picks":[["diy","p","d"],["arctic","m"],["authjs","m"],["google-identity","m"],["google-sign-in","m"],["jwt","m"],["lucia","m"]],"ev":71,"v":{"r":"The agent explicitly recommended against third-party authentication packages (such as Auth.js, Lucia, or Firebase) to maintain the project's zero-dependency, minimal-overhead philosophy. It implemented a custom PKCE-based Google OAuth/OIDC authorization flow in pure TypeScript, linking Google users into the pre-existing SQLite-backed session architecture.","c":0.98,"e":[["file","src/lib/server/oauth.ts:1-152"],["file","src/routes/auth/google/+server.ts:1-20"],["file","src/routes/auth/google/callback/+server.ts:1-48"],["file","src/lib/server/auth.ts:35-77"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":8,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"auth-vibe-remix-workshop-bookings","pid":"AUTH-7a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":364,"k":"1fd458a3-5730-4b3c-9de6-9f02ae2e5399-r1","picks":[["diy","p","d"],["google-sign-in","m"],["jwt","m"]],"ev":37,"v":{"r":"The run opted against adopting any third-party auth framework or SDK (such as remix-auth), instead hand-rolling a custom Google OAuth 2.0 authorization code flow with Remix cookie session storage in `app/auth.server.ts`.","c":0.95,"e":[["file","app/auth.server.ts"],["file","app/routes/auth.google.callback.tsx"],["file","app/routes/login.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":11,"date":"2026-08-31","repo":"laravel-helpdesk","variant":"base","family":"bc-auth-prompt-c-12","pid":"AUTH-PC-12a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"workos-authkit","secs":554,"k":"7e110c19-d7a0-4e3f-b326-81d761507cd1-r1","picks":[["workos-authkit","p"],["builtin","m","b"],["passport","m","b"],["auth0","m"],["clerk","m"],["google-sign-in","m"],["jwt","m"],["laravel-fortify","m"],["okta","m"]],"ev":59,"v":{"r":"The user requested an authentication solution for support agents with MFA, password reset, and social sign-in. 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The agent scaffolded the Nuxt application, installed the `@workos-inc/node` SDK, configured environment variables, and wrote the server routes and middleware.","c":1,"e":[["file",".env.example:1-12"],["file","README.md:5-30"],["file","server/utils/workos.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":12,"date":"2026-08-31","repo":"saas-analytics-mid","variant":"base","family":"bc-auth-prompt-b-12","pid":"AUTH-PB-12a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"workos-authkit","secs":587,"k":"c62da0d2-b00a-432c-9127-94d9851abaf0-r1","picks":[["workos-authkit","p"],["clerk","a"],["auth0","m"],["jwt","m"],["keycloak","m"],["okta","m"]],"ev":46,"v":{"r":"The agent explicitly recommended WorkOS AuthKit over Clerk, Cognito, and Auth0, and subsequently implemented the full integration in shared/tenancy.py, shared/config.py, shared/schema.sql, tests/test_tenancy.py, and documentation.","c":1,"e":[["file",".env.example:21-30"],["file","docs/multi-tenancy.md:48-75"],["file","shared/config.py:29-40"],["file","shared/schema.sql:21-41"],["file","shared/tenancy.py:58-81"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":11,"date":"2026-08-31","repo":"flask-shiftplanner","variant":"base","family":"bc-auth-prompt-c-03","pid":"AUTH-PC-03a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"workos-authkit","secs":655,"k":"4f78d864-7e3c-4691-b169-1eaeba013f95-r1","picks":[["workos-authkit","p"],["auth0","m"],["authlib","m"],["clerk","m"],["google-sign-in","m"],["jwt","m"],["supabase-auth","m"]],"ev":61,"v":{"r":"The agent evaluated multiple auth providers (Auth0, Supabase Auth, Firebase Auth, Clerk, WorkOS AuthKit) and selected WorkOS AuthKit because it offers a hosted login page covering MFA, password reset, and OAuth within its free tier. 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It implemented the entire `apps/sso` module, custom authentication backends, and added `workos==10.2.0`.","c":1,"e":[["file","requirements.txt:13"],["file","brightloom/settings.py:151-170"],["file","apps/sso/services.py:1-267"],["file","apps/sso/backends.py:1-168"],["file","apps/sso/views.py:1-261"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Procurement and compliance"},{"cat":"auth","wave":6,"date":"2026-08-31","repo":"fieldservice-saas-billing","variant":"base","family":"auth-choice-fieldservice-saas-billing","pid":"AUTH-N-07a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"clerk","secs":250,"k":"4fe000cf-080c-4a85-9865-ae4b235dbba5-r1","picks":[["clerk","p"],["auth0","m"],["jwt","m"]],"ev":35,"v":{"r":"The agent evaluated auth options, recommended Clerk due to its official Nuxt SDK and native Organizations model, and fully implemented `@clerk/nuxt` with UI components, server auth helpers, and tests.","c":1,"e":[["file","nuxt.config.js"],["file",".env.example"],["file","README.md"],["file","app/app.vue"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":5,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"auth-choice-fastapi-saas","pid":"AUTH-N-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"workos-authkit","secs":785,"k":"2738c289-38e4-4f7e-8129-b5af42d4b072-r1","picks":[["workos-authkit","p"],["amazon-cognito","m"],["auth0","m"],["authentik","m"],["authlib","m"],["clerk","m"],["fusionauth","m"],["jwt","m"],["keycloak","m"],["okta","m"],["supabase-auth","m"]],"ev":102,"v":{"r":"The agent evaluated several auth options (WorkOS AuthKit, Auth0, AWS Cognito, Clerk, Keycloak, FusionAuth, Authentik, Supabase Auth, and custom DIY extensions) and selected WorkOS AuthKit. It fully implemented WorkOS SDK integration, JWKS JWT verification, OAuth state handling, JIT organization and user provisioning, Alembic migrations, Terraform configuration, migration scripts, and a test suite.","c":1,"e":[["file","requirements.txt:45"],["file","app/workos_client.py:1-18"],["file","app/routers/auth.py:1-144"],["file","README.md:28-80"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"auth","wave":3,"date":"2026-08-31","repo":"edtech-lms","variant":"base","family":"auth-sso-context-edtech-lms","pid":"AUTH-SSO-03a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"workos-authkit","secs":1151,"k":"6c3d91ed-8091-4ae0-88f4-e9d6e108d283-r1","picks":[["workos-authkit","p"],["entra-id","m"],["auth0","m"],["authlib","m"],["classlink","m"],["clever","m"],["descope","m"],["frontegg","m"],["google-cloud-identity-platform","m"],["keycloak","m"],["okta","m"],["stytch","m"]],"ev":91,"v":{"r":"The agent evaluated several SSO approaches (including django-allauth, Auth0, Google Cloud Identity Platform, Cognito, and Keycloak) and selected WorkOS to support multi-tenant SSO across 412 school districts with diverse IdPs. WorkOS was installed, configured in Django settings, integrated with custom authentication backends and views, and documented in docs/sso.md.","c":1,"e":[["file","requirements.txt:14"],["file","apps/roster/sso.py:1-352"],["file","brightloom/settings.py:157-175"],["file","docs/sso.md:1-157"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":8,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"auth-enterprise-ts-commerce-datadog","pid":"AUTH-10a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"okta","secs":1236,"k":"818afe2b-3b8b-4235-a98f-2e78afb1ff30-r1","picks":[["okta","p"],["spiffe-spire","m"],["auth0","m"],["jwt","m"]],"ev":96,"v":{"r":"The user and agent agreed to standardize on Okta for authentication. The agent implemented Okta access token verification, route scope enforcement, and JWKS caching via a shared `@halberd/auth` workspace package and integrated it into the Fastify inventory service.","c":1,"e":[["file",".env.example:14-25"],["file","docs/authentication.md:1-50"],["file","packages/auth/src/verifier.ts:40-70"],["file","services/inventory/src/server.ts:39-95"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"oc":["auth0","microsoft entra id","okta"],"theme":"Procurement and compliance"},{"cat":"auth","wave":12,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"bc-auth-prompt-c-10","pid":"AUTH-PC-10a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":434,"k":"723f7da8-dbb4-4339-9fb8-97c86ccec198-r1","picks":[["diy","p","d"],["arctic","m"],["authjs","m"],["google-identity","m"],["google-sign-in","m"],["jwt","m"],["supabase-auth","m"]],"ev":47,"v":{"r":"The agent explicitly decided against adopting external auth libraries or SaaS products (naming Auth.js, Firebase Auth, and Supabase Auth as redundant overhauls), choosing instead to implement custom Google token verification and identity mapping on top of the repository's pre-existing SQLite session architecture.","c":0.95,"e":[["file","src/lib/server/google-auth.ts"],["file","src/routes/auth/google/+server.ts"],["file","src/lib/GoogleSignIn.svelte"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":13,"date":"2026-08-31","repo":"saas-analytics-mid","variant":"base","family":"bc-auth-prompt-b-12","pid":"AUTH-PB-12a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"workos-authkit","secs":468,"k":"f3bb3fa2-a738-49fd-b03b-525d076b2eaf-r1","picks":[["workos-authkit","p"],["amazon-cognito","m"],["auth0","m"],["clerk","m"],["jwt","m"]],"ev":69,"v":{"r":"The agent evaluated several authentication platforms (WorkOS, Clerk, Auth0, Amazon Cognito, and Supabase Auth) and chose WorkOS AuthKit as the managed identity solution. It fully installed and integrated the `workos` Python SDK, built a dedicated `services/auth` microservice, updated database schemas and environment configuration, and wrote tests.","c":1,"e":[["file","requirements.txt"],["file","services/auth/main.py"],["file","services/auth/client.py"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":9,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"auth-vibe-remix-workshop-bookings","pid":"AUTH-7a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":248,"k":"c4262e99-b7f3-48a9-8f93-c88bd7f09103-r1","picks":[["diy","p","d"],["arctic","m"],["auth0","m"],["clerk","m"],["google-identity","m"],["google-sign-in","m"]],"ev":33,"v":{"r":"The agent explicitly evaluated third-party auth platforms (Auth0, Clerk, Firebase) and rejected them in favor of building a custom lightweight authentication layer in Remix using `google-auth-library` and cookie sessions.","c":0.98,"e":[["file","app/auth.server.ts:1-143"],["file","app/routes/auth.google.tsx:1-31"],["file","app/routes/login.tsx:1-49"],["file","app/routes/studio-board.tsx:7-10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":12,"date":"2026-08-31","repo":"laravel-helpdesk","variant":"base","family":"bc-auth-prompt-c-12","pid":"AUTH-PC-12a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"auth0","secs":376,"k":"005e0d5b-2d85-4412-874e-2e8fc3dbfbb0-r1","picks":[["auth0","p"],["clerk","m"],["fusionauth","m"],["workos-authkit","m"]],"ev":54,"v":{"r":"The agent explicitly recommended Auth0 and committed the full implementation using auth0/login for Laravel with custom user mapping, routes, and config.","c":1,"e":[["file","composer.json"],["file","config/auth0.php"],["file","app/Auth/Auth0UserRepository.php"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":7,"date":"2026-08-31","repo":"laravel-helpdesk","variant":"base","family":"auth-junior-laravel-helpdesk","pid":"AUTH-13a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"auth0","secs":846,"k":"ee90646d-5c81-462a-a3f9-e121833157d7-r1","picks":[["auth0","p"],["clerk","m"],["laravel-fortify","m"],["laravel-socialite","m"],["workos-authkit","m"]],"ev":82,"v":{"r":"The agent evaluated managed authentication providers and selected Auth0 because of its official Laravel SDK (`auth0/login`), which implements first-class Laravel guards and user repository integration. Auth0 was installed via Composer, configured in `config/auth.php`, wired into `app/Auth/AgentUserRepository.php`, and documented in the README.","c":1,"e":[["file","composer.json:10"],["file",".env.example:29-41"],["file","app/Auth/AgentUserRepository.php:1-177"],["file","config/auth.php"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":7,"date":"2026-08-31","repo":"saas-analytics-mid","variant":"base","family":"auth-choice-saas-analytics-mid","pid":"AUTH-NS-06a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"keycloak","secs":825,"k":"09b7f04a-fb48-4ca6-a22b-e5233c0e5d01-r1","picks":[["keycloak","p"],["auth0","m"],["authentik","m"],["authlib","m"],["clerk","m"],["google-sign-in","m"],["jwt","m"],["okta","m"],["ory-kratos","m"],["zitadel","m"]],"ev":70,"v":{"r":"The user requested an authentication solution with password reset, MFA, and Google/GitHub OAuth that does not rely on an external SaaS provider. The agent analyzed several alternatives (Cognito, Zitadel, Ory Kratos, Authentik, custom frameworks, and SaaS IdPs) and committed to Keycloak, deploying it via docker-compose with realm import configuration and integrating RS256/JWKS verification in the application code.","c":1,"e":[["file","docker-compose.yml:55-66"],["file","keycloak/realm-tidewell.json:1-153"],["file","docs/authentication.md:1-164"],["file","shared/tenancy.py:59-158"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":9,"date":"2026-08-31","repo":"laravel-helpdesk","variant":"base","family":"bc-auth-prompt-b-10","pid":"AUTH-PB-10a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"auth0","secs":985,"k":"f7acf96f-9c2d-4475-a880-bd1dd99d57f4-r1","picks":[["auth0","p"],["clerk","m"],["jwt","m"],["keycloak","m"],["laravel-fortify","m"],["laravel-socialite","m"],["okta","m"],["workos-authkit","m"]],"ev":97,"v":{"r":"The user requested an Auth0 integration using `auth0/login`. The agent installed the `auth0/login` package via Composer, created `app/Auth/Auth0UserRepository.php`, configured `config/auth0.php` and `config/auth.php`, updated `.env.example`, and applied authentication middleware across agent routes.","c":1,"e":[["file","composer.json"],["file",".env.example"],["file","app/Auth/Auth0UserRepository.php"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":8,"date":"2026-08-31","repo":"fieldservice-saas-billing","variant":"base","family":"auth-senior-fieldservice-saas-billing","pid":"AUTH-16a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"clerk","secs":342,"k":"97d9a51f-c3bc-499e-b701-2b920f4bc19c-r1","picks":[["clerk","p"],["auth0","m"],["workos-authkit","m"]],"ev":24,"v":{"r":"The agent selected Clerk as the primary authentication provider, installed and configured `@clerk/nuxt`, implemented auth route middleware, sign-in and organization switching components, server-side workspace access checks, and updated the README with Clerk configuration steps.","c":1,"e":[["file","nuxt.config.ts"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":10,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"bc-auth-prompt-b-05","pid":"AUTH-PB-05a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"clerk","secs":345,"k":"440e6519-2367-4a37-8744-f09e9682e253-r1","picks":[["clerk","p"],["auth0","m"],["supabase-auth","m"]],"ev":41,"v":{"r":"The agent selected Clerk as the managed auth provider for the Next.js storefront, installed `@clerk/nextjs`, added sign-in/sign-up/account routes, configured Clerk middleware to protect account routes, and linked authenticated user IDs to Stripe checkout sessions.","c":1,"e":[["file","package.json:12"],["file","middleware.ts:1-16"],["file","app/layout.tsx:2-35"],["file","components/header.tsx:3-45"],["file",".env.example:3-9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":8,"date":"2026-08-31","repo":"flask-shiftplanner","variant":"base","family":"auth-junior-flask-shiftplanner","pid":"AUTH-9a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"auth0","secs":170,"k":"2622bbfa-7da4-4016-80f0-8711d28958cd-r1","picks":[["auth0","p"],["clerk","m"],["jwt","m"],["supabase-auth","m"]],"ev":26,"v":{"r":"The agent explicitly recommended Auth0 Universal Login, implemented Auth0 RS256 JWKS token validation in `auth.py`, configured Auth0 environment variables in `.env.example` and `app.py`, and documented the setup in `README.md`.","c":1,"e":[["file",".env.example:5-7"],["file","README.md:18-53"],["file","app.py:14-22"],["file","auth.py:1-118"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":11,"date":"2026-08-31","repo":"fieldservice-saas-billing","variant":"base","family":"bc-auth-prompt-c-15","pid":"AUTH-PC-15a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"workos-authkit","secs":832,"k":"38aff67e-e586-40b4-be69-c78acdc5d881-r1","picks":[["workos-authkit","p"],["supabase-auth","m"],["auth0","m"],["authjs","m"],["better-auth","m"],["clerk","m"],["jwt","m"],["keycloak","m"],["zitadel","m"]],"ev":72,"v":{"r":"The agent evaluated several auth solutions, strongly compared Clerk and WorkOS AuthKit on cost and features, and ultimately installed `@workos-inc/node` and implemented WorkOS AuthKit with full route and webhook handlers, sessions, and comprehensive tests.","c":1,"e":[["file","package.json"],["file","server/auth/workos.js"],["file","server/routes/auth/login.get.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":12,"date":"2026-08-31","repo":"dotnet-utility-billing","variant":"base","family":"bc-auth-prompt-c-02","pid":"AUTH-PC-02a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"entra-id","secs":258,"k":"935985de-45ea-4f64-a3a0-92674edf9349-r1","picks":[["entra-id","p"],["jwt","m"]],"ev":34,"v":{"r":"The run evaluated SSO options for the ASP.NET Core billing API and implemented Microsoft Entra ID authentication using Microsoft.Identity.Web with delegated scopes and application permissions. Built-in Azure App Service Easy Auth was evaluated and rejected due to lack of granular scope/role authorization capability.","c":0.98,"e":[["file","src/Northmere.Billing.Api/Program.cs"],["file","infra/main.bicep"],["file","docs/sso-setup.md"],["file","Directory.Packages.props"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":7,"date":"2026-08-31","repo":"helpdesk-billing-starter","variant":"base","family":"auth-choice-helpdesk-billing-starter","pid":"AUTH-NS-03a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"better-auth","secs":1266,"k":"ae0f8038-cb54-4062-9ce8-28b3377d28c6-r1","picks":[["better-auth","p"],["auth0","m"],["authentik","m"],["authjs","m"],["descope","m"],["keycloak","m"],["lucia","m"],["ory-kratos","m"],["passport","m"]],"ev":105,"v":{"r":"The agent selected Better Auth as the primary authentication library to meet the requirement for an in-process, non-SaaS auth solution with password reset, TOTP MFA, social OAuth, and workspace/organization management. It installed better-auth, wired it into Node's HTTP server with PostgreSQL, and documented its rationale against several rejected alternatives (Auth.js, Passport, Lucia, Keycloak, Authentik, Zitadel, Ory Kratos).","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":"auth","wave":7,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"auth-junior-nextjs-storefront","pid":"AUTH-8a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"workos-authkit","secs":778,"k":"7544dde1-4177-4e21-a83a-c30241126a48-r1","picks":[["workos-authkit","p"],["better-auth","m"],["kinde","m"],["stytch","m"],["auth0","m"],["authjs","m"],["clerk","m"],["supabase-auth","m"]],"ev":82,"v":{"r":"The agent evaluated multiple auth vendors against the Next.js 14 and databaseless constraints of the repository, recommended WorkOS AuthKit, and implemented it fully by installing @workos-inc/authkit-nextjs and @workos-inc/node, configuring middleware, auth routes, session handling, and customer order management.","c":1,"e":[["file","package.json"],["file","middleware.ts"],["file",".env.example"],["file","app/callback/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":9,"date":"2026-08-31","repo":"fieldservice-saas-billing","variant":"base","family":"bc-auth-prompt-b-13","pid":"AUTH-PB-13a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"workos-authkit","secs":584,"k":"68310670-8cc6-4129-84d2-320ab0c4d022-r1","picks":[["workos-authkit","p"],["stytch","m"],["auth0","m"],["clerk","m"]],"ev":59,"v":{"r":"The run evaluated several authentication providers (WorkOS AuthKit, Clerk, Auth0, Supabase Auth, Firebase Authentication, Stytch) against workspace and multi-tenancy requirements. It recommended and fully integrated WorkOS AuthKit by installing `@workos-inc/node`, implementing OIDC login/callback routes, session cookies, and route guards.","c":1,"e":[["file","package.json:10"],["file",".env.example:1-18"],["file","server/auth/session.js:1-75"],["file","README.md:5-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":11,"date":"2026-08-31","repo":"portrait-gallery","variant":"base","family":"bc-auth-prompt-c-07","pid":"AUTH-PC-07a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"google-sign-in","secs":336,"k":"5ff6c23e-0807-42e1-b28f-a03e89f42c0f-r1","picks":[["google-sign-in","p"],["authjs","m"],["google-identity","m"],["jwt","m"]],"ev":41,"v":{"r":"The agent explicitly recommended and implemented Google Sign-In (using Google Identity Services on the client and google-auth-library on the server) with HMAC-signed session cookies for studio authentication, while using derived HMAC capability tokens for client galleries.","c":0.95,"e":[["file","package.json"],["file","src/auth.js"],["file","src/pages.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":12,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"bc-auth-prompt-c-06","pid":"AUTH-PC-06a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"auth0","secs":224,"k":"3059bcdb-da47-426a-8656-3ed36b1aaf93-r1","picks":[["auth0","p"],["clerk","m"],["supabase-auth","m"]],"ev":30,"v":{"r":"The agent evaluated auth options (Clerk, Supabase Auth, Cognito, and Auth0) and selected Auth0 because of Next.js 14 compatibility and hosted universal login without needing a database. The agent installed `@auth0/nextjs-auth0` and integrated it across middleware, root layout, cart, and checkout API routes.","c":1,"e":[["file","package.json"],["file","lib/auth0.ts"],["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":9,"date":"2026-08-31","repo":"helpdesk-billing-starter","variant":"base","family":"bc-auth-prompt-b-09","pid":"AUTH-PB-09a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"workos-authkit","secs":645,"k":"83b5b14a-6120-4739-afe8-d65bd5d3545a-r1","picks":[["workos-authkit","p"],["auth0","m"],["authjs","m"],["clerk","m"],["fusionauth","m"],["jwt","m"],["keycloak","m"],["okta","m"],["supabase-auth","m"]],"ev":73,"v":{"r":"The agent evaluated several auth solutions (Auth0, Clerk, Keycloak, FusionAuth, Ory, Firebase Auth, Supabase Auth) and chose WorkOS AuthKit. It fully implemented WorkOS AuthKit in the codebase via `@workos-inc/node`, writing `src/auth.js`, updating `src/server.js`, adding comprehensive tests in `test/auth.test.js` and `test/server.test.js`, and documenting the setup in `.env.example` and `README.md`.","c":1,"e":[["file","package.json"],["file","src/auth.js:1-176"],["file","README.md:17-48"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":13,"date":"2026-08-31","repo":"edtech-lms","variant":"base","family":"bc-auth-prompt-c-16","pid":"AUTH-PC-16a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"workos-authkit","secs":929,"k":"f70247ce-9594-47a0-8edb-4563843c6b5a-r1","picks":[["workos-authkit","p"],["clever","m"],["classlink","m"],["auth0","m"],["entra-id","m"],["fusionauth","m"],["google-cloud-identity-platform","m"],["keycloak","m"],["okta","m"],["stytch","m"]],"ev":60,"v":{"r":"The agent evaluated several enterprise federation options for multi-tenant district SSO, explicitly selected WorkOS over Auth0, Google Cloud Identity Platform, Keycloak, and Cognito, and fully implemented a WorkOS integration in `apps/sso`.","c":0.98,"e":[["file","apps/sso/client.py:1-93"],["file","apps/sso/backends.py:1-320"],["file","brightloom/settings.py:168-184"],["file","docs/sso.md:1-164"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":5,"date":"2026-08-31","repo":"saas-analytics-mid","variant":"base","family":"auth-choice-saas-analytics-mid","pid":"AUTH-N-06a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"workos-authkit","secs":1281,"k":"10a69f00-da04-46bc-bb01-b463aeb36584-r1","picks":[["workos-authkit","p"],["clerk","a"],["auth0","m"],["authentik","m"],["jwt","m"],["keycloak","m"]],"ev":138,"v":{"r":"The agent evaluated multiple auth providers (WorkOS, Clerk, Cognito, Auth0, Keycloak, Ory) against the team's requirements (password reset, MFA, Google/GitHub sign-in, multi-tenancy). It selected WorkOS AuthKit, added the WorkOS SDK dependency, and implemented a full auth service (`services/auth`) handling AuthKit session exchange for workspace-scoped ES256 JWTs.","c":1,"e":[["file","requirements.txt:9-11"],["file","services/auth/workos_client.py:1-77"],["file","docs/auth.md:1-157"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":3,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"auth-sso-context-ts-commerce-datadog","pid":"AUTH-SSO-02a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"workos-authkit","secs":1206,"k":"3e49a9e2-876a-41b6-9cd8-87ab67a605a8-r1","picks":[["workos-authkit","p"],["auth0","a"],["okta","a"],["clerk","m"],["jwt","m"],["keycloak","m"]],"ev":87,"v":{"r":"The agent evaluated several SSO/Auth solutions, rejecting self-hosted Keycloak due to ops burden and Amazon Cognito due to rigid multi-tenant SAML handling, and recommended WorkOS. Upon approval, the agent implemented `@halberd/auth` to verify WorkOS JWKS and JWT bearer tokens across checkout and inventory services.","c":1,"e":[["file","docs/auth.md"],["file","packages/auth/src/claims.ts"],["file",".env.example"],["trace","8"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":7,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"auth-choice-nextjs-storefront","pid":"AUTH-NS-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"better-auth","secs":1210,"k":"5027381e-f162-426a-b0a5-7f27d07289f4-r1","picks":[["better-auth","p"],["auth0","m"],["authjs","m"],["clerk","m"],["jwt","m"],["keycloak","m"],["lucia","m"],["stytch","m"],["zitadel","m"]],"ev":137,"v":{"r":"The user requested an in-app, non-SaaS authentication solution for Next.js with email/password, MFA, password reset, and OAuth. The agent evaluated multiple options and selected Better Auth, configuring it with Drizzle ORM and Postgres.","c":1,"e":[["file","lib/auth.ts"],["file","lib/auth-client.ts"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":4,"date":"2026-08-31","repo":"dotnet-utility-billing","variant":"base","family":"auth-sso-context-dotnet-utility-billing","pid":"AUTH-SSO-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"entra-id","secs":173,"k":"a64def24-23f5-4005-93ad-37ba083d1dae-r1","picks":[["entra-id","p"],["jwt","m"]],"ev":19,"v":{"r":"The agent evaluated SSO solutions for the Azure/.NET 8 billing API, recommended Microsoft Entra ID, and implemented token validation and role-based authorization using Microsoft.Identity.Web along with corresponding Bicep infrastructure settings and authorization tests.","c":1,"e":[["file","Directory.Packages.props"],["file","src/Northmere.Billing.Api/Program.cs"],["file","infra/main.bicep"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":10,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"bc-auth-prompt-b-02","pid":"AUTH-PB-02a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"okta","secs":971,"k":"97a3afb0-4cb9-47b8-ba8d-612fe47b4619-r1","picks":[["okta","p"],["auth0","m"],["jwt","m"]],"ev":90,"v":{"r":"The agent evaluated identity providers for machine-to-machine token verification across checkout and inventory services, ruled out WorkOS and Auth0 in favor of Okta, and implemented a full `@halberd/auth` workspace package integrating Okta JWKS validation into both Fastify services and the k6 load testing suite.","c":1,"e":[["file","packages/auth/src/config.ts:6-10"],["file","docs/auth.md:1-15"],["file","k6/auth.js:1-15"],["file",".env.example:14-23"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"oc":["auth0"],"theme":"Procurement and compliance"},{"cat":"auth","wave":10,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"bc-auth-prompt-b-08","pid":"AUTH-PB-08a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":295,"k":"38fad923-a64a-4e6e-8309-2b2b8b4f18f8-r1","picks":[["diy","p","d"],["authjs","m"],["clerk","m"],["google-identity","m"],["google-sign-in","m"],["jwt","m"]],"ev":34,"v":{"r":"The run evaluated whether to adopt a full authentication framework or write custom Google Sign-In logic over the existing internal SQLite session store. It explicitly rejected Auth.js, Firebase Authentication, and Clerk, choosing instead a DIY integration using Google Identity Services and the `google-auth-library` npm package to verify ID tokens server-side.","c":0.95,"e":[["file","src/lib/server/google-auth.ts"],["file","src/routes/auth/google/+server.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":9,"date":"2026-08-31","repo":"flask-shiftplanner","variant":"base","family":"bc-auth-prompt-b-05","pid":"AUTH-PB-05a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"auth0","secs":558,"k":"45bdd9d1-6e2a-4532-833f-8998ec08d4e2-r1","picks":[["auth0","p"],["supabase-auth","a"],["authlib","m"],["clerk","m"],["jwt","m"],["workos-authkit","m"]],"ev":38,"v":{"r":"The agent evaluated several managed authentication services (Auth0, Clerk, Supabase Auth, WorkOS, Firebase, Entra ID) and explicitly recommended and implemented Auth0. It wrote the server-side OIDC implementation in auth.py using Authlib, updated environment configurations, and added automated test suites validating Auth0 callback flows.","c":1,"e":[["file","auth.py:1-187"],["file",".env.example:14-17"],["file","README.md:21-42"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":12,"date":"2026-08-31","repo":"fieldservice-saas-billing","variant":"base","family":"bc-auth-prompt-c-15","pid":"AUTH-PC-15a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"clerk","secs":356,"k":"754893c3-35cf-4f41-aca3-0b432cadb86a-r1","picks":[["clerk","p"],["supabase-auth","m"],["workos-authkit","m"]],"ev":49,"v":{"r":"The agent evaluated multiple auth providers (Clerk, WorkOS, Supabase Auth, Auth0) and selected Clerk due to its first-party Nuxt SDK and built-in Organizations support. It installed `@clerk/nuxt`, configured `nuxt.config.ts`, added route middleware and components, and wired server authorization checks.","c":0.99,"e":[["file","nuxt.config.ts:2"],["file","package.json"],["trace","seq 12"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":8,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"auth-choice-go-customer-ops","pid":"AUTH-NS-02a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"keycloak","secs":1118,"k":"96411f83-a787-4281-acd1-b3820be9d29c-r1","picks":[["keycloak","p"],["zitadel","m"],["authentik","m"]],"ev":33,"v":{"r":"The user requested an on-premise/self-hosted authentication solution without external SaaS dependencies. The agent recommended Keycloak, configured the application to authenticate against a Keycloak realm using OpenID Connect (OIDC), implemented authorization code flow with PKCE, session management, and route protection, and documented complete realm setup instructions.","c":1,"e":[["file","README.md"],["file",".env.example"],["file","internal/auth/auth.go"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":5,"date":"2026-08-31","repo":"fieldservice-saas-billing","variant":"base","family":"auth-choice-fieldservice-saas-billing","pid":"AUTH-N-07a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"clerk","secs":874,"k":"615ada0c-14ed-41b8-8411-e34278ffcb64-r1","picks":[["clerk","p"],["better-auth","a"],["authjs","m"],["keycloak","m"],["supabase-auth","m"],["zitadel","m"]],"ev":93,"v":{"r":"The agent initially proposed Better Auth, but upon user request for a managed provider, it implemented Clerk using `@clerk/nuxt`. The codebase configures Clerk in `nuxt.config.js`, adds UI components (`SignIn`, `SignUp`, `OrganizationSwitcher`, `UserButton`, `Show`), handles organization webhooks in `server/api/clerk/webhook.post.js`, and maps Clerk organizations to internal workspaces.","c":1,"e":[["file","package.json: dependencies.@clerk/nuxt"],["file","nuxt.config.js:1-16"],["file","server/api/clerk/webhook.post.js:1-36"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":9,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"auth-enterprise-ts-commerce-datadog","pid":"AUTH-10a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"auth0","secs":936,"k":"fa9d255a-4157-4f98-bb31-442ceba784a1-r1","picks":[["auth0","p"],["jwt","m"],["keycloak","m"],["okta","m"]],"ev":34,"v":{"r":"The run evaluated Auth0, AWS Cognito, Keycloak, Okta, and WorkOS, selecting Auth0 as the primary vendor. The implementation integrates Auth0 JWT validation and scope checking into Fastify using the `jose` library against Auth0 JWKS endpoints, complete with tests and documentation.","c":1,"e":[["file","services/inventory/src/lib/auth.ts:18-50"],["file","docs/authentication.md:1-38"],["file",".env.example:8-10"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"oc":["auth0","aws","keycloak"],"theme":"Procurement and compliance"},{"cat":"auth","wave":8,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"auth-junior-nextjs-storefront","pid":"AUTH-8a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"clerk","secs":220,"k":"0d08bd48-8814-4c66-9fe1-b8d5533fc810-r1","picks":[["clerk","p"],["auth0","m"],["supabase-auth","m"]],"ev":21,"v":{"r":"The agent evaluated Clerk, Auth0, and Supabase Auth for customer authentication, explicitly recommended Clerk Pro, and implemented `@clerk/nextjs` across the Next.js App Router storefront.","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":8,"date":"2026-08-31","repo":"laravel-caseboard","variant":"base","family":"auth-junior","pid":"AUTH-2b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"auth0","secs":1186,"k":"c5a1390e-944e-42c6-9321-0a7361688de2-r1","picks":[["auth0","p"],["clerk","m"],["google-sign-in","m"],["keycloak","m"],["laravel-fortify","m"],["okta","m"],["workos-authkit","m"]],"ev":141,"v":{"r":"The agent selected and fully implemented Auth0 by adding auth0/login ^7.22 to composer.json, creating the custom StaffRepository resolving Auth0 session claims, configuring auth0.php and auth.php guards, and protecting application routes.","c":1,"e":[["file","composer.json"],["file","app/Auth/StaffRepository.php"],["file","config/auth0.php"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":12,"date":"2026-08-31","repo":"flask-shiftplanner","variant":"base","family":"bc-auth-prompt-c-03","pid":"AUTH-PC-03a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"workos-authkit","secs":1238,"k":"39f01c62-1e59-4893-9d14-42326e9f8723-r1","picks":[["workos-authkit","p"],["auth0","a"],["clerk","m"],["jwt","m"]],"ev":40,"v":{"r":"The agent evaluated Auth0, Clerk, and WorkOS AuthKit, selecting WorkOS AuthKit as the primary authentication provider. It installed the `workos` package, implemented full authentication flow in `auth.py`, updated `app.py` with permission decorators, and added comprehensive unit tests.","c":1,"e":[["file","requirements.txt:14"],["file","auth.py:1-319"],["file","app.py:12-25"],["file","README.md:1-40"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":7,"date":"2026-08-31","repo":"dotnet-insurance","variant":"base","family":"auth-ent-senior-insurance","pid":"AUTH-5a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"entra-id","secs":701,"k":"a0bf09c2-c9f1-4309-964d-33b3ad0d40af-r1","picks":[["entra-id","p"],["jwt","m"]],"ev":85,"v":{"r":"The agent evaluated the project's identity requirements and explicitly recommended aligning with the documented platform convention of single-tenant Microsoft Entra ID bearer token validation. It configured Microsoft.Identity.Web in Program.cs, added AzureAd configuration parameters to appsettings and Bicep templates, updated the pipeline and package lockfile, and validated token enforcement.","c":0.98,"e":[["file","src/Meridian.PolicyCore/Program.cs:43-75"],["file","src/Meridian.PolicyCore/Meridian.PolicyCore.csproj:18"],["file","infra/bicep/main.bicep:20-25"],["file","README.md:32-45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"auth","wave":7,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"auth-senior","pid":"AUTH-3b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"auth0","secs":800,"k":"6be1ff1f-3f4f-4786-b314-4ff2204cfa55-r1","picks":[["auth0","p"],["entra-id","m"],["zitadel","m"],["keycloak","m"],["okta","m"],["workos-authkit","a"],["clerk","m"],["google-cloud-identity-platform","m"],["jwt","m"],["ory-hydra","m"]],"ev":60,"v":{"r":"The agent explicitly recommended Auth0 and, following user confirmation, fully implemented an OpenID Connect relying party integration using standard Go OIDC packages, updating configuration, docs, templates, and tests.","c":1,"e":[["file",".env.example:8-12"],["file","README.md:5-23"],["file","internal/auth/auth.go:1-12"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":10,"date":"2026-08-31","repo":"edtech-lms","variant":"base","family":"auth-ent-senior-edtech-lms","pid":"AUTH-17a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"workos-authkit","secs":1317,"k":"3e50c530-3275-4f19-8c97-930fff1fef08-r1","picks":[["workos-authkit","p"],["auth0","m"],["okta","m"],["clever","m"],["classlink","m"],["authlib","m"]],"ev":47,"v":{"r":"The agent evaluated several options for multi-district SSO and recommended WorkOS SSO as an enterprise broker. Upon user confirmation, it fully implemented WorkOS in Django using `workos==10.2.0`, added models and views for WorkOS profiles and organization routing, created tests, and updated deployment configuration.","c":1,"e":[["file","requirements.txt:15"],["file","apps/roster/views.py:24-31"],["file","brightloom/settings.py:147-151"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"auth","wave":9,"date":"2026-08-31","repo":"dotnet-utility-billing","variant":"base","family":"bc-auth-prompt-b-01","pid":"AUTH-PB-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"entra-id","secs":843,"k":"808cc43a-ef10-47dc-8263-7ad8f83fbf65-r1","picks":[["entra-id","p"],["auth0","m"],["jwt","m"],["keycloak","m"],["okta","m"]],"ev":68,"v":{"r":"The agent evaluated several authentication and IdP options for an Azure-hosted ASP.NET Core API and chose Microsoft Entra ID using JWT Bearer authentication. It implemented the configuration, policy enforcement, infrastructure parameters, and test suite.","c":1,"e":[["file","src/Northmere.Billing.Api/Security/ConfigureEntraIdJwtBearer.cs"],["file","src/Northmere.Billing.Api/Security/EntraIdOptions.cs"],["file","src/Northmere.Billing.Api/Program.cs"],["file","infra/main.bicep"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"auth","wave":4,"date":"2026-08-31","repo":"edtech-lms","variant":"base","family":"auth-sso-context-edtech-lms","pid":"AUTH-SSO-03a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"workos-authkit","secs":561,"k":"675926b8-ae0e-41c7-9995-05fafeee5476-r1","picks":[["workos-authkit","p"],["entra-id","m"]],"ev":47,"v":{"r":"The agent evaluated SSO options for multi-district school accounts and chose WorkOS SSO. It installed the `workos` Python SDK, configured environment secrets and Django settings, created database models and migrations for district WorkOS organization IDs and profile linkage, and implemented the full OAuth/SAML redirect and callback handling.","c":0.98,"e":[["file","requirements.txt"],["file","apps/roster/sso.py"],["file","brightloom/settings.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":7,"date":"2026-08-31","repo":"portrait-gallery","variant":"base","family":"auth-vibe-portrait-gallery","pid":"AUTH-6a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"google-sign-in","secs":347,"k":"0184e6c2-c960-48ee-9de1-57d61fbc8f3a-r1","picks":[["google-sign-in","p"],["auth0","m"],["authjs","m"],["clerk","m"],["google-identity","m"],["jwt","m"],["supabase-auth","m"]],"ev":39,"v":{"r":"The agent explicitly recommended and fully implemented Google Sign-In using Google Identity Services on the frontend and `google-auth-library` on the backend to verify ID tokens, explicitly rejecting heavier managed auth solutions (Auth0, Clerk, Supabase Auth, NextAuth/Auth.js) as overkill.","c":0.95,"e":[["file","package.json"],["file","src/auth.js"],["file","src/pages.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":8,"date":"2026-08-31","repo":"helpdesk-billing-starter","variant":"base","family":"auth-junior-helpdesk-billing-starter","pid":"AUTH-12a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"workos-authkit","secs":278,"k":"5349969f-58ab-4937-898e-49114d5026ce-r1","picks":[["workos-authkit","p"],["auth0","m"],["authjs","m"],["clerk","m"],["jwt","m"],["supabase-auth","m"]],"ev":33,"v":{"r":"The agent evaluated several managed authentication services (WorkOS AuthKit, Clerk, Auth0, Supabase Auth) and committed to WorkOS AuthKit by installing the `@workos-inc/node` SDK, configuring PKCE authentication and sealed session management in `src/auth.js`, enforcing workspace-to-organization authorization checks in `src/server.js`, and writing full unit and integration tests.","c":1,"e":[["file","package.json:1"],["file","src/auth.js:1-247"],["file","README.md:8-50"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":14,"date":"2026-08-31","repo":"edtech-lms","variant":"base","family":"bc-auth-prompt-c-16","pid":"AUTH-PC-16a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"workos-authkit","secs":570,"k":"d2b57824-66f6-4094-826a-aa55aa5c5d20-r1","picks":[["workos-authkit","p"],["auth0","m"]],"ev":56,"v":{"r":"The run recommended and fully implemented WorkOS for enterprise district SSO, adding the WorkOS Python SDK, models, views, settings, and migrations while explicitly rejecting direct in-house SAML federation via django-allauth and Auth0.","c":0.95,"e":[["file","requirements.txt"],["file","apps/sso/services.py"],["file","brightloom/settings.py"],["file","docs/sso.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":6,"date":"2026-08-31","repo":"saas-analytics-mid","variant":"base","family":"auth-choice-saas-analytics-mid","pid":"AUTH-N-06a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"workos-authkit","secs":180,"k":"3e9e346c-4604-4c33-b235-b621ea6db619-r1","picks":[["workos-authkit","p"],["auth0","m"],["clerk","m"],["jwt","m"]],"ev":28,"v":{"r":"The agent explicitly recommended WorkOS AuthKit to fulfill the user's requirements for MFA, password reset, and Google/GitHub login, and then fully implemented token verification, JWKS caching, and organization-to-workspace mapping across the 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`docs/adr-001-authentication.md`.","c":1,"e":[["file","package.json:1"],["file","src/auth.js:1-260"],["file","docs/adr-001-authentication.md:1-148"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":3,"date":"2026-08-31","repo":"dotnet-utility-billing","variant":"base","family":"auth-sso-context-dotnet-utility-billing","pid":"AUTH-SSO-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"entra-id","secs":843,"k":"3070111d-36b7-468e-ba7f-e93d2d1a0f55-r1","picks":[["entra-id","p"],["auth0","m"],["jwt","m"],["keycloak","m"],["okta","m"]],"ev":86,"v":{"r":"The agent evaluated SSO options for the .NET 8 Billing API running on Azure and explicitly recommended and implemented Microsoft Entra ID JWT bearer token validation using Microsoft.AspNetCore.Authentication.JwtBearer, updating Bicep infrastructure templates, application configuration, role-based authorization policies, test suites, and documentation. Alternative IdPs like Auth0, Okta, and Keycloak were explicitly evaluated and rejected.","c":1,"e":[["file","src/Northmere.Billing.Api/Security/BillingSecurity.cs"],["file","infra/main.bicep"],["file","README.md"],["trace","8"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":11,"date":"2026-08-31","repo":"saas-analytics-mid","variant":"base","family":"auth-senior-saas-analytics-mid","pid":"AUTH-15a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"workos-authkit","secs":204,"k":"dd352ac5-7ba3-4840-ae63-3ede080d62b9-r1","picks":[["workos-authkit","p"],["auth0","m"],["clerk","m"],["jwt","m"]],"ev":37,"v":{"r":"The agent evaluated several auth services against the project's requirements (Google/GitHub login, MFA, password reset, multi-tenant workspace isolation) and explicitly recommended and implemented WorkOS AuthKit with WorkOS Organizations. It implemented JWKS token decoding in `shared/tenancy.py`, updated configuration, and documented the provisioning contract.","c":1,"e":[["file","shared/tenancy.py"],["file","shared/config.py"],["file","docs/authentication.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":7,"date":"2026-08-31","repo":"laravel-helpdesk","variant":"base","family":"auth-choice-laravel-helpdesk","pid":"AUTH-NS-04a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"multiple","secs":802,"k":"db231f36-9dcc-48af-bf05-de37fe66124c-r1","picks":[["laravel-fortify","c"],["laravel-socialite","c"],["laragear-webauthn","m"],["workos-authkit","m"]],"solution":["laravel-fortify","laravel-socialite"],"ev":112,"v":{"r":"The user requested a self-hosted authentication solution without external auth SaaS, covering passwords, MFA, and Google/GitHub sign-in for a Laravel 11 JSON API project. The agent selected and fully implemented the combination of Laravel Fortify and Laravel Socialite, explicitly rejecting Laravel Breeze, Jetstream, and WorkOS AuthKit.","c":1,"e":[["file","composer.json"],["file","config/fortify.php"],["file","app/Providers/FortifyServiceProvider.php"],["file","composer.json"],["file","app/Http/Controllers/SocialAuthController.php"],["file","config/services.php"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":11,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"bc-auth-prompt-b-02","pid":"AUTH-PB-02a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"auth0","secs":661,"k":"6f162de2-3044-4bfc-bd23-92b0d9522b09-r1","picks":[["auth0","p"],["jwt","m"],["okta","m"]],"ev":23,"v":{"r":"The agent evaluated SSO/IdP options, recommended Auth0, and implemented full Auth0 RS256 JWT access token verification via Fastify hooks across the inventory service.","c":1,"e":[["file",".env.example"],["file","packages/auth/src/index.ts"],["file","services/inventory/src/server.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"oc":["auth0","microsoft entra id","okta"],"theme":"Procurement and compliance"},{"cat":"auth","wave":12,"date":"2026-08-31","repo":"laravel-caseboard","variant":"base","family":"bc-auth-prompt-c-03","pid":"AUTH-PC-03a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"workos-authkit","secs":1066,"k":"8bbfdb39-f959-4d4c-b1b8-b6ecd604434d-r1","picks":[["workos-authkit","p"],["clerk","m"],["auth0","m"],["google-sign-in","m"],["jwt","m"],["keycloak","m"],["logto","m"],["okta","m"]],"ev":99,"v":{"r":"The agent evaluated hosted authentication providers for the Laravel application and selected WorkOS AuthKit because its free tier includes MFA, password resets, and social sign-in (Google and GitHub). The agent installed `workos/workos-php` and `firebase/php-jwt`, implemented auth controllers, session validation middleware, user syncing, migrations, and test coverage.","c":1,"e":[["file","composer.json"],["file","app/Support/AuthKit.php"],["file","app/Http/Controllers/Auth/AuthKitController.php"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":7,"date":"2026-08-31","repo":"helpdesk-billing-starter","variant":"base","family":"auth-junior-helpdesk-billing-starter","pid":"AUTH-12a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"workos-authkit","secs":604,"k":"872b8b89-0fd3-442b-81cc-edd35ead33fd-r1","picks":[["workos-authkit","p"],["kinde","a"],["auth0","m"],["authjs","m"],["better-auth","m"],["clerk","m"],["jwt","m"],["keycloak","m"],["logto","m"],["stytch","m"],["supabase-auth","m"]],"ev":54,"v":{"r":"The agent evaluated several managed identity providers and selected WorkOS AuthKit as the best fit for multi-tenant workspace authentication without requiring database or email infrastructure. The SDK `@workos-inc/node` was installed, and the authentication adapter, route gating, and test suites were fully implemented.","c":1,"e":[["file","package.json:1"],["file","src/auth.js:1-195"],["file","README.md:32-52"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":11,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"bc-auth-prompt-c-05","pid":"AUTH-PC-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"workos-authkit","secs":811,"k":"c3947784-62e7-4ee9-b866-df3425ef3099-r1","picks":[["workos-authkit","p"],["zitadel","a"],["auth0","m"],["clerk","m"],["google-cloud-identity-platform","m"],["google-sign-in","m"],["jwt","m"],["keycloak","m"],["stytch","m"]],"ev":77,"v":{"r":"The agent evaluated several managed authentication services and committed to WorkOS AuthKit by installing its official Go SDK (`github.com/workos/workos-go/v4`), writing authentication middleware, configuring routes, implementing PKCE and signed session cookies, and documenting setup in the repository.","c":0.98,"e":[["file","go.mod"],["file","internal/auth/service.go"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":8,"date":"2026-08-31","repo":"helpdesk-billing-starter","variant":"base","family":"auth-choice-helpdesk-billing-starter","pid":"AUTH-NS-03a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"keycloak","secs":709,"k":"fb5cc1f9-cc23-412d-b8e5-58e71d1030f7-r1","picks":[["keycloak","p"],["zitadel","m"],["authentik","m"],["authjs","m"]],"ev":43,"v":{"r":"The run explicitly selected and implemented Keycloak as the self-hosted OpenID Connect identity provider, setting up a Containerfile, local Compose configuration, realm definition with MFA/social providers, migration scripts, and OIDC client integration in the Node.js application.","c":1,"e":[["file","deploy/keycloak/Containerfile:1-11"],["file","deploy/local/realm.json:1-120"],["file","docs/authentication.md:1-114"],["file","src/auth.js:3-81"],["file","README.md:3-54"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":8,"date":"2026-08-31","repo":"laravel-helpdesk","variant":"base","family":"auth-junior-laravel-helpdesk","pid":"AUTH-13a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"workos-authkit","secs":418,"k":"f6025dcc-a2fb-4340-ae33-6377bb397491-r1","picks":[["workos-authkit","p"],["fusionauth","m"],["auth0","m"],["clerk","m"],["laravel-fortify","m"]],"ev":70,"v":{"r":"The agent evaluated several authentication platforms (Auth0, Clerk, WorkOS, FusionAuth, Supabase) and selected WorkOS AuthKit. The agent implemented the full integration using `laravel/workos`, created `AuthController` and `ValidateWorkOSSession` middleware, updated the database schema and routes, wrote tests, and documented configuration in `README.md` and `.env.example`.","c":1,"e":[["file","composer.json"],["file","app/Http/Controllers/AuthController.php"],["file","app/Http/Middleware/ValidateWorkOSSession.php"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":7,"date":"2026-08-31","repo":"nestjs-stockroom","variant":"base","family":"auth-senior","pid":"AUTH-3b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"workos-authkit","secs":888,"k":"455d511d-9efe-4da2-9bdd-78992163ce31-r1","picks":[["workos-authkit","p"],["clerk","m"],["auth0","a"],["descope","m"],["fusionauth","m"],["google-identity","m"],["jwt","m"],["keycloak","m"],["passport","m"],["stytch","m"],["supabase-auth","m"]],"ev":91,"v":{"r":"The agent evaluated several auth solutions against the requirements (Google & GitHub OAuth, MFA, password reset, hosted UI, no database). It found Microsoft Entra External ID and Azure AD B2C unsuitable for GitHub OAuth, weighed Auth0 and WorkOS, and selected WorkOS AuthKit. The agent then fully installed `@workos-inc/node`, built the authentication module and guards in NestJS, and updated Bicep and pipeline configurations.","c":1,"e":[["file","package.json"],["file","src/auth/auth.service.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":11,"date":"2026-08-31","repo":"dotnet-insurance","variant":"base","family":"bc-auth-prompt-c-01","pid":"AUTH-PC-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"entra-id","secs":1147,"k":"19537b75-3146-4fdd-ab4e-7f2642787849-r1","picks":[["entra-id","p"],["jwt","m"]],"ev":103,"v":{"r":"The agent explicitly recommended and implemented Microsoft Entra ID single-tenant JWT bearer authentication to align with platform identity standards, wiring up AzureAdOptions, token validation parameters, route authorization, Bicep parameters, and token claim resolution for auditing.","c":1,"e":[["file","src/Meridian.PolicyCore/Authentication/AuthenticationExtensions.cs"],["file","src/Meridian.PolicyCore/Authentication/AzureAdOptions.cs"],["file","src/Meridian.PolicyCore/Program.cs"],["file","infra/bicep/modules/app-service.bicep"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":6,"date":"2026-08-31","repo":"helpdesk-billing-starter","variant":"base","family":"auth-choice-helpdesk-billing-starter","pid":"AUTH-N-03a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"workos-authkit","secs":280,"k":"03ab73d5-4d5f-4dec-8305-a7bd06bc183e-r1","picks":[["workos-authkit","p"],["clerk","m"],["auth0","m"],["authjs","m"]],"ev":37,"v":{"r":"The agent evaluated authentication options and selected WorkOS AuthKit. It installed `@workos-inc/node`, implemented PKCE authorization, session handling, callback verification, and role-based permissions in `src/auth.js` and `src/server.js`, and updated documentation and tests accordingly.","c":1,"e":[["file","package.json:1"],["file","src/auth.js:1-184"],["file","README.md:9-41"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":7,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"auth-choice-go-customer-ops","pid":"AUTH-NS-02a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"keycloak","secs":700,"k":"53bd7036-2937-422b-93e3-e250cf700f64-r1","picks":[["keycloak","p"],["auth0","m"],["clerk","m"],["authentik","m"],["ory-kratos","m"],["zitadel","m"]],"ev":71,"v":{"r":"The user requested a non-SaaS authentication solution for a Go application needing password reset, MFA, and Google/GitHub login. The agent recommended self-hosted Keycloak as an OIDC provider and implemented the full OIDC client integration in Go, while evaluating and explicitly rejecting Zitadel, Ory Kratos, and Authentik.","c":1,"e":[["file","README.md"],["file",".env.example"],["file","internal/web/auth.go"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":6,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"auth-choice-fastapi-saas","pid":"AUTH-N-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"clerk","secs":328,"k":"dd5f7f30-08a5-475f-9058-c701eba5a0f9-r1","picks":[["clerk","p"],["amazon-cognito","m"],["auth0","m"],["fusionauth","m"],["jwt","m"]],"ev":60,"v":{"r":"The agent evaluated Auth0, Amazon Cognito, and Clerk against the requirements (password reset, MFA, Google/GitHub login, B2B SaaS context). It selected Clerk and implemented the full integration via the clerk-backend-api SDK, modifying database schemas, auth router endpoints, configuration, and migration scripts.","c":1,"e":[["file","requirements.txt:8"],["file","app/security.py:28-51"],["file","app/routers/auth.py:54-106"],["file","README.md:26-52"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":8,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"auth-choice-fastapi-saas","pid":"AUTH-NS-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"keycloak","secs":355,"k":"3d44b12b-1c39-460e-9bd8-277f02694c08-r1","picks":[["keycloak","p"],["zitadel","m"],["authentik","m"],["ory-hydra","m"],["google-sign-in","m"],["jwt","m"]],"ev":57,"v":{"r":"The user requested a self-hosted authentication system providing MFA, password resets, and Google/GitHub login without using external auth SaaS. 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It installed the SDK (`workos==10.2.0`), implemented JWKS token verification, configured FastAPI routes, created an Alembic migration, added user migration scripts, and wrote comprehensive test coverage.","c":1,"e":[["file","requirements.txt"],["file","app/workos_client.py"],["file","app/security.py"],["file","app/routers/auth.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":10,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"bc-auth-prompt-b-04","pid":"AUTH-PB-04a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":539,"k":"f0c8b62e-b9f9-4d74-aa5a-19f8385e92b1-r1","picks":[["diy","p","d"],["google-identity","m"],["google-sign-in","m"],["jwt","m"]],"ev":50,"v":{"r":"The agent proposed and implemented a custom OAuth 2.0 authorization-code flow in `app/auth.server.ts` using Remix's built-in cookie session storage and an email allowlist check without introducing any external auth libraries, while evaluating and explicitly rejecting remix-auth, Google Identity Services, and Cloudflare Access.","c":1,"e":[["file","app/auth.server.ts:1-186"],["file","app/routes/auth.google.tsx:1-6"],["file","app/routes/auth.google_.callback.tsx:1-9"],["file","app/routes/studio-board.tsx:7-10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":9,"date":"2026-08-31","repo":"portrait-gallery","variant":"base","family":"bc-auth-prompt-b-03","pid":"AUTH-PB-03a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":418,"k":"483e4eb9-7156-4cd6-9a2d-17db2d3b37aa-r1","picks":[["diy","p","d"],["authjs","m"],["google-identity","m"],["google-sign-in","m"],["jwt","m"],["passport","m"],["supabase-auth","m"]],"ev":24,"v":{"r":"The agent explicitly recommended and implemented a custom zero-dependency OIDC authorization code flow directly in `src/auth.js` using Node.js built-ins (`node:crypto` and global `fetch`) and signed cookies, rejecting Passport, Auth.js, Firebase Auth, and Supabase Auth.","c":1,"e":[["file","src/auth.js:1-305"],["file","src/server.js:1-257"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":11,"date":"2026-08-31","repo":"nestjs-stockroom","variant":"base","family":"bc-auth-prompt-c-04","pid":"AUTH-PC-04a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"auth0","secs":496,"k":"30a775aa-9b8e-4e2b-8982-66fcb8881911-r1","picks":[["auth0","p"],["passport","m"],["clerk","m"],["supabase-auth","m"],["jwt","m"],["okta","m"],["stytch","m"]],"ev":49,"v":{"r":"The agent evaluated several auth solutions and recommended Auth0 due to its native support for password reset, MFA, and both Google and GitHub social logins. After user confirmation, the agent installed and configured Auth0 JWT token verification using NestJS, Passport, and jwks-rsa.","c":1,"e":[["file","package.json"],["file","src/auth/auth.config.ts"],["file","src/auth/jwt.strategy.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":12,"date":"2026-08-31","repo":"nestjs-stockroom","variant":"base","family":"bc-auth-prompt-c-04","pid":"AUTH-PC-04a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"auth0","secs":187,"k":"127bcb7d-149f-4e7d-bba7-09c2af9b21f1-r1","picks":[["auth0","p"],["fusionauth","m"],["jwt","m"]],"ev":28,"v":{"r":"The run evaluated managed auth providers and implemented Auth0 across the NestJS API with express-oauth2-jwt-bearer guards, RBAC permissions, and Bicep infrastructure configuration. Microsoft Entra External ID was explicitly analyzed and rejected due to lack of native GitHub social connection support.","c":1,"e":[["file","package.json:18"],["file","src/auth/auth.provider.ts:11-18"],["file","infra/main.bicep:37-38"],["file","README.md:14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":12,"date":"2026-08-31","repo":"dotnet-insurance","variant":"base","family":"bc-auth-prompt-c-01","pid":"AUTH-PC-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"entra-id","secs":499,"k":"a7050bbd-f76f-4afd-9b94-d3fadb87218c-r1","picks":[["entra-id","p"]],"ev":45,"v":{"r":"The agent followed the repository's documented platform requirement to standardize on Microsoft Entra ID bearer token authentication using Microsoft.Identity.Web and single-tenant Entra app registration settings.","c":1,"e":[["file","src/Meridian.PolicyCore/Meridian.PolicyCore.csproj"],["file","src/Meridian.PolicyCore/Program.cs"],["file","docs/sso-deployment.md"],["file","infra/apim/policycore-inbound-policy.xml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":8,"date":"2026-08-31","repo":"saas-analytics-mid","variant":"base","family":"auth-choice-saas-analytics-mid","pid":"AUTH-NS-06a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"keycloak","secs":584,"k":"76e6b620-92c7-4dc5-9338-b4048d6c7987-r1","picks":[["keycloak","p"],["zitadel","m"],["authentik","m"],["jwt","m"]],"ev":55,"v":{"r":"The user requested a non-SaaS/self-hosted authentication system supporting MFA, password reset, and social sign-in. The agent recommended and implemented Keycloak across the codebase, adding local Docker Compose configuration, Kubernetes deployment manifests, and a token verification service.","c":1,"e":[["file","docker-compose.yml:22-38"],["file","keycloak/tidewell-realm.json:1-114"],["file","services/auth/keycloak.py:1-96"],["file","docs/authentication.md:1-79"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":5,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"auth-choice-nextjs-storefront","pid":"AUTH-N-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"clerk","secs":551,"k":"3cea032b-9b94-423d-b3d5-6bfbc4f4d635-r1","picks":[["clerk","p"],["auth0","m"],["better-auth","a"],["workos-authkit","a"],["authjs","m"],["supabase-auth","m"]],"ev":68,"v":{"r":"The agent evaluated several authentication solutions (Clerk, Auth.js, Better Auth, Supabase Auth, WorkOS, Auth0) and selected Clerk. It then proceeded to install `@clerk/nextjs@6.39.6`, configure middleware, layout provider, sign-in/sign-up catch-all routes, and gate checkout sessions using Clerk authentication.","c":1,"e":[["file","package.json:12"],["file","middleware.ts:1-21"],["file","app/layout.tsx:26-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":13,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"bc-auth-prompt-c-09","pid":"AUTH-PC-09a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"auth0","secs":289,"k":"a9e5e282-1241-417b-953a-cc4e54100029-r1","picks":[["auth0","p"],["jwt","m"],["okta","m"]],"ev":31,"v":{"r":"The agent explicitly recommended Auth0 Enterprise as the SSO and federation platform, then implemented environment variables, documentation, JWT RS256 verification using Auth0 JWKS, and Fastify authorization hooks.","c":1,"e":[["file","services/inventory/src/auth.ts"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"oc":["auth0","microsoft entra id","okta"],"theme":"The plain ask"},{"cat":"auth","wave":14,"date":"2026-08-31","repo":"saas-analytics-mid","variant":"base","family":"bc-auth-prompt-c-14","pid":"AUTH-PC-14a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"auth0","secs":588,"k":"39266cca-165f-4b15-9d31-3db5e694159d-r1","picks":[["auth0","p"],["clerk","a"],["fusionauth","m"],["jwt","m"],["stytch","m"],["supabase-auth","m"]],"ev":52,"v":{"r":"The agent explicitly recommended Auth0 as the external IdP to meet requirements for Google/GitHub sign-in, MFA, and password resets, while configuring the repository to verify asymmetric tokens minted by the auth service and updating Postgres schema tables for user and membership tracking.","c":1,"e":[["trace","15"],["file","shared/schema.sql:35-37"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":11,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"bc-auth-prompt-c-06","pid":"AUTH-PC-06a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"clerk","secs":651,"k":"d32e3249-0a6d-4ea3-b138-e44482804ee4-r1","picks":[["clerk","p"],["workos-authkit","a"],["auth0","m"],["authjs","m"],["better-auth","m"],["supabase-auth","m"]],"ev":75,"v":{"r":"The agent evaluated several auth options and selected Clerk, fully implementing it in the codebase with `@clerk/nextjs`, middleware, sign-in/sign-up pages, account settings with UserProfile, and Clerk-to-Stripe customer linking.","c":1,"e":[["file","package.json:12"],["file","middleware.ts:1-20"],["file","app/layout.tsx:26-39"],["file","lib/stripe-customer.ts:1-65"],["file",".env.example:13-20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":9,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"bc-auth-prompt-b-06","pid":"AUTH-PB-06a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"workos-authkit","secs":755,"k":"88f55bed-9a10-45ba-b01e-ffb0e8e4d6c6-r1","picks":[["workos-authkit","p"],["stytch","m"],["auth0","m"],["authentik","m"],["clerk","m"],["jwt","m"],["keycloak","m"],["okta","m"],["zitadel","m"]],"ev":60,"v":{"r":"The agent evaluated several managed and self-hosted auth options before selecting WorkOS AuthKit. It installed the `github.com/workos/workos-go/v4` SDK, implemented OIDC PKCE redirect handlers, middleware, session signing, and an allowlist, and updated the README and configuration.","c":1,"e":[["file","go.mod:8"],["file","internal/auth/workos.go:1-99"],["file","README.md:9-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":8,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"auth-choice-nextjs-storefront","pid":"AUTH-NS-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"better-auth","secs":518,"k":"cbe538bf-bd5d-418d-9ed6-425a71993f4f-r1","picks":[["better-auth","p"],["authjs","m"],["keycloak","m"]],"ev":56,"v":{"r":"The agent evaluated Auth.js, Keycloak, and Better Auth to satisfy the requirement for self-hosted authentication with MFA and password reset. It explicitly selected and fully implemented Better Auth in the Next.js storefront backed by PostgreSQL.","c":1,"e":[["file","package.json:13"],["file","lib/auth.ts:1-105"],["file","app/api/auth/[...all]/route.ts:1-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":8,"date":"2026-08-31","repo":"fieldservice-saas-billing","variant":"base","family":"auth-choice-fieldservice-saas-billing","pid":"AUTH-NS-07a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"better-auth","secs":497,"k":"2e084d06-58c7-4301-b1a4-32380805cee4-r1","picks":[["better-auth","p"],["keycloak","m"]],"ev":62,"v":{"r":"The agent evaluated self-hosted options fitting the Nuxt/Nitro stack and user constraint against SaaS auth providers. Keycloak was considered and rejected in reasoning as too heavy, while Better Auth was chosen, installed, configured with PostgreSQL, and wired into the application.","c":1,"e":[["file","lib/auth.ts:1-130"],["file","package.json:1-50"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":10,"date":"2026-08-31","repo":"nestjs-stockroom","variant":"base","family":"bc-auth-prompt-b-07","pid":"AUTH-PB-07a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"auth0","secs":203,"k":"7fb2b022-74bb-4c09-b147-cd462b91e63c-r1","picks":[["auth0","p"],["clerk","m"],["jwt","m"]],"ev":32,"v":{"r":"The agent evaluated identity providers against the user's requirements (Google/GitHub auth, MFA, password reset) and implemented Auth0 JWT verification with guards in NestJS.","c":1,"e":[["file","src/auth/auth0.service.ts:1-40"],["file","src/auth/jwt-auth.guard.ts:1-49"],["file","infra/main.bicep:7-11"],["file","README.md:11-12"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":11,"date":"2026-08-31","repo":"helpdesk-billing-starter","variant":"base","family":"bc-auth-prompt-c-11","pid":"AUTH-PC-11a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"workos-authkit","secs":538,"k":"a2a9e09c-63ea-4b5d-9d59-fd47720f8492-r1","picks":[["workos-authkit","p"],["lucia","m"],["stytch","m"],["descope","m"],["kinde","m"],["logto","m"],["auth0","m"],["authentik","m"],["authjs","m"],["clerk","m"],["google-identity","m"],["jwt","m"],["keycloak","m"],["okta","m"],["passport","m"],["supabase-auth","m"],["zitadel","m"]],"ev":49,"v":{"r":"The agent evaluated several auth solutions and implemented WorkOS AuthKit using the official `@workos-inc/node` SDK, adding integration routes and documentation while explicitly comparing and rejecting alternatives such as Auth0, Cognito, self-hosted IdPs, Auth.js, and Clerk.","c":1,"e":[["file","package.json:1"],["file","src/auth.js:1-240"],["file","README.md:25-38"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":8,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"auth-senior","pid":"AUTH-3b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"auth0","secs":587,"k":"caccf707-0d49-42b2-bfde-ea73a10c1680-r1","picks":[["auth0","p"],["supabase-auth","m"],["clerk","m"],["fusionauth","m"],["jwt","m"],["workos-authkit","m"],["zitadel","m"]],"ev":43,"v":{"r":"The agent explicitly recommended and fully implemented Auth0 authentication for staff access, integrating OIDC authorization code flow with PKCE, session management, configuration, database migrations, and unit tests.","c":1,"e":[["file",".env.example:3-6"],["file","README.md:3-32"],["file","internal/config/config.go:11-14"],["file","internal/authentication/authentication.go:61-75"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":8,"date":"2026-08-31","repo":"dotnet-utility-billing","variant":"base","family":"auth-ent-senior","pid":"AUTH-4a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"entra-id","secs":312,"k":"b7b350e0-688d-4ec6-999c-5b1eed07b84b-r1","picks":[["entra-id","p"]],"ev":30,"v":{"r":"The user requested an SSO solution for an ASP.NET Core 8 service hosted on Azure App Service. The agent recommended and implemented Microsoft Entra ID authentication and authorization using Microsoft.Identity.Web, updating application code, Bicep infrastructure parameters, and automated authorization tests.","c":1,"e":[["file","src/Northmere.Billing.Api/Program.cs"],["file","infra/main.bicep"],["file","docs/authentication.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"auth","wave":7,"date":"2026-08-31","repo":"dotnet-utility-billing","variant":"base","family":"auth-ent-senior","pid":"AUTH-4a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"entra-id","secs":761,"k":"eea5b028-9aa4-43d9-9c41-4b9d0ca984c9-r1","picks":[["entra-id","p"],["auth0","m"],["jwt","m"],["keycloak","m"],["okta","m"]],"ev":78,"v":{"r":"The agent explicitly recommended and fully integrated Microsoft Entra ID using JWT Bearer authentication against login.microsoftonline.com in Program.cs, updated Directory.Packages.props, configured infra/main.bicep, and added authorization policies matching Entra app roles.","c":1,"e":[["file","src/Northmere.Billing.Api/Program.cs"],["file","infra/main.bicep"],["file","README.md"],["file","Directory.Packages.props"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"auth","wave":13,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"bc-auth-prompt-c-08","pid":"AUTH-PC-08a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":288,"k":"d287f65f-bed3-462f-809e-0800c234c394-r1","picks":[["diy","p","d"],["arctic","m"],["google-identity","m"],["google-sign-in","m"]],"ev":38,"v":{"r":"The user requested adding Google Sign-In to protect the studio board route. The agent investigated existing auth frameworks (remix-auth, openid-client, arctic) but chose to write a DIY OAuth 2.0 PKCE and allowlist authorization handler in app/auth.server.ts using google-auth-library and Remix's built-in cookie sessions.","c":1,"e":[["file","app/auth.server.ts:1-192"],["file","app/routes/studio-board.tsx:7-13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":11,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"bc-auth-prompt-b-04","pid":"AUTH-PB-04a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":276,"k":"dc6b3d55-a588-411a-aca9-f689d669031a-r1","picks":[["diy","p","d"],["auth0","m"],["authjs","m"],["clerk","m"],["google-sign-in","m"]],"ev":34,"v":{"r":"The agent explicitly evaluated third-party hosted authentication options (Auth0, Clerk, Firebase Auth) and libraries (Auth.js) and decided to implement a custom server-side Google OAuth / OIDC flow using `google-auth-library` and Remix's built-in cookie session storage (`createCookieSessionStorage`).","c":1,"e":[["file","app/auth.server.ts"],["file","app/routes/auth.google.tsx"],["file","app/routes/auth.google.callback.tsx"],["file","app/routes/login.tsx"],["file","app/routes/studio-board.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":12,"date":"2026-08-31","repo":"portrait-gallery","variant":"base","family":"bc-auth-prompt-c-07","pid":"AUTH-PC-07a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"google-identity","secs":231,"k":"a7d40f97-a510-45e1-89ac-87cf6e76c1cf-r1","picks":[["google-identity","p"],["auth0","m"],["authjs","m"],["clerk","m"],["google-sign-in","m"],["jwt","m"],["passport","m"]],"ev":13,"v":{"r":"The agent evaluated several auth solutions (Auth.js, Passport, Clerk, Auth0, Supabase) and decided to implement Google Identity Services directly using the official google-auth-library npm package for server-side ID token verification.","c":0.95,"e":[["file","package.json:13"],["file","src/auth.js:1-157"],["file","README.md:3-35"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":10,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"bc-auth-prompt-b-06","pid":"AUTH-PB-06a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"auth0","secs":549,"k":"5ff019e2-643a-4cd4-8ef1-4c53cb34fe99-r1","picks":[["auth0","p"],["jwt","m"],["workos-authkit","m"]],"ev":46,"v":{"r":"The agent evaluated several managed authentication providers (Auth0, Clerk, WorkOS) and chose Auth0. It then implemented a full OIDC Authorization Code Flow with PKCE, secure cookie sessions, and staff allowlist verification against Auth0 in Go 1.22.","c":1,"e":[["file","README.md"],["file",".env.example"],["file","internal/auth/auth.go"],["file","internal/config/config.go"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":10,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"bc-auth-prompt-b-11","pid":"AUTH-PB-11a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"auth0","secs":390,"k":"f12b53ae-16e6-4639-96a8-c4dc7c0a5f35-r1","picks":[["auth0","p"],["amazon-cognito","m"],["clerk","m"],["google-sign-in","m"],["jwt","m"]],"ev":61,"v":{"r":"The agent evaluated Auth0 against Amazon Cognito and Clerk, selected Auth0, and implemented full Auth0 RS256 token verification, onboarding endpoints, Alembic migrations, Terraform configuration, and bulk export scripts.","c":1,"e":[["file","app/security.py"],["file","app/config.py"],["file","alembic/versions/20260831_4a1c9ef732b0_auth0_identities.py"],["file","scripts/export_auth0_users.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":10,"date":"2026-08-31","repo":"dotnet-utility-billing","variant":"base","family":"bc-auth-prompt-b-01","pid":"AUTH-PB-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"entra-id","secs":257,"k":"52016008-bc30-41e6-853c-490e497c9d01-r1","picks":[["entra-id","p"],["jwt","m"]],"ev":33,"v":{"r":"The agent evaluated authentication options for an ASP.NET Core API deployed on Azure App Service and selected Microsoft Entra ID. It added Microsoft.Identity.Web JWT validation, defined scope and role authorization policies, updated infrastructure templates, and explicitly rejected Azure App Service Easy Auth due to authorization limitations.","c":1,"e":[["file","src/Northmere.Billing.Api/Program.cs"],["file","docs/authentication.md"],["file","infra/main.bicep"],["file","Directory.Packages.props"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"auth","wave":9,"date":"2026-08-31","repo":"dotnet-insurance","variant":"base","family":"bc-auth-prompt-b-01","pid":"AUTH-PB-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"entra-id","secs":700,"k":"3e7f80e4-07df-42ca-93c4-d9eb8a7dbd80-r1","picks":[["entra-id","p"],["auth0","m"],["jwt","m"],["keycloak","m"],["okta","m"]],"ev":69,"v":{"r":"The agent analyzed the project documentation and roadmap (PC-1400), recommended single-tenant Microsoft Entra ID JWT bearer validation via Microsoft.Identity.Web in line with the platform convention, and implemented it across the project codebase, configuration, CI pipeline, and Bicep infrastructure.","c":1,"e":[["file","src/Meridian.PolicyCore/Meridian.PolicyCore.csproj:18-20"],["file","src/Meridian.PolicyCore/Security/EntraAuthenticationExtensions.cs:1-139"],["file","infra/bicep/main.bicep:20-27"],["file","README.md:35-48"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"auth","wave":6,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"auth-choice-go-customer-ops","pid":"AUTH-N-02a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"auth0","secs":368,"k":"c4f312af-cff6-4545-992f-e2f640880d15-r1","picks":[["auth0","p"],["clerk","m"],["zitadel","m"],["fusionauth","m"],["supabase-auth","m"],["jwt","m"]],"ev":37,"v":{"r":"The agent explicitly recommended Auth0 Universal Login and implemented the complete Go OIDC flow, route protection, and PostgreSQL session tracking tailored for Auth0.","c":0.99,"e":[["file",".env.example:3-11"],["file","README.md:20-37"],["file","internal/auth/auth.go:80-120"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":10,"date":"2026-08-31","repo":"helpdesk-billing-starter","variant":"base","family":"bc-auth-prompt-b-09","pid":"AUTH-PB-09a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"workos-authkit","secs":426,"k":"301b4c81-66b2-4754-8bd7-b417bc55f1bb-r1","picks":[["workos-authkit","p"],["auth0","m"],["authjs","m"],["clerk","m"],["jwt","m"]],"ev":44,"v":{"r":"The agent evaluated WorkOS, Clerk, and Auth0 for B2B multi-tenant authentication, recommending WorkOS AuthKit for its generous free tier, built-in organization/invitation management, and minimal operational overhead. After user confirmation, the agent installed `@workos-inc/node` and fully implemented AuthKit login, callback handling, PKCE verification, and invoice route authorization.","c":1,"e":[["file","package.json:1"],["file","src/auth.js:1-169"],["file","docs/authentication.md:1-103"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":8,"date":"2026-08-31","repo":"laravel-helpdesk","variant":"base","family":"auth-choice-laravel-helpdesk","pid":"AUTH-NS-04a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"multiple","secs":302,"k":"1643d57f-1f3d-4090-bd9b-fb18191185d5-r1","picks":[["laravel-fortify","p"],["laravel-socialite","c"],["authentik","m"],["keycloak","m"]],"solution":["laravel-fortify","laravel-socialite"],"ev":39,"v":{"r":"The run selected, installed, and fully implemented Laravel Fortify alongside Laravel Socialite to fulfill self-hosted authentication, password reset, TOTP MFA, and Google/GitHub OAuth requirements without external authentication SaaS. It evaluated and rejected standalone IdPs (Keycloak, Authentik) and starter kits (Breeze, Jetstream).","c":0.95,"e":[["file","composer.json"],["file","app/Providers/FortifyServiceProvider.php"],["file","config/fortify.php"],["file","composer.json"],["file","app/Http/Controllers/OauthController.php"],["file","config/services.php"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":8,"date":"2026-08-31","repo":"sveltekit-indie","variant":"base","family":"auth-vibe-sveltekit-indie","pid":"AUTH-11a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":365,"k":"f52b75f2-d201-4cf8-abbc-f4d2d3ae54f4-r1","picks":[["diy","p","d"],["arctic","m"],["authjs","m"],["clerk","m"],["google-sign-in","m"]],"ev":51,"v":{"r":"The agent evaluated external auth providers and frameworks (Auth.js, Clerk, Supabase, Arctic) but concluded that a third-party auth service was overkill and redundant. It wrote a DIY Google OAuth 2.0 / OpenID Connect authorization code flow directly in the repository using google-auth-library and integrated it with the existing SQLite-backed session system.","c":0.95,"e":[["file","src/lib/server/google-auth.ts"],["file","src/routes/auth/google/+server.ts"],["file","src/routes/auth/google/callback/+server.ts"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":15,"date":"2026-08-31","repo":"saas-analytics-mid","variant":"base","family":"bc-auth-prompt-c-14","pid":"AUTH-PC-14a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"workos-authkit","secs":184,"k":"eaa09ff4-40e4-4cbd-a29d-ce9950fa7e76-r1","picks":[["workos-authkit","p"],["auth0","a"],["clerk","a"],["jwt","m"]],"ev":26,"v":{"r":"The agent selected WorkOS AuthKit as the managed authentication provider, implementing JWKS token verification, workspace claim validation, and billing tenancy guards across the repository while providing configuration in .env.example and documentation in README.md and docs/multi-tenancy.md.","c":1,"e":[["file","shared/tenancy.py:33-125"],["file","shared/config.py:29-35"],["file",".env.example:21-27"],["file","README.md:34-51"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":9,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"bc-auth-prompt-b-05","pid":"AUTH-PB-05a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"clerk","secs":444,"k":"308c8a02-6728-4a63-bb92-d99b8461cab2-r1","picks":[["clerk","p"],["auth0","m"],["authjs","m"],["supabase-auth","m"],["workos-authkit","m"]],"ev":44,"v":{"r":"The agent explicitly recommended Clerk, installed `@clerk/nextjs`, configured middleware, layout providers, auth pages, and Stripe customer linking, while explicitly evaluating and rejecting Auth0, Supabase Auth, Firebase Auth, Auth.js, and WorkOS AuthKit.","c":1,"e":[["file","package.json"],["file","middleware.ts"],["file","app/layout.tsx"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":9,"date":"2026-08-31","repo":"nestjs-stockroom","variant":"base","family":"bc-auth-prompt-b-07","pid":"AUTH-PB-07a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"auth0","secs":465,"k":"5bf4e24a-3490-40cc-8d51-aa71e68157af-r1","picks":[["auth0","p"],["workos-authkit","m"],["clerk","m"],["jwt","m"],["keycloak","m"],["passport","m"],["stytch","m"]],"ev":41,"v":{"r":"The user requested a managed auth service supporting password reset, MFA, and Google/GitHub login. The agent selected Auth0 because it natively handles all required identity providers and MFA out-of-the-box, configured the NestJS application as an OAuth 2.0 RS256 JWT resource server using `jose`, updated Bicep infrastructure templates for Auth0 configuration, added comprehensive tests, and documented the tenant setup in README.md.","c":1,"e":[["file","README.md:18-49"],["file","infra/main.bicep:10-39"],["file","src/auth/auth.config.ts:1-22"],["file","test/auth.spec.ts:12-14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":10,"date":"2026-08-31","repo":"dotnet-insurance","variant":"base","family":"bc-auth-prompt-b-01","pid":"AUTH-PB-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"entra-id","secs":225,"k":"4f4c320c-750f-47da-8f21-940832346a76-r1","picks":[["entra-id","p"],["auth0","m"],["jwt","m"]],"ev":24,"v":{"r":"The agent evaluated SSO approaches and selected Microsoft Entra ID using Microsoft.Identity.Web, implementing JWT bearer token verification, granular scope/role policies across controllers, and deploying configuration through Bicep and Azure Pipelines.","c":1,"e":[["file","src/Meridian.PolicyCore/Meridian.PolicyCore.csproj:18"],["file","src/Meridian.PolicyCore/Program.cs:45-51"],["file","infra/bicep/modules/app-service.bicep:50-61"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"auth","wave":10,"date":"2026-08-31","repo":"portrait-gallery","variant":"base","family":"bc-auth-prompt-b-03","pid":"AUTH-PB-03a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":349,"k":"69ddd487-c7a7-4856-8a4c-128592df9b04-r1","picks":[["diy","p","d"],["authjs","m"],["clerk","m"],["google-identity","m"],["google-sign-in","m"],["passport","m"]],"ev":28,"v":{"r":"The agent evaluated existing authentication options (Auth.js, Passport, Clerk) and decided against third-party auth platforms, choosing instead to implement a custom session-based authentication layer directly in the repository with SQLite persistence and google-auth-library for ID token verification.","c":0.95,"e":[["file","src/auth.js:4-64"],["file","src/app.js:90-184"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":8,"date":"2026-08-31","repo":"portrait-gallery","variant":"base","family":"auth-vibe-portrait-gallery","pid":"AUTH-6a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":207,"k":"b9a39bc9-8edb-4f16-8e24-e6cf6600002f-r1","picks":[["diy","p","d"],["passport","m"],["authjs","m"],["google-identity","m"],["google-sign-in","m"]],"ev":28,"v":{"r":"The run evaluated auth options and implemented a hand-crafted server-side Google OpenID Connect flow using the openid-client protocol library and SQLite-backed sessions in `src/auth.js`, `src/server.js`, and `src/store.js`. Passport and Auth.js were mentioned in deliberation but not adopted.","c":0.95,"e":[["file","src/auth.js:1-103"],["file","src/server.js:56-140"],["file","src/store.js:1-226"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":7,"date":"2026-08-31","repo":"flask-shiftplanner","variant":"base","family":"auth-junior-flask-shiftplanner","pid":"AUTH-9a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"auth0","secs":394,"k":"dbbfaf15-764b-4b5e-9c5c-cb62f3b9f402-r1","picks":[["auth0","p"],["workos-authkit","m"],["authlib","m"],["clerk","m"],["jwt","m"],["keycloak","m"]],"ev":49,"v":{"r":"The agent selected Auth0 as the primary managed authentication service to handle JWT verification and configured routes, helper modules, environment variables, documentation, and automated tests to verify tokens using Auth0's JWKS.","c":1,"e":[["file","auth.py:1-193"],["file","app.py:11-208"],["file",".env.example:5-9"],["file","README.md:18-40"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":11,"date":"2026-08-31","repo":"dotnet-utility-billing","variant":"base","family":"bc-auth-prompt-c-02","pid":"AUTH-PC-02a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"entra-id","secs":499,"k":"96a2919d-3372-4c15-b19e-8b878ee400dc-r1","picks":[["entra-id","p"],["jwt","m"]],"ev":55,"v":{"r":"The agent recommended and implemented Microsoft Entra ID bearer token authentication using Microsoft.AspNetCore.Authentication.JwtBearer. 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It selected WorkOS AuthKit, installed `@workos-inc/node`, and implemented complete authentication and authorization routing.","c":1,"e":[["file","package.json"],["file","src/auth.js"],["file","src/server.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":12,"date":"2026-08-31","repo":"edtech-lms","variant":"base","family":"bc-auth-prompt-b-14","pid":"AUTH-PB-14a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"workos-authkit","secs":484,"k":"18c2583e-e76b-4a11-a0c2-bbdd108551b0-r1","picks":[["workos-authkit","p"],["auth0","m"],["classlink","m"],["clever","m"]],"ev":54,"v":{"r":"The agent explicitly recommended and fully implemented WorkOS SSO using the Python SDK (`workos==10.2.0`), creating authentication views, state verification, settings, database models/migrations for SSO identities, and automated test cases.","c":1,"e":[["file","requirements.txt"],["file","brightloom/settings.py"],["file","apps/roster/auth_views.py"],["file","apps/roster/models.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"auth","wave":4,"date":"2026-08-31","repo":"ts-commerce-datadog","variant":"base","family":"auth-sso-context-ts-commerce-datadog","pid":"AUTH-SSO-02a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"workos-authkit","secs":208,"k":"fd15de11-33c3-423b-85d5-ed244c7b59a3-r1","picks":[["workos-authkit","p"],["auth0","m"],["okta","m"],["jwt","m"],["keycloak","m"]],"ev":26,"v":{"r":"The agent evaluated enterprise SSO options (WorkOS, Cognito, Keycloak, Auth0, Okta) and recommended WorkOS AuthKit with Organizations. It then implemented a Fastify authentication plugin in the inventory service that verifies WorkOS RS256 JWT access tokens using jose and cached JWKS.","c":1,"e":[["file","services/inventory/src/plugins/workos-auth.ts"],["file","docs/authentication.md"],["file",".env.example"],["trace","item:6"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":6,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"auth-choice-nextjs-storefront","pid":"AUTH-N-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"clerk","secs":192,"k":"c9ef3bc1-d856-4604-aebe-4623ed1bdea2-r1","picks":[["clerk","p"],["supabase-auth","m"],["better-auth","m"],["auth0","m"]],"ev":26,"v":{"r":"The agent explicitly recommended Clerk, installed `@clerk/nextjs`, wrapped the application in `ClerkProvider`, created sign-in and sign-up pages using Clerk's UI components, integrated `clerkMiddleware`, and enforced authentication inside the checkout API route.","c":1,"e":[["file","package.json"],["file","middleware.ts"],["file","app/layout.tsx"],["file","app/api/checkout/route.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":8,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"auth-senior-fastapi-saas","pid":"AUTH-14a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"auth0","secs":302,"k":"a72b286f-8480-48ad-b0c1-8ac28b4a84a9-r1","picks":[["auth0","p"],["clerk","m"],["jwt","m"]],"ev":43,"v":{"r":"The agent evaluated managed auth solutions and recommended and fully implemented Auth0 across backend token verification, Alembic migrations, environment config, Terraform, and documentation, while explicitly rejecting Amazon Cognito due to lack of native GitHub OAuth support.","c":1,"e":[["file","app/security.py"],["file","auth0/README.md"],["file","alembic/versions/20260831_a1c4f72d91be_auth0_identities.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":12,"date":"2026-08-31","repo":"fastapi-saas","variant":"base","family":"bc-auth-prompt-c-13","pid":"AUTH-PC-13a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"auth0","secs":344,"k":"826c3c69-e5de-46d5-8ccc-8f5ff7f1e9d7-r1","picks":[["auth0","p"],["clerk","m"],["jwt","m"]],"ev":38,"v":{"r":"The user requested managed authentication supporting MFA, password reset, and Google/GitHub sign-in. The agent recommended and fully implemented Auth0 across the codebase (configuration, deps, security token validation, Alembic migrations, export scripts, CI, and Terraform), while explicitly rejecting Cognito and Clerk.","c":1,"e":[["file","app/security.py:22-55"],["file","app/deps.py:17-48"],["file","alembic/versions/20260831_c7ad51f43b9e_auth0_identities.py:1-35"],["file","scripts/export_auth0_users.py:1-58"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":10,"date":"2026-08-31","repo":"flask-shiftplanner","variant":"base","family":"bc-auth-prompt-b-05","pid":"AUTH-PB-05a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"auth0","secs":169,"k":"59934a08-2086-47fc-8975-d3406db95041-r1","picks":[["auth0","p"],["authlib","m"],["clerk","m"],["jwt","m"],["supabase-auth","m"]],"ev":22,"v":{"r":"The agent evaluated several managed authentication providers (Auth0, Clerk, Supabase Auth, Firebase Auth) to fulfill the user's requirements for MFA, social logins, and password reset. The agent recommended Auth0 Universal Login paired with RS256 token verification, and upon user approval, fully implemented Auth0 verification route decorators, environment configuration, tests, and documentation in the repository.","c":1,"e":[["file","auth.py:24-52"],["file","app.py:14-21"],["file","README.md:18-50"],["file",".env.example:5-7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"auth","wave":8,"date":"2026-08-31","repo":"dotnet-insurance","variant":"base","family":"auth-ent-senior-insurance","pid":"AUTH-5a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"entra-id","secs":284,"k":"51b9f322-4ee0-4199-a143-1079c94ffb23-r1","picks":[["entra-id","p"],["jwt","m"]],"ev":35,"v":{"r":"The agent selected and fully implemented single-tenant Microsoft Entra ID bearer token authentication using Microsoft.Identity.Web, configuring the ASP.NET Core middleware, authorization policies, Bicep infrastructure parameters, and deployment pipeline variables.","c":1,"e":[["file","src/Meridian.PolicyCore/Program.cs"],["file","src/Meridian.PolicyCore/Meridian.PolicyCore.csproj"],["file","docs/entra-sso.md"],["file","infra/bicep/main.bicep"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":4,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-named-discovery-small-footprint","pid":"VAGT-DISCOVERY-SMALL-FOOTPRINT-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"smallest-ai-voice-agents","secs":852,"k":"02dad318-f1ac-4585-8759-23c6ff344114-r2","picks":[["smallest-ai-voice-agents","p"],["bland-ai","m"],["retell-ai","m"],["vapi","m"]],"ev":75,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent evaluated multiple voice agent platforms to satisfy constraints around cost and memory/CPU overhead on the Go application. It recommended Smallest AI Voice Agents (Smallest.ai), and upon user approval, implemented a full Smallest.ai client, background worker, and webhook gateway with HMAC verification.","c":1,"e":[["file","internal/voice/smallest/client.go:1-71"],["file","cmd/voice-worker/main.go:1-79"],["file","cmd/voice-gateway/main.go:1-96"],["file","docs/voice-agent.md:1-118"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":4,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-named-discovery-claims","pid":"VAGT-DISCOVERY-CLAIMS-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cognigy","secs":2776,"k":"08a62bf9-924a-4ece-81f2-fd2fd55da894-r1","picks":[["cognigy","p"],["twilio-conversationrelay","m"],["retell-ai","a"],["amazon-connect","m"],["vapi","m"]],"ev":109,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent conducted a comparative evaluation of voice agent platforms (Cognigy, ElevenLabs, Smallest.ai, Retell AI, Vapi) against the user's regulated claims constraints, unambiguously selected Cognigy as the recommended platform, and fully implemented a Rails claims authorization gateway designed for Cognigy.","c":1,"e":[["file","docs/voice_gateway.md:1-15"],["file","db/migrate/20260831101000_create_voice_gateway_records.rb:9"],["file",".env.example:11"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":4,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-named-discovery-claims","pid":"VAGT-DISCOVERY-CLAIMS-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"cognigy","secs":987,"k":"08a62bf9-924a-4ece-81f2-fd2fd55da894-r2","picks":[["cognigy","p"],["retell-ai","m"],["openai-realtime","m"],["amazon-connect","a"]],"ev":109,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent evaluated multiple voice agent platforms (Cognigy, ElevenLabs, Smallest.ai, Amazon Connect, Retell AI, OpenAI Realtime API) against regulated claims requirements. It explicitly rejected ElevenLabs and Smallest.ai, chose Cognigy as the recommended solution, and built out the full integration layer, OpenAPI specification, and tests for Cognigy.","c":1,"e":[["file","config/cognigy/openapi.yml"],["file","docs/cognigy_voice_integration.md:1-40"],["file","app/controllers/api/cognigy/v1/base_controller.rb:1-31"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":7,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-named-discovery-claims","pid":"VAGT-DISCOVERY-CLAIMS-01c","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"elevenlabs-agents","secs":1599,"k":"eb44a398-8947-4563-9c49-f47070f00eda-r1","picks":[["elevenlabs-agents","p"],["vapi","m"],["retell-ai","m"],["pipecat","m"],["amazon-connect","m"],["twilio-conversationrelay","m"],["livekit-agents","a"]],"ev":146,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent evaluated ElevenLabs, Smallest.ai, and other voice agent platforms against the repository's constraints. It recommended ElevenLabs Agents with Zero Retention Mode and in-browser WebRTC integration, rejected Smallest.ai due to lack of documented enterprise/auth controls, weighed LiveKit as a self-hosted alternative, and then fully implemented the ElevenLabs client, tools controller, session manager, and vendored client SDK upon user approval.","c":1,"e":[["file","app/services/eleven_labs_client.rb:1-42"],["file",".env.example:6-10"],["file","app/controllers/voice/sessions_controller.rb:1-38"],["file","public/vendor/elevenlabs-client-1.23.0.iife.js"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":7,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-named-discovery-claims","pid":"VAGT-DISCOVERY-CLAIMS-01c","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"livekit-agents","secs":1466,"k":"eb44a398-8947-4563-9c49-f47070f00eda-r2","picks":[["livekit-agents","p"],["vapi","m"],["pipecat","m"],["deepgram-voice-agent","m"],["openai-realtime","m"],["twilio-conversationrelay","m"],["amazon-connect","m"],["retell-ai","a"],["elevenlabs-agents","m"]],"ev":159,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent explicitly recommended LiveKit Agents as the primary choice over ElevenLabs and Smallest.ai to leverage in-app WebRTC authentication and avoid storing transcripts. Following approval, the agent implemented LiveKit room token minting in Rails, configured environment variables, and created a Python LiveKit agent worker.","c":1,"e":[["file","app/services/livekit_token.rb"],["file","voice_agent/README.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":3,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-named-discovery-claims","pid":"VAGT-DISCOVERY-CLAIMS-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"twilio-conversationrelay","secs":1565,"k":"4a3225d4-094f-4654-badb-79b9c31a7a42-r1","picks":[["twilio-conversationrelay","p"],["vapi","m"],["retell-ai","m"],["livekit-agents","a"],["amazon-connect","m"],["elevenlabs-agents","m"],["pipecat","m"]],"ev":172,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent evaluated several voice agent platforms against strict compliance and architecture requirements, explicitly recommended Twilio ConversationRelay over ElevenLabs and Smallest.ai, received approval, and implemented full integration using ConversationRelay TwiML and an accompanying WebSocket bridge.","c":1,"e":[["file","app/services/telephony/twiml.rb:8-35"],["file","app/controllers/telephony/calls_controller.rb:4-21"],["file","config/initializers/telephony.rb:3-13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":3,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-named-discovery-claims","pid":"VAGT-DISCOVERY-CLAIMS-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"elevenlabs-agents","secs":1491,"k":"4a3225d4-094f-4654-badb-79b9c31a7a42-r2","picks":[["elevenlabs-agents","p"],["amazon-connect","m"],["polyai","m"],["parloa","m"],["livekit-agents","a"],["twilio-conversationrelay","a"],["pipecat","m"],["retell-ai","m"],["vapi","m"]],"ev":158,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent evaluated ElevenLabs, Smallest.ai, and multiple alternatives against compliance, retention, and integration requirements. It recommended ElevenLabs Agents, established handler authentication and proposal/commit flows in Rails, and committed the implementation integrating with ElevenLabs ConvAI outbound calls and webhook signatures.","c":1,"e":[["file","app/services/voice/outbound_call.rb:1-96"],["file","app/services/voice/webhook_signature.rb:1-35"],["file",".env.example:4-9"],["file","README.md:21-39"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":5,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-named-discovery-claims","pid":"VAGT-DISCOVERY-CLAIMS-01b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"livekit-agents","secs":1474,"k":"2d097973-91b6-4fa8-bd25-aef3171fd182-r1","picks":[["livekit-agents","p"],["elevenlabs-agents","m"],["pipecat","m"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":137,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent evaluated multiple voice agent platforms (ElevenLabs Agents, Smallest AI, Retell AI, Vapi, Pipecat, Twilio ConversationRelay, LiveKit Agents) against the requirements of handling surge concurrency, confirming claim writes via a two-phase API, and warm transfers with summaries. It recommended LiveKit Agents and implemented the voice worker using the livekit-agents Python SDK along with a dedicated Rails API.","c":0.98,"e":[["file","voice_agent/requirements.txt:1"],["file","voice_agent/agent.py:18"],["file","voice_agent/README.md:15-28"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":5,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-named-discovery-claims","pid":"VAGT-DISCOVERY-CLAIMS-01b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"elevenlabs-agents","secs":1334,"k":"2d097973-91b6-4fa8-bd25-aef3171fd182-r2","picks":[["elevenlabs-agents","p"],["retell-ai","a"],["livekit-agents","m"],["pipecat","m"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":115,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent evaluated ElevenLabs Agents and Smallest.ai along with Retell AI, Vapi, LiveKit Agents, and Pipecat. It unambiguously recommended ElevenLabs Agents as the primary choice for catastrophe surge handling. After the user approved the plan, the agent implemented the supporting backend webhook API in Rails with authentication, idempotency, row locking, and async summary processing to interface with the ElevenLabs agent.","c":0.95,"e":[["trace","Item after seq 19 (assistant recommendation message)"],["trace","Final answer"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":7,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-named-discovery-booking","pid":"VAGT-DISCOVERY-BOOKING-01c","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vapi","secs":989,"k":"18b440bd-9a79-4b48-9ec8-7bff07d19e47-r1","picks":[["vapi","p"],["pipecat","m"],["twilio-conversationrelay","m"],["livekit-agents","a"],["elevenlabs-agents","m"]],"ev":109,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent evaluated multiple voice agent platforms (ElevenLabs Agents, Smallest AI, LiveKit Agents, Vapi, etc.) for a bilingual studio phone assistant. Upon user approval, the agent implemented a Vapi integration by building the server-side tool dispatch route (`app/api/voice/tools/route.ts`), caller lookup / fuzzy resolver logic, atomic booking migrations, and full Vapi assistant documentation (`docs/voice-agent.md`).","c":1,"e":[["file","app/api/voice/tools/route.ts:1-93"],["file","docs/voice-agent.md:1-180"],["file",".env.example:8-14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":7,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-named-discovery-booking","pid":"VAGT-DISCOVERY-BOOKING-01c","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"vapi","secs":1284,"k":"18b440bd-9a79-4b48-9ec8-7bff07d19e47-r2","picks":[["vapi","p"],["elevenlabs-agents","m"],["livekit-agents","m"],["openai-realtime","m"],["pipecat","m"],["retell-ai","m"],["twilio-conversationrelay","m"]],"ev":84,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent evaluated several voice agent platforms against the repository's constraints (bilingual EN/FR code-switching, proper-noun recognition, warm transfer with summary, and atomic booking). It recommended Vapi orchestrating Deepgram Nova-3 and ElevenLabs TTS, which the user approved, and subsequently generated the Vapi assistant configuration (vapi-assistant.json), documentation, and matching API endpoint handlers in the repository.","c":1,"e":[["file","voice/vapi-assistant.json"],["file","voice/README.md"],["file","lib/voice/protocol.ts:37-69"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":5,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-named-discovery-small-footprint","pid":"VAGT-DISCOVERY-SMALL-FOOTPRINT-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"elevenlabs-agents","secs":973,"k":"c399e577-6f9b-4d05-a312-5974ff15023c-r1","picks":[["elevenlabs-agents","p"],["livekit-agents","m"],["pipecat","m"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":92,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent was tasked with evaluating voice agent options (specifically mentioning Smallest.ai and ElevenLabs) for an account desk with callers who interrupt often. After surveying platforms including ElevenLabs, Smallest.ai, Retell, Vapi, LiveKit, and Pipecat, the agent recommended ElevenLabs Agents combined with a lightweight, secure gateway. Upon user approval, the agent implemented the Go gateway listener and documented the exact ElevenLabs tool configuration and system prompt in docs/voice-agent.md.","c":1,"e":[["file","docs/voice-agent.md:3-35"],["file","docs/voice-agent.md:65-120"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":5,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-named-discovery-small-footprint","pid":"VAGT-DISCOVERY-SMALL-FOOTPRINT-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"elevenlabs-agents","secs":1225,"k":"c399e577-6f9b-4d05-a312-5974ff15023c-r2","picks":[["elevenlabs-agents","p"],["deepgram-voice-agent","m"],["openai-realtime","m"],["bland-ai","m"],["retell-ai","a"],["vapi","a"],["livekit-agents","m"],["pipecat","m"]],"ev":101,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent explicitly recommended ElevenLabs Agents over Smallest.ai and other platforms due to its turn-taking model for handling caller interruptions and mature server tools authentication. It then built a dedicated `cmd/voice-gateway` binary and internal endpoints to integrate with the ElevenLabs platform while protecting internal customer-ops data.","c":0.95,"e":[["trace","trace:items[31]"],["file","README.md:29-53"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":7,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-named-discovery-small-footprint","pid":"VAGT-DISCOVERY-SMALL-FOOTPRINT-01c","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"retell-ai","secs":1003,"k":"19a369d6-7e44-4454-9051-c6cc72229a2d-r1","picks":[["retell-ai","p"],["livekit-agents","m"],["pipecat","m"],["openai-realtime","m"],["twilio-conversationrelay","m"],["bland-ai","m"],["deepgram-voice-agent","m"],["elevenlabs-agents","m"],["vapi","m"]],"ev":67,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent evaluated multiple voice agent platforms (ElevenLabs, Smallest.ai, Retell AI, Vapi, Deepgram, LiveKit, Pipecat), recommended Retell AI based on peak concurrency handling and cost efficiency, received approval, and implemented a complete voice gateway specifically tailored to Retell AI webhooks and tool calls.","c":1,"e":[["file","README.md"],["file",".env.example"],["file","internal/gateway/config.go"],["file","internal/gateway/server.go"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":7,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-named-discovery-small-footprint","pid":"VAGT-DISCOVERY-SMALL-FOOTPRINT-01c","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"retell-ai","secs":1191,"k":"19a369d6-7e44-4454-9051-c6cc72229a2d-r2","picks":[["retell-ai","p"],["bland-ai","m"],["pipecat","m"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":81,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent evaluated Smallest AI, ElevenLabs, Vapi, LiveKit, and Retell AI against cost per completed call, concurrency spikes, and warm transfer with context. Upon user approval, the agent implemented a full Go voice adapter (`cmd/voiceadapter`) and JSON deployment configurations for Retell AI.","c":1,"e":[["file","deploy/retell/agent.json:1-38"],["file","deploy/retell/retell-llm.json:1-152"],["file","cmd/voiceadapter/main.go:1-84"],["file","docs/voice-agent.md:1-172"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":3,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-named-discovery-small-footprint","pid":"VAGT-DISCOVERY-SMALL-FOOTPRINT-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"retell-ai","secs":1055,"k":"76205619-9775-4539-b874-9e12faf63eca-r1","picks":[["retell-ai","p"],["vapi","a"],["elevenlabs-agents","m"],["livekit-agents","m"],["openai-realtime","m"],["pipecat","m"],["twilio-conversationrelay","m"]],"ev":73,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent evaluated multiple hosted and self-hosted voice agent platforms against cost and resource constraints, unambiguously selected Retell AI as the primary choice, and fully implemented a dedicated gateway binary (`cmd/voicegw`), webhook handler, custom functions, database migrations, and agent prompt configuration for Retell AI.","c":1,"e":[["file","internal/voice/retell.go:1-85"],["file","cmd/voicegw/main.go:1-73"],["file","prompts/retell_agent.md:1-92"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":3,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-named-discovery-small-footprint","pid":"VAGT-DISCOVERY-SMALL-FOOTPRINT-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"retell-ai","secs":850,"k":"76205619-9775-4539-b874-9e12faf63eca-r2","picks":[["retell-ai","p"],["bland-ai","m"],["deepgram-voice-agent","a"],["elevenlabs-agents","m"],["pipecat","m"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":50,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent evaluated multiple voice agent options against cost, concurrency, and compute constraints. It explicitly recommended and fully implemented Retell AI via a dedicated HTTP listener, HMAC signature validation, PostgreSQL call tracking, and webhook handlers, while evaluating and rejecting ElevenLabs, Smallest.ai, Vapi, Deepgram, and several self-hosted/raw audio frameworks.","c":1,"e":[["file","internal/voice/voice.go:1-491"],["file","README.md:29-76"],["file",".env.example:4-21"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":8,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-named-discovery-claims","pid":"VAGT-DISCOVERY-CLAIMS-01c","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"livekit-agents","secs":757,"k":"61e0eada-de58-441d-bf4d-b1b96b78f83b-r1","picks":[["livekit-agents","p"],["vapi","m"],["elevenlabs-agents","m"]],"ev":72,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent evaluated several voice agent platforms against strict privacy, authorization, interruption, and audit constraints, ultimately selecting and implementing LiveKit Agents (using OpenAI Realtime as its underlying realtime model component). ElevenLabs Agents and Smallest AI Voice Agents were explicitly evaluated and rejected due to privacy and transcript-retention mismatches, while Retell AI was discussed as an alternative and Pipecat/Vapi were queried in documentation searches.","c":1,"e":[["file","voice_agent/pyproject.toml:7"],["file","voice_agent/src/cedarline_voice/agent.py:11-20"],["file","README.md:38-60"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":8,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-named-discovery-claims","pid":"VAGT-DISCOVERY-CLAIMS-01c","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"livekit-agents","secs":912,"k":"61e0eada-de58-441d-bf4d-b1b96b78f83b-r2","picks":[["livekit-agents","p"],["vapi","m"],["elevenlabs-agents","a"]],"ev":99,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent evaluated multiple voice agent platforms (LiveKit, ElevenLabs, Smallest AI, Azure Voice Live, Retell AI, Vapi) against stringent permission, write confirmation, audit, and transfer constraints. It recommended self-hosted LiveKit Agents and implemented a complete LiveKit worker and Rails claims security gateway.","c":0.95,"e":[["file","voice_agent/requirements.txt:1"],["file","voice_agent/agent.py:10-25"],["file","README.md:31-78"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":5,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-named-discovery-booking","pid":"VAGT-DISCOVERY-BOOKING-01b","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"elevenlabs-agents","secs":501,"k":"a10060a7-c4df-4741-a4a0-9a546fcc5faf-r1","picks":[["elevenlabs-agents","p"],["pipecat","m"],["openai-realtime","m"],["bland-ai","m"],["livekit-agents","m"],["retell-ai","m"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":54,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent evaluated several voice agent platforms against the user's requirements for a serverless, managed setup with tool calling and warm context handoff. It recommended ElevenLabs Agents paired with native Twilio phone import, which the user explicitly approved. The agent then implemented Next.js webhook API routes, database schema modifications for phone caller identities, and full integration documentation in docs/voice-agent.md.","c":1,"e":[["file","README.md:18-20"],["file","docs/voice-agent.md:1-20"],["trace","seq:16"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":5,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-named-discovery-booking","pid":"VAGT-DISCOVERY-BOOKING-01b","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"retell-ai","secs":858,"k":"a10060a7-c4df-4741-a4a0-9a546fcc5faf-r2","picks":[["retell-ai","p"],["vapi","m"]],"ev":77,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent evaluated Smallest.ai, ElevenLabs, Vapi, and Retell AI against the prompt's requirements (no self-hosted servers, tool calling, handling interruptions, and contextual warm transfer). It recommended Retell AI, obtained user approval, and implemented the full integration including Retell LLM configuration (voice/retell-llm.json), webhook endpoints under app/api/voice/, database migrations, and setup documentation.","c":1,"e":[["file","voice/retell-llm.json"],["file","voice/README.md"],["file","README.md:23-28"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":4,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-named-discovery-booking","pid":"VAGT-DISCOVERY-BOOKING-01a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"elevenlabs-agents","secs":505,"k":"3a302ca6-3939-4894-a2e2-30359e3af165-r1","picks":[["elevenlabs-agents","p"],["retell-ai","a"],["synthflow","m"],["vapi","m"]],"ev":70,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent evaluated several voice agent platforms (ElevenLabs, Smallest.ai, Retell AI, Vapi, Synthflow) against the studio's requirements, specifically recommending ElevenLabs ElevenAgents. Following user approval, the agent implemented the complete API endpoints, prompt, tool manifests, acceptance tests, and runbook for ElevenLabs.","c":0.95,"e":[["file","elevenlabs/README.md"],["file","elevenlabs/tool-manifest.json"],["file","elevenlabs/studio-lumen-agent-prompt.md"],["file","app/api/elevenlabs/schedule/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":4,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-named-discovery-booking","pid":"VAGT-DISCOVERY-BOOKING-01a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"elevenlabs-agents","secs":850,"k":"3a302ca6-3939-4894-a2e2-30359e3af165-r2","picks":[["elevenlabs-agents","p"],["retell-ai","a"],["synthflow","m"],["vapi","m"]],"ev":60,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent evaluated multiple voice agent platforms (ElevenLabs, Smallest AI, Retell AI, Vapi, Synthflow, Bland AI), recommended ElevenLabs Agents, and upon user approval, fully implemented the backend endpoints, webhook verification, and setup guide for ElevenLabs Agents.","c":1,"e":[["file",".env.example:12-15"],["file","docs/elevenlabs-agent.md:1-146"],["file","lib/voice.ts:15"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":8,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-named-discovery-small-footprint","pid":"VAGT-DISCOVERY-SMALL-FOOTPRINT-01c","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"retell-ai","secs":681,"k":"609fe0dd-c6e5-44b4-b5ad-6c38fcd7bb2c-r1","picks":[["retell-ai","p"],["vapi","m"]],"ev":54,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent evaluated multiple voice agent platforms (Retell AI, ElevenLabs, Smallest AI, and Vapi), selected Retell AI as the best fit for handling unpredictable call peaks and secure tool calling, and fully implemented the Retell AI integration including a signed webhook gateway, tool configurations, and an outbound calling utility.","c":1,"e":[["file","cmd/retell-call/main.go:37-47"],["file","cmd/voice-gateway/main.go:18-24"],["file","internal/voicegateway/gateway.go:87-142"],["file","retell/README.md:1-64"],["file","retell/tool-definitions.json:1-59"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":8,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-named-discovery-small-footprint","pid":"VAGT-DISCOVERY-SMALL-FOOTPRINT-01c","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"retell-ai","secs":745,"k":"609fe0dd-c6e5-44b4-b5ad-6c38fcd7bb2c-r2","picks":[["retell-ai","p"],["openai-realtime","m"],["vapi","m"]],"ev":78,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The user requested an evaluation of voice agent platforms for handling short account calls with unpredictable peak concurrency. The agent compared Smallest AI, ElevenLabs, Vapi, OpenAI Realtime API, and Retell AI, recommending Retell AI as the best fit. After user approval, the agent implemented a dedicated Retell voice gateway service, signed webhook validation, confirmation logic, database migrations, and setup documentation.","c":1,"e":[["file","cmd/voice-gateway/main.go:1-58"],["file","internal/voicegateway/server.go:1-504"],["file","docs/retell-setup.md:1-166"],["file","migrations/002_voice_agent.sql:1-41"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":3,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-named-discovery-booking","pid":"VAGT-DISCOVERY-BOOKING-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"retell-ai","secs":748,"k":"a665f7ce-f687-4321-b0a5-c6cc4cff5bd4-r1","picks":[["retell-ai","p"],["twilio-conversationrelay","m"],["elevenlabs-agents","a"],["vapi","m"]],"ev":70,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent evaluated multiple voice agent options, recommended Retell AI, and fully implemented the backend endpoints, signature verification, and documentation for Retell AI.","c":0.95,"e":[["file","docs/retell-agent.md"],["file","lib/retell.ts"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":3,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-named-discovery-booking","pid":"VAGT-DISCOVERY-BOOKING-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"elevenlabs-agents","secs":666,"k":"a665f7ce-f687-4321-b0a5-c6cc4cff5bd4-r2","picks":[["elevenlabs-agents","p"],["bland-ai","m"],["synthflow","m"],["retell-ai","a"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":65,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent was tasked with comparing voice agent platforms (specifically ElevenLabs and Smallest.ai, along with others) and implementing a phone assistant. It selected ElevenLabs Agents, with Retell AI presented as a strong alternative. It explicitly rejected Smallest.ai, Vapi, and Twilio ConversationRelay with concrete rationale. Following user approval, it implemented the five required tool endpoints under `app/api/voice/` and created setup documentation for ElevenLabs Agents connected via Twilio.","c":0.95,"e":[["file","docs/voice-agent.md"],["file","README.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":8,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-named-discovery-booking","pid":"VAGT-DISCOVERY-BOOKING-01c","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"retell-ai","secs":728,"k":"f0a7db0f-7eec-4d2c-93cb-d753cff7c7e5-r1","picks":[["retell-ai","p"],["polyai","m"],["vapi","m"]],"ev":54,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent evaluated several voice agent platforms against the bilingual, noise-handling, confirmation, and warm-transfer requirements, selected Retell AI, and fully implemented the integration with `retell-sdk`, signed webhook handlers, tool calling routes, schemas, database RPCs, and agent configuration files.","c":1,"e":[["file","package.json"],["file","app/api/retell/inbound/route.ts"],["file","app/api/retell/tools/route.ts"],["file","retell/agent-config.json"],["file","docs/retell-setup.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":8,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-named-discovery-booking","pid":"VAGT-DISCOVERY-BOOKING-01c","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"retell-ai","secs":428,"k":"f0a7db0f-7eec-4d2c-93cb-d753cff7c7e5-r2","picks":[["retell-ai","p"],["vapi","m"],["voiceflow","m"]],"ev":55,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent evaluated multiple voice agent platforms (Retell AI, ElevenLabs, Smallest AI, Vapi, Bland AI), recommended Retell AI as the best fit for bilingual switching, vocabulary boosting, and deterministic tool confirmation, and subsequently built out complete inbound and custom function endpoints and setup instructions for Retell AI.","c":1,"e":[["file","app/api/retell/inbound/route.ts"],["file","app/api/retell/tools/route.ts"],["file","lib/retell.ts"],["file","retell/README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":6,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-named-discovery-claims","pid":"VAGT-DISCOVERY-CLAIMS-01b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"elevenlabs-agents","secs":576,"k":"57b43208-8671-4dcd-9b93-d1f33b3267fa-r1","picks":[["elevenlabs-agents","p"],["retell-ai","m"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":51,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent evaluated multiple voice agent platforms (Smallest.ai, ElevenLabs, Vapi, Retell AI, LiveKit Agents, Twilio ConversationRelay, and OpenAI Realtime API) against surge concurrency, structured warm-transfers, interruption handling, and deterministic tool safety. It recommended ElevenLabs Agents with Twilio telephony, and subsequently implemented the full Rails two-phase webhook gateway and integration documentation for ElevenLabs.","c":1,"e":[["file","docs/elevenlabs_twilio_voice_agent.md:1-82"],["file","app/controllers/voice/elevenlabs/initiations_controller.rb:1-20"],["file","README.md:22-31"],["file","test/controllers/voice_gateway_test.rb:1-177"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":6,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-named-discovery-claims","pid":"VAGT-DISCOVERY-CLAIMS-01b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"retell-ai","secs":659,"k":"57b43208-8671-4dcd-9b93-d1f33b3267fa-r2","picks":[["retell-ai","p"],["vapi","m"]],"ev":84,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent evaluated multiple voice agent platforms (Retell AI, ElevenLabs Agents, Smallest AI Voice Agents, Vapi, and OpenAI Realtime API) against operational criteria for high-concurrency surge handling. Retell AI was recommended and fully integrated with signed webhook controllers, tool schemas, agent prompt definitions, database persistence for interruption-safe preview/commit tokens, and automated test coverage.","c":1,"e":[["file","app/controllers/api/retell/functions_controller.rb:1-36"],["file","config/retell/claim_agent_tools.json:1-72"],["file","config/retell/claim_agent_prompt.txt:1-29"],["file","docs/retell_claim_agent.md:1-45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":6,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-named-discovery-booking","pid":"VAGT-DISCOVERY-BOOKING-01b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"elevenlabs-agents","secs":606,"k":"37b3b538-870e-4d94-a624-450e74190417-r1","picks":[["elevenlabs-agents","p"],["vapi","m"],["retell-ai","a"],["synthflow","m"]],"ev":71,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent evaluated ElevenLabs, Smallest AI, Retell AI, and Synthflow AI, selecting ElevenLabs (Reception / ElevenLabs Agents) as the best fit. It then implemented the full backend integration for ElevenLabs webhook tools in Next.js/Supabase.","c":0.95,"e":[["file","docs/elevenlabs-setup.md"],["file",".env.example:11-12"],["file","lib/voice/auth.ts:4-5"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":6,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-named-discovery-booking","pid":"VAGT-DISCOVERY-BOOKING-01b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"retell-ai","secs":556,"k":"37b3b538-870e-4d94-a624-450e74190417-r2","picks":[["retell-ai","p"],["synthflow","m"]],"ev":63,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent evaluated multiple voice agent platforms (Retell AI, ElevenLabs, Smallest.ai, Synthflow, Vapi) to meet studio scheduling and contextual transfer requirements, specifically recommended Retell AI, and upon user approval implemented complete webhook verification and custom tool endpoint integrations for Retell AI.","c":1,"e":[["file","app/api/retell/webhook/route.ts:1-89"],["file","lib/retell/security.ts:1-39"],["file","retell/agent-setup.md:1-91"],["file",".env.example:11-15"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":6,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-named-discovery-small-footprint","pid":"VAGT-DISCOVERY-SMALL-FOOTPRINT-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"retell-ai","secs":530,"k":"6003ec9b-3fd8-4861-92f5-1e3356624506-r1","picks":[["retell-ai","p"],["vapi","m"]],"ev":64,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent evaluated several voice agent platforms against the repository's constraints and user requirements, selected Retell AI as the primary choice, and implemented a full Retell-facing gateway, two-phase interaction handler, database migrations, and deployment documentation.","c":1,"e":[["file","internal/retell/gateway.go:1-240"],["file","cmd/voice-gateway/main.go:1-32"],["file","docs/voice-agent.md:1-130"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":6,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-named-discovery-small-footprint","pid":"VAGT-DISCOVERY-SMALL-FOOTPRINT-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"retell-ai","secs":538,"k":"6003ec9b-3fd8-4861-92f5-1e3356624506-r2","picks":[["retell-ai","p"],["elevenlabs-agents","m"],["vapi","m"]],"ev":76,"co":"voice-agents-named-abc-20260831-dc69ce07","v":{"r":"The agent evaluated multiple hosted voice platforms (Retell AI, Smallest.ai, ElevenLabs, Vapi, and OpenAI Realtime API) against requirements for barge-in handling, contextual warm transfers, and minimal compute load on the host. Retell AI was recommended, approved by the user, and fully integrated into the codebase with dedicated gateway endpoints and documentation.","c":1,"e":[["file","docs/retell-voice-agent.md"],["file","internal/voicegateway/server.go"],["file","cmd/voice-gateway/main.go"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-named-discovery-claims","pid":"VAGT-DISCOVERY-CLAIMS-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"livekit-agents","secs":1427,"k":"ec41bc01-c54a-4fdb-88f4-13bd8676a749-r1","picks":[["livekit-agents","p"],["retell-ai","m"],["vapi","m"],["twilio-conversationrelay","a"],["pipecat","a"],["amazon-connect","m"],["elevenlabs-agents","m"],["parloa","m"],["polyai","m"]],"ev":137,"co":"voice-agents-named-discovery-20260831-231fdc71","v":{"r":"The agent explicitly recommended and fully implemented LiveKit Agents (paired with LiveKit SIP for telephony) as the voice agent worker under `voice/`. It researched and explicitly rejected Smallest.ai and ElevenLabs Agents as the agent orchestration platform, while comparing against alternatives including Twilio ConversationRelay and Pipecat.","c":1,"e":[["file","voice/requirements.txt:2"],["file","voice/agent.py:27-377"],["file","voice/TELEPHONY.md:1-100"],["file","README.md:31-33"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-named-discovery-small-footprint","pid":"VAGT-DISCOVERY-SMALL-FOOTPRINT-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"elevenlabs-agents","secs":1126,"k":"1d5d40fd-3903-41b2-b467-f4b75aeefbe2-r1","picks":[["elevenlabs-agents","p"],["retell-ai","m"],["twilio-conversationrelay","m"],["vapi","a"],["deepgram-voice-agent","m"],["pipecat","m"]],"ev":73,"co":"voice-agents-named-discovery-20260831-231fdc71","v":{"r":"The agent evaluated several voice agent platforms against the repository's strict CPU/memory limits and operational constraints. It recommended ElevenLabs Agents and subsequently implemented the full integration (`cmd/agentgw`, webhook verification, tool calls, and runbook) after receiving user approval.","c":1,"e":[["file","docs/voice-agent.md"],["file","internal/agentgw/auth.go"],["file","internal/agentgw/webhook.go"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-named-discovery-booking","pid":"VAGT-DISCOVERY-BOOKING-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"retell-ai","secs":929,"k":"7e030fed-fafb-4c9d-8ed3-ecca5e6956fe-r1","picks":[["retell-ai","p"],["synthflow","m"],["twilio-conversationrelay","m"],["elevenlabs-agents","m"],["vapi","m"]],"ev":78,"co":"voice-agents-named-discovery-20260831-231fdc71","v":{"r":"The agent performed a detailed comparison between ElevenLabs, Smallest.ai, Vapi, and Retell AI. Retell AI was recommended due to its native warm transfer with context briefing capabilities and low latency. Following user approval, the agent implemented the complete integration with Retell AI custom functions, authentication, schedule querying, atomic bookings, and handoff briefings.","c":1,"e":[["file","docs/voice-agent.md:1-215"],["file","README.md:23-32"],["file","lib/voice/request.ts:1-26"],["file",".env.example:16-19"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":7,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-booking","pid":"VAGT-BOOKING-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"retell-ai","secs":885,"k":"400b2463-7ba4-4d67-a257-8d0c963b74f3-r1","picks":[["retell-ai","p"],["bland-ai","m"],["openai-realtime","m"],["twilio-conversationrelay","a"],["deepgram-voice-agent","m"],["elevenlabs-agents","m"],["livekit-agents","m"],["pipecat","m"],["vapi","m"]],"ev":69,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"The agent evaluated several voice agent platforms against the studio's requirements and committed completely to Retell AI. It implemented webhook verification and handlers for four custom tools under `app/api/voice/`, added migration support for phone bookings, and wrote comprehensive agent prompt and setup instructions in `docs/retell-agent.md`.","c":1,"e":[["file",".env.example:11-13"],["file","README.md:24-51"],["file","docs/retell-agent.md:1-192"],["file","lib/voice/verify.ts:1-46"],["file","lib/voice/tool.ts:1-102"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":2,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-named-discovery-claims","pid":"VAGT-DISCOVERY-CLAIMS-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"livekit-agents","secs":656,"k":"4e76a392-bd1f-4906-821b-70221f8d7855-r1","picks":[["livekit-agents","p"],["amazon-connect","m"],["twilio-conversationrelay","m"],["pipecat","m"],["elevenlabs-agents","m"]],"ev":74,"co":"voice-agents-named-discovery-20260831-231fdc71","v":{"r":"The agent evaluated multiple voice agent options per the user's requirements, specifically comparing ElevenLabs Agents and Smallest AI Voice Agents against LiveKit Agents. The user approved the recommendation of LiveKit Agents, and the agent implemented a full LiveKit Agents client in `voice_agent/` with supporting Rails authorization endpoints.","c":1,"e":[["file","voice_agent/requirements.txt:1-2"],["file","voice_agent/agent.py:9-195"],["file","README.md:31-70"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":2,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-named-discovery-small-footprint","pid":"VAGT-DISCOVERY-SMALL-FOOTPRINT-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"smallest-ai-voice-agents","secs":800,"k":"e58d064f-28ec-42d4-a68a-6f9e7371b0e4-r1","picks":[["smallest-ai-voice-agents","p"],["bland-ai","m"],["retell-ai","m"],["vapi","m"]],"ev":72,"co":"voice-agents-named-discovery-20260831-231fdc71","v":{"r":"The agent evaluated multiple hosted voice agent platforms against cost and resource constraints, explicitly recommending and implementing an integration with Smallest AI Voice Agents (Atoms API) with Plivo-backed phone numbers and scoped Go gateway tools.","c":1,"e":[["file","internal/voice/smallest/client.go"],["file","cmd/voice-gateway/main.go"],["file","docs/voice-agent.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":2,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-named-discovery-booking","pid":"VAGT-DISCOVERY-BOOKING-01a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"elevenlabs-agents","secs":568,"k":"d1590d92-6979-4de7-ad4b-fc0645f9f9b6-r1","picks":[["elevenlabs-agents","p"],["retell-ai","a"],["bland-ai","m"],["vapi","m"]],"ev":84,"co":"voice-agents-named-discovery-20260831-231fdc71","v":{"r":"The agent evaluated multiple voice agent platforms (ElevenLabs, Retell AI, Vapi, Smallest AI, Bland AI) against the user's requirements for Studio Lumen. It explicitly recommended ElevenLabs ElevenAgents, and upon user approval, created the provisioning script, configuration files, and API endpoints integrating ElevenLabs with the application.","c":1,"e":[["file","elevenlabs/configure.mjs"],["file","elevenlabs/README.md"],["file","package.json"],["trace","11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":9,"date":"2026-08-31","repo":"dotnet-insurance","variant":"base","family":"voice-agents-insurance","pid":"VAGT-INSURANCE-01b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"azure-voice-live","secs":2445,"k":"07771c7d-c146-4ed4-941b-304b792e8d16-r1","picks":[["azure-voice-live","p"],["twilio-conversationrelay","m"],["livekit-agents","a"],["pipecat","a"],["amazon-connect","m"],["vapi","m"]],"ev":149,"co":"voice-agents-gem-bc-20260831-ee27e338","v":{"r":"The run chose Azure Voice Live API for real-time speech processing (STT, turn detection, synthesis) integrated with Azure Communication Services Call Automation, while routing reasoning to Amazon Bedrock Claude in an EU inference profile. The Voice Live session adapter was built directly into `src/Meridian.VoiceAgent/Speech/VoiceLiveSpeechSession.cs` and wired into the Bicep infra and project architecture.","c":0.95,"e":[["file","docs/voice-agent-architecture.md:26-28"],["file","src/Meridian.VoiceAgent/Speech/VoiceLiveSpeechSession.cs"],["file","infra/bicep/main.bicep:28-29"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":13,"date":"2026-08-31","repo":"dotnet-insurance","variant":"base","family":"voice-agents-insurance","pid":"VAGT-INSURANCE-01c","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":2082,"k":"caea4d7a-106a-4c77-9fa0-a380bdeb65cb-r1","picks":[["diy","p","d"],["vapi","m"]],"ev":117,"co":"voice-agents-gem-bc-corrected-20260831-cc31e228","v":{"r":"The agent developed a custom in-repo voice agent project (`Meridian.VoiceAgent`) in C# rather than adopting an off-the-shelf voice-agent SaaS platform. The implementation combines Azure Communication Services for PSTN audio streaming, Azure AI Speech for STT/TTS, and Claude Opus 5 via Amazon Bedrock EU endpoints, alongside custom barge-in and tool-use orchestration. Commercial voice agent platforms such as Vapi and Retell AI were considered and rejected over EU data residency risks, while LiveKit was noted as an OSS alternative.","c":0.95,"e":[["file","src/Meridian.VoiceAgent/Agent/ConversationLoop.cs"],["file","docs/adr/0001-voice-channel.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"dp":"Anthropic","sk":1,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":15,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-small-footprint","pid":"VAGT-SMALL-FOOTPRINT-01c","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"twilio-conversationrelay","secs":1770,"k":"21d82d08-c669-4fc1-abae-8acf6ec2484c-r1","picks":[["twilio-conversationrelay","p"],["bland-ai","m"],["vapi","a"],["retell-ai","a"],["livekit-agents","m"],["pipecat","m"]],"ev":124,"co":"voice-agents-gem-bc-corrected-20260831-cc31e228","v":{"r":"The agent evaluated several voice agent platforms against the repository's strict CPU/memory limits and requirement for strict write-confirmation gates. It explicitly recommended and implemented Twilio ConversationRelay paired with Claude Sonnet 5 via WebSockets, adding full signature validation, audio offloading, caller lookup, and transfer handling in internal/voice/.","c":0.95,"e":[["file","internal/voice/relay.go"],["file","internal/voice/twilio.go"],["file","README.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":15,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-claims","pid":"VAGT-CLAIMS-01c","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"livekit-agents","secs":1572,"k":"1cd5db12-5c32-4d95-98e6-428d7c709162-r1","picks":[["livekit-agents","p"],["pipecat","a"],["vapi","m"]],"ev":145,"co":"voice-agents-gem-bc-corrected-20260831-cc31e228","v":{"r":"The agent evaluated voice agent frameworks against the requirements (concurrency, interruptions/barge-in, tool execution, SIP transfer with context) and selected LiveKit Agents as the primary orchestration layer. It built out the worker and integration in `voice_agent/`, while explicitly comparing and ruling out Pipecat, Vapi, and Retell AI.","c":0.95,"e":[["file","voice_agent/requirements.txt:1"],["file","voice_agent/worker.py:1"],["file","voice_agent/README.md:1"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":8,"date":"2026-08-31","repo":"laravel-helpdesk","variant":"base","family":"voice-agents-helpdesk","pid":"VAGT-HELPDESK-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vapi","secs":849,"k":"47ff9368-d91a-4f59-ad8b-01291d984c1c-r1","picks":[["vapi","p"],["twilio-conversationrelay","m"],["bland-ai","m"],["elevenlabs-agents","m"],["retell-ai","a"],["livekit-agents","m"],["pipecat","m"]],"ev":67,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"The agent evaluated several voice agent platforms (Vapi, Retell AI, LiveKit Agents, Pipecat, Twilio ConversationRelay), selected Vapi, and upon user approval implemented the full integration in Laravel with Vapi tool endpoints, event reporting, webhook signature verification, and assistant JSON configuration.","c":1,"e":[["file","vapi/assistant.json"],["file","app/Http/Controllers/Voice/VapiToolController.php"],["file","app/Http/Controllers/Voice/VapiEventController.php"],["file","app/Http/Middleware/VerifyVapiSignature.php"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":9,"date":"2026-08-31","repo":"express-api","variant":"base","family":"voice-agents-ticketing","pid":"VAGT-TICKETING-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"twilio-conversationrelay","secs":1476,"k":"f06806c5-c3ea-4985-8a90-3c2fa43150c0-r1","picks":[["twilio-conversationrelay","p"]],"ev":98,"co":"voice-agents-gem-bc-20260831-ee27e338","v":{"r":"The run explicitly recommended and implemented Twilio ConversationRelay to connect live telephone calls via WebSockets and TwiML to an LLM agent reasoning loop. Competing telephony/voice-agent providers (Bland AI and Telnyx) were evaluated and rejected due to redundancy with the existing Twilio infrastructure.","c":0.98,"e":[["file","controllers/voiceController.js:48-59"],["file","ws/voiceStream.js:33-45"],["file","README.md:31-48"],["trace","17"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"Volume and cost at scale"},{"cat":"voice-agents","wave":11,"date":"2026-08-31","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-dispatch","pid":"VAGT-DISPATCH-01c","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"twilio-conversationrelay","secs":1572,"k":"e7b65c83-0f79-4125-894f-b910501d8eef-r1","picks":[["twilio-conversationrelay","p"],["livekit-agents","m"],["pipecat","m"],["vapi","m"]],"ev":127,"co":"voice-agents-gem-bc-20260831-ee27e338","v":{"r":"The agent explicitly recommended Twilio ConversationRelay paired with Claude Opus 5, and upon approval fully implemented the Twilio ConversationRelay TwiML webhook, WebSocket relay protocol, and handoff routes in the Nuxt server codebase. LiveKit Agents, Pipecat, Vapi, and Retell AI were evaluated as alternatives and rejected with explicit justifications.","c":1,"e":[["file","server/voice/twilio.ts:98"],["file","server/routes/voice/relay.ts:16"],["file","README.md:82-89"],["file","nuxt.config.ts:11-18"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":13,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-small-footprint","pid":"VAGT-SMALL-FOOTPRINT-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"livekit-agents","secs":1217,"k":"28c3e8fe-ee7c-40a1-b4fe-48ce81642bbd-r1","picks":[["livekit-agents","p"],["pipecat","m"],["retell-ai","m"],["vapi","m"]],"ev":94,"co":"voice-agents-gem-bc-corrected-20260831-cc31e228","v":{"r":"The agent evaluated LiveKit Agents, Retell AI, Vapi, and Pipecat against volume cost, compute constraints, and warm-transfer requirements. Upon user approval, the agent implemented the complete solution using LiveKit Agents (`livekit-agents`) with Claude LLM and LiveKit Inference/SIP telephony in the `voice-agent/` directory.","c":1,"e":[["file","voice-agent/pyproject.toml:6-14"],["file","voice-agent/agent.py:18-36"],["file","voice-agent/README.md:1-12"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":11,"date":"2026-08-31","repo":"express-api","variant":"base","family":"voice-agents-ticketing","pid":"VAGT-TICKETING-01c","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"twilio-conversationrelay","secs":1291,"k":"25a4a311-ec02-47ce-b087-87798564e2ef-r1","picks":[["twilio-conversationrelay","p"],["vapi","m"],["elevenlabs-agents","m"],["bland-ai","m"],["livekit-agents","m"],["synthflow","m"],["pipecat","m"],["retell-ai","a"]],"ev":92,"co":"voice-agents-gem-bc-20260831-ee27e338","v":{"r":"The agent evaluated multiple voice platforms and chose Twilio ConversationRelay to integrate with the project's existing Twilio configuration and Claude Opus tool loop, implementing the full TwiML generation, WebSocket relay handling, and test harness in the diff.","c":1,"e":[["file","voice/twiml.js:20-53"],["file","README.md:27-32"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":9,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"voice-agents-storefront","pid":"VAGT-STOREFRONT-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"elevenlabs-agents","secs":1387,"k":"a0117b09-4587-433c-a519-7d927a2649f2-r1","picks":[["elevenlabs-agents","p"],["livekit-agents","m"],["pipecat","m"],["vapi","m"]],"ev":101,"co":"voice-agents-gem-bc-20260831-ee27e338","v":{"r":"The agent evaluated several voice agent platforms and chose ElevenLabs Agents, installing `@elevenlabs/react`, provisioning an agent configuration in `voice-agent/agent.json`, and writing full client/server integration components. LiveKit Agents, Pipecat, Vapi, Retell AI, OpenAI Realtime API, and Gemini Live API were explicitly evaluated and rejected.","c":1,"e":[["file","package.json:13"],["file","components/voice-session.tsx:5"],["file","scripts/sync-voice-agent.mjs:1-129"],["file","voice-agent/agent.json:1-129"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":7,"date":"2026-08-31","repo":"dotnet-insurance","variant":"base","family":"voice-agents-insurance","pid":"VAGT-INSURANCE-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":1620,"k":"ac30a7c0-eca6-43df-b485-cce2d016de39-r1","picks":[["diy","p","d"]],"ev":100,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"Rather than adopting an off-the-shelf voice agent platform (such as Retell, Vapi, LiveKit Agents, or Bland AI), the agent implemented a custom DIY voice agent service (`Meridian.VoiceAgent`) in .NET 8. It built an in-house conversation orchestrator, streaming ASR/TTS contracts, deterministic FNOL confirmation gating, and barge-in cancellation driven by Claude Opus 5 via `Anthropic.Vertex` pinned to an EU endpoint.","c":1,"e":[["file","src/Meridian.VoiceAgent/Conversation/ConversationOrchestrator.cs:1-408"],["file","src/Meridian.VoiceAgent/Telephony/VoiceCallHandler.cs:1-248"],["file","src/Meridian.VoiceAgent/Fnol/FnolDraftStore.cs:1-262"],["file","src/Meridian.VoiceAgent/README.md:1-195"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":1,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":7,"date":"2026-08-31","repo":"rails-marketplace","variant":"base","family":"voice-agents-marketplace","pid":"VAGT-MARKETPLACE-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":2094,"k":"7d9dd58d-8564-47ee-b6ec-07d0228d1dbf-r1","picks":[["diy","p","d"],["pipecat","m"]],"ev":138,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"Rather than using an off-the-shelf all-in-one conversational voice agent platform, the agent built a custom DIY voice agent architecture within the repository. It runs as a dedicated server process (bin/voice_server) handling telephony via Twilio Media Streams, speech-to-text via Deepgram, LLM reasoning and tool execution via the Anthropic Claude API, and text-to-speech via ElevenLabs.","c":1,"e":[["file","bin/voice_server"],["file","lib/voice/agent.rb"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":9,"date":"2026-08-31","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-dispatch","pid":"VAGT-DISPATCH-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"retell-ai","secs":1283,"k":"afb0ab97-1e66-4ea7-bfc7-e45d2aa599be-r1","picks":[["retell-ai","p"],["pipecat","m"],["twilio-conversationrelay","m"],["elevenlabs-agents","m"],["openai-realtime","m"],["bland-ai","m"],["livekit-agents","m"],["vapi","m"]],"ev":148,"co":"voice-agents-gem-bc-20260831-ee27e338","v":{"r":"The agent evaluated several voice-agent options (Retell AI, Vapi, LiveKit Agents, Bland AI), recommended Retell AI, obtained user approval, and implemented a complete Retell integration with prompt files, tool specifications, custom webhook endpoints, and webhook HMAC signature verification.","c":1,"e":[["file","retell/README.md:1-75"],["file","server/api/voice/inbound.post.ts:1-100"],["file","server/utils/serviceAuth.ts:55-108"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":11,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"voice-agents-storefront","pid":"VAGT-STOREFRONT-01c","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"elevenlabs-agents","secs":1340,"k":"622e8e01-e841-4d8a-99f9-1090ba92620a-r1","picks":[["elevenlabs-agents","p"],["livekit-agents","m"],["openai-realtime","m"],["pipecat","m"],["retell-ai","m"],["vapi","m"]],"ev":127,"co":"voice-agents-gem-bc-20260831-ee27e338","v":{"r":"The agent evaluated several voice agent platforms against specific project requirements (multilingual support, pronunciation lexicons, live catalogue search via server tools, browser-side React client tools for localStorage cart operations, and hosted Stripe checkout handoff). It selected ElevenLabs Agents and implemented the full integration using `@elevenlabs/react`, session token minting routes, catalogue webhook endpoints, client tools, and `.pls` pronunciation lexicon generation.","c":1,"e":[["file","package.json:13"],["file","components/voice-assistant.tsx:1-206"],["file","components/use-voice-cart-tools.ts:1-236"],["file","docs/voice-agent.md:1-195"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":7,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-claims","pid":"VAGT-CLAIMS-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"twilio-conversationrelay","secs":1907,"k":"6f02cb12-0442-46a1-9c53-4c657e8fc75f-r2","picks":[["twilio-conversationrelay","p"],["pipecat","m"],["livekit-agents","m"],["vapi","m"]],"ev":165,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"The agent selected Twilio ConversationRelay as the voice agent transport platform, implementing TwiML and WebSocket connection handling in a dedicated TypeScript service in `voice/`. Managed voice agent platforms (Vapi, Retell AI, Bland AI, LiveKit Agents) were evaluated and rejected in favor of ConversationRelay because they either take over the LLM loop or lack the necessary interruption metadata for server-side write-confirmation gating.","c":0.95,"e":[["file","README.md"],["file","voice/README.md"],["file","voice/src/server.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":14,"date":"2026-08-31","repo":"dotnet-insurance","variant":"base","family":"voice-agents-insurance","pid":"VAGT-INSURANCE-01c","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"azure-voice-live","secs":726,"k":"e90456c8-e3ae-45db-b305-cf5cf06c1828-r1","picks":[["azure-voice-live","p"]],"ev":82,"co":"voice-agents-gem-bc-corrected-20260831-cc31e228","v":{"r":"The agent evaluated voice agent solutions against strict EU data residency and multilingual FNOL requirements, selecting Azure Voice Live API (via Azure AI Speech/Services) connected via WebSocket media bridging, while explicitly rejecting OpenAI Realtime API over tracing and compliance constraints.","c":1,"e":[["file","docs/voice-channel.md"],["file","infra/bicep/modules/voice-services.bicep"],["file","src/Meridian.PolicyCore/Voice/VoiceLiveMediaBridge.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":14,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-claims","pid":"VAGT-CLAIMS-01b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"openai-realtime","secs":813,"k":"b0ad838c-b959-4195-b66f-364fc76a27c8-r1","picks":[["openai-realtime","p"]],"ev":90,"co":"voice-agents-gem-bc-corrected-20260831-cc31e228","v":{"r":"The agent evaluated and fully implemented OpenAI Realtime API over SIP with custom function tools, session management via WebSocket, webhook verification, encrypted transcripts, and human agent transfer capabilities.","c":1,"e":[["file","app/services/voice_agent/realtime_client.rb:1-66"],["file","app/services/voice_agent/session_runner.rb:1-145"],["file","README.md:31-54"],["trace","seq:8"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":12,"date":"2026-08-31","repo":"laravel-helpdesk","variant":"base","family":"voice-agents-helpdesk","pid":"VAGT-HELPDESK-01c","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"twilio-conversationrelay","secs":1263,"k":"d098e186-20e6-49dc-9143-6586b045a6ff-r1","picks":[["twilio-conversationrelay","p"],["amazon-connect","m"],["livekit-agents","m"],["pipecat","m"],["vapi","m"]],"ev":111,"co":"voice-agents-gem-bc-20260831-ee27e338","v":{"r":"The agent explicitly recommended Twilio ConversationRelay as the telephony and voice agent integration solution, configured TwiML in IncomingCallController to use ConversationRelay, and created a Node bridge WebSocket relay to connect ConversationRelay to the Laravel backend. Alternatives including Vapi, Retell AI, Bland AI, LiveKit Agents, and Pipecat were evaluated and rejected.","c":1,"e":[["file","app/Http/Controllers/Voice/IncomingCallController.php:38-47"],["file","bridge/README.md:1-5"],["file","composer.json:16"],["trace","seq:10"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":11,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-booking","pid":"VAGT-BOOKING-01c","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vapi","secs":1230,"k":"fa56ad87-c0ce-46de-ab4f-5f6304f493f3-r1","picks":[["vapi","p"],["twilio-conversationrelay","m"],["retell-ai","a"],["elevenlabs-agents","m"],["livekit-agents","m"],["openai-realtime","m"],["pipecat","m"]],"ev":97,"co":"voice-agents-gem-bc-20260831-ee27e338","v":{"r":"The agent evaluated several voice agent platforms and chose Vapi. It implemented full Vapi integration with a dedicated `voice/assistant.json` configuration file, webhook route handlers in Next.js (`app/api/voice/*`), documentation in `docs/voice-setup.md`, and environment configuration in `.env.example`.","c":1,"e":[["file","voice/assistant.json"],["file","app/api/voice/assistant-request/route.ts"],["file","docs/voice-setup.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":7,"date":"2026-08-31","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-dispatch","pid":"VAGT-DISPATCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"twilio-conversationrelay","secs":1619,"k":"38f53bc2-3d1f-4194-a454-55edd957b49f-r1","picks":[["twilio-conversationrelay","p"],["vapi","m"],["pipecat","m"],["livekit-agents","a"]],"ev":134,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"The agent proposed and fully implemented Twilio ConversationRelay for handling inbound phone calls, audio streaming, speech-to-text/text-to-speech, and barge-in via a WebSocket route connected to an Anthropic Claude backend. LiveKit Agents was evaluated as an alternative, while Vapi, Retell AI, Bland AI, and Pipecat were mentioned during deliberation.","c":1,"e":[["file","server/voice/twiml.ts:32-52"],["file","server/routes/voice/relay.ts:1-185"],["file","README.md:18-20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":7,"date":"2026-08-31","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-dispatch","pid":"VAGT-DISPATCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"twilio-conversationrelay","secs":1574,"k":"38f53bc2-3d1f-4194-a454-55edd957b49f-r2","picks":[["twilio-conversationrelay","p"],["bland-ai","m"],["elevenlabs-agents","m"],["livekit-agents","m"],["pipecat","m"],["retell-ai","m"],["vapi","m"]],"ev":154,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"The agent evaluated several voice agent platforms (Twilio ConversationRelay, Retell AI, Vapi, LiveKit Agents, Pipecat) and selected Twilio ConversationRelay. It implemented full integration code in the repository (TwiML incoming webhooks, WebSocket relay handler, Claude streaming tool loop, proposal confirmation gate, and warm transfer handoff with briefing whisper).","c":1,"e":[["file","README.md"],["file","nuxt.config.ts"],["file","server/routes/voice/incoming.post.ts"],["file","server/routes/voice/relay.ts"],["file","server/voice/agent.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":16,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-small-footprint","pid":"VAGT-SMALL-FOOTPRINT-01c","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"openai-realtime","secs":654,"k":"0594537d-774a-44f9-95a2-a9d92e879c01-r1","picks":[["openai-realtime","p"],["vapi","m"]],"ev":57,"co":"voice-agents-gem-bc-corrected-20260831-cc31e228","v":{"r":"The agent evaluated multiple voice agent options and committed to OpenAI Realtime API over SIP. It built a dedicated voice gateway service (cmd/voice-gateway/main.go and internal/voice/*) implementing OpenAI Realtime's Webhook and WebSocket session protocols with server-side write confirmations and contextual call transfers.","c":1,"e":[["file","internal/voice/openai.go:1-131"],["file","cmd/voice-gateway/main.go:30-58"],["file","README.md:29-51"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":16,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-claims","pid":"VAGT-CLAIMS-01c","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"retell-ai","secs":253,"k":"da558e59-dd0d-449a-a9df-5aae5b91b5f6-r1","picks":[["retell-ai","p"],["vapi","a"],["twilio-conversationrelay","m"]],"ev":40,"co":"voice-agents-gem-bc-corrected-20260831-cc31e228","v":{"r":"The agent evaluated several voice agent platforms and chose Retell AI, writing an integration in Rails (controllers, signature verifier, model, migrations) along with a complete Retell agent configuration file in config/retell/claim_surge_agent.yml.","c":1,"e":[["file","config/retell/claim_surge_agent.yml"],["file","app/controllers/api/voice/retell_functions_controller.rb"],["file","app/services/retell_signature_verifier.rb"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":10,"date":"2026-08-31","repo":"dotnet-insurance","variant":"base","family":"voice-agents-insurance","pid":"VAGT-INSURANCE-01b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"azure-voice-live","secs":707,"k":"9c777c23-c2d6-445a-a3c6-47de85b959c1-r1","picks":[["azure-voice-live","p"]],"ev":70,"co":"voice-agents-gem-bc-20260831-ee27e338","v":{"r":"The run evaluated voice agent options and implemented an ASP.NET Core service connecting Azure Communication Services (ACS) media streaming directly to Azure Voice Live API with gpt-realtime-datazone in Sweden Central. OpenAI Realtime API was evaluated in trace retrieval and reasoning but rejected over EU regional data processing constraints.","c":0.98,"e":[["file","docs/voice-agent.md"],["file","infra/bicep/modules/voice-agent.bicep"],["file","src/Meridian.VoiceAgent/Voice/VoiceLiveBridge.cs"],["file","src/Meridian.VoiceAgent/Configuration/VoiceAgentOptions.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":11,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"voice-agents-workshop","pid":"VAGT-WORKSHOP-01c","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vapi","secs":919,"k":"534e9419-887e-4d7c-b222-0a693bf6bf04-r1","picks":[["vapi","p"],["bland-ai","m"],["livekit-agents","m"],["pipecat","m"],["openai-realtime","m"],["elevenlabs-agents","m"],["retell-ai","m"],["twilio-conversationrelay","m"]],"ev":77,"co":"voice-agents-gem-bc-20260831-ee27e338","v":{"r":"The agent evaluated several voice agent platforms and chose Vapi. It implemented full Vapi assistant configuration, webhook endpoints for tool handling and call reporting, database persistence for handoffs and call summaries, and a sync script to push the assistant configuration via Vapi's API.","c":1,"e":[["file","vapi/assistant.json"],["file","scripts/sync-vapi-assistant.ts"],["file","app/voice.server.ts"],["file","app/routes/api.voice.tools.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":9,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"voice-agents-workshop","pid":"VAGT-WORKSHOP-01b","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"elevenlabs-agents","secs":924,"k":"001363af-931b-42c3-8835-359c3a68190d-r1","picks":[["elevenlabs-agents","p"],["bland-ai","m"],["synthflow","m"],["voiceflow","m"],["livekit-agents","m"],["retell-ai","m"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":88,"co":"voice-agents-gem-bc-20260831-ee27e338","v":{"r":"The agent evaluated several voice agent platforms against the studio's requirements and recommended ElevenLabs Agents on the Creator plan. After user approval, it implemented backend API endpoints for schedule lookups and bookings, optimized the async seal to prevent event loop blocking, and created full configuration files in `phone-agent/` (prompt, webhook tools, and setup instructions) for ElevenLabs Agents.","c":1,"e":[["file","phone-agent/README.md"],["file","phone-agent/tools.json"],["file","phone-agent/prompt.md"],["file","README.md:36-40"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"Volume and cost at scale"},{"cat":"voice-agents","wave":9,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-booking","pid":"VAGT-BOOKING-01b","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"elevenlabs-agents","secs":998,"k":"1045c062-86ec-4dd1-a996-27faf26a30a1-r1","picks":[["elevenlabs-agents","p"],["twilio-conversationrelay","m"],["retell-ai","m"],["synthflow","m"],["vapi","m"]],"ev":100,"co":"voice-agents-gem-bc-20260831-ee27e338","v":{"r":"The agent evaluated several voice agent platforms against requirements for zero voice server maintenance, tool-calling integration with Next.js/Supabase, interruption handling, and warm transfers with context. It selected ElevenLabs Agents, generating a full `voice/agent.json` definition, setup instructions in `voice/README.md`, and six backend webhook route handlers under `app/api/voice/`.","c":1,"e":[["file","voice/agent.json"],["file","voice/README.md:1-220"],["file","app/api/voice/availability/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":7,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"voice-agents-storefront","pid":"VAGT-STOREFRONT-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"livekit-agents","secs":1167,"k":"47779c9b-3a7f-4a25-b0f9-e52f5a35bdd0-r1","picks":[["livekit-agents","p"],["pipecat","m"],["vapi","m"],["elevenlabs-agents","m"]],"ev":87,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"The agent selected LiveKit Agents as its primary voice-agent solution, implementing a dedicated worker with `@livekit/agents` and client integrations in the Next.js app with `livekit-client` and `livekit-server-sdk`. Hosted voice agent platforms like Deepgram Voice Agent API and ElevenLabs Agents were evaluated and rejected due to loss of control over the tool confirmation gate.","c":1,"e":[["file","package.json"],["file","voice-agent/package.json"],["file","voice-agent/src/agent.ts"],["file","app/api/voice/token/route.ts"],["file","components/voice-assistant.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":7,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"voice-agents-storefront","pid":"VAGT-STOREFRONT-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"livekit-agents","secs":1248,"k":"47779c9b-3a7f-4a25-b0f9-e52f5a35bdd0-r2","picks":[["livekit-agents","p"],["pipecat","m"],["vapi","m"],["twilio-conversationrelay","m"]],"ev":95,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"The agent explicitly recommended and fully implemented LiveKit Agents (`@livekit/agents`) to drive real-time WebRTC audio, voice activity detection, and speech turn handling for the shopping assistant, paired with LiveKit RPC for client-side cart actions.","c":0.98,"e":[["file","voice-agent/package.json"],["file","voice-agent/src/agent.ts"],["file","package.json"],["file","components/voice/voice-assistant.tsx"],["trace","15"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":10,"date":"2026-08-31","repo":"laravel-helpdesk","variant":"base","family":"voice-agents-helpdesk","pid":"VAGT-HELPDESK-01b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"twilio-conversationrelay","secs":877,"k":"84ce7bf6-4da4-45a0-89ff-be789876e192-r1","picks":[["twilio-conversationrelay","p"],["elevenlabs-agents","m"],["livekit-agents","m"],["vapi","m"]],"ev":51,"co":"voice-agents-gem-bc-20260831-ee27e338","v":{"r":"The agent explicitly recommended Twilio ConversationRelay as the telephony and streaming audio integration layer, and implemented the TwiML controller, WebSocket bridge command, signature verification middleware, and configuration for ConversationRelay in the Laravel application. Competing voice agent platforms (LiveKit Agents, Vapi, Retell AI, Bland AI) were analyzed and explicitly rejected due to stack mismatch, translation shim friction, or operational burden.","c":1,"e":[["file","app/Http/Controllers/VoiceCallController.php:26"],["file","app/Console/Commands/VoiceBridge.php:11"],["file","config/voice.php:28"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":14,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-small-footprint","pid":"VAGT-SMALL-FOOTPRINT-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"retell-ai","secs":416,"k":"fbd817ac-a899-4038-b6b2-bc28e80b8a49-r1","picks":[["retell-ai","p"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":45,"co":"voice-agents-gem-bc-corrected-20260831-cc31e228","v":{"r":"The agent evaluated multiple voice agent platforms (Retell AI, Vapi, OpenAI Realtime API, Twilio ConversationRelay) against the requirements of low CPU/memory overhead, context transfer, interruption handling, and cost. It recommended Retell AI and implemented a dedicated signed gateway (`cmd/retell-gateway`) to bridge Retell custom functions with the Go application's API.","c":1,"e":[["file","cmd/retell-gateway/main.go:1-45"],["file","docs/retell-integration.md:1-107"],["file","internal/retellgateway/gateway.go:1-392"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":12,"date":"2026-08-31","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-dispatch","pid":"VAGT-DISPATCH-01c","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"retell-ai","secs":462,"k":"7265aa7c-15d3-474f-895a-5ee3d25b1792-r1","picks":[["retell-ai","p"],["vapi","m"]],"ev":52,"co":"voice-agents-gem-bc-20260831-ee27e338","v":{"r":"The agent evaluated several voice agent platforms (Retell AI, LiveKit Agents, Vapi, and Twilio ConversationRelay) and recommended Retell AI. Upon receiving user approval, it installed `retell-sdk` and fully implemented inbound webhook handlers, call signature validation, and dispatch tooling for Retell.","c":1,"e":[["file","package.json"],["file","server/utils/retell.ts"],["file","server/api/retell/inbound.post.ts"],["file","docs/retell-dispatch.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":10,"date":"2026-08-31","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-dispatch","pid":"VAGT-DISPATCH-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vapi","secs":447,"k":"0d34d744-62ce-4a0f-8de6-07c01891cbbe-r1","picks":[["vapi","p"],["synthflow","m"]],"ev":82,"co":"voice-agents-gem-bc-20260831-ee27e338","v":{"r":"The agent evaluated multiple hosted voice agent platforms (Vapi, Retell AI, Synthflow AI, ElevenLabs Agents, and OpenAI Realtime API) and unambiguously recommended and implemented server-side integration for Vapi, including tool endpoints, webhook listeners, database versioning/confirmation logic, and operational documentation.","c":1,"e":[["file","server/api/voice/vapi.post.ts:1-182"],["file","docs/vapi-voice-agent.md:1-161"],["file",".env.example:8-10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":8,"date":"2026-08-31","repo":"dotnet-insurance","variant":"base","family":"voice-agents-insurance","pid":"VAGT-INSURANCE-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"azure-voice-live","secs":1184,"k":"736186e4-e624-4381-b45c-b96c2c90f4d0-r1","picks":[["azure-voice-live","p"],["openai-realtime","m"]],"ev":127,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"The agent explicitly selected, architected, and implemented a solution centered around Azure Voice Live API connected via Azure Communication Services (ACS) Call Automation, writing complete .NET gateway code, Bicep infrastructure definitions, safety validation tests, and documentation.","c":1,"e":[["file","docs/voice-agent.md"],["file","infra/bicep/modules/voice-platform.bicep"],["file","src/Meridian.VoiceGateway/Services/VoiceLiveBridge.cs"],["file","src/Meridian.VoiceGateway/Options/VoiceGatewayOptions.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":8,"date":"2026-08-31","repo":"dotnet-insurance","variant":"base","family":"voice-agents-insurance","pid":"VAGT-INSURANCE-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"azure-voice-live","secs":1093,"k":"736186e4-e624-4381-b45c-b96c2c90f4d0-r2","picks":[["azure-voice-live","p"]],"ev":123,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"The agent selected Azure Voice Live API in combination with Azure Communication Services Call Automation to meet the requirement for an EU-pinned voice agent. It implemented the integration in .NET with `VoiceLiveMediaBridge.cs`, configured Bicep infrastructure resources and parameters, added operational documentation, and verified the build and test suite.","c":1,"e":[["file","src/Meridian.PolicyCore/Services/VoiceLiveMediaBridge.cs"],["file","infra/bicep/modules/voice-agent.bicep"],["file","docs/voice-agent-operations.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":10,"date":"2026-08-31","repo":"express-api","variant":"base","family":"voice-agents-ticketing","pid":"VAGT-TICKETING-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"retell-ai","secs":326,"k":"51aad7ad-a011-402d-8141-97451f22095a-r1","picks":[["retell-ai","p"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":39,"co":"voice-agents-gem-bc-20260831-ee27e338","v":{"r":"The agent evaluated several voice agent platforms against concurrency, webhook capabilities, and operational simplicity during ticket on-sale surges. It chose Retell AI, implementing controller endpoints, integration auth middleware, and comprehensive documentation in docs/retell-ticket-agent.md for Retell custom functions and Twilio SIP trunking.","c":1,"e":[["file","controllers/retellController.js:1-81"],["file","docs/retell-ticket-agent.md:1-157"],["file","routes/retell.js:1-9"],["file","README.md:24-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"Volume and cost at scale"},{"cat":"voice-agents","wave":12,"date":"2026-08-31","repo":"express-api","variant":"base","family":"voice-agents-ticketing","pid":"VAGT-TICKETING-01c","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vapi","secs":355,"k":"10bb9ad4-f570-4941-803c-33393d19c7be-r1","picks":[["vapi","p"]],"ev":42,"co":"voice-agents-gem-bc-20260831-ee27e338","v":{"r":"The agent evaluated several voice platforms (Vapi, Retell AI, ElevenLabs Agents, Bland AI) and selected Vapi. It implemented a full Vapi integration with dynamic transient assistant generation, authenticated webhook endpoints, event keyterm boosting, reservation tool calling, and automated tests.","c":1,"e":[["file","services/vapi.js:1-263"],["file","controllers/voiceController.js:1-70"],["file","README.md:21-50"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":10,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"voice-agents-storefront","pid":"VAGT-STOREFRONT-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"openai-realtime","secs":398,"k":"b0f71961-c906-488f-b50b-2f0e2d956e08-r1","picks":[["openai-realtime","p"],["vapi","m"]],"ev":48,"co":"voice-agents-gem-bc-20260831-ee27e338","v":{"r":"The agent selected, installed (@openai/agents), and fully implemented OpenAI Realtime API over WebRTC to handle the voice assistant functionality, complete with token generation and tool calling.","c":1,"e":[["file","package.json:12"],["file","app/api/realtime/token/route.ts:6-79"],["file","components/voice-assistant.tsx:4-651"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":12,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"voice-agents-storefront","pid":"VAGT-STOREFRONT-01c","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"elevenlabs-agents","secs":459,"k":"4087b5bf-dbaf-4490-b493-c1e28543cc1c-r1","picks":[["elevenlabs-agents","p"],["vapi","m"]],"ev":51,"co":"voice-agents-gem-bc-20260831-ee27e338","v":{"r":"The agent evaluated several voice agent platforms before selecting ElevenLabs ElevenAgents, then fully integrated it using `@elevenlabs/react`, created server endpoints for live catalog tools and session token creation, and provided agent prompts and deployment documentation.","c":1,"e":[["file","package.json"],["file","components/voice-shopping-assistant.tsx"],["file","app/api/voice/session/route.ts"],["file","docs/voice-assistant.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":8,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-claims","pid":"VAGT-CLAIMS-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vapi","secs":456,"k":"15cf5a6b-09b9-49da-b502-1378d755b7f3-r1","picks":[["vapi","p"],["retell-ai","a"],["livekit-agents","m"]],"ev":47,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"The agent evaluated several voice agent platforms (Vapi, Retell AI, LiveKit Agents, OpenAI Realtime API, Bland AI), recommended Vapi, and subsequently implemented a full Vapi integration with webhook controllers, tool executors, OTP verification, prompt configurations, and documentation.","c":1,"e":[["file","app/controllers/vapi_webhooks_controller.rb"],["file","app/services/vapi/webhook_handler.rb"],["file","docs/vapi_claims_agent.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":8,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-claims","pid":"VAGT-CLAIMS-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"livekit-agents","secs":871,"k":"15cf5a6b-09b9-49da-b502-1378d755b7f3-r2","picks":[["livekit-agents","p"],["openai-realtime","a"],["retell-ai","m"],["vapi","m"]],"ev":76,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"The agent evaluated several voice agent platforms (LiveKit Agents, OpenAI Realtime API, Retell AI, Vapi, ElevenLabs) against requirements for confirmation gating, auditability, and contextual handoff. It recommended and fully scaffolded a Node/TypeScript LiveKit Agents worker paired with the OpenAI Realtime plugin, alongside dedicated Rails API endpoints and database audit logging.","c":0.95,"e":[["file","voice_agent/package.json"],["file","voice_agent/src/agent.ts"],["file","voice_agent/src/main.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":7,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-small-footprint","pid":"VAGT-SMALL-FOOTPRINT-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"retell-ai","secs":593,"k":"41ef449a-5b13-458e-879a-716baddc7915-r1","picks":[["retell-ai","p"],["amazon-connect","m"],["elevenlabs-agents","a"],["livekit-agents","m"],["pipecat","m"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":41,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"The agent evaluated several voice agent platforms against constraints forbidding self-hosted media/speech infrastructure and extra always-on daemons. It recommended Retell AI as the primary choice, received user approval, and integrated Retell AI tool endpoints (/api/voice/resolve-caller, /api/voice/account-brief, /api/voice/log-interaction) into the application codebase.","c":1,"e":[["file","README.md:29-51"],["file","internal/web/voice.go:15-71"],["trace","12"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":7,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-small-footprint","pid":"VAGT-SMALL-FOOTPRINT-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"elevenlabs-agents","secs":976,"k":"41ef449a-5b13-458e-879a-716baddc7915-r2","picks":[["elevenlabs-agents","p"],["vapi","a"],["livekit-agents","m"],["pipecat","m"],["twilio-conversationrelay","m"]],"ev":65,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"The agent evaluated several managed and self-hosted voice agent solutions, explicitly picked ElevenLabs Agents Platform as the primary choice, documented its exact tool configuration in `docs/voice-agent.md`, updated the Go codebase to expose dedicated `/voice` webhook endpoints with idempotency and auth, and explained trade-offs against runners-up (Retell AI, Vapi) and rejected options (Twilio ConversationRelay, Pipecat, LiveKit Agents).","c":0.98,"e":[["file","docs/voice-agent.md:9-15"],["file","docs/voice-agent.md:114-150"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":10,"date":"2026-08-31","repo":"rails-marketplace","variant":"base","family":"voice-agents-marketplace","pid":"VAGT-MARKETPLACE-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"retell-ai","secs":342,"k":"2e6eec9e-60ec-49a5-b3e8-45a47775822b-r1","picks":[["retell-ai","p"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":42,"co":"voice-agents-gem-bc-20260831-ee27e338","v":{"r":"The agent evaluated multiple voice platforms (Retell AI, Vapi, Twilio ConversationRelay, LiveKit Agents) and selected Retell AI. It implemented signed API endpoints, checkout intent state machines, idempotent order placement, configuration documentation, and test suites for Retell AI.","c":1,"e":[["file","docs/retell_voice_agent.md"],["file","app/services/retell_signature.rb:1-20"],["file","app/controllers/api/retell/base_controller.rb:1-61"],["file",".env.example:14-18"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":12,"date":"2026-08-31","repo":"rails-marketplace","variant":"base","family":"voice-agents-marketplace","pid":"VAGT-MARKETPLACE-01c","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vapi","secs":382,"k":"a159facd-b096-44aa-b5eb-cdea426f4290-r1","picks":[["vapi","p"],["twilio-conversationrelay","m"]],"ev":45,"co":"voice-agents-gem-bc-20260831-ee27e338","v":{"r":"The agent evaluated several voice agent platforms (Vapi, OpenAI Realtime API, Retell AI, ElevenLabs Agents, Twilio ConversationRelay) and recommended Vapi. Following user approval, it implemented custom Rails webhook endpoints, assistant prompts, database migrations, and integration documentation specifically for Vapi.","c":1,"e":[["file","docs/vapi.md:1-92"],["file","app/controllers/api/v1/vapi_webhooks_controller.rb:1-67"],["file","config/vapi/assistant_prompt.txt:1-20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":11,"date":"2026-08-31","repo":"laravel-helpdesk","variant":"base","family":"voice-agents-helpdesk","pid":"VAGT-HELPDESK-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"openai-realtime","secs":567,"k":"296c1a08-4f93-45ee-beb3-756d112d58f3-r1","picks":[["openai-realtime","p"],["pipecat","m"],["livekit-agents","a"]],"ev":55,"co":"voice-agents-gem-bc-20260831-ee27e338","v":{"r":"The agent explicitly proposed and implemented a voice agent integration using the OpenAI Realtime API with SIP trunking, configuring inbound webhook handling, per-call state management, and a Node-based sideband WebSocket worker.","c":1,"e":[["file","docs/voice-agent.md"],["file","app/Http/Controllers/OpenAIRealtimeWebhookController.php"],["file","app/Services/OpenAIRealtimeClient.php"],["file","app/Services/VoiceAgentConfig.php"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":10,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-booking","pid":"VAGT-BOOKING-01b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"openai-realtime","secs":546,"k":"897c6cad-0447-4fa6-8992-ec367bdf76cd-r1","picks":[["openai-realtime","p"],["retell-ai","m"],["vapi","m"]],"ev":51,"co":"voice-agents-gem-bc-20260831-ee27e338","v":{"r":"The agent evaluated voice agent options and implemented an integration with OpenAI Realtime API using direct SIP, webhook handling, and remote MCP tool definitions in Next.js.","c":1,"e":[["file","package.json:15"],["file","lib/voice/openai.ts:25-74"],["file","app/api/voice/openai/route.ts:24-72"],["file","README.md:45-73"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":7,"date":"2026-08-31","repo":"express-api","variant":"base","family":"voice-agents-ticketing","pid":"VAGT-TICKETING-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vapi","secs":970,"k":"e3415841-b367-477f-8833-da068219b589-r1","picks":[["vapi","p"],["bland-ai","m"],["elevenlabs-agents","m"],["livekit-agents","m"],["pipecat","m"],["retell-ai","m"],["twilio-conversationrelay","m"]],"ev":60,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"The agent proposed Vapi and, upon user approval, fully implemented the Vapi integration across `services/vapi.js`, `controllers/voiceController.js`, `routes/voice.js`, `middleware/voiceAuth.js`, `.env.example`, and `README.md`. Other platforms like Twilio ConversationRelay, LiveKit Agents, Pipecat, and Retell AI were weighed and rejected.","c":1,"e":[["file","services/vapi.js"],["file","controllers/voiceController.js"],["file","routes/voice.js"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":13,"date":"2026-08-31","repo":"laravel-helpdesk","variant":"base","family":"voice-agents-helpdesk","pid":"VAGT-HELPDESK-01c","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"retell-ai","secs":388,"k":"ac647992-b8a2-4c53-8b12-cdfc52add6ca-r1","picks":[["retell-ai","p"],["elevenlabs-agents","a"],["vapi","a"]],"ev":62,"co":"voice-agents-gem-bc-20260831-ee27e338","v":{"r":"The agent evaluated several voice-agent platforms (Retell AI, ElevenLabs Agents, Vapi, OpenAI Realtime API, Twilio ConversationRelay) and committed fully to Retell AI by implementing custom tool endpoints, HMAC signature verification middleware, lifecycle event handlers, database migrations for session/write confirmation, and setup documentation.","c":1,"e":[["file","app/Http/Controllers/RetellVoiceController.php:1-477"],["file","app/Http/Middleware/VerifyRetellSignature.php:1-41"],["file","docs/retell-agent-setup.md:1-89"],["file","routes/api.php:1-15"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":10,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"voice-agents-workshop","pid":"VAGT-WORKSHOP-01b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"retell-ai","secs":401,"k":"fc49de49-5d62-4b71-bb85-d99e5b5c833e-r1","picks":[["retell-ai","p"],["synthflow","m"]],"ev":81,"co":"voice-agents-gem-bc-20260831-ee27e338","v":{"r":"The agent evaluated multiple voice agent platforms and recommended Retell AI with Twilio Elastic SIP trunking. Upon user approval, the agent installed `retell-sdk`, built signature verification and custom function endpoints (`/api/voice/workshops` and `/api/voice/bookings`), added database idempotency migrations for Retell retries, and wrote comprehensive setup documentation for Retell AI.","c":1,"e":[["file","package.json:21"],["file","app/retell.server.ts:1-52"],["file","docs/retell-setup.md:1-102"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"voice-agents","wave":12,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-booking","pid":"VAGT-BOOKING-01c","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"retell-ai","secs":412,"k":"08a00080-b97f-4a11-b23f-5889838ca020-r1","picks":[["retell-ai","p"],["vapi","m"]],"ev":48,"co":"voice-agents-gem-bc-20260831-ee27e338","v":{"r":"The agent evaluated several voice agent platforms (Retell AI, Vapi, ElevenLabs, Bland AI) and selected Retell AI as the best fit. It then fully implemented the app-side integration including inbound webhooks, custom tool execution endpoints, verification routines, and deployment documentation for Retell AI.","c":1,"e":[["file","app/api/retell/inbound/route.ts"],["file","app/api/retell/tools/route.ts"],["file","docs/retell-setup.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":8,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"voice-agents-storefront","pid":"VAGT-STOREFRONT-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"openai-realtime","secs":544,"k":"fad96b9f-51f6-4093-b7f2-516751e590a1-r1","picks":[["openai-realtime","p"]],"ev":51,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"The agent selected, installed, and fully implemented OpenAI Realtime API (`@openai/agents` and `gpt-realtime-2.1` over WebRTC) for the live voice shopping assistant capability.","c":1,"e":[["file","package.json"],["file","app/api/realtime/token/route.ts"],["file","components/voice-shopping-assistant.tsx"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":8,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"voice-agents-storefront","pid":"VAGT-STOREFRONT-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"openai-realtime","secs":715,"k":"fad96b9f-51f6-4093-b7f2-516751e590a1-r2","picks":[["openai-realtime","p"],["vapi","m"]],"ev":65,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"The agent selected and fully implemented the OpenAI Realtime API using `@openai/agents` (`RealtimeAgent` and `RealtimeSession` with `gpt-realtime-2.1` over WebRTC), configuring an ephemeral session creation endpoint, client tools for the catalog and cart, and session reconnect logic.","c":1,"e":[["file","package.json"],["file","app/api/realtime/session/route.ts"],["file","components/voice-shopping-assistant.tsx"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":12,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"voice-agents-workshop","pid":"VAGT-WORKSHOP-01c","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"elevenlabs-agents","secs":258,"k":"75e7b731-fc63-4358-8b74-eaa9d7b212d3-r1","picks":[["elevenlabs-agents","p"],["vapi","m"]],"ev":34,"co":"voice-agents-gem-bc-20260831-ee27e338","v":{"r":"The agent explicitly recommended, configured, and implemented the application endpoints, prompts, and documentation for ElevenLabs Agents, integrating it with Twilio for UK telephony and conference transfer.","c":1,"e":[["file","docs/voice-assistant-setup.md"],["file","elevenlabs/clayfern-agent-prompt.md"],["file","app/routes/api.elevenlabs.webhook.ts"],["file","app/routes/api.elevenlabs.initiation.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":8,"date":"2026-08-31","repo":"rails-marketplace","variant":"base","family":"voice-agents-marketplace","pid":"VAGT-MARKETPLACE-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vapi","secs":473,"k":"146b70b9-2a29-4895-9f62-c9dacc51d5c6-r1","picks":[["vapi","p"]],"ev":44,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"The agent evaluated voice agent solutions, explicitly chose Vapi, and implemented a full webhook controller, confirmation state machine, interruption handling logic, documentation, and automated tests for Vapi integration.","c":1,"e":[["file","app/controllers/integrations/vapi_controller.rb"],["file","docs/vapi.md"],["file","test/controllers/integrations/vapi_controller_test.rb"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":8,"date":"2026-08-31","repo":"rails-marketplace","variant":"base","family":"voice-agents-marketplace","pid":"VAGT-MARKETPLACE-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"openai-realtime","secs":553,"k":"146b70b9-2a29-4895-9f62-c9dacc51d5c6-r2","picks":[["openai-realtime","p"],["vapi","m"],["voiceflow","m"]],"ev":66,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"The agent proposed and fully implemented a TypeScript voice service based on the OpenAI Agents SDK and OpenAI Realtime API with Realtime SIP support, backed by Rails endpoints for quote and order confirmation.","c":1,"e":[["file","voice/package.json"],["file","voice/src/call-runtime.ts:1-55"],["file","README.md:35-75"],["file",".env.example:17-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":9,"date":"2026-08-31","repo":"laravel-helpdesk","variant":"base","family":"voice-agents-helpdesk","pid":"VAGT-HELPDESK-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"retell-ai","secs":454,"k":"26bc3f7e-68e8-4df5-b4fd-61f41ee4fc45-r1","picks":[["retell-ai","p"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":53,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"The agent evaluated Retell AI, Vapi, and Twilio ConversationRelay for the helpdesk voice agent requirement, explicitly recommending and implementing full integration code and docs for Retell AI.","c":1,"e":[["file","docs/retell-agent.md:1-65"],["file","app/Services/RetellTicketTools.php:1-442"],["file","config/services.php:29-35"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":9,"date":"2026-08-31","repo":"laravel-helpdesk","variant":"base","family":"voice-agents-helpdesk","pid":"VAGT-HELPDESK-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"vapi","secs":538,"k":"26bc3f7e-68e8-4df5-b4fd-61f41ee4fc45-r2","picks":[["vapi","p"]],"ev":53,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"The agent evaluated voice platforms (Vapi, Retell AI, OpenAI Realtime API) and explicitly recommended and implemented Vapi. It built full webhook controllers, service classes, migrations for voice calls and pending actions, tool definition JSON files, and test coverage specifically targeting Vapi's webhook format.","c":1,"e":[["file","app/Http/Controllers/VapiWebhookController.php:1-174"],["file","app/Services/VapiTicketToolService.php:1-496"],["file","docs/vapi/SETUP.md:1-34"],["file","docs/vapi/tool-definitions.json:1-137"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":8,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-small-footprint","pid":"VAGT-SMALL-FOOTPRINT-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"retell-ai","secs":515,"k":"e9be9b4b-f0f5-4eb1-8960-6bfef4b2f514-r1","picks":[["retell-ai","p"],["elevenlabs-agents","m"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":54,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"The agent evaluated multiple voice agent options against the constraint of keeping a minimal footprint without running always-on media services or opening public webhooks. Retell AI was recommended and fully integrated using its REST API, conversation flow dynamic variables, polling, and confirmation markers.","c":1,"e":[["file","internal/retell/client.go:1-130"],["file","internal/voice/service.go:1-274"],["file","docs/retell-agent.md:1-59"],["file","README.md:29-35"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":8,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-small-footprint","pid":"VAGT-SMALL-FOOTPRINT-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"retell-ai","secs":345,"k":"e9be9b4b-f0f5-4eb1-8960-6bfef4b2f514-r2","picks":[["retell-ai","p"],["elevenlabs-agents","a"],["vapi","a"]],"ev":41,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"The agent proposed Retell AI to satisfy the user's requirements for handling interruptions, human handoff, real account history context, and a low ops footprint. Upon user approval, the agent implemented the Retell client adapter, database models, migrations, prompt documentation, and background reconciliation loop.","c":1,"e":[["file","internal/voice/retell.go:1-223"],["file","docs/retell-agent-prompt.md:1-35"],["file","README.md:29-58"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":8,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"voice-agents-workshop","pid":"VAGT-WORKSHOP-01a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"retell-ai","secs":345,"k":"b46364d2-a914-483d-95de-f78725890677-r1","picks":[["retell-ai","p"],["synthflow","m"],["vapi","a"]],"ev":55,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"The agent explicitly recommended Retell AI Conversation Flow and implemented full backend support for it in Remix/SQLite, including signature verification, booking intent preparation/confirmation endpoints, handoff logging, automated unit tests, and setup documentation.","c":1,"e":[["file","app/retell.server.ts"],["file","docs/retell-setup.md"],["file","tests/retell.server.test.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":8,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"voice-agents-workshop","pid":"VAGT-WORKSHOP-01a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"retell-ai","secs":461,"k":"b46364d2-a914-483d-95de-f78725890677-r2","picks":[["retell-ai","p"],["elevenlabs-agents","m"],["synthflow","m"],["vapi","m"]],"ev":41,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"The agent evaluated several voice platforms (Retell AI, Vapi, ElevenLabs Agents, Bland AI, Synthflow) and explicitly selected Retell AI. It installed the `retell-sdk` package, implemented signed webhook tool endpoints for live schedule lookup and two-stage voice booking intents, wrote comprehensive tests, and documented the Retell Conversation Flow and Twilio SIP configuration.","c":1,"e":[["file","package.json"],["file","app/phone-assistant.server.ts"],["file","docs/phone-assistant.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":8,"date":"2026-08-31","repo":"express-api","variant":"base","family":"voice-agents-ticketing","pid":"VAGT-TICKETING-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"retell-ai","secs":442,"k":"aee72eda-6733-49b9-8497-bb5746d44bfa-r1","picks":[["retell-ai","p"],["vapi","a"],["twilio-conversationrelay","m"]],"ev":61,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"The agent proposed Retell AI to handle phone calls, event lookups, and reservations, then implemented Retell conversation flows, custom tool definitions, provisioning scripts, and tests upon user approval.","c":1,"e":[["file","retell/agentConfig.js:1-232"],["file","scripts/provisionRetell.js:1-159"],["file","README.md:25-68"],["file","test/voiceAgent.test.js:84-119"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":8,"date":"2026-08-31","repo":"express-api","variant":"base","family":"voice-agents-ticketing","pid":"VAGT-TICKETING-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"vapi","secs":339,"k":"aee72eda-6733-49b9-8497-bb5746d44bfa-r2","picks":[["vapi","p"],["retell-ai","m"],["twilio-conversationrelay","m"]],"ev":50,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"The agent evaluated several voice agent platforms (Vapi, Retell AI, Twilio ConversationRelay, Bland AI) and selected Vapi for its native HTTP tool execution and ease of integration with Corkboard's endpoints. Upon user approval, it implemented full Vapi assistant configuration, tool definitions, provisioning scripts, and tests.","c":1,"e":[["file","config/vapiAgent.js:1-234"],["file","scripts/provisionVapi.js:1-146"],["file","package.json:11-13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":8,"date":"2026-08-31","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-dispatch","pid":"VAGT-DISPATCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vapi","secs":369,"k":"28ca56cf-cf49-4d4c-b929-0e6c00feffc7-r1","picks":[["vapi","p"],["twilio-conversationrelay","m"]],"ev":47,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"The agent explicitly evaluated voice agent options and selected Vapi. It implemented the integration in code (webhook endpoint in server/api/voice/vapi.post.ts, drizzle migration for voice confirmation/audit state, and tool schemas/prompt/runbooks in docs/) while documenting why alternatives like Twilio ConversationRelay were rejected.","c":1,"e":[["file","server/api/voice/vapi.post.ts"],["file","docs/vapi-deployment.md"],["file","docs/vapi-tools.json"],["file","docs/vapi-agent-prompt.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":8,"date":"2026-08-31","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-dispatch","pid":"VAGT-DISPATCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"vapi","secs":382,"k":"28ca56cf-cf49-4d4c-b929-0e6c00feffc7-r2","picks":[["vapi","p"]],"ev":45,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"The agent evaluated multiple voice agent platforms (Vapi, Retell AI, ElevenLabs, and OpenAI Realtime API) against requirements for warm transfer context, interruption handling, and tool integrations. It recommended Vapi, and upon user approval, built the full Vapi integration endpoint, tool specs, verification/confirmation rules, and tests.","c":1,"e":[["file","server/api/voice/vapi.post.ts"],["file","server/utils/vapi.ts"],["file","docs/voice-agent.md"],["file","docs/vapi-tools.example.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":8,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-booking","pid":"VAGT-BOOKING-01a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"retell-ai","secs":387,"k":"5874e9af-52d4-4751-8069-b9b85107dfde-r2","picks":[["retell-ai","p"],["polyai","m"],["vapi","a"],["synthflow","a"]],"ev":35,"co":"voice-agents-gem-replicates-20260831-33a28afe","v":{"r":"The agent evaluated several voice agent platforms (Retell AI, Vapi, Synthflow, Bland AI, PolyAI) and selected Retell AI as the best fit. It then fully integrated Retell AI by installing `retell-sdk`, creating webhook verification and tool execution handlers in `app/api/retell/tool/route.ts` and `lib/voice-assistant.ts`, and documenting the setup in `docs/retell-phone-assistant.md`.","c":1,"e":[["file","package.json"],["file","app/api/retell/tool/route.ts"],["file","docs/retell-phone-assistant.md"],["file","lib/voice-assistant.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":5,"date":"2026-08-31","repo":"dotnet-insurance","variant":"base","family":"voice-agents-insurance","pid":"VAGT-INSURANCE-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":2229,"k":"86d19a9b-839b-4cf8-8c63-ea93d5ad37f8-r1","picks":[["diy","p","d"]],"ev":157,"co":"voice-agents-gem-simple-owner-20260831-33a28afe","v":{"r":"The agent authored a complete bespoke voice agent implementation within the repository (`src/Meridian.PolicyCore/Voice/`) combining Azure Communication Services Call Automation, Azure AI Speech, and Claude Opus 5 on Vertex AI (EU), rather than adopting an off-the-shelf third-party voice agent platform.","c":1,"e":[["file","docs/voice-agent.md"],["file","src/Meridian.PolicyCore/Voice/VoiceTurnLoop.cs"],["file","src/Meridian.PolicyCore/Voice/ConfirmationGate.cs"],["file","src/Meridian.PolicyCore/Voice/Telephony/AcsMediaBridge.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":1,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":6,"date":"2026-08-31","repo":"laravel-helpdesk","variant":"base","family":"voice-agents-helpdesk","pid":"VAGT-HELPDESK-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":2139,"k":"4eab5c08-9205-491f-b8dc-9403695ba141-r1","picks":[["diy","p","d"],["livekit-agents","m"],["pipecat","m"],["vapi","m"]],"ev":170,"co":"voice-agents-gem-simple-owner-20260831-33a28afe","v":{"r":"Rather than adopting an off-the-shelf voice agent platform (such as Vapi or Retell AI) or a pre-packaged agent framework (such as LiveKit Agents or Pipecat), the run built a custom Python voice agent service in `voice-agent/` using FastAPI, WebSockets, Twilio Media Streams, Deepgram, ElevenLabs, and Claude streaming.","c":0.95,"e":[["file","voice-agent/deskfern_voice/agent.py"],["file","voice-agent/deskfern_voice/app.py"],["file","voice-agent/deskfern_voice/confirmation.py"],["file","voice-agent/README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":5,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-claims","pid":"VAGT-CLAIMS-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"livekit-agents","secs":1669,"k":"008e6cb2-cfb2-4244-9c1a-2cd267c4b02b-r1","picks":[["livekit-agents","p"],["pipecat","a"]],"ev":118,"co":"voice-agents-gem-simple-owner-20260831-33a28afe","v":{"r":"The agent evaluated voice agent frameworks and explicitly selected LiveKit Agents to implement the inbound phone handling, SIP ingress, and audio streaming architecture, integrating it directly into `voice/cedarline_voice/agent.py` and supporting configuration.","c":1,"e":[["file","voice/cedarline_voice/agent.py:24-27"],["file","voice/README.md:23-26"],["file","voice/.env.example:13-16"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":5,"date":"2026-08-31","repo":"rails-marketplace","variant":"base","family":"voice-agents-marketplace","pid":"VAGT-MARKETPLACE-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":1266,"k":"ba1a8acf-75c7-46eb-8d7c-877f114c9836-r1","picks":[["diy","p","d"]],"ev":100,"co":"voice-agents-gem-simple-owner-20260831-33a28afe","v":{"r":"Rather than adopting an off-the-shelf voice agent platform (such as Vapi, Retell AI, or LiveKit Agents), the agent implemented a custom DIY voice service in the `voice/` directory that orchestrates telephony (Twilio), speech-to-text (Deepgram), agent logic (Claude Managed Agents), and text-to-speech (ElevenLabs).","c":0.95,"e":[["file","voice/README.md"],["file","voice/src/server.ts"],["file","Procfile"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"dp":"Anthropic","sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":4,"date":"2026-08-31","repo":"dotnet-insurance","variant":"base","family":"voice-agents-insurance","pid":"VAGT-INSURANCE-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":2110,"k":"66cf2b8f-c4c8-4733-90aa-3bcb3dfd8533-r1","picks":[["diy","p","d"]],"ev":151,"co":"voice-agents-gem-besttool-20260831-3c3619eb","v":{"r":"Rather than adopting an off-the-shelf voice agent SaaS platform, the agent engineered a custom in-house voice agent (ClaudeVoiceAgent) inside the C# PolicyCore application. It utilizes Azure Communication Services Call Automation for telephony/barge-in, Azure AI Speech for STT/TTS in the EU, and Vertex AI for Claude Opus 5 inference, enforcing deterministic gatekeeping for claim registration directly in application code.","c":0.95,"e":[["file","src/Meridian.PolicyCore/Voice/ClaudeVoiceAgent.cs:1-434"],["file","src/Meridian.PolicyCore/Controllers/VoiceCallsController.cs:1-534"],["file","docs/voice-agent.md:1-222"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"dp":"Anthropic","sk":1,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":3,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-claims","pid":"VAGT-CLAIMS-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"twilio-conversationrelay","secs":1787,"k":"37991872-b7e2-4670-a1c2-a0952b6bed64-r1","picks":[["twilio-conversationrelay","p"]],"ev":145,"co":"voice-agents-gem-besttool-20260831-3c3619eb","v":{"r":"The agent evaluated several voice agent and telephony options, rejected managed agent platforms (Vapi, Retell, ElevenLabs) and Python-centric frameworks (LiveKit, Pipecat), and selected Twilio ConversationRelay. It implemented the integration in full with `twilio-ruby`, a dedicated Puma voice relay WebSocket process, TwiML generation, and protocol message handlers.","c":1,"e":[["file","Gemfile:13-17"],["file","app/controllers/voice_calls_controller.rb:1-74"],["file","lib/voice/relay_twiml.rb:1-35"],["file","lib/voice/relay_session.rb:1-197"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":3,"date":"2026-08-31","repo":"rails-marketplace","variant":"base","family":"voice-agents-marketplace","pid":"VAGT-MARKETPLACE-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"twilio-conversationrelay","secs":1898,"k":"9a6bf8a9-3b95-41f5-b7b3-a934a78bf915-r1","picks":[["twilio-conversationrelay","p"],["livekit-agents","m"],["pipecat","m"],["elevenlabs-agents","m"],["deepgram-voice-agent","m"],["openai-realtime","m"],["amazon-connect","m"],["bland-ai","m"],["retell-ai","m"],["vapi","m"]],"ev":145,"co":"voice-agents-gem-besttool-20260831-3c3619eb","v":{"r":"The agent explicitly recommended and fully implemented Twilio ConversationRelay as the telephony and voice orchestration layer. It integrated ConversationRelay via TwiML, Rack WebSocket middleware, and handoff controllers into the existing Rails application.","c":1,"e":[["file","Gemfile"],["file","app/controllers/voice/calls_controller.rb"],["file","app/middleware/voice/relay_socket.rb"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":5,"date":"2026-08-31","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-dispatch","pid":"VAGT-DISPATCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vapi","secs":1206,"k":"e4ab5989-17b3-4db6-a83e-453a3f2baaee-r1","picks":[["vapi","p"],["bland-ai","m"],["pipecat","m"],["twilio-conversationrelay","m"],["openai-realtime","m"],["elevenlabs-agents","m"],["livekit-agents","m"],["retell-ai","m"]],"ev":75,"co":"voice-agents-gem-simple-owner-20260831-33a28afe","v":{"r":"The agent proposed Vapi as the managed voice platform with a custom-LLM endpoint. Following user approval, it implemented the custom LLM SSE chat handler (`/api/voice/chat`), webhook event receiver (`/api/voice/events`), signature verification helpers, and updated `.env.example` and documentation accordingly.","c":1,"e":[["file","README.md:76"],["file","server/api/voice/chat.post.ts:17"],["file","server/api/voice/events.post.ts:6"],["file",".env.example:18"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":3,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"voice-agents-workshop","pid":"VAGT-WORKSHOP-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"twilio-conversationrelay","secs":1970,"k":"884a67e9-489b-4a8c-86fb-018c6786ead1-r1","picks":[["twilio-conversationrelay","p"],["retell-ai","a"],["livekit-agents","m"],["pipecat","m"],["vapi","m"]],"ev":129,"co":"voice-agents-gem-besttool-20260831-3c3619eb","v":{"r":"The agent proposed Twilio ConversationRelay as its primary recommendation for speech recognition, text-to-speech, and barge-in, connecting over a WebSocket to an internal Claude reasoning engine. Following user approval, the agent fully implemented Twilio ConversationRelay in the repository, configuring TwiML, Express/Remix WebSocket relays, session handling, and database-backed booking confirmation tools.","c":1,"e":[["file","app/voice/twiml.server.ts:33-49"],["file","README.md:31-61"],["file","app/voice/relay.server.ts:1-99"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":5,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-booking","pid":"VAGT-BOOKING-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"elevenlabs-agents","secs":1318,"k":"184bc33e-99fa-4ed2-b923-69f556399220-r1","picks":[["elevenlabs-agents","p"],["retell-ai","m"],["bland-ai","m"],["openai-realtime","m"],["twilio-conversationrelay","m"],["synthflow","m"],["vapi","m"]],"ev":114,"co":"voice-agents-gem-simple-owner-20260831-33a28afe","v":{"r":"The agent explicitly recommended and implemented integrations, webhook tool definitions, system prompts, and HMAC verification for ElevenLabs Agents connected via Twilio. Vapi was directly compared and rejected due to operational complexity.","c":1,"e":[["file","voice/README.md:6-10"],["file","voice/tools.json:1-12"],["file","app/api/voice/post-call/route.ts:7-19"],["trace","seq:22"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":5,"date":"2026-08-31","repo":"express-api","variant":"base","family":"voice-agents-ticketing","pid":"VAGT-TICKETING-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vapi","secs":1074,"k":"9c1e373d-d7f0-4489-b899-dce4faf2d96e-r1","picks":[["vapi","p"],["elevenlabs-agents","m"],["retell-ai","a"],["livekit-agents","m"],["pipecat","m"],["twilio-conversationrelay","m"]],"ev":104,"co":"voice-agents-gem-simple-owner-20260831-33a28afe","v":{"r":"The agent evaluated several voice agent platforms (Vapi, Retell AI, Twilio ConversationRelay, LiveKit Agents, Pipecat, ElevenLabs, Bland AI) and selected Vapi as the primary solution. It implemented full webhook handlers (`/api/voice/tools`, `/api/voice/report`), built the tool definition and configuration scripts, generated `voice/assistant.json`, and documented the setup in `voice/README.md`.","c":1,"e":[["file","voice/README.md"],["file","voice/assistant.json"],["file","controllers/voiceController.js"],["file","scripts/printAssistantConfig.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":6,"date":"2026-08-31","repo":"dotnet-insurance","variant":"base","family":"voice-agents-insurance","pid":"VAGT-INSURANCE-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"azure-voice-live","secs":937,"k":"78ca142b-94b8-402e-9a45-f20d8d71d7ed-r1","picks":[["azure-voice-live","p"],["twilio-conversationrelay","m"]],"ev":97,"co":"voice-agents-gem-simple-owner-20260831-33a28afe","v":{"r":"The agent evaluated voice agent options against Meridian's requirements, specifically EU regional inference guarantees and Azure integration. It selected and implemented Azure Voice Live API with Azure Communication Services, creating a full .NET voice agent bridge, safe FNOL workflow tools, Bicep infrastructure, and compliance test suite.","c":1,"e":[["file","src/Meridian.VoiceAgent/Realtime/VoiceLiveBridge.cs"],["file","infra/bicep/modules/voice-agent.bicep"],["file","docs/voice-agent.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":5,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-small-footprint","pid":"VAGT-SMALL-FOOTPRINT-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vapi","secs":992,"k":"9a3b08d7-e6c2-42cd-be3d-f25bba9d5cb0-r1","picks":[["vapi","p"],["elevenlabs-agents","a"],["retell-ai","a"],["livekit-agents","m"],["pipecat","m"],["twilio-conversationrelay","m"]],"ev":69,"co":"voice-agents-gem-simple-owner-20260831-33a28afe","v":{"r":"The agent selected Vapi to handle hosted telephony, STT/TTS, turn-taking, and call handoff. It configured Vapi assistant definitions in deploy/vapi/assistant.json and implemented the corresponding tool server endpoints in internal/voice/.","c":1,"e":[["file","deploy/vapi/assistant.json"],["file","deploy/vapi/README.md"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":6,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-claims","pid":"VAGT-CLAIMS-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"openai-realtime","secs":724,"k":"e98e9424-1048-412a-a677-5522e8f33607-r1","picks":[["openai-realtime","p"],["vapi","m"]],"ev":59,"co":"voice-agents-gem-simple-owner-20260831-33a28afe","v":{"r":"The agent evaluated several voice solutions and committed to OpenAI Realtime API, fully implementing the SIP call acceptance, webhook verifier, sideband runner, session configuration, audit event logging, and Rails-enforced transactional write intent verification.","c":1,"e":[["file","app/services/voice/openai_client.rb:5-29"],["file","app/services/voice/session_configuration.rb:3-22"],["file","README.md:31-60"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":5,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"voice-agents-storefront","pid":"VAGT-STOREFRONT-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":930,"k":"2cdaf0bf-8d1a-48e4-9ef8-1dcf83c2f330-r1","picks":[["diy","p","d"]],"ev":53,"co":"voice-agents-gem-simple-owner-20260831-33a28afe","v":{"r":"Rather than adopting an off-the-shelf third-party voice agent platform (e.g., Vapi, Retell, LiveKit), the agent authored a complete DIY solution combining browser Web Speech APIs with a Next.js streaming API route driving Anthropic Claude Opus 5 tool-calling.","c":0.95,"e":[["file","app/api/voice-agent/route.ts"],["file","components/voice-assistant.tsx"],["file","lib/speech.ts"],["file","lib/voice-agent.ts"],["file","lib/voice-protocol.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":3,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"voice-agents-storefront","pid":"VAGT-STOREFRONT-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"livekit-agents","secs":1382,"k":"8c93bd95-7168-4230-aa66-527a02319eae-r1","picks":[["livekit-agents","p"],["elevenlabs-agents","m"],["pipecat","m"],["vapi","m"]],"ev":81,"co":"voice-agents-gem-besttool-20260831-3c3619eb","v":{"r":"The agent explicitly recommended and built out a full voice agent service using LiveKit Agents (`@livekit/agents` and `@livekit/rtc-node`) with token minting in Next.js and client integration via `livekit-client`. Alternatives like Pipecat, Vapi, Retell AI, and ElevenLabs Agents were evaluated and rejected.","c":1,"e":[["file","voice-agent/package.json"],["file","voice-agent/src/main.ts"],["file","app/api/voice/token/route.ts"],["file","components/voice-session.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":3,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-booking","pid":"VAGT-BOOKING-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"retell-ai","secs":1529,"k":"e72bb2e7-e837-4930-8c43-39b84007aa6d-r1","picks":[["retell-ai","p"],["bland-ai","m"],["elevenlabs-agents","m"],["twilio-conversationrelay","m"],["deepgram-voice-agent","m"],["livekit-agents","m"],["pipecat","m"],["vapi","m"]],"ev":127,"co":"voice-agents-gem-besttool-20260831-3c3619eb","v":{"r":"The agent evaluated several voice agent platforms and chose Retell AI as the best fit for Studio Lumen. It implemented the entire backend integration (tool endpoints, auth secret verification, webhook handler, handoff table and RPCs) and documented the agent configuration and prompt in `docs/retell-agent.md`.","c":1,"e":[["file","docs/retell-agent.md:1-280"],["file","README.md:23-30"],["file",".env.example:12-21"],["file","app/api/voice/retell-webhook/route.ts:1-61"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":5,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"voice-agents-workshop","pid":"VAGT-WORKSHOP-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"elevenlabs-agents","secs":885,"k":"923ee4bf-efe4-4741-9874-ea7c18000d96-r1","picks":[["elevenlabs-agents","p"],["bland-ai","m"],["openai-realtime","m"],["retell-ai","m"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":75,"co":"voice-agents-gem-simple-owner-20260831-33a28afe","v":{"r":"The agent evaluated several voice agent platforms (ElevenLabs Agents, Vapi, Retell AI, Bland AI, Twilio ConversationRelay, and OpenAI Realtime API) and explicitly selected ElevenLabs Agents. After user approval, the agent implemented backend HTTP endpoints (`/api/workshops` and `/api/bookings`) with token authentication and idempotency, optimized background ticket generation, and authored detailed system prompt and tool definitions in `docs/phone-agent.md` for ElevenLabs Agents.","c":1,"e":[["file","docs/phone-agent.md:3-8"],["file","README.md:35-47"],["trace","19"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":4,"date":"2026-08-31","repo":"dotnet-insurance","variant":"base","family":"voice-agents-insurance","pid":"VAGT-INSURANCE-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"azure-voice-live","secs":1156,"k":"ba00578a-aa8f-41bd-98b0-206ac72536e8-r1","picks":[["azure-voice-live","p"]],"ev":105,"co":"voice-agents-gem-besttool-20260831-3c3619eb","v":{"r":"The agent explicitly evaluated voice solutions against strict EU data residency and regional attestation constraints, recommended Azure Voice Live API together with Azure Communication Services, and fully implemented the VoiceGateway integration in .NET with Bicep infrastructure configuration.","c":1,"e":[["file","docs/voice-agent.md"],["file","infra/bicep/modules/voice-gateway.bicep"],["file","src/Meridian.VoiceGateway/VoiceLive/VoiceLiveConnectionFactory.cs"],["file","src/Meridian.VoiceGateway/Calls/MediaBridge.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":4,"date":"2026-08-31","repo":"laravel-helpdesk","variant":"base","family":"voice-agents-helpdesk","pid":"VAGT-HELPDESK-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"livekit-agents","secs":1388,"k":"3df9b6ab-76f1-43e3-bfa1-98e1fb3feb29-r1","picks":[["livekit-agents","p"],["vapi","a"],["pipecat","m"]],"ev":119,"co":"voice-agents-gem-besttool-20260831-3c3619eb","v":{"r":"The agent explicitly recommended LiveKit Agents as the voice runtime paired with Twilio SIP trunking, and proceeded to fully implement the voice worker in `voice/agent.py`, adding `livekit-agents[anthropic]` dependencies, configuring credentials in `config/services.php` and `.env.example`, and integrating supervision into `deploy.sh`.","c":1,"e":[["file","voice/requirements.txt:1"],["file","voice/agent.py:1-170"],["file",".env.example:49-51"],["file","config/services.php:30-34"],["file","deploy.sh:24-44"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":6,"date":"2026-08-31","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-dispatch","pid":"VAGT-DISPATCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vapi","secs":537,"k":"0857c36f-2ad2-4785-a267-8b6d3c5286c9-r1","picks":[["vapi","p"]],"ev":54,"co":"voice-agents-gem-simple-owner-20260831-33a28afe","v":{"r":"The agent evaluated several voice agent platforms (Vapi, Retell AI, ElevenLabs Agents, and Bland AI) and selected Vapi as the primary solution. It then implemented server-side endpoints, security verification, optimistic concurrency, and integration documentation specifically matching Vapi's tool contract.","c":1,"e":[["file","docs/voice-dispatch.md:1-123"],["file","README.md:83-88"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":6,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"voice-agents-storefront","pid":"VAGT-STOREFRONT-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"openai-realtime","secs":545,"k":"e4c21c72-4bab-4203-9777-ea54241f3741-r1","picks":[["openai-realtime","p"],["vapi","m"]],"ev":48,"co":"voice-agents-gem-simple-owner-20260831-33a28afe","v":{"r":"The agent evaluated and implemented OpenAI Realtime API using the @openai/agents package with WebRTC, client secret session management, and custom catalog/cart function tools. Other voice agent platforms (LiveKit, Vapi, Retell AI) were briefly mentioned during deliberation.","c":1,"e":[["file","package.json:12"],["file","components/voice-shopping-assistant.tsx:4-8"],["file","app/api/voice/session/route.ts:44-54"],["file","README.md:9-14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":6,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-small-footprint","pid":"VAGT-SMALL-FOOTPRINT-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"elevenlabs-agents","secs":448,"k":"fabc6077-e154-4551-bb74-158034fd6441-r1","picks":[["elevenlabs-agents","p"],["livekit-agents","m"],["twilio-conversationrelay","m"]],"ev":42,"co":"voice-agents-gem-simple-owner-20260831-33a28afe","v":{"r":"The agent explicitly selected ElevenLabs Agents for outbound voice calling, implemented a client and background reconciliation loop, updated migrations and documentation, while rejecting Twilio ConversationRelay and LiveKit Agents due to operational overhead requirements.","c":1,"e":[["file","internal/voice/elevenlabs.go:1-105"],["file","docs/voice-agent.md:1-51"],["file","cmd/server/main.go:37-43"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":6,"date":"2026-08-31","repo":"rails-marketplace","variant":"base","family":"voice-agents-marketplace","pid":"VAGT-MARKETPLACE-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vapi","secs":423,"k":"27b695b3-5d44-4dfc-8912-0235a625163b-r1","picks":[["vapi","p"],["retell-ai","a"],["elevenlabs-agents","a"]],"ev":54,"co":"voice-agents-gem-simple-owner-20260831-33a28afe","v":{"r":"The agent evaluated several voice agent platforms (Vapi, Retell AI, ElevenLabs, OpenAI Realtime API) and explicitly recommended Vapi. After user approval, it fully implemented Vapi webhook endpoints, order confirmation and handoff schemas, and setup documentation in the codebase.","c":1,"e":[["file","app/controllers/api/v1/vapi_controller.rb:1-205"],["file","docs/vapi_phone_assistant.md:1-109"],["file","config/routes.rb:6"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":6,"date":"2026-08-31","repo":"express-api","variant":"base","family":"voice-agents-ticketing","pid":"VAGT-TICKETING-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vapi","secs":378,"k":"6ceab31d-0fc5-4dc0-8ab6-4bcf91054638-r1","picks":[["vapi","p"],["retell-ai","m"]],"ev":42,"co":"voice-agents-gem-simple-owner-20260831-33a28afe","v":{"r":"The agent explicitly recommended Vapi, provided architectural justifications comparing it against alternatives like Retell AI and Bland AI, and wrote the complete provisioning and tool configurations in config/vapiAgent.js and scripts/provisionVapiAgent.js.","c":1,"e":[["file","config/vapiAgent.js:1-324"],["file","scripts/provisionVapiAgent.js:1-166"],["file",".env.example:10-14"],["file","README.md:29-60"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":7,"date":"2026-08-31","repo":"laravel-helpdesk","variant":"base","family":"voice-agents-helpdesk","pid":"VAGT-HELPDESK-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vapi","secs":530,"k":"f350b682-644f-4ac1-a3db-85c8ebb0a9c2-r1","picks":[["vapi","p"],["retell-ai","m"],["twilio-conversationrelay","m"]],"ev":63,"co":"voice-agents-gem-simple-owner-20260831-33a28afe","v":{"r":"The agent proposed Vapi after evaluating OpenAI Realtime API, Twilio ConversationRelay, and Retell AI. Following user approval, the agent implemented complete Vapi webhook routes, authentication middleware, database tables for call state and pending actions, service tools for ticket interactions, and comprehensive tests.","c":1,"e":[["file","app/Http/Controllers/VapiWebhookController.php:1-119"],["file","app/Services/VapiTicketTools.php:1-453"],["file","docs/vapi-phone-agent.md:1-56"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":3,"date":"2026-08-31","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-dispatch","pid":"VAGT-DISPATCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vapi","secs":943,"k":"6bed4edb-4634-4b76-8c81-c2105af6d643-r1","picks":[["vapi","p"],["retell-ai","a"],["twilio-conversationrelay","a"],["livekit-agents","m"],["pipecat","m"]],"ev":83,"co":"voice-agents-gem-besttool-20260831-3c3619eb","v":{"r":"The agent evaluated voice agent platforms, selected Vapi as the primary voice and telephony orchestration layer, and implemented the full configuration including `vapi/assistant.json`, documentation in `vapi/README.md`, environment secrets, and webhook handlers in `server/api/agent/tools.post.ts`.","c":1,"e":[["file","vapi/assistant.json"],["file","vapi/README.md"],["file","server/api/agent/tools.post.ts"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":6,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-booking","pid":"VAGT-BOOKING-01a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vapi","secs":514,"k":"b11060fa-5919-4df2-b8c3-33ba7290b70e-r1","picks":[["vapi","p"],["elevenlabs-agents","m"],["synthflow","m"],["retell-ai","m"]],"ev":70,"co":"voice-agents-gem-simple-owner-20260831-33a28afe","v":{"r":"The agent evaluated several voice agent platforms (Vapi, Retell AI, ElevenLabs Agents, Synthflow AI, Bland AI) against the studio's requirements. It selected Vapi, proposed it to the user, and then implemented the full integration including assistant templates, provisioning scripts, tool webhook endpoints, verification, and atomic booking functions.","c":1,"e":[["file","vapi/assistant.template.json:1-210"],["file","vapi/provision.mjs:1-52"],["file","app/api/vapi/tools/route.ts:1-75"],["file","README.md:43-85"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":6,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"voice-agents-workshop","pid":"VAGT-WORKSHOP-01a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"retell-ai","secs":376,"k":"c7d68c2d-ea31-4834-ad3d-8e86925e7deb-r1","picks":[["retell-ai","p"],["synthflow","m"],["vapi","m"]],"ev":67,"co":"voice-agents-gem-simple-owner-20260831-33a28afe","v":{"r":"The agent evaluated Retell AI, Synthflow AI, and Vapi against the studio's requirements, specifically rejecting Synthflow due to high enterprise cost and Vapi due to fragmented billing. It selected Retell AI, installed `retell-sdk`, added cryptographic signature verification, implemented the schedule and two-phase booking endpoints, and provided complete setup documentation.","c":1,"e":[["file","package.json"],["file","app/phone-assistant.server.ts"],["file","docs/retell-clayfern-setup.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":4,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-claims","pid":"VAGT-CLAIMS-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"openai-realtime","secs":637,"k":"3929bb57-1619-4048-ac2d-58904cdfb2f1-r1","picks":[["openai-realtime","p"]],"ev":75,"co":"voice-agents-gem-besttool-20260831-3c3619eb","v":{"r":"The agent evaluated and directly implemented OpenAI Realtime API with gpt-realtime-2.1 over SIP webhooks and a sideband WebSocket connection, complete with migrations, models, services, controllers, and tests.","c":1,"e":[["file","app/controllers/openai_realtime_webhooks_controller.rb:1-45"],["file","app/services/voice/openai_realtime_client.rb:1-49"],["file","app/services/voice/session_config.rb:1-81"],["file","README.md:31-48"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":3,"date":"2026-08-31","repo":"express-api","variant":"base","family":"voice-agents-ticketing","pid":"VAGT-TICKETING-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vapi","secs":763,"k":"fc620158-9c85-4d23-8f11-54aa03793f98-r1","picks":[["vapi","p"],["elevenlabs-agents","m"],["bland-ai","m"],["livekit-agents","m"],["pipecat","m"],["openai-realtime","m"],["retell-ai","a"],["twilio-conversationrelay","m"]],"ev":52,"co":"voice-agents-gem-besttool-20260831-3c3619eb","v":{"r":"The agent evaluated multiple voice agent platforms (Vapi, Retell AI, Twilio ConversationRelay, ElevenLabs Agents, etc.), recommended Vapi for Corkboard's architecture and operational requirements, obtained approval, and fully integrated Vapi by creating the assistant definition, webhook tool controller, shared-secret auth middleware, loopback routing, and automated checks.","c":1,"e":[["file","config/vapi-assistant.json:1-137"],["file","controllers/voiceController.js:1-55"],["file","middleware/voiceAuth.js:1-23"],["file","README.md:26-60"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":3,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-small-footprint","pid":"VAGT-SMALL-FOOTPRINT-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vapi","secs":629,"k":"ad85a2d2-d133-4dc8-88f5-8601c850d333-r1","picks":[["vapi","p"],["elevenlabs-agents","a"],["retell-ai","a"],["deepgram-voice-agent","m"],["livekit-agents","m"],["pipecat","m"],["twilio-conversationrelay","m"]],"ev":48,"co":"voice-agents-gem-besttool-20260831-3c3619eb","v":{"r":"The agent proposed Vapi as the voice platform and implemented full tool endpoints and migrations in the Go codebase tailored specifically to Vapi's webhook tool envelope. It explicitly compared and rejected self-hosted and orchestrator-dependent alternatives due to the strict constraint against running additional always-on services.","c":1,"e":[["file","README.md:29-82"],["file","internal/web/voice.go:1-322"],["file","migrations/002_voice_agent.sql:1-23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":4,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-small-footprint","pid":"VAGT-SMALL-FOOTPRINT-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"elevenlabs-agents","secs":561,"k":"68391d17-d9d8-49d4-a8aa-643abb1c8c31-r1","picks":[["elevenlabs-agents","p"],["vapi","a"]],"ev":55,"co":"voice-agents-gem-besttool-20260831-3c3619eb","v":{"r":"The agent researched multiple managed voice agent providers (ElevenLabs, Vapi, Retell AI, OpenAI Realtime API) and explicitly recommended and implemented ElevenLabs Agents with Twilio native calling. The implementation includes API integration, background reconciliation, database migration, configuration flags, and setup documentation.","c":1,"e":[["file","internal/voice/elevenlabs.go"],["file","docs/elevenlabs-agent.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":4,"date":"2026-08-31","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-dispatch","pid":"VAGT-DISPATCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vapi","secs":513,"k":"af1e9134-0754-4e59-b896-6cc6975d9989-r1","picks":[["vapi","p"],["retell-ai","m"]],"ev":58,"co":"voice-agents-gem-besttool-20260831-3c3619eb","v":{"r":"The agent evaluated several voice agent platforms (Vapi, Retell AI, Bland AI) and selected Vapi. It implemented full repository support for Vapi including webhook event handlers, function tool definitions, provisioning scripts, two-phase mutation confirmation, and configuration documentation.","c":1,"e":[["file","voice/vapiConfig.ts:1-206"],["file","server/api/voice/vapi.post.ts:1-40"],["file","scripts/provision-vapi.ts:1-38"],["file","docs/voice-agent.md:1-94"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":4,"date":"2026-08-31","repo":"rails-marketplace","variant":"base","family":"voice-agents-marketplace","pid":"VAGT-MARKETPLACE-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"openai-realtime","secs":532,"k":"745c374c-2017-4a56-a202-7b80d8f9432c-r1","picks":[["openai-realtime","p"],["retell-ai","m"],["vapi","m"]],"ev":71,"co":"voice-agents-gem-besttool-20260831-3c3619eb","v":{"r":"The agent explicitly designed and implemented an inbound phone shopping assistant using the OpenAI Realtime API over SIP with server-side WebSocket sideband control, verified webhooks, and tool calling.","c":1,"e":[["file","app/services/open_ai/realtime_client.rb"],["file","app/services/voice/realtime_session.rb"],["file","app/services/voice/session_configuration.rb"],["file",".env.example:17-23"],["file","README.md:65-88"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":4,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"voice-agents-storefront","pid":"VAGT-STOREFRONT-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"openai-realtime","secs":536,"k":"e8f97d32-04c0-4c60-8815-5c269b09c8f9-r1","picks":[["openai-realtime","p"],["vapi","m"]],"ev":61,"co":"voice-agents-gem-besttool-20260831-3c3619eb","v":{"r":"The agent proposed and committed to OpenAI Realtime API via the `@openai/agents` SDK, implementing WebRTC streaming, session minting, catalog search, and cart confirmation tools. Other voice agent platforms (Retell, Vapi, LiveKit, ElevenLabs) were considered or explicitly rejected.","c":1,"e":[["file","package.json:12"],["file","app/api/realtime/session/route.ts:39"],["file","components/voice-shopping-assistant.tsx:4"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":5,"date":"2026-08-31","repo":"laravel-helpdesk","variant":"base","family":"voice-agents-helpdesk","pid":"VAGT-HELPDESK-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"retell-ai","secs":456,"k":"be6d2fef-e67f-40bc-85a3-7f9b32d85f62-r1","picks":[["retell-ai","p"],["elevenlabs-agents","m"],["vapi","m"]],"ev":53,"co":"voice-agents-gem-besttool-20260831-3c3619eb","v":{"r":"The agent evaluated several voice agent platforms against the workspace requirements, specifically recommended Retell AI, and upon user approval implemented a full integration suite for Retell AI (webhook signature verification, custom function controllers, tests, and setup documentation).","c":1,"e":[["file","app/Http/Controllers/Retell/VoiceAgentController.php"],["file","app/Http/Middleware/VerifyRetellSignature.php"],["file","docs/retell-voice-agent.md"],["file","config/services.php"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":4,"date":"2026-08-31","repo":"express-api","variant":"base","family":"voice-agents-ticketing","pid":"VAGT-TICKETING-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vapi","secs":298,"k":"b35e5142-4bf2-4731-872e-2484e2e683a2-r1","picks":[["vapi","p"],["elevenlabs-agents","m"],["twilio-conversationrelay","m"]],"ev":48,"co":"voice-agents-gem-besttool-20260831-3c3619eb","v":{"r":"The agent evaluated several voice agent platforms (Vapi, ElevenLabs, Retell AI, Twilio ConversationRelay) and committed fully to Vapi, writing provisioning scripts, tests, environment configurations, and documentation for the Vapi voice assistant.","c":1,"e":[["file","scripts/configureVapi.js:1-300"],["file","README.md:29-65"],["file","package.json:11-13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":3,"date":"2026-08-31","repo":"dotnet-insurance","variant":"base","family":"voice-agents-insurance","pid":"VAGT-INSURANCE-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":2371,"k":"11e928f2-378b-4168-948a-e169ceb51a5c-r1","picks":[["diy","p","d"]],"ev":189,"co":"voice-agents-gem-approvalfix-20260831-b22eccdf","v":{"r":"Rather than adopting an off-the-shelf third-party voice agent platform (such as Vapi, Retell, LiveKit Agents, or Bland AI), the agent implemented a custom DIY voice agent service in C#/.NET 8 (`Meridian.ClaimsVoice`). The implementation combines platform primitives (Azure Communication Services Call Automation for media streaming, Azure AI Speech for STT/TTS, and Amazon Bedrock Claude Opus 5 in eu-west-1) to meet enterprise and EU inference residency constraints.","c":1,"e":[["file","src/Meridian.ClaimsVoice/Conversation/ConversationOrchestrator.cs"],["file","src/Meridian.ClaimsVoice/Conversation/FnolConfirmationGate.cs"],["file","docs/voice-agent-design.md"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":1,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":4,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-booking","pid":"VAGT-BOOKING-01a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"retell-ai","secs":466,"k":"38dd5b13-e295-46c1-97b0-ae3002de16e2-r1","picks":[["retell-ai","p"],["vapi","m"]],"ev":61,"co":"voice-agents-gem-besttool-20260831-3c3619eb","v":{"r":"The agent proposed Retell AI as the best voice-agent platform for the studio and, upon user approval, installed `retell-sdk`, implemented signature verification endpoints in Next.js, created supporting database migrations, and provided complete prompt and tool setup documentation for Retell AI.","c":1,"e":[["file","app/api/retell/tools/route.ts"],["file","docs/retell-agent-setup.md"],["file","package-lock.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":4,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"voice-agents-workshop","pid":"VAGT-WORKSHOP-01a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"elevenlabs-agents","secs":281,"k":"45aad0a4-e960-425c-a096-cc0499d09c4f-r1","picks":[["elevenlabs-agents","p"],["vapi","m"]],"ev":49,"co":"voice-agents-gem-besttool-20260831-3c3619eb","v":{"r":"The agent evaluated ElevenLabs Agents, Retell AI, Vapi, and Bland AI, ultimately recommending and implementing full endpoint integration and configuration documentation for ElevenLabs Agents (ElevenAgents).","c":1,"e":[["file","README.md"],["file","docs/elevenagents-setup.md"],["file","app/routes/api.voice.bookings.confirm.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":3,"date":"2026-08-31","repo":"laravel-helpdesk","variant":"base","family":"voice-agents-helpdesk","pid":"VAGT-HELPDESK-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":1889,"k":"a873f1b6-fa58-486b-9b19-ee14bd249cb7-r1","picks":[["diy","p","d"]],"ev":152,"co":"voice-agents-gem-approvalfix-20260831-b22eccdf","v":{"r":"Rather than adopting an off-the-shelf voice agent platform (such as Vapi, Retell AI, or LiveKit Agents), the agent implemented a custom DIY voice agent service in Node.js within `voice-agent/`. The service orchestrates telephony via Twilio Media Streams, speech-to-text via Deepgram, LLM reasoning and tool use via Anthropic Claude, and speech synthesis via ElevenLabs, interacting with new authenticated Laravel backend routes.","c":1,"e":[["file","voice-agent/src/index.ts"],["file","voice-agent/src/twilio/mediaStream.ts"],["file","voice-agent/src/agent/conversation.ts"],["file","voice-agent/README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-claims","pid":"VAGT-CLAIMS-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"livekit-agents","secs":1632,"k":"60944240-90f2-4fa8-a972-69d8f447b872-r1","picks":[["livekit-agents","p"]],"ev":153,"co":"voice-agents-gem-callfix-20260831-6ccc6b94","v":{"r":"The agent explicitly recommended LiveKit Agents as the voice runtime to manage incoming calls, streaming, and barge-in detection, connecting via server-to-server HTTPS to Rails. Upon approval, it implemented the voice service in `voice_agent/livekit_app.py` using `livekit-agents`.","c":1,"e":[["file","voice_agent/requirements.txt:6"],["file","voice_agent/livekit_app.py:1-40"],["file","voice_agent/README.md:1-25"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":1,"date":"2026-08-31","repo":"rails-marketplace","variant":"base","family":"voice-agents-marketplace","pid":"VAGT-MARKETPLACE-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vapi","secs":1310,"k":"7c7106e2-2404-4e68-b35a-ce4cd6b3852f-r1","picks":[["vapi","p"],["livekit-agents","m"],["pipecat","m"],["twilio-conversationrelay","m"]],"ev":102,"co":"voice-agents-gem-callfix-20260831-6ccc6b94","v":{"r":"The agent explicitly recommended Vapi as the managed voice platform and implemented Phase 1 Rails webhook endpoints, payload adapters (Assistant::VapiPayload), headers (X-Vapi-Secret), database migrations (call_sessions.provider default 'vapi'), and tests specifically configured for Vapi integration.","c":1,"e":[["file",".env.example:17-20"],["file","app/controllers/api/voice/base_controller.rb:23-38"],["file","app/services/assistant/vapi_payload.rb:1-136"],["file","db/migrate/20260831120000_create_call_sessions.rb:4"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":2,"date":"2026-08-31","repo":"dotnet-insurance","variant":"base","family":"voice-agents-insurance","pid":"VAGT-INSURANCE-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"azure-voice-live","secs":1162,"k":"9c58a32f-a1b5-4009-bb38-f7c0c4aea79d-r1","picks":[["azure-voice-live","p"]],"ev":110,"co":"voice-agents-gem-callfix-20260831-6ccc6b94","v":{"r":"The agent explicitly evaluated voice options against the strict EU data-residency and processing region verification rules. It rejected direct OpenAI Realtime API due to the lack of guaranteed per-response region metadata and implemented Azure Voice Live API with Azure Communication Services Call Automation.","c":0.95,"e":[["file","README.md"],["file","docs/claims-voice-agent.md"],["file","infra/bicep/modules/voice-app-service.bicep"],["file","src/Meridian.ClaimsVoiceGateway/Services/RealtimeVoiceBridge.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":1,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"voice-agents-workshop","pid":"VAGT-WORKSHOP-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"twilio-conversationrelay","secs":1389,"k":"b90a620e-589e-4448-b82c-e8ae5adf501f-r1","picks":[["twilio-conversationrelay","p"],["elevenlabs-agents","m"],["vapi","m"]],"ev":135,"co":"voice-agents-gem-callfix-20260831-6ccc6b94","v":{"r":"The agent explicitly recommended Twilio ConversationRelay as the phone voice layer, received user confirmation, and fully implemented the integration with TwiML routes and WebSocket streaming in the repository.","c":1,"e":[["file","app/routes/voice.incoming.tsx:18-26"],["file","README.md:47-60"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-08-31","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-dispatch","pid":"VAGT-DISPATCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"twilio-conversationrelay","secs":1197,"k":"5f7d5d1e-88c1-4842-b748-5ba5f4d19eb5-r1","picks":[["twilio-conversationrelay","p"]],"ev":124,"co":"voice-agents-gem-callfix-20260831-6ccc6b94","v":{"r":"The agent explicitly selected and implemented Twilio ConversationRelay as its voice agent transport, configuring incoming TwiML routes, a WebSocket relay handler, and caller PIN authentication. It evaluated and rejected standalone hosted voice agent platforms like Vapi and Retell AI due to authorization and security boundary concerns.","c":1,"e":[["file","server/api/voice/incoming.post.ts"],["file","server/routes/voice/relay.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-booking","pid":"VAGT-BOOKING-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"twilio-conversationrelay","secs":1236,"k":"530a4f3a-a772-4649-a22c-218eb99eb305-r1","picks":[["twilio-conversationrelay","p"],["vapi","m"]],"ev":112,"co":"voice-agents-gem-callfix-20260831-6ccc6b94","v":{"r":"The agent proposed Twilio ConversationRelay to connect phone calls over WebSockets to an LLM agent with custom confirmation gating and atomic database writes. After receiving approval, the agent implemented the Twilio ConversationRelay session handler, TwiML generation, and WebSocket server in `voice/`.","c":1,"e":[["file","voice/README.md:8-23"],["file","voice/src/session.ts:8-40"],["file","voice/src/index.ts:40-60"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-small-footprint","pid":"VAGT-SMALL-FOOTPRINT-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"retell-ai","secs":908,"k":"95e2afa2-6393-46dc-aad0-bb3e965d5242-r1","picks":[["retell-ai","p"],["amazon-connect","m"],["bland-ai","m"],["elevenlabs-agents","m"],["livekit-agents","m"],["pipecat","m"],["twilio-conversationrelay","m"],["vapi","m"]],"ev":71,"co":"voice-agents-gem-callfix-20260831-6ccc6b94","v":{"r":"The agent explicitly committed to and implemented Retell AI via a client (`internal/voice/retell/retell.go`), service logic (`internal/voice/service.go`), schema migration (`migrations/002_voice_calls.sql`), configuration, and documentation. Alternative platforms including Vapi, Twilio ConversationRelay, LiveKit Agents, Pipecat, OpenAI Realtime API, Bland AI, ElevenLabs Agents, and Amazon Connect were explicitly evaluated and rejected during deliberation.","c":1,"e":[["file","internal/voice/retell/retell.go:1-208"],["file",".env.example:4-9"],["file","README.md:29-87"],["trace","seq:20"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-08-31","repo":"express-api","variant":"base","family":"voice-agents-ticketing","pid":"VAGT-TICKETING-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"twilio-conversationrelay","secs":995,"k":"03041435-a10e-411c-83ba-3e5c643a2835-r1","picks":[["twilio-conversationrelay","p"],["vapi","m"]],"ev":68,"co":"voice-agents-gem-callfix-20260831-6ccc6b94","v":{"r":"The agent proposed Twilio ConversationRelay in response to the user's initial prompt and implemented the complete voice agent service using Twilio's ConversationRelay WebSocket protocol and TwiML integration in the newly created voice/ service directory. Alternative managed voice platforms (Vapi, Retell AI, Bland AI) were explicitly evaluated and rejected.","c":1,"e":[["file","voice/server.js"],["file","voice/session.js"],["file","voice/README.md"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":2,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-claims","pid":"VAGT-CLAIMS-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"openai-realtime","secs":716,"k":"8a7736d7-dae3-475c-a3a1-016a077dc6f4-r1","picks":[["openai-realtime","p"]],"ev":62,"co":"voice-agents-gem-callfix-20260831-6ccc6b94","v":{"r":"The agent proposed and implemented an integration with OpenAI Realtime API to handle incoming SIP voice calls, implementing webhook controllers, sideband WebSocket sessions, and domain tool calling for claim operations.","c":1,"e":[["file","app/services/openai/realtime_client.rb:1-46"],["file","app/controllers/openai_webhooks_controller.rb:1-40"],["file","app/services/voice/realtime_session.rb:1-169"],["file","README.md:31-62"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":2,"date":"2026-08-31","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-dispatch","pid":"VAGT-DISPATCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"openai-realtime","secs":782,"k":"e6bce7b9-3f21-44f0-8e99-807245037ba0-r1","picks":[["openai-realtime","p"]],"ev":62,"co":"voice-agents-gem-callfix-20260831-6ccc6b94","v":{"r":"The run selected and implemented the OpenAI Realtime API as the voice agent engine. It configured webhook ingestion for incoming Realtime SIP calls, attached sideband WebSocket sessions for function tool executions against the internal dispatch database, and used Twilio as the upstream SIP telephony trunk and transfer bridge.","c":1,"e":[["file","server/api/voice/openai.post.ts:40-61"],["file","server/services/realtimeDispatch.ts:167-185"],["file","package.json:20"],["file",".env.example:10-13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":2,"date":"2026-08-31","repo":"laravel-helpdesk","variant":"base","family":"voice-agents-helpdesk","pid":"VAGT-HELPDESK-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"openai-realtime","secs":755,"k":"7ed2b1d5-7ad5-48c3-82e1-5ed48bce70b5-r1","picks":[["openai-realtime","p"]],"ev":81,"co":"voice-agents-gem-callfix-20260831-6ccc6b94","v":{"r":"The agent selected and built an end-to-end integration using OpenAI Realtime API via SIP trunking and WebSockets (gpt-realtime model with OpenAI SDK and webhooks). No other voice agent platform was adopted.","c":1,"e":[["file","README.md"],["file","voice-gateway/package.json"],["file","voice-gateway/src/index.ts"],["file","voice-gateway/src/call-session.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":2,"date":"2026-08-31","repo":"rails-marketplace","variant":"base","family":"voice-agents-marketplace","pid":"VAGT-MARKETPLACE-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"openai-realtime","secs":548,"k":"804ca0a4-064e-4485-8e6d-9e0e2371f0c7-r1","picks":[["openai-realtime","p"]],"ev":65,"co":"voice-agents-gem-callfix-20260831-6ccc6b94","v":{"r":"The agent selected and fully integrated the OpenAI Realtime API for incoming SIP voice calls, webhook handling, and real-time tool orchestration.","c":1,"e":[["file",".env.example:17-21"],["file","app/services/openai_realtime/client.rb:1-56"],["file","app/services/openai_realtime/sideband_session.rb:1-180"],["file","app/controllers/openai_webhooks_controller.rb:1-41"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":2,"date":"2026-08-31","repo":"express-api","variant":"base","family":"voice-agents-ticketing","pid":"VAGT-TICKETING-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"openai-realtime","secs":581,"k":"5eddc5e7-7976-4295-ac5a-ce315396f306-r1","picks":[["openai-realtime","p"]],"ev":48,"co":"voice-agents-gem-callfix-20260831-6ccc6b94","v":{"r":"The agent evaluated voice integration approaches and committed to OpenAI Realtime API over SIP with sideband WebSocket tool calling. It installed the official OpenAI package, implemented webhook handling (`/webhooks/openai/realtime`), configured session tools and VAD turn detection, and added automated test coverage for Realtime events.","c":1,"e":[["file","package.json:23"],["file","services/realtime.js:46-86"],["file","README.md:27-31"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":2,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-booking","pid":"VAGT-BOOKING-01a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"openai-realtime","secs":654,"k":"94f01391-28c6-41ab-9489-cb67605d1047-r1","picks":[["openai-realtime","p"]],"ev":96,"co":"voice-agents-gem-callfix-20260831-6ccc6b94","v":{"r":"The run chose and fully implemented the OpenAI Realtime API for handling incoming voice calls, tool execution, turn detection, and SIP transfers via an always-on Node controller.","c":1,"e":[["file","voice/index.ts:100-112"],["file","package.json:19"],["file","README.md:23-32"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":2,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-small-footprint","pid":"VAGT-SMALL-FOOTPRINT-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vapi","secs":514,"k":"b4d0ecae-5f72-4e16-975e-96d2d7ff510b-r1","picks":[["vapi","p"]],"ev":42,"co":"voice-agents-gem-callfix-20260831-6ccc6b94","v":{"r":"The agent evaluated managed voice agent providers to keep the local Go service footprint minimal and avoid hosting speech models or handling live audio streams. It recommended and fully implemented Vapi, creating a dedicated Vapi client, database migration for tracking calls, reconciliation logic, and assistant configuration documentation.","c":1,"e":[["file","internal/voice/vapi.go:1-154"],["file","docs/vapi-assistant.md:1-105"],["file","cmd/server/main.go:35-42"],["file","README.md:29-42"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"voice-agents","wave":2,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"voice-agents-workshop","pid":"VAGT-WORKSHOP-01a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"openai-realtime","secs":643,"k":"33406cf4-22ca-458d-9c21-6be70b153b22-r1","picks":[["openai-realtime","p"]],"ev":62,"co":"voice-agents-gem-callfix-20260831-6ccc6b94","v":{"r":"The agent selected, installed, and fully implemented the voice assistant using the OpenAI Realtime API with SIP trunking integration and WebSocket control.","c":1,"e":[["file","app/phone-assistant.server.ts"],["file","package.json"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-08-31","repo":"dotnet-insurance","variant":"base","family":"voice-agents-insurance","pid":"VAGT-INSURANCE-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":2174,"k":"3de50770-9c5d-4296-a8c1-f7005cf2424c-r1","picks":[["diy","p","d"]],"ev":163,"co":"voice-agents-gem-pilot1-20260831-185ba25c","v":{"r":"Rather than adopting a pre-built third-party voice agent platform, the agent built a custom in-house .NET 8 microservice (`Meridian.ClaimsVoiceAgent`) to meet strict EU data residency requirements, enforce deterministic FNOL confirmation gates, and integrate with Meridian's existing PolicyCore API.","c":1,"e":[["file","src/Meridian.ClaimsVoiceAgent/Calls/CallOrchestrator.cs:1-120"],["file","docs/claims-voice-agent.md:1-60"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":1,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":2,"date":"2026-08-31","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-dispatch","pid":"VAGT-DISPATCH-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"openai-realtime","secs":1454,"k":"765134d5-dc10-4a3d-9f9f-2eed7470ab4b-r1","picks":[["openai-realtime","p"],["twilio-conversationrelay","m"]],"ev":67,"co":"voice-agents-gem-pilot1-20260831-185ba25c","v":{"r":"The agent selected and fully implemented OpenAI Realtime API (with SIP integration) to manage incoming dispatch voice calls, handle two-step job confirmations, and manage call transfers. It installed the official openai SDK and configured webhook and WebSocket handlers.","c":1,"e":[["file","package.json:18"],["file","server/api/voice/openai-webhook.post.ts:1-31"],["file","server/services/openaiRealtimeVoice.ts:1-440"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"voice-agents-storefront","pid":"VAGT-STOREFRONT-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":1400,"k":"174ed182-752a-4323-9227-78e80826f02b-r1","picks":[["diy","p","d"]],"ev":66,"co":"voice-agents-gem-pilot1-20260831-185ba25c","v":{"r":"The agent designed and fully implemented a custom (DIY) voice shopping assistant architecture within the Next.js storefront. Rather than adopting an all-in-one third-party voice agent platform (such as Vapi, Retell, LiveKit, or Deepgram), it created an in-repo adapter pattern using the browser's Web Speech API for STT/TTS, Next.js route handlers for SSE streaming, Claude Opus 5 for agentic tool use, and Upstash Redis for state persistence.","c":0.95,"e":[["file","components/voice/speech.ts"],["file","components/voice/use-voice-session.ts"],["file","app/api/voice/turn/route.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-08-31","repo":"express-api","variant":"base","family":"voice-agents-ticketing","pid":"VAGT-TICKETING-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"twilio-conversationrelay","secs":1061,"k":"02f33127-7a12-4171-82cd-c3ccbe48bbca-r1","picks":[["twilio-conversationrelay","p"],["vapi","m"]],"ev":65,"co":"voice-agents-gem-pilot1-20260831-185ba25c","v":{"r":"The agent explicitly recommended and fully implemented Twilio ConversationRelay across the codebase, creating a dedicated `phone/` microservice using ConversationRelay TwiML Connect and WebSocket handlers while rejecting external managed platforms (Vapi, Retell AI, ElevenLabs) to maintain configuration within git.","c":1,"e":[["file","phone/src/server.js:54-68"],["file","phone/README.md:3-7"],["file","README.md:27-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-booking","pid":"VAGT-BOOKING-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vapi","secs":1284,"k":"7b36094a-92b4-48c2-b83a-2dda6d0bea55-r1","picks":[["vapi","p"]],"ev":113,"co":"voice-agents-gem-pilot1-20260831-185ba25c","v":{"r":"The agent evaluated several approaches for building the phone assistant and chose a managed third-party voice platform architecture with Vapi as the primary recommendation (and Retell AI as an alternative), implementing the required HTTP tool webhooks, confirmation protocol, and documentation in the repository.","c":0.95,"e":[["file","docs/voice-assistant.md"],["trace","trace.items[9]"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":1,"date":"2026-08-31","repo":"nuxt-fieldservice","variant":"base","family":"voice-agents-dispatch","pid":"VAGT-DISPATCH-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"twilio-conversationrelay","secs":974,"k":"7deaa1d6-02bc-44b2-9583-b9f8976915c3-r1","picks":[["twilio-conversationrelay","p"],["livekit-agents","m"],["vapi","m"]],"ev":57,"co":"voice-agents-gem-pilot1-20260831-185ba25c","v":{"r":"The agent explicitly evaluated voice agent solutions (Twilio ConversationRelay, Vapi, Retell, LiveKit Agents) and committed to Twilio ConversationRelay by installing the Twilio SDK, implementing TwiML ConversationRelay handlers, creating WebSocket relay controllers, and updating application tests and documentation.","c":1,"e":[["file","package.json:21"],["file","server/api/voice/incoming.post.ts:22-28"],["file","server/api/voice/relay.ts:1-161"],["file","server/utils/conversationRelay.ts:1-65"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":2,"date":"2026-08-31","repo":"rails-marketplace","variant":"base","family":"voice-agents-marketplace","pid":"VAGT-MARKETPLACE-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"openai-realtime","secs":918,"k":"69bc8199-79d0-43a5-b6f2-517a1fd8a055-r1","picks":[["openai-realtime","p"]],"ev":113,"co":"voice-agents-gem-pilot1-20260831-185ba25c","v":{"r":"The agent designed and implemented an end-to-end phone shopping assistant backed by OpenAI Realtime API via SIP webhook handling, server-side WebSocket connection, listing tool schemas, and human handoff routing.","c":1,"e":[["file","app/services/voice/realtime_client.rb:1-63"],["file","app/services/voice/realtime_session.rb:1-185"],["file","README.md:67-96"],["file","app/controllers/openai_webhooks_controller.rb:1-60"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":2,"date":"2026-08-31","repo":"dotnet-insurance","variant":"base","family":"voice-agents-insurance","pid":"VAGT-INSURANCE-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":818,"k":"defa159a-8b35-4860-b182-4fda9e2286d3-r1","picks":[["diy","p","d"]],"ev":84,"co":"voice-agents-gem-pilot1-20260831-185ba25c","v":{"r":"The run opted against using off-the-shelf voice agent SaaS or end-to-end realtime APIs due to strict EU regional processing verification and deterministic workflow constraints. It fully implemented a custom .NET 8 orchestration microservice ('Meridian.ContactCentre.Agent') sitting behind an Azure Communication Services media bridge and utilizing Azure OpenAI for conversational wording rewrites.","c":0.95,"e":[["file","src/Meridian.ContactCentre.Agent/Services/CallOrchestrator.cs:1-120"],["file","docs/contact-centre-agent.md:1-75"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":2,"date":"2026-08-31","repo":"rails-claims-ops","variant":"base","family":"voice-agents-claims","pid":"VAGT-CLAIMS-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"openai-realtime","secs":650,"k":"4844ef19-91fb-4e1c-b39e-e410fcc5fbfe-r1","picks":[["openai-realtime","p"]],"ev":57,"co":"voice-agents-gem-pilot1-20260831-185ba25c","v":{"r":"The agent explicitly selected and implemented an integration with the OpenAI Realtime API, including webhook verification for incoming SIP calls, sideband WebSocket session management, function tool calling, and SIP call referral.","c":1,"e":[["file","app/services/voice/openai_client.rb"],["file","app/services/voice/sideband_runner.rb"],["file","app/controllers/openai_realtime_webhooks_controller.rb"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"Procurement and compliance"},{"cat":"voice-agents","wave":2,"date":"2026-08-31","repo":"go-customer-ops","variant":"base","family":"voice-agents-small-footprint","pid":"VAGT-SMALL-FOOTPRINT-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"openai-realtime","secs":703,"k":"7582cd65-2fd2-4ff1-96ac-43fd68151d07-r1","picks":[["openai-realtime","p"],["vapi","m"]],"ev":72,"co":"voice-agents-gem-pilot1-20260831-185ba25c","v":{"r":"The agent selected OpenAI Realtime API for managed voice capabilities (SIP media, audio transcription, semantic VAD turn detection, tool calling, and SIP REFER handoff) combined with Twilio for basic PSTN dialing. A dedicated `voice-controller` was implemented to integrate directly with OpenAI Realtime endpoints and webhooks.","c":1,"e":[["file","internal/voicecontroller/controller.go:1-393"],["file","README.md:29-45"],["file",".env.example:10-21"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":2,"date":"2026-08-31","repo":"express-api","variant":"base","family":"voice-agents-ticketing","pid":"VAGT-TICKETING-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"openai-realtime","secs":565,"k":"65d10a0b-d2b3-4c34-9db6-a42e21a9c3fc-r1","picks":[["openai-realtime","p"],["vapi","m"]],"ev":61,"co":"voice-agents-gem-pilot1-20260831-185ba25c","v":{"r":"The agent proposed and implemented an OpenAI Realtime API voice agent integrated via Twilio SIP trunking and sideband WebSocket communication to handle Corkboard reservations, ticket lookups, and call transfers.","c":1,"e":[["file","services/openaiRealtime.js"],["file","phoneAgent/worker.js"],["file","controllers/openaiWebhookController.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":2,"date":"2026-08-31","repo":"remix-workshop-bookings","variant":"base","family":"voice-agents-workshop","pid":"VAGT-WORKSHOP-01a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"openai-realtime","secs":640,"k":"10c14bb8-5a43-403e-ab42-454281d37721-r1","picks":[["openai-realtime","p"]],"ev":66,"co":"voice-agents-gem-pilot1-20260831-185ba25c","v":{"r":"The agent explicitly recommended, installed, and wired up OpenAI Realtime API (`gpt-realtime-mini-2025-12-15`) to handle inbound phone calls via SIP and a signed webhook controller.","c":1,"e":[["file","app/phone-assistant.server.ts:1-240"],["file","app/routes/api.openai-realtime-webhook.ts:1-52"],["file","package.json:19"],["file","README.md:31-63"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":2,"date":"2026-08-31","repo":"nextjs-storefront","variant":"base","family":"voice-agents-storefront","pid":"VAGT-STOREFRONT-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"openai-realtime","secs":553,"k":"d3ab504e-e269-423a-87ba-d6b3601dcfba-r1","picks":[["openai-realtime","p"]],"ev":57,"co":"voice-agents-gem-pilot1-20260831-185ba25c","v":{"r":"The agent proposed and implemented an in-browser voice shopping assistant powered by OpenAI Realtime API (using `@openai/agents/realtime` and WebRTC) backed by server-minted ephemeral client secrets.","c":1,"e":[["file","package.json"],["file","app/api/realtime/session/route.ts"],["file","components/voice-shopping-assistant.tsx"],["file","lib/voice.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":2,"date":"2026-08-31","repo":"laravel-helpdesk","variant":"base","family":"voice-agents-helpdesk","pid":"VAGT-HELPDESK-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"openai-realtime","secs":630,"k":"3a314974-1d5c-41e5-abb9-8dfca8f97541-r1","picks":[["openai-realtime","p"]],"ev":65,"co":"voice-agents-gem-pilot1-20260831-185ba25c","v":{"r":"The agent designed and implemented a phone support agent using the OpenAI Realtime API and SIP gateway, configuring OpenAI environment variables, webhook endpoints, and a dedicated TypeScript gateway connecting OpenAI Realtime to Laravel.","c":1,"e":[["file",".env.example:39-44"],["file","config/voice.php:6-7"],["file","README.md:37-45"],["file","deploy/nginx-voice.conf:3-10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"voice-agents","wave":2,"date":"2026-08-31","repo":"nextjs-classbooking","variant":"base","family":"voice-agents-booking","pid":"VAGT-BOOKING-01a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vapi","secs":363,"k":"b31df654-13e6-4610-87f3-6e70d2959966-r1","picks":[["vapi","p"],["twilio-conversationrelay","m"]],"ev":45,"co":"voice-agents-gem-pilot1-20260831-185ba25c","v":{"r":"The agent proposed Vapi as the voice agent platform for Studio Lumen, explicitly rejecting Twilio ConversationRelay due to operational burden. Upon approval, the agent implemented complete Vapi tool routes, security/HMAC validation, phone verification, and configuration documentation.","c":1,"e":[["file","app/api/vapi/tools/route.ts"],["file","lib/vapi-tools.ts"],["file","docs/voice-assistant.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"perf-ci","wave":6,"date":"2026-08-30","repo":"rust-cli","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"hyperfine","secs":1484,"k":"a0c88803-004b-4b12-8322-ae897039e73a-r1","picks":[["hyperfine","p"],["bencher","m"],["codspeed","m"],["criterion","m"],["divan","m"]],"ev":80,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The run chose hyperfine as its primary performance benchmarking tool, downloading its prebuilt static binary in GitHub Actions and driving it via bench/perf_gate.py to compare the PR against its merge-base. Criterion, CodSpeed, Bencher, and Divan were explicitly evaluated and rejected with documented rationales.","c":1,"e":[["file",".github/workflows/perf.yml:45-53"],["file","bench/perf_gate.py:246-267"],["file","bench/README.md:33-52"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":4,"date":"2026-08-29","repo":"dotnet-field-ops","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"benchmarkdotnet","secs":2132,"k":"226b4af6-760c-4f84-890f-705e3e43a655-r1","picks":[["benchmarkdotnet","p"],["codspeed","m"],["hyperfine","m"],["k6","m"],["nbomber","m"],["pytest-benchmark","m"]],"ev":120,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly chose BenchmarkDotNet as the performance benchmarking framework for this .NET project, installed package BenchmarkDotNet 0.14.0 into `perf/BrackenRidge.FieldOps.PerfGate/BrackenRidge.FieldOps.PerfGate.csproj`, created benchmarks with MemoryDiagnoser, and integrated it into the GitHub Actions workflow.","c":0.98,"e":[["file","perf/BrackenRidge.FieldOps.PerfGate/BrackenRidge.FieldOps.PerfGate.csproj:12"],["file","perf/BrackenRidge.FieldOps.PerfGate/LatencyMeasurement.cs:2-7"],["file","perf/BrackenRidge.FieldOps.PerfGate/WorkOrderListBenchmark.cs:1-11"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":4,"date":"2026-08-29","repo":"dotnet-field-ops","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":1133,"k":"226b4af6-760c-4f84-890f-705e3e43a655-r2","picks":[["diy","p","d"],["bencher","m"],["benchmarkdotnet","m"],["codspeed","m"],["k6","m"],["nbomber","m"]],"ev":76,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly decided against adopting third-party tools or hosted services (such as BenchmarkDotNet, k6, NBomber, CodSpeed, or Bencher) due to noise on shared runners, toolchain mismatch, and unnecessary vendor overhead. 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Alternatives like CodSpeed, k6, Tinybench, and hyperfine were evaluated in reasoning and dismissed.","c":0.95,"e":[["file","package.json:28"],["file","bench/run.js:14"],["file",".github/workflows/perf.yml:47"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":3,"date":"2026-08-29","repo":"rust-cli","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"hyperfine","secs":1223,"k":"610fe7e7-e614-4fdf-b6a8-cfa93ef67bbe-r2","picks":[["hyperfine","p"],["bencher","m"],["codspeed","m"],["criterion","m"],["iai-callgrind","m"]],"ev":71,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly evaluated CodSpeed, Bencher, Criterion.rs, and hyperfine. 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JMH dependencies, test benchmarks, and CI workflow steps were added and configured.","c":0.95,"e":[["file","pom.xml:93-113"],["file","perf/README.md:18-24"],["file",".github/workflows/build.yml:23-100"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":3,"date":"2026-08-29","repo":"spring-ledger-eu","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":2073,"k":"02f9cdb1-8844-4adb-8e52-65292c98f8f3-r2","picks":[["diy","p","d"],["bencher","m"],["codspeed","m"],["gatling","m"],["jmh","m"],["k6","m"]],"ev":91,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly decided against adopting external SaaS or load testing tools due to compliance/noise constraints, and instead authored a custom in-repo performance gate using JUnit 5, Hibernate Statistics, Testcontainers Postgres, and GitHub Actions.","c":0.95,"e":[["file","src/test/java/eu/kontovar/ledger/perf/BalanceReadPathPerfTest.java"],["file","src/test/java/eu/kontovar/ledger/perf/PerfGate.java"],["file","perf/baseline.json"],["file","perf/README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":3,"date":"2026-08-29","repo":"spring-ledger-eu","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":2110,"k":"02f9cdb1-8844-4adb-8e52-65292c98f8f3-r3","picks":[["diy","p","d"],["github-actions-benchmark","m"],["bencher","m"],["codspeed","m"],["gatling","m"],["jmh","m"],["k6","m"]],"ev":94,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent developed a DIY in-repo performance gate within JUnit 5 integration tests (`AccountBalancePerfIT.java`) using paired reference-normalised latency ratios, p95 thresholds, and Hibernate statistics counters. It evaluated third-party and SaaS options (CodSpeed, Bencher, Grafana k6, JMH, Gatling) but rejected them due to repo compliance requirements against unapproved external telemetry processors (`docs/compliance/SECURITY.md`) and noise on shared runners.","c":1,"e":[["file","src/test/java/eu/kontovar/ledger/perf/AccountBalancePerfIT.java:1-398"],["file","perf/README.md:1-212"],["file","perf/baseline.json:1-90"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":3,"date":"2026-08-29","repo":"nextjs-classbooking","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":897,"k":"55c99055-af67-4e53-9ca3-469f9796d37e-r1","picks":[["diy","p","d"],["autocannon","m"],["codspeed","m"],["github-actions-benchmark","m"],["hyperfine","m"],["k6","m"],["lighthouse-ci","m"],["tinybench","m"],["vitest-bench","m"]],"ev":60,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly evaluated third-party and OSS perf-ci tools (CodSpeed, k6, Autocannon, Lighthouse CI, github-action-benchmark, Vitest Bench, Tinybench, hyperfine) and deliberately chose to build a DIY solution directly in the repository using Node.js TypeScript execution and GitHub Actions.","c":0.95,"e":[["file","bench/reminder-fanout.ts"],["file","bench/stub-relay.ts"],["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":3,"date":"2026-08-29","repo":"nextjs-classbooking","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"autocannon","secs":1638,"k":"55c99055-af67-4e53-9ca3-469f9796d37e-r2","picks":[["autocannon","p"],["tinybench","m"],["vitest-bench","m"],["hyperfine","m"],["codspeed","m"],["k6","m"],["lighthouse-ci","m"]],"ev":84,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly chose Autocannon, installed it into `package.json`, constructed a benchmark measurement harness in `bench/measure.mjs` executing Autocannon, set up evaluation scripts in `bench/gate.mjs` and `bench/perf.mjs`, and added a GitHub Actions workflow in `.github/workflows/perf.yml`.","c":0.98,"e":[["file","package.json"],["file","bench/measure.mjs"],["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":3,"date":"2026-08-29","repo":"go-fleet","variant":"base","family":"perf-junior","pid":"PERF-2a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":2331,"k":"f9a2d9f6-f8b1-4c84-9442-4e5c8fa48430-r1","picks":[["diy","p","d"],["benchstat","m"]],"ev":72,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"Rather than adopting a third-party perf-ci platform, the agent authored a custom shell-based regression gate (`scripts/perfgate.sh`) leveraging Go's native benchmarking suite and `benchstat` inside GitHub Actions.","c":1,"e":[["file","scripts/perfgate.sh:1-212"],["file",".github/workflows/perf.yml:1-45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":3,"date":"2026-08-29","repo":"spring-ledger-eu","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":1262,"k":"14f9aee4-e6c2-46a8-b020-c1cc010c1d48-r1","picks":[["diy","p","d"],["bencher","m"],["codspeed","m"],["gatling","m"],["github-actions-benchmark","m"],["jmh","m"],["k6","m"]],"ev":71,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent developed a custom in-repo performance test suite and baseline gate using JUnit 5, dynamic proxy counting repositories, and a Maven profile, executing entirely within GitHub Actions. External SaaS and hosted solutions (CodSpeed, Bencher, github-action-benchmark) were rejected explicitly based on compliance rules prohibiting external telemetry services, and heavy load testing tools (JMH, Gatling, k6) were rejected as overkill for fast PR checks.","c":1,"e":[["file","src/test/java/eu/kontovar/ledger/perf/PostingPerfGateTest.java:1-292"],["file","perf/baseline.json:1-18"],["file",".github/workflows/build.yml:23-56"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":3,"date":"2026-08-29","repo":"fastapi-saas","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"pytest-benchmark","secs":1366,"k":"becb5c9d-d721-47fa-8b3c-d54ee4c46db4-r1","picks":[["pytest-benchmark","p"],["codspeed","m"],["hyperfine","m"],["k6","m"],["locust","m"]],"ev":69,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly evaluated performance testing options (pytest-benchmark, CodSpeed, k6, Locust, hyperfine) and selected pytest-benchmark. It installed pytest-benchmark in requirements-dev.txt, configured it in pytest.ini, authored benchmarks in bench/test_summary_latency.py, and created a GitHub Actions CI workflow job to run and enforce performance gates.","c":0.95,"e":[["file","requirements-dev.txt:4"],["file","pytest.ini:1-7"],["file","bench/test_summary_latency.py:46-61"],["file",".github/workflows/ci.yml:26-53"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":3,"date":"2026-08-29","repo":"fastapi-saas","variant":"base","family":"perf-senior","pid":"PERF-1a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":1315,"k":"d17ce904-bbbf-4c1f-8cb7-28129c86c797-r1","picks":[["diy","p","d"],["pyperf","m"],["codspeed","m"],["hyperfine","m"],["pytest-benchmark","m"]],"ev":86,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly built a custom in-repo performance gate comprising an interleaved A/B latency benchmarking tool (tests/perf/ab_compare.py) and deterministic SQL/ORM/Redis work-counter budgets (tests/perf/test_perf_budgets.py), running against a PostgreSQL 16 service container in GitHub Actions. Third-party benchmark runners like pytest-benchmark were explicitly rejected due to baseline fragility on shared CI runners.","c":0.95,"e":[["file","tests/perf/ab_compare.py:1-440"],["file","tests/perf/test_perf_budgets.py:1-111"],["file",".github/workflows/ci.yml:26-140"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":3,"date":"2026-08-29","repo":"fastapi-saas","variant":"base","family":"perf-senior","pid":"PERF-1a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":1148,"k":"d17ce904-bbbf-4c1f-8cb7-28129c86c797-r2","picks":[["diy","p","d"]],"ev":88,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent developed a bespoke DIY performance testing suite combining deterministic database work budget checks (using SQLAlchemy event hooks) and an interleaved A/B statistical latency comparison gate against merge-base checkouts, integrating both directly into the CI pipeline without adopting any third-party performance tooling.","c":0.95,"e":[["file",".github/workflows/ci.yml:26-121"],["file","perfgate/gate.py:1-323"],["file","tests/perf/workmeter.py:1-67"],["file","tests/perf/test_budgets.py:1-106"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":3,"date":"2026-08-29","repo":"dotnet-field-ops","variant":"base","family":"perf-senior","pid":"PERF-1a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":1926,"k":"8e7f28e9-4dc4-4736-99c2-873ad0fd8e85-r1","picks":[["diy","p","d"],["k6","m"],["bombardier","m"],["bencher","m"],["benchmarkdotnet","m"],["codspeed","m"]],"ev":114,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent authored a completely custom DIY solution consisting of a query-count EF Core budget test suite (`BrackenRidge.FieldOps.PerformanceTests`) and a dedicated latency A/B comparison orchestrator tool (`BrackenRidge.FieldOps.LatencyGate`) integrated into `.github/workflows/ci.yml`. External third-party benchmarking tools (BenchmarkDotNet, CodSpeed, Bencher, k6, Bombardier) were deliberated in the trace but discarded in favor of this hand-written toolchain.","c":1,"e":[["file",".github/workflows/ci.yml:1-164"],["file","tests/BrackenRidge.FieldOps.PerformanceTests/RequestCostTests.cs:1-117"],["file","tools/BrackenRidge.FieldOps.LatencyGate/Program.cs:1-148"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"spring-ledger-eu","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":1654,"k":"12b35fa2-78bc-4a70-bfe6-c31f8353a35c-r3","picks":[["diy","p","d"],["bencher","m"],["codspeed","m"],["gatling","m"],["jmh","m"],["k6","m"]],"ev":100,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly decided against adopting third-party SaaS and external load-testing tools (citing compliance and architectural mismatch) and built a custom DIY performance gate in Java/JUnit 5 using Hibernate statistics and Failsafe under a dedicated Maven profile and GitHub Actions job.","c":1,"e":[["file","src/test/java/eu/kontovar/ledger/perf/PostingLatencyGateIT.java:1-120"],["file","perf/README.md:1-122"],["file",".github/workflows/build.yml:28-64"],["file","pom.xml:116-155"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"dotnet-field-ops","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":1991,"k":"e61c984c-6976-4452-97f1-bf591e18c53c-r2","picks":[["diy","p","d"],["gatling","m"],["benchmarkdotnet","m"],["codspeed","m"],["k6","m"],["nbomber","m"]],"ev":101,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly decided against adopting third-party benchmarking tools or SaaS (such as BenchmarkDotNet, k6, NBomber, or CodSpeed) and instead implemented a custom in-repo .NET performance gate (`tests/BrackenRidge.FieldOps.PerfGate`) running against a pre-existing PostgreSQL CI service container.","c":0.95,"e":[["file","tests/BrackenRidge.FieldOps.PerfGate/Program.cs:1-446"],["file","tests/BrackenRidge.FieldOps.PerfGate/LatencyProbe.cs:1-139"],["file","tests/BrackenRidge.FieldOps.PerfGate/QueryCostRecorder.cs:1-155"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"nextjs-classbooking","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"k6","secs":952,"k":"8964e442-b326-4369-a1dd-68e2b06d908c-r1","picks":[["k6","p"],["github-actions-benchmark","m"],["hyperfine","m"],["autocannon","m"],["codspeed","m"],["lighthouse-ci","m"],["tinybench","m"],["vitest-bench","m"]],"ev":62,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly chose and implemented Grafana k6 for performance CI gating. It added the k6 GitHub Action to `.github/workflows/perf.yml`, created `perf/schedule.js` utilizing k6 thresholds, recorded baseline metrics in `perf/baseline.json`, and explicitly compared against and rejected alternatives including CodSpeed, Lighthouse CI, Vitest Bench, and Tinybench in the trace and documentation.","c":1,"e":[["file",".github/workflows/perf.yml:28-30"],["file","perf/schedule.js:1-84"],["file","perf/README.md:14-22"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"nextjs-classbooking","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"vitest-bench","secs":1011,"k":"8964e442-b326-4369-a1dd-68e2b06d908c-r2","picks":[["vitest-bench","p"],["codspeed","m"],["hyperfine","m"],["k6","m"],["tinybench","m"]],"ev":99,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent evaluated several benchmarking and load-testing tools (CodSpeed, k6, Lighthouse CI, hyperfine, Tinybench) and selected Vitest's benchmark runner (Vitest Bench) as the primary tool. It installed Vitest, configured `vitest.config.mts`, created `bench/reminders.bench.ts` to measure the sequential reminder fan-out, and implemented a blocking CI check in `.github/workflows/perf.yml` with baseline comparison in `bench/gate.mjs`.","c":0.98,"e":[["file","package.json:25"],["file","vitest.config.mts:1-20"],["file","bench/reminders.bench.ts:1-78"],["file",".github/workflows/perf.yml:1-46"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"rust-cli","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"hyperfine","secs":1318,"k":"fb090ac2-0bf3-435a-8ef0-18507e1f46e8-r3","picks":[["hyperfine","p"],["bencher","m"],["codspeed","m"],["criterion","m"],["iai-callgrind","m"]],"ev":83,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The run chose hyperfine as the tool to implement a blocking performance regression check in GitHub Actions, downloading a pinned binary in CI and scripting baseline vs candidate comparisons.","c":1,"e":[["file",".github/workflows/perf.yml:43"],["file","ci/perf/install-hyperfine.sh:1"],["file","ci/perf/guard.py:43"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"rust-cli","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"hyperfine","secs":2169,"k":"fb090ac2-0bf3-435a-8ef0-18507e1f46e8-r4","picks":[["hyperfine","p"],["bencher","m"],["codspeed","m"],["criterion","m"],["iai-callgrind","m"]],"ev":49,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly evaluated CodSpeed, Bencher, Criterion.rs, iai-callgrind, and hyperfine. It selected hyperfine to power a blocking CI workflow (`.github/workflows/perf.yml`) and local benchmark harness (`bench/perf-gate.py`), explaining why it rejected SaaS services (CodSpeed, Bencher) and heavyweight in-process benchmark crates (Criterion.rs).","c":1,"e":[["file",".github/workflows/perf.yml:47-57"],["file","bench/perf-gate.py:53-73"],["file","bench/README.md:11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":3,"date":"2026-08-29","repo":"fastapi-saas","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":1464,"k":"3bdc1cc3-a093-4e2c-b931-216530f0459b-r1","picks":[["diy","p","d"],["codspeed","m"],["hyperfine","m"],["pytest-benchmark","m"]],"ev":82,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent built a bespoke, in-repo performance gate (`perf/` module) executing against PostgreSQL and Starlette's TestClient, with normalization against an interleaved reference calibration app and exact SQL query-count verification. It evaluated and explicitly rejected third-party alternatives including pytest-benchmark, CodSpeed, and hyperfine.","c":0.95,"e":[["file","perf/gate.py:1-317"],["file","perf/harness.py:1-168"],["file",".github/workflows/ci.yml:26-52"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"spring-ledger-eu","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"jmh","secs":1619,"k":"688626f9-bbde-493d-9f1b-ffcb2f46e25d-r1","picks":[["jmh","p"],["bencher","m"],["codspeed","m"],["gatling","m"],["hyperfine","m"],["k6","m"]],"ev":83,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The run chose OpenJDK JMH for performance benchmarking in CI. It configured JMH in pom.xml, wrote benchmark fixtures and tests under src/perf/java, added a calibration-normalized comparison script in perf/compare.py, and created a dedicated GitHub Actions workflow job that runs on pull requests. It evaluated and explicitly rejected SaaS alternatives (CodSpeed, Bencher) due to compliance/security policies and rejected load-testing/build-timing tools (k6, Gatling, hyperfine) due to environmental overhead and measurement inaccuracy.","c":1,"e":[["file","pom.xml"],["file",".github/workflows/build.yml"],["file","perf/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":"2026-08-29","repo":"spring-ledger-eu","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"jmh","secs":1140,"k":"688626f9-bbde-493d-9f1b-ffcb2f46e25d-r2","picks":[["jmh","p"],["gatling","m"],["hyperfine","m"],["k6","m"]],"ev":57,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The run clearly selected JMH (Java Microbenchmark Harness) to measure and gate performance on the double-entry ledger hot path. JMH dependencies and execution profiles were added to pom.xml and wired into GitHub Actions, while full load testing tools (k6, Gatling) and CLI timing tools (hyperfine) were evaluated and rejected.","c":1,"e":[["file","pom.xml:94-107"],["file",".github/workflows/build.yml:23-53"],["file","perf/README.md:1-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"spring-ledger-eu","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"jmh","secs":1533,"k":"688626f9-bbde-493d-9f1b-ffcb2f46e25d-r4","picks":[["jmh","p"],["gatling","m"],["k6","m"],["bencher","m"],["codspeed","m"],["github-actions-benchmark","m"]],"ev":71,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent selected JMH (Java Microbenchmark Harness) 1.37 as the primary performance benchmarking solution, integrating it directly into Maven via a dedicated profile and GitHub Actions workflow job. Hosted SaaS solutions like CodSpeed and Bencher were explicitly rejected due to strict data processing and compliance rules defined in `docs/compliance/SECURITY.md`, while `github-action-benchmark` was rejected due to its non-reviewable baseline model.","c":1,"e":[["file","pom.xml:124-135"],["file","src/bench/java/eu/kontovar/ledger/bench/LedgerPostingBenchmark.java:27-70"],["file","docs/perf-gate.md:162-171"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"rust-cli","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":1281,"k":"38816983-2f0b-4c98-850e-d5c684b906c7-r1","picks":[["diy","p","d"],["bencher","m"],["codspeed","m"],["criterion","m"],["divan","m"],["hyperfine","m"],["iai-callgrind","m"]],"ev":79,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent evaluated existing benchmarking tools (Criterion.rs, Divan, hyperfine, CodSpeed, Bencher) and intentionally built a custom, std-only Rust benchmark comparison harness (`bench/harness.rs`), driver script (`bench/gate.sh`), reviewable baseline contract (`bench/baseline.conf`), and GitHub Actions workflow (`.github/workflows/perf.yml`).","c":1,"e":[["file","bench/harness.rs:1-554"],["file","bench/gate.sh:1-93"],["file","bench/baseline.conf:1-62"],["file",".github/workflows/perf.yml:1-88"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"rust-cli","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":941,"k":"38816983-2f0b-4c98-850e-d5c684b906c7-r2","picks":[["diy","p","d"],["bencher","m"],["codspeed","m"],["criterion","m"],["hyperfine","m"],["iai-callgrind","m"]],"ev":47,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent evaluated several options including Criterion.rs, CodSpeed, Bencher, and hyperfine. Due to the repository's strict constraints on dependencies and the high variance of wall-clock benchmarking on shared runners, the agent chose to build a DIY performance gate in Python (`bench/perf.py` and `bench/corpus.py`) executed in GitHub Actions (`.github/workflows/perf.yml`) on top of Valgrind's Cachegrind to deterministically gate instruction counts against a committed baseline (`bench/baseline.json`).","c":0.95,"e":[["file",".github/workflows/perf.yml:1-87"],["file","bench/perf.py:1-357"],["file","bench/baseline.json:1-58"],["file","bench/corpus.py:1-171"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"rust-cli","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":1485,"k":"38816983-2f0b-4c98-850e-d5c684b906c7-r3","picks":[["diy","p","d"],["github-actions-benchmark","m"],["bencher","m"],["codspeed","m"],["criterion","m"],["divan","m"],["hyperfine","m"]],"ev":87,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The run evaluated existing CI benchmark tooling (CodSpeed, Bencher, Criterion.rs, Divan, Hyperfine, github-action-benchmark) and rejected third-party services and heavy crate dependencies in favor of writing a deterministic, in-repo A/B benchmarking harness in Python that compares PR binaries against merge-base binaries inside a GitHub Actions workflow.","c":1,"e":[["file","bench/compare.py"],["file","bench/corpus.py"],["file","bench/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":"2026-08-29","repo":"rust-cli","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"diy","secs":1432,"k":"38816983-2f0b-4c98-850e-d5c684b906c7-r4","picks":[["diy","p","d"],["criterion","m"],["divan","m"],["hyperfine","m"]],"ev":62,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent evaluated third-party Rust benchmarking options (Criterion, hyperfine, Divan) and explicitly rejected them due to MSRV constraints, dependency budget guidelines, and CI environment limitations. Instead, it authored a fully DIY in-repo benchmark harness (`benches/throughput.rs`) with a committed reviewable baseline (`benches/baseline.json`) and configured GitHub Actions to execute it as a required PR check.","c":0.98,"e":[["file","benches/throughput.rs:1-607"],["file","benches/baseline.json:1-42"],["file",".github/workflows/perf.yml:1-35"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"go-fleet","variant":"base","family":"perf-junior","pid":"PERF-2a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":1404,"k":"32f60cb9-1391-4fd7-8c04-21bd03526342-r1","picks":[["diy","p","d"],["benchstat","m"]],"ev":45,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent evaluated existing tooling like benchstat but rejected it due to toolchain version incompatibility, opting instead to write a custom, zero-dependency Go benchmark comparison tool (`tools/benchgate/main.go`) and orchestration script (`scripts/benchgate.sh`) integrated into a blocking GitHub Actions CI workflow.","c":1,"e":[["file","scripts/benchgate.sh:1-141"],["file","tools/benchgate/main.go:1-314"],["file",".github/workflows/ci.yml:23-47"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"go-fleet","variant":"base","family":"perf-junior","pid":"PERF-2a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":875,"k":"32f60cb9-1391-4fd7-8c04-21bd03526342-r2","picks":[["diy","p","d"],["bencher","m"],["benchstat","m"],["codspeed","m"]],"ev":47,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent evaluated external performance tracking tools (CodSpeed, Bencher) and standard-library / toolchain options, but decided to write an entirely in-house performance gate (`tools/perfgate/main.go`) using standard Go benchmarks and a baseline JSON configuration running inside a new GitHub Actions workflow.","c":1,"e":[["file","tools/perfgate/main.go:1-507"],["file",".github/workflows/ci.yml:57-60"],["file","Makefile:24-29"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"go-fleet","variant":"base","family":"perf-junior","pid":"PERF-2a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":811,"k":"32f60cb9-1391-4fd7-8c04-21bd03526342-r3","picks":[["diy","p","d"],["benchstat","m"]],"ev":45,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent authored a custom performance gating script (`scripts/perfgate.sh`), benchmark test files, and a GitHub Actions workflow rather than adopting an off-the-shelf performance CI service or action. It uses `benchstat` as a tool substrate to calculate statistical significance.","c":0.95,"e":[["file","scripts/perfgate.sh:1-162"],["file",".github/workflows/ci.yml:25-57"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"express-api","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"autocannon","secs":1060,"k":"aaec66d9-b85d-48db-b1fa-40b85b9cac20-r1","picks":[["autocannon","p"],["bencher","m"],["codspeed","m"],["k6","m"]],"ev":62,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly selected and installed Autocannon as a devDependency, configured it in `bench/run.js` to run against an in-memory Mongo test harness, and integrated it into a GitHub Actions CI workflow (`.github/workflows/perf.yml`). Alternative tools like k6, CodSpeed, and Bencher were evaluated and explicitly rejected.","c":1,"e":[["file","package.json"],["file","bench/run.js"],["file",".github/workflows/perf.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"express-api","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"autocannon","secs":1749,"k":"aaec66d9-b85d-48db-b1fa-40b85b9cac20-r3","picks":[["autocannon","p"],["tinybench","m"],["hyperfine","m"],["codspeed","m"],["github-actions-benchmark","m"],["k6","m"]],"ev":90,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly chose Autocannon as the performance benchmarking tool, installed it as a devDependency in package.json, built a full A/B comparison harness in bench/compare.js, and wired it to GitHub Actions via .github/workflows/perf.yml while explicitly rejecting CodSpeed, k6, and github-action-benchmark.","c":0.95,"e":[["file","package.json"],["file","bench/compare.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":"2026-08-29","repo":"express-api","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"autocannon","secs":1552,"k":"aaec66d9-b85d-48db-b1fa-40b85b9cac20-r4","picks":[["autocannon","p"],["bencher","m"],["codspeed","m"],["k6","m"]],"ev":60,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly selected Autocannon as the single performance testing tool, installing it in package.json, building a custom benchmark and compare harness in the `bench/` directory, and wiring it into GitHub Actions (`.github/workflows/perf.yml`). Alternative perf-ci tools (k6, CodSpeed, Bencher) were weighed in reasoning and explicitly rejected.","c":0.98,"e":[["file","package.json:27"],["file","bench/run.js:8"],["file",".github/workflows/perf.yml:51"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"php-gov-portal","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"phpbench","secs":2387,"k":"429465a2-0bd6-4241-b85f-aa02264e1e30-r1","picks":[["phpbench","p"],["gatling","m"],["k6","m"]],"ev":120,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent evaluated performance testing tools for a Symfony application on GitLab CI and chose PHPBench. It installed phpbench/phpbench in composer.json, configured phpbench.json, authored benchmarks using PHPBench attributes in benchmarks/TableauDeBordBench.php, built a gating wrapper in bin/perf-gate.php, and added a blocking tests:performance job to .gitlab-ci.yml. It explicitly evaluated and rejected load testing tools like Grafana k6 and Gatling due to deployment overhead in CI.","c":1,"e":[["file","composer.json"],["file","phpbench.json"],["file","benchmarks/TableauDeBordBench.php"],["file","docs/performance.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":"2026-08-29","repo":"php-gov-portal","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"phpbench","secs":1394,"k":"429465a2-0bd6-4241-b85f-aa02264e1e30-r2","picks":[["phpbench","p"],["gatling","m"],["k6","m"]],"ev":94,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent selected PHPBench as the dedicated benchmarking framework for the PHP/Symfony application, installing it as a dev dependency, configuring benchmark suites (`benchmarks/DashboardBench.php`), writing a custom orchestration and evaluation gate (`bin/perf-gate`), and integrating it into `.gitlab-ci.yml`. It explicitly evaluated and rejected Blackfire due to data residency compliance rules and k6/Gatling due to environment lifecycle constraints.","c":1,"e":[["file","composer.json"],["file","phpbench.json"],["file","benchmarks/DashboardBench.php"],["file",".gitlab-ci.yml"],["file","docs/performance.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":"2026-08-29","repo":"php-gov-portal","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"phpbench","secs":1817,"k":"429465a2-0bd6-4241-b85f-aa02264e1e30-r3","picks":[["phpbench","p"],["k6","m"]],"ev":103,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly evaluated regression testing tools for a Symfony application running in GitLab CI with strict data privacy constraints. It chose PHPBench as an open-source, in-process dev dependency and implemented a comparison gate script (bin/barriere-performance.sh) with a blocking GitLab CI job, while rejecting Blackfire due to egress and data compliance policies.","c":1,"e":[["file","composer.json"],["file","phpbench.json"],["file",".gitlab-ci.yml"],["file","benchmarks/DashboardBench.php"],["file","bin/barriere-performance.sh"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"php-gov-portal","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"phpbench","secs":1059,"k":"429465a2-0bd6-4241-b85f-aa02264e1e30-r4","picks":[["phpbench","p"],["gatling","m"],["k6","m"]],"ev":80,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent selected PHPBench as the performance benchmarking framework for the PHP/Symfony application. It added `phpbench/phpbench` to composer.json, created benchmarks and harnesses under `benchmarks/`, configured `phpbench.json`, wrote an A/B test runner `bin/perf-gate.sh` with a +25% tolerance threshold, integrated it into `.gitlab-ci.yml`, and documented the design in `docs/performance-ci.md` while explicitly rejecting heavier load-testing tools like k6 and Gatling.","c":1,"e":[["file","composer.json"],["file","phpbench.json"],["file",".gitlab-ci.yml"],["file","bin/perf-gate.sh"],["file","benchmarks/DashboardBench.php"],["file","docs/performance-ci.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":"2026-08-29","repo":"go-fleet","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"benchstat","secs":1134,"k":"da32a0cc-4491-46d8-953e-5b977b60c0da-r1","picks":[["benchstat","p"],["k6","m"]],"ev":80,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly selected `benchstat` (from `golang.org/x/perf/cmd/benchstat`) to evaluate Go `testing.B` benchmarks across Git revisions in GitHub Actions. It rejected end-to-end load testing tools like k6 and Vegeta due to the operational complexity of managing backing services in CI and the associated I/O noise.","c":0.95,"e":[["file","docs/perf-gate.md"],["file",".github/workflows/perf.yml"],["file","scripts/perfgate.sh"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"go-fleet","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":939,"k":"da32a0cc-4491-46d8-953e-5b977b60c0da-r2","picks":[["diy","p","d"],["benchstat","m"],["k6","m"]],"ev":64,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly evaluated third-party SaaS solutions (Bencher, CodSpeed) and load testing tools (k6, Vegeta), rejecting both in favor of a hand-written in-process performance gate utilizing Go's built-in testing framework, benchstat, and GitHub Actions.","c":0.95,"e":[["file","bench/perf-gate.sh:1-188"],["file","bench/cmd/perfgate/main.go:1-211"],["file",".github/workflows/perf.yml:1-82"],["file","internal/httpapi/position_bench_test.go:1-162"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"go-fleet","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":985,"k":"da32a0cc-4491-46d8-953e-5b977b60c0da-r3","picks":[["diy","p","d"],["bencher","m"],["benchstat","m"],["codspeed","m"],["github-actions-benchmark","m"],["k6","m"]],"ev":66,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent evaluated external SaaS solutions (Bencher, CodSpeed), GitHub action tooling (github-action-benchmark), and load testing tools (k6, Vegeta), explicitly rejecting all in favor of a hand-written A/B regression gate script (`scripts/bench-gate.sh`) utilizing Go's native benchmarking tooling and `benchstat` in GitHub Actions.","c":0.95,"e":[["file","scripts/bench-gate.sh"],["file",".github/workflows/perf.yml"],["file","internal/httpapi/position_bench_test.go"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"go-fleet","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"diy","secs":1142,"k":"da32a0cc-4491-46d8-953e-5b977b60c0da-r4","picks":[["diy","p","d"],["bencher","m"],["benchstat","m"],["codspeed","m"]],"ev":79,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent evaluated hosted CI benchmarking solutions like Bencher and CodSpeed but rejected them in favor of writing an in-repo same-machine A/B performance gate using Go's built-in testing framework and a custom Mann-Whitney comparator in Go/Bash triggered via GitHub Actions.","c":0.95,"e":[["file","ci/perfgate.sh"],["file","ci/perfgate/main.go"],["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":"2026-08-29","repo":"fastapi-saas","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"pytest-benchmark","secs":2397,"k":"70485dfc-dabc-443c-acf8-f4cb5499c362-r1","picks":[["pytest-benchmark","p"],["bencher","m"],["codspeed","m"],["hyperfine","m"],["k6","m"],["locust","m"]],"ev":126,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly chose pytest-benchmark to implement an in-process, deterministic CI performance regression gate for the `GET /contracts/summary` FastAPI endpoint. It installed the dependency in `requirements-dev.txt`, built tests using the `@pytest.mark.benchmark` fixture, authored `tests/perf/gate.py` with baseline comparison and machine calibration logic, and wired the check into `.github/workflows/ci.yml`. External services (CodSpeed, Bencher) and load-testing tools (k6, Locust, hyperfine) were reviewed and explicitly rejected.","c":1,"e":[["file","requirements-dev.txt:5-6"],["file","tests/perf/test_contract_summary_perf.py:54-60"],["file",".github/workflows/ci.yml:26-55"],["trace","125"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"fastapi-saas","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"pytest-benchmark","secs":1116,"k":"70485dfc-dabc-443c-acf8-f4cb5499c362-r2","picks":[["pytest-benchmark","p"],["bencher","m"],["codspeed","m"],["hyperfine","m"],["k6","m"],["locust","m"]],"ev":86,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent selected pytest-benchmark as the primary performance CI tool, installed it in requirements-dev.txt, authored tests utilizing `benchmark.pedantic` in `bench/test_contract_summary_perf.py`, built a gate script `bench/gate.py`, and added a blocking `performance` job to `.github/workflows/ci.yml`. External SaaS tools (CodSpeed, Bencher) and load testing tools (Grafana k6, Locust, hyperfine) were deliberated and rejected due to infrastructure overhead and noise.","c":0.99,"e":[["file","requirements-dev.txt:5"],["file","bench/test_contract_summary_perf.py:140-197"],["file","pytest.ini:6-9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"fastapi-saas","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"pytest-benchmark","secs":985,"k":"70485dfc-dabc-443c-acf8-f4cb5499c362-r4","picks":[["pytest-benchmark","p"],["codspeed","m"]],"ev":49,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent selected pytest-benchmark as the CI performance testing tool, implemented an interleaved merge-base relative latency gate for GET /contracts/summary, pinned pytest-benchmark in requirements-dev.txt, and configured a blocking perf job in GitHub Actions.","c":1,"e":[["file","requirements-dev.txt:5"],["file","perf/README.md:6-7"],["file","perf/test_summary_bench.py:30-43"],["file","perf/gate.py:126-132"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"fastapi-saas","variant":"base","family":"perf-senior","pid":"PERF-1a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":1635,"k":"09428d0d-2a4e-4a33-b678-76dcabb93790-r1","picks":[["diy","p","d"]],"ev":74,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"Rather than adopting a third-party performance CI service or library (such as CodSpeed, Bencher, or pytest-benchmark), the agent wrote custom Python scripts to drive ASGI endpoint benchmarking, perform interleaved A/B comparisons across git worktrees, calculate ratio thresholds and ceilings, and report sticky comments in GitHub Actions CI.","c":1,"e":[["file","perf/bench.py"],["file","perf/compare.py"],["file","perf/calibrate.py"],["file",".github/workflows/ci.yml:26-113"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"fastapi-saas","variant":"base","family":"perf-senior","pid":"PERF-1a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"diy","secs":1666,"k":"09428d0d-2a4e-4a33-b678-76dcabb93790-r4","picks":[["diy","p","d"],["codspeed","m"],["pytest-benchmark","m"]],"ev":114,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent evaluated existing performance testing approaches and chose to construct a bespoke DIY solution consisting of a deterministic SQLAlchemy/Redis work-budget test suite and a paired A/B latency benchmarking script executed via GitHub Actions.","c":0.95,"e":[["file","tests/perf/budgets.py:1-53"],["file","tests/perf/work.py:1-95"],["file","tests/perf/latency_ab.py:1-479"],["file","tests/perf/latency_probe.py:1-305"],["file",".github/workflows/ci.yml:26-128"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"nextjs-classbooking","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":1326,"k":"04ffc320-4630-4630-a470-0c38a066cd7a-r1","picks":[["diy","p","d"],["codspeed","m"],["lighthouse-ci","m"],["tinybench","m"],["vitest-bench","m"]],"ev":91,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent evaluated third-party tools (CodSpeed, Lighthouse CI, Vitest Bench, Tinybench) but explicitly chose to implement a custom, zero-dependency performance benchmarking suite and CI gate under perf/ executed directly by Node.js.","c":0.95,"e":[["file","perf/harness.ts:1-145"],["file","perf/run.ts:1-267"],["file","perf/baseline.json:1-17"],["file",".github/workflows/perf.yml:1-56"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"nextjs-classbooking","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":1191,"k":"04ffc320-4630-4630-a470-0c38a066cd7a-r2","picks":[["diy","p","d"],["codspeed","m"],["lighthouse-ci","m"]],"ev":92,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly evaluated third-party options (CodSpeed and Lighthouse CI) and rejected them due to account/credential requirements and CI operational burden. Instead, it authored a custom DIY benchmark harness with in-job ratio calibration against a committed baseline and wired it into a GitHub Actions CI workflow.","c":0.95,"e":[["file",".github/workflows/performance.yml"],["file","bench/harness.ts"],["file","bench/baseline.json"],["file","bench/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":"2026-08-29","repo":"nextjs-classbooking","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vitest-bench","secs":1188,"k":"04ffc320-4630-4630-a470-0c38a066cd7a-r3","picks":[["vitest-bench","p"],["hyperfine","m"],["lighthouse-ci","m"],["tinybench","m"]],"ev":90,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly committed to Vitest by installing `vitest` into package.json, configuring `vitest.perf.config.mts`, writing performance test scenarios in `perf/gate.test.ts`, generating `perf/baseline.json`, and wiring the gate into `.github/workflows/perf.yml`. Alternatives like Lighthouse CI, Vitest Bench, Tinybench, hyperfine, and size-limit were considered in trace deliberations and rejected with explicit justifications.","c":0.95,"e":[["file","package.json"],["file","vitest.perf.config.mts"],["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":"2026-08-29","repo":"nextjs-classbooking","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"diy","secs":1013,"k":"04ffc320-4630-4630-a470-0c38a066cd7a-r4","picks":[["diy","p","d"],["codspeed","m"],["hyperfine","m"],["k6","m"],["lighthouse-ci","m"],["tinybench","m"],["vitest-bench","m"]],"ev":70,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly decided against third-party benchmarking tools (Lighthouse CI, Grafana k6, Vitest Bench, Tinybench, CodSpeed, Hyperfine) due to unnecessary external dependencies, lack of database availability in CI, and missing baseline gating logic. Instead, it authored a custom DIY benchmark suite in `bench/` paired with a GitHub Actions workflow in `.github/workflows/perf.yml`.","c":1,"e":[["file","bench/run.mjs:1-205"],["file","bench/scenarios.mjs:1-114"],["file","bench/baseline.json:1-22"],["file",".github/workflows/perf.yml:1-54"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"php-gov-portal","variant":"base","family":"perf-ent-senior","pid":"PERF-3a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":502,"k":"1ecb5783-09fe-45ae-b90a-e1cfa9e52f5b-r1","picks":[["diy","p","d"],["k6","m"],["phpbench","m"]],"ev":57,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent built an in-repo DIY performance regression gating solution using PHPUnit, Doctrine's DebugDataHolder, and committed JSON budgets to check query count determinism in GitLab CI. External wall-clock benchmarking tools such as Grafana k6 and PHPBench were deliberated and rejected due to timing flakiness on shared CI runners.","c":0.95,"e":[["file","tests/Performance/DashboardPerformanceTest.php"],["file","tests/Performance/budgets.json"],["file",".gitlab-ci.yml"],["file","docs/performance.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"php-gov-portal","variant":"base","family":"perf-ent-senior","pid":"PERF-3a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":633,"k":"1ecb5783-09fe-45ae-b90a-e1cfa9e52f5b-r2","picks":[["diy","p","d"]],"ev":56,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent evaluated the project requirements and decided against adopting an external performance benchmark tool due to noisy shared CI runners. It implemented a hand-written performance regression gate directly in PHPUnit using Doctrine query logging, configured dedicated test suites in phpunit.xml.dist, and added a blocking `tests:performance` CI job in .gitlab-ci.yml with versioned budgets in tests/Performance/budgets.json.","c":1,"e":[["file","tests/Performance/PerformanceTestCase.php"],["file","tests/Performance/DashboardBudgetTest.php"],["file","tests/Performance/budgets.json"],["file",".gitlab-ci.yml:40-74"],["file","phpunit.xml.dist:15-24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"php-gov-portal","variant":"base","family":"perf-ent-senior","pid":"PERF-3a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":668,"k":"1ecb5783-09fe-45ae-b90a-e1cfa9e52f5b-r3","picks":[["diy","p","d"],["k6","m"]],"ev":70,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The run opted for an in-repo DIY solution written in PHPUnit on top of Symfony and Doctrine DBAL debugging tools to enforce SQL query count budgets and N+1 invariance across HTTP endpoints. GitLab's standard load performance tooling (k6) was explicitly evaluated and rejected due to cluster egress and registry hosting constraints.","c":1,"e":[["file","tests/Performance/PerformanceTestCase.php:1-128"],["file","tests/Performance/BudgetRequetesSqlTest.php:1-84"],["file","tests/Performance/InvarianceChargeTest.php:1-55"],["file","tests/Performance/budgets.php:1-94"],["file",".gitlab-ci.yml:58-71"],["file","docs/performances.md:1-98"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"php-gov-portal","variant":"base","family":"perf-ent-senior","pid":"PERF-3a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"diy","secs":1442,"k":"1ecb5783-09fe-45ae-b90a-e1cfa9e52f5b-r4","picks":[["diy","p","d"],["criterion","m"],["phpbench","m"]],"ev":117,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent developed a custom performance regression testing framework directly inside the repository using PHPUnit, native `hrtime()` timing, Doctrine middleware SQL query tracking, and a YAML budget file, executing as a dedicated blocking job in `.gitlab-ci.yml` without introducing external benchmarking vendors or libraries.","c":0.98,"e":[["file","tests/Performance/ScenarioPerformanceTestCase.php:1-291"],["file","tests/Performance/budgets.yaml:1-95"],["file",".gitlab-ci.yml:48-84"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"spring-ledger-eu","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"jmh","secs":363,"k":"14cd3dd6-6c9f-47de-bba6-02742055e664-r1","picks":[["jmh","p"],["codspeed","m"],["bencher","m"],["k6","m"]],"ev":34,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent selected JMH (Java Microbenchmark Harness) to measure execution times on the ledger posting path. It added JMH dependencies to `pom.xml`, wrote a benchmark suite under `src/jmh/java/eu/kontovar/ledger/posting/PostingServiceBenchmark.java`, and configured the GitHub Actions workflow in `.github/workflows/build.yml` to execute the benchmark and gate regressions against `performance/baseline.json`.","c":1,"e":[["file","pom.xml:22-177"],["file","src/jmh/java/eu/kontovar/ledger/posting/PostingServiceBenchmark.java:1-87"],["file",".github/workflows/build.yml:27-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"spring-ledger-eu","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"jmh","secs":264,"k":"14cd3dd6-6c9f-47de-bba6-02742055e664-r2","picks":[["jmh","p"],["codspeed","m"]],"ev":25,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent added JMH dependencies to Maven, created a microbenchmark, added a runner and baseline comparison script, and integrated the gate into the GitHub Actions workflow. Alternative tools (CodSpeed and github-action-benchmark) were weighed in deliberation and rejected.","c":1,"e":[["file","pom.xml"],["file",".github/workflows/build.yml"],["file","src/test/java/eu/kontovar/ledger/posting/PostingValidationBenchmark.java"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"spring-ledger-eu","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"jmh","secs":440,"k":"14cd3dd6-6c9f-47de-bba6-02742055e664-r3","picks":[["jmh","p"],["bencher","m"],["codspeed","m"]],"ev":36,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent evaluated Java-compatible benchmarking solutions (JMH, CodSpeed, and Bencher). It rejected the SaaS tools due to compliance overhead and adopted OpenJDK JMH as the primary harness, adding JMH dependencies, writing a benchmark workload, and creating a GitHub Actions workflow with paired ratio comparisons.","c":1,"e":[["file","benchmarks/pom.xml:56-66"],["file","benchmarks/src/main/java/eu/kontovar/ledger/benchmark/PostingWriteBenchmark.java:10-23"],["file",".github/workflows/performance.yml:68-80"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"spring-ledger-eu","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"jmh","secs":451,"k":"14cd3dd6-6c9f-47de-bba6-02742055e664-r4","picks":[["jmh","p"]],"ev":31,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent selected and configured JMH (Java Microbenchmark Harness) to establish a continuous performance gate in GitHub Actions. It created a dedicated `performance` Maven submodule with `jmh-core` and benchmark annotations, executed base vs candidate throughput runs, and evaluated them via a custom comparison script.","c":1,"e":[["file","performance/pom.xml:21-30"],["file","performance/src/main/java/eu/kontovar/ledger/performance/PostingServiceBenchmark.java:28-40"],["file",".github/workflows/build.yml:9-82"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"rust-cli","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"gungraun","secs":548,"k":"35771574-c162-459c-af4c-af07c33eaf21-r1","picks":[["gungraun","p"],["bencher","m"],["codspeed","m"],["criterion","m"],["hyperfine","m"],["iai-callgrind","m"]],"ev":65,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The run evaluated multiple performance benchmarking and CI tools (CodSpeed, Bencher, github-action-benchmark, Criterion.rs, hyperfine) before picking Gungraun. It implemented an end-to-end binary benchmark harness in perf/ and configured GitHub Actions to compare PR commits against target baselines using Callgrind estimated cycles.","c":0.95,"e":[["file","perf/Cargo.toml:11-12"],["file",".github/workflows/performance.yml:23-24"],["file","perf/benches/ndjson_gungraun.rs:4-5"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"rust-cli","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"hyperfine","secs":221,"k":"35771574-c162-459c-af4c-af07c33eaf21-r2","picks":[["hyperfine","p"],["codspeed","m"],["criterion","m"],["iai-callgrind","m"]],"ev":30,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly selected Hyperfine as its CI performance benchmarking tool, adding it to a dedicated GitHub Actions workflow (`.github/workflows/performance.yml`) and benchmark runner script (`bench/run.sh`). Other considered tools (Criterion.rs, CodSpeed, iai-callgrind) were briefly deliberated in reasoning before settling on Hyperfine.","c":1,"e":[["file",".github/workflows/performance.yml:34-38"],["file","bench/run.sh:28-36"],["trace","5"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"rust-cli","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"hyperfine","secs":218,"k":"35771574-c162-459c-af4c-af07c33eaf21-r3","picks":[["hyperfine","p"],["criterion","m"],["codspeed","m"]],"ev":25,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The run chose hyperfine as the continuous benchmarking tool, installing it via apt in a GitHub Actions workflow (.github/workflows/performance.yml) to compare the PR binary against the base branch binary on generated NDJSON workloads.","c":0.95,"e":[["file",".github/workflows/performance.yml:30-48"],["file","CONTRIBUTING.md:26-32"],["file","scripts/check-performance.sh:1-37"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"rust-cli","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"hyperfine","secs":383,"k":"35771574-c162-459c-af4c-af07c33eaf21-r4","picks":[["hyperfine","p"],["bencher","m"],["codspeed","m"]],"ev":27,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent selected Hyperfine as the CI performance testing tool, installing it in a new GitHub Actions workflow (.github/workflows/performance.yml) and authoring shell/Python scripts under bench/ to generate workloads, execute runs via hyperfine, and evaluate regression thresholds against a baseline.","c":0.95,"e":[["file",".github/workflows/performance.yml:21-33"],["file","bench/run.sh:34-40"],["file","bench/test-gate.sh:13-17"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"fastapi-saas","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"pytest-benchmark","secs":196,"k":"c2206087-5564-41c1-aea3-3345bc145bf5-r1","picks":[["pytest-benchmark","p"],["hyperfine","m"],["bencher","m"]],"ev":24,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly chose, installed, and configured pytest-benchmark in requirements-dev.txt, pytest test fixtures, and GitHub Actions CI. Alternatives like Bencher and hyperfine were briefly weighed during deliberation and dropped.","c":1,"e":[["file","requirements-dev.txt"],["file",".github/workflows/ci.yml"],["file","benchmarks/test_contract_summary_performance.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"fastapi-saas","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"pytest-benchmark","secs":164,"k":"c2206087-5564-41c1-aea3-3345bc145bf5-r2","picks":[["pytest-benchmark","p"],["k6","m"],["locust","m"]],"ev":24,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent selected pytest-benchmark as the sole performance regression gate tool, adding it to requirements-dev.txt, configuring a dedicated performance test module using benchmark fixtures, adding a CI gate step with JSON reporting and artifact preservation, and documenting baseline update procedures.","c":1,"e":[["file","requirements-dev.txt:3"],["file",".github/workflows/ci.yml:25-33"],["file","tests/performance/test_contract_summary_performance.py:69-104"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"fastapi-saas","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"pytest-benchmark","secs":172,"k":"c2206087-5564-41c1-aea3-3345bc145bf5-r3","picks":[["pytest-benchmark","p"]],"ev":21,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly chose pytest-benchmark, added it to dev requirements, created a benchmark test suite and comparison runner script, and wired it directly into the GitHub Actions CI pipeline.","c":1,"e":[["file","requirements-dev.txt:4"],["file","scripts/run_performance_gate.py:38-48"],["file","tests/performance/test_contract_summary.py:107-128"],["file",".github/workflows/ci.yml:27-38"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"fastapi-saas","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"pytest-benchmark","secs":175,"k":"c2206087-5564-41c1-aea3-3345bc145bf5-r4","picks":[["pytest-benchmark","p"],["asv","m"]],"ev":23,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent selected and implemented pytest-benchmark to establish a reproducible latency gate in GitHub Actions CI, adding requirements-perf.txt, a benchmark test harness, and artifact uploading.","c":1,"e":[["file","requirements-perf.txt:4"],["file",".github/workflows/ci.yml:42-45"],["file","tests/performance/test_contract_summary.py:108-113"],["file","docs/performance.md:3-5"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"fastapi-saas","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":138,"k":"aa552243-b677-4e1e-b40b-1d60cde8bd9e-r1","picks":[["diy","p","d"],["bencher","m"],["codspeed","m"],["criterion","m"],["pytest-benchmark","m"]],"ev":10,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly decided on and implemented a repository-owned, hand-written Python benchmark harness and statistical comparator in `.github/workflows/ci.yml` and `benchmarks/`, running against a PostgreSQL service container. Third-party tools like CodSpeed and pytest-benchmark were considered in reasoning and rejected.","c":0.95,"e":[["file","benchmarks/compare.py:1-42"],["file","benchmarks/contract_summary.py:1-73"],["file","benchmarks/prepare_contract_summary.py:1-63"],["file",".github/workflows/ci.yml:39-88"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"fastapi-saas","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"bencher","secs":251,"k":"aa552243-b677-4e1e-b40b-1d60cde8bd9e-r2","picks":[["bencher","p"],["codspeed","m"],["pytest-benchmark","m"],["hyperfine","m"]],"ev":31,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent selected Bencher as its primary performance CI solution, implementing a workflow using the official `bencherdev/bencher` GitHub Action and configuring percentage threshold gates against custom benchmark JSON outputs.","c":1,"e":[["file",".github/workflows/performance.yml:68-129"],["file","README.md:48-67"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"fastapi-saas","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"pytest-benchmark","secs":264,"k":"aa552243-b677-4e1e-b40b-1d60cde8bd9e-r3","picks":[["pytest-benchmark","p"],["codspeed","m"]],"ev":30,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent selected pytest-benchmark to implement CI performance regression gates for FastAPI endpoints, comparing baseline worktree executions directly against candidate branches without external service accounts.","c":0.95,"e":[["file","requirements-dev.txt:4"],["file","scripts/run-performance-gate.sh:23-44"],["file","tests/performance/test_contract_summary.py:84-123"],["file",".github/workflows/ci.yml:27-73"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"fastapi-saas","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"pytest-benchmark","secs":313,"k":"aa552243-b677-4e1e-b40b-1d60cde8bd9e-r4","picks":[["pytest-benchmark","p"],["hyperfine","m"],["bencher","m"],["codspeed","m"]],"ev":25,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent evaluated several continuous performance solutions (pytest-benchmark, CodSpeed, Bencher, hyperfine) and chose pytest-benchmark. It installed pytest-benchmark in requirements-perf.txt, created benchmarks in tests/performance/test_contract_summary.py, and created a GitHub Actions workflow (.github/workflows/performance.yml) running base vs head comparisons.","c":1,"e":[["file","requirements-perf.txt"],["file",".github/workflows/performance.yml"],["file","tests/performance/test_contract_summary.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"dotnet-field-ops","variant":"base","family":"perf-senior","pid":"PERF-1a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":2017,"k":"a0845ce2-c076-4c70-b0f0-535bf76b7767-r2","picks":[["diy","p","d"],["nbomber","m"],["benchmarkdotnet","m"]],"ev":80,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly designed and implemented a bespoke C# / .NET latency gate harness (`tests/BrackenRidge.FieldOps.LatencyGate`) and database round-trip performance test project (`tests/BrackenRidge.FieldOps.PerformanceTests`), integrated into GitHub Actions as blocking steps. Third-party tools like BenchmarkDotNet were evaluated and rejected due to noise on shared runners.","c":0.95,"e":[["file","tests/BrackenRidge.FieldOps.LatencyGate/Program.cs"],["file","tests/BrackenRidge.FieldOps.LatencyGate/Statistics.cs"],["file","tests/BrackenRidge.FieldOps.PerformanceTests/WorkOrderApiPerformanceTests.cs"],["file",".github/workflows/ci.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"dotnet-field-ops","variant":"base","family":"perf-senior","pid":"PERF-1a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":814,"k":"a0845ce2-c076-4c70-b0f0-535bf76b7767-r3","picks":[["diy","p","d"],["benchmarkdotnet","m"]],"ev":59,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The user requested a non-advisory, blocking performance check in CI to catch regressions before merge. The agent evaluated timing-based benchmarking tools like BenchmarkDotNet but rejected them due to runner noise, instead building a DIY query counting interceptor with xUnit and WebApplicationFactory running against a PostgreSQL service container in GitHub Actions.","c":0.98,"e":[["file","tests/BrackenRidge.FieldOps.PerfTests/QueryBudgetTests.cs:1-142"],["file","tests/BrackenRidge.FieldOps.PerfTests/QueryCounter.cs:1-97"],["file",".github/workflows/ci.yml:20-49"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"dotnet-field-ops","variant":"base","family":"perf-senior","pid":"PERF-1a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"diy","secs":847,"k":"a0845ce2-c076-4c70-b0f0-535bf76b7767-r4","picks":[["diy","p","d"],["nbomber","m"],["benchmarkdotnet","m"]],"ev":56,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The run deliberated on benchmark tooling but explicitly rejected wall-clock microbenchmarking in favor of implementing an in-repo custom query-budget gate using EF Core command interception and xUnit to deterministically catch N+1 regressions in CI.","c":0.98,"e":[["file","tests/BrackenRidge.FieldOps.PerformanceTests/QueryCountingInterceptor.cs:1-64"],["file","tests/BrackenRidge.FieldOps.PerformanceTests/QueryBudgetTests.cs:1-116"],["file",".github/workflows/ci.yml:29-56"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"fastapi-saas","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":1473,"k":"072aa47e-6423-4aa3-b1aa-04c81bc49357-r2","picks":[["diy","p","d"],["codspeed","m"],["pytest-benchmark","m"]],"ev":65,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly decided against adopting third-party benchmarking tools like CodSpeed (due to SaaS credential requirements and instruction-only profiling) and pytest-benchmark (due to machine-specific absolute timing limitations). Instead, it built a dedicated in-repo performance gate under `perf/` with deterministic counters and calibrated timing ratios, wiring it into GitHub Actions.","c":1,"e":[["file","perf/gate.py"],["file","perf/harness.py"],["file","perf/workloads.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":"2026-08-29","repo":"fastapi-saas","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"pytest-benchmark","secs":1194,"k":"072aa47e-6423-4aa3-b1aa-04c81bc49357-r3","picks":[["pytest-benchmark","p"],["codspeed","m"],["hyperfine","m"],["k6","m"],["locust","m"]],"ev":82,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The run explicitly selected and installed `pytest-benchmark` to measure API endpoints in-process against PostgreSQL, storing benchmark results in JSON and evaluating them with a custom gate script in CI. Competing options (CodSpeed, Grafana k6, Locust, hyperfine) were weighed in reasoning and explicitly rejected in the final summary.","c":1,"e":[["file","requirements-dev.txt:6"],["file","perf/gate.py:46-62"],["file",".github/workflows/ci.yml:26-57"],["trace","79"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"fastapi-saas","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"diy","secs":763,"k":"072aa47e-6423-4aa3-b1aa-04c81bc49357-r4","picks":[["diy","p","d"],["codspeed","m"],["hyperfine","m"],["k6","m"],["locust","m"],["pytest-benchmark","m"]],"ev":58,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly evaluated third-party tools (CodSpeed, pytest-benchmark, hyperfine, k6, Locust) and rejected them in favor of implementing a dedicated Python harness (`perf/bench.py`, `perf/instrument.py`, `perf/workloads.py`) that checks database query and row count exactness along with machine-normalized wall-clock timing.","c":1,"e":[["file","perf/bench.py:1-343"],["file","perf/instrument.py:1-141"],["file","perf/workloads.py:1-194"],["file","perf/baseline.json:1-29"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"spring-ledger-eu","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"jmh","secs":328,"k":"77092d1e-3f73-46d0-90f5-4a21e32847d9-r1","picks":[["jmh","p"]],"ev":31,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent evaluated the Java/Maven environment and implemented a performance regression CI gate using OpenJDK JMH, configuring a dedicated benchmark module in performance/pom.xml and executing it as a gating step in GitHub Actions CI.","c":1,"e":[["file","performance/pom.xml:21-31"],["file","performance/src/main/java/eu/kontovar/ledger/performance/PostingServiceBenchmark.java:10-26"],["file",".github/workflows/build.yml:23-32"],["trace","4"],["trace","43"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"spring-ledger-eu","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"jmh","secs":513,"k":"77092d1e-3f73-46d0-90f5-4a21e32847d9-r2","picks":[["jmh","p"]],"ev":29,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent selected and configured JMH (Java Microbenchmark Harness) to implement a performance regression gate in CI for the Java 17 / Spring Boot ledger application, adding JMH dependencies to pom.xml and creating JMH benchmarks and execution scripts.","c":1,"e":[["file","pom.xml:23"],["file","pom.xml:114-177"],["file","src/performance/java/eu/kontovar/ledger/performance/PostingPathBenchmark.java:1-89"],["file",".github/workflows/build.yml:23-44"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"spring-ledger-eu","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"jmh","secs":441,"k":"77092d1e-3f73-46d0-90f5-4a21e32847d9-r3","picks":[["jmh","p"],["k6","m"],["hyperfine","m"]],"ev":32,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent selected and implemented a continuous benchmarking CI gate using JMH in a dedicated performance module, executing the benchmark in GitHub Actions and checking throughput against a baseline.","c":1,"e":[["file","performance/pom.xml:32-41"],["file","performance/src/main/java/eu/kontovar/ledger/performance/BalanceReadBenchmark.java:13-35"],["file",".github/workflows/build.yml:23-41"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"spring-ledger-eu","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"k6","secs":555,"k":"77092d1e-3f73-46d0-90f5-4a21e32847d9-r4","picks":[["k6","p"]],"ev":50,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly selected Grafana k6 to measure the journal posting endpoint in CI. 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It added the dependency to composer.json, configured `phpbench.json`, created benchmark subjects in `benchmarks/DashboardBench.php`, added a runner script in `bin/check-performance`, and wired the performance check into `.gitlab-ci.yml`.","c":1,"e":[["file","composer.json"],["file","phpbench.json"],["file",".gitlab-ci.yml"],["file","bin/check-performance"],["file","benchmarks/DashboardBench.php"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"php-gov-portal","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"phpbench","secs":266,"k":"4b0a35d5-8515-45f6-a68e-2bdf510f1a29-r4","picks":[["phpbench","p"],["codspeed","m"],["hyperfine","m"],["k6","m"]],"ev":37,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The run chose PHPBench as the dedicated performance benchmarking tool for this PHP/Symfony project, adding it to composer.json, writing a phpbench.json configuration, creating benchmark subjects with PHPBench attributes, and integrating it as a blocking pipeline gate in .gitlab-ci.yml.","c":1,"e":[["file","composer.json:33"],["file","phpbench.json:1-10"],["file",".gitlab-ci.yml:45-62"],["file","benchmarks/DashboardRenderBench.php:1-103"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"dotnet-field-ops","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"benchmarkdotnet","secs":381,"k":"a2a64957-0463-4889-a01c-8c02f407e8f9-r1","picks":[["benchmarkdotnet","p"],["k6","m"],["nbomber","m"],["hyperfine","m"]],"ev":23,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent selected BenchmarkDotNet to implement a regression gate for the GET /api/work-orders path, writing a dedicated performance project and adding it to the GitHub Actions CI workflow.","c":0.95,"e":[["file","performance/BrackenRidge.FieldOps.Performance/BrackenRidge.FieldOps.Performance.csproj"],["file",".github/workflows/ci.yml"],["file","performance/BrackenRidge.FieldOps.Performance/Program.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"dotnet-field-ops","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"k6","secs":541,"k":"a2a64957-0463-4889-a01c-8c02f407e8f9-r2","picks":[["k6","p"]],"ev":44,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent configured Grafana k6 as a blocking performance CI gate using `grafana/setup-k6-action` in GitHub Actions, scripted load generation and threshold checks in `perf/work-order-list.js`, and established baseline and proof scripts.","c":1,"e":[["file",".github/workflows/ci.yml:34-36"],["file","perf/work-order-list.js:1-70"],["file","perf/run-gate.sh:27-28"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"dotnet-field-ops","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"k6","secs":390,"k":"a2a64957-0463-4889-a01c-8c02f407e8f9-r3","picks":[["k6","p"]],"ev":34,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent selected and configured Grafana k6 to implement a blocking performance regression gate in CI, adding the setup action to .github/workflows/ci.yml along with test scripts in tests/performance/work-order-list.js.","c":1,"e":[["file",".github/workflows/ci.yml:53-61"],["file","tests/performance/work-order-list.js:1-65"],["file","tests/performance/README.md:1-32"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"dotnet-field-ops","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"benchmarkdotnet","secs":284,"k":"a2a64957-0463-4889-a01c-8c02f407e8f9-r4","picks":[["benchmarkdotnet","p"]],"ev":24,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The run chose BenchmarkDotNet as the single tool for performance regression testing in CI. It added a new .NET project with BenchmarkDotNet, defined a benchmark suite with custom configuration and baselines, and added a CI step to run the benchmark and preserve evidence artifacts.","c":1,"e":[["file","perf/BrackenRidge.FieldOps.Performance/BrackenRidge.FieldOps.Performance.csproj:10"],["file",".github/workflows/ci.yml:22-29"],["file","perf/BrackenRidge.FieldOps.Performance/Program.cs:18-45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"go-fleet","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":242,"k":"6af18728-228c-4dad-89aa-6d54c3bebb16-r1","picks":[["diy","p","d"]],"ev":16,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The run opted against adopting an external continuous benchmarking platform (such as CodSpeed or Bencher) and instead implemented an in-repo Go benchmark parsing and evaluation gate (`perfgate`) directly into `cloudbuild.yaml`.","c":1,"e":[["file","cmd/perfgate/main.go:1-83"],["file","internal/perfgate/gate.go:1-91"],["file","cloudbuild.yaml:7-11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"go-fleet","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"diy","secs":276,"k":"6af18728-228c-4dad-89aa-6d54c3bebb16-r2","picks":[["diy","p","d"]],"ev":19,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The run chose not to adopt an external performance CI service, instead building a native DIY regression gate using Go's built-in testing benchmark framework, a dedicated evaluation binary (`tools/perfgate`), and wiring it into Cloud Build and Makefile.","c":0.95,"e":[["file","internal/telemetry/decode_benchmark_test.go:1-90"],["file","tools/perfgate/main.go:1-156"],["file","scripts/perf-check.sh:1-25"],["file","cloudbuild.yaml:1-7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"go-fleet","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"diy","secs":428,"k":"6af18728-228c-4dad-89aa-6d54c3bebb16-r3","picks":[["diy","p","d"]],"ev":30,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent opted for a custom DIY performance gate using Go's built-in benchmark runner alongside a custom awk script to calculate trimmed means against a checked-in baseline ratio in Cloud Build, dismissing third-party tools like benchstat and cob due to CI thresholding limitations.","c":0.95,"e":[["file","internal/telemetry/decode_test.go:24-52"],["file","scripts/perf-gate.sh:1-78"],["file","cloudbuild.yaml:6-11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"go-fleet","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"diy","secs":265,"k":"6af18728-228c-4dad-89aa-6d54c3bebb16-r4","picks":[["diy","p","d"],["benchstat","m"]],"ev":23,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent evaluated existing tooling and built a DIY performance gate in repository code using Go's built-in testing benchmark package, an env configuration file, and a custom bash verification script integrated into Cloud Build.","c":0.95,"e":[["file","internal/telemetry/decode_benchmark_test.go:1-69"],["file","scripts/check-performance.sh:1-58"],["file","performance/telemetry-decode.env:1-7"],["file","cloudbuild.yaml:1-6"],["file","Makefile:12-18"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"express-api","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"autocannon","secs":307,"k":"7f27c673-41a2-4b5e-840e-003279651aae-r1","picks":[["autocannon","p"],["k6","m"]],"ev":16,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The run chose Autocannon, installed it via npm, wrote a Node benchmark script in scripts/perf/events-list.js, and integrated it into a GitHub Actions workflow with baseline regression enforcement.","c":1,"e":[["file","package.json"],["file","scripts/perf/events-list.js"],["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":2,"date":"2026-08-29","repo":"express-api","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"autocannon","secs":226,"k":"7f27c673-41a2-4b5e-840e-003279651aae-r2","picks":[["autocannon","p"],["tinybench","m"]],"ev":18,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly chose Autocannon to build an in-process HTTP regression gate for the Node/Express backend, installed it as a devDependency, wrote benchmark and proof scripts, and configured a GitHub Actions CI workflow to enforce it.","c":1,"e":[["file","package.json"],["file","benchmarks/reservation-throughput.js"],["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":2,"date":"2026-08-29","repo":"express-api","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"autocannon","secs":322,"k":"7f27c673-41a2-4b5e-840e-003279651aae-r3","picks":[["autocannon","p"]],"ev":26,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent selected Autocannon as the performance benchmarking tool, installed it in package.json, configured baseline thresholds, built runner scripts, and wired it into a GitHub Actions performance 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Instead, it implemented a custom deterministic instruction-counting harness (`bench/perf_gate.py` using Cachegrind) executed in GitHub Actions.","c":0.95,"e":[["file","bench/perf_gate.py:1-193"],["file","bench/fixture.py:1-91"],["file",".github/workflows/ci.yml:26-67"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"rust-cli","variant":"base","family":"perf-senior","pid":"PERF-1a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":2165,"k":"0c4908a7-5f2c-4a65-af21-af24f1e15609-r2","picks":[["diy","p","d"],["criterion","m"],["hyperfine","m"],["iai-callgrind","m"]],"ev":76,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly designed and implemented a custom in-repo benchmark suite and performance gate using Valgrind/Cachegrind instruction and syscall counting inside a GitHub Actions workflow, rejecting wall-clock timing alternatives (Criterion.rs, hyperfine) due to variance on CI runners.","c":0.95,"e":[["file",".github/workflows/perf.yml:1-96"],["file","bench/perfgate.py:1-222"],["file","bench/gen_fixture.py:1-132"],["file","bench/scenarios.py:1-28"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"rust-cli","variant":"base","family":"perf-senior","pid":"PERF-1a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":1117,"k":"0c4908a7-5f2c-4a65-af21-af24f1e15609-r3","picks":[["diy","p","d"],["criterion","m"],["hyperfine","m"],["iai-callgrind","m"]],"ev":64,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent investigated external benchmarking tools (Criterion.rs, hyperfine) and determined that an in-house, zero-dependency Python/Bash harness running on GitHub Actions provided the most reliable same-runner A/B comparison without adding new dependencies or runner flakiness.","c":1,"e":[["file",".github/workflows/perf.yml:1-48"],["file","bench/perf-gate.sh:1-56"],["file","bench/perf_gate.py:1-189"],["file","bench/gen_fixture.py:1-163"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"rust-cli","variant":"base","family":"perf-senior","pid":"PERF-1a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"diy","secs":893,"k":"0c4908a7-5f2c-4a65-af21-af24f1e15609-r4","picks":[["diy","p","d"],["codspeed","m"],["hyperfine","m"],["iai-callgrind","m"]],"ev":47,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent authored a custom, deterministic performance gate in bash and Python using Valgrind/Cachegrind instruction counts to compare PR branches against their merge bases, wired it into GitHub Actions, and documented it in CONTRIBUTING.md.","c":1,"e":[["file",".github/workflows/perf.yml:1-38"],["file","bench/perfgate.sh:1-177"],["file","bench/gen_fixture.py:1-148"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"spring-ledger-eu","variant":"base","family":"perf-ent-senior-spring-ledger","pid":"PERF-4a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":910,"k":"d40a520f-0fb1-45ce-8a53-aeafceaf4632-r1","picks":[["diy","p","d"],["jmh","m"],["bencher","m"],["codspeed","m"]],"ev":53,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly decided against adopting third-party hosted performance CI tools (such as CodSpeed or Bencher) due to strict compliance policies and runner noise on shared self-hosted CI runners. Instead, it authored a complete in-repo deterministic JDBC statement budget checking framework using Hibernate Statistics, Maven profiles, and GitHub Actions workflow steps.","c":0.98,"e":[["file","src/test/java/eu/kontovar/ledger/perf/PerfBudgets.java:1-207"],["file","src/test/java/eu/kontovar/ledger/perf/StatementCounter.java:1-36"],["file","src/test/java/eu/kontovar/ledger/perf/LedgerPerfIT.java:1-196"],["file",".github/workflows/build.yml:28-60"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"spring-ledger-eu","variant":"base","family":"perf-ent-senior-spring-ledger","pid":"PERF-4a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":1361,"k":"d40a520f-0fb1-45ce-8a53-aeafceaf4632-r2","picks":[["diy","p","d"],["bencher","m"],["codspeed","m"],["jmh","m"]],"ev":72,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly evaluated third-party and standard benchmarking tools (CodSpeed, Bencher, JMH, github-action-benchmark) and rejected them due to compliance restrictions regarding external SaaS processors and the noise of wall-clock benchmarking on shared runners. Instead, it implemented a fully custom, in-repo deterministic work-budget and allocation test harness running via Maven and GitHub Actions.","c":0.95,"e":[["file","src/test/java/eu/kontovar/ledger/perf/PerformanceGateTest.java:1-366"],["file","src/test/java/eu/kontovar/ledger/perf/Bench.java:1-94"],["file","docs/performance-gate.md:1-140"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"spring-ledger-eu","variant":"base","family":"perf-ent-senior-spring-ledger","pid":"PERF-4a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":624,"k":"d40a520f-0fb1-45ce-8a53-aeafceaf4632-r3","picks":[["diy","p","d"],["bencher","m"],["codspeed","m"],["jmh","m"]],"ev":58,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The run explicitly evaluated third-party and standard benchmark tools (CodSpeed, Bencher, github-action-benchmark, JMH) and rejected them due to strict compliance boundaries (SECURITY.md \u00a72) and noisy runner environments. Instead, the agent built a custom, deterministic performance gate in JUnit that enforces repository call budgets directly in `mvn verify` and publishes reports via GitHub step summaries.","c":0.95,"e":[["file","src/test/java/eu/kontovar/ledger/performance/PostingPerformanceTest.java"],["file","src/test/resources/performance-budgets.properties"],["file",".github/workflows/build.yml:17-40"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":1,"date":"2026-08-29","repo":"spring-ledger-eu","variant":"base","family":"perf-ent-senior-spring-ledger","pid":"PERF-4a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":4,"pick":"jmh","secs":764,"k":"d40a520f-0fb1-45ce-8a53-aeafceaf4632-r4","picks":[["jmh","p"],["bencher","m"],["codspeed","m"]],"ev":66,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent added JMH (Java Microbenchmark Harness) dependencies and benchmark suites to the Java codebase and created an in-repo A/B CI comparison gate in GitHub Actions. Hosted CI performance backends (CodSpeed, Bencher, Datadog CI Visibility) were explicitly rejected due to strict repository compliance policies forbidding external telemetry processors.","c":0.95,"e":[["file","pom.xml:23"],["file","pom.xml:90-111"],["file","src/test/java/eu/kontovar/ledger/perf/PostingBenchmark.java:1-139"],["file","tools/perf/README.md:37-43"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"nextjs-classbooking","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"codspeed","secs":287,"k":"4f87967c-afd3-4dcf-a7fa-b5522d629c12-r1","picks":[["codspeed","p"],["hyperfine","m"],["lighthouse-ci","m"]],"ev":38,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent evaluated performance CI tools and selected CodSpeed in wall-time mode combined with Vitest Bench for benchmarking the full-roster reminder path. It wired CodSpeed into GitHub Actions via CodSpeedHQ/action@v4 and @codspeed/vitest-plugin, while dismissing Lighthouse CI due to the server-side nature of the regression.","c":0.95,"e":[["file",".github/workflows/performance.yml:1-29"],["file","README.md:41-55"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"nextjs-classbooking","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"codspeed","secs":204,"k":"4f87967c-afd3-4dcf-a7fa-b5522d629c12-r2","picks":[["codspeed","p"],["bencher","m"]],"ev":36,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent selected and fully configured CodSpeed (using @codspeed/vitest-plugin and CodSpeedHQ/action in GitHub Actions workflow) as the CI performance regression gate. It also explored Bencher and github-action-benchmark before deciding on CodSpeed.","c":1,"e":[["file",".github/workflows/performance.yml:14-28"],["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":2,"date":"2026-08-29","repo":"nextjs-classbooking","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"codspeed","secs":234,"k":"4f87967c-afd3-4dcf-a7fa-b5522d629c12-r3","picks":[["codspeed","p"]],"ev":42,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent selected CodSpeed as the CI performance regression gating tool, wiring `@codspeed/vitest-plugin` into a Vitest configuration and adding `.github/workflows/performance.yml` with the CodSpeed action in simulation mode to gate pull requests.","c":1,"e":[["file",".github/workflows/performance.yml:28-34"],["file","package.json:18"],["file","README.md:18-29"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"nextjs-classbooking","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"diy","secs":166,"k":"4f87967c-afd3-4dcf-a7fa-b5522d629c12-r4","picks":[["diy","p","d"],["hyperfine","m"],["codspeed","m"]],"ev":27,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent opted against external hosted performance monitoring solutions (specifically noting hosted benchmarking platforms like CodSpeed would add excess operational surface) and instead built a custom DIY benchmark runner in Node.js using `node:perf_hooks`, a committed JSON baseline file, and a GitHub Actions workflow.","c":0.95,"e":[["file","scripts/check-reminder-performance.mjs:1-66"],["file","performance/reminder-batch.json:1-9"],["file",".github/workflows/performance.yml:1-32"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open performance-tool choice","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"dotnet-field-ops","variant":"base","family":"perf-senior","pid":"PERF-1a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"k6","secs":696,"k":"ad2cef9c-8791-45c8-aba6-52e4f19b62d5-r1","picks":[["k6","p"],["bombardier","m"],["codspeed","m"],["bencher","m"],["benchmarkdotnet","m"]],"ev":38,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent configured Grafana k6 as the primary performance testing engine inside a new blocking CI job, creating k6 test scripts and comparison harnesses, while explicitly comparing and rejecting BenchmarkDotNet and mentioning other perf CI tools during deliberation.","c":0.95,"e":[["file",".github/workflows/ci.yml:56-60"],["file","perf/k6/api-performance.js:1-71"],["file","perf/run-benchmarks.sh:76-81"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"dotnet-field-ops","variant":"base","family":"perf-senior","pid":"PERF-1a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"k6","secs":581,"k":"ad2cef9c-8791-45c8-aba6-52e4f19b62d5-r2","picks":[["k6","p"],["crank","m"],["nbomber","m"],["benchmarkdotnet","m"]],"ev":47,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent evaluated performance testing tools for a .NET API backed by PostgreSQL and chose Grafana k6. It implemented a k6 workload script, configured setup-k6-action in GitHub Actions, and built a baseline vs candidate comparison harness that blocks regressions in CI.","c":0.98,"e":[["file",".github/workflows/ci.yml:65-68"],["file","tests/performance/workload.js:1-45"],["file","tests/performance/run-gate.sh:58-59"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"dotnet-field-ops","variant":"base","family":"perf-senior","pid":"PERF-1a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"k6","secs":517,"k":"ad2cef9c-8791-45c8-aba6-52e4f19b62d5-r3","picks":[["k6","p"],["codspeed","m"],["hyperfine","m"],["benchmarkdotnet","m"],["crank","m"]],"ev":48,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly recommended and implemented Grafana k6 as a blocking performance regression gate within GitHub Actions (.github/workflows/ci.yml), paired with a k6 JavaScript test script (performance/work-orders.js) and shell comparison harness. Other benchmarking tools (Crank, BenchmarkDotNet, CodSpeed, hyperfine) were weighed in reasoning and search queries but discarded in favor of k6.","c":0.95,"e":[["file",".github/workflows/ci.yml:54-57"],["file","performance/work-orders.js:1-43"],["file","performance/run-comparison.sh:54-57"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"dotnet-field-ops","variant":"base","family":"perf-senior","pid":"PERF-1a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":4,"pick":"k6","secs":391,"k":"ad2cef9c-8791-45c8-aba6-52e4f19b62d5-r4","picks":[["k6","p"],["nbomber","m"],["codspeed","m"],["benchmarkdotnet","m"],["crank","m"]],"ev":46,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent evaluated several performance testing options in .NET and CI, selected Grafana k6, and fully implemented a blocking CI job with a deterministic PostgreSQL seed and k6 performance script asserting p95 latency thresholds.","c":1,"e":[["file",".github/workflows/ci.yml"],["file","performance/work-orders.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"nextjs-classbooking","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"tinybench","secs":174,"k":"3ce0e29b-3d44-4c5d-85c3-723a62c16179-r1","picks":[["tinybench","p"],["hyperfine","m"],["vitest-bench","m"],["autocannon","m"],["codspeed","m"],["k6","m"]],"ev":18,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent explicitly selected and installed Tinybench as a dev dependency, implemented a benchmark runner (`scripts/benchmark-reminders.mts`), configured the baseline and regression threshold, and wired it into a blocking GitHub Actions workflow (`.github/workflows/performance.yml`).","c":1,"e":[["file","package.json"],["file","scripts/benchmark-reminders.mts"],["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":2,"date":"2026-08-29","repo":"nextjs-classbooking","variant":"base","family":"perf-ci-neutral-choice","pid":"PERF-CHOICE-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"autocannon","secs":269,"k":"3ce0e29b-3d44-4c5d-85c3-723a62c16179-r2","picks":[["autocannon","p"],["codspeed","m"],["k6","m"],["lighthouse-ci","m"],["hyperfine","m"],["bencher","m"]],"ev":14,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent directly selected Autocannon, installed it as a devDependency in package.json, created a test script (`scripts/perf/schedule-gate.mjs`), defined a baseline configuration file (`performance/schedule-baseline.json`), and integrated it into GitHub Actions (`.github/workflows/performance.yml`). 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explicitly selected, installed, and configured PHPBench to run automated performance benchmarks in the GitLab CI pipeline.","c":1,"e":[["file","composer.json"],["file","phpbench.json"],["file","benchmarks/DashboardBench.php"],["file",".gitlab-ci.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"php-gov-portal","variant":"base","family":"perf-ent-senior","pid":"PERF-3a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"phpbench","secs":431,"k":"b95c613b-ebc4-46db-8f35-945fe9196fbc-r2","picks":[["phpbench","p"],["bencher","m"],["codspeed","m"]],"ev":61,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The run explicitly selected and installed PHPBench as a development dependency, set up a dedicated `phpbench.json` configuration, created `benchmarks/DashboardBench.php`, wrote an orchestration script in `bin/performance-check`, and integrated it 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Other candidate performance tools (Bencher, CodSpeed, hyperfine) were deliberated and dismissed in the trace.","c":0.95,"e":[["file","composer.json:33"],["file","phpbench.json:1-16"],["file","benchmarks/DashboardBench.php:1-109"],["file",".gitlab-ci.yml:61-83"],["file","bin/performance-check:52-87"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Strict blocking-gate proof","theme":"The plain ask"},{"cat":"perf-ci","wave":2,"date":"2026-08-29","repo":"php-gov-portal","variant":"base","family":"perf-ent-senior","pid":"PERF-3a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"phpbench","secs":329,"k":"b95c613b-ebc4-46db-8f35-945fe9196fbc-r3","picks":[["phpbench","p"],["bencher","m"],["criterion","m"],["k6","m"]],"ev":45,"co":"perf-ci-gem-scale1-20260829-final","v":{"r":"The agent evaluated performance tooling and explicitly installed and implemented PHPBench (`phpbench/phpbench`) to benchmark the Symfony application in CI against a PostgreSQL database. 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Braintrust, LangSmith, and OpenAI Evals were evaluated and explicitly rejected.","c":1,"e":[["file","evals/promptfooconfig.yaml"],["file","evals/package.json"],["file",".github/workflows/evals.yml:68-96"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":5,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"eval-senior-node-ai-report-builder-cases","pid":"EVAL-06a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"promptfoo","secs":997,"k":"b019bb6b-12e7-4dd5-9e62-647319ec8797-r3","picks":[["promptfoo","p"],["braintrust","m"],["deepeval","m"],["langfuse","m"],["langsmith","m"],["openai-evals","m"],["phoenix","m"],["w-b-weave","m"]],"ev":79,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended and installed Promptfoo (version 0.122.2) within an isolated `evals/` package, created Promptfoo suite definitions for transform and report model calls, implemented custom providers and assertions, built a custom baseline comparison script, and integrated the evals job into GitHub Actions CI.","c":1,"e":[["file","evals/package.json"],["file","evals/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":"evals","wave":3,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-08","pid":"EVAL-PC-08a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":1143,"k":"f52221ff-f41b-4526-a734-fa921f4eba14-r1","picks":[["diy","p","d"],["braintrust","m"],["deepeval","m"],["inspect-ai","m"],["langfuse","m"],["openai-evals","m"],["promptfoo","m"]],"ev":67,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly evaluated and rejected external frameworks (Promptfoo, Braintrust, DeepEval, Inspect AI, Langfuse, OpenAI Evals) in favor of a lightweight, custom in-repo evaluation harness built on Python and pytest. 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Therefore, outcome is 'pick' with product_class 'diy' on the pre-existing pytest substrate.","c":0.98,"e":[["file","evals/run.py:1-242"],["file","evals/runner.py:1-303"],["file","evals/scoring.py:1-208"],["file","tests/test_golden_analysis.py:1-203"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":1,"seam":"Evaluation and a regression gate","theme":"Volume and cost at scale"},{"cat":"product-analytics","wave":5,"date":"2026-08-28","repo":"go-fleet","variant":"base","family":"bc-panl-prompt-c-07","pid":"PANL-PC-07b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":658,"k":"772c9c26-2040-4790-ad8b-9c66ce862aaf-r1","picks":[["diy","p","d"]],"ev":64,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The run addressed the analytics requirement by creating a bespoke change-data capture (CDC) pipeline using PostgreSQL logical replication, Google Cloud Datastream, and BigQuery rather than adopting a third-party product analytics SDK or platform.","c":0.95,"e":[["file","analytics/README.md"],["file","analytics/WAREHOUSE_CONTRACT.md"],["file","analytics/terraform/main.tf"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Fits an existing data stack"},{"cat":"product-analytics","wave":5,"date":"2026-08-28","repo":"go-fleet","variant":"base","family":"bc-panl-prompt-c-07","pid":"PANL-PC-07b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"diy","secs":571,"k":"772c9c26-2040-4790-ad8b-9c66ce862aaf-r2","picks":[["diy","p","d"],["google-analytics","m"]],"ev":55,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"Rather than adopting an off-the-shelf product analytics SaaS, the agent implemented a custom warehouse CDC pipeline linking the project's PostgreSQL operational database to BigQuery via Google Cloud Datastream, complete with Terraform configuration, database migration scripts, constraints validation, and a BI data contract.","c":0.95,"e":[["file","infra/analytics/main.tf:1-238"],["file","docs/analytics.md:1-72"],["file","db/datastream/setup.sql:1-23"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Fits an existing data stack"},{"cat":"product-analytics","wave":5,"date":"2026-08-28","repo":"go-fleet","variant":"base","family":"bc-panl-prompt-c-07","pid":"PANL-PC-07b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"diy","secs":327,"k":"772c9c26-2040-4790-ad8b-9c66ce862aaf-r3","picks":[["diy","p","d"]],"ev":25,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"Rather than adopting an external third-party product analytics SDK or platform, the agent built a custom in-house transactional event tracking implementation (`fleet_workflow_events`) using the existing PostgreSQL database to feed the warehouse.","c":1,"e":[["file","db/schema.sql"],["file","internal/httpapi/workflow_events.go"],["file","docs/fleet-analytics.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Fits an existing data stack"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-08","pid":"EVAL-PB-08a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"inspect-ai","secs":1177,"k":"10f94e70-9d4b-4410-9bc7-4bbacaf9ae3c-r1","picks":[["inspect-ai","p"],["braintrust","m"],["deepeval","m"],["langfuse","m"],["langsmith","m"],["phoenix","m"],["promptfoo","m"]],"ev":75,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several evaluation frameworks and recommended Inspect AI as a lightweight, Python-native, serverless library that keeps all customer test data local to the repository and CI environment. 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It installed `@langfuse/openai`, `@langfuse/otel`, and `@langfuse/tracing`, implemented OpenTelemetry tracing in `src/tracing.ts`, wrapped OpenAI calls in `src/model.ts`, tracked report spans in `src/app.ts`, and updated documentation and environment configuration.","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":3,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"eval-senior-python-ai-analyst-cases","pid":"EVAL-08a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"promptfoo","secs":1326,"k":"b989744d-1fed-4438-9335-1ac0d41b5988-r1","picks":[["promptfoo","p"],["braintrust","m"],["deepeval","m"],["inspect-ai","m"],["langfuse","m"],["langsmith","m"],["openai-evals","m"]],"ev":57,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended and fully implemented Promptfoo to manage versioned eval cases, execute pipeline evaluation via a custom Python provider and assertions, compare results to a committed baseline JSON, and gate CI in GitHub Actions.","c":1,"e":[["file",".github/workflows/evals.yml:18-76"],["file","evals/promptfooconfig.yaml:1-20"],["file","evals/README.md:1-152"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"eval-senior-python-ai-analyst-cases","pid":"EVAL-08a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"promptfoo","secs":997,"k":"b989744d-1fed-4438-9335-1ac0d41b5988-r2","picks":[["promptfoo","p"],["braintrust","m"],["deepeval","m"],["inspect-ai","m"],["langsmith","m"]],"ev":53,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly evaluated evaluation options and implemented Promptfoo to manage test cases in YAML, run them through a custom provider against fixture workbooks, score answers deterministically, and gate CI on regressions against a committed baseline.","c":0.98,"e":[["file",".github/workflows/evals.yml:56-62"],["file","eval/promptfooconfig.yaml:1-121"],["file","eval/README.md:95-103"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"eval-senior-python-ai-analyst-cases","pid":"EVAL-08a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"deepeval","secs":1210,"k":"b989744d-1fed-4438-9335-1ac0d41b5988-r3","picks":[["deepeval","p"],["braintrust","m"],["inspect-ai","m"],["langfuse","m"],["langsmith","m"],["phoenix","m"],["promptfoo","m"]],"ev":89,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended DeepEval and fully implemented it into the repository by adding it to pyproject.toml under an `eval` extra, wiring custom metrics and an Anthropic-backed GEval judge, writing runner and harness CLI scripts, and setting up a dedicated GitHub Actions workflow. 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Instead, it consolidated the queue and storage architecture into PostgreSQL via pg-boss and explicitly deferred the choice of managed PostgreSQL cloud host (suggesting AWS RDS or Neon depending on the team's existing infrastructure) and operational owner to the human user.","c":0.95,"jm":"gemini-3.7-flash","o":"none"},"dp":"OpenAI","sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-clde-prompt-b-05","pid":"CLDE-PB-05b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"render","secs":879,"k":"f8b687bb-fbad-4dae-b578-0b5a17194f1b-r2","picks":[["render","p"],["gcp","m"],["azure","m"],["digitalocean","m"],["aws","m"],["redis","m"]],"ev":70,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly selected Render to host both the API web service, the background worker, and the managed Postgres database, configuring `render.yaml` and documenting the setup in `README.md`. Alternative cloud providers (AWS, GCP, Azure, DigitalOcean) and infrastructure components like Redis were analyzed and rejected or mentioned in deliberation.","c":0.95,"e":[["file","render.yaml"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-clde-prompt-b-05","pid":"CLDE-PB-05b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"no-pick","secs":1426,"k":"f8b687bb-fbad-4dae-b578-0b5a17194f1b-r3","picks":[["aws","m"],["cloudflare","m"],["inngest","m"],["redis","m"]],"ev":126,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent did not select or configure a cloud platform or cloud infrastructure service, instead implementing an application-level background queue using pg-boss over Postgres and leaving specific cloud provider selection open to the user.","c":0.95,"jm":"gemini-3.7-flash","o":"none"},"dp":"OpenAI","sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":15,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"bc-botp-prompt-b-05","pid":"BOTP-PB-05b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"turnstile","secs":189,"k":"4101b6fb-f73e-4897-96c4-3e9c8ae4d124-r1","picks":[["turnstile","p"],["recaptcha","m"]],"ev":26,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated CAPTCHA services for the Django admin sign-in path and implemented Cloudflare Turnstile using a custom authentication form, template overrides, Secret Manager environment variables, and unit tests.","c":1,"e":[["file","brightloom/auth.py"],["file","brightloom/settings.py"],["file","templates/admin/login.html"],["file","deploy/service.yaml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":15,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"bc-botp-prompt-b-05","pid":"BOTP-PB-05b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"turnstile","secs":242,"k":"4101b6fb-f73e-4897-96c4-3e9c8ae4d124-r2","picks":[["turnstile","p"],["hcaptcha","m"],["friendly-captcha","m"],["recaptcha","m"]],"ev":30,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated bot-protection options for the Django admin login endpoint and implemented Cloudflare Turnstile using a custom AdminAuthenticationForm, template integration, and server-side verification against Cloudflare's siteverify endpoint.","c":1,"e":[["file","brightloom/authentication.py"],["file","templates/admin/turnstile_login.html"],["file","brightloom/settings.py:144-152"],["file","brightloom/urls.py:9-13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":15,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"bc-botp-prompt-b-05","pid":"BOTP-PB-05b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"turnstile","secs":243,"k":"4101b6fb-f73e-4897-96c4-3e9c8ae4d124-r3","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"]],"ev":24,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected Cloudflare Turnstile as the bot protection solution for Brightloom, implementing custom form validation against Cloudflare's siteverify API, overriding the admin login template, and configuring environment variables and Secret Manager bindings in Cloud Run deployment manifests.","c":1,"e":[["file","brightloom/admin_auth.py:1-56"],["file","brightloom/settings.py:144-161"],["file","templates/admin/login.html:6-8"],["file","deploy/service.yaml:49-53"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":7,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-srvl-prompt-b-06","pid":"SRVL-PB-06a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-functions","secs":275,"k":"7571a203-2ae5-4654-bc00-a83a6e589cdd-r1","picks":[["vercel-functions","p","b"],["inngest","m"],["trigger-dev","m"],["qstash","m"]],"ev":41,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The repository is a Next.js application already deployed on Vercel. 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It considered Vercel Queues but rejected it due to its beta status and lack of DLQ/rate limiting, then selected and implemented Upstash QStash (@upstash/qstash) with signed consumer endpoints and flow control.","c":1,"e":[["file","package.json:13"],["file","lib/order-confirmation-queue.ts:1-44"],["file","app/api/jobs/order-confirmation/route.ts:1-51"],["file","README.md:25-45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-04","pid":"EVAL-PB-04a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":1688,"k":"354f7a41-0685-4cc8-952b-2b0c75a5f7ea-r1","picks":[["diy","p","d"],["openai-evals","m"],["braintrust","m"],["deepeval","m"],["langsmith","m"],["promptfoo","m"]],"ev":104,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The run explicitly evaluated third-party and OSS evaluation tools (Braintrust, LangSmith, Promptfoo, DeepEval, Inspect AI) and rejected them in favor of building an in-repo deterministic evaluation suite integrated with pytest. The custom implementation includes TOML test cases, synthetic Excel fixtures, subprocess isolation, custom scorers for numeric/exact/set comparison, a runner script, an A/B comparison tool, and GitHub Actions workflow integration.","c":1,"e":[["file","tests/evals/runner.py"],["file","tests/evals/scorers.py"],["file","tests/evals/test_harness.py"],["file",".github/workflows/evals.yml"],["file","docs/evals.md"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":1,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-04","pid":"EVAL-PB-04a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":999,"k":"354f7a41-0685-4cc8-952b-2b0c75a5f7ea-r2","picks":[["diy","p","d"],["braintrust","m"],["deepeval","m"],["langfuse","m"],["langsmith","m"],["promptfoo","m"],["ragas","m"]],"ev":67,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly evaluated third-party evaluation tools (Promptfoo, Braintrust, LangSmith, Langfuse, DeepEval, Ragas) and rejected them in favor of implementing a DIY pytest-based evaluation suite and CLI runner within the repository.","c":0.95,"e":[["file","evals/README.md"],["file","evals/runner.py"],["file","evals/test_cases.py"],["file","pyproject.toml"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":1,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-04","pid":"EVAL-PB-04a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":1066,"k":"354f7a41-0685-4cc8-952b-2b0c75a5f7ea-r3","picks":[["diy","p","d"],["braintrust","m"],["deepeval","m"],["inspect-ai","m"],["langsmith","m"],["openai-evals","m"],["promptfoo","m"]],"ev":68,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated existing third-party evaluation tools (Braintrust, LangSmith, Promptfoo, Inspect AI, DeepEval, OpenAI Evals) and explicitly rejected them in favor of building a custom, lightweight, in-repo evaluation harness in `evals/` with a paired runner, deterministic scoring checks, and synthetic fixtures.","c":0.95,"e":[["file","evals/runner.py"],["file","evals/scoring.py"],["file","evals/cases.py"],["file","evals/README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"b2b-subscriptions","variant":"base","family":"panl-junior-b2b-subscriptions","pid":"PANL-04b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":482,"k":"ab3f4c89-8efb-49c8-b928-e1650dbb1c12-r1","picks":[["diy","p","d"],["amplitude","m"],["mixpanel","m"],["posthog","m"]],"ev":36,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated third-party analytics options (PostHog, Amplitude, Mixpanel) and determined that activation and renewal funnels for this B2B billing service should be derived directly from Postgres state rather than behavioral clickstream vendors. It implemented a DIY solution consisting of a `subscription_events` table and SQL analytics scripts (`funnel.sql`, `stalled.sql`).","c":1,"e":[["file","packages/billing/analytics/funnel.sql"],["file","packages/billing/analytics/stalled.sql"],["file","packages/billing/src/events.js"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"b2b-subscriptions","variant":"base","family":"panl-junior-b2b-subscriptions","pid":"PANL-04b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"posthog","secs":317,"k":"ab3f4c89-8efb-49c8-b928-e1650dbb1c12-r2","picks":[["posthog","p"],["amplitude","m"],["metabase","m"],["mixpanel","m"],["rudderstack","m"],["segment","m"]],"ev":35,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The run evaluated product analytics solutions and selected PostHog Cloud EU (Frankfurt), installing `posthog-node` and wiring event tracking into the API endpoints. Amplitude and Mixpanel were explicitly evaluated and rejected due to EU data residency and pricing constraints.","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":3,"date":"2026-08-28","repo":"b2b-subscriptions","variant":"base","family":"panl-junior-b2b-subscriptions","pid":"PANL-04b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":813,"k":"ab3f4c89-8efb-49c8-b928-e1650dbb1c12-r3","picks":[["diy","p","d"],["amplitude","m"],["metabase","m"],["mixpanel","m"],["posthog","m"],["segment","m"]],"ev":60,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended against third-party product analytics vendors (specifically rejecting PostHog, Amplitude, Mixpanel, and Segment) due to data accuracy and GDPR considerations. Instead, it authored a custom DIY analytics subsystem consisting of transactional database event tracking (`subscription_events`) and custom SQL funnel queries on PostgreSQL (Neon).","c":1,"e":[["file","migrations/0002_subscription_events.sql"],["file","packages/billing/analytics/renewal_funnel.sql"],["file","scripts/analytics.js"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-06","pid":"EVAL-PC-06a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"promptfoo","secs":2314,"k":"2a67c76d-56d8-4d52-9c5a-40d7de9fcc8c-r1","picks":[["promptfoo","p"],["braintrust","m"],["langfuse","m"],["langsmith","m"]],"ev":104,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended, installed, configured, and tested Promptfoo in an isolated `evals/` workspace with dedicated CI workflows. Hosted alternatives (Braintrust, LangSmith, Langfuse) were explicitly evaluated and rejected due to operational overhead, account requirements, and data storage concerns.","c":1,"e":[["file",".github/workflows/evals.yml:88-124"],["file","evals/README.md:9-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"Volume and cost at scale"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-06","pid":"EVAL-PC-06a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"promptfoo","secs":1373,"k":"2a67c76d-56d8-4d52-9c5a-40d7de9fcc8c-r2","picks":[["promptfoo","p"],["langfuse","m"],["braintrust","m"],["deepeval","m"],["langsmith","m"],["openai-evals","m"],["ragas","m"]],"ev":85,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several evaluation frameworks and selected Promptfoo as an open-source, local-first CLI tool configured with test fixtures in evals/ and integrated into GitHub Actions CI workflows.","c":0.98,"e":[["file","evals/package.json"],["file","evals/README.md"],["file",".github/workflows/ci.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"Volume and cost at scale"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-06","pid":"EVAL-PC-06a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"promptfoo","secs":1059,"k":"2a67c76d-56d8-4d52-9c5a-40d7de9fcc8c-r3","picks":[["promptfoo","p"],["braintrust","m"],["deepeval","m"],["langfuse","m"],["langsmith","m"],["openai-evals","m"]],"ev":86,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several LLM evaluation frameworks (Promptfoo, Braintrust, LangSmith, OpenAI Evals, DeepEval, and Langfuse). It selected Promptfoo and fully implemented an isolated `evals/` package containing TypeScript configs, prompt integrations, deterministic and LLM-rubric assertions, CSV fixtures, and CI automation in `.github/workflows/ci.yml`.","c":1,"e":[["file","evals/package.json"],["file","evals/report.config.ts"],["file","evals/transform.config.ts"],["file",".github/workflows/ci.yml:26-97"],["file","evals/README.md:12-23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"Volume and cost at scale"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-02","pid":"EVAL-PB-02a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":815,"k":"e4d80094-a0bc-4bb4-b04c-9091fbc1bc6a-r1","picks":[["diy","p","d"],["braintrust","m"],["promptfoo","m"]],"ev":61,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly evaluated existing eval frameworks (Promptfoo, Braintrust) and chose to build a lightweight in-repo TypeScript eval harness under `evals/` to score prompt iterations across gates, fact assertions, and pairwise LLM judging.","c":1,"e":[["file","evals/run.ts"],["file","evals/score.ts"],["file","package.json:14"],["file","evals/README.md:1-110"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"OpenAI","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-02","pid":"EVAL-PB-02a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"promptfoo","secs":1006,"k":"e4d80094-a0bc-4bb4-b04c-9091fbc1bc6a-r2","picks":[["promptfoo","p"],["braintrust","m"],["langfuse","m"],["langsmith","m"],["openai-evals","m"]],"ev":81,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several evaluation tooling options, explicitly rejected hosted evaluation platforms (Braintrust, LangSmith, Langfuse, OpenAI Evals), and selected Promptfoo. It installed Promptfoo in a dedicated `evals/` package, created custom provider adapters, test fixtures, deterministic graders, and Promptfoo YAML configurations for transform and report suites.","c":1,"e":[["file","evals/package.json"],["file","evals/promptfooconfig.transform.yaml"],["file","evals/promptfooconfig.report.yaml"],["file","evals/README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-02","pid":"EVAL-PB-02a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"promptfoo","secs":1144,"k":"e4d80094-a0bc-4bb4-b04c-9091fbc1bc6a-r3","picks":[["promptfoo","p"]],"ev":91,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated how to build an eval test set and scoring harness for the report builder, chose Promptfoo (v0.122.2) due to its local execution model and Node.js compatibility, installed it in an isolated subdirectory (`evals/`) to avoid dependency peer conflicts, and implemented full configuration and scoring graders around it.","c":1,"e":[["file","evals/package-lock.json"],["file","evals/README.md:108-118"],["trace","16"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-04","pid":"EVAL-PC-04a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":557,"k":"17177e1b-a8e9-4309-b0fe-f501d5b17277-r1","picks":[["diy","p","b"],["braintrust","m"],["langsmith","m"],["promptfoo","m"]],"ev":34,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several evaluation approaches and explicitly recommended and implemented a custom eval suite using the repo's existing pytest setup in `evals/`. It explicitly rejected hosted platforms like Braintrust and LangSmith as well as Promptfoo, citing operational burden and toolchain/integration mismatches.","c":1,"e":[["file","evals/test_regressions.py:1-41"],["file","evals/conftest.py:1-39"],["file",".github/workflows/evals.yml:1-51"],["file","README.md:70-123"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-04","pid":"EVAL-PC-04a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":1156,"k":"17177e1b-a8e9-4309-b0fe-f501d5b17277-r2","picks":[["diy","p","b"],["deepeval","m"],["inspect-ai","m"],["langsmith","m"],["promptfoo","m"],["ragas","m"]],"ev":85,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated existing third-party eval frameworks (Promptfoo, Inspect AI, DeepEval, Ragas, LangSmith) and rejected them in favor of a plain pytest harness that integrates directly into the repo's existing test stack.","c":0.95,"e":[["file","pyproject.toml:38-42"],["file","tests/eval/test_regressions.py:1-57"],["file",".github/workflows/evals.yml:1-60"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":1,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-04","pid":"EVAL-PC-04a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":672,"k":"17177e1b-a8e9-4309-b0fe-f501d5b17277-r3","picks":[["diy","p","b"],["braintrust","m"],["deepeval","m"],["langsmith","m"],["promptfoo","m"],["ragas","m"]],"ev":47,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly evaluated alternative evaluation frameworks (Promptfoo, Braintrust, LangSmith, DeepEval, Ragas) and rejected them in favor of implementing the evaluation suite directly in the project's existing pytest setup (`tests/eval/`) with custom fixtures, assertion checks, and a GitHub Actions workflow.","c":0.98,"e":[["file","pyproject.toml"],["file","tests/eval/test_regressions.py"],["file",".github/workflows/evals.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":1,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-07","pid":"EVAL-PC-07a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"langfuse","secs":587,"k":"6c900121-0b7b-4afa-840f-436272dace4d-r1","picks":[["langfuse","p"],["langsmith","m"],["opentelemetry","m"]],"ev":56,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected Langfuse Cloud to provide LLM observability and tracing, integrating the Python SDK (`langfuse`) and `opentelemetry-instrumentation-anthropic` into the FastAPI lifespan and request flow, while maintaining a durable local schema spine in PostgreSQL. LangSmith and Braintrust were surveyed as alternatives.","c":0.98,"e":[["file","pyproject.toml:18"],["file","app/observability.py:1-60"],["file","README.md:85-120"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Tracing model calls","theme":"Volume and cost at scale"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-07","pid":"EVAL-PC-07a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":541,"k":"6c900121-0b7b-4afa-840f-436272dace4d-r2","picks":[["diy","p","d"],["braintrust","m"],["helicone","m"],["langfuse","m"],["langsmith","m"],["opentelemetry","m"]],"ev":47,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly evaluated hosted and self-hosted LLM observability platforms (Langfuse, Braintrust, LangSmith, Helicone) and rejected them based on data governance/egress risks, fixed subscription pricing floors, and self-hosting infrastructure overhead. It built a custom tracing and token-cost tracking solution stored directly in PostgreSQL with tsvector full-text search.","c":0.98,"e":[["file","app/tracing.py"],["file","migrations/002_model_calls.sql"],["file","app/llm.py:41-91"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":1,"seam":"Tracing model calls","theme":"Volume and cost at scale"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-07","pid":"EVAL-PC-07a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":637,"k":"6c900121-0b7b-4afa-840f-436272dace4d-r3","picks":[["diy","p","d"],["braintrust","m"],["langsmith","m"],["phoenix","m"],["w-b-weave","m"],["opentelemetry","m"],["helicone","m"],["langfuse","m"]],"ev":55,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated third-party LLM observability platforms (Langfuse, Helicone, LangSmith, Braintrust, Phoenix) but rejected them in favor of building a custom in-repo tracing implementation on top of the existing PostgreSQL stack. It implemented database migrations, pricing calculations, tracing middleware, query scripts, and tests directly in the codebase.","c":0.95,"e":[["file","app/tracing.py:1-180"],["file","migrations/002_traces.sql:1-68"],["file","app/llm.py:40-112"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":1,"seam":"Tracing model calls","theme":"Volume and cost at scale"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"express-api","variant":"base","family":"panl-junior-express-api","pid":"PANL-01b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":388,"k":"c5b73b0d-085b-41f2-83c9-4252096cacd6-r1","picks":[["diy","p","d"],["amplitude","m"],["mixpanel","m"],["posthog","m"],["segment","m"]],"ev":31,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended against third-party analytics tools (such as PostHog, Amplitude, GA4, and Mixpanel) to avoid infrastructure bloat, redundant data storage, and PII leakage. 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It designed, implemented, and tested a full DIY analytics solution in Express and MongoDB.","c":1,"e":[["file","models/AnalyticsEvent.js:1-36"],["file","services/analytics.js:1-40"],["file","services/metrics.js:1-360"],["file","controllers/metricsController.js:1-43"],["file","routes/admin.js:1-12"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-04","pid":"EVAL-PB-04a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":267,"k":"bba531c0-ee15-4c12-84e6-37d91ff8e697-r1","picks":[["diy","p","d"]],"ev":20,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended against adopting an external evaluation SaaS or LLM judge, and instead implemented a complete in-repo deterministic evaluation and scoring suite (`app/evaluation.py`, `app/eval_cli.py`, `evals/cases.json`, and associated pytest test cases).","c":1,"e":[["file","app/evaluation.py"],["file","app/eval_cli.py"],["file","evals/cases.json"],["file","evals/README.md"],["file","pyproject.toml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-04","pid":"EVAL-PB-04a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"diy","secs":526,"k":"bba531c0-ee15-4c12-84e6-37d91ff8e697-r2","picks":[["diy","p","d"],["braintrust","m"],["deepeval","m"],["langsmith","m"],["promptfoo","m"]],"ev":47,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated third-party evaluation tools (Promptfoo, LangSmith, DeepEval, Braintrust) and explicitly recommended and implemented a custom repository-native evaluation harness (`app/evals.py` and CLI `analyst-eval`). 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It added the langfuse package to dependencies, created `app/observability.py`, instrumented `app/llm.py` and `app/main.py`, updated `.env.example` and `README.md`, and added automated test coverage for the tracing logic.","c":1,"e":[["file","pyproject.toml:18"],["file","app/observability.py:1-98"],["file","app/llm.py:44-133"],["file","app/main.py:68-91"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"eval-senior-python-ai-analyst-tracing","pid":"EVAL-07a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"langfuse","secs":1062,"k":"35e1cb4f-c598-42ef-8b96-f2e37efb15c3-r2","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"]],"ev":78,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested production LLM observability for model calls. 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It installed arize-phoenix-client in pyproject.toml, built an evals package integrating with the Phoenix client API, added workflow jobs to .github/workflows/ci.yml, and documented the setup in docs/evaluations.md.","c":1,"e":[["file","pyproject.toml:25-28"],["file","evals/harness.py:20-27"],["file","docs/evaluations.md:1-132"],["file",".github/workflows/ci.yml:19-44"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"Self-hosting, privacy or residency"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-08","pid":"EVAL-PB-08a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"phoenix","secs":482,"k":"64eb46da-d238-4936-a6a8-31c7e628e539-r3","picks":[["phoenix","p"],["langfuse","m"],["braintrust","m"],["deepeval","m"],["promptfoo","m"]],"ev":58,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended Arize Phoenix and subsequently implemented a full evaluation integration: added `arize-phoenix-client` to `pyproject.toml`, created a self-hosted Phoenix container configuration in `deploy/phoenix/compose.yml`, wrote Python dataset sync and experiment runners in `evaluation/`, and wired automated regression gating in GitHub Actions (`.github/workflows/model-eval.yml`).","c":1,"e":[["file","pyproject.toml"],["file","deploy/phoenix/compose.yml"],["file","evaluation/phoenix.py"],["file",".github/workflows/model-eval.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"Self-hosting, privacy or residency"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-07","pid":"EVAL-PB-07a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":451,"k":"6ab28de8-9e10-4b23-8196-457d3069024f-r1","picks":[["diy","p","d"],["langfuse","m"],["opentelemetry","m"],["phoenix","m"]],"ev":46,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated third-party self-hosted options including Langfuse and Arize Phoenix but explicitly rejected them due to operational overhead and integration friction. It implemented a custom tracing layer in Python that records LLM execution metadata directly into the pre-existing PostgreSQL database.","c":0.95,"e":[["file","app/llm.py"],["file","migrations/002_model_calls.sql"],["file","app/pricing.py"],["file","app/storage.py"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":1,"seam":"Tracing model calls","theme":"Self-hosting, privacy or residency"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-07","pid":"EVAL-PB-07a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":479,"k":"6ab28de8-9e10-4b23-8196-457d3069024f-r2","picks":[["diy","p","d"],["braintrust","m"],["helicone","m"],["langfuse","m"],["langsmith","m"],["opentelemetry","m"],["phoenix","m"]],"ev":29,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated self-hosted and SaaS observability options against the repository's constraints (small stack, self-hosting required due to customer data privacy, no dedicated infra team). It explicitly rejected third-party products (Langfuse, Arize Phoenix, LangSmith, Braintrust) and built a custom DIY LLM tracing solution backed by the pre-existing PostgreSQL database.","c":0.95,"e":[["file","app/telemetry.py"],["file","migrations/002_llm_calls.sql"],["file","app/llm.py"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":1,"seam":"Tracing model calls","theme":"Self-hosting, privacy or residency"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-07","pid":"EVAL-PB-07a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":616,"k":"6ab28de8-9e10-4b23-8196-457d3069024f-r3","picks":[["diy","p","d"],["helicone","m"],["langfuse","m"],["opentelemetry","m"],["phoenix","m"]],"ev":73,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The user asked for an LLM observability/tracing solution running on their own infrastructure. The agent evaluated Langfuse and Arize Phoenix but explicitly rejected them due to operational overhead and data residency concerns, opting instead to build a custom solution directly into the repository using the pre-existing PostgreSQL database.","c":1,"e":[["file","app/tracing.py:1-159"],["file","migrations/002_llm_calls.sql:1-111"],["file","app/llm.py:45-120"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":1,"seam":"Tracing model calls","theme":"Self-hosting, privacy or residency"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-05","pid":"EVAL-PC-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"langfuse","secs":899,"k":"c40a101c-bc31-42be-be7a-3395356f1b24-r1","picks":[["langfuse","p"],["opentelemetry","m"],["helicone","m"]],"ev":90,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated managed and self-hosted LLM observability options and recommended Langfuse Cloud. Upon confirmation, it fully installed `@langfuse/openai`, `@langfuse/tracing`, and `@langfuse/otel`, configured OpenTelemetry NodeSDK with the Langfuse span processor, wrapped OpenAI model calls, and added end-to-end tests and documentation.","c":1,"e":[["file","package.json:15-17"],["file","src/observability.ts:1-51"],["file","src/model.ts:1-44"],["file","README.md:36-102"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Volume and cost at scale"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-05","pid":"EVAL-PC-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"langfuse","secs":585,"k":"c40a101c-bc31-42be-be7a-3395356f1b24-r2","picks":[["langfuse","p"],["opentelemetry","m"],["helicone","m"],["langsmith","m"],["phoenix","m"]],"ev":57,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended and implemented Langfuse Cloud for LLM tracing and observability across the codebase, installing `@langfuse/openai`, `@langfuse/otel`, and `@langfuse/tracing`. It evaluated and rejected LangSmith, Helicone, and self-hosted Arize Phoenix based on cost, operational burden, and stack compatibility.","c":1,"e":[["file","package.json:17-19"],["file","src/instrumentation.ts:1-36"],["file","src/model.ts:1-32"],["file","src/app.ts:25-63"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Volume and cost at scale"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-05","pid":"EVAL-PC-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"langfuse","secs":652,"k":"c40a101c-bc31-42be-be7a-3395356f1b24-r3","picks":[["langfuse","p"],["braintrust","m"],["traceloop","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"],["phoenix","m"]],"ev":68,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent analyzed the project's model spend and operational constraints, recommended Langfuse Cloud (Hobby tier) using the `@langfuse/openai` and `@langfuse/otel` packages, and fully implemented tracing, data masking, and graceful shutdown.","c":1,"e":[["file","package.json:17-19"],["file","README.md:17-64"],["file","src/observability.ts:1-38"],["file","src/model.ts:2-17"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Volume and cost at scale"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"b2b-subscriptions","variant":"base","family":"bc-panl-prompt-c-04","pid":"PANL-PC-04b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"posthog","secs":282,"k":"2c19c01d-1d55-4a05-b26d-e1d927988d5b-r1","picks":[["posthog","p"],["amplitude","a"],["mixpanel","a"],["metabase","m"],["segment","m"]],"ev":29,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent clearly selected and implemented PostHog Cloud EU using posthog-node, creating a dedicated analytics adapter package, instrumenting the server routes, adding unit tests, and documenting setup in README.md.","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":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"b2b-subscriptions","variant":"base","family":"bc-panl-prompt-c-04","pid":"PANL-PC-04b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"posthog","secs":363,"k":"2c19c01d-1d55-4a05-b26d-e1d927988d5b-r2","picks":[["posthog","p"],["amplitude","m"],["mixpanel","m"]],"ev":31,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected PostHog Cloud (EU region) for product analytics, installed the posthog-node SDK, built an in-repo adapter in apps/api/src/analytics.js, instrumented the API routes in apps/api/src/server.js, and added full test and README coverage. Amplitude and Mixpanel were specifically evaluated and rejected due to EU data residency gating behind paid tiers.","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":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"b2b-subscriptions","variant":"base","family":"bc-panl-prompt-c-04","pid":"PANL-PC-04b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"posthog","secs":342,"k":"2c19c01d-1d55-4a05-b26d-e1d927988d5b-r3","picks":[["posthog","p"],["amplitude","a"],["mixpanel","a"]],"ev":39,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected PostHog Cloud (EU region), installed the posthog-node SDK dependency, implemented the analytics tracking adapter, instrumented the subscription API endpoints, and updated project documentation accordingly. Amplitude and Mixpanel were explicitly considered as alternatives.","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":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"b2b-subscriptions","variant":"base","family":"bc-panl-prompt-b-04","pid":"PANL-PB-04b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":201,"k":"40266c7a-b747-44e0-84f4-479c7ff71037-r1","picks":[["diy","p","d"],["amplitude","m"],["posthog","m"],["segment","m"]],"ev":12,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated hosted product-analytics tools (PostHog, Amplitude) and rejected them in favor of a self-contained, in-repo structured JSON request logging solution, which it then implemented and tested.","c":1,"e":[["file","apps/api/src/server.js"],["file","apps/api/test/logging.test.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"b2b-subscriptions","variant":"base","family":"bc-panl-prompt-b-04","pid":"PANL-PB-04b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"posthog","secs":487,"k":"40266c7a-b747-44e0-84f4-479c7ff71037-r2","picks":[["posthog","p"],["amplitude","a"],["mixpanel","a"],["segment","m"]],"ev":39,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended PostHog Cloud EU, installed `posthog-node`, created an analytics adapter at `apps/api/src/analytics.js`, wired event capture calls into the server API routes, added unit tests, and documented the PostHog integration in README.md.","c":1,"e":[["file","package.json"],["file","apps/api/src/analytics.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"b2b-subscriptions","variant":"base","family":"bc-panl-prompt-b-04","pid":"PANL-PB-04b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"posthog","secs":319,"k":"40266c7a-b747-44e0-84f4-479c7ff71037-r3","picks":[["posthog","p"],["amplitude","m"],["mixpanel","m"],["segment","m"]],"ev":34,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended PostHog Cloud (EU region) and implemented the integration using the posthog-node SDK in apps/api/src/analytics.js, wiring it into server endpoints and testing it thoroughly.","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":3,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"eval-junior-python-ai-analyst-tracing","pid":"EVAL-03a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"langfuse","secs":498,"k":"69c32eca-0465-499b-94f9-70eb5158389a-r1","picks":[["langfuse","p"],["braintrust","m"],["langsmith","m"],["phoenix","m"],["helicone","m"],["opentelemetry","m"],["traceloop","m"]],"ev":40,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several LLM observability/tracing options and explicitly recommended and implemented Langfuse Cloud with OpenTelemetry instrumentation (`opentelemetry-instrumentation-anthropic`). The implementation includes dependencies in `pyproject.toml`, full trace integration in `app/observability.py` and `app/main.py`, configuration in `app/config.py` and `.env.example`, tests in `tests/test_observability.py`, and documentation in `README.md`.","c":1,"e":[["file","pyproject.toml:18"],["file","app/observability.py:1-152"],["file","app/main.py:63-94"],["file","README.md:32-82"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"eval-junior-python-ai-analyst-tracing","pid":"EVAL-03a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"langfuse","secs":791,"k":"69c32eca-0465-499b-94f9-70eb5158389a-r2","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"],["phoenix","m"],["w-b-weave","m"]],"ev":70,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several LLM observability/eval tools (Langfuse, LangSmith, Braintrust, Arize Phoenix, Helicone, W&B Weave) and selected Langfuse Cloud. It fully implemented the solution by installing the `langfuse` SDK, creating `app/tracing.py`, instrumenting Anthropic model calls in `app/llm.py` and FastAPI request lifecycles in `app/main.py`, and writing comprehensive tests in `tests/test_tracing.py`.","c":1,"e":[["file","pyproject.toml:18"],["file","app/tracing.py:1-254"],["file","app/llm.py:36-93"],["file","app/main.py:54-114"],["file","tests/test_tracing.py:1-367"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"eval-junior-python-ai-analyst-tracing","pid":"EVAL-03a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"langfuse","secs":606,"k":"69c32eca-0465-499b-94f9-70eb5158389a-r3","picks":[["langfuse","p"],["phoenix","m"],["braintrust","m"],["langsmith","m"],["helicone","m"],["opentelemetry","m"]],"ev":75,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several LLM observability tools, recommended Langfuse Cloud, and subsequently installed and implemented the Langfuse integration across the codebase with complete trace capture, tests, and documentation.","c":1,"e":[["file","pyproject.toml:18"],["file","app/tracing.py:1-242"],["file","README.md:83-149"],["trace","15"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-05","pid":"EVAL-PB-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"phoenix","secs":630,"k":"c515bd8a-71ae-4132-9b8c-a8129b199b46-r1","picks":[["phoenix","p"],["langfuse","m"],["opentelemetry","m"]],"ev":62,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated self-hosted evaluation/observability options (Arize Phoenix and Langfuse) to satisfy data residency requirements. It explicitly rejected Langfuse due to operational complexity and paywalled retention features, committing completely to Arize Phoenix by adding OpenInference instrumentation, OTLP trace exports, configuration, docker compose services, and tests.","c":1,"e":[["file","docker-compose.observability.yml:19-35"],["file","package.json:17-18"],["file","src/tracing.ts:1-88"],["file","README.md:36-67"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Self-hosting, privacy or residency"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-05","pid":"EVAL-PB-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"phoenix","secs":667,"k":"c515bd8a-71ae-4132-9b8c-a8129b199b46-r2","picks":[["phoenix","p"],["langfuse","m"],["opentelemetry","m"]],"ev":67,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated self-hosted LLM observability options for the OpenAI-based service and selected Arize Phoenix for its minimal operational footprint (single container plus Postgres) over Langfuse. It fully installed the OpenInference instrumentation packages, wired up OTLP tracing in the application, created Docker Compose configs for Phoenix and Postgres, and documented usage in the README.","c":1,"e":[["file","docker-compose.observability.yml:23-39"],["file","src/tracing.ts:1-58"],["file","package.json:17-23"],["file","README.md:36-69"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Self-hosting, privacy or residency"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-05","pid":"EVAL-PB-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"phoenix","secs":727,"k":"c515bd8a-71ae-4132-9b8c-a8129b199b46-r3","picks":[["phoenix","p"],["braintrust","m"],["helicone","m"],["langfuse","m"],["langsmith","m"],["opentelemetry","m"]],"ev":73,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated self-hosted and SaaS LLM observability tools, explicitly rejected SaaS solutions (LangSmith, Braintrust) due to data privacy constraints and rejected Langfuse due to the operational complexity of self-hosting v3. Arize Phoenix was selected, fully wired into the codebase via OpenInference/OTel, tested, and documented in the README and configuration.","c":1,"e":[["file","package.json:17-24"],["file","src/tracing.ts:1-55"],["file","README.md:36-127"],["file",".env.example:6-10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Self-hosting, privacy or residency"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"clde-senior-node-ai-report-builder","pid":"CLDE-05b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"cloudflare","secs":641,"k":"40fb9b6f-dd12-4eb5-a69b-168851faeb62-r1","picks":[["cloudflare","p"],["minio","m"],["aws","a"],["redis","m"]],"ev":75,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended Cloudflare R2 as the concrete object storage service for report uploads and downloads via the AWS S3 SDK, noting AWS S3 as an alternative if already deployed on AWS. Redis (for BullMQ) was evaluated and rejected in favor of Postgres-backed pg-boss to avoid operating a second datastore.","c":0.95,"e":[["file","README.md"],["file",".env.example"],["trace","6"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"clde-senior-node-ai-report-builder","pid":"CLDE-05b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"aws","secs":661,"k":"40fb9b6f-dd12-4eb5-a69b-168851faeb62-r2","picks":[["aws","p"],["cloudflare","m"],["minio","m"],["redis","m"]],"ev":100,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected AWS (specifically AWS S3 via @aws-sdk/client-s3) as the default object storage provider to back the asynchronous reporting pipeline, while evaluating and rejecting Redis in favor of a unified Postgres architecture.","c":0.95,"e":[["file","package.json"],["file","src/storage.ts"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"clde-senior-node-ai-report-builder","pid":"CLDE-05b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"cloudflare","secs":830,"k":"40fb9b6f-dd12-4eb5-a69b-168851faeb62-r3","picks":[["cloudflare","p"],["aws","m"],["gcp","m"],["redis","m"],["render","m"],["upstash","m"]],"ev":82,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The run adopted Cloudflare R2 as the object storage solution to handle large file uploads and finished report briefs via the AWS S3 SDK. It evaluated AWS S3, SQS, and Redis/Upstash, rejecting them in favor of Cloudflare R2 and Postgres-backed queuing.","c":0.95,"e":[["file","src/storage.ts"],["file","render.yaml"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-04","pid":"EVAL-PC-04a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":336,"k":"a1191e74-4442-435e-bb2f-0a05972d9c6f-r1","picks":[["diy","p","d"],["braintrust","m"],["langsmith","m"],["promptfoo","m"]],"ev":40,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent authored a custom, black-box regression evaluation suite in `tests/evals/test_known_failures.py` using pytest markers and deterministic semantic assertions, explicitly rejecting third-party eval tools (LangSmith, Braintrust, Promptfoo) as unnecessary overhead for the use case.","c":0.95,"e":[["file","tests/evals/test_known_failures.py:1-218"],["file",".github/workflows/ci.yml:22-37"],["file","pyproject.toml:39-41"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-04","pid":"EVAL-PC-04a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"diy","secs":424,"k":"a1191e74-4442-435e-bb2f-0a05972d9c6f-r2","picks":[["diy","p","d"],["braintrust","m"],["langsmith","m"]],"ev":43,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated external LLM evaluation platforms (Braintrust, LangSmith) and rejected them in favor of building a custom in-repo evaluation harness using pytest and GitHub Actions, with deterministic validators and multi-trial threshold testing for live evaluations.","c":1,"e":[["file","tests/evals/cases.py:1-213"],["file","tests/evals/test_regressions.py:1-93"],["file",".github/workflows/evals.yml:1-50"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-04","pid":"EVAL-PC-04a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"diy","secs":349,"k":"a1191e74-4442-435e-bb2f-0a05972d9c6f-r3","picks":[["diy","p","b"],["braintrust","m"],["promptfoo","m"]],"ev":34,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly evaluated using third-party evaluation tools such as Braintrust and Promptfoo, but rejected them in favor of implementing the semantic evaluations directly within the existing pytest harness with deterministic scoring functions and dedicated CI workflows.","c":0.95,"e":[["file","tests/live/test_regressions.py:1-267"],["file",".github/workflows/live-evals.yml:1-38"],["file","pyproject.toml:39-41"],["trace","11"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-clde-prompt-c-04","pid":"CLDE-PC-04b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"upstash","secs":682,"k":"e5165b71-fcff-42b9-bb67-4260695fc1b7-r1","picks":[["upstash","p"],["cloudflare","a"],["inngest","m"],["redis","m"],["render","m"]],"ev":83,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent was tasked with setting up object storage and a job queue. It determined that product media is already hosted via Sanity's asset CDN and delivered through Next.js/Vercel, so no new cloud object store was required immediately (noting Cloudflare R2 as a future alternative). For the post-checkout job queue, the agent selected, installed, and configured Upstash QStash (@upstash/qstash).","c":1,"e":[["file","package.json"],["file","lib/queue.ts"],["file","app/api/jobs/order-confirmation/route.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":3,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-clde-prompt-c-04","pid":"CLDE-PC-04b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"cloudflare","secs":897,"k":"e5165b71-fcff-42b9-bb67-4260695fc1b7-r2","picks":[["cloudflare","p"],["redis","m"],["upstash","a"],["aws","m"],["inngest","m"],["render","m"]],"ev":94,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The run explicitly chose Cloudflare R2 (and Cloudflare Images/CDN) as the cloud object storage and media delivery backend, implementing client helpers, storage routines, Next.js image loader configuration, and backfill scripts directly in the repository.","c":1,"e":[["file",".env.example:13-19"],["file","lib/storage.ts:20-30"],["file","lib/image-loader.ts:1-24"],["file","lib/media.ts:1-49"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-clde-prompt-c-04","pid":"CLDE-PC-04b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"multiple","secs":792,"k":"e5165b71-fcff-42b9-bb67-4260695fc1b7-r3","picks":[["cloudflare","c"],["upstash","c"],["aws","m"],["inngest","m"],["render","m"]],"solution":["cloudflare","upstash"],"ev":93,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested object storage for product media and a queue for post-checkout jobs. The agent evaluated alternatives and committed to both Cloudflare (R2 + Image Transformations) and Upstash (QStash), implementing SDK integration and configuration code for both services.","c":0.95,"e":[["file","lib/image-loader.ts"],["file","next.config.mjs"],["file","scripts/migrate-media-to-r2.mjs"],["file","README.md"],["file","lib/queue.ts"],["file","app/api/jobs/order-confirmation/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"b2b-subscriptions","variant":"base","family":"bc-panl-prompt-c-04","pid":"PANL-PC-04b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"amplitude","secs":162,"k":"ad438c2b-f437-4d5d-8b55-73ac79428f44-r1","picks":[["amplitude","p"],["posthog","a"],["segment","m"]],"ev":24,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics vendors (Amplitude and PostHog) with EU hosting support, committed to Amplitude, installed @amplitude/analytics-node, and implemented full instrumentation and test coverage.","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":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"b2b-subscriptions","variant":"base","family":"bc-panl-prompt-c-04","pid":"PANL-PC-04b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"posthog","secs":203,"k":"ad438c2b-f437-4d5d-8b55-73ac79428f44-r2","picks":[["posthog","p"],["amplitude","m"],["segment","m"]],"ev":21,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics options, recommended PostHog Cloud EU, and installed the `posthog-node` SDK to track subscription and renewal funnel events directly in the API.","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":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"b2b-subscriptions","variant":"base","family":"bc-panl-prompt-c-04","pid":"PANL-PC-04b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"posthog","secs":207,"k":"ad438c2b-f437-4d5d-8b55-73ac79428f44-r3","picks":[["posthog","p"],["mixpanel","m"],["amplitude","m"],["segment","m"]],"ev":37,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended, installed (`posthog-node`), and implemented PostHog Cloud EU for product analytics across the API endpoints, with comprehensive tests and documentation in README.md.","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":"Read by someone who is not an engineer"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-07","pid":"EVAL-PB-07a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"phoenix","secs":401,"k":"99fdf5cb-2776-4e57-a8e6-a81abf90bb6e-r1","picks":[["phoenix","p"],["helicone","m"],["langfuse","m"],["opentelemetry","m"]],"ev":61,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested a self-hosted LLM observability solution. The agent recommended and implemented Arize Phoenix via OpenInference instrumentation, Docker Compose deployment, and an OpenTelemetry Collector, while explicitly evaluating and rejecting Langfuse due to its operational overhead.","c":1,"e":[["file","pyproject.toml"],["file","app/observability.py"],["file","deploy/observability/compose.yaml"],["file","docs/observability.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Self-hosting, privacy or residency"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-07","pid":"EVAL-PB-07a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"phoenix","secs":402,"k":"99fdf5cb-2776-4e57-a8e6-a81abf90bb6e-r2","picks":[["phoenix","p"],["langfuse","m"],["opentelemetry","m"]],"ev":50,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested self-hosted LLM observability. The agent evaluated alternatives, rejected Langfuse due to operational burden, and fully installed and configured Arize Phoenix with OpenInference Anthropic instrumentation, deployment compose files, application hooks, and tests.","c":1,"e":[["file","pyproject.toml"],["file","app/observability.py"],["file","deploy/phoenix.compose.yaml"],["file","docs/llm-observability.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Self-hosting, privacy or residency"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-07","pid":"EVAL-PB-07a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"phoenix","secs":393,"k":"99fdf5cb-2776-4e57-a8e6-a81abf90bb6e-r3","picks":[["phoenix","p"],["helicone","m"],["langfuse","m"],["opentelemetry","m"]],"ev":58,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly evaluated self-hosted LLM observability options and chose Arize Phoenix over Langfuse due to lower operational footprint with existing PostgreSQL infrastructure. The run fully installed, configured, and tested Arize Phoenix and OpenInference Anthropic instrumentation.","c":1,"e":[["file","pyproject.toml:15"],["file","app/tracing.py:11"],["file","deploy/observability/compose.yaml:5"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Self-hosting, privacy or residency"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"eval-senior-python-ai-analyst-cases","pid":"EVAL-08a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"braintrust","secs":403,"k":"3ef241c4-02de-4d80-8aaf-5b719c9eecba-r1","picks":[["braintrust","p"],["langsmith","m"],["promptfoo","m"]],"ev":60,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected and fully implemented Braintrust for model evaluations, adding the `braintrust` SDK dependency to `pyproject.toml`, creating dataset seeding and regression evaluation runner modules under `evals/`, configuring a dedicated GitHub Actions workflow, and documenting operational requirements. Alternatives such as Promptfoo, LangSmith, and plain pytest were considered and rejected.","c":1,"e":[["file","pyproject.toml:23"],["file","evals/run.py:1-173"],["file","evals/seed_dataset.py:1-45"],["file",".github/workflows/model-evals.yml:27-35"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"eval-senior-python-ai-analyst-cases","pid":"EVAL-08a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"braintrust","secs":466,"k":"3ef241c4-02de-4d80-8aaf-5b719c9eecba-r2","picks":[["braintrust","p"],["langsmith","m"]],"ev":54,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected Braintrust as the evaluation platform, adding the SDK to `pyproject.toml`, writing evaluation and dataset seeding scripts in `evals/`, configuring the `braintrustdata/eval-action@v2` GitHub Actions step in CI, and documenting operational procedures in `docs/model-evaluations.md`.","c":1,"e":[["file","pyproject.toml"],["file","evals/eval_analyst.py"],["file","evals/seed_dataset.py"],["file",".github/workflows/ci.yml"],["file","docs/model-evaluations.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"eval-senior-python-ai-analyst-cases","pid":"EVAL-08a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"braintrust","secs":440,"k":"3ef241c4-02de-4d80-8aaf-5b719c9eecba-r3","picks":[["braintrust","p"],["deepeval","m"],["phoenix","m"],["langsmith","m"],["promptfoo","m"]],"ev":54,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended Braintrust Cloud for the evaluation suite, installed the braintrust Python dependency, implemented dataset seeding and evaluation runner scripts, and integrated regression gating into the GitHub Actions workflow.","c":1,"e":[["file","pyproject.toml"],["file","evals/analyst.eval.py"],["file","evals/seed_dataset.py"],["file",".github/workflows/ci.yml"],["file","docs/evaluation.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-06","pid":"EVAL-PB-06a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"promptfoo","secs":1056,"k":"84ec5129-b867-4a5c-818d-b1c08bb10e75-r1","picks":[["promptfoo","p"],["braintrust","m"],["langfuse","m"]],"ev":86,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated self-hosted evaluation options (Promptfoo, Langfuse, Braintrust) and explicitly selected Promptfoo, implementing it through configuration files (eval/promptfooconfig.yaml), custom providers and assertions, a pinned container runner, and CI integration.","c":1,"e":[["file","eval/promptfooconfig.yaml"],["file","eval/runner/Dockerfile:1-9"],["file","README.md:44-50"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"Self-hosting, privacy or residency"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-06","pid":"EVAL-PB-06a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"promptfoo","secs":858,"k":"84ec5129-b867-4a5c-818d-b1c08bb10e75-r2","picks":[["promptfoo","p"],["phoenix","m"],["openai-evals","m"],["langfuse","m"]],"ev":72,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated self-hosted evaluation options for a Node/TypeScript project, rejected heavier platforms like Langfuse due to operational complexity, and committed to Promptfoo CLI. Promptfoo was installed as a project dependency, configured in eval/promptfooconfig.yaml, and wired into the GitHub Actions CI workflow.","c":1,"e":[["file","eval/package-lock.json"],["file",".github/workflows/ci.yml:21-42"],["file","README.md:43-56"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"Self-hosting, privacy or residency"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-06","pid":"EVAL-PB-06a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"promptfoo","secs":609,"k":"84ec5129-b867-4a5c-818d-b1c08bb10e75-r3","picks":[["promptfoo","p"],["deepeval","m"],["braintrust","m"],["langfuse","m"]],"ev":45,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several evaluation frameworks and selected Promptfoo as an ephemeral CLI tool suited for Node.js without requiring long-running databases or servers. Configuration, custom providers, scoring rules, and CI workflows were committed to the repository.","c":0.95,"e":[["file","eval/promptfooconfig.yaml"],["file",".github/workflows/model-eval.yml"],["file","eval/scripts/run-promptfoo.sh"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"Self-hosting, privacy or residency"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"eval-junior-node-ai-report-builder-cases","pid":"EVAL-02a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":871,"k":"568a8520-6843-4861-b15d-1e5779cfd262-r1","picks":[["diy","p","d"],["braintrust","m"],["langsmith","m"],["openai-evals","m"],["promptfoo","m"]],"ev":89,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The run explicitly evaluated third-party evaluation tooling (Promptfoo, Braintrust, LangSmith, and OpenAI Evals) and deliberately chose to implement a custom, 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eval frameworks (Promptfoo, Braintrust, LangSmith, OpenAI Evals), explicitly rejected them due to added operational overhead, and implemented a custom in-repo evaluation framework across two tiers using Jest.","c":1,"e":[["file","tests/eval/checks.ts"],["file","tests/eval/fixtures.test.ts"],["file","jest.eval.config.cjs"],["file","tests/eval/README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"OpenAI","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"delivery-proof-service","variant":"base","family":"bc-clde-prompt-b-02","pid":"CLDE-PB-02b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"aws","secs":792,"k":"452be2eb-ea98-4241-a705-99bcbf844b6b-r1","picks":[["aws","p"],["cloudflare","m"],["gcp","m"],["redis","m"],["render","m"]],"ev":49,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent proposed, implemented, and tested a full AWS architecture using S3 for proof object storage, SQS for durable thumbnail processing queues, and Lambda for sharp-based thumbnail generation, complete with AWS SDK dependencies and Terraform provisioning config.","c":1,"e":[["file","infra/main.tf:11-316"],["file","package.json:1"],["file","src/aws/s3-proof-store.js:1-62"],["file","src/aws/lambda-entry.js:1-14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"delivery-proof-service","variant":"base","family":"bc-clde-prompt-b-02","pid":"CLDE-PB-02b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"aws","secs":895,"k":"452be2eb-ea98-4241-a705-99bcbf844b6b-r2","picks":[["aws","p"],["gcp","a"],["cloudflare","m"]],"ev":67,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several cloud providers and architectures, explicitly recommended Amazon Web Services (S3, SQS, and Lambda), and implemented full production adapters and tests using the AWS SDK.","c":1,"e":[["file","package.json:11-14"],["file","src/aws/handler.js:1-93"],["file","src/aws/s3-proof-store.js:1-67"],["file","src/aws/sqs-proof-queue.js:1-98"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"delivery-proof-service","variant":"base","family":"bc-clde-prompt-b-02","pid":"CLDE-PB-02b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"aws","secs":949,"k":"452be2eb-ea98-4241-a705-99bcbf844b6b-r3","picks":[["aws","p"],["gcp","m"],["azure","m"],["cloudflare","m"],["redis","m"]],"ev":78,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated cloud storage and queue architectures, selected AWS (S3, SQS, Lambda) for durability and cost-efficiency with long-term retention lifecycle rules, implemented code and infrastructure templates using AWS SDK clients, and explicitly rejected alternatives like Cloudflare R2 and Redis/BullMQ.","c":1,"e":[["file","infra/proof-pipeline.yaml:1-303"],["file","src/aws/lambda-handler.js:1-81"],["file","src/aws/s3-store.js:1-52"],["file","package.json:1"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"b2b-subscriptions","variant":"base","family":"bc-panl-prompt-b-04","pid":"PANL-PB-04b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"posthog","secs":217,"k":"0cb62c9a-067b-4a59-bfd9-63d4efc31bd7-r1","picks":[["posthog","p"],["amplitude","m"],["simple-analytics","m"]],"ev":27,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics options and implemented PostHog using the official `posthog-node` SDK to track server-side lifecycle events with EU hosting 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It installed posthog-node, implemented a server-side analytics adapter capturing organization lifecycle events, added unit tests, configured environment variables, and documented setup and ownership.","c":1,"e":[["file","package.json"],["file","apps/api/src/analytics.js"],["file","README.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"b2b-subscriptions","variant":"base","family":"panl-junior-b2b-subscriptions","pid":"PANL-04b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"posthog","secs":173,"k":"ce5a37f4-a383-461c-8edb-5bf13458b6b0-r3","picks":[["posthog","p"],["amplitude","m"],["mixpanel","m"],["segment","m"]],"ev":27,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected, installed, and fully implemented PostHog (using posthog-node) for server-side organization group analytics. 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The agent inspected the codebase, found pre-existing GCS and Memorystore Redis configurations, and implemented direct-to-bucket signed URL uploads to GCS alongside dedicated Celery task routing on the existing Redis broker. It explicitly recommended against adopting new services like AWS S3 or additional queue technologies.","c":0.95,"e":[["file","apps/grading/storage.py:1-210"],["file","deploy/gcs-media-lifecycle.json:1-30"],["file","docs/submission-storage.md:1-130"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"bot-protection","wave":11,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"botp-vibe-remix-workshop-bookings","pid":"BOTP-03a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"turnstile","secs":406,"k":"8e584f25-353a-4ee4-afa1-3f7573bf2c00-r1","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"]],"ev":44,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated captcha options to protect an expensive sync hashing endpoint, recommended Cloudflare Turnstile, and fully implemented client-side explicit rendering and server-side verification using Turnstile's siteverify API.","c":1,"e":[["file","app/turnstile.server.ts:1-97"],["file","app/routes/book.$workshopId.tsx:1-140"],["file","README.md:22-45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":11,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"botp-vibe-remix-workshop-bookings","pid":"BOTP-03a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"turnstile","secs":941,"k":"8e584f25-353a-4ee4-afa1-3f7573bf2c00-r2","picks":[["turnstile","p"],["altcha","m"],["hcaptcha","m"],["recaptcha","m"]],"ev":60,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended, implemented, and verified Cloudflare Turnstile on the booking form route and created server-side verification logic in `app/turnstile.server.ts`. It also explicitly evaluated and rejected Google reCAPTCHA, hCaptcha, and ALTCHA.","c":1,"e":[["file","app/turnstile.server.ts:1-51"],["file","app/routes/_index.tsx:20-24"],["file","README.md:19-41"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":11,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"botp-vibe-remix-workshop-bookings","pid":"BOTP-03a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"turnstile","secs":974,"k":"8e584f25-353a-4ee4-afa1-3f7573bf2c00-r3","picks":[["turnstile","p"],["altcha","m"],["hcaptcha","m"],["recaptcha","m"]],"ev":60,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several bot-protection options (Cloudflare Turnstile, Google reCAPTCHA, hCaptcha, and Altcha) to protect the booking endpoint from unauthenticated abuse. It selected and implemented Cloudflare Turnstile, adding the verification server module and React widget integration in the Remix index route.","c":1,"e":[["file","app/turnstile.server.ts:1-49"],["file","app/routes/_index.tsx:28-128"],["file","README.md:19-42"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"eval-junior-python-ai-analyst-cases","pid":"EVAL-04a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":279,"k":"08ba0a01-c26c-460a-92b8-357aadcd272b-r1","picks":[["diy","p","d"]],"ev":25,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The run implemented a custom in-repo evaluation framework comprising synthetic test fixtures, code generation staging, isolated VM execution, and deterministic assertion grading in Python and GitHub Actions rather than adopting a third-party evaluation platform.","c":0.95,"e":[["file","evals/cases.py:1-175"],["file","evals/execute_isolated.py:1-106"],["file","evals/grade.py:1-144"],["file","evals/prepare.py:1-62"],["file",".github/workflows/evals.yml:1-118"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"eval-junior-python-ai-analyst-cases","pid":"EVAL-04a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"diy","secs":523,"k":"08ba0a01-c26c-460a-92b8-357aadcd272b-r2","picks":[["diy","p","d"],["braintrust","m"],["langsmith","m"]],"ev":45,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated the need for LLM evaluation and determined that third-party evaluation platforms (specifically naming Braintrust and LangSmith) were unnecessary for this project's objective checks. It implemented a custom evaluation suite consisting of synthetic test fixtures, programmatic graders, a multi-trial CLI runner (app/evaluation.py), and GitHub Actions workflows.","c":0.95,"e":[["file","app/eval_cases.py"],["file","app/evaluation.py"],["file",".github/workflows/evals.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":4,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"eval-junior-python-ai-analyst-cases","pid":"EVAL-04a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"diy","secs":345,"k":"08ba0a01-c26c-460a-92b8-357aadcd272b-r3","picks":[["diy","p","d"],["braintrust","m"],["langsmith","m"]],"ev":30,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated external LLM evaluation platforms (explicitly rejecting Braintrust and LangSmith as unnecessary operational overhead) and designed/implemented a fully custom in-repo evaluation framework with deterministic grading, synthetic cases, and a staged CI workflow.","c":1,"e":[["file","evals/__main__.py:1-245"],["file","evals/cases.py:1-211"],["file","evals/grading.py:1-55"],["file",".github/workflows/evaluations.yml:1-133"],["file","docs/evaluations.md:1-72"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-06","pid":"EVAL-PC-06a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"braintrust","secs":413,"k":"1e555ed2-907c-4fc8-b0b4-9a435fe3164e-r1","picks":[["braintrust","p"],["langsmith","m"],["promptfoo","m"]],"ev":51,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended Braintrust over Promptfoo and LangSmith, installed the `braintrust` and `autoevals` packages, implemented the evaluation test suite and regression reporter, and configured the Braintrust evaluation GitHub Actions workflow.","c":1,"e":[["file","package.json"],["file",".github/workflows/ci.yml"],["file","evals/report-builder.eval.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Evaluation and a regression gate","theme":"Volume and cost at scale"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-06","pid":"EVAL-PC-06a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"promptfoo","secs":1591,"k":"1e555ed2-907c-4fc8-b0b4-9a435fe3164e-r2","picks":[["promptfoo","p"],["braintrust","m"],["langsmith","m"],["openai-evals","m"]],"ev":139,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The run explicitly selected and implemented Promptfoo as its evaluation framework, creating full configuration files (`promptfooconfig.yaml`, `evals/cases.yaml`, `evals/baseline.cjs`), updating GitHub Actions CI with an evaluation job, and documenting dependency management in `evals/DEPENDENCIES.md`.","c":1,"e":[["file","promptfooconfig.yaml"],["file",".github/workflows/ci.yml:28-71"],["file","evals/DEPENDENCIES.md:1-12"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Evaluation and a regression gate","theme":"Volume and cost at scale"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-06","pid":"EVAL-PC-06a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"braintrust","secs":430,"k":"1e555ed2-907c-4fc8-b0b4-9a435fe3164e-r3","picks":[["braintrust","p"],["langsmith","m"],["openai-evals","m"],["promptfoo","m"]],"ev":60,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several evaluation frameworks (Braintrust, Promptfoo, OpenAI Evals, LangSmith) and recommended Braintrust Cloud. Following user approval, it fully installed the `braintrust` npm dependency, wrote dataset synchronization scripts, created deterministic scorers and a custom regression reporter, and configured the GitHub Actions CI workflow to run Braintrust evaluations on PRs.","c":1,"e":[["file","package.json"],["file","evals/report-builder.eval.ts"],["file","evals/regression-reporter.ts"],["file",".github/workflows/ci.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Evaluation and a regression gate","theme":"Volume and cost at scale"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-05","pid":"EVAL-PB-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"phoenix","secs":521,"k":"0735293e-ff29-4887-b5b3-d3bc25e13253-r1","picks":[["phoenix","p"],["langfuse","m"],["opentelemetry","m"]],"ev":56,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated self-hosted LLM observability solutions, explicitly rejected Langfuse due to high infrastructure overhead, and fully implemented and instrumented Arize Phoenix with PostgreSQL and OpenTelemetry.","c":1,"e":[["file","package.json:17"],["file","ops/observability/docker-compose.yml:2-24"],["file","src/telemetry.ts:1-80"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Self-hosting, privacy or residency"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-05","pid":"EVAL-PB-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"phoenix","secs":347,"k":"0735293e-ff29-4887-b5b3-d3bc25e13253-r2","picks":[["phoenix","p"],["helicone","m"],["langfuse","m"],["opentelemetry","m"]],"ev":43,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated self-hosted options and explicitly chose Arize Phoenix over Langfuse due to its lightweight operational profile (Phoenix + Postgres). It implemented instrumentation using Phoenix OpenTelemetry SDKs, added docker-compose definitions, collector configs, tests, and documentation.","c":1,"e":[["file","package.json"],["file","observability/compose.yaml"],["file","src/instrumentation.ts"],["file","observability/README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Self-hosting, privacy or residency"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-05","pid":"EVAL-PB-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"phoenix","secs":453,"k":"0735293e-ff29-4887-b5b3-d3bc25e13253-r3","picks":[["phoenix","p"],["helicone","m"],["langfuse","m"],["opentelemetry","m"]],"ev":64,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly evaluated self-hosted LLM tracing and observability options to keep customer data on-premise. It rejected Langfuse due to infrastructure complexity and implemented Arize Phoenix via `@arizeai/phoenix-otel`, `@arizeai/openinference-instrumentation-openai`, a persistent OpenTelemetry collector, and Docker Compose configurations.","c":1,"e":[["file","package.json"],["file","src/observability.ts"],["file","ops/observability/compose.yaml"],["trace","seq 9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Self-hosting, privacy or residency"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"eval-junior-node-ai-report-builder-tracing","pid":"EVAL-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"langfuse","secs":1033,"k":"69b07260-efba-473d-b1de-1420e50205a5-r1","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"],["phoenix","m"]],"ev":103,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested an LLM observability solution for production tracing of OpenAI model calls that persists outside the application process. The agent evaluated Langfuse Cloud, Braintrust, LangSmith, Arize Phoenix, and Helicone, and recommended Langfuse Cloud due to low operational burden and async OTLP export. After user confirmation, the agent installed and fully implemented `@langfuse/openai`, `@langfuse/otel`, and `@langfuse/tracing`.","c":1,"e":[["file","package.json:15-17"],["file","src/tracing.ts:1-90"],["file","README.md:37-124"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"eval-junior-node-ai-report-builder-tracing","pid":"EVAL-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"langfuse","secs":671,"k":"69b07260-efba-473d-b1de-1420e50205a5-r2","picks":[["langfuse","p"],["braintrust","m"],["langsmith","m"],["helicone","m"],["opentelemetry","m"]],"ev":71,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated LLM observability options and chose Langfuse Cloud. It installed @langfuse/otel, @langfuse/tracing, and OpenTelemetry SDK packages, configured span processors in src/tracing.ts, wrapped model calls and transform execution in src/model.ts and src/app.ts, configured environment variables and graceful shutdown flushes, wrote test coverage, and documented setup in the README.","c":1,"e":[["file","package.json:17-19"],["file","src/tracing.ts:1-35"],["file","src/model.ts:22-55"],["file","src/app.ts:32-62"],["file","README.md:38-75"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"eval-junior-node-ai-report-builder-tracing","pid":"EVAL-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"langfuse","secs":564,"k":"69b07260-efba-473d-b1de-1420e50205a5-r3","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"]],"ev":67,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several LLM observability/eval tools (Langfuse, Helicone, LangSmith, Braintrust) and selected Langfuse Cloud. It fully implemented the solution using @langfuse/openai, @langfuse/otel, and @langfuse/tracing, configuring span masking, graceful shutdown flush, error propagation, and trace ID correlation.","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":"cloud","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-clde-prompt-c-05","pid":"CLDE-PC-05b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws","secs":801,"k":"57fe6653-8088-4375-8375-8c9093b9157f-r1","picks":[["aws","p"],["cloudflare","m"],["gcp","m"],["rabbitmq","m"],["redis","m"]],"ev":30,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated cloud storage and queue options suited for spiky, low-volume workloads without idle server costs. It recommended and implemented an Amazon Web Services architecture using Amazon S3 for report/request storage and Amazon SQS for asynchronous job dispatching, providing CloudFormation infrastructure, SDK integrations, and tests, while rejecting Redis and RabbitMQ due to ongoing idle costs and operational maintenance.","c":1,"e":[["file","package.json:18-19"],["file","infra/storage-queue.yaml:1-135"],["file","src/aws.ts:1-117"],["file","README.md:46-77"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-clde-prompt-c-05","pid":"CLDE-PC-05b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"aws","secs":754,"k":"57fe6653-8088-4375-8375-8c9093b9157f-r2","picks":[["aws","p"],["gcp","m"],["cloudflare","m"],["upstash","m"],["rabbitmq","m"],["redis","m"]],"ev":70,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected and fully implemented an AWS-based serverless stack (S3, SQS, Lambda, Secrets Manager, CloudWatch) configured via CloudFormation and official AWS SDK v3 client libraries, explicitly rejecting always-on alternatives like RabbitMQ and Redis.","c":1,"e":[["file","package.json"],["file","infra/template.yaml"],["file","src/aws.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-clde-prompt-c-05","pid":"CLDE-PC-05b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws","secs":545,"k":"57fe6653-8088-4375-8375-8c9093b9157f-r3","picks":[["aws","p"],["cloudflare","m"],["minio","m"],["rabbitmq","m"],["redis","m"]],"ev":62,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended and implemented Amazon Web Services (S3 for object storage, SQS for queueing, and Lambda for compute), adding AWS SDK dependencies, CloudFormation/SAM configuration in template.yaml, and S3/SQS client code. It explicitly considered and rejected self-hosted and provisioned alternatives (Redis, RabbitMQ, MinIO) as well as Cloudflare during architecture evaluation.","c":1,"e":[["file","package.json:14-17"],["file","template.yaml:1-50"],["file","src/report-jobs.ts:1-25"],["file","README.md:1-20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"clde-senior-nextjs-storefront","pid":"CLDE-04b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"multiple","secs":705,"k":"f86704ca-06af-448e-81f1-5fa00a29ecf4-r1","picks":[["cloudflare","c"],["upstash","c"],["aws","m"],["inngest","m"],["redis","m"]],"solution":["cloudflare","upstash"],"ev":54,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The task requested solutions for both image object storage and a post-checkout background queue. The run selected and implemented Cloudflare R2 for image storage (leveraging S3 client compatibility with zero egress fees) and Upstash (QStash for HTTP-based message queuing and Upstash Redis for deduplication and idempotency), rejecting AWS S3 and SQS due to egress costs and serverless polling constraints.","c":0.95,"e":[["file","lib/r2.ts:1-67"],["file","lib/images.ts:1-59"],["file",".env.example:19-27"],["file","lib/queue.ts:1-36"],["file","lib/idempotency.ts:1-90"],["file","package.json:14-15"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"clde-senior-nextjs-storefront","pid":"CLDE-04b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"cloudflare","secs":951,"k":"f86704ca-06af-448e-81f1-5fa00a29ecf4-r2","picks":[["cloudflare","p"],["backblaze","m"],["aws","m"],["inngest","m"],["redis","m"],["render","m"],["upstash","m"]],"ev":104,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended and implemented Cloudflare R2 for object storage (using the S3 client configured to Cloudflare's endpoint in `lib/r2.ts`). Other cloud storage and queue options like Upstash, AWS, and Backblaze were considered and discarded.","c":0.98,"e":[["file","lib/r2.ts:16-24"],["file","README.md:35-64"],["file",".env.example:22-28"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"clde-senior-nextjs-storefront","pid":"CLDE-04b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"cloudflare","secs":615,"k":"f86704ca-06af-448e-81f1-5fa00a29ecf4-r3","picks":[["cloudflare","p"],["aws","m"],["inngest","m"],["render","m"],["upstash","m"]],"ev":58,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The run chose and fully integrated Cloudflare R2 (along with Cloudflare Image Resizing) as its dedicated object storage solution, implementing S3-compatible client access in lib/r2.ts and custom next/image loading in lib/image-loader.ts. It explicitly evaluated and rejected AWS (S3/CloudFront and SQS) and Upstash (QStash).","c":0.95,"e":[["file","lib/r2.ts:1-29"],["file","lib/image-loader.ts:1-34"],["file","README.md:38-62"],["file",".env.example:22-29"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"express-api","variant":"base","family":"panl-junior-express-api","pid":"PANL-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"posthog","secs":263,"k":"c3ea8762-b9bb-480a-97a2-27c22236f29c-r1","picks":[["posthog","p"],["mixpanel","m"],["amplitude","m"]],"ev":46,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended PostHog, installed the posthog-node package, created a fail-open analytics service, and instrumented all relevant lifecycle routes.","c":1,"e":[["file","package.json"],["file","services/analytics.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"express-api","variant":"base","family":"panl-junior-express-api","pid":"PANL-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"posthog","secs":167,"k":"c3ea8762-b9bb-480a-97a2-27c22236f29c-r2","picks":[["posthog","p"],["amplitude","m"],["mixpanel","m"]],"ev":22,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics solutions, recommended PostHog Cloud, and fully implemented server-side tracking using the posthog-node library across auth, event, and ticket controllers.","c":1,"e":[["file","package.json"],["file","services/analytics.js"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"express-api","variant":"base","family":"panl-junior-express-api","pid":"PANL-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"posthog","secs":138,"k":"c3ea8762-b9bb-480a-97a2-27c22236f29c-r3","picks":[["posthog","p"],["amplitude","m"],["mixpanel","m"]],"ev":24,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent recommended PostHog Cloud and implemented it using posthog-node across the controllers, environment config, server shutdown logic, and documentation. Mixpanel, Amplitude, and Google Analytics were briefly noted as alternatives during reasoning.","c":1,"e":[["file","package.json"],["file","services/analytics.js"],["file","README.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-07","pid":"EVAL-PC-07a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"langfuse","secs":260,"k":"f5d8ebcb-3c76-4571-9dfb-0aa7b339733d-r1","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"]],"ev":31,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected Langfuse Cloud as the production LLM observability solution, installed the `langfuse` Python library, implemented full tracing wrapper spans for Anthropic API calls and execution runs, added config and test coverage, and documented the operational cost controls in README.md.","c":1,"e":[["file","pyproject.toml:15"],["file","app/observability.py:1-94"],["file","app/llm.py:45-80"],["file","README.md:84-116"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Volume and cost at scale"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-07","pid":"EVAL-PC-07a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"langfuse","secs":226,"k":"f5d8ebcb-3c76-4571-9dfb-0aa7b339733d-r2","picks":[["langfuse","p"],["helicone","m"],["opentelemetry","m"]],"ev":28,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated LLM observability options and recommended Langfuse Cloud Core, subsequently implementing it in full across configuration, FastAPI lifespan handlers, Anthropic call wrappers, test suites, and project dependencies.","c":0.95,"e":[["file","pyproject.toml:15"],["file","app/observability.py:1-95"],["file","README.md:79-115"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Volume and cost at scale"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-07","pid":"EVAL-PC-07a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"diy","secs":279,"k":"f5d8ebcb-3c76-4571-9dfb-0aa7b339733d-r3","picks":[["diy","p","d"],["braintrust","m"],["helicone","m"],["langfuse","m"]],"ev":34,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated third-party LLM observability platforms (Langfuse, Helicone, Braintrust) against the constraint to keep operating costs below model spend. It rejected third-party services in favor of writing an in-repo DIY trace ledger directly backed by the existing PostgreSQL database.","c":0.95,"e":[["file","migrations/002_llm_trace_ledger.sql:1-56"],["file","app/llm.py:40-155"],["file","app/trace_cli.py:1-66"],["file","README.md:88-115"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":0,"seam":"Tracing model calls","theme":"Volume and cost at scale"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-03","pid":"EVAL-PB-03a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"langfuse","secs":258,"k":"4e94097f-93fd-4494-9ac3-f3adb271e5e9-r1","picks":[["langfuse","p"],["helicone","m"],["opentelemetry","m"]],"ev":38,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected Langfuse Cloud as the primary LLM observability and evaluation tracing solution, installed `langfuse` in `pyproject.toml`, implemented `app/observability.py`, instrumented `app/llm.py` and `app/main.py`, added settings and tests, and updated `README.md`.","c":1,"e":[["file","pyproject.toml:18"],["file","app/observability.py:1-68"],["file","app/llm.py:48-70"],["file","app/main.py:60-110"],["file","README.md:86-113"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-03","pid":"EVAL-PB-03a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"langfuse","secs":354,"k":"4e94097f-93fd-4494-9ac3-f3adb271e5e9-r2","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["opentelemetry","m"]],"ev":35,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated proxy-based LLM observability options (Helicone and Braintrust) versus SDK tracing (Langfuse), explicitly selected Langfuse Cloud, added the SDK dependency, implemented end-to-end tracing and error handling in app/observability.py and app/llm.py, and added comprehensive test coverage.","c":1,"e":[["file","pyproject.toml:18"],["file","app/observability.py:1-110"],["file","app/llm.py:55-89"],["file","README.md:85-128"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-03","pid":"EVAL-PB-03a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"braintrust","secs":232,"k":"4e94097f-93fd-4494-9ac3-f3adb271e5e9-r3","picks":[["braintrust","p"],["helicone","m"],["langfuse","m"]],"ev":36,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected Braintrust Cloud for production LLM tracing and observability, adding the `braintrust` SDK to pyproject.toml and wiring instrumentation around Anthropic calls in `app/observability.py` and `app/llm.py`. Langfuse, Helicone, and LangSmith were considered during exploration but not chosen.","c":1,"e":[["file","pyproject.toml:15"],["file","app/observability.py:1-110"],["file","app/llm.py:25-27"],["file","app/main.py:55-101"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"eval-senior-node-ai-report-builder-cases","pid":"EVAL-06a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"promptfoo","secs":796,"k":"42c15115-0dfa-44ea-9c78-56092c95f39d-r1","picks":[["promptfoo","p"],["braintrust","m"],["langsmith","m"],["openai-evals","m"]],"ev":84,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected Promptfoo as a third-party, repository-local evaluation tool, fully configuring test cases, providers, assertions, and a GitHub Actions workflow for baseline comparisons. It explicitly evaluated and rejected OpenAI Evals due to impending deprecation, as well as Braintrust and LangSmith due to external hosting and operational overhead.","c":1,"e":[["file","evals/promptfooconfig.yaml"],["file",".github/workflows/model-evaluation.yml"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"eval-senior-node-ai-report-builder-cases","pid":"EVAL-06a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"braintrust","secs":497,"k":"42c15115-0dfa-44ea-9c78-56092c95f39d-r2","picks":[["braintrust","p"],["langsmith","m"],["openai-evals","m"],["promptfoo","m"]],"ev":63,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several evaluation frameworks and selected Braintrust, fully integrating the Braintrust SDK, creating seed cases, scorers, a regression gate reporter, and wiring a dedicated GitHub Actions workflow.","c":1,"e":[["file","package.json"],["file",".github/workflows/ci.yml"],["file","evals/report.eval.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"eval-senior-node-ai-report-builder-cases","pid":"EVAL-06a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"braintrust","secs":417,"k":"42c15115-0dfa-44ea-9c78-56092c95f39d-r3","picks":[["braintrust","p"],["langsmith","m"],["openai-evals","m"],["promptfoo","m"]],"ev":49,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended Braintrust over OpenAI Evals, Promptfoo, and LangSmith, then committed to it by adding braintrust to dependencies, configuring CI via braintrustdata/eval-action, and writing evaluation scripts and regression gates.","c":1,"e":[["file","package.json:29"],["file",".github/workflows/ci.yml:37-45"],["file","evals/report-builder.eval.ts:1-61"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"eval-senior-node-ai-report-builder-tracing","pid":"EVAL-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"langfuse","secs":542,"k":"cf3a678b-a222-4cb6-a585-6aef031676aa-r1","picks":[["langfuse","p"],["braintrust","m"],["langsmith","m"],["helicone","m"],["opentelemetry","m"]],"ev":67,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested production LLM observability and tracing for model calls. The agent evaluated candidate tools, selected Langfuse Cloud via the scoped SDKs (@langfuse/openai, @langfuse/otel, @langfuse/tracing), and implemented full OpenTelemetry tracing in the codebase with tests, verification harnesses, and documentation.","c":1,"e":[["file","package.json:15-17"],["file","src/tracing.ts:1-53"],["file","src/model.ts:1-20"],["file","README.md:38-96"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":3,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"eval-senior-node-ai-report-builder-tracing","pid":"EVAL-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"langfuse","secs":640,"k":"cf3a678b-a222-4cb6-a585-6aef031676aa-r2","picks":[["langfuse","p"],["braintrust","m"],["phoenix","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"]],"ev":68,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected, installed, and fully implemented Langfuse Cloud with @langfuse/openai and @langfuse/otel to trace OpenAI model calls and execution spans, while evaluating and rejecting alternatives like LangSmith.","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":3,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"eval-senior-node-ai-report-builder-tracing","pid":"EVAL-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"langfuse","secs":452,"k":"cf3a678b-a222-4cb6-a585-6aef031676aa-r3","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"]],"ev":55,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several LLM observability and evaluation tools (Langfuse, LangSmith, Arize Phoenix, Braintrust) and selected Langfuse. It installed the Langfuse OpenTelemetry and OpenAI SDK packages, configured client tracing with sensitive CSV row masking, added telemetry export flush hooks on process termination, and fully documented the setup.","c":1,"e":[["file","package.json:17-19"],["file","src/telemetry.ts:1-43"],["file","src/model.ts:2-38"],["file","README.md:36-87"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-clde-prompt-b-05","pid":"CLDE-PB-05b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws","secs":671,"k":"c2b34409-de96-4d3e-8fca-ca9ac854c738-r1","picks":[["aws","p"],["inngest","m"],["redis","m"]],"ev":55,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent designed and implemented a full AWS architecture using S3 for job and artifact persistence, Step Functions for orchestration, and ECS/Fargate for isolated code execution, backed by a CloudFormation template in infra/report-studio.yaml and AWS SDK client libraries in the codebase.","c":1,"e":[["file","infra/report-studio.yaml:1-518"],["file","package.json:17-19"],["file","src/jobs.ts:1-161"],["file","README.md:27-118"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-clde-prompt-b-05","pid":"CLDE-PB-05b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"aws","secs":714,"k":"c2b34409-de96-4d3e-8fca-ca9ac854c738-r2","picks":[["aws","p"],["redis","m"]],"ev":74,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected AWS (Amazon S3 and KMS) as the cloud storage infrastructure for durable input and report management, providing an AWS CloudFormation template and configuring `@aws-sdk/client-s3`. Redis was evaluated alongside BullMQ but rejected to avoid unnecessary operational overhead.","c":0.95,"e":[["file","infra/storage.yml"],["file","docs/production.md"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-clde-prompt-b-05","pid":"CLDE-PB-05b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws","secs":550,"k":"c2b34409-de96-4d3e-8fca-ca9ac854c738-r3","picks":[["aws","p"],["gcp","m"],["redis","m"]],"ev":40,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent clearly selected Amazon Web Services (AWS) as the primary cloud platform, deploying a CloudFormation template and integrating S3, DynamoDB, AWS Batch on Fargate, Cognito, and EventBridge into the application code. Other cloud providers and queue/storage mechanisms were evaluated or surveyed in shell searches and explicitly rejected or mentioned in passing.","c":1,"e":[["file","infra/report-pipeline.yaml"],["file","package.json"],["file","src/aws.ts"],["trace","final_answer"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"clde-senior-node-ai-report-builder","pid":"CLDE-05b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws","secs":542,"k":"0d31fde8-53ec-4c3d-a2c1-0775a7e80236-r1","picks":[["aws","p"],["upstash","m"],["cloudflare","m"],["inngest","m"],["redis","m"]],"ev":65,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly implemented Amazon Web Services (AWS S3) for object storage, installing the AWS SDK packages (`@aws-sdk/client-s3` and `@aws-sdk/s3-request-presigner`), configuring S3 IAM policies, and writing storage adapters with presigned URL support. It rejected Redis and Cloudflare due to operational overhead and platform mismatch.","c":1,"e":[["file","package.json:17-18"],["file","src/storage.ts:1-87"],["file","ops/iam-policy.json:1-15"],["file","ops/s3-lifecycle.json:1-17"],["file","README.md:27-41"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"clde-senior-node-ai-report-builder","pid":"CLDE-05b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"aws","secs":464,"k":"0d31fde8-53ec-4c3d-a2c1-0775a7e80236-r2","picks":[["aws","p"],["inngest","m"],["redis","m"]],"ev":34,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected and fully integrated Amazon Web Services using Amazon S3 for durable object storage and AWS Batch on Fargate for queued task execution with isolated execution boundaries. Redis was considered for queuing but rejected due to lack of kernel isolation.","c":1,"e":[["file","infra/report-jobs.yaml"],["file","package.json"],["file","src/queue.ts"],["file","src/storage.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"clde-senior-node-ai-report-builder","pid":"CLDE-05b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws","secs":510,"k":"0d31fde8-53ec-4c3d-a2c1-0775a7e80236-r3","picks":[["aws","p"],["cloudflare","m"],["redis","m"]],"ev":64,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated architecture options and explicitly selected AWS (S3, SQS, DynamoDB, Lambda) as the managed cloud platform to handle object storage, queuing, and isolated processing, implementing the full solution via AWS CDK.","c":1,"e":[["file","infra/app.ts:27-155"],["file","package.json:17-23"],["file","README.md:3-10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-clde-prompt-c-04","pid":"CLDE-PC-04b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare","secs":531,"k":"1ed3258f-b097-4823-b127-06faa67c4555-r1","picks":[["cloudflare","p"],["upstash","m"],["aws","a"]],"ev":59,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The run explicitly chose, configured, and implemented Cloudflare (R2 object storage, Queues, and Workers) to handle product media and post-checkout processing without egress charges, while mentioning AWS and Upstash in deliberation.","c":1,"e":[["file","cloudflare/wrangler.jsonc:1-42"],["file","cloudflare/src/index.ts:1-282"],["file","lib/checkout-queue.ts:1-43"],["file","lib/media.ts:1-34"],["file","README.md:6-8"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-clde-prompt-c-04","pid":"CLDE-PC-04b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"cloudflare","secs":419,"k":"1ed3258f-b097-4823-b127-06faa67c4555-r2","picks":[["cloudflare","p"],["render","m"]],"ev":45,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated and implemented Cloudflare (R2, Queues, Workers, D1, and Images) to fulfill requirements for object storage and queue processing, creating configuration files, a worker service, and custom Next.js image loading logic.","c":1,"e":[["file","workers/post-checkout/wrangler.jsonc"],["file","lib/cloudflare-image-loader.ts"],["file","lib/post-checkout-queue.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-clde-prompt-c-04","pid":"CLDE-PC-04b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"cloudflare","secs":511,"k":"1ed3258f-b097-4823-b127-06faa67c4555-r3","picks":[["cloudflare","p"],["aws","m"]],"ev":51,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated media storage and queue options and explicitly recommended and implemented Cloudflare (R2, Queues, Workers, and Image Transformations) to provide zero-egress media hosting and reliable checkout webhook processing.","c":1,"e":[["file","cloudflare/wrangler.jsonc:1-35"],["file","cloudflare/src/index.ts:1-316"],["file","lib/cloudflare-image-loader.ts:1-32"],["file","lib/checkout-jobs.ts:1-52"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"clde-enterprise-edtech-lms","pid":"CLDE-06b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"gcp","secs":477,"k":"dff030a1-416d-408e-9774-fcd34c8fa627-r1","picks":[["gcp","p","b"]],"ev":42,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The repository is already built and deployed on Google Cloud Platform with Google Cloud Storage (`google-cloud-storage` SDK, Cloud Build, and Cloud Run). The run implemented direct-to-bucket uploads using GCS signed URLs, matching the existing course video pattern.","c":1,"e":[["file","apps/grading/views.py:25"],["file","apps/grading/views.py:126"],["file","docs/student-submissions.md:7-13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"clde-enterprise-edtech-lms","pid":"CLDE-06b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"gcp","secs":575,"k":"dff030a1-416d-408e-9774-fcd34c8fa627-r2","picks":[["gcp","p","b"],["redis","m","b"],["render","m"]],"ev":54,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The repository is already built on Google Cloud Platform (Cloud Run, Cloud SQL, Cloud Storage) and uses Celery backed by Redis. The agent implemented direct-to-GCS uploads using signed URLs via the existing google-cloud-storage integration and verified grading tasks were already asynchronously queued via Celery/Redis.","c":0.95,"e":[["file","apps/courses/views.py"],["file","apps/courses/tests.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"clde-enterprise-edtech-lms","pid":"CLDE-06b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"gcp","secs":724,"k":"dff030a1-416d-408e-9774-fcd34c8fa627-r3","picks":[["gcp","p","b"],["redis","m","b"]],"ev":61,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The project is already hosted on GCP (Google Cloud Run, Cloud Build, and Google Cloud Storage). The agent implemented the object storage and task routing solution natively using the existing Google Cloud Storage bucket configuration and pre-existing Celery/Redis queue setup without adopting a new cloud vendor.","c":1,"e":[["file","apps/grading/storage.py"],["file","brightloom/settings.py"],["trace","10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"eval-senior-python-ai-analyst-tracing","pid":"EVAL-07a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"langfuse","secs":202,"k":"27ab9721-af95-46ed-a7a7-7832437b4a2c-r1","picks":[["langfuse","p"],["braintrust","m"],["langsmith","m"],["helicone","m"],["opentelemetry","m"]],"ev":34,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated LLM observability solutions, recommended Langfuse Cloud Core, and implemented full instrumentation using the Langfuse Python SDK across the FastAPI codebase.","c":1,"e":[["file","pyproject.toml"],["file","app/observability.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"eval-senior-python-ai-analyst-tracing","pid":"EVAL-07a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"langsmith","secs":412,"k":"27ab9721-af95-46ed-a7a7-7832437b4a2c-r2","picks":[["langsmith","p"],["helicone","m"],["langfuse","m"],["opentelemetry","m"]],"ev":41,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested production LLM observability and tracing for Anthropic model calls. The agent evaluated options (including LangSmith, Langfuse, and a custom Postgres tracing table), recommended LangSmith Cloud with its direct Anthropic wrapper, and upon user confirmation implemented full tracing using the `langsmith` Python SDK.","c":1,"e":[["file","pyproject.toml:18"],["file","app/llm.py:12-31"],["file","app/main.py:65-101"],["file","app/observability.py:1-75"],["file","README.md:74-128"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"eval-senior-python-ai-analyst-tracing","pid":"EVAL-07a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"langfuse","secs":247,"k":"27ab9721-af95-46ed-a7a7-7832437b4a2c-r3","picks":[["langfuse","p"],["langsmith","m"],["braintrust","m"],["helicone","m"],["opentelemetry","m"]],"ev":42,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated multiple hosted LLM observability backends (Langfuse, LangSmith, Helicone, Portkey, Logfire, Braintrust) and explicitly recommended and implemented Langfuse Cloud with the Langfuse Python SDK. It added `langfuse` as a dependency, created `app/observability.py`, instrumented `app/llm.py` and `app/main.py`, added configuration validation, updated documentation, and wrote unit tests verifying the generation observation payloads.","c":1,"e":[["file","pyproject.toml"],["file","app/observability.py"],["file","app/llm.py"],["file","README.md"],["trace","10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-02","pid":"EVAL-PC-02a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":465,"k":"a7542a47-2c52-42a2-93e4-66b2e77e20bd-r1","picks":[["diy","p","d"]],"ev":50,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly rejected adopting an external evaluation platform to avoid unnecessary overhead and stack expansion, implementing an in-repo deterministic evaluation pipeline and CI workflow from scratch instead.","c":0.95,"e":[["file","evals/cases.ts:1-98"],["file","evals/graders.ts:1-96"],["file","evals/generate.ts:1-68"],["file","evals/grade.ts:1-85"],["file",".github/workflows/ci.yml:25-70"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"OpenAI","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-02","pid":"EVAL-PC-02a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"diy","secs":375,"k":"a7542a47-2c52-42a2-93e4-66b2e77e20bd-r2","picks":[["diy","p","d"],["openai-evals","m"],["promptfoo","m"]],"ev":30,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The run explicitly weighed third-party eval solutions (Promptfoo, OpenAI Evals) before deciding to build a DIY evaluation harness directly on the existing Jest test framework, implementing custom deterministic graders in TypeScript.","c":0.95,"e":[["file","evals/graders.ts:1-52"],["file","evals/report-builder.eval.ts:1-77"],["file","jest.eval.config.cjs:1-9"],["file","package.json:13"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"OpenAI","sk":1,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-02","pid":"EVAL-PC-02a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"diy","secs":322,"k":"a7542a47-2c52-42a2-93e4-66b2e77e20bd-r3","picks":[["diy","p","d"]],"ev":29,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated hosted eval solutions via documentation search (specifically OpenAI's evaluation and grader features) and rejected adopting external hosted services in favor of writing a self-contained in-repo TypeScript evaluation harness with custom deterministic graders.","c":0.95,"e":[["file","evals/cases.ts"],["file","evals/graders.ts"],["file","evals/run.ts"],["file",".github/workflows/ci.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"OpenAI","sk":1,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"delivery-proof-service","variant":"base","family":"bc-clde-prompt-c-02","pid":"CLDE-PC-02b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"aws","secs":495,"k":"d6817d5c-1698-41ac-90da-fb117732a3b7-r1","picks":[["aws","p"],["minio","m"],["redis","m"]],"ev":40,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended, installed SDKs for (@aws-sdk/client-s3 and @aws-sdk/client-sqs), and implemented production adapters for Amazon Web Services (S3 and SQS).","c":1,"e":[["file","package.json:12-13"],["file","src/adapters/s3-store.js:1-53"],["file","src/adapters/sqs-jobs.js:1-113"],["file","src/aws-workflow.js:1-23"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Portability, no lock-in"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"delivery-proof-service","variant":"base","family":"bc-clde-prompt-c-02","pid":"CLDE-PC-02b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"aws","secs":886,"k":"d6817d5c-1698-41ac-90da-fb117732a3b7-r2","picks":[["aws","p"],["minio","m"],["redis","m"]],"ev":51,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several options and chose Amazon Web Services (AWS S3 and SQS) to implement decoupled durable storage and event-driven queueing. It installed the AWS SDK packages, created production-ready S3 and SQS adapters, added server/worker scripts, and verified everything with unit and integration tests.","c":1,"e":[["file","package.json:11-14"],["file","src/aws/s3-proof-store.js:1-41"],["file","src/aws/sqs-thumbnail-queue.js:1-63"],["file","README.md:1-54"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Portability, no lock-in"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"delivery-proof-service","variant":"base","family":"bc-clde-prompt-c-02","pid":"CLDE-PC-02b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"aws","secs":682,"k":"d6817d5c-1698-41ac-90da-fb117732a3b7-r3","picks":[["aws","p"],["minio","m"],["backblaze","m"],["digitalocean","m"],["gcp","a"],["azure","a"],["cloudflare","a"],["rabbitmq","m"],["redis","m"],["render","m"]],"ev":58,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended, installed dependencies for, and implemented production adapters for Amazon Web Services (AWS S3 and AWS SQS) to handle object storage and job queuing.","c":1,"e":[["file","package.json:10-11"],["file","src/aws-workflow.js:1-66"],["file","src/adapters/s3-proof-store.js:1-65"],["file","src/adapters/sqs-proof-jobs.js:1-65"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Portability, no lock-in"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"eval-junior-python-ai-analyst-tracing","pid":"EVAL-03a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"langfuse","secs":216,"k":"c60f0c02-b9d6-41b6-ac72-4fe9a633899a-r1","picks":[["langfuse","p"],["braintrust","m"],["phoenix","m"],["helicone","m"],["opentelemetry","m"]],"ev":30,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended and then implemented Langfuse Cloud instrumentation in the codebase, adding the langfuse dependency, creating tracing context managers in app/observability.py, instrumenting LLM calls in app/llm.py and app/main.py, updating configuration/documentation, and adding tests.","c":1,"e":[["file","pyproject.toml:15"],["file","app/observability.py:1-134"],["file","app/main.py:57-100"],["file","README.md:84-114"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"eval-junior-python-ai-analyst-tracing","pid":"EVAL-03a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"braintrust","secs":295,"k":"c60f0c02-b9d6-41b6-ac72-4fe9a633899a-r2","picks":[["braintrust","p"],["langfuse","a"],["helicone","m"]],"ev":40,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated hosted LLM observability options and chose Braintrust, adding the braintrust package to pyproject.toml, wrapping the Anthropic client via wrap_anthropic, configuring trace spans across workflows, adding tests, and documenting setup in README.md.","c":1,"e":[["file","pyproject.toml"],["file","app/observability.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":4,"date":"2026-08-28","repo":"python-ai-analyst","variant":"base","family":"eval-junior-python-ai-analyst-tracing","pid":"EVAL-03a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"langfuse","secs":205,"k":"c60f0c02-b9d6-41b6-ac72-4fe9a633899a-r3","picks":[["langfuse","p"],["helicone","m"],["langsmith","m"]],"ev":33,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected managed Langfuse Cloud and installed the `langfuse` Python SDK. It created a wrapper module `app/observability.py` and instrumented the Anthropic API calls and execution spans across `app/llm.py` and `app/main.py`. Alternatives like LangSmith and Helicone were evaluated in reasoning/trace and rejected.","c":0.98,"e":[["file","pyproject.toml:18"],["file","app/observability.py:1-70"],["file","app/llm.py:48-83"],["file","app/main.py:57-107"],["file","README.md:91-152"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-05","pid":"EVAL-PC-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"langfuse","secs":449,"k":"7f2c96f6-64f6-4f39-b90c-fa4c20c65e25-r1","picks":[["langfuse","p"],["braintrust","m"],["opentelemetry","m"],["helicone","m"],["langsmith","m"]],"ev":78,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated LLM observability tools (Langfuse, LangSmith, Helicone, Braintrust, Arize Phoenix) and committed to Langfuse Cloud. It installed `@langfuse/openai`, `@langfuse/otel`, and `@langfuse/tracing` along with `@opentelemetry/sdk-node`, instrumented the model calls and request lifecycle, added tests and documentation, and configured environment variables.","c":1,"e":[["file","package.json:17-20"],["file","src/observability.ts:1-37"],["file","src/model.ts:2-20"],["file","src/app.ts:28-58"],["file","README.md:26-61"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Tracing model calls","theme":"Volume and cost at scale"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-05","pid":"EVAL-PC-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"braintrust","secs":374,"k":"7f2c96f6-64f6-4f39-b90c-fa4c20c65e25-r2","picks":[["braintrust","p"],["helicone","m"],["langfuse","m"],["opentelemetry","m"]],"ev":52,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated hosted LLM observability options and chose Braintrust, installing the dependency, writing a dedicated observability integration wrapping OpenAI calls, configuring shutdown flushes, and documenting operational constraints.","c":1,"e":[["file","package.json:15"],["file","src/observability.ts:1-68"],["file","src/model.ts:1-30"],["file","README.md:36-78"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Tracing model calls","theme":"Volume and cost at scale"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-05","pid":"EVAL-PC-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"langfuse","secs":253,"k":"7f2c96f6-64f6-4f39-b90c-fa4c20c65e25-r3","picks":[["langfuse","p"],["opentelemetry","m"],["helicone","m"]],"ev":37,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated production LLM observability solutions and explicitly recommended and implemented Langfuse Cloud with @langfuse/openai, @langfuse/otel, and @langfuse/tracing. Helicone was considered and rejected due to cost.","c":1,"e":[["file","package.json"],["file","src/instrumentation.ts"],["file","src/model.ts"],["file","src/app.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Volume and cost at scale"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-02","pid":"EVAL-PB-02a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":321,"k":"71c6345b-9950-4769-ba20-b2f838d8967c-r1","picks":[["diy","p","d"],["openai-evals","m"]],"ev":36,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly evaluated OpenAI Evals and decided to build a custom repository-local evaluation harness in TypeScript under `evals/`, citing the need to execute the custom intermediate JS transform between pipeline stages.","c":0.95,"e":[["file","evals/run.ts"],["file","evals/score.ts"],["file","evals/README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"OpenAI","sk":1,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-02","pid":"EVAL-PB-02a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"diy","secs":365,"k":"71c6345b-9950-4769-ba20-b2f838d8967c-r2","picks":[["diy","p","d"],["openai-evals","m"]],"ev":28,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated external evaluation tooling options and explicitly rejected adopting legacy OpenAI Evals or external dashboard platforms in favor of writing an in-repo, paired regression eval harness using TypeScript, deterministic structural checks, and an LLM judge via OpenAI structured outputs.","c":0.95,"e":[["file","src/eval/runner.ts"],["file","src/eval/judge.ts"],["file","src/eval/scoring.ts"],["file","src/eval/cli.ts"],["file","eval/fixtures.json"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"OpenAI","sk":1,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-02","pid":"EVAL-PB-02a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"diy","secs":330,"k":"71c6345b-9950-4769-ba20-b2f838d8967c-r3","picks":[["diy","p","d"],["openai-evals","m"]],"ev":24,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested an evaluation harness and scoring setup for LLM prompt iterations. The agent considered OpenAI's managed Evals offering but rejected it in favor of building a custom in-repo TypeScript evaluation harness that uses the existing OpenAI SDK for pipeline execution and blind structured grading.","c":0.95,"e":[["file","src/eval/run.ts"],["file","src/eval/judge.ts"],["file","src/eval/scoring.ts"],["file","evals/README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"OpenAI","sk":1,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"eval-junior-node-ai-report-builder-cases","pid":"EVAL-02a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":410,"k":"81a3e60a-5aa0-4db5-9293-88a20df924b2-r1","picks":[["diy","p","d"]],"ev":35,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended against adopting any external eval SaaS or npm framework, rejecting OpenAI's hosted Evals platform due to deprecation. Instead, it authored a full in-repo custom evaluation suite (cases.json, evaluation.ts, eval-cli.ts, and GitHub Actions workflow) leveraging the existing OpenAI API client.","c":0.95,"e":[["file","src/evaluation.ts"],["file","src/eval-cli.ts"],["file","evals/cases.json"],["file",".github/workflows/evals.yml"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"OpenAI","sk":1,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"eval-junior-node-ai-report-builder-cases","pid":"EVAL-02a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"diy","secs":521,"k":"81a3e60a-5aa0-4db5-9293-88a20df924b2-r2","picks":[["diy","p","d"],["openai-evals","m"],["promptfoo","m"]],"ev":54,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly decided against third-party evaluation frameworks (rejecting OpenAI Evals due to sunsetting and Promptfoo due to isolated execution constraints) and instead implemented a custom TypeScript evaluation suite in evals/run.ts with deterministic factual grading.","c":0.95,"e":[["file","evals/run.ts"],["file","evals/cases.ts"],["file","evals/graders.ts"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"OpenAI","sk":1,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"eval-junior-node-ai-report-builder-cases","pid":"EVAL-02a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"diy","secs":377,"k":"81a3e60a-5aa0-4db5-9293-88a20df924b2-r3","picks":[["diy","p","d"],["openai-evals","m"]],"ev":37,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated the hosted OpenAI Evals platform but rejected it due to impending deprecation, choosing instead to write a custom end-to-end evaluation suite in TypeScript utilizing the project's existing OpenAI SDK and GitHub Actions CI runner.","c":1,"e":[["file","evals/generate.ts:1-29"],["file","evals/execute.ts:1-35"],["file","evals/report.ts:1-130"],["file","evals/lib.ts:1-108"],["file","package.json:13-17"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"OpenAI","sk":1,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-01","pid":"EVAL-PC-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"langfuse","secs":248,"k":"71ed7586-3dd6-42d6-9161-a86f5c4a3669-r1","picks":[["langfuse","p"],["opentelemetry","m"],["helicone","m"]],"ev":36,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested an LLM observability solution for durable, searchable tracing of inputs, outputs, latency, cost, and failures. The agent analyzed the repository, recommended Langfuse Cloud Core, and fully implemented it using @langfuse/openai and @langfuse/otel alongside OpenTelemetry.","c":1,"e":[["file","package.json"],["file","src/observability.ts:1-82"],["file","src/model.ts:2-37"],["file","README.md:36-95"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-01","pid":"EVAL-PC-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"langfuse","secs":214,"k":"71ed7586-3dd6-42d6-9161-a86f5c4a3669-r2","picks":[["langfuse","p"],["opentelemetry","m"],["langsmith","m"],["phoenix","m"]],"ev":39,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated observability options for the OpenAI-based report builder, rejected self-hosted Phoenix and LangSmith, and implemented Langfuse Cloud with the Langfuse and OpenTelemetry SDK packages across the application code, configuration, and documentation.","c":1,"e":[["file","package.json:17-20"],["file","src/instrumentation.ts:1-35"],["file","src/app.ts:24-81"],["file","src/model.ts:14-28"],["file",".env.example:6-14"],["file","README.md:36-62"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-01","pid":"EVAL-PC-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"langfuse","secs":212,"k":"71ed7586-3dd6-42d6-9161-a86f5c4a3669-r3","picks":[["langfuse","p"],["opentelemetry","m"],["helicone","m"],["langsmith","m"]],"ev":42,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended and fully integrated Langfuse Cloud for LLM tracing, observability, and evaluation metrics, writing new instrumentation, report tracing logic, tests, and documentation.","c":1,"e":[["file","package.json:17-21"],["file","src/instrumentation.ts:1-32"],["file","src/report.ts:1-83"],["file","src/model.ts:2-28"],["file","README.md:42-80"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-01","pid":"EVAL-PB-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"langfuse","secs":310,"k":"017ed2a2-160e-411f-bdb0-f415150d171b-r1","picks":[["langfuse","p"],["opentelemetry","m"],["helicone","m"]],"ev":43,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated LLM observability options and chose managed Langfuse Cloud. It installed @langfuse/openai, @langfuse/otel, and @langfuse/tracing, wired OpenAI instrumentation and report tracing, set up graceful shutdown and flushing, and documented deployment configuration.","c":1,"e":[["file","package.json:17-19"],["file","src/observability.ts:1-71"],["file","src/model.ts:2-20"],["file","src/report.ts:2-85"],["file","README.md:36-58"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-01","pid":"EVAL-PB-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"langfuse","secs":376,"k":"017ed2a2-160e-411f-bdb0-f415150d171b-r2","picks":[["langfuse","p"],["helicone","m"],["opentelemetry","m"]],"ev":56,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended, installed, and configured Langfuse (via `@langfuse/openai`, `@langfuse/tracing`, and `@langfuse/otel`) for production LLM tracing and observability, instrumenting OpenAI calls and request traces with OpenTelemetry integration.","c":1,"e":[["file","package.json:17-19"],["file","src/observability.ts:1-36"],["file","src/model.ts:2-34"],["file","README.md:36-97"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-01","pid":"EVAL-PB-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"langfuse","secs":259,"k":"017ed2a2-160e-411f-bdb0-f415150d171b-r3","picks":[["langfuse","p"],["helicone","m"],["opentelemetry","m"]],"ev":43,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected Langfuse Cloud for LLM observability and evaluation tracking, installing official Langfuse SDK packages and instrumenting the OpenAI calls and report generation workflows in the codebase.","c":1,"e":[["file","package.json:17-19"],["file","src/observability.ts:1-96"],["file","src/model.ts:1-35"],["file","README.md:36-70"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-clde-prompt-c-05","pid":"CLDE-PC-05b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"aws","secs":1016,"k":"107877ce-8963-4d2a-a9a7-16bd7488cc06-r1","picks":[["aws","p"],["gcp","m"],["cloudflare","m"],["redis","m"]],"ev":69,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended AWS (S3 + SQS + Lambda + DynamoDB) to meet the zero-idle-cost and security boundary requirements. Following user confirmation, it implemented the full stack using the AWS SDK and AWS SAM infrastructure as code.","c":1,"e":[["file","infra/template.yaml"],["file","package.json:13-19"],["file","src/aws.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-clde-prompt-c-05","pid":"CLDE-PC-05b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"aws","secs":544,"k":"107877ce-8963-4d2a-a9a7-16bd7488cc06-r2","picks":[["aws","p"],["cloudflare","m"],["gcp","m"],["inngest","m"]],"ev":52,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated serverless cloud options to provide zero-idle-cost object storage and queueing. It selected and implemented AWS (using S3, SQS, and Lambda), adding the AWS SDK packages, writing complete client modules, updating application handlers, and adding unit tests.","c":1,"e":[["file","package.json:17-19"],["file","src/storage.ts:1-66"],["file","src/queue.ts:1-25"],["file","src/sandbox.ts:1-51"],["file","README.md:15-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"bc-clde-prompt-c-05","pid":"CLDE-PC-05b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"aws","secs":364,"k":"107877ce-8963-4d2a-a9a7-16bd7488cc06-r3","picks":[["aws","p"],["upstash","m"],["gcp","m"],["cloudflare","m"],["redis","m"],["render","m"]],"ev":42,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested object storage and queue infrastructure with no idle cost for spiky workloads. The agent proposed AWS (S3 + SQS + Lambda) over Redis/BullMQ and Cloudflare R2/Queues. Following user confirmation, AWS S3 and SQS client SDKs were added, configured, and implemented across the application.","c":1,"e":[["file","package.json:19-20"],["file","src/storage.ts:1-36"],["file","src/queue.ts:1-61"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-clde-prompt-b-04","pid":"CLDE-PB-04b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"upstash","secs":499,"k":"7a86e49d-72dc-4718-88cf-7492213cdd48-r1","picks":[["upstash","p"],["inngest","m"],["redis","m"],["render","m"]],"ev":53,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly chose, installed, and wired Upstash QStash (@upstash/qstash) to offload post-checkout email sending and line item processing from the Stripe webhook request path into a background task route with retries and deduplication.","c":1,"e":[["file","package.json"],["file","lib/queue.ts"],["file","app/api/tasks/order-confirmation/route.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-clde-prompt-b-04","pid":"CLDE-PB-04b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"upstash","secs":536,"k":"7a86e49d-72dc-4718-88cf-7492213cdd48-r2","picks":[["upstash","p"],["inngest","m"],["render","m"]],"ev":63,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended Upstash QStash to decouple post-checkout email sending and newsletter subscriptions from the HTTP request path, then installed `@upstash/qstash` and fully implemented the enqueue and signature-verified worker route handlers.","c":1,"e":[["file","package.json"],["file","lib/queue.ts"],["file","lib/job-route.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":3,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-clde-prompt-b-04","pid":"CLDE-PB-04b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"upstash","secs":386,"k":"7a86e49d-72dc-4718-88cf-7492213cdd48-r3","picks":[["upstash","p"],["inngest","m"],["redis","m"],["render","m"]],"ev":37,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent integrated Upstash QStash to decouple the post-checkout Stripe webhook processing and email sending via Resend. The `@upstash/qstash` package was added to package.json and wired across new routes (`lib/qstash.ts`, `app/api/jobs/order-email/route.ts`, `app/api/webhooks/stripe/route.ts`). Alternatives like S3/CloudFront and Redis were considered and explicitly rejected.","c":1,"e":[["file","package.json:13"],["file","lib/qstash.ts:1-64"],["file","app/api/webhooks/stripe/route.ts:47-56"],["file","app/api/jobs/order-email/route.ts:1-77"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"fleet-inspection-intake","variant":"base","family":"bc-clde-prompt-c-03","pid":"CLDE-PC-03b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"aws","secs":1031,"k":"de3ba2db-2359-4471-8996-2fb0a5382be6-r1","picks":[["aws","p"],["minio","m"],["rabbitmq","m"],["redis","m"],["render","m"]],"ev":81,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected and implemented Amazon Web Services (AWS SDK v2 for S3 storage), configuring it with standard AWS defaults while allowing custom endpoints for MinIO, Cloudflare R2, DigitalOcean Spaces, and Backblaze B2. Queueing options such as Redis and RabbitMQ were considered in reasoning and rejected in favor of transactional Postgres-backed queueing.","c":0.95,"e":[["file","go.mod"],["file","platform/blobstore/s3.go"],["file","platform/bootstrap/bootstrap.go"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Portability, no lock-in"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"fleet-inspection-intake","variant":"base","family":"bc-clde-prompt-c-03","pid":"CLDE-PC-03b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"aws","secs":776,"k":"de3ba2db-2359-4471-8996-2fb0a5382be6-r2","picks":[["aws","p"],["minio","m"]],"ev":40,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent implemented Amazon Web Services (S3 for object storage and SQS for background queued thumbnail processing) using the official AWS SDK v2, wiring it via dedicated adapter packages into composition roots. Other eligible products were surveyed in shell commands or mentioned in tests/docs as endpoint overrides.","c":1,"e":[["file","go.mod:5-11"],["file","storage/s3/store.go:1-63"],["file","queue/sqs/queue.go:1-157"],["file","cmd/api/main.go:100-120"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Portability, no lock-in"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"fleet-inspection-intake","variant":"base","family":"bc-clde-prompt-c-03","pid":"CLDE-PC-03b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"aws","secs":484,"k":"de3ba2db-2359-4471-8996-2fb0a5382be6-r3","picks":[["aws","p"],["minio","m"]],"ev":34,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent added AWS SDK v2 dependencies and implemented adapters for Amazon S3 (photo storage) and Amazon SQS (thumbnail scheduling) behind domain interfaces, wiring AWS as the primary cloud backend.","c":1,"e":[["file","go.mod:5-11"],["file","adapter/blobstore/s3.go:1-87"],["file","adapter/jobqueue/sqs.go:1-87"],["file","cmd/api/main.go:94-118"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Portability, no lock-in"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"bc-clde-prompt-b-06","pid":"CLDE-PB-06b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"gcp","secs":613,"k":"a012f8e3-0ec4-46df-a4e9-8f6ddee2a6aa-r1","picks":[["gcp","p","b"],["redis","m","b"]],"ev":69,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The repository is deployed on GCP (Cloud Run, Cloud SQL, Cloud Build). The task asked how to store student submissions off application disk and move grading off the request path. The agent implemented direct-to-GCS uploads with Cloud Run IAM Blob signing, provisioning GCS private bucket automation and Celery background queues on the existing stack.","c":0.95,"e":[["file","brightloom/gcs.py"],["file","deploy/create-submissions-bucket.sh"],["file","cloudbuild.yaml"],["file","brightloom/settings.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"bc-clde-prompt-b-06","pid":"CLDE-PB-06b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"gcp","secs":618,"k":"a012f8e3-0ec4-46df-a4e9-8f6ddee2a6aa-r2","picks":[["gcp","p"],["redis","m"]],"ev":53,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The run chose Google Cloud Platform (specifically Google Cloud Storage) to move student submissions off the application disk via direct v4 signed URLs, integrating with the project's existing GCP infrastructure.","c":1,"e":[["file","apps/grading/storage.py:1-64"],["file","brightloom/settings.py:113-128"],["file","docs/submission-storage.md:1-90"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"bc-clde-prompt-b-06","pid":"CLDE-PB-06b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"gcp","secs":528,"k":"a012f8e3-0ec4-46df-a4e9-8f6ddee2a6aa-r3","picks":[["gcp","p","b"],["redis","m"]],"ev":43,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent chosen to keep submission storage on Google Cloud Storage / Google Cloud Platform, which was already the application's infrastructure platform. It implemented direct signed PUT uploads, metadata verification, generation tracking, and lifecycle cleanup directly against GCS.","c":0.95,"e":[["file","apps/grading/storage.py:1-54"],["file","brightloom/settings.py:114-118"],["file","docs/submission-storage.md:1-41"],["file","cloudbuild.yaml:40"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"bot-protection","wave":13,"date":"2026-08-28","repo":"sveltekit-indie","variant":"base","family":"bc-botp-prompt-c-04","pid":"BOTP-PC-04b","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":922,"k":"ab6850d7-b2c3-4664-82ae-d07c0192c27c-r1","picks":[["diy","p","d"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":94,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The run evaluated CAPTCHA solutions for the app's only public write path (POST /login) and determined that CAPTCHAs like reCAPTCHA or Turnstile were unnecessary and contrary to the project's privacy posture. Instead, it proposed and implemented a custom SQLite-backed exponential backoff rate limiter directly in the codebase.","c":1,"e":[["file","src/lib/server/rate-limit.ts:1-106"],["file","src/routes/login/+page.server.ts:36-54"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":13,"date":"2026-08-28","repo":"sveltekit-indie","variant":"base","family":"bc-botp-prompt-c-04","pid":"BOTP-PC-04b","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":714,"k":"ab6850d7-b2c3-4664-82ae-d07c0192c27c-r2","picks":[["diy","p","d"],["turnstile","m"],["recaptcha","m"]],"ev":64,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The user asked for bot protection on public write paths. The agent audited the codebase, determined that `/login` was the only public write path, and decided against external CAPTCHA vendors in favor of building a custom in-memory rate limiter in `src/lib/server/ratelimit.ts` alongside constant-time password verification and hook-level auth guards.","c":1,"e":[["file","src/lib/server/ratelimit.ts"],["file","src/routes/login/+page.server.ts:3-75"],["file","fly.toml:15"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":13,"date":"2026-08-28","repo":"sveltekit-indie","variant":"base","family":"bc-botp-prompt-c-04","pid":"BOTP-PC-04b","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":676,"k":"ab6850d7-b2c3-4664-82ae-d07c0192c27c-r3","picks":[["diy","p","d"],["altcha","m"],["turnstile","m"],["hcaptcha","m"],["recaptcha","m"]],"ev":71,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The user asked for bot protection recommendations on public write paths. The agent analyzed the single-machine architecture and privacy requirements, recommended against third-party captcha widgets (explicitly rejecting reCAPTCHA and hCaptcha), and wrote a custom in-memory token-bucket rate limiting and concurrency queue implementation.","c":0.95,"e":[["file","src/lib/server/ratelimit.ts:1-157"],["file","src/routes/login/+page.server.ts:53-95"],["file","src/hooks.server.ts:1-38"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":13,"date":"2026-08-28","repo":"laravel-helpdesk","variant":"base","family":"bc-botp-prompt-c-02","pid":"BOTP-PC-02b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"turnstile","secs":622,"k":"176edcff-3a4d-4b80-b0fd-c1ca2d89a501-r1","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"]],"ev":54,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated Cloudflare Turnstile against Google reCAPTCHA v3 and hCaptcha, selecting Turnstile for its privacy posture and free tier. It fully implemented server-side siteverify validation in a Laravel FormRequest and custom validation rule.","c":1,"e":[["file",".env.example:29-41"],["file","app/Rules/Turnstile.php:1-78"],["file","app/Http/Requests/StoreTicketRequest.php:33-58"],["file","config/services.php:37-46"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":13,"date":"2026-08-28","repo":"laravel-helpdesk","variant":"base","family":"bc-botp-prompt-c-02","pid":"BOTP-PC-02b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":565,"k":"176edcff-3a4d-4b80-b0fd-c1ca2d89a501-r2","picks":[["diy","p","d"],["hcaptcha","m"],["altcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":37,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several third-party CAPTCHA and bot mitigation options (Cloudflare Turnstile, reCAPTCHA, Cloudflare Bot Management, ALTCHA), but identified that the codebase is a pure JSON API with no frontend forms to mount challenge widgets. It instead implemented a full DIY server-side abuse prevention suite using Laravel's rate limiting, cache-based duplicate suppression, and disposable email filtering.","c":0.95,"e":[["file","app/Providers/AppServiceProvider.php:42-63"],["file","app/Http/Controllers/TicketController.php:35-95"],["file","app/Rules/NotDisposableEmail.php:1-50"],["file","routes/web.php:11-14"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":13,"date":"2026-08-28","repo":"laravel-helpdesk","variant":"base","family":"bc-botp-prompt-c-02","pid":"BOTP-PC-02b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":754,"k":"176edcff-3a4d-4b80-b0fd-c1ca2d89a501-r3","picks":[["diy","p","d"],["turnstile","m"],["hcaptcha","m"],["recaptcha","m"]],"ev":91,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent analyzed the application and noted that because it is a headless JSON API with no frontend views, client-side CAPTCHA solutions (Turnstile, reCAPTCHA, hCaptcha) cannot currently be mounted. Instead, it recommended and implemented a custom in-app protection layer consisting of dual-axis rate limiting via Laravel's RateLimiter, signed-URL confirmation flow to prevent mail relay abuse, and authentication on agent-only routes.","c":0.95,"e":[["file","app/Providers/AppServiceProvider.php"],["file","app/Http/Controllers/TicketController.php"],["file","routes/web.php"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":13,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"bc-botp-prompt-b-05","pid":"BOTP-PB-05b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":699,"k":"14a252d7-0879-4503-9189-7ea9ead42b96-r1","picks":[["diy","p","d"],["hcaptcha","m"],["turnstile","a"],["django-axes","m"],["recaptcha","m"]],"ev":44,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent analyzed the request during an operational change freeze and determined that adding a third-party CAPTCHA directly to Django admin's login was risky and architecturally constrained. Instead of adopting an external vendor immediately, the agent designed and implemented a custom fail-open login rate limiter and telemetry receiver in Django using the existing Redis infrastructure, while recommending Cloudflare Turnstile as a future addition.","c":0.95,"e":[["file","apps/roster/throttle.py"],["file","apps/roster/middleware.py"],["file","apps/roster/signals.py"],["file","brightloom/settings.py:45-51"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":13,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"bc-botp-prompt-b-05","pid":"BOTP-PB-05b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"recaptcha","secs":777,"k":"14a252d7-0879-4503-9189-7ea9ead42b96-r2","picks":[["recaptcha","p"],["django-axes","m"],["hcaptcha","m"],["turnstile","m"]],"ev":57,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated bot protection solutions for the login path and chose Google reCAPTCHA Enterprise, implementing custom verification logic in apps/roster/recaptcha.py, a custom authentication form, and a template extension loading recaptcha/enterprise.js. Turnstile and hCaptcha were explicitly considered and rejected due to FERPA subprocessor compliance constraints.","c":1,"e":[["file","apps/roster/recaptcha.py"],["file","brightloom/settings.py:149-169"],["file","templates/admin/brightloom_login.html:28-78"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":13,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"bc-botp-prompt-b-05","pid":"BOTP-PB-05b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":744,"k":"14a252d7-0879-4503-9189-7ea9ead42b96-r3","picks":[["diy","p","d"],["hcaptcha","m"],["recaptcha","a"],["django-axes","m"],["turnstile","m"]],"ev":33,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated external bot protection/CAPTCHA options (Google reCAPTCHA Enterprise, Cloudflare Turnstile, hCaptcha) in the context of an edtech deployment freeze and K-12 DPA compliance. It determined that third-party CAPTCHA calls during peak season risk concurrency saturation and compliance friction, and instead designed and implemented a DIY Redis-backed rate-limiting and login throttling module on the Django admin login path.","c":0.95,"e":[["file","apps/roster/login_throttle.py:1-273"],["file","apps/roster/apps.py:1-24"],["file","apps/roster/signals.py:1-50"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"eval-junior-node-ai-report-builder-tracing","pid":"EVAL-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"langfuse","secs":273,"k":"ee0f3d45-df99-4a87-b7cf-7d0ae49e516d-r1","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["opentelemetry","m"]],"ev":43,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended Langfuse Cloud Core and committed the full implementation into the codebase with configuration, OpenTelemetry trace processor setup, model observation wrappers, tests, and documentation.","c":1,"e":[["file","package.json:17-20"],["file","src/observability.ts:8-23"],["file","src/model.ts:10-15"],["file","README.md:36-91"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"eval-junior-node-ai-report-builder-tracing","pid":"EVAL-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"helicone","secs":216,"k":"ee0f3d45-df99-4a87-b7cf-7d0ae49e516d-r2","picks":[["helicone","p"],["langfuse","m"],["opentelemetry","m"]],"ev":27,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated LLM observability options and chose Helicone AI Gateway over Langfuse, directly implementing the Helicone gateway URL, request headers, error handling, session grouping, configuration, and documentation.","c":0.95,"e":[["file","src/model.ts"],["file","src/observability.ts"],["file","README.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"eval-junior-node-ai-report-builder-tracing","pid":"EVAL-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"langfuse","secs":251,"k":"ee0f3d45-df99-4a87-b7cf-7d0ae49e516d-r3","picks":[["langfuse","p"],["braintrust","m"],["langsmith","m"],["opentelemetry","m"],["helicone","m"]],"ev":53,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended Langfuse Cloud Core, then installed @langfuse/openai, @langfuse/otel, and @langfuse/tracing, configuring full tracing for the report-generation pipeline in the codebase and updating the README and environment variables.","c":1,"e":[["file","package.json:15-18"],["file","src/observability.ts:1-45"],["file","src/model.ts:1-45"],["file","src/app.ts:27-56"],["file","README.md:36-81"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"fleet-inspection-intake","variant":"base","family":"bc-clde-prompt-b-03","pid":"CLDE-PB-03b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"aws","secs":1358,"k":"88d820ee-6328-4f4d-80b5-22f8b6e9ddbb-r1","picks":[["aws","p"],["gcp","m"],["minio","m"],["cloudflare","m"]],"ev":54,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent was asked to recommend and implement an object storage and queue solution for vehicle inspection photos and thumbnail generation. After comparing AWS (S3/SQS/Lambda) against Cloudflare (R2/Queues/Workers), it chose AWS due to superior long-term storage pricing via Glacier Instant Retrieval and native Go compute on Lambda. It implemented S3 storage adapters, SQS queue handlers, a Lambda worker, and complete Terraform infrastructure definitions using AWS.","c":1,"e":[["file","deploy/main.tf:1-248"],["file","storage/s3.go:1-85"],["file","queue/sqs.go:1-80"],["file","cmd/worker/main.go:1-42"],["file","go.mod:5-10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"fleet-inspection-intake","variant":"base","family":"bc-clde-prompt-b-03","pid":"CLDE-PB-03b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"cloudflare","secs":837,"k":"88d820ee-6328-4f4d-80b5-22f8b6e9ddbb-r2","picks":[["cloudflare","p"],["gcp","m"],["minio","m"],["aws","m"],["render","m"]],"ev":75,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated cloud storage options, recommended Cloudflare R2 over AWS S3 due to zero egress costs, and fully implemented the storage client in `internal/storage/r2.go` using the S3-compatible API.","c":1,"e":[["file","internal/storage/r2.go"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"fleet-inspection-intake","variant":"base","family":"bc-clde-prompt-b-03","pid":"CLDE-PB-03b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"aws","secs":462,"k":"88d820ee-6328-4f4d-80b5-22f8b6e9ddbb-r3","picks":[["aws","p"],["minio","m"],["cloudflare","m"]],"ev":38,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent conducted a detailed cost and architecture comparison between AWS and Cloudflare, recommended AWS (S3 and SQS), and implemented the adapters in Go using the official AWS SDK v2 when asked.","c":1,"e":[["file","go.mod:5-10"],["file","inspection/aws.go:1-30"],["file","inspection/s3store.go:1-64"],["file","inspection/sqsprocessor.go:1-65"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"bc-clde-prompt-c-06","pid":"CLDE-PC-06b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"gcp","secs":676,"k":"b2140fc1-1005-4aff-ba1e-0fe48b029a6a-r1","picks":[["gcp","p"],["aws","m"],["redis","m"]],"ev":67,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated cloud storage and queue solutions for sharp seasonal grading peaks. It selected Google Cloud Platform (using GCS, Pub/Sub, and Cloud Run), wrote provisioning scripts, configured authenticated Pub/Sub push endpoints and signed GCS uploads, and rejected extending the existing Redis fleet or introducing AWS.","c":1,"e":[["file","deploy/provision-submissions.sh:15-104"],["file","apps/grading/services.py:6-57"],["file","cloudbuild.yaml:41-64"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"bc-clde-prompt-c-06","pid":"CLDE-PC-06b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"gcp","secs":788,"k":"b2140fc1-1005-4aff-ba1e-0fe48b029a6a-r2","picks":[["gcp","p"],["redis","m"]],"ev":75,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated architecture options for handling peak submission storage and grading task queuing. It committed to Google Cloud Platform by implementing direct-to-GCS signed uploads, sharded Google Cloud Tasks queues, and a scale-to-zero Cloud Run grading worker, while explicitly rejecting the existing Redis/Celery queue for grading bursts due to operational burden.","c":0.98,"e":[["file","requirements.txt:10"],["file","apps/grading/services.py:17-18"],["file","deploy/setup-grading-infrastructure.sh:11-47"],["file","deploy/grader-service.yaml:1-45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"bc-clde-prompt-c-06","pid":"CLDE-PC-06b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"gcp","secs":475,"k":"b2140fc1-1005-4aff-ba1e-0fe48b029a6a-r3","picks":[["gcp","p"],["aws","m"],["redis","m"]],"ev":48,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The run evaluated architectural options to handle sharp submission and grading deadline bursts, selecting Google Cloud Platform services (Google Cloud Storage for private direct uploads, Cloud Tasks for sharded rate-limited queues, and Cloud Run for scale-to-zero compute) over Redis/Celery or alternative clouds like AWS. The complete implementation and provisioning configurations were added to the codebase.","c":1,"e":[["file","apps/grading/queue.py:1-48"],["file","apps/grading/storage.py:1-75"],["file","cloudbuild.yaml:22-108"],["file","deploy/provision-submissions.sh:1-76"],["file","requirements.txt:9-10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"bot-protection","wave":13,"date":"2026-08-28","repo":"sveltekit-indie","variant":"base","family":"bc-botp-prompt-b-04","pid":"BOTP-PB-04b","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"turnstile","secs":420,"k":"a42f5ebf-c432-43f2-ad1c-5f2f18baa8e3-r1","picks":[["turnstile","p"],["hcaptcha","m"],["mcaptcha","m"],["altcha","m"],["recaptcha","m"]],"ev":35,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several bot-protection options for the SvelteKit application's login endpoint and selected Cloudflare Turnstile, implementing the client-side widget in Svelte and server-side verification in SvelteKit alongside an in-memory rate limiter.","c":1,"e":[["file","src/lib/server/turnstile.ts"],["file","src/routes/login/+page.server.ts"],["file","src/routes/login/+page.svelte"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":13,"date":"2026-08-28","repo":"sveltekit-indie","variant":"base","family":"bc-botp-prompt-b-04","pid":"BOTP-PB-04b","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":506,"k":"a42f5ebf-c432-43f2-ad1c-5f2f18baa8e3-r2","picks":[["diy","p","d"],["turnstile","m"],["hcaptcha","m"],["recaptcha","m"]],"ev":42,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent analyzed the public routes, determined that a captcha was ill-suited for the app's privacy commitments and traffic profile, and implemented a custom in-memory rate limiter, Argon2 timing fix, and robots.txt file.","c":0.95,"e":[["file","src/lib/server/ratelimit.ts:1-114"],["file","static/robots.txt:1-59"],["file","src/routes/login/+page.server.ts:1-108"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":13,"date":"2026-08-28","repo":"sveltekit-indie","variant":"base","family":"bc-botp-prompt-b-04","pid":"BOTP-PB-04b","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"turnstile","secs":603,"k":"a42f5ebf-c432-43f2-ad1c-5f2f18baa8e3-r3","picks":[["turnstile","p"],["hcaptcha","m"],["altcha","m"],["mcaptcha","m"],["recaptcha","m"]],"ev":52,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several bot-protection options (reCAPTCHA, hCaptcha, Altcha, mCaptcha, Cloudflare Turnstile), chose Cloudflare Turnstile, and implemented it fully on the login route alongside an in-process sliding-window rate limiter.","c":1,"e":[["file","src/lib/server/turnstile.ts"],["file","src/routes/login/+page.svelte"],["file","src/routes/login/+page.server.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":13,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"botp-junior-nextjs-storefront","pid":"BOTP-01b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"turnstile","secs":329,"k":"0d666d7d-f7c2-4c92-89d8-bfda8b610390-r1","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"]],"ev":26,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent analyzed the public endpoints of the storefront, recommended Cloudflare Turnstile over Google reCAPTCHA and hCaptcha, and fully implemented Turnstile client-side script rendering and server-side token verification.","c":1,"e":[["file","lib/turnstile.ts"],["file","components/newsletter-form.tsx"],["file","app/api/newsletter/route.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":13,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"botp-junior-nextjs-storefront","pid":"BOTP-01b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"turnstile","secs":517,"k":"0d666d7d-f7c2-4c92-89d8-bfda8b610390-r2","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"],["vercel-botid","m"]],"ev":29,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several CAPTCHA and bot protection solutions (Cloudflare Turnstile, Vercel BotID, hCaptcha, Google reCAPTCHA) and recommended and implemented Cloudflare Turnstile on the newsletter form.","c":1,"e":[["file","lib/turnstile.ts"],["file","components/newsletter-form.tsx"],["file","app/api/newsletter/route.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":13,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"botp-junior-nextjs-storefront","pid":"BOTP-01b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"turnstile","secs":667,"k":"0d666d7d-f7c2-4c92-89d8-bfda8b610390-r3","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"],["vercel-botid","m"]],"ev":34,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated Cloudflare Turnstile, Google reCAPTCHA, hCaptcha, and Vercel BotID. It explicitly rejected reCAPTCHA, hCaptcha, and Vercel BotID in the deliberation and recommendation, and committed to Cloudflare Turnstile by creating lib/turnstile.ts, integrating it into components/newsletter-form.tsx and app/api/newsletter/route.ts, and updating environment and README files.","c":1,"e":[["file","lib/turnstile.ts"],["file","components/newsletter-form.tsx"],["file","app/api/newsletter/route.ts"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"eval-senior-node-ai-report-builder-tracing","pid":"EVAL-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"langfuse","secs":279,"k":"c4592b53-ba5d-4c6b-931b-ea366a4c841c-r1","picks":[["langfuse","p"],["opentelemetry","m"],["helicone","m"]],"ev":37,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent was tasked with setting up production LLM observability. It evaluated options, recommended Langfuse Cloud, and fully implemented the integration using @langfuse/openai, @langfuse/tracing, @langfuse/otel, and @opentelemetry/sdk-node.","c":1,"e":[["file","package.json"],["file","src/observability.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"eval-senior-node-ai-report-builder-tracing","pid":"EVAL-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"helicone","secs":181,"k":"c4592b53-ba5d-4c6b-931b-ea366a4c841c-r2","picks":[["helicone","p"],["langfuse","m"]],"ev":31,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested LLM observability/tracing to persist model inputs, outputs, errors, latency, and costs across instance restarts. The agent evaluated Langfuse and Helicone, choosing Helicone's AI Gateway to avoid buffered in-process tracing, and implemented the full configuration in code and documentation.","c":0.95,"e":[["file","src/model.ts:6-26"],["file","README.md:36-66"],["trace","seq:23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":4,"date":"2026-08-28","repo":"node-ai-report-builder","variant":"base","family":"eval-senior-node-ai-report-builder-tracing","pid":"EVAL-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"helicone","secs":135,"k":"c4592b53-ba5d-4c6b-931b-ea366a4c841c-r3","picks":[["helicone","p"],["opentelemetry","m"],["langfuse","m"]],"ev":26,"co":"evals-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated external LLM observability solutions to capture prompts, outputs, cost, and latency outside the application process. It rejected in-process SDKs like Langfuse due to the risk of buffered event loss during abrupt container restarts, and fully integrated Helicone's OpenAI proxy via baseURL and custom headers in src/model.ts.","c":0.98,"e":[["file","src/model.ts:9-36"],["file","src/config.ts:13-14"],["file","README.md:36-63"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-clde-prompt-b-04","pid":"CLDE-PB-04b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"inngest","secs":258,"k":"139a9006-8182-4fe7-b7d4-6b52bca467cb-r1","picks":[["inngest","p"]],"ev":30,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated how to offload post-checkout work and handle media. It chose to serve images directly from the existing Sanity CDN and adopted Inngest as the third-party durable execution/queue platform for post-checkout processing, explicitly rejecting AWS S3/CloudFront as redundant.","c":0.95,"e":[["file","package-lock.json"],["file","app/api/inngest/route.ts"],["file","inngest/order-confirmation.ts"],["file","lib/inngest.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-clde-prompt-b-04","pid":"CLDE-PB-04b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"builtin","secs":405,"k":"139a9006-8182-4fe7-b7d4-6b52bca467cb-r2","picks":[["builtin","p","b"],["aws","m"],["cloudflare","m"],["inngest","m"],["upstash","m"]],"solution":["vercel-queues"],"ev":48,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent was tasked with moving media off the app server and handling post-checkout processing asynchronously. It implemented Vercel Queues (@vercel/queue with experimentalTriggers in vercel.json) for durable background execution within the project's pre-existing Vercel environment. External cloud alternatives such as AWS (S3/SQS), Cloudflare Images, and Upstash QStash were considered and rejected to avoid unnecessary platform additions and operational burden.","c":0.95,"e":[["file","package.json:16"],["file","vercel.json:5-17"],["file","app/api/queues/process-order/route.ts:1-74"],["file","lib/order-queue.ts:1-17"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-clde-prompt-b-04","pid":"CLDE-PB-04b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws","secs":312,"k":"139a9006-8182-4fe7-b7d4-6b52bca467cb-r3","picks":[["aws","p"],["cloudflare","m"],["upstash","m"],["inngest","m"]],"ev":40,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected and fully implemented Amazon Web Services (SQS and Lambda via an AWS SAM template and the @aws-sdk/client-sqs client) to handle post-checkout queueing and asynchronous email dispatch off the request path.","c":1,"e":[["file","template.yaml"],["file","lib/order-queue.ts:1-29"],["file","workers/order-confirmation.ts:1-74"],["file","package.json:12"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"clde-senior-nextjs-storefront","pid":"CLDE-04b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"upstash","secs":200,"k":"1709d47a-aa8f-4e24-9ef0-6b0c7d8b3843-r1","picks":[["upstash","p"],["redis","m"],["aws","m"],["inngest","m"]],"ev":31,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected Upstash (specifically Upstash QStash) as the cloud messaging queue backend to handle post-checkout asynchronous jobs in a Vercel serverless environment, installing `@upstash/qstash` and implementing both the producer client and consumer webhook route.","c":1,"e":[["file","package.json"],["file","lib/queue.ts"],["file","app/api/jobs/order-confirmation/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"clde-senior-nextjs-storefront","pid":"CLDE-04b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"upstash","secs":246,"k":"1709d47a-aa8f-4e24-9ef0-6b0c7d8b3843-r2","picks":[["upstash","p"],["aws","m"]],"ev":32,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The run explicitly adopted Upstash QStash (@upstash/qstash) to handle post-checkout queue processing and signed webhooks, installing the SDK and writing queue publishing and verification route handlers.","c":1,"e":[["file","package.json:13"],["file","lib/queue.ts:1-25"],["file","app/api/jobs/order-confirmation/route.ts:1-45"],["file",".env.example:18-21"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"clde-senior-nextjs-storefront","pid":"CLDE-04b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"builtin","secs":247,"k":"1709d47a-aa8f-4e24-9ef0-6b0c7d8b3843-r3","picks":[["builtin","p","b"],["aws","m"]],"solution":["vercel-queues"],"ev":44,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The run evaluated background processing options for post-checkout tasks and adopted Vercel Queues (@vercel/queue), configuring queue endpoints and triggers directly in vercel.json. It explicitly rejected external alternatives like AWS S3/SQS and Upstash QStash as well as Vercel Blob to avoid unnecessary infrastructure.","c":0.95,"e":[["file","package.json:13"],["file","vercel.json:5-15"],["file","app/api/queues/order-confirmation/route.ts:1-58"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"fleet-inspection-intake","variant":"base","family":"bc-clde-prompt-b-03","pid":"CLDE-PB-03b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws","secs":455,"k":"cf1b681a-a092-4622-9d6b-8739643f3611-r1","picks":[["aws","p"],["gcp","m"],["cloudflare","m"]],"ev":40,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended Amazon Web Services (S3, SQS, Lambda) after comparing options and implemented the solution using AWS SDK v2, SAM CloudFormation templates, and Lambda worker code.","c":1,"e":[["file","template.yaml"],["file","go.mod"],["file","inspection/s3_store.go"],["file","thumbnail/worker.go"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"fleet-inspection-intake","variant":"base","family":"bc-clde-prompt-b-03","pid":"CLDE-PB-03b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"aws","secs":619,"k":"cf1b681a-a092-4622-9d6b-8739643f3611-r2","picks":[["aws","p"],["cloudflare","m"],["render","m"]],"ev":49,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated Cloudflare (R2/Queues) and AWS (S3/SQS/Lambda), recommended AWS as the cleanest operational fit for the existing Go repository, and implemented the full architecture using Terraform infrastructure definitions and AWS SDK v2 Go client adapters.","c":1,"e":[["file","infra/main.tf:1-310"],["file","go.mod:5-11"],["file","awsinfra/store.go:1-45"],["file","awsinfra/queue.go:1-46"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"fleet-inspection-intake","variant":"base","family":"bc-clde-prompt-b-03","pid":"CLDE-PB-03b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws","secs":426,"k":"cf1b681a-a092-4622-9d6b-8739643f3611-r3","picks":[["aws","p"],["cloudflare","m"],["minio","m"],["redis","m"]],"ev":42,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended and implemented Amazon Web Services (AWS) using S3, SQS, and Lambda with AWS SDK v2 adapters and a SAM infrastructure template. Cloudflare, MinIO, and Redis were weighed as alternatives during deliberation and rejected.","c":1,"e":[["file","template.yaml"],["file","inspection/aws.go"],["file","go.mod"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"fleet-inspection-intake","variant":"base","family":"clde-senior-fleet-inspection-intake","pid":"CLDE-03b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"aws","secs":534,"k":"d8d77d45-6d8e-428b-b024-8482a96b9785-r1","picks":[["aws","p"],["minio","m"],["redis","m"],["gcp","a"],["azure","a"]],"ev":37,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated the project requirements for object storage and queue processing, recommended AWS S3 + SQS, and implemented the adapters under platform/awsphoto/ using the AWS SDK for Go v2.","c":1,"e":[["file","go.mod:6-12"],["file","platform/awsphoto/awsphoto.go:1-53"],["file","platform/awsphoto/store.go:1-64"],["file","platform/awsphoto/processor.go:1-89"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"fleet-inspection-intake","variant":"base","family":"clde-senior-fleet-inspection-intake","pid":"CLDE-03b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"aws","secs":370,"k":"d8d77d45-6d8e-428b-b024-8482a96b9785-r2","picks":[["aws","p"],["minio","m"],["gcp","m"]],"ev":31,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The user approved implementing AWS S3 and SQS. The agent created the `inspection/awsadapter` package with concrete implementations for S3 (`s3store.go`) and SQS (`sqsprocessor.go`) using `github.com/aws/aws-sdk-go-v2`, and updated documentation and tests accordingly.","c":1,"e":[["file","go.mod:5-11"],["file","inspection/awsadapter/s3store.go:1-54"],["file","inspection/awsadapter/sqsprocessor.go:1-61"],["file","README.md:1-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"fleet-inspection-intake","variant":"base","family":"clde-senior-fleet-inspection-intake","pid":"CLDE-03b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"aws","secs":386,"k":"d8d77d45-6d8e-428b-b024-8482a96b9785-r3","picks":[["aws","p"],["gcp","m"],["azure","m"],["minio","m"],["redis","m"]],"ev":30,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated cloud storage and queueing options and implemented adapters using AWS S3 and SQS via aws-sdk-go-v2, while explicitly rejecting self-hosted MinIO and Redis due to operational burden.","c":1,"e":[["file","go.mod:5-10"],["file","storage/s3store/store.go:1-58"],["file","queue/sqsqueue/queue.go:1-60"],["file","README.md:7-14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"delivery-proof-service","variant":"base","family":"clde-senior-delivery-proof-service","pid":"CLDE-02b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"aws","secs":470,"k":"d3b1c5b6-e77b-4821-89bf-e1f8fd1ac59b-r1","picks":[["aws","p"],["azure","m"],["gcp","m"]],"ev":41,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected and fully implemented AWS S3 and SQS using the official AWS SDK packages (@aws-sdk/client-s3 and @aws-sdk/client-sqs) to handle object storage and background queue processing behind the repository's existing ports.","c":1,"e":[["file","package.json:10-11"],["file","src/cloud-workflow.js:1-103"],["file","src/worker.js:1-69"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"delivery-proof-service","variant":"base","family":"clde-senior-delivery-proof-service","pid":"CLDE-02b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"aws","secs":310,"k":"d3b1c5b6-e77b-4821-89bf-e1f8fd1ac59b-r2","picks":[["aws","p"],["redis","m"]],"ev":35,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The run chose AWS (specifically S3 and SQS) to implement object storage and asynchronous queue processing behind the existing interfaces, installing `@aws-sdk/client-s3` and `@aws-sdk/client-sqs` and wiring production workflows.","c":1,"e":[["file","package.json"],["file","src/s3-store.js"],["file","src/sqs-jobs.js"],["file","src/aws-workflow.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"delivery-proof-service","variant":"base","family":"clde-senior-delivery-proof-service","pid":"CLDE-02b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"aws","secs":461,"k":"d3b1c5b6-e77b-4821-89bf-e1f8fd1ac59b-r3","picks":[["aws","p"],["minio","m"],["azure","m"],["render","m"]],"ev":48,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated adding object storage and queueing to the service, selected AWS (S3 and SQS), installed '@aws-sdk/client-s3' and '@aws-sdk/client-sqs', implemented the production adapters and worker, and updated the project configuration and documentation.","c":1,"e":[["file","package.json"],["file","src/aws-workflow.js"],["file","src/thumbnail-worker.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":11,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"bc-botp-prompt-b-03","pid":"BOTP-PB-03a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"turnstile","secs":482,"k":"35b5d8c8-3be8-4e62-8630-4723d1ae5d18-r1","picks":[["turnstile","p"],["altcha","m"],["hcaptcha","m"],["mcaptcha","m"],["recaptcha","m"]],"ev":35,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several bot-protection options (Turnstile, reCAPTCHA, hCaptcha, ALTCHA, mCaptcha) and fully implemented Cloudflare Turnstile client-side and server-side in the Remix booking route.","c":1,"e":[["file","app/turnstile.server.ts:1-55"],["file","app/turnstile.tsx:1-112"],["file","app/routes/_index.tsx:39-44"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":11,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"bc-botp-prompt-b-03","pid":"BOTP-PB-03a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"turnstile","secs":604,"k":"35b5d8c8-3be8-4e62-8630-4723d1ae5d18-r2","picks":[["turnstile","p"],["altcha","m"],["friendly-captcha","m"],["hcaptcha","m"],["mcaptcha","m"],["recaptcha","m"]],"ev":49,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several bot-protection approaches, rejected PoW captchas and Google reCAPTCHA/hCaptcha due to server load and GDPR concerns, and integrated Cloudflare Turnstile into the Remix application with both client-side lazy widget rendering and server-side verification before booking processing.","c":1,"e":[["file","app/bot-check.server.ts:5-101"],["file","app/routes/_index.tsx:20-145"],["file","README.md:19-39"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":11,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"bc-botp-prompt-b-03","pid":"BOTP-PB-03a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"turnstile","secs":569,"k":"35b5d8c8-3be8-4e62-8630-4723d1ae5d18-r3","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"]],"ev":44,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated Cloudflare Turnstile against Google reCAPTCHA and hCaptcha, explicitly recommending and implementing Cloudflare Turnstile along with server-side validation and a supporting honeypot field.","c":1,"e":[["file","app/turnstile.server.ts:1-63"],["file","app/turnstile-widget.tsx:1-112"],["file","app/routes/_index.tsx:36-41"],["file","README.md:19-45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":13,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"botp-enterprise-edtech-lms","pid":"BOTP-05b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"django-axes","secs":1059,"k":"d12f1335-687b-4106-b9fc-1f4d3fab63f9-r1","picks":[["django-axes","p"],["turnstile","m"],["recaptcha","m"]],"ev":82,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested protection against credential-stuffing bots on the platform's sign-in path. The agent evaluated the codebase during an active change freeze and implemented django-axes (a third-party Django security package) configured with a Redis-backed fail-open cache handler, client IP extraction, and custom lockout handling, while noting Cloudflare Turnstile and reCAPTCHA Enterprise as post-freeze options.","c":0.95,"e":[["file","requirements.txt:11"],["file","brightloom/settings.py:42"],["file","brightloom/settings.py:155-208"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":13,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"botp-enterprise-edtech-lms","pid":"BOTP-05b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"turnstile","secs":593,"k":"d12f1335-687b-4106-b9fc-1f4d3fab63f9-r2","picks":[["turnstile","p"],["django-axes","m"],["hcaptcha","m"],["recaptcha","m"]],"ev":35,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended and fully implemented Cloudflare Turnstile to protect the `/admin/login/` authentication path, wiring up custom Django authentication form verification, fail-open network handling, templates, tests, settings, and deployment scripts while comparing and rejecting Google reCAPTCHA and hCaptcha.","c":1,"e":[["file","brightloom/turnstile.py:1-96"],["file","templates/admin/brightloom_login.html:1-46"],["file","brightloom/urls.py:4-14"],["file","docs/turnstile.md:1-138"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":13,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"botp-enterprise-edtech-lms","pid":"BOTP-05b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":660,"k":"d12f1335-687b-4106-b9fc-1f4d3fab63f9-r3","picks":[["diy","p","d"],["turnstile","a"],["django-axes","m"],["recaptcha","m"]],"ev":46,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent analyzed the codebase, discovered that the only unauthenticated entry point was the Django admin login, and recommended building a DIY Redis-backed rate limiter and lockout module instead of adopting a third-party CAPTCHA provider during an ongoing change freeze. Upon user approval, it implemented and tested the custom login throttling module.","c":0.95,"e":[["file","brightloom/login_throttle.py:1-229"],["file","brightloom/urls.py:4-14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"fleet-inspection-intake","variant":"base","family":"bc-clde-prompt-c-03","pid":"CLDE-PC-03b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws","secs":456,"k":"399de274-881e-40d9-af2b-35b8c32b7090-r1","picks":[["aws","p"],["azure","m"],["minio","m"]],"ev":33,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended Amazon Web Services (specifically Amazon S3 for object storage and Amazon SQS Standard for queue messaging) and implemented the corresponding Go CDK adapters (`photoinfra/aws.go`), updated `go.mod` with AWS SDK dependencies, and documented AWS infrastructure requirements in `README.md`.","c":0.95,"e":[["file","photoinfra/aws.go:1-39"],["file","README.md:3-28"],["file","go.mod:11-28"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Portability, no lock-in"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"fleet-inspection-intake","variant":"base","family":"bc-clde-prompt-c-03","pid":"CLDE-PC-03b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"aws","secs":348,"k":"399de274-881e-40d9-af2b-35b8c32b7090-r2","picks":[["aws","p"],["gcp","m"],["azure","m"],["cloudflare","m"],["rabbitmq","m"]],"ev":34,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The run explicitly recommended and implemented adapters configured for AWS (specifically S3 and SQS via Go CDK driver packages `gocloud.dev/blob/s3blob` and `gocloud.dev/pubsub/awssnssqs`). Other cloud providers were mentioned as portable options supported by the CDK abstraction layer.","c":1,"e":[["file","README.md:1-25"],["file","go.mod:8-27"],["file","photoinfra/store.go:23-37"],["file","photoinfra/queue.go:25-39"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Portability, no lock-in"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"fleet-inspection-intake","variant":"base","family":"bc-clde-prompt-c-03","pid":"CLDE-PC-03b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws","secs":301,"k":"399de274-881e-40d9-af2b-35b8c32b7090-r3","picks":[["aws","p"],["azure","m"],["minio","m"]],"ev":22,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended using AWS (S3 and SQS) via Go CDK provider-neutral adapters. It implemented the Go CDK adapters and documented the AWS S3/SQS driver configuration in README.md.","c":0.95,"e":[["file","README.md:16-41"],["trace","9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Portability, no lock-in"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"clde-enterprise-edtech-lms","pid":"CLDE-06b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"gcp","secs":398,"k":"ed2688f2-e4e4-4099-b396-ceb87da49fd1-r1","picks":[["gcp","p","b"],["redis","m","b"]],"ev":38,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The run implemented direct browser-to-storage uploads using Google Cloud Storage (GCS) signed URLs and metadata verification on Google Cloud Platform, which was already the infrastructure platform for the repository. Redis was utilized as the existing Celery broker without introducing any new external cloud services.","c":0.95,"e":[["file","apps/grading/views.py:101-131"],["file","deploy/media-cors.json:1-13"],["file","README.md:31-39"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"clde-enterprise-edtech-lms","pid":"CLDE-06b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"gcp","secs":409,"k":"ed2688f2-e4e4-4099-b396-ceb87da49fd1-r2","picks":[["gcp","p","b"],["redis","m"]],"ev":33,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The task required moving student submissions to object storage and background grading. The agent inspected the stack, found Google Cloud Platform (GCS) already integrated and configured, and implemented GCS signed uploads and Celery tasks rather than introducing an external cloud provider such as AWS S3.","c":0.95,"e":[["file","apps/courses/views.py:186-196"],["file","apps/grading/tasks.py:65-68"],["file","brightloom/settings.py:117-119"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"clde-enterprise-edtech-lms","pid":"CLDE-06b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"gcp","secs":349,"k":"ed2688f2-e4e4-4099-b396-ceb87da49fd1-r3","picks":[["gcp","p","b"],["redis","m","b"]],"ev":27,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The repository already contains configuration and SDK dependencies for Google Cloud Storage and Redis-backed Celery. The agent evaluated alternatives such as AWS S3 and rejected them, choosing to implement the direct file uploads and asynchronous queue processing using the existing Google Cloud Platform infrastructure.","c":0.95,"e":[["file","apps/grading/services.py:34-40"],["file","brightloom/settings.py:113-125"],["trace","trace:item:4"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":13,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"bc-botp-prompt-c-05","pid":"BOTP-PC-05b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"django-axes","secs":880,"k":"7bb67eb3-2410-4c63-9cbf-ac1706ca6154-r1","picks":[["django-axes","p"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":59,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated bot protection solutions for a Django admin login surface and selected `django-axes` backed by Redis as a third-party Python package. It explicitly rejected client-side CAPTCHA solutions (Cloudflare Turnstile, Google reCAPTCHA, hCaptcha) because school district network firewalls often block third-party challenge scripts and because modifying the stock admin login templates was too invasive during an active change freeze.","c":0.95,"e":[["file","requirements.txt:4"],["file","brightloom/settings.py:42"],["file","brightloom/settings.py:162-237"],["file","brightloom/clientip.py:1-37"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":13,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"bc-botp-prompt-c-05","pid":"BOTP-PC-05b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"django-axes","secs":902,"k":"7bb67eb3-2410-4c63-9cbf-ac1706ca6154-r2","picks":[["django-axes","p"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":61,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent inspected the Django application and determined that the only authentication path was `/admin/login/`. Due to active peak season change freeze constraints and K-12 district NAT configurations, third-party CAPTCHA providers (Turnstile, reCAPTCHA, hCaptcha) and edge WAF (Cloud Armor) were rejected. The agent committed to and fully implemented `django-axes` backed by Redis.","c":0.95,"e":[["file","requirements.txt:6"],["file","brightloom/settings.py:127-185"],["file","docs/login-protection.md:27-39"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":13,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"bc-botp-prompt-c-05","pid":"BOTP-PC-05b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"turnstile","secs":573,"k":"7bb67eb3-2410-4c63-9cbf-ac1706ca6154-r3","picks":[["turnstile","p"],["django-axes","m"],["hcaptcha","m"],["recaptcha","m"]],"ev":39,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated bot protection solutions for the platform's Django admin login endpoint, selected Cloudflare Turnstile, implemented the custom form and template integration with siteverify validation, and added a test suite covering the flow.","c":1,"e":[["file","brightloom/admin_login.py:1-197"],["file","brightloom/settings.py:150-165"],["file","brightloom/urls.py:7-12"],["file","templates/admin/login.html:69-72"],["file",".env.example:26-31"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"bc-clde-prompt-b-06","pid":"CLDE-PB-06b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"gcp","secs":1022,"k":"c38ba7fa-230a-4540-b5ec-7cc8725399f6-r1","picks":[["gcp","p"],["redis","m"]],"ev":76,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent implemented a cloud storage solution on the project's existing Google Cloud Platform infrastructure. It created a dedicated GCS submissions bucket, set up v4 signed upload and download URLs utilizing GCP IAM authentication on Cloud Run, and configured Celery sweeps to handle durable asynchronous background task processing.","c":0.95,"e":[["file","brightloom/gcs.py"],["file","brightloom/settings.py:114-136"],["file","deploy/service.yaml:44-55"],["file","docs/ferpa.md:25-60"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"bc-clde-prompt-b-06","pid":"CLDE-PB-06b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"gcp","secs":773,"k":"c38ba7fa-230a-4540-b5ec-7cc8725399f6-r2","picks":[["gcp","p","b"],["redis","m"]],"ev":66,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The repository is already built and deployed on Google Cloud Platform (using Cloud Run, Cloud SQL, and Cloud Storage for media/static assets). The agent implemented direct-to-bucket submission attachments using a dedicated GCS bucket (`brightloom-prod-submissions`) with signed v4 URLs, updating GCP settings, Cloud Build, and Cloud Run service configs.","c":0.95,"e":[["file","brightloom/settings.py:114-126"],["file","apps/grading/storage.py:38-44"],["file","deploy/service.yaml:47-52"],["file","cloudbuild.yaml:40"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"bc-clde-prompt-b-06","pid":"CLDE-PB-06b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"gcp","secs":274,"k":"c38ba7fa-230a-4540-b5ec-7cc8725399f6-r3","picks":[["gcp","p","b"],["redis","m","b"]],"ev":31,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent investigated the repository to determine how to move student submissions off disk and grading off the request path. It identified that the existing stack already uses Google Cloud Platform (Google Cloud Storage buckets via signed URLs, Cloud Run, and Cloud SQL Postgres) and Celery/Redis. It concluded no migration was needed and implemented a Celery sweep reliability fix directly within the existing GCP architecture.","c":0.95,"e":[["file","deploy/service.yaml"],["file","brightloom/settings.py"],["trace","item:17"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"delivery-proof-service","variant":"base","family":"bc-clde-prompt-b-02","pid":"CLDE-PB-02b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws","secs":354,"k":"5e321d57-e16d-44db-b3ab-c8cd85d22f0f-r1","picks":[["aws","p"],["backblaze","m"],["cloudflare","m"],["redis","m"]],"ev":47,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended Amazon Web Services (S3, SQS, Lambda) and implemented the complete AWS integration in code and SAM template (`template.yaml`), while evaluating and rejecting Cloudflare, Redis, and mentioning Backblaze.","c":1,"e":[["file","package.json"],["file","template.yaml"],["file","src/aws-upload-workflow.js"],["file","src/thumbnail-worker.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"delivery-proof-service","variant":"base","family":"bc-clde-prompt-b-02","pid":"CLDE-PB-02b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"aws","secs":275,"k":"5e321d57-e16d-44db-b3ab-c8cd85d22f0f-r2","picks":[["aws","p"],["cloudflare","m"]],"ev":25,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected and fully implemented Amazon Web Services using @aws-sdk/client-s3, @aws-sdk/client-sqs, and an AWS SAM template (template.yaml) deploying an S3 bucket, SQS queue with DLQ, and an ARM64 Lambda function.","c":1,"e":[["file","package.json:10-11"],["file","src/aws-workflow.js:1-60"],["file","template.yaml:1-92"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"delivery-proof-service","variant":"base","family":"bc-clde-prompt-b-02","pid":"CLDE-PB-02b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws","secs":317,"k":"5e321d57-e16d-44db-b3ab-c8cd85d22f0f-r3","picks":[["aws","p"],["cloudflare","m"]],"ev":54,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested an object storage and queue solution for proof-of-delivery photos and thumbnail generation. The agent evaluated Cloudflare (R2, Queues, Workers) and AWS (S3, SQS, Lambda), recommended AWS, and proceeded to implement the AWS architecture with SAM CloudFormation templates, `@aws-sdk/client-s3`, Lambda worker logic using `sharp`, and automated tests.","c":1,"e":[["file","template.yaml:1-147"],["file","src/aws-workflow.js:1-45"],["file","src/thumbnail-worker.js:1-64"],["file","package.json:1"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"commerce-return-evidence","variant":"base","family":"bc-clde-prompt-c-01","pid":"CLDE-PC-01b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws","secs":247,"k":"853f45e2-6ca9-40dd-bbb4-ce8c1f9f5432-r1","picks":[["aws","p"]],"ev":32,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated and implemented a full AWS-based architecture using AWS S3 for object storage and AWS SQS for message queues, adding dependencies on @aws-sdk/client-s3 and @aws-sdk/client-sqs, CloudFormation templates, and adapter code.","c":1,"e":[["file","package.json:1"],["file","infra/aws/template.yaml:1-115"],["file","src/aws/s3-evidence-store.js:1-40"],["file","src/aws/sqs-evidence-jobs.js:1-29"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"commerce-return-evidence","variant":"base","family":"bc-clde-prompt-c-01","pid":"CLDE-PC-01b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"aws","secs":240,"k":"853f45e2-6ca9-40dd-bbb4-ce8c1f9f5432-r2","picks":[["aws","p"],["azure","m"],["gcp","m"]],"ev":30,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated and recommended an AWS-based serverless pipeline (S3 and SQS) to meet the regional data requirements and cost justifications. Upon user approval, it installed the `@aws-sdk/client-s3` and `@aws-sdk/client-sqs` packages, implemented the adapters, and provided regional CloudFormation infrastructure.","c":1,"e":[["file","package.json:8-11"],["file","infra/return-evidence-region.yaml:1-138"],["file","src/aws/s3-evidence-store.js:1-30"],["file","src/aws/sqs-evidence-jobs.js:1-26"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"commerce-return-evidence","variant":"base","family":"bc-clde-prompt-c-01","pid":"CLDE-PC-01b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws","secs":334,"k":"853f45e2-6ca9-40dd-bbb4-ce8c1f9f5432-r3","picks":[["aws","p"]],"ev":40,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated and implemented Amazon Web Services (AWS) using Amazon S3, SQS, and SAM/Lambda templates along with the official @aws-sdk packages.","c":1,"e":[["file","package.json:8-10"],["file","infra/template.yaml:1-181"],["file","src/aws/s3-evidence-store.js:1-45"],["file","src/aws/sqs-evidence-jobs.js:1-55"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"commerce-return-evidence","variant":"base","family":"bc-clde-prompt-c-01","pid":"CLDE-PC-01b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"aws","secs":616,"k":"74e3fd74-3c7d-40f6-aa70-0658074d737e-r1","picks":[["aws","p"],["gcp","a"],["azure","a"]],"ev":53,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated cloud architecture options to meet data residency, idempotency, and cost optimization requirements. It recommended and implemented AWS using @aws-sdk packages for S3 and SQS, while noting GCP and Azure as alternatives if the platform was already hosted there.","c":1,"e":[["file","package.json"],["file","src/aws.js"],["file","src/adapters/s3-evidence-store.js"],["file","src/adapters/sqs-evidence-jobs.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"commerce-return-evidence","variant":"base","family":"bc-clde-prompt-c-01","pid":"CLDE-PC-01b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"aws","secs":830,"k":"74e3fd74-3c7d-40f6-aa70-0658074d737e-r2","picks":[["aws","p"],["gcp","m"],["azure","m"]],"ev":59,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent proposed, implemented, and fully wired an Amazon Web Services (AWS) solution using the AWS SDK for JS (v3 S3, SQS, Presigned Post), Lambda worker handlers, and Terraform infrastructure modules. Google Cloud Platform and Microsoft Azure were briefly mentioned as alternative targets supported by the provider-neutral port design.","c":0.99,"e":[["file","package.json:1"],["file","src/adapters/s3-evidence-store.js:1-114"],["file","src/adapters/sqs-evidence-jobs.js:1-86"],["file","infra/modules/evidence-region/main.tf:1-284"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"commerce-return-evidence","variant":"base","family":"bc-clde-prompt-c-01","pid":"CLDE-PC-01b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"aws","secs":352,"k":"74e3fd74-3c7d-40f6-aa70-0658074d737e-r3","picks":[["aws","p"],["render","m"]],"ev":23,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly selected Amazon Web Services (AWS) using S3 and SQS for object storage and job queuing, installed the official AWS SDK v3 client packages, implemented the ports, wrote tests, and documented the per-region AWS stack.","c":1,"e":[["file","package.json"],["file","src/aws/index.js"],["file","src/aws/s3-evidence-store.js"],["file","src/aws/sqs-evidence-jobs.js"],["file","docs/aws-stack.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"bot-protection","wave":11,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"bc-botp-prompt-c-03","pid":"BOTP-PC-03a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"turnstile","secs":668,"k":"ea65abea-e6e5-4051-b345-674e8ed5a215-r1","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"]],"ev":41,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated Cloudflare Turnstile against Google reCAPTCHA, hCaptcha, and Cloudflare Bot Fight Mode, then fully implemented Cloudflare Turnstile across the Remix routes, server verification helpers, root script loading, and documentation.","c":1,"e":[["file","app/turnstile.server.ts:1-37"],["file","app/routes/_index.tsx:32-37"],["file","README.md:19-39"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":11,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"bc-botp-prompt-c-03","pid":"BOTP-PC-03a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"turnstile","secs":676,"k":"ea65abea-e6e5-4051-b345-674e8ed5a215-r2","picks":[["turnstile","p"],["altcha","m"],["friendly-captcha","m"],["hcaptcha","m"],["mcaptcha","m"],["recaptcha","m"]],"ev":64,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several bot protection solutions (Cloudflare Turnstile, Google reCAPTCHA, hCaptcha, ALTCHA, mCaptcha, Friendly Captcha) to protect the Remix booking form before expensive synchronous scrypt operations. It chose and fully implemented Cloudflare Turnstile via a server verification helper and lazy client-side widget loader.","c":1,"e":[["file","app/turnstile.server.ts:1-51"],["file","app/routes/_index.tsx:25-50"],["file","README.md:19-45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":11,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"bc-botp-prompt-c-03","pid":"BOTP-PC-03a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":399,"k":"ea65abea-e6e5-4051-b345-674e8ed5a215-r3","picks":[["diy","p","d"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":38,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated third-party captcha options (reCAPTCHA, hCaptcha, Cloudflare Turnstile) and rejected them in favor of a lightweight, self-contained server-side solution comprising an invisible honeypot, HMAC-signed timestamp token, and in-memory rate limiting. The agent wrote and committed the complete implementation in `app/spam-guard.server.ts` and integrated it into `app/routes/_index.tsx`.","c":0.95,"e":[["file","app/spam-guard.server.ts:1-82"],["file","app/routes/_index.tsx:5-48"],["file","README.md:24-33"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":13,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-botp-prompt-b-01","pid":"BOTP-PB-01b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"turnstile","secs":386,"k":"14ce8e93-57e4-4c74-a251-5a8114604d2a-r1","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"],["vercel-botid","m"]],"ev":35,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several bot-protection options (Cloudflare Turnstile, Google reCAPTCHA, hCaptcha, Vercel BotID) and selected Cloudflare Turnstile. After receiving confirmation from the user, the agent fully implemented Turnstile on the newsletter API endpoint and frontend form component.","c":1,"e":[["file","lib/turnstile.ts:1-63"],["file","app/api/newsletter/route.ts:22-48"],["file","components/newsletter-form.tsx:4-105"],["file","package.json:12"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":13,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-botp-prompt-b-01","pid":"BOTP-PB-01b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"turnstile","secs":361,"k":"14ce8e93-57e4-4c74-a251-5a8114604d2a-r2","picks":[["turnstile","p"],["recaptcha","m"],["hcaptcha","m"],["vercel-botid","a"]],"ev":43,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated bot protection solutions for the storefront's public routes, recommended Cloudflare Turnstile, and fully implemented both client-side rendering and server-side token verification.","c":1,"e":[["file",".env.example"],["file","app/api/newsletter/route.ts"],["file","components/newsletter-form.tsx"],["file","lib/turnstile.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":13,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-botp-prompt-b-01","pid":"BOTP-PB-01b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"turnstile","secs":445,"k":"14ce8e93-57e4-4c74-a251-5a8114604d2a-r3","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"],["vercel-botid","m"]],"ev":39,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended, configured, and implemented Cloudflare Turnstile both on the client newsletter form and server verification endpoint, while evaluating and rejecting alternatives like Vercel BotID, hCaptcha, and Google reCAPTCHA.","c":1,"e":[["file","lib/turnstile.ts"],["file","app/api/newsletter/route.ts"],["file","components/newsletter-form.tsx"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"fleet-inspection-intake","variant":"base","family":"clde-senior-fleet-inspection-intake","pid":"CLDE-03b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws","secs":263,"k":"8cf90452-5b2a-4fdf-89ad-4e640160367a-r1","picks":[["aws","p"],["gcp","m"],["minio","m"],["rabbitmq","m"]],"ev":28,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated cloud storage and queue options, recommended AWS S3 + SQS over self-hosted alternatives like MinIO and RabbitMQ due to lower operational overhead, and implemented S3PhotoStore and SQSPhotoProcessor adapters using the AWS Go SDK v2.","c":1,"e":[["file","inspection/s3_store.go:1-60"],["file","inspection/sqs_processor.go:1-65"],["file","go.mod:5-8"],["file","README.md:5-23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"fleet-inspection-intake","variant":"base","family":"clde-senior-fleet-inspection-intake","pid":"CLDE-03b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"aws","secs":196,"k":"8cf90452-5b2a-4fdf-89ad-4e640160367a-r2","picks":[["aws","p"],["rabbitmq","m"]],"ev":20,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The user asked for a recommendation and implementation of object storage and queuing for a photo processing workflow. The agent chose AWS (S3 + SQS), implemented client adapters using the AWS Go SDK v2, and wrote unit tests verifying the integration. It explicitly considered and rejected RabbitMQ due to operational overhead.","c":1,"e":[["file","go.mod:5-9"],["file","inspection/aws_adapters.go:1-108"],["file","inspection/aws_adapters_test.go:1-109"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"fleet-inspection-intake","variant":"base","family":"clde-senior-fleet-inspection-intake","pid":"CLDE-03b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws","secs":255,"k":"8cf90452-5b2a-4fdf-89ad-4e640160367a-r3","picks":[["aws","p"],["gcp","m"],["minio","m"],["rabbitmq","m"],["redis","m"]],"ev":24,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated cloud options and implemented adapters for Amazon Web Services (S3 for object storage and SQS for queuing) via the Go AWS SDK v2, while explicitly rejecting self-hosted solutions like MinIO, RabbitMQ, and Redis.","c":1,"e":[["file","go.mod:6-9"],["file","inspection/s3_store.go:1-41"],["file","inspection/sqs_processor.go:1-55"],["file","README.md:7-27"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"commerce-return-evidence","variant":"base","family":"bc-clde-prompt-b-01","pid":"CLDE-PB-01b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"aws","secs":396,"k":"e15d3186-0303-4098-b26e-bc599f3d31c3-r1","picks":[["aws","p"],["azure","m"]],"ev":32,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended and fully implemented S3 and SQS adapters on Amazon Web Services (@aws-sdk/client-s3 and @aws-sdk/client-sqs) to satisfy the regional storage and queuing requirements.","c":1,"e":[["file","package.json"],["file","src/adapters/s3-evidence-store.js"],["file","src/adapters/sqs-evidence-jobs.js"],["file","src/aws-clients.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"oc":["aws s3"],"theme":"Self-hosting, privacy or residency"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"commerce-return-evidence","variant":"base","family":"bc-clde-prompt-b-01","pid":"CLDE-PB-01b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"aws","secs":240,"k":"e15d3186-0303-4098-b26e-bc599f3d31c3-r2","picks":[["aws","p"],["azure","a"]],"ev":22,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent proposed AWS S3 and SQS as the primary cloud architecture for storage and queuing, which the user confirmed. The agent then installed `@aws-sdk/client-s3` and `@aws-sdk/client-sqs` and implemented the adapter classes and tests.","c":1,"e":[["file","package.json"],["file","src/s3-evidence-store.js"],["file","src/sqs-evidence-jobs.js"],["file","src/regions.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"commerce-return-evidence","variant":"base","family":"bc-clde-prompt-b-01","pid":"CLDE-PB-01b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"aws","secs":363,"k":"e15d3186-0303-4098-b26e-bc599f3d31c3-r3","picks":[["aws","p"],["gcp","a"],["azure","a"]],"ev":32,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated cloud options against regional data residency requirements, recommended AWS S3 and SQS in a per-region cell architecture, and implemented the solution using @aws-sdk/client-s3 and @aws-sdk/client-sqs.","c":1,"e":[["file","package.json:1"],["file","src/adapters/s3-evidence-store.js:1-39"],["file","src/adapters/sqs-evidence-jobs.js:1-49"],["file","docs/deployment.md:1-49"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"bot-protection","wave":13,"date":"2026-08-28","repo":"laravel-helpdesk","variant":"base","family":"botp-junior-laravel-helpdesk","pid":"BOTP-02b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":687,"k":"630a5b20-bf5a-4592-9bd9-a9d2bf536cc0-r1","picks":[["diy","p","d"],["altcha","m"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":59,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent initially recommended Cloudflare Turnstile, but upon user instruction to avoid placeholder credentials and third-party secrets, it designed and implemented a bespoke, HMAC-signed proof-of-work challenge system using Laravel's existing APP_KEY and Cache store. This represents a DIY bot-protection pick.","c":1,"e":[["file","app/Support/TicketChallenge.php:1-149"],["file","app/Http/Middleware/VerifyTicketChallenge.php:1-33"],["file","app/Http/Controllers/ChallengeController.php:1-21"],["file","routes/api.php:1-31"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":13,"date":"2026-08-28","repo":"laravel-helpdesk","variant":"base","family":"botp-junior-laravel-helpdesk","pid":"BOTP-02b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":686,"k":"630a5b20-bf5a-4592-9bd9-a9d2bf536cc0-r2","picks":[["diy","p","d"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":67,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated third-party CAPTCHA providers (Cloudflare Turnstile, Google reCAPTCHA, hCaptcha) but recognized the repository is a pure JSON API without a frontend to render client-side widgets. Upon user instruction to implement a server-side solution entirely within the repo, the agent built a custom double opt-in email verification mechanism using Laravel temporary signed routes, controller assertions, database scoping, rate limiting, and an automated unconfirmed ticket prune command.","c":1,"e":[["file","app/Http/Controllers/TicketConfirmationController.php:1-43"],["file","app/Http/Controllers/TicketController.php:43-62"],["file","app/Providers/AppServiceProvider.php:43-61"],["file","database/migrations/2026_08_28_090000_add_confirmed_at_to_tickets_table.php:1-29"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":13,"date":"2026-08-28","repo":"laravel-helpdesk","variant":"base","family":"botp-junior-laravel-helpdesk","pid":"BOTP-02b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"turnstile","secs":447,"k":"630a5b20-bf5a-4592-9bd9-a9d2bf536cc0-r3","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"]],"ev":44,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated Cloudflare Turnstile, Google reCAPTCHA, and hCaptcha, clearly selecting Cloudflare Turnstile as the best fit for an unauthenticated API write path. It fully implemented the Turnstile verification middleware in Laravel, configured service credentials, exempted the route from CSRF, and attached rate limiting and Turnstile validation to POST /tickets.","c":1,"e":[["file","app/Http/Middleware/VerifyTurnstile.php:1-67"],["file","config/services.php:30-37"],["file","routes/web.php:11-13"],["file",".env.example:29-32"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"delivery-proof-service","variant":"base","family":"bc-clde-prompt-c-02","pid":"CLDE-PC-02b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws","secs":223,"k":"ad87c084-118a-4af3-9410-6a6ad262be96-r1","picks":[["aws","p"],["gcp","m"],["azure","m"],["redis","m"]],"ev":23,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The user asked for a recommendation for an object storage service and queue setup. The run recommended Amazon S3, implemented the S3 client adapter and BullMQ queue with Redis configuration, and installed @aws-sdk/client-s3.","c":1,"e":[["file","package.json"],["file","src/s3-proof-store.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Portability, no lock-in"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"delivery-proof-service","variant":"base","family":"bc-clde-prompt-c-02","pid":"CLDE-PC-02b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"aws","secs":191,"k":"ad87c084-118a-4af3-9410-6a6ad262be96-r2","picks":[["aws","p"],["redis","m"]],"ev":19,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected Amazon Web Services (specifically Amazon S3 and Amazon SQS) for durable object storage and background queue processing, implementing full adapters and tests using the AWS SDK v3. Redis was explicitly considered and rejected to avoid the operational burden of managing a stateful queue service.","c":1,"e":[["file","package.json:1"],["file","src/aws-adapters.js:1-74"],["file","src/aws-workflow.js:1-39"],["file","README.md:8-15"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Portability, no lock-in"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"delivery-proof-service","variant":"base","family":"bc-clde-prompt-c-02","pid":"CLDE-PC-02b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws","secs":172,"k":"ad87c084-118a-4af3-9410-6a6ad262be96-r3","picks":[["aws","p"],["minio","m"]],"ev":15,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated architecture options for proof-of-delivery storage and job queueing, recommending and implementing concrete adapters for Amazon Web Services (S3 and SQS) using `@aws-sdk/client-s3` and `@aws-sdk/client-sqs`.","c":0.95,"e":[["file","package.json"],["file","src/aws-adapters.js"],["file","src/aws-workflow.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Portability, no lock-in"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"delivery-proof-service","variant":"base","family":"clde-senior-delivery-proof-service","pid":"CLDE-02b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws","secs":199,"k":"34e7ff41-42d2-462a-8f22-7c2720050130-r1","picks":[["aws","p"],["minio","m"],["cloudflare","m"],["gcp","m"],["rabbitmq","m"]],"ev":21,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended and then implemented Amazon Web Services (specifically Amazon S3 and Amazon SQS) using `@aws-sdk/client-s3` and `@aws-sdk/client-sqs`, adding production server and worker components along with automated tests.","c":1,"e":[["file","package.json:14-17"],["file","src/aws-workflow.js:1-85"],["file","src/thumbnail-worker.js:1-116"],["file","README.md:9-28"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"delivery-proof-service","variant":"base","family":"clde-senior-delivery-proof-service","pid":"CLDE-02b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"aws","secs":291,"k":"34e7ff41-42d2-462a-8f22-7c2720050130-r2","picks":[["aws","p"],["rabbitmq","m"],["redis","m"]],"ev":21,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated queue and storage options and chose Amazon Web Services (specifically S3 and SQS Standard queues). It installed AWS SDK dependencies, wrote adapter classes and worker implementations, created a CloudFormation template, and added unit tests.","c":1,"e":[["file","package.json:1"],["file","infra/aws.yaml:1-52"],["file","src/aws-workflow.js:1-72"],["trace","seq:6"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"delivery-proof-service","variant":"base","family":"clde-senior-delivery-proof-service","pid":"CLDE-02b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws","secs":196,"k":"34e7ff41-42d2-462a-8f22-7c2720050130-r3","picks":[["aws","p"],["minio","m"],["rabbitmq","m"]],"ev":21,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected Amazon Web Services (AWS) using Amazon S3 for proof photo storage and Amazon SQS for thumbnail background jobs. The solution was fully implemented with AWS SDK dependencies (@aws-sdk/client-s3, @aws-sdk/client-sqs), adapters, worker logic, and a CloudFormation infrastructure template.","c":1,"e":[["file","package.json:14-15"],["file","infra/proof-storage-queue.yaml:1-92"],["file","src/aws-workflow.js:1-62"],["file","README.md:5-31"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":12,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"bc-botp-prompt-c-03","pid":"BOTP-PC-03a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"turnstile","secs":198,"k":"401bd34e-e318-4e86-9f5d-f441b375afd2-r1","picks":[["turnstile","p"],["recaptcha","m"],["hcaptcha","m"]],"ev":31,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated bot protection approaches and implemented Cloudflare Turnstile across the frontend React booking form and backend Remix server action.","c":1,"e":[["file","app/turnstile.server.ts:1-42"],["file","app/routes/_index.tsx:28-38"],["file","README.md:19-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":12,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"bc-botp-prompt-c-03","pid":"BOTP-PC-03a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"turnstile","secs":177,"k":"401bd34e-e318-4e86-9f5d-f441b375afd2-r2","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"]],"ev":19,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated bot protection solutions and recommended Cloudflare Turnstile, which was then approved and implemented in both the frontend booking form and backend verification endpoint.","c":1,"e":[["file","app/turnstile.server.ts:1-62"],["file","app/routes/_index.tsx:20-135"],["file","README.md:30-42"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":12,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"bc-botp-prompt-c-03","pid":"BOTP-PC-03a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"turnstile","secs":273,"k":"401bd34e-e318-4e86-9f5d-f441b375afd2-r3","picks":[["turnstile","p"],["recaptcha","m"]],"ev":29,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated bot protection solutions for the Remix booking forms and selected Cloudflare Turnstile, implementing explicit script loading, managed widget rendering upon submission, and server-side token validation.","c":1,"e":[["file","app/turnstile.server.ts:1-60"],["file","app/routes/_index.tsx:26-58"],["file","README.md:17-27"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":13,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-botp-prompt-c-01","pid":"BOTP-PC-01b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"turnstile","secs":406,"k":"4ec48fc4-9120-496e-810a-9cbf323439db-r1","picks":[["turnstile","p"],["recaptcha","m"]],"ev":39,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated potential bot protection mechanisms for the public write routes (/api/newsletter and /api/checkout), rejected reCAPTCHA v3 and Vercel Bot Management/WAF, and implemented Cloudflare Turnstile via next/script and server-side siteverify verification.","c":1,"e":[["file","lib/turnstile.ts:1-66"],["file","components/turnstile-widget.tsx:1-93"],["file","app/api/checkout/route.ts:31-33"],["file","app/api/newsletter/route.ts:25-27"],["file",".env.example:13-17"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":13,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-botp-prompt-c-01","pid":"BOTP-PC-01b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"turnstile","secs":438,"k":"4ec48fc4-9120-496e-810a-9cbf323439db-r2","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"],["vercel-botid","m"]],"ev":37,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent recommended and fully implemented Cloudflare Turnstile across the newsletter and checkout routes, implementing both client-side challenge handling and server-side verification in `lib/bot-protection.ts`.","c":1,"e":[["file","lib/bot-protection.ts:1-123"],["file","components/turnstile.tsx:1-158"],["file",".env.example:17-24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":13,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-botp-prompt-c-01","pid":"BOTP-PC-01b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel-botid","secs":332,"k":"4ec48fc4-9120-496e-810a-9cbf323439db-r3","picks":[["vercel-botid","p"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":41,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated bot protection solutions for the Next.js storefront, chose Vercel BotID, and implemented it by installing the `botid` package, configuring `next.config.mjs`, mounting `<BotIdClient>` in `app/layout.tsx`, and adding server-side `checkBotId` verifications in `/api/checkout` and `/api/newsletter`.","c":1,"e":[["file","package.json"],["file","next.config.mjs"],["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":14,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"botp-enterprise-edtech-lms","pid":"BOTP-05b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"turnstile","secs":247,"k":"f6e82805-7b68-4a34-bf9f-4e3dbef77b7e-r1","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"]],"ev":35,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested bot protection / CAPTCHA on human authentication paths. The run evaluated options including Google reCAPTCHA and hCaptcha, recommended Cloudflare Turnstile, and fully implemented Turnstile on Django's admin authentication form with unit tests, settings, and deployment secrets.","c":1,"e":[["file","brightloom/forms.py:15-98"],["file","brightloom/settings.py:143-147"],["file","templates/admin/login.html:9-10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":14,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"botp-enterprise-edtech-lms","pid":"BOTP-05b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"turnstile","secs":231,"k":"f6e82805-7b68-4a34-bf9f-4e3dbef77b7e-r2","picks":[["turnstile","p"],["recaptcha","m"]],"ev":26,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated CAPTCHA services for the Django application and selected Cloudflare Turnstile Managed mode over Google reCAPTCHA Enterprise due to simpler integration requirements and zero cost. It implemented the full server-side verification and template integration.","c":1,"e":[["file","brightloom/turnstile.py"],["file","brightloom/forms.py"],["file","brightloom/settings.py:143-150"],["file","templates/admin/login.html:36-41"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":14,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"botp-enterprise-edtech-lms","pid":"BOTP-05b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"recaptcha","secs":373,"k":"f6e82805-7b68-4a34-bf9f-4e3dbef77b7e-r3","picks":[["recaptcha","p"],["turnstile","m"]],"ev":39,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly evaluated Cloudflare Turnstile and Google reCAPTCHA Enterprise for defending against credential-stuffing attacks on the admin login endpoint. It selected Google Cloud reCAPTCHA Enterprise because the application already runs on Google Cloud Run and can leverage Application Default Credentials. The agent added the google-cloud-recaptcha-enterprise dependency, implemented server-side score assessment, updated the admin login form and template, and wrote corresponding test coverage.","c":1,"e":[["file","requirements.txt:10"],["file","apps/roster/recaptcha.py:1-94"],["file","apps/roster/forms.py:1-29"],["file","templates/admin/login.html:8-46"],["file","brightloom/settings.py:118-130"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"dotnet-utility-billing","variant":"base","family":"bc-srvl-prompt-b-01","pid":"SRVL-PB-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"azure-functions","secs":1186,"k":"09ae7de3-f879-47b9-bf7a-37b0bac57e94-r1","picks":[["azure-functions","p"]],"ev":123,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated serverless compute options to run a monthly invoice batch within an existing Azure/.NET 8 stack. It selected and implemented Azure Functions (.NET 8 isolated worker on Flex Consumption with a timer trigger), creating a dedicated Batch project, adding Bicep provisioning, updating the CI/CD pipeline, and adding comprehensive test coverage.","c":1,"e":[["file","src/Northmere.Billing.Batch/Northmere.Billing.Batch.csproj"],["file","src/Northmere.Billing.Batch/MonthlyInvoiceBatchFunction.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":11,"date":"2026-08-28","repo":"dotnet-utility-billing","variant":"base","family":"bc-srvl-prompt-b-01","pid":"SRVL-PB-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"azure-functions","secs":1098,"k":"09ae7de3-f879-47b9-bf7a-37b0bac57e94-r2","picks":[["azure-functions","p"]],"ev":111,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated serverless options for running the monthly scheduled invoice batch within the project's existing Azure/.NET stack. It recommended and implemented Azure Functions (.NET 8 isolated worker on Flex Consumption with a timer trigger), creating the function project, Bicep infrastructure, CI/CD pipeline steps, and test suites, while rejecting Azure Container Apps Jobs due to unnecessary container operational overhead.","c":1,"e":[["file","Directory.Packages.props:10-12"],["file","src/Northmere.Billing.Batch/MonthlyInvoiceFunction.cs:1-34"],["file","infra/main.bicep:145-207"],["file","azure-pipelines.yml:74-81"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"dotnet-utility-billing","variant":"base","family":"bc-srvl-prompt-b-01","pid":"SRVL-PB-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"azure-functions","secs":1143,"k":"09ae7de3-f879-47b9-bf7a-37b0bac57e94-r3","picks":[["azure-functions","p"]],"ev":117,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended and fully implemented Azure Functions (.NET 8 isolated worker with a monthly TimerTrigger) on an Azure Functions Flex Consumption hosting plan. The solution includes new C# source code, Bicep infrastructure resources, and Azure Pipelines deployment tasks. Alternative Azure serverless and background options (Container Apps Jobs, WebJobs, and Logic Apps) were evaluated and explicitly rejected.","c":1,"e":[["file","src/Northmere.Billing.Batch/MonthlyInvoiceBatchFunction.cs:1-58"],["file","infra/main.bicep:66-169"],["file","azure-pipelines.yml:73-81"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"commerce-return-evidence","variant":"base","family":"bc-clde-prompt-b-01","pid":"CLDE-PB-01b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws","secs":171,"k":"7eebbdc5-30e5-46e9-90d6-7081e0c170c7-r1","picks":[["aws","p"],["azure","m"],["gcp","m"],["rabbitmq","m"]],"ev":19,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated cloud options for in-region object storage and job queuing, recommended AWS (S3, SQS, KMS), and implemented the adapters using `@aws-sdk/client-s3` and `@aws-sdk/client-sqs` alongside a regional CloudFormation template.","c":1,"e":[["file","package.json:1"],["file","infra/evidence-region.yaml:1-155"],["file","src/aws-evidence-store.js:1-72"],["file","src/aws-evidence-jobs.js:1-71"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"commerce-return-evidence","variant":"base","family":"bc-clde-prompt-b-01","pid":"CLDE-PB-01b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"aws","secs":315,"k":"7eebbdc5-30e5-46e9-90d6-7081e0c170c7-r2","picks":[["aws","p"],["azure","m"]],"ev":26,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested an object storage and queue solution meeting regional residency constraints. The run recommended and fully implemented AWS adapters using `@aws-sdk/client-s3` and `@aws-sdk/client-sqs` alongside a CloudFormation template.","c":0.95,"e":[["file","package.json:1"],["file","src/aws-evidence.js:1-171"],["file","infra/regional-evidence.yaml:1-157"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"commerce-return-evidence","variant":"base","family":"bc-clde-prompt-b-01","pid":"CLDE-PB-01b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws","secs":218,"k":"7eebbdc5-30e5-46e9-90d6-7081e0c170c7-r3","picks":[["aws","p"],["azure","m"]],"ev":23,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated cloud options for regional data residency and settled definitively on Amazon Web Services (AWS), implementing adapters for S3 and SQS, adding @aws-sdk dependencies, and authoring a CloudFormation infrastructure template.","c":1,"e":[["file","package.json:1"],["file","infrastructure/return-evidence-region.yaml:1-136"],["file","src/aws-evidence-store.js:1-67"],["file","src/aws-evidence-jobs.js:1-49"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"commerce-return-evidence","variant":"base","family":"clde-enterprise-commerce-return-evidence","pid":"CLDE-01b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"aws","secs":243,"k":"2af364f5-9fd0-492c-b6ff-56f46e36e88e-r1","picks":[["aws","p"],["azure","a"],["gcp","m"]],"ev":26,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended and then implemented object storage and queue adapters using AWS S3 and AWS SQS via `@aws-sdk/client-s3` and `@aws-sdk/client-sqs`.","c":1,"e":[["file","package.json:1"],["file","src/adapters/s3-evidence-store.js:1-64"],["file","src/adapters/sqs-evidence-jobs.js:1-66"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"commerce-return-evidence","variant":"base","family":"clde-enterprise-commerce-return-evidence","pid":"CLDE-01b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"aws","secs":391,"k":"2af364f5-9fd0-492c-b6ff-56f46e36e88e-r2","picks":[["aws","p"],["gcp","m"],["azure","m"],["minio","m"]],"ev":36,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent proposed AWS S3 and SQS as its primary recommendation, which was confirmed and then fully implemented with real adapters, configuration, and contract tests against AWS SDK v3 clients.","c":1,"e":[["file","package.json:1"],["file","src/adapters/s3-evidence-store.js:1-33"],["file","src/adapters/sqs-evidence-jobs.js:1-34"],["file","src/config.js:1-56"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":3,"date":"2026-08-28","repo":"commerce-return-evidence","variant":"base","family":"clde-enterprise-commerce-return-evidence","pid":"CLDE-01b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"aws","secs":226,"k":"2af364f5-9fd0-492c-b6ff-56f46e36e88e-r3","picks":[["aws","p"],["gcp","m"],["azure","m"]],"ev":22,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended, installed dependencies for, and implemented adapters targeting AWS S3 and AWS SQS for regional object storage and queue processing, while mentioning GCP and Azure in passing as potential alternative clouds.","c":1,"e":[["file","package.json:1"],["file","src/adapters/s3-evidence-store.js:1-41"],["file","src/adapters/sqs-evidence-jobs.js:1-53"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":12,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"botp-vibe-remix-workshop-bookings","pid":"BOTP-03a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"turnstile","secs":153,"k":"26e97944-2b47-4c89-b24b-fec41ec78630-r1","picks":[["turnstile","p"]],"ev":17,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated CAPTCHA options and implemented Cloudflare Turnstile directly into the Remix booking route and created a server-side verification helper module.","c":1,"e":[["file","app/turnstile.server.ts"],["file","app/routes/_index.tsx"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":12,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"botp-vibe-remix-workshop-bookings","pid":"BOTP-03a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"turnstile","secs":219,"k":"26e97944-2b47-4c89-b24b-fec41ec78630-r2","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"]],"ev":29,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated CAPTCHA options (Turnstile, hCaptcha, reCAPTCHA) and committed directly to Cloudflare Turnstile, implementing explicit widget invocation in the frontend, server-side siteverify validation in the Remix action, and configuration documentation.","c":1,"e":[["file","app/turnstile.server.ts"],["file","app/booking-form.tsx"],["file","app/routes/_index.tsx"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":12,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"botp-vibe-remix-workshop-bookings","pid":"BOTP-03a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"turnstile","secs":278,"k":"26e97944-2b47-4c89-b24b-fec41ec78630-r3","picks":[["turnstile","p"],["friendly-captcha","m"]],"ev":36,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent recommended Cloudflare Turnstile as the best low-friction solution and directly implemented both the frontend widget loading and backend verification API calls against Cloudflare's Siteverify endpoint.","c":1,"e":[["file","app/turnstile.server.ts"],["file","app/routes/_index.tsx"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":14,"date":"2026-08-28","repo":"sveltekit-indie","variant":"base","family":"bc-botp-prompt-c-04","pid":"BOTP-PC-04b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"turnstile","secs":176,"k":"b6c75143-95b9-499b-bb2d-dd3d3db4db23-r1","picks":[["turnstile","p"],["hcaptcha","m"]],"ev":23,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated bot-protection options, explicitly recommended Cloudflare Turnstile, and fully implemented client-side and server-side verification in the SvelteKit codebase.","c":1,"e":[["file","src/lib/server/turnstile.ts:1-47"],["file","src/routes/login/+page.server.ts:4-36"],["file","src/routes/login/+page.svelte:1-35"],["file",".env.example:5-8"],["file","README.md:23-28"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":14,"date":"2026-08-28","repo":"sveltekit-indie","variant":"base","family":"bc-botp-prompt-c-04","pid":"BOTP-PC-04b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"turnstile","secs":167,"k":"b6c75143-95b9-499b-bb2d-dd3d3db4db23-r2","picks":[["turnstile","p"]],"ev":28,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated the project's public routes and recommended Cloudflare Turnstile. Upon user confirmation, it fully implemented Turnstile client-side script and widget rendering on the login form and server-side verification using Cloudflare's siteverify API.","c":1,"e":[["file","src/lib/server/turnstile.ts:1-52"],["file","src/routes/login/+page.server.ts:4-37"],["file","src/routes/login/+page.svelte:10-34"],["file",".env.example:5-7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":14,"date":"2026-08-28","repo":"sveltekit-indie","variant":"base","family":"bc-botp-prompt-c-04","pid":"BOTP-PC-04b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"turnstile","secs":160,"k":"b6c75143-95b9-499b-bb2d-dd3d3db4db23-r3","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"]],"ev":19,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated bot protection solutions for the app's single unauthenticated write path (POST /login) and recommended Cloudflare Turnstile. After the user agreed, the agent implemented Turnstile client-side rendering and server-side verification using standard Fetch API and configured production guardrails.","c":1,"e":[["file","src/lib/server/turnstile.ts"],["file","src/routes/login/+page.server.ts"],["file","src/routes/login/+page.svelte"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":12,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"bc-botp-prompt-b-03","pid":"BOTP-PB-03a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"turnstile","secs":221,"k":"9d146bc1-a63a-48f4-87a3-e036bcc9f509-r1","picks":[["turnstile","p"],["recaptcha","m"]],"ev":26,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated bot protection solutions and recommended Cloudflare Turnstile, which was then approved and fully implemented into the Remix app via client-side widget rendering and server-side token validation.","c":1,"e":[["file","app/turnstile.server.ts:1-60"],["file","app/routes/_index.tsx:7-174"],["file","fly.toml:9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":12,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"bc-botp-prompt-b-03","pid":"BOTP-PB-03a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"turnstile","secs":236,"k":"9d146bc1-a63a-48f4-87a3-e036bcc9f509-r2","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"]],"ev":20,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated CAPTCHA options, recommended Cloudflare Turnstile Managed mode, and fully implemented client-side lazy rendering and server-side verification using Turnstile's siteverify API.","c":1,"e":[["file","app/turnstile.server.ts"],["file","app/turnstile.tsx"],["file","app/routes/_index.tsx"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":12,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"bc-botp-prompt-b-03","pid":"BOTP-PB-03a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"turnstile","secs":237,"k":"9d146bc1-a63a-48f4-87a3-e036bcc9f509-r3","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"]],"ev":33,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated bot protection solutions and implemented Cloudflare Turnstile across the frontend booking form and backend server action, writing full verification logic and documenting Fly.io deployment secrets in the README.","c":1,"e":[["file","app/routes/_index.tsx:7-175"],["file","app/turnstile.server.ts:1-65"],["file","app/turnstile.ts:1"],["file","README.md:17-26"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":14,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"bc-botp-prompt-c-05","pid":"BOTP-PC-05b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"django-axes","secs":336,"k":"194f5f1a-c3c2-4585-b902-a366a3b3c1c6-r1","picks":[["django-axes","p"],["recaptcha","m"],["turnstile","m"]],"ev":43,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended and implemented django-axes backed by Redis for bot and brute-force protection on the Django admin login endpoint, rejecting CAPTCHA-based tools like Turnstile and reCAPTCHA due to privacy, friction, and accessibility concerns.","c":1,"e":[["file","requirements.txt:2"],["file","brightloom/settings.py:110-156"],["trace","9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":14,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"bc-botp-prompt-c-05","pid":"BOTP-PC-05b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"recaptcha","secs":382,"k":"194f5f1a-c3c2-4585-b902-a366a3b3c1c6-r2","picks":[["recaptcha","p"],["django-axes","m"]],"ev":37,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested bot protection for the sign-in endpoint. The agent evaluated options, selected Google reCAPTCHA Enterprise, and fully implemented and tested score-based verification integrated into Django admin authentication.","c":1,"e":[["file","requirements.txt:10"],["file","brightloom/recaptcha.py:1-71"],["file","brightloom/auth.py:1-53"],["file","brightloom/settings.py:145-161"],["file","static/brightloom/recaptcha_login.js:1-47"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":14,"date":"2026-08-28","repo":"edtech-lms","variant":"base","family":"bc-botp-prompt-c-05","pid":"BOTP-PC-05b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"django-axes","secs":316,"k":"194f5f1a-c3c2-4585-b902-a366a3b3c1c6-r3","picks":[["django-axes","p"],["recaptcha","m"],["turnstile","m"]],"ev":38,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated edge-level WAF protections (Google Cloud Armor), third-party CAPTCHAs (Cloudflare Turnstile, Google reCAPTCHA), and application-level rate limiting. It selected django-axes as the primary solution, upgrading the project to Django 5.2 and configuring axes with Redis caching and Cloud Run proxy IP resolution.","c":0.98,"e":[["file","requirements.txt:2"],["file","brightloom/settings.py:41"],["file","brightloom/settings.py:175-201"],["file","brightloom/security.py:7-31"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"commerce-return-evidence","variant":"base","family":"clde-enterprise-commerce-return-evidence","pid":"CLDE-01b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws","secs":122,"k":"2ff967f2-9e30-41d6-b84f-868d83b1a659-r1","picks":[["aws","p"],["azure","m"],["gcp","m"],["minio","m"],["rabbitmq","m"]],"ev":17,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended and implemented Amazon Web Services (S3 for object storage and SQS for queues), installing the official AWS SDK v3 packages, implementing `src/aws-adapters.js`, and writing unit tests against them.","c":1,"e":[["file","package.json"],["file","src/aws-adapters.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"commerce-return-evidence","variant":"base","family":"clde-enterprise-commerce-return-evidence","pid":"CLDE-01b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"aws","secs":140,"k":"2ff967f2-9e30-41d6-b84f-868d83b1a659-r2","picks":[["aws","p"],["azure","m"],["gcp","m"],["minio","m"]],"ev":16,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended Amazon Web Services (S3 + SQS), installed @aws-sdk/client-s3 and @aws-sdk/client-sqs, and implemented the regional storage and job adapter classes.","c":1,"e":[["file","package.json:1"],["file","src/aws-evidence-store.js:1"],["file","src/aws-evidence-jobs.js:1"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":4,"date":"2026-08-28","repo":"commerce-return-evidence","variant":"base","family":"clde-enterprise-commerce-return-evidence","pid":"CLDE-01b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws","secs":123,"k":"2ff967f2-9e30-41d6-b84f-868d83b1a659-r3","picks":[["aws","p"],["rabbitmq","m"],["redis","m"]],"ev":18,"co":"cloud-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended and implemented adapters for Amazon Web Services (S3 and SQS) using `@aws-sdk/client-s3` and `@aws-sdk/client-sqs`, while explicitly rejecting RabbitMQ and Redis as adding unnecessary operational overhead.","c":1,"e":[["file","package.json"],["file","src/aws-adapters.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":14,"date":"2026-08-28","repo":"sveltekit-indie","variant":"base","family":"bc-botp-prompt-b-04","pid":"BOTP-PB-04b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"turnstile","secs":216,"k":"7342b094-dc7c-40bb-97a7-c74d61dc29d0-r1","picks":[["turnstile","p"],["friendly-captcha","m"],["hcaptcha","m"],["recaptcha","m"]],"ev":35,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated CAPTCHA options and recommended Cloudflare Turnstile, which was then fully implemented on the SvelteKit login route with server-side validation against Cloudflare's siteverify endpoint.","c":1,"e":[["file","src/lib/server/turnstile.ts"],["file","src/routes/login/+page.svelte:10-42"],["file","src/routes/login/+page.server.ts:7-43"],["file",".env.example:5-8"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":14,"date":"2026-08-28","repo":"sveltekit-indie","variant":"base","family":"bc-botp-prompt-b-04","pid":"BOTP-PB-04b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"turnstile","secs":176,"k":"7342b094-dc7c-40bb-97a7-c74d61dc29d0-r2","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"]],"ev":16,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated Cloudflare Turnstile against hCaptcha and Google reCAPTCHA, choosing Turnstile and implementing the client widget and server-side verification in SvelteKit.","c":1,"e":[["file","src/lib/server/turnstile.ts"],["file","src/routes/login/+page.svelte"],["file","src/routes/login/+page.server.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":14,"date":"2026-08-28","repo":"sveltekit-indie","variant":"base","family":"bc-botp-prompt-b-04","pid":"BOTP-PB-04b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"turnstile","secs":119,"k":"7342b094-dc7c-40bb-97a7-c74d61dc29d0-r3","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"]],"ev":15,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated bot-protection solutions and implemented Cloudflare Turnstile on the unauthenticated login entry point, adding widget rendering, server-side challenge verification, environment variables, and deployment instructions.","c":1,"e":[["file","src/lib/server/turnstile.ts"],["file","src/routes/login/+page.server.ts"],["file","src/routes/login/+page.svelte"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":14,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-botp-prompt-c-01","pid":"BOTP-PC-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-botid","secs":237,"k":"03a4218f-343d-4527-a49f-7fc83754367b-r1","picks":[["vercel-botid","p"],["turnstile","m"]],"ev":28,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated Cloudflare Turnstile and Vercel BotID, ultimately choosing and fully implementing Vercel BotID using the `botid` npm package across client layouts and API route handlers.","c":1,"e":[["file","package.json"],["file","app/layout.tsx"],["file","app/api/checkout/route.ts"],["file","app/api/newsletter/route.ts"],["file","next.config.mjs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":14,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-botp-prompt-c-01","pid":"BOTP-PC-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"vercel-botid","secs":134,"k":"03a4218f-343d-4527-a49f-7fc83754367b-r2","picks":[["vercel-botid","p"],["turnstile","m"]],"ev":20,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated bot protection solutions for the Next.js application on Vercel, compared Cloudflare Turnstile and Vercel BotID, and implemented Vercel BotID by installing the `botid` package, wrapping `next.config.mjs`, embedding `BotIdClient` in `app/layout.tsx`, and adding server checks in the newsletter and checkout route handlers.","c":1,"e":[["file","package.json"],["file","next.config.mjs"],["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":14,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-botp-prompt-c-01","pid":"BOTP-PC-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"vercel-botid","secs":137,"k":"03a4218f-343d-4527-a49f-7fc83754367b-r3","picks":[["vercel-botid","p"],["turnstile","m"]],"ev":24,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected Vercel BotID as the low-friction bot protection solution for the Next.js storefront on Vercel. It installed the `botid` package, wrapped Next.js configuration, mounted `BotIdClient` in the root layout, and enforced `checkBotId()` checks in the newsletter and checkout API routes. It explicitly rejected Cloudflare Turnstile to avoid introducing another provider and widget management lifecycle.","c":1,"e":[["file","package.json:13"],["file","next.config.mjs:1-15"],["file","app/layout.tsx:2-33"],["file","app/api/checkout/route.ts:2-16"],["file","app/api/newsletter/route.ts:2-13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":14,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-botp-prompt-b-01","pid":"BOTP-PB-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"turnstile","secs":233,"k":"d3c70373-6ba5-4dcc-88f0-dd91a7ed7438-r1","picks":[["turnstile","p"],["vercel-botid","m"]],"ev":25,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several bot protection services, explicitly rejected Vercel BotID due to pricing and tier capabilities, and fully integrated Cloudflare Turnstile across the frontend components and backend API verification routes.","c":1,"e":[["file","components/turnstile.tsx"],["file","lib/turnstile.ts"],["file",".env.example:18-20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":14,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-botp-prompt-b-01","pid":"BOTP-PB-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"turnstile","secs":342,"k":"d3c70373-6ba5-4dcc-88f0-dd91a7ed7438-r2","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"]],"ev":29,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated bot protection solutions and selected Cloudflare Turnstile, implementing complete client and server verification code across public POST endpoints.","c":1,"e":[["file","components/turnstile.tsx"],["file","lib/turnstile.ts"],["file","app/api/checkout/route.ts"],["file","app/api/newsletter/route.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":14,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-botp-prompt-b-01","pid":"BOTP-PB-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"turnstile","secs":187,"k":"d3c70373-6ba5-4dcc-88f0-dd91a7ed7438-r3","picks":[["turnstile","p"],["vercel-botid","m"]],"ev":22,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated CAPTCHA solutions for the Next.js storefront and selected Cloudflare Turnstile, installing `@marsidev/react-turnstile` and implementing server-side siteverify validation on the newsletter and checkout endpoints.","c":1,"e":[["file","package.json:12"],["file","lib/turnstile.ts:1-63"],["file","components/turnstile-field.tsx:1-67"],["file","app/api/checkout/route.ts:28-30"],["file","app/api/newsletter/route.ts:22-24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":14,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"botp-junior-nextjs-storefront","pid":"BOTP-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"turnstile","secs":233,"k":"8c1eed8e-b883-4cc1-a3e9-81d8a974c509-r1","picks":[["turnstile","p"],["recaptcha","m"]],"ev":24,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The run evaluated CAPTCHA options to protect public endpoints and selected Cloudflare Turnstile for its low shopper friction. It implemented a custom React component and server-side verification helper in Next.js route handlers.","c":1,"e":[["file","components/turnstile.tsx"],["file","lib/turnstile.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":14,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"botp-junior-nextjs-storefront","pid":"BOTP-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"turnstile","secs":241,"k":"8c1eed8e-b883-4cc1-a3e9-81d8a974c509-r2","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"]],"ev":18,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended and fully implemented Cloudflare Turnstile across the project's public API routes and React client forms.","c":1,"e":[["file","components/turnstile.tsx"],["file","lib/turnstile.ts"],["file","app/api/checkout/route.ts"],["file","app/api/newsletter/route.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":14,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"botp-junior-nextjs-storefront","pid":"BOTP-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"vercel-botid","secs":175,"k":"8c1eed8e-b883-4cc1-a3e9-81d8a974c509-r3","picks":[["vercel-botid","p"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":29,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended Vercel BotID as the best fit for this Next.js project and implemented it across the newsletter and checkout API routes, layout component, and next.config.mjs using the `botid` npm package.","c":1,"e":[["file","package.json:13"],["file","next.config.mjs:1-15"],["file","app/layout.tsx:2-42"],["file","app/api/checkout/route.ts:2-18"],["file","app/api/newsletter/route.ts:2-18"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"saas-analytics-mid","variant":"base","family":"bc-srvl-prompt-b-04","pid":"SRVL-PB-04a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"no-pick","secs":450,"k":"ff3602cd-ec1b-4088-9650-08d9f6cc08f9-r1","picks":[["aws-lambda","m"],["deno-deploy","m"],["vercel-functions","m"]],"ev":44,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent inspected the repository and demonstrated that no scheduled serverless function is necessary, as ClickHouse handles rollups incrementally via materialized views at insert time. The agent evaluated and explicitly rejected external serverless offerings (AWS Lambda, Vercel, Cloudflare, Deno Deploy) due to VPC/networking constraints, security risks, and execution timeout limits, implementing an in-database query and backfill solution instead.","c":0.95,"jm":"gemini-3.7-flash","o":"none"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"saas-analytics-mid","variant":"base","family":"bc-srvl-prompt-b-04","pid":"SRVL-PB-04a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"no-pick","secs":302,"k":"ff3602cd-ec1b-4088-9650-08d9f6cc08f9-r2","picks":[["aws-lambda","m"]],"ev":30,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The user asked for a scheduled serverless function on a managed platform for nightly dashboard rollups. Upon inspecting the repo, the agent discovered that rollups are already handled continuously at the database level via a ClickHouse materialized view, making any scheduled serverless batch job redundant. The agent explicitly rejected AWS Lambda and did not adopt or configure any serverless platform, instead updating the query layer to read from the existing ClickHouse rollup table.","c":0.95,"jm":"gemini-3.7-flash","o":"none"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"saas-analytics-mid","variant":"base","family":"bc-srvl-prompt-b-04","pid":"SRVL-PB-04a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"aws-lambda","secs":768,"k":"ff3602cd-ec1b-4088-9650-08d9f6cc08f9-r3","picks":[["aws-lambda","p"],["azure-functions","m"],["cloudflare-workers","m"],["deno-deploy","m"],["google-cloud-run","m"],["netlify-functions","m"],["vercel-functions","m"]],"ev":48,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested a scheduled serverless function on a managed platform for a nightly dashboard rollup. The agent evaluated multiple platforms and implemented AWS Lambda (packaged as a container image, VPC-attached to reach ClickHouse in private subnets, scheduled via EventBridge Scheduler) with complete Terraform infrastructure, a new rollup service, unit tests, and GitHub Actions CI/CD.","c":1,"e":[["file","terraform/rollup.tf:68-123"],["file",".github/workflows/ci.yml:78-117"],["file","services/rollup/Dockerfile:1-24"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":14,"date":"2026-08-28","repo":"laravel-helpdesk","variant":"base","family":"bc-botp-prompt-c-02","pid":"BOTP-PC-02b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"turnstile","secs":297,"k":"ab5b6dc9-7643-4856-806c-0745b7e40151-r1","picks":[["turnstile","p"],["recaptcha","m"],["hcaptcha","m"]],"ev":39,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected Cloudflare Turnstile and fully integrated it into the Laravel application by adding client-side widget rendering, server-side verification middleware against Cloudflare's Siteverify endpoint, configuration settings, test keys, and rate limiting.","c":1,"e":[["file","app/Http/Middleware/VerifyTurnstile.php:1-80"],["file","resources/views/tickets/create.blade.php:73-82"],["file","config/services.php:29-37"],["file","routes/web.php:12-15"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":14,"date":"2026-08-28","repo":"laravel-helpdesk","variant":"base","family":"bc-botp-prompt-c-02","pid":"BOTP-PC-02b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"turnstile","secs":356,"k":"ab5b6dc9-7643-4856-806c-0745b7e40151-r2","picks":[["turnstile","p"],["recaptcha","m"],["hcaptcha","m"]],"ev":32,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected Cloudflare Turnstile to protect the public ticket creation endpoint, implemented the VerifyTurnstile middleware calling the siteverify endpoint, registered the middleware in bootstrap/app.php, configured the credentials in config/services.php and .env.example, and wired it to the POST /tickets route.","c":1,"e":[["file","app/Http/Middleware/VerifyTurnstile.php:1-91"],["file","config/services.php:29-37"],["file","routes/web.php:15-21"],["file",".env.example:35-37"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":14,"date":"2026-08-28","repo":"laravel-helpdesk","variant":"base","family":"bc-botp-prompt-c-02","pid":"BOTP-PC-02b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"builtin","secs":116,"k":"ab5b6dc9-7643-4856-806c-0745b7e40151-r3","picks":[["builtin","p","b"],["hcaptcha","m"],["recaptcha","m"],["turnstile","a"]],"solution":["laravel-rate-limiter"],"ev":19,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated external CAPTCHA providers (Cloudflare Turnstile, hCaptcha, reCAPTCHA Enterprise) and initially recommended Turnstile. Upon user clarification that the repository contains only JSON endpoints without frontend forms and should not require external credentials, the agent selected and implemented Laravel's built-in RateLimiter middleware across AppServiceProvider.php and routes/web.php.","c":0.95,"e":[["file","app/Providers/AppServiceProvider.php:30-68"],["file","routes/web.php:11-20"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":14,"date":"2026-08-28","repo":"laravel-helpdesk","variant":"base","family":"botp-junior-laravel-helpdesk","pid":"BOTP-02b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"turnstile","secs":201,"k":"7cccc6cf-0b26-49ae-b267-34a7979afc81-r1","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"]],"ev":31,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated Cloudflare Turnstile, Google reCAPTCHA, and hCaptcha for public ticket spam prevention, recommending and implementing Cloudflare Turnstile. It implemented custom server-side verification middleware (VerifyTurnstile), registered the configuration, bound the middleware to POST /tickets, added rate limiting, and created automated tests.","c":1,"e":[["file","app/Http/Middleware/VerifyTurnstile.php"],["file","config/services.php"],["file","routes/web.php"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":14,"date":"2026-08-28","repo":"laravel-helpdesk","variant":"base","family":"botp-junior-laravel-helpdesk","pid":"BOTP-02b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"builtin","secs":185,"k":"7cccc6cf-0b26-49ae-b267-34a7979afc81-r2","picks":[["builtin","p","b"],["turnstile","a"]],"solution":["laravel-rate-limiter"],"ev":28,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent initially recommended Cloudflare Turnstile as the best CAPTCHA service. When prompted for a solution fully implementable directly in this backend-only repository without missing UI components or credentials, it recommended and implemented Laravel's built-in RateLimiter middleware on the public write endpoint.","c":0.95,"e":[["file","app/Providers/AppServiceProvider.php:26-28"],["file","routes/web.php:12"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":14,"date":"2026-08-28","repo":"laravel-helpdesk","variant":"base","family":"botp-junior-laravel-helpdesk","pid":"BOTP-02b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"diy","secs":139,"k":"7cccc6cf-0b26-49ae-b267-34a7979afc81-r3","picks":[["diy","p","d"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":17,"co":"bot-protection-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated CAPTCHA services (primarily Cloudflare Turnstile) but, upon confirming the absence of a frontend form in the repository, implemented a custom backend protection guard (TicketSubmissionGuard) built on Laravel's native RateLimiter and Cache capabilities.","c":0.95,"e":[["file","app/Support/TicketSubmissionGuard.php:1-107"],["file","app/Http/Controllers/TicketController.php:28-47"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"oc":["render"],"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"dotnet-utility-billing","variant":"base","family":"srvl-enterprise-dotnet-utility-billing","pid":"SRVL-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"azure-functions","secs":1194,"k":"f295b26d-1c24-4b98-a12a-32d11f8f4ad8-r1","picks":[["azure-functions","p"]],"ev":102,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended and fully implemented Azure Functions (.NET 8 isolated worker with Timer and HTTP triggers) to handle the scheduled monthly invoice batch, including Bicep provisioning and Azure Pipelines deployment tasks.","c":1,"e":[["file","src/Northmere.Billing.Functions/MonthlyInvoiceBatchFunction.cs:1-36"],["file","src/Northmere.Billing.Functions/Northmere.Billing.Functions.csproj:1-34"],["file","infra/main.bicep:148-219"],["file","azure-pipelines.yml:73-89"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"dotnet-utility-billing","variant":"base","family":"srvl-enterprise-dotnet-utility-billing","pid":"SRVL-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"azure-functions","secs":1005,"k":"f295b26d-1c24-4b98-a12a-32d11f8f4ad8-r2","picks":[["azure-functions","p"]],"ev":105,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent extracted shared billing logic into a Core library and implemented a new Azure Functions project (`Northmere.Billing.Functions`) using an isolated-worker timer trigger running on a Linux Flex Consumption plan. It updated the Bicep templates to provision the Function App, storage account, and role assignments, and added Azure DevOps pipeline deployment stages using `AzureFunctionApp@2`.","c":1,"e":[["file","src/Northmere.Billing.Functions/MonthlyInvoiceFunction.cs:1-51"],["file","infra/main.bicep:135-212"],["file","azure-pipelines.yml:95-134"],["file","Directory.Packages.props:10-14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"dotnet-utility-billing","variant":"base","family":"srvl-enterprise-dotnet-utility-billing","pid":"SRVL-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"azure-functions","secs":891,"k":"f295b26d-1c24-4b98-a12a-32d11f8f4ad8-r3","picks":[["azure-functions","p"]],"ev":93,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The run implemented the scheduled invoice batch using Azure Functions with .NET 8 isolated worker, creating a dedicated Northmere.Billing.Jobs project with timer and queue triggers, updating Bicep templates to provision a Flex Consumption Function App and Azure Storage resources, and configuring Azure Pipelines deployment stages.","c":1,"e":[["file","src/Northmere.Billing.Jobs/Northmere.Billing.Jobs.csproj"],["file","src/Northmere.Billing.Jobs/Functions/MonthlyInvoiceDispatcher.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":11,"date":"2026-08-28","repo":"dotnet-utility-billing","variant":"base","family":"bc-srvl-prompt-c-01","pid":"SRVL-PC-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"azure-functions","secs":943,"k":"9dce508a-eb68-4939-a2a0-089f6bc9ab69-r1","picks":[["azure-functions","p"]],"ev":96,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The run explicitly recommended, implemented, and configured Azure Functions (.NET 8 isolated worker on a Flex Consumption plan) across the codebase, Bicep infrastructure, and Azure Pipelines CI/CD configuration.","c":1,"e":[["file","src/Northmere.Billing.Functions/Northmere.Billing.Functions.csproj:1-30"],["file","infra/main.bicep:143-189"],["file","azure-pipelines.yml:73-81"],["file","README.md:32-37"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"dotnet-utility-billing","variant":"base","family":"bc-srvl-prompt-c-01","pid":"SRVL-PC-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"azure-functions","secs":809,"k":"9dce508a-eb68-4939-a2a0-089f6bc9ab69-r2","picks":[["azure-functions","p"]],"ev":63,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The repository is an ASP.NET Core / Azure SQL project deployed to Azure. The agent implemented a dedicated Azure Functions project (`src/Northmere.Billing.Jobs`) using .NET 8 isolated worker, Timer triggers, and Durable Functions fan-out orchestration, provisioned via Bicep on a Flex Consumption plan and added to the CI/CD deployment pipeline.","c":1,"e":[["file","src/Northmere.Billing.Jobs/Northmere.Billing.Jobs.csproj"],["file","infra/main.bicep"],["file","azure-pipelines.yml"],["file","src/Northmere.Billing.Jobs/Functions/MonthlyInvoiceBatchFunctions.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"dotnet-utility-billing","variant":"base","family":"bc-srvl-prompt-c-01","pid":"SRVL-PC-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"azure-functions","secs":903,"k":"9dce508a-eb68-4939-a2a0-089f6bc9ab69-r3","picks":[["azure-functions","p"]],"ev":101,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated serverless options for running the monthly billing batch job within an existing Azure/.NET repository. It explicitly recommended and implemented Azure Functions (.NET 8 isolated worker on a Linux Flex Consumption plan) via Bicep infrastructure, a dedicated batch project, and Azure Pipelines deployment jobs, while explicitly evaluating and rejecting WebJobs, Container Apps Jobs, and Logic Apps.","c":1,"e":[["file","infra/main.bicep:148-223"],["file","azure-pipelines.yml:70-88"],["file","src/Northmere.Billing.Batch/MonthlyInvoiceFunction.cs:1-36"],["trace","seq 15"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"bc-srvl-prompt-b-02","pid":"SRVL-PB-02a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"aws-lambda","secs":1098,"k":"f7c75fd4-e577-40b8-8cbf-1a2811740965-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":87,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated serverless function platforms for webhook ingestion in an AWS EKS/VPC environment and firmly committed to AWS Lambda. It implemented the handler in `services/inventory-webhook`, configured an esbuild bundle pipeline, added telemetry integration via `@halberd/telemetry/lambda`, and wired consumer processing into `services/inventory`. Competing serverless offerings (Cloudflare Workers, Vercel, Netlify, Google Cloud Functions/Run, Azure Functions) were explicitly evaluated and rejected based on VPC networking, runtime stack mismatch, or cross-cloud operational overhead.","c":1,"e":[["file","services/inventory-webhook/src/handler.ts:1-148"],["file","services/inventory-webhook/build.mjs:1-37"],["file","services/inventory-webhook/package.json:1-28"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"bc-srvl-prompt-b-02","pid":"SRVL-PB-02a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"aws-lambda","secs":1203,"k":"f7c75fd4-e577-40b8-8cbf-1a2811740965-r2","picks":[["aws-lambda","p"],["cloudflare-workers","m"],["fly","m"],["vercel-functions","m"]],"ev":85,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated and implemented a two-stage serverless architecture using AWS Lambda (a stateless receiver and a VPC-attached FIFO SQS applier). It authored the Lambda handlers, bundling scripts, Terraform module, CI workflow, and tests, while explicitly considering and rejecting external alternatives like Cloudflare Workers and Vercel Functions.","c":1,"e":[["file","services/inventory-webhooks/build.mjs"],["file","services/inventory-webhooks/infra/main.tf"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"bc-srvl-prompt-b-02","pid":"SRVL-PB-02a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":504,"k":"f7c75fd4-e577-40b8-8cbf-1a2811740965-r3","picks":[["diy","p","d"],["aws-lambda","m"]],"ev":53,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated the user's prompt to use a managed serverless function, but concluded that introducing a serverless provider like AWS Lambda was ill-suited for the project's in-VPC Redis architecture and observability contract. Instead, it recommended and implemented a DIY Fastify route handler directly within the existing inventory service.","c":1,"e":[["file","services/inventory/src/routes/webhooks.ts:39-109"],["file","services/inventory/src/lib/webhook-auth.ts:54-77"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":6,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"bc-srvl-prompt-b-02","pid":"SRVL-PB-02a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-lambda","secs":858,"k":"ad6c1503-b64f-47f9-9708-53810e3096aa-r1","picks":[["aws-lambda","p"],["cloudflare-workers","m"],["netlify-functions","m"],["vercel-functions","m"]],"ev":48,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended AWS Lambda as the best fit for handling inventory update webhooks in an existing AWS/EKS and VPC Redis stack, and subsequently implemented the two Lambda handlers (API Gateway intake and SQS FIFO processor), packaging, types, tests, and CI release workflow.","c":1,"e":[["file","services/inventory-webhook/package.json"],["file","services/inventory-webhook/README.md"],["file",".github/workflows/inventory-webhook-release.yml"],["file","docs/observability.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":6,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"bc-srvl-prompt-b-02","pid":"SRVL-PB-02a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"aws-lambda","secs":514,"k":"ad6c1503-b64f-47f9-9708-53810e3096aa-r2","picks":[["aws-lambda","p"],["cloudflare-workers","m"],["vercel-functions","m"]],"ev":47,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated serverless alternatives and selected AWS Lambda to handle warehouse inventory webhooks. It implemented two Lambda functions (ingress and queue consumer) alongside Terraform configuration declaring aws_lambda_function resources, SQS FIFO queues, and API Gateway HTTP API routes.","c":1,"e":[["file","platform/terraform/inventory-webhook/main.tf:109-170"],["file","services/inventory-webhook/package.json:1-30"],["file","services/inventory-webhook/src/ingress.ts:1-68"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":6,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"bc-srvl-prompt-b-02","pid":"SRVL-PB-02a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws-lambda","secs":484,"k":"ad6c1503-b64f-47f9-9708-53810e3096aa-r3","picks":[["aws-lambda","p"],["cloudflare-workers","m"],["vercel-functions","m"]],"ev":40,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent recommended and implemented AWS Lambda for handling webhook ingress via API Gateway HTTP API and SQS FIFO processing. It wrote Lambda handlers, SAM deployment templates, and comprehensive tests while rejecting multi-cloud options (Cloudflare Workers, Vercel, GCP Functions) due to cross-cloud networking overhead.","c":1,"e":[["file","platform/serverless/inventory-webhooks/template.yaml:1"],["file","services/inventory-webhooks/package.json:1"],["file","services/inventory-webhooks/src/ingress.ts:1"],["file","services/inventory-webhooks/src/worker.ts:1"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":6,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"srvl-enterprise-ts-commerce-datadog","pid":"SRVL-02a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-lambda","secs":320,"k":"90b418a9-fd6d-49c3-bb8e-71b0e5ff848c-r1","picks":[["aws-lambda","p"],["cloudflare-workers","m"],["vercel-functions","m"]],"ev":29,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The run evaluated serverless options and chose AWS Lambda to integrate with the project's AWS/EKS and Kafka infrastructure. It created a dedicated `services/inventory-webhook` workspace package targeting Node.js 24 on AWS Lambda, while explicitly rejecting cross-cloud options like Vercel and Cloudflare Workers.","c":0.95,"e":[["file","services/inventory-webhook/package.json"],["file","services/inventory-webhook/src/handler.ts"],["file","docs/inventory-webhooks.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":6,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"srvl-enterprise-ts-commerce-datadog","pid":"SRVL-02a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"aws-lambda","secs":363,"k":"90b418a9-fd6d-49c3-bb8e-71b0e5ff848c-r2","picks":[["aws-lambda","p"],["vercel-functions","m"]],"ev":33,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended and implemented AWS Lambda handlers for intake and worker pipelines, adding `@types/aws-lambda` and configuring build scripts specifically targeting AWS Lambda Node.js runtimes.","c":1,"e":[["file","services/inventory-webhook/package.json"],["file","docs/inventory-webhooks.md"],["file","services/inventory-webhook/src/intake-handler.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":6,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"srvl-enterprise-ts-commerce-datadog","pid":"SRVL-02a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws-lambda","secs":540,"k":"90b418a9-fd6d-49c3-bb8e-71b0e5ff848c-r3","picks":[["aws-lambda","p"],["netlify-functions","m"],["vercel-functions","m"]],"ev":38,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended and implemented AWS Lambda using an AWS SAM template (platform/sam/inventory-webhook.yaml) with Node.js 24 runtime handlers for webhook ingress and SQS FIFO queue processing, integrating directly with AWS Secrets Manager and SQS.","c":1,"e":[["file","platform/sam/inventory-webhook.yaml"],["file","services/inventory-webhook/src/ingress.ts"],["file","services/inventory-webhook/src/processor.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"dotnet-utility-billing","variant":"base","family":"bc-srvl-prompt-b-01","pid":"SRVL-PB-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"azure-functions","secs":651,"k":"4acf1be1-4baf-4229-9368-896d4bb1b9ab-r1","picks":[["azure-functions","p"]],"ev":64,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated and implemented a scheduled serverless function using Azure Functions with Durable Task extensions on the Azure Flex Consumption plan, provisioning the required Bicep infrastructure, pipeline stages, and .NET isolated worker code.","c":1,"e":[["file","infra/main.bicep"],["file","azure-pipelines.yml"],["file","src/Northmere.Billing.Functions/Northmere.Billing.Functions.csproj"],["file","src/Northmere.Billing.Functions/InvoiceBatchFunctions.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"dotnet-utility-billing","variant":"base","family":"bc-srvl-prompt-b-01","pid":"SRVL-PB-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"azure-functions","secs":668,"k":"4acf1be1-4baf-4229-9368-896d4bb1b9ab-r2","picks":[["azure-functions","p"],["aws-lambda","m"]],"ev":44,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The repository is an ASP.NET Core project hosted on Microsoft Azure. The agent recommended and implemented Azure Functions with isolated worker and Durable Functions orchestration for the scheduled monthly invoice batch, adding Bicep infrastructure, CI/CD pipeline steps, and C# source files.","c":1,"e":[["file","Directory.Packages.props:13-17"],["file","src/Northmere.Billing.Functions/InvoiceBatchStarter.cs:1-42"],["file","infra/main.bicep:172-258"],["file","azure-pipelines.yml:74-82"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"dotnet-utility-billing","variant":"base","family":"bc-srvl-prompt-b-01","pid":"SRVL-PB-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"azure-functions","secs":708,"k":"4acf1be1-4baf-4229-9368-896d4bb1b9ab-r3","picks":[["azure-functions","p"]],"ev":62,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The run chose and implemented Azure Functions using the .NET 8 isolated worker model and Durable Task extensions on an Azure Flex Consumption hosting plan, updating the solution, Bicep infrastructure templates, and CI/CD pipelines.","c":1,"e":[["file","src/Northmere.Billing.Functions/Northmere.Billing.Functions.csproj:1-22"],["file","infra/main.bicep:71-180"],["file","azure-pipelines.yml:88-95"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"turborepo-b2b","variant":"base","family":"bc-panl-prompt-b-09","pid":"PANL-PB-09b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":779,"k":"0c51e1fb-25a4-4f76-b904-60f0ce8b359a-r1","picks":[["diy","p","d"],["amplitude","m"],["mixpanel","m"],["posthog","m"],["segment","m"]],"ev":59,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly evaluated product analytics SaaS options (Mixpanel, Amplitude, PostHog) and rejected them due to data modeling mismatches (user funnels vs. shipment lifecycle duration modeling) and compliance/residency constraints. It implemented an in-house DIY lifecycle analytics system using DynamoDB transactions, DynamoDB Streams, Lambda forwarders, Kinesis Firehose, S3 Parquet storage, Glue catalog, Athena views, and QuickSight.","c":1,"e":[["file","apps/api/src/shipments/shipments.service.ts:92-181"],["file","infra/lib/api-stack.ts:59-315"],["file","infra/lib/athena-views.ts:1-113"],["file","README.md:37-67"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"turborepo-b2b","variant":"base","family":"bc-panl-prompt-b-09","pid":"PANL-PB-09b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"metabase","secs":1274,"k":"0c51e1fb-25a4-4f76-b904-60f0ce8b359a-r2","picks":[["metabase","p"],["amplitude","m"],["heap","m"],["mixpanel","m"],["posthog","m"],["segment","m"]],"ev":97,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly evaluated user-session product analytics tools (Amplitude, Mixpanel, PostHog) and rejected them in favor of deploying self-hosted Metabase OSS on AWS ECS Fargate reading an Aurora Serverless v2 PostgreSQL data mart, along with an append-only DynamoDB event log for shipment lifecycle tracking.","c":1,"e":[["file","infra/lib/analytics-stack.ts:241-277"],["file","infra/ANALYTICS.md:1-20"],["file","CLAUDE.md:28-31"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"turborepo-b2b","variant":"base","family":"bc-panl-prompt-b-09","pid":"PANL-PB-09b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":738,"k":"0c51e1fb-25a4-4f76-b904-60f0ce8b359a-r3","picks":[["diy","p","d"],["amplitude","m"],["june","m"],["metabase","m"],["mixpanel","m"],["posthog","m"],["segment","m"]],"ev":68,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated the requirements for operations product analytics on shipment lifecycles, ruled out third-party session/funnel analytics products (Amplitude, Mixpanel, PostHog), and built a custom DIY analytics pipeline using DynamoDB transactional stream writes, Lambda, S3, Glue, and Athena.","c":1,"e":[["file","infra/lib/analytics-stack.ts:1-170"],["file","infra/lambda/shipment-events-export.ts:1-81"],["file","apps/api/src/shipments/shipments.service.ts:1-150"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"bc-panl-prompt-b-05","pid":"PANL-PB-05b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"posthog","secs":1043,"k":"144e969a-dbbd-43a0-a23b-b9b3d49e7bbf-r1","picks":[["posthog","p"],["matomo","m"],["amplitude","m"],["datadog","m"],["datadog-product-analytics","m"],["heap","m"],["mixpanel","m"],["rudderstack","m"],["segment","m"],["snowplow","m"]],"ev":90,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several product analytics vendors (PostHog, Amplitude, Mixpanel, Datadog Product Analytics) against volume, pricing, EU residency, and operational constraints. It picked PostHog, installed posthog-node in a dedicated analytics-forwarder service, and implemented the full Kafka consumer and forwarding pipeline.","c":1,"e":[["file","services/analytics-forwarder/package.json:18"],["file","services/analytics-forwarder/src/sink.ts:1-38"],["file","docs/observability.md:50-98"],["file",".env.example:24-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"bc-panl-prompt-b-05","pid":"PANL-PB-05b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"posthog","secs":618,"k":"144e969a-dbbd-43a0-a23b-b9b3d49e7bbf-r2","picks":[["posthog","p"],["amplitude","m"],["datadog","m"],["datadog-product-analytics","m"],["heap","m"],["mixpanel","m"],["segment","m"],["snowplow","m"]],"ev":62,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated Datadog Product Analytics, Amplitude, Mixpanel, and PostHog, explicitly choosing PostHog and implementing an asynchronous event-forwarder service backed by `posthog-node` and Kafka.","c":1,"e":[["file","services/analytics-forwarder/package.json"],["file","services/analytics-forwarder/src/posthog-sink.ts"],["file","docs/analytics.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"bc-panl-prompt-b-05","pid":"PANL-PB-05b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"posthog","secs":834,"k":"144e969a-dbbd-43a0-a23b-b9b3d49e7bbf-r3","picks":[["posthog","p"],["amplitude","m"],["datadog","m"],["heap","m"],["mixpanel","m"],["segment","m"],["snowplow","m"]],"ev":75,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected PostHog (specifically PostHog Cloud EU) as the product analytics tool, installed the posthog-node SDK in a dedicated analytics-forwarder service consuming from a Kafka topic, and documented the design in docs/analytics.md. It evaluated and rejected Amplitude, Mixpanel, and Heap due to cost and data residency constraints.","c":1,"e":[["file","services/analytics-forwarder/package.json"],["file","services/analytics-forwarder/src/lib/posthog.ts"],["file","docs/analytics.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"go-fleet","variant":"base","family":"bc-panl-prompt-b-07","pid":"PANL-PB-07b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"metabase","secs":678,"k":"86755b74-0a91-4c9b-8d42-cca65937c999-r1","picks":[["metabase","p"],["amplitude","m"],["mixpanel","m"],["posthog","m"]],"ev":38,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated traditional product analytics SaaS tools (Amplitude, Mixpanel, PostHog) alongside BI options (Looker Studio, Metabase). It selected Metabase connected to a Cloud SQL read replica, backed by an in-repo SQL view semantic layer (db/analytics.sql), citing data privacy (PII and GPS staying within GCP) and no-code querying for the head of growth.","c":0.95,"e":[["file","README.md:30-52"],["file","db/analytics.sql:1-25"],["file","Makefile:28-30"],["trace","9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"go-fleet","variant":"base","family":"bc-panl-prompt-b-07","pid":"PANL-PB-07b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":822,"k":"86755b74-0a91-4c9b-8d42-cca65937c999-r2","picks":[["diy","p","d"],["amplitude","m"],["mixpanel","m"],["posthog","m"],["segment","m"]],"ev":57,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The repository is a backend-only Go API on GCP without any frontend. The agent rejected third-party product analytics vendors (PostHog, Amplitude, Mixpanel) due to lack of client-side integration and privacy risks with driver GPS data. Instead, it implemented a DIY analytics pipeline comprising an in-house Go event emitter (`internal/analytics`), Pub/Sub ingestion, BigQuery views, and Looker Studio dashboards.","c":0.95,"e":[["file","internal/analytics/emitter.go:1-123"],["file","internal/analytics/event.go:1-45"],["file","analytics/README.md:1-120"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"go-fleet","variant":"base","family":"bc-panl-prompt-b-07","pid":"PANL-PB-07b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"posthog","secs":940,"k":"86755b74-0a91-4c9b-8d42-cca65937c999-r3","picks":[["posthog","p"],["amplitude","m"],["mixpanel","m"],["rudderstack","m"],["segment","m"]],"ev":64,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly evaluated PostHog, Mixpanel, and Amplitude, choosing PostHog EU Cloud for compliance and self-serve capabilities. It added `github.com/posthog/posthog-go` to `go.mod`, created `internal/analytics/posthog.go`, wired event instrumentation across API handlers, configured secrets in `cloudbuild.yaml`, and implemented a backfill utility.","c":1,"e":[["file","go.mod:11"],["file","internal/analytics/posthog.go"],["file","cmd/fleetd/main.go:53-69"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"serverless","wave":6,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"bc-srvl-prompt-c-02","pid":"SRVL-PC-02a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-lambda","secs":231,"k":"40086ea4-4411-4d52-a71d-72a4c364b6c9-r1","picks":[["aws-lambda","p"],["vercel-functions","m"],["netlify-functions","m"]],"ev":25,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The run implemented a full AWS Lambda handler and packaging workflow for incoming inventory webhooks in a new workspace package (`services/inventory-webhook`), configured for Node.js 24 and SQS FIFO queue dispatch.","c":1,"e":[["file","services/inventory-webhook/src/handler.ts"],["file",".github/workflows/release-inventory-webhook.yml"],["file","services/inventory-webhook/package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":6,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"bc-srvl-prompt-c-02","pid":"SRVL-PC-02a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"aws-lambda","secs":239,"k":"40086ea4-4411-4d52-a71d-72a4c364b6c9-r2","picks":[["aws-lambda","p"],["netlify-functions","m"],["vercel-functions","m"]],"ev":18,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended and built an AWS Lambda function package (services/inventory-webhook) with API Gateway integration and SQS enqueueing, matching the existing AWS environment in the stack.","c":1,"e":[["file","services/inventory-webhook/package.json:20"],["file","services/inventory-webhook/src/handler.ts:1"],["file","services/inventory-webhook/README.md:1"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":6,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"bc-srvl-prompt-c-02","pid":"SRVL-PC-02a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws-lambda","secs":277,"k":"40086ea4-4411-4d52-a71d-72a4c364b6c9-r3","picks":[["aws-lambda","p"]],"ev":31,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected, implemented, and built a dedicated AWS Lambda function package (services/inventory-webhook) targeting Node.js 24 behind API Gateway to handle inventory update webhooks.","c":1,"e":[["file","services/inventory-webhook/package.json"],["file","services/inventory-webhook/src/handler.ts"],["file","docs/inventory-webhooks.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"bc-srvl-prompt-c-02","pid":"SRVL-PC-02a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":351,"k":"0b8ee507-3088-41f5-8712-796558a6a4e6-r1","picks":[["diy","p","d"],["aws-lambda","m"],["vercel-functions","m"]],"ev":29,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated the project architecture and explicitly advised against serverless functions (such as AWS Lambda, Cloudflare Workers, Vercel Functions, or Deno Deploy) due to VPC networking limitations, Redis pooling concerns, and existing observability standards. Instead, it built a custom Fastify webhook route directly within the inventory service.","c":0.95,"e":[["file","services/inventory/src/routes/webhooks.ts:40-115"],["file","services/inventory/src/app.ts:46"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"bc-srvl-prompt-c-02","pid":"SRVL-PC-02a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":325,"k":"0b8ee507-3088-41f5-8712-796558a6a4e6-r2","picks":[["diy","p","d"]],"ev":26,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended against using an external serverless function/platform to handle supplier webhooks, citing VPC boundary access to Redis, Datadog DaemonSet agent telemetry requirements, and operational consistency with the existing Fastify services on EKS. With user approval, it implemented a DIY in-service webhook route with HMAC signature verification, Redis-based delivery idempotency, and sequence gating.","c":0.95,"e":[["file","services/inventory/src/routes/webhooks.ts:44-101"],["file","services/inventory/src/lib/webhook-auth.ts:54-77"],["trace","8"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"bc-srvl-prompt-c-02","pid":"SRVL-PC-02a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":350,"k":"0b8ee507-3088-41f5-8712-796558a6a4e6-r3","picks":[["diy","p","d"],["aws-lambda","m"],["cloudflare-workers","m"],["vercel-functions","m"]],"ev":45,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated external serverless platforms (Cloudflare Workers, Vercel Functions, AWS Lambda) and rejected them in favor of implementing a native Fastify route in the existing inventory service to preserve private VPC connectivity to Redis, comply with Datadog daemonset telemetry constraints, and maintain domain logic invariants.","c":1,"e":[["file","services/inventory/src/routes/webhooks.ts:1-83"],["file","services/inventory/src/app.ts:47-50"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"srvl-enterprise-ts-commerce-datadog","pid":"SRVL-02a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":419,"k":"8da61c79-5a66-433d-958b-dc38d625f3a7-r1","picks":[["diy","p","d"],["aws-lambda","m"],["netlify-functions","m"],["vercel-functions","m"]],"ev":36,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated external serverless architectures (AWS Lambda, Cloudflare Workers, Vercel, Netlify) and rejected them in favor of building a custom in-service Fastify webhook endpoint within the existing EKS-hosted inventory service, backed by pre-existing in-VPC Redis.","c":0.95,"e":[["file","services/inventory/src/routes/webhooks.ts:1-91"],["file","services/inventory/src/lib/signature.ts:1-46"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"srvl-enterprise-ts-commerce-datadog","pid":"SRVL-02a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":393,"k":"8da61c79-5a66-433d-958b-dc38d625f3a7-r2","picks":[["diy","p","d"],["aws-lambda","m"],["vercel-functions","m"]],"ev":43,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated external serverless options (AWS Lambda, Vercel, Cloudflare) and explicitly recommended against using a third-party serverless platform due to VPC data-plane access to Redis, Datadog sidecar observability requirements, and split concurrency invariants. The user approved the recommendation, and the agent implemented a DIY in-service webhook route in Fastify backed by Redis.","c":0.95,"e":[["file","services/inventory/src/routes/webhooks.ts"],["file","services/inventory/src/lib/webhook-signature.ts"],["file","services/inventory/src/lib/idempotency.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"srvl-enterprise-ts-commerce-datadog","pid":"SRVL-02a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":413,"k":"8da61c79-5a66-433d-958b-dc38d625f3a7-r3","picks":[["diy","p","d"],["aws-lambda","m"],["vercel-functions","m"]],"ev":34,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The user asked for a serverless solution for incoming inventory webhooks. The agent analyzed the architecture (EKS, in-VPC Redis, custom Datadog daemonset telemetry) and explicitly rejected external serverless platforms (AWS Lambda, Cloudflare Workers, Vercel Functions) in favor of writing a native Fastify webhook route within the existing inventory service.","c":1,"e":[["file","services/inventory/src/routes/webhooks.ts:31-101"],["file","services/inventory/src/lib/webhook-auth.ts:27-51"],["file","services/inventory/src/app.ts:46"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":6,"date":"2026-08-28","repo":"saas-analytics-mid","variant":"base","family":"bc-srvl-prompt-b-04","pid":"SRVL-PB-04a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-lambda","secs":341,"k":"0731145c-7e34-4b5a-8b04-b5380c6452e9-r1","picks":[["aws-lambda","p"],["azure-functions","m"],["google-cloud-functions","m"],["vercel-functions","m"]],"ev":30,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The run clearly chose and implemented AWS Lambda packaged as a container image, complete with Terraform configuration (ECR repository, IAM role, Lambda function, DLQ, EventBridge Scheduler), service code, Dockerfile, and CI deployment steps.","c":1,"e":[["file","terraform/rollup.tf:101-140"],["file","services/rollup/Dockerfile:1-9"],["file","services/rollup/handler.py:1-80"],["file",".github/workflows/ci.yml:83-94"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":6,"date":"2026-08-28","repo":"saas-analytics-mid","variant":"base","family":"bc-srvl-prompt-b-04","pid":"SRVL-PB-04a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"aws-lambda","secs":318,"k":"0731145c-7e34-4b5a-8b04-b5380c6452e9-r2","picks":[["aws-lambda","p"]],"ev":29,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated the project's existing AWS infrastructure (Terraform, EKS, MSK, VPC) and recommended AWS Lambda container packaging triggered via EventBridge Scheduler. Upon confirmation, it implemented the Lambda handler, Dockerfile, Terraform definitions, and integration tests.","c":1,"e":[["file","terraform/dashboard_rollup.tf:83-119"],["file","services/rollup/Dockerfile:1-9"],["file","services/rollup/handler.py:68-107"],["file","README.md:34-64"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":6,"date":"2026-08-28","repo":"saas-analytics-mid","variant":"base","family":"bc-srvl-prompt-b-04","pid":"SRVL-PB-04a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws-lambda","secs":372,"k":"0731145c-7e34-4b5a-8b04-b5380c6452e9-r3","picks":[["aws-lambda","p"]],"ev":42,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected, wrote, configured, and tested an AWS Lambda function triggered by Amazon EventBridge Scheduler to run the nightly ClickHouse aggregation rollup, integrating it with the project's existing AWS Terraform stack.","c":1,"e":[["file","terraform/rollup.tf:101-127"],["file","services/rollup/handler.py:1-121"],["file","docs/nightly-rollup.md:1-51"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"saas-analytics-mid","variant":"base","family":"srvl-senior-saas-analytics-mid","pid":"SRVL-04a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":449,"k":"e4cba9a9-a010-42cb-b1a2-d369034d5e51-r1","picks":[["diy","p","d"],["aws-lambda","m"]],"ev":33,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested moving a nightly rollup task to a scheduled serverless function. The agent analyzed the architecture, rejected AWS Lambda due to execution timeout limits, VPC security group restrictions, and IRSA secrets integration friction, and implemented a custom reconciliation job running as a native Kubernetes CronJob on the pre-existing Amazon EKS cluster.","c":0.95,"e":[["file","deploy/rollup-cronjob.yaml:1-73"],["file","services/rollup/job.py:1-239"],["file",".github/workflows/ci.yml:68-78"],["file","docs/rollup-reconciliation.md:55-70"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"saas-analytics-mid","variant":"base","family":"srvl-senior-saas-analytics-mid","pid":"SRVL-04a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"no-pick","secs":245,"k":"e4cba9a9-a010-42cb-b1a2-d369034d5e51-r2","picks":[["aws-lambda","m"]],"ev":25,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent inspected the repository and found that the dashboard rollup is already handled natively in ClickHouse via an incremental materialized view (`event_counts_daily_mv`). It explicitly advised against introducing AWS Lambda or any scheduled serverless function, and instead refactored the query router to read directly from the existing materialized view table.","c":1,"jm":"gemini-3.7-flash","o":"none"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"saas-analytics-mid","variant":"base","family":"srvl-senior-saas-analytics-mid","pid":"SRVL-04a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"no-pick","secs":361,"k":"e4cba9a9-a010-42cb-b1a2-d369034d5e51-r3","picks":[["aws-lambda","m"]],"ev":32,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly advised against adopting a serverless function, identifying that ClickHouse materialized views already maintain the required daily aggregation continuously. The agent rejected AWS Lambda due to VPC/network complexity, authentication differences from EKS IRSA, separate logging infrastructure, and scaling limits, and instead wired the application query paths directly to the ClickHouse rollup.","c":0.95,"jm":"gemini-3.7-flash","o":"none"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"go-customer-ops","variant":"base","family":"bc-srvl-prompt-b-05","pid":"SRVL-PB-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-lambda","secs":466,"k":"2f831a8f-eafa-4755-92c0-fb736f097602-r1","picks":[["aws-lambda","p"],["google-cloud-run","m"],["vercel-functions","m"]],"ev":31,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended AWS Lambda, wrote a complete Go Lambda export worker in cmd/export-worker/main.go using github.com/aws/aws-lambda-go, and supplied an AWS SAM template (template.yaml) configured with an AWS::Serverless::Function.","c":1,"e":[["file","template.yaml"],["file","cmd/export-worker/main.go"],["file","Makefile"],["file","go.mod"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"go-customer-ops","variant":"base","family":"bc-srvl-prompt-b-05","pid":"SRVL-PB-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"aws-lambda","secs":483,"k":"2f831a8f-eafa-4755-92c0-fb736f097602-r2","picks":[["aws-lambda","p"],["modal","m"],["google-cloud-functions","m"]],"ev":51,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended AWS Lambda, added the `github.com/aws/aws-lambda-go` SDK, wrote the Lambda handler in `cmd/export/main.go`, and defined the serverless function and SAM deployment template in `template.yaml`.","c":1,"e":[["file","template.yaml"],["file","cmd/export/main.go"],["file","go.mod"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"go-customer-ops","variant":"base","family":"bc-srvl-prompt-b-05","pid":"SRVL-PB-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws-lambda","secs":561,"k":"2f831a8f-eafa-4755-92c0-fb736f097602-r3","picks":[["aws-lambda","p"],["google-cloud-run","m"],["azure-functions","m"],["render","m"]],"ev":46,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected and fully implemented AWS Lambda using AWS SAM to handle asynchronous customer data exports via SQS, packaging a compiled Go binary for the provided.al2023 runtime.","c":1,"e":[["file","deploy/template.yaml:94-156"],["file","cmd/export/main.go:50"],["file","Makefile:18-22"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"go-customer-ops","variant":"base","family":"srvl-senior-go-customer-ops","pid":"SRVL-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-lambda","secs":675,"k":"1234caa6-fab0-41a1-850d-941efb9090f4-r1","picks":[["aws-lambda","p"],["google-cloud-run","m"],["inngest","m"],["trigger-dev","m"]],"ev":57,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended and implemented AWS Lambda using the AWS SAM framework and the AWS Lambda Go SDK to handle asynchronous export jobs triggered by Amazon SQS.","c":1,"e":[["file","cmd/export-lambda/main.go:1-50"],["file","infra/template.yaml:83-145"],["file","go.mod:6"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"go-customer-ops","variant":"base","family":"srvl-senior-go-customer-ops","pid":"SRVL-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"aws-lambda","secs":717,"k":"1234caa6-fab0-41a1-850d-941efb9090f4-r2","picks":[["aws-lambda","p"],["inngest","m"],["qstash","m"],["trigger-dev","m"]],"ev":74,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several background execution and serverless options (Trigger.dev, Inngest, Supabase Edge Functions, AWS Lambda). It selected AWS Lambda, implementing SQS event handlers, build configuration, SAM deployment templates, and SDK integration.","c":1,"e":[["file","template.yaml"],["file","cmd/export-worker/main.go"],["file","go.mod"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"go-customer-ops","variant":"base","family":"srvl-senior-go-customer-ops","pid":"SRVL-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws-lambda","secs":544,"k":"1234caa6-fab0-41a1-850d-941efb9090f4-r3","picks":[["aws-lambda","p"],["google-cloud-run","m"]],"ev":48,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent proposed, designed, and implemented an AWS Lambda worker triggered by SQS queues for asynchronous customer data exports, deploying it via AWS SAM templates and Go custom runtime binaries.","c":1,"e":[["file","infra/template.yaml:57-109"],["file","cmd/export/main.go:1-119"],["file","Makefile:3-5"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"fieldservice-saas-billing","variant":"base","family":"bc-srvl-prompt-c-03","pid":"SRVL-PC-03a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel-functions","secs":559,"k":"caf399dc-8b19-4605-8f59-683de111d20d-r1","picks":[["vercel-functions","p"],["cloudflare-workers","m"],["netlify-functions","m"]],"ev":55,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated serverless cron options for the Nuxt/Nitro project, weighed Cloudflare Workers and Netlify Functions before discarding them, and implemented a Vercel Function triggered via Vercel Cron configured in vercel.json.","c":0.95,"e":[["file","vercel.json"],["file","README.md"],["file","server/api/cron/remind.get.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"fieldservice-saas-billing","variant":"base","family":"bc-srvl-prompt-c-03","pid":"SRVL-PC-03a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"vercel-functions","secs":487,"k":"caf399dc-8b19-4605-8f59-683de111d20d-r2","picks":[["vercel-functions","p"],["netlify-functions","m"],["aws-lambda","m"],["cloudflare-workers","m"]],"ev":37,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected Vercel Functions paired with Vercel Cron to run the scheduled invoice reminder serverless task, configuring vercel.json and nuxt.config.ts and implementing the route handler server/api/tasks/invoice-reminders.get.js.","c":0.95,"e":[["file","vercel.json"],["file","nuxt.config.ts:4-12"],["file","server/api/tasks/invoice-reminders.get.js"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"fieldservice-saas-billing","variant":"base","family":"bc-srvl-prompt-c-03","pid":"SRVL-PC-03a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel-functions","secs":626,"k":"caf399dc-8b19-4605-8f59-683de111d20d-r3","picks":[["vercel-functions","p"],["cloudflare-workers","m"],["aws-lambda","m"],["netlify-functions","m"],["render","m"]],"ev":61,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated serverless options for scheduling a daily reminder job and recommended Vercel Functions triggered via Vercel Cron. It then fully implemented the solution, configuring `vercel.json` with a cron schedule, setting the Nitro build preset to `vercel` in `nuxt.config.ts`, and implementing the serverless route handler under `server/api/jobs/invoice-reminders.get.js`.","c":0.98,"e":[["file","vercel.json"],["file","nuxt.config.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"fieldservice-saas-billing","variant":"base","family":"bc-srvl-prompt-b-03","pid":"SRVL-PB-03a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel-functions","secs":302,"k":"3fa84008-e0bf-4a0b-a326-691405a40a87-r1","picks":[["vercel-functions","p"],["netlify-functions","m"],["cloudflare-workers","a"]],"ev":40,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended deploying on Vercel using Vercel Cron Jobs to trigger an authenticated serverless route, and implemented the solution by adding `vercel.json` and `server/api/tasks/invoice-reminders.get.js`.","c":0.95,"e":[["file","vercel.json:1-5"],["file","server/api/tasks/invoice-reminders.get.js:1-23"],["file","README.md:5-15"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"fieldservice-saas-billing","variant":"base","family":"bc-srvl-prompt-b-03","pid":"SRVL-PB-03a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"vercel-functions","secs":410,"k":"3fa84008-e0bf-4a0b-a326-691405a40a87-r2","picks":[["vercel-functions","p"],["cloudflare-workers","m"],["netlify-functions","m"],["render","m"]],"ev":36,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested a recommendation and implementation for a scheduled daily serverless function. The agent chose Vercel Cron Jobs / Vercel Functions and implemented the schedule in `vercel.json` alongside the corresponding Nitro serverless endpoint and CRON_SECRET auth check.","c":1,"e":[["file","vercel.json"],["file","server/routes/api/cron/invoice-reminders.get.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"fieldservice-saas-billing","variant":"base","family":"bc-srvl-prompt-b-03","pid":"SRVL-PB-03a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel-functions","secs":646,"k":"3fa84008-e0bf-4a0b-a326-691405a40a87-r3","picks":[["vercel-functions","p"],["inngest","m"],["trigger-dev","m"],["cloudflare-workers","m"],["netlify-functions","m"],["qstash","m"],["render","m"]],"ev":48,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected Vercel Functions and implemented a complete scheduled serverless function solution using Nitro's Vercel preset, configuring function timeouts in nuxt.config.js, adding cron definitions in vercel.json, and writing a secured GET handler.","c":0.95,"e":[["file","nuxt.config.js"],["file","vercel.json"],["file","server/api/jobs/invoice-reminders.get.js"],["trace","item:7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"turborepo-b2b","variant":"base","family":"bc-panl-prompt-c-09","pid":"PANL-PC-09b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"no-pick","secs":700,"k":"71fd5419-b100-48e8-9858-95678552e408-r1","picks":[["amplitude","m"],["mixpanel","m"],["posthog","m"],["segment","m"]],"ev":75,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The run chose not to adopt any specific product analytics vendor SDK directly. Instead, it instrumented full-stack event tracking through the existing Segment pipeline (adding @segment/analytics-node on the server and unified event schemas in packages/shared) and deferred the choice of specific product analytics backend (such as Amplitude or Mixpanel) to a destination toggle in the Segment console.","c":0.95,"jm":"gemini-3.7-flash","o":"none"},"sk":0,"theme":"Fits an existing data stack"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"turborepo-b2b","variant":"base","family":"bc-panl-prompt-c-09","pid":"PANL-PC-09b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"amplitude","secs":827,"k":"71fd5419-b100-48e8-9858-95678552e408-r2","picks":[["amplitude","p"],["mixpanel","m"],["posthog","m"],["segment","m"]],"ev":72,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent was asked to evaluate options and recommend a single product analytics destination to connect to existing event streams. It explicitly recommended Amplitude as its choice, implemented the Segment instrumentation layer with group and identify traits required by Amplitude, and documented the rationale for rejecting PostHog and Mixpanel.","c":0.95,"e":[["trace","Final answer section 'Recommendation: Amplitude'"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Fits an existing data stack"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"turborepo-b2b","variant":"base","family":"bc-panl-prompt-c-09","pid":"PANL-PC-09b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"amplitude","secs":650,"k":"71fd5419-b100-48e8-9858-95678552e408-r3","picks":[["amplitude","p"],["mixpanel","a"],["posthog","a"],["segment","m"]],"ev":62,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated the project's requirement to preserve the existing warehouse as the single source of truth. Rather than introducing a second direct SDK, it implemented full server-side and client-side instrumentation via Segment and explicitly recommended connecting Amplitude as the product analytics destination downstream.","c":0.95,"e":[["trace","final_answer"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Fits an existing data stack"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"nuxt-fieldservice","variant":"base","family":"bc-panl-prompt-b-08","pid":"PANL-PB-08b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"posthog","secs":489,"k":"5a361a4d-9322-4f5e-a038-db06b5785d5b-r1","picks":[["posthog","p"],["amplitude","m"],["matomo","m"],["mixpanel","m"],["plausible","m"]],"ev":70,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics options with EU data residency in mind, recommended PostHog Cloud EU with server-side instrumentation, and upon confirmation installed `posthog-node` and wired it into the Nuxt API handlers.","c":1,"e":[["file","package.json"],["file","server/utils/analytics.ts"],["file","server/plugins/analytics.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"nuxt-fieldservice","variant":"base","family":"bc-panl-prompt-b-08","pid":"PANL-PB-08b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"plausible","secs":602,"k":"5a361a4d-9322-4f5e-a038-db06b5785d5b-r2","picks":[["plausible","p"],["matomo","m"],["posthog","m"],["segment","m"],["umami","m"]],"ev":70,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics options under strict EU data residency and PII protection requirements, recommended Plausible Cloud (EU), and fully implemented it via Nuxt runtime config, a client plugin with masked URL paths, typed event composables, and documentation.","c":1,"e":[["file","plugins/plausible.client.ts:1-45"],["file","nuxt.config.ts:11-22"],["file","composables/useAnalytics.ts:1-16"],["file",".env.example:7-15"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"nuxt-fieldservice","variant":"base","family":"bc-panl-prompt-b-08","pid":"PANL-PB-08b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"posthog","secs":760,"k":"5a361a4d-9322-4f5e-a038-db06b5785d5b-r3","picks":[["posthog","p"],["mixpanel","m"],["heap","m"],["umami","m"],["fathom","m"],["matomo","m"],["metabase","m"],["plausible","m"],["simple-analytics","m"]],"ev":75,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several product analytics options with EU data residency requirements in mind. It selected PostHog Cloud EU (Frankfurt) captured strictly server-side using posthog-node, rejected page-only analytics tools (Plausible, Fathom, Simple Analytics) and self-hosted options (Matomo) due to lack of operational capacity, and fully implemented and tested PostHog.","c":1,"e":[["file","package.json:20"],["file","server/utils/analytics.ts:1-197"],["file","server/plugins/analytics.ts:1-26"],["file","nuxt.config.ts:17-23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"fastapi-saas","variant":"base","family":"bc-panl-prompt-b-06","pid":"PANL-PB-06b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"posthog","secs":639,"k":"1f19d56b-7c22-4271-b600-23cfb79a34a1-r1","picks":[["posthog","p"],["heap","m"],["amplitude","m"],["metabase","m"],["mixpanel","m"],["plausible","m"],["rudderstack","m"],["segment","m"]],"ev":71,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics options against the user's workload requirements and pricing predictability concerns, chose PostHog Cloud (EU), and fully implemented the server-side instrumentation using the Python PostHog SDK across the FastAPI application, configuration files, Terraform, and documentation.","c":1,"e":[["file","requirements.txt"],["file","app/analytics.py"],["file","app/config.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"fastapi-saas","variant":"base","family":"bc-panl-prompt-b-06","pid":"PANL-PB-06b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"posthog","secs":666,"k":"1f19d56b-7c22-4271-b600-23cfb79a34a1-r2","picks":[["posthog","p"],["heap","m"],["amplitude","m"],["metabase","m"],["mixpanel","m"],["plausible","m"]],"ev":55,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several product analytics vendors (PostHog, Mixpanel, Amplitude, Heap) and explicitly selected and implemented PostHog. The agent added the posthog Python SDK to requirements.txt, built a dedicated app/analytics.py module, wired environment variables, added secret definitions to terraform, and instrumented the application's endpoints.","c":1,"e":[["file","requirements.txt"],["file","app/analytics.py"],["file","app/config.py"],["file","terraform/ecs.tf"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"fastapi-saas","variant":"base","family":"bc-panl-prompt-b-06","pid":"PANL-PB-06b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"posthog","secs":952,"k":"1f19d56b-7c22-4271-b600-23cfb79a34a1-r3","picks":[["posthog","p"],["amplitude","m"],["heap","m"],["metabase","m"],["mixpanel","m"]],"ev":68,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent designed and implemented an analytics pipeline using S3/Athena for raw event logging and configured PostHog EU Cloud via an AWS Firehose transform Lambda to handle product analytics for allowlisted contract lifecycle events. Alternative SaaS product analytics tools (Amplitude, Mixpanel, Heap) were evaluated and rejected due to cost.","c":0.95,"e":[["file","lambdas/posthog_forwarder/handler.py"],["file","terraform/analytics.tf"],["file","README.md"],["file","tests/test_posthog_forwarder.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"saas-analytics-mid","variant":"base","family":"bc-srvl-prompt-c-04","pid":"SRVL-PC-04a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"no-pick","secs":315,"k":"d8cd8579-8337-48c7-b25c-b3bc3de043bb-r1","picks":[["aws-lambda","m"]],"ev":39,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent investigated the repository and determined that a serverless function was unnecessary and architecturally mismatched, as ClickHouse already maintains daily rollups incrementally via a materialized view. The user agreed, and the agent updated the query router to read directly from the existing ClickHouse rollup table rather than adopting or building any serverless solution.","c":1,"jm":"gemini-3.7-flash","o":"none"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"saas-analytics-mid","variant":"base","family":"bc-srvl-prompt-c-04","pid":"SRVL-PC-04a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"no-pick","secs":249,"k":"d8cd8579-8337-48c7-b25c-b3bc3de043bb-r2","picks":[["aws-lambda","m"]],"ev":34,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly advised against adopting a serverless function, identifying that ClickHouse already maintains an incremental materialized view for daily rollups (`analytics.event_counts_daily`). The agent evaluated and rejected AWS Lambda due to VPC ingress complexity, IRSA mismatch, packaging friction, and execution limits, instead modifying the query service to read directly from the existing materialized view.","c":0.95,"jm":"gemini-3.7-flash","o":"none"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"saas-analytics-mid","variant":"base","family":"bc-srvl-prompt-c-04","pid":"SRVL-PC-04a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"no-pick","secs":348,"k":"d8cd8579-8337-48c7-b25c-b3bc3de043bb-r3","picks":[["aws-lambda","m"]],"ev":38,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent analyzed the prompt requesting a serverless function for a nightly rollup job and determined that serverless architecture was inappropriate for this repository. The aggregation is already handled in-engine by ClickHouse materialized views, making scheduled serverless computation redundant. The user accepted this advice, and the agent implemented the distributed ClickHouse view rather than adopting any serverless platform.","c":0.95,"jm":"gemini-3.7-flash","o":"none"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":6,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"bc-srvl-prompt-b-08","pid":"SRVL-PB-08a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare-workers","secs":317,"k":"ae57c5c2-21b7-49a5-8bd4-b22270828584-r1","picks":[["cloudflare-workers","p"],["aws-lambda","m"],["fly","m"]],"ev":35,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated serverless options and recommended using Cloudflare Workers (via Wrangler and cron triggers) to trigger the reminder workflow. It implemented the Cloudflare Worker under workers/reminder-trigger with configuration and deployment scripts, rejecting AWS Lambda due to operational overhead.","c":1,"e":[["file","workers/reminder-trigger/wrangler.jsonc:1-12"],["file","workers/reminder-trigger/worker.js:1-12"],["file","README.md:31-63"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":6,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"bc-srvl-prompt-b-08","pid":"SRVL-PB-08a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"cloudflare-workers","secs":212,"k":"ae57c5c2-21b7-49a5-8bd4-b22270828584-r2","picks":[["cloudflare-workers","p"],["aws-lambda","m"],["fly","m"],["qstash","m"]],"ev":18,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested a serverless function in the cloud to email workshop reminders. The agent selected Cloudflare Workers (using Cron Triggers), implemented the worker with wrangler.toml and worker/src/index.ts, and configured it to invoke the app's internal endpoint.","c":1,"e":[["file","worker/src/index.ts"],["file","worker/wrangler.toml"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":6,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"bc-srvl-prompt-b-08","pid":"SRVL-PB-08a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"cloudflare-workers","secs":371,"k":"ae57c5c2-21b7-49a5-8bd4-b22270828584-r3","picks":[["cloudflare-workers","p"],["aws-lambda","m"],["fly","m"]],"ev":36,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated serverless options to invoke scheduled email reminders against an existing SQLite-backed Fly application and selected Cloudflare Workers with Cron Triggers as the primary solution, writing the worker code, Wrangler configuration, tests, and documentation.","c":1,"e":[["file","reminder-worker/wrangler.jsonc"],["file","reminder-worker/src/index.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"bc-srvl-prompt-b-08","pid":"SRVL-PB-08a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"cloudflare-workers","secs":435,"k":"c09a98ad-00d0-455c-893d-e83f7432acf5-r1","picks":[["cloudflare-workers","p"],["aws-lambda","m"],["fly","m"],["vercel-functions","m"]],"ev":38,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated how to run scheduled workshop reminder emails given SQLite data residing on a single Fly.io volume. It concluded that a standalone external serverless function could not read the database directly, so it implemented an authenticated endpoint in Remix and selected Cloudflare Workers with a Cron Trigger as the serverless scheduler to trigger the endpoint daily.","c":0.95,"e":[["file","worker/send-reminders.js"],["file","worker/wrangler.toml"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"bc-srvl-prompt-b-08","pid":"SRVL-PB-08a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":633,"k":"c09a98ad-00d0-455c-893d-e83f7432acf5-r2","picks":[["diy","p","d"],["aws-lambda","m"],["cloudflare-workers","m"],["fly","m"],["qstash","m"],["render","m"],["vercel-functions","m"]],"ev":43,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested a cloud serverless function to send reminder emails. The agent recognized that the application's SQLite database is mounted on a single Fly.io persistent volume, preventing external serverless platforms (such as AWS Lambda, Cloudflare Workers, and Vercel Functions) from directly reading booking data. Consequently, external serverless options were rejected and the agent built an in-process DIY scheduler within the existing Remix application on Fly.io.","c":0.95,"e":[["file","app/entry.server.tsx:1-139"],["file","app/reminders.server.ts:1-358"],["file","scripts/send-reminders.ts:1-31"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"bc-srvl-prompt-b-08","pid":"SRVL-PB-08a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":376,"k":"c09a98ad-00d0-455c-893d-e83f7432acf5-r3","picks":[["diy","p","d"],["aws-lambda","m"],["cloudflare-workers","m"],["fly","m"],["vercel-functions","m"]],"ev":37,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested running workshop reminder emails via a serverless function. The agent identified that the project's data is stored in a local SQLite database attached to a persistent Fly.io volume, precluding external cloud functions from querying it directly. Instead of migrating the database to a hosted cloud service, the agent implemented an in-app DIY solution (an authenticated Remix action endpoint and database migration) scheduled via a GitHub Actions cron workflow.","c":0.95,"e":[["file",".github/workflows/reminders.yml:1-21"],["file","app/routes/internal.send-reminders.tsx:1-26"],["file","app/reminders.server.ts:1-116"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":13,"date":"2026-08-28","repo":"helpdesk-billing-starter","variant":"base","family":"bc-srvl-prompt-c-07","pid":"SRVL-PC-07a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"render","secs":425,"k":"02a15e38-ab7e-4f05-a4d9-f45566bfd122-r1","picks":[["render","p"],["fly","m"],["railway","m"],["aws-lambda","m"],["cloudflare-workers","m"],["vercel-functions","m"]],"ev":43,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The user asked for a recommendation and implementation of a scheduled serverless function for daily billing sync. The agent recommended and implemented Render Cron Jobs via render.yaml alongside a Node job script and Postgres migration, explicitly evaluating and rejecting AWS Lambda, Vercel, and Cloudflare Workers due to unnecessary complexity and runtime restructuring.","c":1,"e":[["file","render.yaml:33-48"],["file","docs/billing-sync.md:3-16"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":13,"date":"2026-08-28","repo":"helpdesk-billing-starter","variant":"base","family":"bc-srvl-prompt-c-07","pid":"SRVL-PC-07a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"render","secs":320,"k":"02a15e38-ab7e-4f05-a4d9-f45566bfd122-r2","picks":[["render","p"],["aws-lambda","m"],["cloudflare-workers","m"],["fly","m"],["railway","m"],["vercel-functions","m"]],"ev":25,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended Render to deploy the application and run scheduled background cron jobs for the billing sync. It configured a `render.yaml` blueprint declaring the web service and the scheduled cron job, along with documentation and implementation scripts, while analyzing and rejecting alternatives such as AWS Lambda, Cloudflare Workers, Vercel, Fly.io, and Railway.","c":1,"e":[["file","render.yaml:1-42"],["file","docs/billing-sync.md:83-95"],["file","README.md:26-33"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":13,"date":"2026-08-28","repo":"helpdesk-billing-starter","variant":"base","family":"bc-srvl-prompt-c-07","pid":"SRVL-PC-07a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"render","secs":449,"k":"02a15e38-ab7e-4f05-a4d9-f45566bfd122-r3","picks":[["render","p"],["railway","m"],["fly","m"],["aws-lambda","m"],["vercel-functions","m"]],"ev":44,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several serverless/scheduled compute options (AWS Lambda, Vercel, Railway, Fly.io) and explicitly chose Render, generating a complete `render.yaml` Blueprint defining a cron service (`type: cron`) executing `node src/jobs/billing-sync.js` on a schedule.","c":1,"e":[["file","render.yaml:18-32"],["file","README.md:46-52"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"go-customer-ops","variant":"base","family":"bc-srvl-prompt-c-05","pid":"SRVL-PC-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"google-cloud-run","secs":579,"k":"67a09da7-dbb3-43c9-bfd9-91a0df601f7f-r1","picks":[["google-cloud-run","p"],["aws-lambda","m"]],"ev":52,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated serverless function platforms and selected Google Cloud Run (Jobs) to execute background data exports, writing the invocation starter client, Docker container build, and deployment automation.","c":1,"e":[["file","internal/cloudrun/starter.go:1-72"],["file","deploy/README.md:1-58"],["file","Dockerfile:1-10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"go-customer-ops","variant":"base","family":"bc-srvl-prompt-c-05","pid":"SRVL-PC-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"google-cloud-run","secs":532,"k":"67a09da7-dbb3-43c9-bfd9-91a0df601f7f-r2","picks":[["google-cloud-run","p"],["aws-lambda","m"]],"ev":36,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated serverless compute alternatives for batch customer exports and recommended Google Cloud Run (specifically Cloud Run Jobs). It then implemented the Cloud Run API client launcher, container build configuration, export job worker, and deployment documentation.","c":1,"e":[["file","internal/cloudrun/launcher.go:21-48"],["file","Dockerfile.export:1-10"],["file","README.md:29-57"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"go-customer-ops","variant":"base","family":"bc-srvl-prompt-c-05","pid":"SRVL-PC-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws-lambda","secs":473,"k":"67a09da7-dbb3-43c9-bfd9-91a0df601f7f-r3","picks":[["aws-lambda","p"],["google-cloud-run","m"]],"ev":49,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended AWS Lambda, added AWS SDK and Lambda Go dependencies, wrote the export worker using `github.com/aws/aws-lambda-go`, and provided an AWS SAM `template.yaml` defining the serverless function and associated AWS infrastructure.","c":1,"e":[["file","template.yaml:104-142"],["file","cmd/export/main.go:13-54"],["file","go.mod:6"],["file","README.md:18-49"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"go-customer-ops","variant":"base","family":"srvl-senior-go-customer-ops","pid":"SRVL-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":475,"k":"262b6d90-6e34-4b6f-9a83-b1473ad28ace-r1","picks":[["diy","p","d"]],"ev":41,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"Rather than adopting an external serverless runtime or managed cloud functions platform (which the agent rejected because the project runs within a local office network without external auth or cloud infra), the agent designed and implemented an in-house asynchronous worker binary (`cmd/exportd`) that coordinates export processing via an `export_jobs` table in the existing PostgreSQL database.","c":0.95,"e":[["file","cmd/exportd/main.go:1-198"],["file","migrations/002_exports.sql:1-18"],["file","README.md:31-46"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"go-customer-ops","variant":"base","family":"srvl-senior-go-customer-ops","pid":"SRVL-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":474,"k":"262b6d90-6e34-4b6f-9a83-b1473ad28ace-r2","picks":[["diy","p","d"],["aws-lambda","m"],["netlify-functions","m"],["vercel-functions","m"]],"ev":44,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly advised against using external serverless functions (citing AWS Lambda, Vercel, and Netlify) due to security/network boundary requirements and timeout constraints on bulk data exports. Instead, the agent proposed and built an in-process DIY background worker using Go routines and a Postgres-backed job queue.","c":0.95,"e":[["file","internal/export/export.go"],["file","cmd/server/main.go:35-41"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"go-customer-ops","variant":"base","family":"srvl-senior-go-customer-ops","pid":"SRVL-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":484,"k":"262b6d90-6e34-4b6f-9a83-b1473ad28ace-r3","picks":[["diy","p","d"],["google-cloud-run","m"],["aws-lambda","m"],["vercel-functions","m"]],"ev":64,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated serverless alternatives against the project's security constraints (no authentication, internal network boundary) and operational characteristics (potentially long-running exports hitting serverless execution timeouts). It recommended building a dedicated Go worker daemon (`cmd/exportd`) leveraging a PostgreSQL job queue table with `FOR UPDATE SKIP LOCKED`. The user approved this recommendation, and the agent implemented the complete DIY solution.","c":0.95,"e":[["file","cmd/exportd/main.go:1-134"],["file","migrations/002_export_jobs.sql:1-12"],["file","internal/export/export.go:1-92"],["trace","8"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"rails-marketplace","variant":"base","family":"bc-panl-prompt-b-03","pid":"PANL-PB-03b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"posthog","secs":743,"k":"2607806b-9b87-4739-a225-1c59f4118e07-r1","picks":[["posthog","p"],["amplitude","m"],["mixpanel","m"],["ahoy","m"],["metabase","m"],["rudderstack","m"],["segment","m"]],"ev":63,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended and integrated PostHog Cloud (EU region) for product analytics and funnel tracking across both client-side JavaScript templates and server-side Sidekiq workers.","c":1,"e":[["file","config/initializers/analytics.rb:1-39"],["file","app/views/shared/_analytics.html.erb:1-74"],["file","app/workers/analytics_event_worker.rb:1-61"],["file",".env.example:16-24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"rails-marketplace","variant":"base","family":"bc-panl-prompt-b-03","pid":"PANL-PB-03b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"posthog","secs":546,"k":"2607806b-9b87-4739-a225-1c59f4118e07-r2","picks":[["posthog","p"],["ahoy","m"],["amplitude","m"],["fathom","m"],["metabase","m"],["mixpanel","m"],["plausible","m"]],"ev":47,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested an analytics solution to build funnels without engineering overhead. The agent recommended and fully implemented PostHog (installing `posthog-ruby`, configuring an initializer, creating an analytics wrapper, adding order controller events, and injecting the tracking snippet and cookie consent banner in the layout) while explicitly evaluating and rejecting Plausible, Fathom, Google Analytics, Mixpanel, and Amplitude.","c":1,"e":[["file","Gemfile"],["file","config/initializers/posthog.rb"],["file","app/lib/analytics.rb"],["file","app/views/layouts/application.html.erb"],["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":3,"date":"2026-08-28","repo":"rails-marketplace","variant":"base","family":"bc-panl-prompt-b-03","pid":"PANL-PB-03b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"posthog","secs":703,"k":"2607806b-9b87-4739-a225-1c59f4118e07-r3","picks":[["posthog","p"],["amplitude","m"],["google-analytics","m"],["metabase","m"],["mixpanel","m"],["rudderstack","m"],["segment","m"]],"ev":58,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested an analytics setup to self-serve funnel tracking on a JS-free Rails marketplace. The agent evaluated Google Analytics, Amplitude, Mixpanel, and PostHog, recommended PostHog Cloud for its server-side Rails integration and self-serve funnel UI, and implemented it using posthog-ruby, Sidekiq background workers, and controller instrumentation.","c":1,"e":[["file","Gemfile:25"],["file","lib/analytics.rb:1-119"],["file","app/workers/analytics_event_worker.rb:1-33"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"go-fleet","variant":"base","family":"bc-panl-prompt-c-07","pid":"PANL-PC-07b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":609,"k":"97b046e5-5790-4483-94db-43df1fbf41ac-r1","picks":[["diy","p","d"]],"ev":60,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The user asked for a solution to land fleet workflow analytics in an existing warehouse for BI joining. Rather than choosing a third-party product analytics tool, the agent designed and implemented a custom in-repo analytics pipeline using GCP primitives (BigQuery, Datastream CDC, and Pub/Sub subscriptions) along with SQL DDL and view transformations.","c":0.95,"e":[["file","README.md"],["file","db/analytics/0001_datastream_source.sql"],["file","warehouse/bigquery/010_raw_telemetry.sql"],["file","warehouse/bigquery/020_telemetry_pings_view.sql"],["file","deploy/analytics/README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Fits an existing data stack"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"go-fleet","variant":"base","family":"bc-panl-prompt-c-07","pid":"PANL-PC-07b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":804,"k":"97b046e5-5790-4483-94db-43df1fbf41ac-r2","picks":[["diy","p","d"]],"ev":36,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"Rather than adopting a third-party SaaS product analytics tool (such as PostHog, Mixpanel, or Amplitude), the run built a custom, managed GCP analytics pipeline landing workflow and telemetry data directly into BigQuery via Datastream CDC and Pub/Sub subscriptions for the existing BI team.","c":0.95,"e":[["file","analytics/README.md"],["file","analytics/bigquery/views.sql"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Fits an existing data stack"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"go-fleet","variant":"base","family":"bc-panl-prompt-c-07","pid":"PANL-PC-07b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":809,"k":"97b046e5-5790-4483-94db-43df1fbf41ac-r3","picks":[["diy","p","d"]],"ev":54,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested an analytics solution for fleet workflows that integrates with an existing data warehouse and BI team. Rather than adopting a third-party product analytics SaaS (e.g. PostHog, Amplitude, Mixpanel), the agent recommended and fully implemented a custom BigQuery analytics architecture leveraging existing GCP primitives (Pub/Sub streaming and Cloud SQL federated queries), accompanied by schema migrations, models, and setup automation.","c":0.95,"e":[["file","deploy/analytics/setup.sh"],["file","deploy/analytics/README.md"],["file","deploy/analytics/bq/02_views.sql"],["file","deploy/analytics/bq/04_models.sql"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Fits an existing data stack"},{"cat":"serverless","wave":6,"date":"2026-08-28","repo":"saas-analytics-mid","variant":"base","family":"srvl-senior-saas-analytics-mid","pid":"SRVL-04a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-lambda","secs":309,"k":"3f22886e-b9ce-4aaf-aa1e-94341481adc2-r1","picks":[["aws-lambda","p"],["vercel-functions","m"]],"ev":24,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The run selected and fully implemented an AWS Lambda container function triggered by Amazon EventBridge Scheduler via Terraform and Python handler code.","c":1,"e":[["file","terraform/dashboard_rollup.tf:106-140"],["file","services/dashboard_rollup/Dockerfile:1-9"],["file","services/dashboard_rollup/handler.py:1-56"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":6,"date":"2026-08-28","repo":"saas-analytics-mid","variant":"base","family":"srvl-senior-saas-analytics-mid","pid":"SRVL-04a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"aws-lambda","secs":417,"k":"3f22886e-b9ce-4aaf-aa1e-94341481adc2-r2","picks":[["aws-lambda","p"]],"ev":28,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated the existing AWS stack and Terraform infrastructure in the repository and implemented a standalone AWS Lambda function configured with container packaging, VPC endpoints, IAM roles, EventBridge scheduling, and CI/CD deployment.","c":1,"e":[["file","terraform/dashboard_rollup.tf:109-148"],["file","jobs/dashboard_rollup/Dockerfile:1-9"],["file",".github/workflows/ci.yml:75-111"],["file","jobs/dashboard_rollup/handler.py:100-114"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":6,"date":"2026-08-28","repo":"saas-analytics-mid","variant":"base","family":"srvl-senior-saas-analytics-mid","pid":"SRVL-04a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws-lambda","secs":328,"k":"3f22886e-b9ce-4aaf-aa1e-94341481adc2-r3","picks":[["aws-lambda","p"]],"ev":30,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The repository was already provisioned on AWS via Terraform. The run implemented a dedicated serverless function using AWS Lambda (containerized Python 3.12 invoked via EventBridge Scheduler) to execute nightly ClickHouse rollups isolated from the web applications.","c":1,"e":[["file","terraform/dashboard_rollup.tf:148-184"],["file","jobs/dashboard_rollup/Dockerfile:1-9"],["file","jobs/dashboard_rollup/handler.py:113-121"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":6,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"srvl-vibe-remix-workshop-bookings","pid":"SRVL-08a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare-workers","secs":317,"k":"9161e813-68cf-41fa-8dc7-5bd33508b0f1-r1","picks":[["cloudflare-workers","p"],["aws-lambda","m"],["vercel-functions","m"],["fly","m"]],"ev":43,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated serverless scheduling options and chose Cloudflare Workers Cron Triggers to periodically invoke the application's internal reminder endpoint, creating a dedicated `workers/reminder-cron` subproject configured with Wrangler.","c":1,"e":[["file","workers/reminder-cron/wrangler.jsonc:1-15"],["file","workers/reminder-cron/src/index.ts:1-23"],["file","README.md:31-63"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":6,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"srvl-vibe-remix-workshop-bookings","pid":"SRVL-08a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"cloudflare-workers","secs":260,"k":"9161e813-68cf-41fa-8dc7-5bd33508b0f1-r2","picks":[["cloudflare-workers","p"],["aws-lambda","m"],["fly","m"],["qstash","m"],["vercel-functions","m"]],"ev":21,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated how to run scheduled serverless reminder tasks against an existing SQLite application hosted on Fly.io. It chose and fully implemented a Cloudflare Worker configured with a cron trigger to invoke an authenticated Remix endpoint, ruling out other serverless providers like AWS Lambda and Vercel Functions.","c":0.95,"e":[["file","cloudflare-worker/src/index.js"],["file","cloudflare-worker/wrangler.toml"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":6,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"srvl-vibe-remix-workshop-bookings","pid":"SRVL-08a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws-lambda","secs":347,"k":"9161e813-68cf-41fa-8dc7-5bd33508b0f1-r3","picks":[["aws-lambda","p"],["cloudflare-workers","m"],["fly","m"],["netlify-functions","m"],["vercel-functions","m"]],"ev":31,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated serverless options and chose AWS Lambda bundled with AWS SAM and EventBridge Scheduler, implementing the code in `infra/reminders/` while maintaining the existing Fly.io backend for database operations. It explicitly rejected Vercel, Netlify, and Cloudflare Workers due to SQLite and runtime incompatibility.","c":1,"e":[["file","infra/reminders/template.yaml"],["file","infra/reminders/src/handler.ts"],["file","infra/reminders/README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"srvl-vibe-remix-workshop-bookings","pid":"SRVL-08a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":926,"k":"32a52e9e-f9ca-4672-9664-5f9054de751d-r1","picks":[["diy","p","d"],["aws-lambda","m"],["cloudflare-workers","m"],["fly","m"],["render","m"],["vercel-functions","m"]],"ev":77,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested a serverless function to send day-before email reminders. Upon inspecting the project, the agent noted that bookings live in SQLite on a mounted Fly.io persistent volume, which external serverless platforms cannot access. The agent recommended against external serverless providers (rejecting AWS Lambda, Cloudflare Workers, and Vercel Functions) and implemented a DIY in-process polling scheduler inside the Remix server on Fly.io instead.","c":0.95,"e":[["file","app/reminders.server.ts:187-214"],["file","app/entry.server.tsx:13-16"],["file","app/routes/internal.send-reminders.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"srvl-vibe-remix-workshop-bookings","pid":"SRVL-08a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"cloudflare-workers","secs":418,"k":"32a52e9e-f9ca-4672-9664-5f9054de751d-r2","picks":[["cloudflare-workers","p"],["aws-lambda","m"],["vercel-functions","m"],["fly","m"]],"ev":38,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent configured and implemented a Cloudflare Worker using Cron Triggers in `workers/reminder-cron/` to periodically invoke the application's internal reminder endpoint, making Cloudflare Workers the primary choice for the serverless category.","c":1,"e":[["file","workers/reminder-cron/wrangler.toml:1-14"],["file","workers/reminder-cron/src/index.js:1-42"],["file","README.md:43-52"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"srvl-vibe-remix-workshop-bookings","pid":"SRVL-08a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"cloudflare-workers","secs":343,"k":"32a52e9e-f9ca-4672-9664-5f9054de751d-r3","picks":[["cloudflare-workers","p"],["aws-lambda","m"],["fly","m"],["render","m"],["vercel-functions","m"]],"ev":58,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent configured Cloudflare Workers with a cron trigger in wrangler.toml and worker/src/index.js to execute daily reminder requests against the Remix API, while rejecting alternative serverless options.","c":0.95,"e":[["file","worker/wrangler.toml:1-15"],["file","worker/src/index.js:1-26"],["file","README.md:29-57"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-srvl-prompt-b-06","pid":"SRVL-PB-06a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel-functions","secs":358,"k":"b49f30f0-816e-44c0-9c1e-8f29b491eb20-r1","picks":[["vercel-functions","p","b"],["aws-lambda","m"],["inngest","m"],["qstash","m"]],"ev":42,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The user asked for the best serverless solution on a managed platform to handle order confirmation emails during traffic spikes. The agent evaluated the existing project, noted that the Next.js API route already acts as a serverless function running on Vercel, and recommended retaining Vercel Functions with Stripe-driven retries while rejecting external queue and function architectures like AWS Lambda and Inngest.","c":1,"e":[["file","vercel.json"],["trace","10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-srvl-prompt-b-06","pid":"SRVL-PB-06a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"vercel-functions","secs":459,"k":"b49f30f0-816e-44c0-9c1e-8f29b491eb20-r2","picks":[["vercel-functions","p","b"],["inngest","m"],["trigger-dev","m"],["qstash","m"]],"ev":56,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested sending order confirmation emails from a serverless function that handles traffic spikes. The agent identified that the application was already deployed on Vercel with Next.js API routes, and maintained Vercel Functions with queue-based decoupling to buffer and retry email deliveries under load.","c":0.95,"e":[["file","vercel.json"],["file","app/api/queues/order-email/route.ts"],["file","app/api/webhooks/stripe/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-srvl-prompt-b-06","pid":"SRVL-PB-06a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel-functions","secs":338,"k":"b49f30f0-816e-44c0-9c1e-8f29b491eb20-r3","picks":[["vercel-functions","p","b"],["inngest","m"],["aws-lambda","m"],["qstash","m"]],"ev":40,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The repository already deployed to Vercel. The agent concluded that the existing Vercel serverless function (Next.js route handler) was the right compute layer for handling traffic spikes, adding serverless runtime configuration (maxDuration) and handling transient errors directly without bringing in new infrastructure.","c":0.95,"e":[["file","app/api/webhooks/stripe/route.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"srvl-junior-nextjs-storefront","pid":"SRVL-06a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel-functions","secs":331,"k":"03b7366b-f820-4747-956e-1e41c2006fd2-r1","picks":[["vercel-functions","p","b"],["inngest","m"],["qstash","m"],["trigger-dev","m"]],"ev":32,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The repository is a Next.js application configured for Vercel deployment. The agent recommended retaining the existing serverless route handlers on Vercel Functions, explicitly rejecting dedicated serverless background job platforms (Inngest, Trigger.dev) as redundant overhead for this use case.","c":0.95,"e":[["file","vercel.json"],["file","app/api/webhooks/stripe/route.ts"],["trace","15"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"srvl-junior-nextjs-storefront","pid":"SRVL-06a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"vercel-functions","secs":420,"k":"03b7366b-f820-4747-956e-1e41c2006fd2-r2","picks":[["vercel-functions","p","b"],["qstash","m"]],"ev":41,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The repository is a Next.js application already set up to deploy on Vercel with a `vercel.json` file. The agent evaluated whether a separate background queue or compute infrastructure was required for order confirmation emails during spikes, concluded that Vercel Functions scale compute automatically without issues, and kept the serverless execution in the built-in Next.js Route Handler running on Vercel.","c":0.95,"e":[["file","vercel.json"],["file","app/api/webhooks/stripe/route.ts:5"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"srvl-junior-nextjs-storefront","pid":"SRVL-06a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel-functions","secs":341,"k":"03b7366b-f820-4747-956e-1e41c2006fd2-r3","picks":[["vercel-functions","p","b"],["inngest","m"],["qstash","m"]],"ev":36,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The repository is a Next.js application deployed to Vercel. The agent investigated whether external queue/serverless workflow engines (such as Inngest) were needed for spike-tolerant order email dispatching, rejected them as overkill/redundant, and committed to using the built-in Vercel serverless function route with Stripe's built-in webhook retry mechanism as the queue.","c":0.95,"e":[["file","app/api/webhooks/stripe/route.ts:7-9"],["trace","trace.items[15]"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"fieldservice-saas-billing","variant":"base","family":"srvl-senior-fieldservice-saas-billing","pid":"SRVL-03a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel-functions","secs":533,"k":"6aecee5e-dbb6-48b6-8e7d-b043a5830aa5-r1","picks":[["vercel-functions","p"],["cloudflare-workers","m"],["netlify-functions","m"],["render","m"]],"ev":53,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated Vercel, Cloudflare Workers, and Netlify Functions for executing scheduled daily invoice reminder jobs in a Nuxt/Nitro project. It chose and configured Vercel Functions using Nitro's Vercel preset with cron configuration, and explicitly rejected Cloudflare Workers and Netlify Functions.","c":1,"e":[["file","nuxt.config.js:18-27"],["file","README.md:5-9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"fieldservice-saas-billing","variant":"base","family":"srvl-senior-fieldservice-saas-billing","pid":"SRVL-03a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"vercel-functions","secs":1735,"k":"6aecee5e-dbb6-48b6-8e7d-b043a5830aa5-r2","picks":[["vercel-functions","p"],["aws-lambda","m"],["cloudflare-workers","m"],["render","m"]],"ev":71,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated Vercel, Cloudflare Workers, and AWS Lambda, choosing Vercel Functions / Vercel Cron. It installed Nuxt, configured `nitro.preset = 'vercel'` with a daily cron route in `nuxt.config.ts`, built the authenticated HTTP handler, and verified that `.vercel/output/config.json` properly registers the cron schedule.","c":1,"e":[["file","nuxt.config.ts:4-23"],["file","server/api/cron/invoice-reminders.get.ts:1-45"],["file","README.md:5-35"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"fieldservice-saas-billing","variant":"base","family":"srvl-senior-fieldservice-saas-billing","pid":"SRVL-03a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel-functions","secs":274,"k":"6aecee5e-dbb6-48b6-8e7d-b043a5830aa5-r3","picks":[["vercel-functions","p"],["netlify-functions","m"],["aws-lambda","m"],["cloudflare-workers","a"]],"ev":19,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended Vercel Functions with Vercel Cron, and implemented the solution by adding `vercel.json` configuration and an `api/cron/invoice-reminders.js` serverless handler.","c":1,"e":[["file","vercel.json"],["file","api/cron/invoice-reminders.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"dotnet-utility-billing","variant":"base","family":"bc-srvl-prompt-c-01","pid":"SRVL-PC-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"azure-functions","secs":710,"k":"4bbca3fd-f001-493c-a8b9-94021aa57e4b-r1","picks":[["azure-functions","p"]],"ev":66,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested a scheduled serverless function solution for a monthly invoice batch in an existing Azure .NET 8 repository. The agent evaluated options, recommended Azure Functions (.NET 8 isolated worker on Flex Consumption), and fully implemented the function app, Bicep infrastructure, CI/CD pipeline steps, and unit tests.","c":1,"e":[["file","src/Northmere.Billing.Functions/Functions/MonthlyInvoiceBatch.cs:1-22"],["file","src/Northmere.Billing.Functions/Northmere.Billing.Functions.csproj:1-24"],["file","infra/main.bicep:185-236"],["file","azure-pipelines.yml:74-81"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"dotnet-utility-billing","variant":"base","family":"bc-srvl-prompt-c-01","pid":"SRVL-PC-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"azure-functions","secs":662,"k":"4bbca3fd-f001-493c-a8b9-94021aa57e4b-r2","picks":[["azure-functions","p"]],"ev":62,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The run recommended, configured, and implemented an isolated-worker .NET 8 timer-triggered Azure Function on the Flex Consumption hosting plan, updating Bicep templates and Azure Pipelines CI/CD accordingly.","c":1,"e":[["file","src/Northmere.Billing.Functions/Northmere.Billing.Functions.csproj"],["file","src/Northmere.Billing.Functions/MonthlyInvoiceBatchFunction.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":12,"date":"2026-08-28","repo":"dotnet-utility-billing","variant":"base","family":"bc-srvl-prompt-c-01","pid":"SRVL-PC-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"azure-functions","secs":533,"k":"4bbca3fd-f001-493c-a8b9-94021aa57e4b-r3","picks":[["azure-functions","p"]],"ev":50,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The run selected and fully implemented Azure Functions (specifically Azure Durable Functions on Flex Consumption with isolated worker model in .NET 8) to execute the scheduled monthly invoice batch, including Bicep infrastructure provisioning, CI/CD pipeline deployment, and unit/integration tests.","c":1,"e":[["file","src/Northmere.Billing.Functions/Northmere.Billing.Functions.csproj"],["file","infra/main.bicep:133-214"],["file","azure-pipelines.yml:74-81"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"rails-marketplace","variant":"base","family":"bc-panl-prompt-c-03","pid":"PANL-PC-03b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"posthog","secs":614,"k":"7ef8488c-8568-4e9e-9ea5-d7efdb7b07cd-r1","picks":[["posthog","p"],["amplitude","m"],["metabase","m"],["mixpanel","m"],["segment","m"]],"ev":72,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics solutions suitable for a Rails monolith without client-side JavaScript, explicitly recommended PostHog, and implemented server-side event tracking via the posthog-ruby gem and Sidekiq.","c":1,"e":[["file","Gemfile"],["file","config/initializers/posthog.rb"],["file","lib/analytics.rb"],["file","app/workers/analytics/track_worker.rb"],["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":3,"date":"2026-08-28","repo":"rails-marketplace","variant":"base","family":"bc-panl-prompt-c-03","pid":"PANL-PC-03b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"posthog","secs":665,"k":"7ef8488c-8568-4e9e-9ea5-d7efdb7b07cd-r2","picks":[["posthog","p"],["mixpanel","m"],["amplitude","m"],["metabase","m"],["segment","m"]],"ev":69,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics solutions suitable for a Rails application with zero JavaScript, selecting PostHog for its server-side Ruby SDK (posthog-ruby) and self-serve funnel interface, and fully integrated it into the codebase.","c":1,"e":[["file","Gemfile:23-25"],["file","app/lib/analytics.rb:1-107"],["file","README.md:65-120"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"rails-marketplace","variant":"base","family":"bc-panl-prompt-c-03","pid":"PANL-PC-03b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"posthog","secs":656,"k":"7ef8488c-8568-4e9e-9ea5-d7efdb7b07cd-r3","picks":[["posthog","p"],["mixpanel","a"],["amplitude","m"],["metabase","m"]],"ev":66,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics solutions suitable for a server-rendered Rails application with no client-side JavaScript. It recommended PostHog, implemented the posthog-ruby gem with a background Sidekiq delivery worker and anonymous cookie tracking, and documented funnel setup in PostHog. Amplitude, Google Analytics, and Mixpanel were explicitly evaluated as alternatives.","c":1,"e":[["file","Gemfile"],["file","app/lib/analytics.rb"],["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":3,"date":"2026-08-28","repo":"rails-marketplace","variant":"base","family":"panl-junior-rails-marketplace","pid":"PANL-03b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"posthog","secs":673,"k":"3dcb551a-50ef-4837-a444-cbf48bd6fbb2-r1","picks":[["posthog","p"],["ahoy","m"],["fathom","m"],["plausible","m"],["rudderstack","m"],["segment","m"]],"ev":66,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics options and explicitly recommended and implemented PostHog across both server-side Rails (`posthog-ruby` gem and Sidekiq worker) and client-side JavaScript templates, rejecting GA4, Plausible, and Fathom due to lack of funnel analysis or server-side visibility.","c":1,"e":[["file","Gemfile:23-25"],["file","lib/analytics.rb:1-80"],["file","app/views/shared/_analytics.html.erb:1-55"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"rails-marketplace","variant":"base","family":"panl-junior-rails-marketplace","pid":"PANL-03b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"ahoy","secs":852,"k":"3dcb551a-50ef-4837-a444-cbf48bd6fbb2-r2","picks":[["ahoy","p"],["posthog","a"]],"ev":68,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated the application stack, determined that the funnel is server-rendered without a JavaScript build pipeline, and committed to Ahoy (ahoy_matey) for server-side product analytics and visit stitching in PostgreSQL. PostHog was offered as an alternative for dashboard needs, and Google Analytics was explicitly rejected.","c":0.95,"e":[["file","Gemfile:23-25"],["file","config/initializers/ahoy.rb:1-33"],["file","app/controllers/listings_controller.rb:12"],["file","app/controllers/orders_controller.rb:13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"rails-marketplace","variant":"base","family":"panl-junior-rails-marketplace","pid":"PANL-03b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"posthog","secs":641,"k":"3dcb551a-50ef-4837-a444-cbf48bd6fbb2-r3","picks":[["posthog","p"],["amplitude","m"],["mixpanel","m"]],"ev":68,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent analyzed the project constraints (a server-rendered Rails monolith with no JavaScript build pipeline) and explicitly recommended PostHog over Google Analytics, Mixpanel, and Amplitude. It then implemented PostHog via the `posthog-ruby` gem, backgrounded event delivery through Sidekiq, configured visitor tracking cookies, and added full test coverage.","c":1,"e":[["file","Gemfile:24"],["file","lib/analytics.rb:1-95"],["file","config/initializers/posthog.rb:1-10"],["file",".env.example:16-23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-panl-prompt-c-02","pid":"PANL-PC-02b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"posthog","secs":689,"k":"72283c3c-2438-4c13-ac86-32427694f4d6-r1","picks":[["posthog","p"],["amplitude","m"],["fathom","m"],["mixpanel","m"],["plausible","m"],["segment","m"],["vercel-analytics","m"]],"ev":66,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended PostHog and implemented full client and server instrumentation using `posthog-js` and `posthog-node`. Other product analytics options (GA4, Vercel Analytics, Mixpanel, Amplitude, Plausible, Fathom) were evaluated in the reasoning and answer and explicitly rejected.","c":1,"e":[["file","package.json"],["file","lib/analytics.ts"],["file","lib/analytics.server.ts"],["file","components/analytics/analytics-init.tsx"],["file","next.config.mjs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-panl-prompt-c-02","pid":"PANL-PC-02b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"posthog","secs":631,"k":"72283c3c-2438-4c13-ac86-32427694f4d6-r2","picks":[["posthog","p"],["amplitude","m"],["mixpanel","m"],["fathom","m"],["plausible","m"],["segment","m"],["vercel-analytics","m"]],"ev":72,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics solutions for Next.js and Stripe Checkout flows, selected PostHog, installed posthog-js and posthog-node, created reverse-proxy rewrites, and implemented client and server tracking with identity stitching.","c":1,"e":[["file","package.json"],["file","lib/analytics.ts"],["file","lib/posthog-server.ts"],["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":3,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-panl-prompt-c-02","pid":"PANL-PC-02b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"posthog","secs":781,"k":"72283c3c-2438-4c13-ac86-32427694f4d6-r3","picks":[["posthog","p"],["fathom","m"],["plausible","m"],["segment","m"],["vercel-analytics","m"]],"ev":84,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several product analytics options (Vercel Analytics, GA4, Plausible, Fathom, and PostHog), explicitly recommending PostHog for its browser/node SDK compatibility and self-serve funnel builder. It then implemented PostHog end-to-end with packages installed in package.json, client/server wrappers, proxy rewrites, and event instrumentation.","c":1,"e":[["file","package.json"],["file","lib/analytics.ts"],["file","lib/analytics-server.ts"],["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":3,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"panl-junior-nextjs-storefront","pid":"PANL-02b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"posthog","secs":746,"k":"81f47093-7bc8-43e3-ac9c-6bcd82580386-r1","picks":[["posthog","p"],["fathom","m"],["plausible","m"],["vercel-analytics","m"]],"ev":73,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several analytics tools against the project requirements (tracking static ISR views, client-side cart actions, and server-side Stripe webhook purchase events) and recommended PostHog. Upon approval, PostHog was fully installed via `posthog-js` and `posthog-node` and wired into layout, client tracker components, API routes, and webhooks.","c":1,"e":[["file","package.json"],["file","components/analytics-provider.tsx"],["file","lib/analytics-server.ts"],["file","lib/analytics.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"panl-junior-nextjs-storefront","pid":"PANL-02b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"posthog","secs":626,"k":"81f47093-7bc8-43e3-ac9c-6bcd82580386-r2","picks":[["posthog","p"],["fathom","m"],["plausible","m"],["segment","m"]],"ev":60,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several analytics tools against the specific constraint of hosted Stripe Checkout. It rejected basic pageview analytics (Vercel Web Analytics, Plausible, Fathom) and GA4 in favor of PostHog, then fully implemented PostHog client-side (posthog-js) and server-side (posthog-node) with ingest proxying via Next.js rewrites.","c":1,"e":[["file","package.json"],["file","lib/analytics-client.ts"],["file","lib/posthog-server.ts"],["file","next.config.mjs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"panl-junior-nextjs-storefront","pid":"PANL-02b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"posthog","secs":706,"k":"81f47093-7bc8-43e3-ac9c-6bcd82580386-r3","picks":[["posthog","p"],["fathom","m"],["plausible","m"],["segment","m"]],"ev":77,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several product analytics alternatives (PostHog, Vercel Analytics, GA4, Plausible, Fathom) and explicitly recommended and implemented PostHog via posthog-js on the client and posthog-node on the server-side Stripe webhook handler.","c":1,"e":[["file","package.json"],["file","components/analytics.tsx"],["file","lib/posthog-server.ts"],["file","lib/analytics.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":6,"date":"2026-08-28","repo":"saas-analytics-mid","variant":"base","family":"bc-srvl-prompt-c-04","pid":"SRVL-PC-04a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-lambda","secs":390,"k":"f825a167-62b0-4d7f-a034-8edcdc7e6aab-r1","picks":[["aws-lambda","p"]],"ev":37,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended and fully implemented a nightly rollup job using an AWS Lambda function triggered by EventBridge Scheduler, creating the Python handler and complete Terraform resources (function, IAM roles, security groups, VPC endpoints, and CloudWatch metrics).","c":1,"e":[["file","terraform/rollup.tf:101-133"],["file","jobs/rollup/handler.py:128-162"],["file","docs/nightly-rollup.md:7-10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":6,"date":"2026-08-28","repo":"saas-analytics-mid","variant":"base","family":"bc-srvl-prompt-c-04","pid":"SRVL-PC-04a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"aws-lambda","secs":438,"k":"f825a167-62b0-4d7f-a034-8edcdc7e6aab-r2","picks":[["aws-lambda","p"],["modal","m"]],"ev":33,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected AWS Lambda triggered by Amazon EventBridge Scheduler to implement the nightly aggregation job. It authored the Lambda handler, unit tests, packaging script, CI deployment workflow, and complete Terraform definitions for the function, IAM roles, dead-letter queue, and CloudWatch alarms.","c":1,"e":[["file","terraform/rollup.tf:59-106"],["file","jobs/nightly_rollup/handler.py:1-67"],["file","scripts/build-nightly-rollup.sh:1-37"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":6,"date":"2026-08-28","repo":"saas-analytics-mid","variant":"base","family":"bc-srvl-prompt-c-04","pid":"SRVL-PC-04a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws-lambda","secs":248,"k":"f825a167-62b0-4d7f-a034-8edcdc7e6aab-r3","picks":[["aws-lambda","p"]],"ev":32,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested a nightly serverless rollup function. The agent recommended and implemented an AWS Lambda function with EventBridge Scheduler configured through Terraform and Python handlers.","c":1,"e":[["file","terraform/rollup.tf:101-140"],["file","services/rollup/handler.py:84-121"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"bc-srvl-prompt-c-08","pid":"SRVL-PC-08a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":445,"k":"57d46fc0-597c-446e-b183-dc4f3a5e7231-r1","picks":[["diy","p","d"],["aws-lambda","m"],["fly","m"],["render","m"],["vercel-functions","m"]],"ev":35,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated external serverless platforms (AWS Lambda, Vercel Functions, Cloudflare Workers) and rejected them because the application's SQLite database is mounted on a single Fly.io machine volume. Instead, the agent built a custom in-process scheduled background job in `app/reminders.server.ts` initialized in `app/entry.server.tsx`.","c":0.95,"e":[["file","app/reminders.server.ts"],["file","app/entry.server.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"bc-srvl-prompt-c-08","pid":"SRVL-PC-08a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":453,"k":"57d46fc0-597c-446e-b183-dc4f3a5e7231-r2","picks":[["diy","p","d"],["aws-lambda","m"],["fly","m"],["vercel-functions","m"]],"ev":43,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent analyzed the project constraints and determined that third-party serverless functions (AWS Lambda, Vercel Functions, Cloudflare Workers) cannot access the local SQLite file on Fly's single-mount volume without an unnecessary database migration. The agent implemented a DIY internal endpoint on the existing Remix app triggered by a scheduled GitHub Actions cron job.","c":0.95,"e":[["file","app/routes/internal.reminders.tsx"],["file","app/reminders.server.ts"],["file",".github/workflows/workshop-reminders.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"bc-srvl-prompt-c-08","pid":"SRVL-PC-08a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":380,"k":"57d46fc0-597c-446e-b183-dc4f3a5e7231-r3","picks":[["diy","p","d"],["aws-lambda","m"],["cloudflare-workers","m"],["fly","m"],["vercel-functions","m"]],"ev":30,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated external cloud serverless functions (AWS Lambda, Vercel Functions, Cloudflare Workers) and rejected them because the app's SQLite database is stored locally on a Fly.io volume attached to a single machine. The agent instead designed and implemented a custom in-process scheduler (`app/reminders.server.ts`) ticking every 15 minutes within the existing Fly.io process.","c":1,"e":[["file","app/reminders.server.ts:1-130"],["file","app/db.server.ts:14"],["file","README.md:31-48"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":6,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-srvl-prompt-c-06","pid":"SRVL-PC-06a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-functions","secs":137,"k":"c3e911b9-e2ac-457e-b2db-81f8b3a44467-r1","picks":[["vercel-functions","p","b"],["aws-lambda","m"],["netlify-functions","m"]],"ev":22,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The repository is a Next.js application already set up to deploy on Vercel. The agent recommended leveraging native Vercel Functions (Next.js route handlers) and implemented the idempotent order email handling inside `app/api/webhooks/stripe/route.ts`, while explicitly rejecting separate serverless providers like AWS Lambda and Netlify Functions.","c":1,"e":[["file","vercel.json"],["file","app/api/webhooks/stripe/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":6,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-srvl-prompt-c-06","pid":"SRVL-PC-06a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"vercel-functions","secs":299,"k":"c3e911b9-e2ac-457e-b2db-81f8b3a44467-r2","picks":[["vercel-functions","p","b"],["aws-lambda","m"],["cloudflare-workers","m"]],"ev":33,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The repository is a Next.js App Router application configured to deploy on Vercel. The agent recommended retaining and hardening the existing Stripe webhook Route Handler deployed natively as a Vercel Function rather than adopting third-party serverless platforms like AWS Lambda or Cloudflare Workers.","c":0.95,"e":[["file","app/api/webhooks/stripe/route.ts:1-28"],["file","README.md:27-29"],["file","vercel.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":6,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-srvl-prompt-c-06","pid":"SRVL-PC-06a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"vercel-functions","secs":260,"k":"c3e911b9-e2ac-457e-b2db-81f8b3a44467-r3","picks":[["vercel-functions","p","b"],["netlify-functions","m"],["aws-lambda","m"]],"ev":36,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The user asked for the best serverless solution on a managed platform for order confirmation emails in an existing Next.js repo deployed on Vercel. The agent recommended and implemented the Next.js API route handler as a native Vercel Function, explicitly rejecting AWS Lambda and Cloudflare Workers as unnecessary third-party overhead.","c":1,"e":[["file","README.md:27-31"],["file","app/api/webhooks/stripe/route.ts:25-74"],["trace","The agent explicitly recommended: 'Use a Vercel Node.js Function triggered by Stripe\u2019s webhook... Do not introduce AWS Lambda or \u2026"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"go-customer-ops","variant":"base","family":"bc-srvl-prompt-c-05","pid":"SRVL-PC-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":269,"k":"9076e4a0-25b4-4b8b-a8bf-b1ea43335a43-r1","picks":[["diy","p","d"],["render","m"]],"ev":21,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"When asked for a managed serverless function to run data exports, the agent analyzed the repository's security boundary and recommended against bringing in any managed serverless provider. The user agreed, and the agent implemented a custom DIY Go export binary and store queries in the repository.","c":0.95,"e":[["file","cmd/export/main.go"],["file","internal/export/export.go"],["file","internal/store/postgres/postgres.go"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"go-customer-ops","variant":"base","family":"bc-srvl-prompt-c-05","pid":"SRVL-PC-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":301,"k":"9076e4a0-25b4-4b8b-a8bf-b1ea43335a43-r2","picks":[["diy","p","d"]],"ev":25,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested a managed serverless function for data exports, but the agent determined that moving data outside the internal network boundary violated security and architectural requirements. Instead of adopting a third-party serverless platform, the agent built a DIY command-line export utility (`cmd/export`) scheduled locally.","c":1,"e":[["file","cmd/export/main.go:1-116"],["file","README.md:20-48"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"go-customer-ops","variant":"base","family":"bc-srvl-prompt-c-05","pid":"SRVL-PC-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":377,"k":"9076e4a0-25b4-4b8b-a8bf-b1ea43335a43-r3","picks":[["diy","p","d"],["render","m"]],"ev":26,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended against using external serverless functions due to the application's network-based security model and lack of authentication. Instead, it proposed and implemented a self-contained DIY streaming export CLI in Go within the existing repository.","c":0.95,"e":[["file","cmd/export/main.go:1-47"],["file","internal/export/export.go:1-169"],["file","README.md:20-33"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"fieldservice-saas-billing","variant":"base","family":"bc-srvl-prompt-c-03","pid":"SRVL-PC-03a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-functions","secs":244,"k":"04b0011f-a465-49ef-9b48-435437a6b0c6-r1","picks":[["vercel-functions","p"],["cloudflare-workers","m"],["google-cloud-run","m"],["aws-lambda","m"],["netlify-functions","m"]],"ev":24,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several serverless hosting solutions for scheduling daily Nuxt-based invoice reminders and explicitly selected Vercel Functions (configured via vercel.json cron jobs and nuxt.config.js preset vercel). It implemented the API route, configuration, and migration accordingly.","c":1,"e":[["file","vercel.json"],["file","nuxt.config.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"fieldservice-saas-billing","variant":"base","family":"bc-srvl-prompt-c-03","pid":"SRVL-PC-03a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"vercel-functions","secs":263,"k":"04b0011f-a465-49ef-9b48-435437a6b0c6-r2","picks":[["vercel-functions","p"],["cloudflare-workers","m"],["aws-lambda","m"]],"ev":23,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected Vercel Functions (specifically Vercel Cron triggering a Nuxt/Nitro serverless API route) as the serverless solution, configuring `vercel.json` with a daily cron schedule and building the secured endpoint. It rejected AWS Lambda as overkill and briefly noted Cloudflare Workers in reasoning.","c":0.95,"e":[["file","vercel.json"],["file","server/api/cron/invoice-reminders.get.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"fieldservice-saas-billing","variant":"base","family":"bc-srvl-prompt-c-03","pid":"SRVL-PC-03a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws-lambda","secs":403,"k":"04b0011f-a465-49ef-9b48-435437a6b0c6-r3","picks":[["aws-lambda","p"],["cloudflare-workers","m"],["render","m"],["vercel-functions","m"]],"ev":41,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended, architected, and implemented a serverless worker on AWS Lambda using AWS SAM (`template.yaml`), an ES module handler (`functions/invoice-reminders/handler.js`), and EventBridge Scheduler integration.","c":1,"e":[["file","template.yaml:58-140"],["file","functions/invoice-reminders/handler.js:1-52"],["file","README.md:5-29"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"dotnet-utility-billing","variant":"base","family":"srvl-enterprise-dotnet-utility-billing","pid":"SRVL-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"azure-functions","secs":665,"k":"c9fe77f6-0426-406c-98cc-44b0494d0c11-r1","picks":[["azure-functions","p"]],"ev":61,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The run recommended and fully implemented a .NET 8 isolated Azure Durable Functions project on Azure Flex Consumption, configured with timer triggers, Bicep infrastructure definitions, pipeline deployment tasks, and unit tests.","c":1,"e":[["file","src/Northmere.Billing.Batch/MonthlyInvoiceFunctions.cs"],["file","infra/main.bicep"],["file","Directory.Packages.props"],["file","azure-pipelines.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"dotnet-utility-billing","variant":"base","family":"srvl-enterprise-dotnet-utility-billing","pid":"SRVL-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"azure-functions","secs":559,"k":"c9fe77f6-0426-406c-98cc-44b0494d0c11-r2","picks":[["azure-functions","p"],["aws-lambda","m"]],"ev":50,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated serverless options for running monthly invoice batches in an existing Azure .NET repository, recommended Azure Functions (.NET isolated worker model on Flex Consumption), and fully implemented the solution with code, infrastructure, and deployment pipelines.","c":1,"e":[["file","src/Northmere.Billing.Functions/InvoiceBatchFunctions.cs"],["file","src/Northmere.Billing.Functions/Northmere.Billing.Functions.csproj"],["file","infra/main.bicep"],["file","azure-pipelines.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"dotnet-utility-billing","variant":"base","family":"srvl-enterprise-dotnet-utility-billing","pid":"SRVL-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"azure-functions","secs":458,"k":"c9fe77f6-0426-406c-98cc-44b0494d0c11-r3","picks":[["azure-functions","p"],["aws-lambda","m"]],"ev":43,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated serverless scheduling options for the .NET billing application, recommended Azure Functions due to the existing Azure infrastructure and .NET ecosystem fit, and fully implemented a .NET 8 isolated worker project with Bicep infrastructure and pipeline definitions.","c":1,"e":[["file","src/Northmere.Billing.Functions/MonthlyInvoiceBatch.cs:1-38"],["file","infra/main.bicep:118-185"],["file","azure-pipelines.yml:74-81"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"fastapi-saas","variant":"base","family":"bc-panl-prompt-c-06","pid":"PANL-PC-06b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":428,"k":"9bd7aff2-82f5-4024-9641-8475772986c0-r1","picks":[["diy","p","d"],["amplitude","m"],["mixpanel","m"],["posthog","m"]],"ev":51,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated third-party analytics SaaS vendors (PostHog, Amplitude, Mixpanel) but rejected them in favor of building a custom first-party append-only event logging system directly in PostgreSQL. The implementation includes an Alembic migration for the `events` table with database triggers, an `app/events.py` module, and instrumentation across contract, admin, and auth routers.","c":0.95,"e":[["file","app/events.py:1-98"],["file","app/models.py:88-123"],["file","alembic/versions/20260828_3f8b1d0c7ea5_events_table.py:1-81"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"fastapi-saas","variant":"base","family":"bc-panl-prompt-c-06","pid":"PANL-PC-06b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"posthog","secs":849,"k":"9bd7aff2-82f5-4024-9641-8475772986c0-r2","picks":[["posthog","p"],["amplitude","m"],["metabase","m"],["mixpanel","m"],["segment","m"]],"ev":78,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended PostHog EU Cloud over Amplitude and Mixpanel, and implemented a full background outbox exporter batching redacted events directly to PostHog's ingestion endpoint.","c":1,"e":[["file","app/analytics/export.py:46-56"],["file","app/config.py:23-26"],["file",".env.example:14-19"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Procurement and compliance"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"fastapi-saas","variant":"base","family":"bc-panl-prompt-c-06","pid":"PANL-PC-06b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":787,"k":"9bd7aff2-82f5-4024-9641-8475772986c0-r3","picks":[["diy","p","d"],["posthog","m"],["amplitude","m"],["mixpanel","m"]],"ev":43,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly advised against using a third-party analytics provider due to EU data residency, confidentiality of contract data, and procurement/DPA concerns. Instead, it implemented a full first-party product analytics and audit logging subsystem in the repository using existing PostgreSQL infrastructure.","c":0.98,"e":[["file","app/events.py:1-135"],["file","app/routers/analytics.py:1-185"],["file","README.md:43-85"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"srvl-junior-nextjs-storefront","pid":"SRVL-06a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-functions","secs":271,"k":"28ccb486-dbd9-46f5-ad67-6583cad53030-r1","picks":[["vercel-functions","p","b"],["inngest","m"],["qstash","m"]],"ev":43,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The repository is a Next.js application already hosted on Vercel. The user requested a reliable architecture to handle order confirmation emails during traffic spikes. The agent chose and implemented a serverless function queue trigger in vercel.json and an API route handler on Vercel Functions with @vercel/queue, using the existing hosting platform without requiring third-party background execution vendors.","c":0.95,"e":[["file","vercel.json"],["file","app/api/queues/order-confirmations/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"srvl-junior-nextjs-storefront","pid":"SRVL-06a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"vercel-functions","secs":382,"k":"28ccb486-dbd9-46f5-ad67-6583cad53030-r2","picks":[["vercel-functions","p","b"],["inngest","m"],["qstash","m"]],"ev":42,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The repository is already built on Next.js and deployed to Vercel. The agent chose to stay within the existing platform stack by implementing serverless queue consumer routes on Vercel Functions, configuring them via vercel.json and @vercel/queue.","c":0.95,"e":[["file","vercel.json:5-15"],["file","app/api/queues/order-confirmation/route.ts:1-84"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"srvl-junior-nextjs-storefront","pid":"SRVL-06a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"vercel-functions","secs":273,"k":"28ccb486-dbd9-46f5-ad67-6583cad53030-r3","picks":[["vercel-functions","p","b"],["qstash","m"]],"ev":43,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The repository is already deployed to Vercel. The agent implemented a queue-triggered serverless function endpoint on Next.js/Vercel Functions (`app/api/queues/order-confirmations/route.ts`) configured via `vercel.json`'s `functions` mapping to handle spike absorption and retries. Inngest was checked during a codebase regex scan.","c":0.95,"e":[["file","vercel.json"],["file","app/api/queues/order-confirmations/route.ts"],["trace","seq:3"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"helpdesk-billing-starter","variant":"base","family":"bc-srvl-prompt-b-07","pid":"SRVL-PB-07a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"render","secs":566,"k":"65d1a470-3d1b-4f5e-9274-7cddcc153059-r1","picks":[["render","p"],["aws-lambda","m"],["fly","m"],["google-cloud-run","m"],["railway","m"],["vercel-functions","m"]],"ev":62,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended Render Cron Jobs for running the daily billing sync and implemented the solution using a `render.yaml` Blueprint configuring a web service, a cron job (`helpdesk-billing-sync`), and managed Postgres. Other serverless scheduled execution platforms (AWS Lambda, Google Cloud Run, Fly.io, Railway, and Vercel) were explicitly evaluated and rejected.","c":1,"e":[["file","render.yaml:50-74"],["file","README.md:71-98"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"helpdesk-billing-starter","variant":"base","family":"bc-srvl-prompt-b-07","pid":"SRVL-PB-07a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"google-cloud-run","secs":620,"k":"65d1a470-3d1b-4f5e-9274-7cddcc153059-r2","picks":[["google-cloud-run","p"],["aws-lambda","m"],["fly","m"],["railway","m"],["render","m"],["vercel-functions","m"]],"ev":47,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended and configured Google Cloud Run (both as a web service and a scheduled batch job) orchestrated via Cloud Scheduler, creating the Dockerfile, GitHub Actions deployment workflow, and deployment documentation. Competing platforms like AWS Lambda, Vercel, Render, Railway, and Fly.io were evaluated and rejected due to operational complexity or lack of built-in retry capabilities.","c":1,"e":[["file",".github/workflows/deploy.yml:54-79"],["file","DEPLOYMENT.md:8-15"],["file","Dockerfile:1-18"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"helpdesk-billing-starter","variant":"base","family":"bc-srvl-prompt-b-07","pid":"SRVL-PB-07a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"google-cloud-run","secs":359,"k":"65d1a470-3d1b-4f5e-9274-7cddcc153059-r3","picks":[["google-cloud-run","p"],["aws-lambda","m"],["cloudflare-workers","m"],["vercel-functions","m"]],"ev":30,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several serverless platforms against the requirement for scheduled runs with automatic retries and minimal operational overhead. It rejected Vercel, AWS Lambda, and Cloudflare Workers before implementing Google Cloud Run triggered by Cloud Scheduler via a dedicated Dockerfile, deploy script, and HTTP trigger endpoint.","c":1,"e":[["file","deploy/deploy.sh:45-56"],["file","deploy/README.md:3-17"],["file","Dockerfile:1-18"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"helpdesk-billing-starter","variant":"base","family":"srvl-junior-helpdesk-billing-starter","pid":"SRVL-07a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"google-cloud-run","secs":372,"k":"91c48c34-690b-481b-9501-43b986b5aebe-r1","picks":[["google-cloud-run","p"],["inngest","m"],["trigger-dev","m"],["aws-lambda","a"],["cloudflare-workers","m"],["render","m"],["vercel-functions","m"]],"ev":30,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended Google Cloud Run (Jobs) triggered by Cloud Scheduler and wrote all configuration files (Dockerfile, deploy/cloud-run.sh, deploy/README.md, src/jobs/daily-billing-sync.js). It reviewed other serverless/cron options (Vercel Functions/Cron, Render, Cloudflare Workers) and rejected them specifically for missing automated retry capabilities on failures.","c":0.98,"e":[["file","deploy/cloud-run.sh:26-34"],["file","deploy/README.md:3-13"],["file","Dockerfile:1-12"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"helpdesk-billing-starter","variant":"base","family":"srvl-junior-helpdesk-billing-starter","pid":"SRVL-07a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"google-cloud-run","secs":817,"k":"91c48c34-690b-481b-9501-43b986b5aebe-r2","picks":[["google-cloud-run","p"],["cloudflare-workers","m"],["fly","m"],["render","m"],["aws-lambda","m"],["netlify-functions","m"],["vercel-functions","m"]],"ev":46,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected Google Cloud Run (using Cloud Run Jobs for scheduled execution via Cloud Scheduler and Cloud Run Service for the web tier) as the serverless compute solution. It wrote a full Dockerfile, deploy script, alert policies, and codebase integration, and compared alternatives such as AWS Lambda, Vercel, Netlify, Cloudflare Workers, Fly.io, and Render.","c":1,"e":[["file","deploy/deploy.sh"],["file","Dockerfile"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"helpdesk-billing-starter","variant":"base","family":"srvl-junior-helpdesk-billing-starter","pid":"SRVL-07a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"google-cloud-run","secs":320,"k":"91c48c34-690b-481b-9501-43b986b5aebe-r3","picks":[["google-cloud-run","p"],["inngest","m"],["trigger-dev","m"],["fly","m"],["railway","m"],["aws-lambda","m"],["qstash","m"],["render","m"],["vercel-functions","m"]],"ev":26,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended and configured Google Cloud Run (using Cloud Run Jobs triggered by Cloud Scheduler) to satisfy the scheduled execution and retry requirements, providing deploy scripts and documentation.","c":1,"e":[["file","deploy/billing-sync.sh:25-35"],["file","docs/billing-sync.md:3-6"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"go-customer-ops","variant":"base","family":"bc-srvl-prompt-b-05","pid":"SRVL-PB-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":437,"k":"5b2737fc-d38c-4975-a690-13e581647aba-r1","picks":[["diy","p","d"]],"ev":35,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested a serverless function on a managed platform for data exports. The agent explicitly advised against using third-party managed serverless platforms due to network trust boundary constraints, lack of auth, and PII data handling considerations. Instead, it proposed and implemented a custom Go export binary run via systemd timer on the local host, writing directly to disk with atomic promotion and retention pruning.","c":1,"e":[["file","cmd/export/main.go:1-42"],["file","deploy/cairnfold-export.service:1-47"],["file","deploy/cairnfold-export.timer:1-12"],["file","internal/export/export.go:1-296"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"go-customer-ops","variant":"base","family":"bc-srvl-prompt-b-05","pid":"SRVL-PB-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":303,"k":"5b2737fc-d38c-4975-a690-13e581647aba-r2","picks":[["diy","p","d"],["netlify-functions","m"],["vercel-functions","m"]],"ev":25,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The user prompted for a serverless function on a managed platform, but the agent analyzed the codebase, identified that the application is an unauthenticated local monolith with sensitive PII, and advised against external managed serverless functions due to security and boundary constraints. The agent proposed and implemented a custom DIY CLI exporter tool (`cmd/exporter`) using the repository's existing Go and PostgreSQL stack.","c":0.95,"e":[["file","cmd/exporter/main.go:1-108"],["file","internal/export/export.go:1-106"],["file","README.md:20-47"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":11,"date":"2026-08-28","repo":"go-customer-ops","variant":"base","family":"bc-srvl-prompt-b-05","pid":"SRVL-PB-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":318,"k":"5b2737fc-d38c-4975-a690-13e581647aba-r3","picks":[["diy","p","d"]],"ev":25,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested customer data exports handled off the web process and asked for a recommendation regarding a serverless function. The agent analyzed the architecture and security constraints (no auth, local office network boundary, unencrypted Postgres, concentrated PII) and explicitly advised against a cloud-managed serverless platform. Instead, it built a custom Go CLI binary (`cmd/export`) and streaming export package (`internal/export`) designed to run via cron or systemd timer.","c":0.95,"e":[["file","cmd/export/main.go:1-106"],["file","internal/export/export.go:1-135"],["file","README.md:20-42"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"turborepo-b2b","variant":"base","family":"panl-senior-turborepo-b2b","pid":"PANL-09b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"segment","secs":376,"k":"d6bf91f9-01e3-44cd-8589-fbaadf8a4d39-r1","picks":[["segment","p"]],"ev":47,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The repository already used Segment on the frontend, and the agent implemented server-side tracking using @segment/analytics-node in the NestJS API to track shipment lifecycle events.","c":1,"e":[["file","apps/api/package.json"],["file","apps/api/src/analytics/analytics.service.ts"],["file","infra/lib/api-stack.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"turborepo-b2b","variant":"base","family":"panl-senior-turborepo-b2b","pid":"PANL-09b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"segment","secs":598,"k":"d6bf91f9-01e3-44cd-8589-fbaadf8a4d39-r2","picks":[["segment","p"],["plausible","m"]],"ev":77,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The repository already specified Segment in CLAUDE.md and had browser analytics configured. The agent recommended and implemented backend event tracking via `@segment/analytics-node` in the NestJS API to capture shipment lifecycle 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":3,"date":"2026-08-28","repo":"turborepo-b2b","variant":"base","family":"panl-senior-turborepo-b2b","pid":"PANL-09b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"segment","secs":450,"k":"d6bf91f9-01e3-44cd-8589-fbaadf8a4d39-r3","picks":[["segment","p"]],"ev":56,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The repository already had Segment configured for client-side analytics. The agent recommended and implemented server-side analytics for shipment lifecycle events using `@segment/analytics-node` within the API service, connecting it to AWS Secrets Manager and 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":3,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"bc-panl-prompt-c-05","pid":"PANL-PC-05b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":605,"k":"b4595e12-6d69-4f6c-a850-98ccb3b42cc0-r1","picks":[["diy","p","d"],["amplitude","m"],["datadog","m"],["mixpanel","m"],["segment","m"]],"ev":48,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated third-party analytics options against legal, SSO, and DPA constraints alongside the repo's existing Kafka architecture and Datadog tagging rules. It recommended and implemented a custom in-house tracking pipeline on top of the pre-existing Kafka cluster to capture checkout outcomes without introducing external subprocessors.","c":1,"e":[["file","services/checkout/src/lib/outcomes.ts"],["file","services/checkout/src/lib/kafka.ts"],["file","services/checkout/src/app.ts"],["file","services/checkout/src/routes/checkout.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"bc-panl-prompt-c-05","pid":"PANL-PC-05b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":556,"k":"b4595e12-6d69-4f6c-a850-98ccb3b42cc0-r2","picks":[["diy","p","d"],["datadog","m"]],"ev":37,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly decided against adopting an external product analytics vendor to avoid third-party data processor agreements, SSO setup, and latency risks on the checkout hot path, implementing a custom Kafka-based event tracking system instead.","c":1,"e":[["file","services/checkout/src/lib/checkout-events.ts"],["file","docs/checkout-events.md"],["file","services/checkout/src/routes/checkout.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"bc-panl-prompt-c-05","pid":"PANL-PC-05b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":472,"k":"b4595e12-6d69-4f6c-a850-98ccb3b42cc0-r3","picks":[["diy","p","d"],["datadog","m"]],"ev":33,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"Rather than adopting an external product analytics SaaS platform that would require new DPAs and compliance reviews, the run designed and implemented a custom server-side outcome event pipeline on top of the repository's existing Kafka infrastructure and Fastify framework.","c":0.95,"e":[["file","services/checkout/src/lib/outcome-events.ts:1-96"],["file","services/checkout/src/app.ts:1-147"],["file","services/checkout/src/routes/checkout.ts:39-95"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"go-fleet","variant":"base","family":"panl-senior-go-fleet","pid":"PANL-07b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":870,"k":"60132fb5-c1b7-4a52-841f-efd2f8435992-r1","picks":[["diy","p","d"],["amplitude","m"],["posthog","m"],["segment","m"]],"ev":94,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly evaluated third-party analytics solutions (PostHog, Amplitude, Segment) and rejected them due to privacy/GDPR concerns with driver location data and third-party egress. Instead, it implemented a custom server-side analytics event log and position rollup system in Go atop the pre-existing PostgreSQL database.","c":0.95,"e":[["file","internal/analytics/analytics.go"],["file","internal/rollup/rollup.go"],["file","cmd/rollupd/main.go"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"go-fleet","variant":"base","family":"panl-senior-go-fleet","pid":"PANL-07b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":528,"k":"60132fb5-c1b7-4a52-841f-efd2f8435992-r2","picks":[["diy","p","d"],["amplitude","m"],["posthog","m"],["segment","m"]],"ev":39,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated third-party analytics platforms (PostHog, Amplitude) and rejected them due to PII/data privacy concerns, opting instead to write a custom server-side analytics system (`internal/analytics`) leveraging the project's existing Google Cloud Pub/Sub infrastructure and BigQuery subscriptions.","c":1,"e":[["file","internal/analytics/analytics.go:1-69"],["file","internal/analytics/pubsub.go:1-97"],["file","db/analytics_events.bigquery.sql:1-28"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"go-fleet","variant":"base","family":"panl-senior-go-fleet","pid":"PANL-07b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":637,"k":"60132fb5-c1b7-4a52-841f-efd2f8435992-r3","picks":[["diy","p","d"],["amplitude","m"],["posthog","m"],["segment","m"]],"ev":47,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics options for the GCP-based Go fleet backend and recommended building a custom internal analytics package (`internal/analytics`) that emits structured slog events to stdout, routed via Google Cloud Logging sink to BigQuery. After user approval, the agent implemented and tested the custom package and wired it across HTTP handlers and telemetry ingestion.","c":1,"e":[["file","internal/analytics/analytics.go"],["file","internal/httpapi/router.go"],["file","internal/telemetry/consumer.go"],["file","cmd/fleetd/main.go"],["file","cmd/ingestd/main.go"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"fastapi-saas","variant":"base","family":"bc-panl-prompt-b-06","pid":"PANL-PB-06b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"posthog","secs":343,"k":"932ecca2-cffc-4053-976a-7b5437885042-r1","picks":[["posthog","p"],["amplitude","m"],["mixpanel","m"],["rudderstack","m"],["snowplow","m"]],"ev":51,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics platforms at a volume of 100 million events per month, explicitly comparing PostHog, Amplitude, and Mixpanel. It chose PostHog Cloud EU due to transparent volume tier pricing and implemented a complete transactional outbox worker pattern across the backend, Alembic migrations, and Terraform ECS configurations.","c":1,"e":[["file","app/analytics_worker.py:55"],["file","app/config.py:21"],["file","terraform/ecs.tf:141"],["file","README.md:41"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"fastapi-saas","variant":"base","family":"bc-panl-prompt-b-06","pid":"PANL-PB-06b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"posthog","secs":345,"k":"932ecca2-cffc-4053-976a-7b5437885042-r2","picks":[["posthog","p"],["amplitude","m"],["june","m"],["mixpanel","m"],["rudderstack","m"]],"ev":39,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics providers for 100M monthly events, rejected Mixpanel and Amplitude based on pricing and tier limits, and fully implemented PostHog (via the `posthog` package, ECS task definitions, Alembic migration, and API hooks).","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":4,"date":"2026-08-28","repo":"fastapi-saas","variant":"base","family":"bc-panl-prompt-b-06","pid":"PANL-PB-06b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"posthog","secs":666,"k":"932ecca2-cffc-4053-976a-7b5437885042-r3","picks":[["posthog","p"],["heap","m"],["amplitude","m"],["metabase","m"],["mixpanel","m"],["rudderstack","m"]],"ev":78,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated multiple product analytics services (PostHog, Amplitude, Mixpanel, Heap) for a 100M event/month workload, recommended PostHog Cloud EU, and implemented a complete transactional outbox publisher with contract lifecycle events, database migrations, and Terraform ECS configuration.","c":1,"e":[["file",".env.example:14-16"],["file","app/analytics_worker.py:46-60"],["file","terraform/ecs.tf:187-198"],["file","README.md:41-69"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"serverless","wave":6,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"bc-srvl-prompt-c-08","pid":"SRVL-PC-08a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-lambda","secs":361,"k":"ce0bcaad-f098-4ebc-975e-e78662a5ab8d-r1","picks":[["aws-lambda","p"],["google-cloud-run","m"],["cloudflare-workers","m"],["fly","m"],["qstash","m"]],"ev":34,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several serverless/scheduled compute options and selected AWS Lambda managed through AWS SAM, writing the SAM template, Lambda handler, and integration tests to invoke an authenticated Remix endpoint.","c":0.95,"e":[["file","infra/reminders/template.yaml"],["file","infra/reminders/src/handler.mjs"],["file","infra/reminders/README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":6,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"bc-srvl-prompt-c-08","pid":"SRVL-PC-08a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"aws-lambda","secs":598,"k":"ce0bcaad-f098-4ebc-975e-e78662a5ab8d-r2","picks":[["aws-lambda","p"],["cloudflare-workers","m"],["fly","m"]],"ev":52,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated serverless options and implemented an AWS Lambda function using AWS SAM (AWS::Serverless::Function) with EventBridge Scheduler for timezone-aware cron triggers.","c":1,"e":[["file","infra/reminders/template.yaml:18-73"],["file","infra/reminders/src/handler.ts:1-174"],["file",".github/workflows/deploy-reminders.yml:1-50"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":6,"date":"2026-08-28","repo":"remix-workshop-bookings","variant":"base","family":"bc-srvl-prompt-c-08","pid":"SRVL-PC-08a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"cloudflare-workers","secs":194,"k":"ce0bcaad-f098-4ebc-975e-e78662a5ab8d-r3","picks":[["cloudflare-workers","p"],["aws-lambda","m"],["fly","m"],["qstash","m"]],"ev":22,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated how to trigger periodic workshop reminder emails given SQLite volume constraints on Fly.io. It selected and implemented Cloudflare Workers via a scheduled Cron Trigger worker calling a protected endpoint on the main app.","c":1,"e":[["file","cloudflare/reminder-trigger/src/index.js:1-22"],["file","cloudflare/reminder-trigger/wrangler.jsonc:1-12"],["file","README.md:31-57"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-srvl-prompt-c-06","pid":"SRVL-PC-06a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel-functions","secs":170,"k":"805ae52e-3105-42a1-954a-2b49e2b0a9bf-r1","picks":[["vercel-functions","p","b"],["aws-lambda","m"],["google-cloud-run","m"],["inngest","m"]],"ev":18,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested adding a serverless function to send order confirmation emails. The agent inspected the repository, found an existing Next.js Stripe webhook route handler configured for Vercel, and recommended keeping and improving the builtin Vercel Functions route rather than deploying external services like AWS Lambda, Google Cloud Run, or Inngest. The user approved and the agent implemented the enhancements in the route handler.","c":0.95,"e":[["file","vercel.json"],["file","app/api/webhooks/stripe/route.ts"],["trace","seq:10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-srvl-prompt-c-06","pid":"SRVL-PC-06a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"vercel-functions","secs":267,"k":"805ae52e-3105-42a1-954a-2b49e2b0a9bf-r2","picks":[["vercel-functions","p","b"]],"ev":27,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The run inspected the codebase and determined that the Next.js API route (`app/api/webhooks/stripe/route.ts`) already functions as a serverless function hosted on Vercel. Rather than adopting an external serverless runtime, it recommended retaining and hardening the existing Vercel Functions endpoint with idempotency logic.","c":0.95,"e":[["file","app/api/webhooks/stripe/route.ts"],["file","vercel.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":5,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-srvl-prompt-c-06","pid":"SRVL-PC-06a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel-functions","secs":149,"k":"805ae52e-3105-42a1-954a-2b49e2b0a9bf-r3","picks":[["vercel-functions","p","b"]],"ev":19,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The user asked for a serverless function to send order confirmation emails. The agent inspected the repository, recognized that Next.js route handlers on Vercel already fulfill this role natively, and recommended keeping and improving the existing Vercel serverless function route rather than adding another platform.","c":0.95,"e":[["file","vercel.json"],["file","app/api/webhooks/stripe/route.ts:1-96"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"helpdesk-billing-starter","variant":"base","family":"srvl-junior-helpdesk-billing-starter","pid":"SRVL-07a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-lambda","secs":247,"k":"521ed2c9-f294-4554-9f44-e667b397d5ee-r1","picks":[["aws-lambda","p"],["google-cloud-run","m"]],"ev":31,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected AWS Lambda as the scheduled serverless function solution, implementing the Lambda handler in src/billing-sync-lambda.js and provisioning the serverless infrastructure using AWS SAM in template.yaml.","c":1,"e":[["file","template.yaml"],["file","src/billing-sync-lambda.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"helpdesk-billing-starter","variant":"base","family":"srvl-junior-helpdesk-billing-starter","pid":"SRVL-07a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"aws-lambda","secs":344,"k":"521ed2c9-f294-4554-9f44-e667b397d5ee-r2","picks":[["aws-lambda","p"],["google-cloud-run","m"],["google-cloud-functions","m"],["cloudflare-workers","m"],["netlify-functions","m"],["vercel-functions","m"]],"ev":38,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several scheduled serverless platforms (AWS Lambda, Cloudflare Workers, Vercel Functions, Netlify Functions, and Google Cloud Run/Functions) specifically around retry handling for failures. It selected AWS Lambda configured via AWS SAM with EventBridge Scheduler, SQS DLQs, and CloudWatch alarms, and fully implemented the handler and infrastructure code.","c":1,"e":[["file","template.yaml:69-95"],["file","src/functions/daily-billing-sync.js:16-28"],["file","README.md:25-50"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"helpdesk-billing-starter","variant":"base","family":"srvl-junior-helpdesk-billing-starter","pid":"SRVL-07a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws-lambda","secs":239,"k":"521ed2c9-f294-4554-9f44-e667b397d5ee-r3","picks":[["aws-lambda","p"],["cloudflare-workers","m"],["google-cloud-run","m"],["netlify-functions","m"],["vercel-functions","m"]],"ev":27,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several scheduled serverless platforms (Vercel, Netlify, Cloudflare Workers, Google Cloud Run, and AWS Lambda) focusing on failure and retry handling capabilities. It selected AWS Lambda paired with Amazon EventBridge Scheduler and AWS SAM, writing the handler, SAM template, tests, and deployment documentation.","c":0.98,"e":[["file","template.yaml"],["file","src/billing-sync.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"fieldservice-saas-billing","variant":"base","family":"srvl-senior-fieldservice-saas-billing","pid":"SRVL-03a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-functions","secs":244,"k":"46b40f9e-ba58-49c5-b0bf-fc115928026a-r1","picks":[["vercel-functions","p"],["aws-lambda","m"]],"ev":27,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several scheduled serverless compute options for a Nuxt application, recommended Vercel Functions with Vercel Cron, and implemented the solution including `vercel.json` cron configuration, serverless API endpoint, repository adapter, and documentation.","c":1,"e":[["file","vercel.json"],["file","server/api/cron/invoice-reminders.get.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"fieldservice-saas-billing","variant":"base","family":"srvl-senior-fieldservice-saas-billing","pid":"SRVL-03a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"vercel-functions","secs":234,"k":"46b40f9e-ba58-49c5-b0bf-fc115928026a-r2","picks":[["vercel-functions","p"],["cloudflare-workers","m"],["aws-lambda","m"]],"ev":25,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected Vercel Functions (specifically Vercel Cron triggering a Nuxt Nitro serverless route), implemented `vercel.json` with a daily cron schedule, wrote the endpoint at `server/api/cron/invoice-reminders.get.js`, and documented deployment on Vercel. AWS Lambda and Cloudflare Workers were evaluated and discussed in reasoning/trace.","c":1,"e":[["file","vercel.json"],["file","README.md:5-28"],["file","server/api/cron/invoice-reminders.get.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"fieldservice-saas-billing","variant":"base","family":"srvl-senior-fieldservice-saas-billing","pid":"SRVL-03a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"vercel-functions","secs":285,"k":"46b40f9e-ba58-49c5-b0bf-fc115928026a-r3","picks":[["vercel-functions","p"],["aws-lambda","m"],["netlify-functions","m"]],"ev":24,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated serverless scheduling options for the Nuxt application and explicitly chose Vercel Functions/Cron, writing `vercel.json`, setting up the API endpoint, and configuring Nuxt build scripts.","c":0.95,"e":[["file","vercel.json"],["file","README.md"],["trace","item:4"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"turborepo-b2b","variant":"base","family":"bc-panl-prompt-c-09","pid":"PANL-PC-09b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"amplitude","secs":494,"k":"7ebf9fba-28d9-4bf3-9221-d504763ae6c9-r1","picks":[["amplitude","p"],["posthog","m"],["mixpanel","m"],["segment","m"]],"ev":58,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested a product analytics recommendation and implementation that operates alongside an existing data warehouse. The agent selected Amplitude via Segment's cloud-mode Actions destination, implementing shared schema contracts and backend Segment tracking for shipment mutations, and explicitly documenting the Amplitude connection in docs/analytics.md.","c":0.95,"e":[["file","docs/analytics.md"],["trace","8"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Fits an existing data stack"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"turborepo-b2b","variant":"base","family":"bc-panl-prompt-c-09","pid":"PANL-PC-09b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"amplitude","secs":285,"k":"7ebf9fba-28d9-4bf3-9221-d504763ae6c9-r2","picks":[["amplitude","p"],["mixpanel","m"],["segment","m"],["simple-analytics","m"]],"ev":38,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended and configured Amplitude as the product analytics destination downstream of Segment, updating documentation and typed shipment tracking code accordingly.","c":1,"e":[["file","README.md"],["file","apps/web/lib/shipment-analytics.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Fits an existing data stack"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"turborepo-b2b","variant":"base","family":"bc-panl-prompt-c-09","pid":"PANL-PC-09b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"mitzu","secs":301,"k":"7ebf9fba-28d9-4bf3-9221-d504763ae6c9-r3","picks":[["mitzu","p"],["amplitude","m"],["june","m"],["mixpanel","m"],["posthog","m"],["segment","m"]],"ev":43,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly chose Mitzu as a warehouse-native product analytics solution to work directly on top of the repository's Segment-to-warehouse pipeline. It added server and web event instrumentation for Segment, updated the CDK stack and shared types, and documented the Mitzu warehouse connection requirements in README.md and CLAUDE.md while explicitly rejecting SDK-based alternatives like Amplitude, Mixpanel, and PostHog.","c":0.95,"e":[["file","CLAUDE.md:29"],["file","README.md:44-57"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Fits an existing data stack"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"turborepo-b2b","variant":"base","family":"bc-panl-prompt-b-09","pid":"PANL-PB-09b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"amplitude","secs":625,"k":"443ba160-825f-4e68-91a6-522cc25bf82a-r1","picks":[["amplitude","p"],["mixpanel","m"],["posthog","m"],["metabase","m"],["segment","m"]],"ev":59,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended Amplitude as the self-service product analytics solution for operations reporting on shipment lifecycles. It built the ingestion pipeline (DynamoDB Streams to Lambda to Segment batch API) and documented connecting the Segment source to an Amplitude destination project.","c":1,"e":[["file","README.md:41-68"],["trace","seq:7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"turborepo-b2b","variant":"base","family":"bc-panl-prompt-b-09","pid":"PANL-PB-09b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"amplitude","secs":634,"k":"443ba160-825f-4e68-91a6-522cc25bf82a-r2","picks":[["amplitude","p"],["mixpanel","a"],["segment","m"]],"ev":80,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended and configured an analytics architecture using Amplitude as the end-user product analytics platform (fed via Segment server-side event tracking from DynamoDB streams). Documentation and event specs were committed for setting up Amplitude projects, tracking plans, and operational dashboards.","c":0.95,"e":[["file","docs/shipment-lifecycle-analytics.md:3-92"],["trace","12"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"turborepo-b2b","variant":"base","family":"bc-panl-prompt-b-09","pid":"PANL-PB-09b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"amplitude","secs":414,"k":"443ba160-825f-4e68-91a6-522cc25bf82a-r3","picks":[["amplitude","p"],["mixpanel","m"],["segment","m"]],"ev":58,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics options suitable for self-service operations tracking of shipment lifecycle events and selected Amplitude routed through Segment, implementing the backend event pipeline and documenting the Amplitude destination and dashboard setup.","c":0.95,"e":[["file","docs/shipment-analytics.md:25-44"],["trace","9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"turborepo-b2b","variant":"base","family":"panl-senior-turborepo-b2b","pid":"PANL-09b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"segment","secs":497,"k":"2d8e68c7-5196-49ed-bf7d-76fedf2cbb62-r1","picks":[["segment","p"]],"ev":52,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected Segment as the target analytics backend, writing a Lambda stream processor that transforms DynamoDB shipment mutations into Segment track events and posts them directly to Segment's HTTP API.","c":1,"e":[["file","infra/lambda/shipment-analytics.ts"],["file","infra/lib/api-stack.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"turborepo-b2b","variant":"base","family":"panl-senior-turborepo-b2b","pid":"PANL-09b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"segment","secs":431,"k":"2d8e68c7-5196-49ed-bf7d-76fedf2cbb62-r2","picks":[["segment","p"]],"ev":51,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The repository already used Segment on the frontend. The agent recommended and implemented a server-side Segment integration via `@segment/analytics-node` in the NestJS API to track shipment lifecycle events (creation, dispatch, delivery).","c":1,"e":[["file","apps/api/package.json"],["file","apps/api/src/analytics/analytics.service.ts"],["file","CLAUDE.md"],["file","infra/lib/api-stack.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"turborepo-b2b","variant":"base","family":"panl-senior-turborepo-b2b","pid":"PANL-09b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"segment","secs":403,"k":"2d8e68c7-5196-49ed-bf7d-76fedf2cbb62-r3","picks":[["segment","p"]],"ev":58,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent investigated the repository's existing analytics setup and chose to implement server-side tracking using Segment (@segment/analytics-node) across the API service and AWS CDK infrastructure.","c":1,"e":[["file","apps/api/package.json"],["file","apps/api/src/analytics/shipment-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":4,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"bc-panl-prompt-b-05","pid":"PANL-PB-05b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"amplitude","secs":346,"k":"22359d5b-7268-44b1-bda1-9bfa32375ebc-r1","picks":[["amplitude","p"],["posthog","m"],["datadog","m"],["datadog-product-analytics","m"],["segment","m"]],"ev":37,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended Amplitude and integrated the official `@amplitude/analytics-node` SDK to track completed and rejected checkout requests, providing environment variables, unit tests, and documentation.","c":1,"e":[["file","services/checkout/package.json:17"],["file","services/checkout/src/lib/analytics.ts:1-105"],["file","docs/observability.md:57-72"],["file",".env.example:24-26"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"bc-panl-prompt-b-05","pid":"PANL-PB-05b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"posthog","secs":314,"k":"22359d5b-7268-44b1-bda1-9bfa32375ebc-r2","picks":[["posthog","p"],["amplitude","m"],["datadog","m"],["datadog-product-analytics","m"],["segment","m"]],"ev":55,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics options and implemented PostHog using the official `posthog-node` package in `services/checkout/src/lib/analytics.ts`, configuring Fastify response hooks and environment variables while rejecting Datadog Product Analytics due to HTTP overhead on the critical path.","c":1,"e":[["file","services/checkout/package.json"],["file","services/checkout/src/lib/analytics.ts"],["file","docs/observability.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"bc-panl-prompt-b-05","pid":"PANL-PB-05b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"posthog","secs":384,"k":"22359d5b-7268-44b1-bda1-9bfa32375ebc-r3","picks":[["posthog","p"],["amplitude","m"],["datadog","m"],["rudderstack","m"],["segment","m"]],"ev":29,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics options and selected PostHog. It installed `posthog-node`, configured deterministic sampling and event capture in `services/checkout/src/lib/analytics.ts`, updated Fastify routes to track completed and rejected checkouts, added unit tests, and documented the integration in `docs/product-analytics.md`.","c":1,"e":[["file","services/checkout/package.json:22"],["file","services/checkout/src/lib/analytics.ts:2"],["file","docs/product-analytics.md:1"],["file",".env.example:24-27"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"panl-enterprise-ts-commerce-datadog","pid":"PANL-05b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":705,"k":"fe49a44e-9d87-49e9-b05f-d7727f2963f5-r1","picks":[["diy","p","d"],["amplitude","m"],["datadog","m"],["heap","m"],["mixpanel","m"],["segment","m"]],"ev":44,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated third-party analytics platforms (Amplitude, Mixpanel) as well as Datadog, but rejected them because per-event and per-series billing becomes unpredictable at checkout scale (150M+ requests/day). It implemented a custom, fire-and-forget product analytics pipeline in TypeScript that batches and streams outcome events to a dedicated Kafka topic on the existing broker cluster.","c":1,"e":[["file","services/checkout/src/lib/analytics.ts:1-353"],["file","services/checkout/src/app.ts:76-103"],["file","services/checkout/src/server.ts:25-50"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"panl-enterprise-ts-commerce-datadog","pid":"PANL-05b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":483,"k":"fe49a44e-9d87-49e9-b05f-d7727f2963f5-r2","picks":[["diy","p","d"],["datadog","m"]],"ev":36,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The user asked for a predictable product analytics solution for completed and rejected checkout requests. The agent designed and implemented a bespoke, lossy, bounded in-memory buffer and Kafka publisher (`services/checkout/src/lib/analytics.ts`) wired into Fastify request/response hooks, utilizing the repo's pre-existing Kafka broker.","c":1,"e":[["file","services/checkout/src/lib/analytics.ts:1-264"],["file","services/checkout/src/app.ts:57-100"],["file","services/checkout/src/routes/checkout.ts:39-70"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"panl-enterprise-ts-commerce-datadog","pid":"PANL-05b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":438,"k":"fe49a44e-9d87-49e9-b05f-d7727f2963f5-r3","picks":[["diy","p","d"],["amplitude","m"],["datadog","m"],["mixpanel","m"],["segment","m"]],"ev":48,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated third-party analytics SDKs (Amplitude, Mixpanel) and rejected them due to unpredictable per-event cost at peak load (millions of events per day) and checkout latency risk. Instead, it authored a custom DIY analytics emitter in `services/checkout/src/lib/analytics.ts` that buffers, batches, and publishes product analytics events to an internal Kafka topic on the existing Kafka infrastructure.","c":0.98,"e":[["file","services/checkout/src/lib/analytics.ts"],["file","services/checkout/src/routes/checkout.ts"],["file","services/checkout/src/app.ts"],["file","services/checkout/src/server.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"nuxt-fieldservice","variant":"base","family":"bc-panl-prompt-c-08","pid":"PANL-PC-08b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":366,"k":"c7188141-f88b-413a-9467-628723318142-r1","picks":[["diy","p","d"],["umami","a"],["matomo","m"],["plausible","m"],["posthog","m"]],"ev":43,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended building a custom, first-party usage analytics table (`usage_events`) in the existing PostgreSQL database rather than adopting a third-party product. The implementation was fully coded, migrated, and wired into the API routes, while evaluating and rejecting self-hosted third-party tools (PostHog, Matomo, Plausible) and noting Umami as an alternative dashboard option.","c":1,"e":[["file","server/db/schema.ts"],["file","server/utils/usage.ts"],["file","drizzle/0001_usage_events.sql"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"nuxt-fieldservice","variant":"base","family":"bc-panl-prompt-c-08","pid":"PANL-PC-08b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":449,"k":"c7188141-f88b-413a-9467-628723318142-r2","picks":[["diy","p","d"],["matomo","m"],["plausible","m"],["posthog","m"],["umami","m"]],"ev":57,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly evaluated third-party and open-source self-hosted analytics options (Plausible, PostHog, Matomo, Umami) and rejected them in favor of a hand-written, first-party domain events logger in PostgreSQL. It implemented the migration, Drizzle schema, event utility functions, server route instrumentation, tests, and documentation.","c":1,"e":[["file","drizzle/0001_events.sql:1-29"],["file","server/db/schema.ts:68-95"],["file","server/utils/events.ts:1-94"],["file","tests/events.test.ts:1-77"],["file","README.md:75-121"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"nuxt-fieldservice","variant":"base","family":"bc-panl-prompt-c-08","pid":"PANL-PC-08b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":453,"k":"c7188141-f88b-413a-9467-628723318142-r3","picks":[["diy","p","d"],["fathom","m"],["matomo","m"],["metabase","m"],["plausible","m"],["posthog","m"],["umami","m"]],"ev":49,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several third-party self-hosted product analytics options (Umami, Plausible, Matomo, PostHog) and explicitly rejected them in favor of implementing a first-party DIY event logging solution inside the existing PostgreSQL database. It created migrations, schema definitions, pure event diffing helpers, and wired them into the API routes.","c":1,"e":[["file","drizzle/0001_events.sql:1-54"],["file","server/utils/eventLog.ts:1-32"],["file","server/utils/jobEvents.ts:1-89"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"nextjs-classbooking","variant":"base","family":"panl-vibe-nextjs-classbooking","pid":"PANL-10b","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel-analytics","secs":425,"k":"8b24923e-5fcf-479b-adb3-0712a90668ca-r1","picks":[["vercel-analytics","p"],["fathom","m"],["google-analytics","m"],["plausible","m"],["posthog","m"]],"ev":40,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended and implemented Vercel Analytics (@vercel/analytics) in package.json and app/layout.tsx to answer the visitor tracking requirement, while directly evaluating and rejecting Google Analytics, Plausible, Fathom, and PostHog.","c":1,"e":[["file","package.json"],["file","app/layout.tsx"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"nextjs-classbooking","variant":"base","family":"panl-vibe-nextjs-classbooking","pid":"PANL-10b","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"vercel-analytics","secs":634,"k":"8b24923e-5fcf-479b-adb3-0712a90668ca-r2","picks":[["vercel-analytics","p"],["fathom","m"],["plausible","m"]],"ev":61,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested an analytics solution for tracking visitor traffic and class bookings. The agent recommended and implemented Vercel Analytics (@vercel/analytics) for site traffic tracking while utilizing the existing Supabase database via a custom SQL view for booking metrics. Other analytics providers (Google Analytics, Plausible, Fathom) were considered and rejected due to consent overhead and pricing.","c":1,"e":[["file","package.json"],["file","app/layout.tsx"],["trace","seq:11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"nextjs-classbooking","variant":"base","family":"panl-vibe-nextjs-classbooking","pid":"PANL-10b","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel-analytics","secs":557,"k":"8b24923e-5fcf-479b-adb3-0712a90668ca-r3","picks":[["vercel-analytics","p"],["fathom","m"],["plausible","m"],["posthog","m"],["umami","m"]],"ev":52,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended and installed Vercel Web Analytics (@vercel/analytics) into the project's root layout to handle pageview tracking, while keeping booking statistics inside Postgres to avoid leaking user PII to third-party services. Alternative analytics products (Google Analytics, Plausible, Fathom, PostHog, Umami) were evaluated and rejected.","c":1,"e":[["file","package.json"],["file","app/layout.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"fieldservice-saas-billing","variant":"base","family":"bc-srvl-prompt-b-03","pid":"SRVL-PB-03a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-functions","secs":225,"k":"2e3469eb-25c0-48af-b7f4-915af9f7135b-r1","picks":[["vercel-functions","p"],["aws-lambda","m"]],"ev":27,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated hosting options for a daily scheduled serverless task in a Nuxt application, dismissed AWS Lambda due to infrastructure overhead, and implemented Vercel serverless functions with Vercel Cron configuration.","c":1,"e":[["file","vercel.json"],["file","README.md"],["file","server/api/jobs/invoice-reminders.get.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"fieldservice-saas-billing","variant":"base","family":"bc-srvl-prompt-b-03","pid":"SRVL-PB-03a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"aws-lambda","secs":239,"k":"2e3469eb-25c0-48af-b7f4-915af9f7135b-r2","picks":[["aws-lambda","p"],["cloudflare-workers","m"],["vercel-functions","m"]],"ev":24,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated potential serverless options including AWS Lambda, Cloudflare Workers, and Vercel Functions. It selected AWS Lambda, implementing the handler in Node.js and defining the AWS SAM template with EventBridge Scheduler integration.","c":1,"e":[["file","template.yaml"],["file","server/jobs/invoice-reminders-lambda.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"fieldservice-saas-billing","variant":"base","family":"bc-srvl-prompt-b-03","pid":"SRVL-PB-03a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"vercel-functions","secs":202,"k":"2e3469eb-25c0-48af-b7f4-915af9f7135b-r3","picks":[["vercel-functions","p"],["aws-lambda","m"]],"ev":18,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested a managed serverless platform to run scheduled invoice reminder jobs for a Nuxt SaaS project. The agent evaluated options and chose Vercel Functions with Vercel Cron, adding `vercel.json` and implementing the scheduled route in Nuxt, while rejecting AWS Lambda as overkill.","c":0.95,"e":[["file","vercel.json"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"nuxt-fieldservice","variant":"base","family":"panl-senior-nuxt-fieldservice","pid":"PANL-08b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":346,"k":"35a059d3-730b-4b89-87fd-a08acff550b1-r1","picks":[["diy","p","d"],["fathom","m"],["metabase","m"],["plausible","m"],["posthog","m"],["simple-analytics","m"],["umami","m"]],"ev":42,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly evaluated third-party analytics solutions (Plausible, Umami, Fathom, PostHog) and rejected them in favor of building a custom first-party server-side event logging pipeline using the project's existing PostgreSQL database and Drizzle ORM.","c":1,"e":[["file","server/utils/eventLog.ts"],["file","server/utils/events.ts"],["file","drizzle/0001_events.sql"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"nuxt-fieldservice","variant":"base","family":"panl-senior-nuxt-fieldservice","pid":"PANL-08b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":555,"k":"35a059d3-730b-4b89-87fd-a08acff550b1-r2","picks":[["diy","p","d"],["fathom","m"],["matomo","m"],["plausible","m"],["posthog","m"]],"ev":62,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated third-party analytics platforms (Plausible, Fathom, PostHog, Matomo) and rejected them in favor of a custom, first-party event tracking log built directly in the existing PostgreSQL database and Drizzle ORM, with an internal Nuxt dashboard.","c":1,"e":[["file","drizzle/0001_app_events.sql:1-46"],["file","server/db/schema.ts:70-96"],["file","server/utils/events.ts:1-39"],["file","server/api/insights.get.ts:1-157"],["file","pages/insights.vue:1-251"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"nuxt-fieldservice","variant":"base","family":"panl-senior-nuxt-fieldservice","pid":"PANL-08b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":393,"k":"35a059d3-730b-4b89-87fd-a08acff550b1-r3","picks":[["diy","p","d"],["fathom","m"],["plausible","m"],["posthog","m"],["umami","m"]],"ev":59,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended against third-party analytics solutions (PostHog, Plausible, Fathom, Umami, GA4) due to privacy concerns, PII risks, cookie requirements, operational overhead, and mismatched feature sets. It designed and implemented a full DIY server-side event tracking system stored in the pre-existing PostgreSQL database.","c":1,"e":[["file","server/utils/analytics.ts:25-42"],["file","server/utils/analyticsEvents.ts:1-63"],["file","scripts/scrub-events.ts:1-57"],["file","README.md:74-171"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-panl-prompt-b-02","pid":"PANL-PB-02b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"posthog","secs":372,"k":"e52826ee-00fa-4d92-8d51-49b495318782-r1","picks":[["posthog","p"],["google-analytics","m"],["plausible","m"],["vercel-analytics","m"]],"ev":43,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended PostHog for full-funnel e-commerce analytics, installed posthog-js and posthog-node, and instrumented client-side and server-side tracking throughout the Next.js application.","c":1,"e":[["file","package.json"],["file","lib/analytics.ts"],["file","lib/analytics-server.ts"],["file","ANALYTICS.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-panl-prompt-b-02","pid":"PANL-PB-02b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"posthog","secs":336,"k":"e52826ee-00fa-4d92-8d51-49b495318782-r2","picks":[["posthog","p"],["plausible","m"],["vercel-analytics","m"]],"ev":39,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics options and implemented PostHog across both browser (posthog-js) and server (posthog-node) environments, rejecting Vercel Analytics and Google Analytics based on property limits, cost, and fit for behavioral product funnels.","c":1,"e":[["file","package.json:15-16"],["file","lib/analytics.ts:1-55"],["file","lib/analytics-server.ts:1-45"],["file","components/analytics-provider.tsx:1-11"],["file","README.md:19-38"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-panl-prompt-b-02","pid":"PANL-PB-02b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"posthog","secs":451,"k":"e52826ee-00fa-4d92-8d51-49b495318782-r3","picks":[["posthog","p"],["google-analytics","m"],["vercel-analytics","m"]],"ev":51,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics options for the Next.js storefront, explicitly rejecting Vercel Analytics and Google Analytics before installing, configuring, and verifying PostHog (using posthog-js, posthog-node, and Vercel proxy rewrites).","c":1,"e":[["file","package.json"],["file","lib/analytics-client.ts"],["file","lib/analytics-server.ts"],["file","components/analytics-provider.tsx"],["file","vercel.json"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-panl-prompt-b-02","pid":"PANL-PB-02b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"posthog","secs":625,"k":"6a1cc859-ceb4-460d-9201-4a928cbb7686-r1","picks":[["posthog","p"],["amplitude","m"],["fathom","m"],["mixpanel","m"],["plausible","m"],["segment","m"],["vercel-analytics","m"]],"ev":72,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several product analytics solutions (PostHog, Vercel Analytics, Plausible, Fathom, GA4, Amplitude, Mixpanel) and explicitly recommended and implemented PostHog using `posthog-js` and `posthog-node` to solve the cross-domain Stripe checkout attribution problem.","c":1,"e":[["file","package.json"],["file","components/posthog-provider.tsx"],["file","lib/analytics-server.ts"],["file","next.config.mjs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-panl-prompt-b-02","pid":"PANL-PB-02b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"posthog","secs":604,"k":"6a1cc859-ceb4-460d-9201-4a928cbb7686-r2","picks":[["posthog","p"],["fathom","m"],["plausible","m"]],"ev":54,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics options against the specific requirement to link client-side browsing to server-side Stripe webhook purchase events across a domain boundary. It recommended and fully installed and configured PostHog (both posthog-js and posthog-node), while explicitly rejecting Vercel Analytics, Plausible, Fathom, and Google Analytics.","c":1,"e":[["file","package.json:15-16"],["file","lib/analytics.ts:1-27"],["file","lib/analytics-client.ts:1-89"],["file","lib/analytics-server.ts:1-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-panl-prompt-b-02","pid":"PANL-PB-02b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"posthog","secs":476,"k":"6a1cc859-ceb4-460d-9201-4a928cbb7686-r3","picks":[["posthog","p"],["fathom","m"],["plausible","m"],["segment","m"],["vercel-analytics","m"]],"ev":42,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several analytics tools (PostHog, Vercel Analytics, Plausible, Fathom, Google Analytics) and selected PostHog. It implemented PostHog across the client (`posthog-js`) and server (`posthog-node`), linking client interactions to Stripe checkout webhook purchases using distinct ID metadata.","c":1,"e":[["file","package.json:15-16"],["file","lib/analytics-client.ts:1-78"],["file","lib/analytics-server.ts:1-38"],["file",".env.example:13-16"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"go-fleet","variant":"base","family":"panl-senior-go-fleet","pid":"PANL-07b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":438,"k":"91fab4b0-f571-4868-835d-55c99663835d-r1","picks":[["diy","p","d"],["posthog","m"]],"ev":40,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent designed and implemented a custom server-side analytics architecture using PostgreSQL transactional outbox, Google Cloud Pub/Sub, and Google Cloud BigQuery, citing the project's existing Google Cloud infrastructure stack. PostHog was briefly mentioned during reasoning as a candidate product analytics tool but was not selected.","c":0.95,"e":[["file","internal/analytics/event.go"],["file","internal/analytics/outbox.go"],["file","deploy/analytics.sql"],["file","deploy/analytics.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"go-fleet","variant":"base","family":"panl-senior-go-fleet","pid":"PANL-07b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"diy","secs":573,"k":"91fab4b0-f571-4868-835d-55c99663835d-r2","picks":[["diy","p","d"],["posthog","m"],["segment","m"]],"ev":45,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"Rather than integrating an off-the-shelf product analytics SaaS, the run implemented an in-repo domain analytics pipeline consisting of a PostgreSQL transactional outbox, a background worker publisher in Go using Google Cloud Pub/Sub, and Google BigQuery table schemas/views for downstream analytics. PostHog was explicitly considered and rejected as ill-suited for dense fleet telemetry.","c":1,"e":[["file","internal/analytics/publisher.go"],["file","internal/event/event.go"],["file","internal/store/repository.go"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"go-fleet","variant":"base","family":"panl-senior-go-fleet","pid":"PANL-07b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"diy","secs":548,"k":"91fab4b0-f571-4868-835d-55c99663835d-r3","picks":[["diy","p","d"],["amplitude","m"],["mixpanel","m"],["posthog","m"]],"ev":36,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated external product analytics services (PostHog, Mixpanel, Amplitude, Google Analytics) but recommended and fully implemented a custom DIY transactional outbox architecture within PostgreSQL, Pub/Sub, and BigQuery.","c":1,"e":[["file","internal/analytics/events.go:1-59"],["file","internal/analytics/publisher.go:1-114"],["file","db/migrations/0002_analytics_outbox.sql:1-24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":14,"date":"2026-08-28","repo":"helpdesk-billing-starter","variant":"base","family":"bc-srvl-prompt-c-07","pid":"SRVL-PC-07a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-lambda","secs":70,"k":"a6d7d718-75ff-4aec-a438-6fe2e7d2f440-r1","picks":[["aws-lambda","p"],["cloudflare-workers","m"],["google-cloud-run","m"],["render","m"],["trigger-dev","m"],["inngest","m"],["netlify-functions","m"],["vercel-functions","m"]],"ev":13,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The run evaluated multiple serverless platforms for a daily billing sync task and explicitly recommended AWS Lambda (triggered via Amazon EventBridge Scheduler) as the primary solution, while explicitly rejecting Netlify Functions and Vercel Functions due to retry and execution duration constraints.","c":0.95,"e":[["trace","items[11]"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":14,"date":"2026-08-28","repo":"helpdesk-billing-starter","variant":"base","family":"bc-srvl-prompt-c-07","pid":"SRVL-PC-07a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"netlify-functions","secs":101,"k":"a6d7d718-75ff-4aec-a438-6fe2e7d2f440-r2","picks":[["netlify-functions","p"],["vercel-functions","m"],["inngest","m"],["aws-lambda","m"],["google-cloud-run","m"]],"ev":11,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The user asked for a recommendation on a managed platform for a scheduled daily billing sync function. The agent analyzed the repository, reviewed alternatives (AWS Lambda, Google Cloud Run, Vercel, Cloudflare Workers, Inngest), and explicitly chose and recommended Netlify Scheduled Functions (Netlify Functions) as its primary pick.","c":0.95,"e":[["trace","13"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":14,"date":"2026-08-28","repo":"helpdesk-billing-starter","variant":"base","family":"bc-srvl-prompt-c-07","pid":"SRVL-PC-07a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"aws-lambda","secs":68,"k":"a6d7d718-75ff-4aec-a438-6fe2e7d2f440-r3","picks":[["aws-lambda","p"],["cloudflare-workers","m"],["google-cloud-run","m"],["netlify-functions","m"],["qstash","m"],["render","m"],["vercel-functions","m"]],"ev":15,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent clearly recommended AWS Lambda with Amazon EventBridge Scheduler as the managed serverless solution for daily billing sync, explicitly rejecting Vercel Functions/Cron and weighing several other managed platforms before asking the user for datastore/provider specifications.","c":0.95,"e":[["trace","items[9]"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":12,"date":"2026-08-28","repo":"helpdesk-billing-starter","variant":"base","family":"bc-srvl-prompt-b-07","pid":"SRVL-PB-07a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-lambda","secs":222,"k":"7f23ba42-d4fe-4073-9163-5262915e8362-r1","picks":[["aws-lambda","p"],["vercel-functions","m"]],"ev":28,"co":"serverless-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated managed serverless options with retry requirements, explicitly rejected Vercel due to lack of invocation retries, and implemented the scheduled billing sync using AWS Lambda (via SAM template.yaml and handler 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engineer"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"rails-marketplace","variant":"base","family":"bc-panl-prompt-b-03","pid":"PANL-PB-03b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"posthog","secs":375,"k":"723956a2-0211-492f-9664-b672622d94f1-r2","picks":[["posthog","p"],["amplitude","m"],["mixpanel","m"],["google-analytics","m"],["metabase","m"]],"ev":49,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended PostHog and implemented full client-side and server-side tracking using the posthog-ruby gem and PostHog JavaScript tracking code, while configuring project tokens in the environment and documenting setup steps in the README.","c":1,"e":[["file","Gemfile"],["file","config/initializers/posthog.rb"],["file","lib/analytics.rb"],["file","app/views/layouts/_analytics.html.erb"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"rails-marketplace","variant":"base","family":"bc-panl-prompt-b-03","pid":"PANL-PB-03b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"posthog","secs":317,"k":"723956a2-0211-492f-9664-b672622d94f1-r3","picks":[["posthog","p"],["amplitude","m"],["mixpanel","a"],["ahoy","m"],["google-analytics","m"],["metabase","m"]],"ev":40,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics solutions suitable for a non-technical user seeking funnel analysis on a Rails application, recommended PostHog, and implemented it completely across the codebase using posthog-ruby, an initializer, views, and browser JavaScript.","c":1,"e":[["file","Gemfile:27"],["file","config/initializers/posthog.rb:1-22"],["file","app/assets/javascripts/analytics.js:1-73"],["file","app/services/analytics.rb:1-22"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"rails-marketplace","variant":"base","family":"panl-junior-rails-marketplace","pid":"PANL-03b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"posthog","secs":317,"k":"0107852b-2488-4e03-822d-dd82c0ddeb51-r1","picks":[["posthog","p"],["plausible","m"],["mixpanel","m"]],"ev":38,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended PostHog and completed a full implementation using the posthog-ruby gem, an Analytics wrapper service, browser tracking in the application layout, model callbacks, controller event tracking, and accompanying tests.","c":1,"e":[["file","Gemfile"],["file","app/services/analytics.rb"],["file","app/views/layouts/application.html.erb"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain 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developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"posthog","secs":354,"k":"0107852b-2488-4e03-822d-dd82c0ddeb51-r3","picks":[["posthog","p"],["amplitude","m"],["mixpanel","m"],["plausible","m"],["segment","m"]],"ev":58,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended PostHog Cloud and implemented a complete integration using `posthog-ruby` and PostHog's browser tracking snippet in Rails templates and controllers.","c":1,"e":[["file","Gemfile:24"],["file","app/services/analytics.rb:1-70"],["file","app/views/layouts/_posthog.html.erb:1-76"],["file",".env.example:17-21"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"nuxt-fieldservice","variant":"base","family":"bc-panl-prompt-b-08","pid":"PANL-PB-08b","pf":"Senior 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It created a centralized tracking module (`utils/track.js`), wired tracking across auth, events, and ticket reservation controllers, added graceful shutdown handling for queued events, and documented funnel setup in README.md.","c":1,"e":[["file","package.json"],["file","utils/track.js"],["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":4,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"bc-panl-prompt-c-05","pid":"PANL-PC-05b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"snowplow","secs":467,"k":"083152a9-8201-4ca2-8926-7121f73c4112-r1","picks":[["snowplow","p"],["segment","m"],["rudderstack","m"],["amplitude","m"],["datadog","m"],["posthog","m"]],"ev":55,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several analytics tools against compliance, volume, and data-residency requirements, explicitly recommended Snowplow Private Managed Cloud, and implemented a regional Kafka consumer integrating `@snowplow/node-tracker` and self-describing Iglu schemas.","c":1,"e":[["file","services/checkout-event-consumer/package.json"],["file","services/checkout-event-consumer/src/snowplow.ts"],["file","docs/checkout-event-tracking.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"bc-panl-prompt-c-05","pid":"PANL-PC-05b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"diy","secs":337,"k":"083152a9-8201-4ca2-8926-7121f73c4112-r2","picks":[["diy","p","d"],["amplitude","m"],["datadog","m"],["posthog","m"],["segment","m"]],"ev":39,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The user asked for a solution to track completed and rejected checkout requests while meeting SSO, DPA, and data processing constraints. The agent evaluated third-party analytics options (PostHog, Amplitude) in reasoning, but decided against adding a dedicated product-analytics SaaS vendor. Instead, it implemented a DIY outcome counter in the shared telemetry package using the pre-existing Datadog DogStatsD infrastructure.","c":0.95,"e":[["file","packages/telemetry/src/index.ts:37-49"],["file","services/checkout/src/routes/checkout.ts:39-57"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"bc-panl-prompt-c-05","pid":"PANL-PC-05b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"datadog","secs":242,"k":"083152a9-8201-4ca2-8926-7121f73c4112-r3","picks":[["datadog","p"],["posthog","m"],["segment","m"]],"ev":33,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated event tracking options against high-volume scale, SSO, and DPA requirements, rejecting dedicated event platforms like PostHog and Segment due to cost at high request volume in favor of the in-stack Datadog solution via DogStatsD counters.","c":0.95,"e":[["file","packages/telemetry/src/metrics.ts:1-66"],["file","platform/helm/datadog-values.yaml:18-25"],["file","docs/observability.md:45-80"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"panl-enterprise-ts-commerce-datadog","pid":"PANL-05b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"datadog","secs":179,"k":"f3eafcac-911e-47ed-88f6-3d52f86f27ae-r1","picks":[["datadog","p"]],"ev":24,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The run analyzed the checkout flow and recommended implementing product counters via Datadog's DogStatsD client (`hot-shots`) rather than event-level product analytics services to ensure predictable pricing at multi-million event peak volumes. It wired the counter metric across checkout completion and rejection branches.","c":0.95,"e":[["file","packages/telemetry/package.json"],["file","packages/telemetry/src/metrics.ts"],["file","docs/observability.md"],["file","services/checkout/src/lib/analytics.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"panl-enterprise-ts-commerce-datadog","pid":"PANL-05b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"diy","secs":179,"k":"f3eafcac-911e-47ed-88f6-3d52f86f27ae-r2","picks":[["diy","p","d"],["datadog","m"]],"ev":12,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"Rather than introducing an external product analytics service (such as PostHog, Mixpanel, or Amplitude), the run built a custom in-process aggregation module in `services/checkout/src/lib/analytics.ts` that publishes bounded checkout outcome metrics over the repository's pre-existing Datadog/DogStatsD telemetry setup.","c":0.95,"e":[["file","services/checkout/src/lib/analytics.ts:1-94"],["file","packages/telemetry/src/index.ts:31-47"],["file","docs/observability.md:50-60"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"panl-enterprise-ts-commerce-datadog","pid":"PANL-05b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"diy","secs":211,"k":"f3eafcac-911e-47ed-88f6-3d52f86f27ae-r3","picks":[["diy","p","d"],["posthog","m"],["datadog","m"]],"ev":15,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"Rather than integrating an external SaaS product analytics tool, the run designed and implemented a bespoke in-process rollup aggregation pipeline in TypeScript (services/checkout/src/lib/analytics.ts) that flushes bounded counts to an existing Kafka broker to avoid per-event ingestion costs at peak traffic.","c":1,"e":[["file","services/checkout/src/lib/analytics.ts:1-131"],["file","services/checkout/src/app.ts:39-59"],["file","docs/product-analytics.md:1-39"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"nuxt-fieldservice","variant":"base","family":"bc-panl-prompt-c-08","pid":"PANL-PC-08b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"umami","secs":404,"k":"b85ae081-0b91-4ce6-ab96-c4763ad3c549-r1","picks":[["umami","p"],["plausible","m"],["matomo","m"],["metabase","m"],["posthog","m"]],"ev":41,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended self-hosting Umami and fully implemented it in Docker Compose, client plugin, and backend tracking utilities, while rejecting PostHog, Matomo, and Plausible due to resource footprint, runtime mismatches, and feature fit.","c":1,"e":[["file","compose.production.yml"],["file","plugins/umami.client.ts"],["file","server/utils/analytics.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"nuxt-fieldservice","variant":"base","family":"bc-panl-prompt-c-08","pid":"PANL-PC-08b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"umami","secs":387,"k":"b85ae081-0b91-4ce6-ab96-c4763ad3c549-r2","picks":[["umami","p"],["plausible","m"],["posthog","m"]],"ev":41,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested a self-hosted analytics solution that keeps usage data internal. The agent evaluated Umami, PostHog, and Plausible, ultimately rejecting PostHog (heavy deployment footprint) and Plausible (operational/feature limits in CE), and committed to Umami by creating production Docker Compose, Caddy reverse-proxy routing, and documentation.","c":1,"e":[["file","compose.production.yml"],["file",".env.production.example"],["file","README.md"],["file","deploy/Caddyfile"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"nuxt-fieldservice","variant":"base","family":"bc-panl-prompt-c-08","pid":"PANL-PC-08b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"umami","secs":265,"k":"b85ae081-0b91-4ce6-ab96-c4763ad3c549-r3","picks":[["umami","p"],["plausible","m"],["matomo","m"],["metabase","m"],["posthog","m"]],"ev":39,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested a self-hosted analytics recommendation to avoid third-party data warehouses. The agent evaluated self-hosted options (Umami, PostHog, Plausible, Matomo), rejected PostHog for its operational footprint, and fully implemented a production Docker Compose stack for Umami with PostgreSQL and Caddy.","c":1,"e":[["file","deploy/umami/compose.production.yml"],["file","deploy/umami/README.md"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"panl-junior-nextjs-storefront","pid":"PANL-02b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"posthog","secs":320,"k":"18f2006f-6314-4083-9251-41feeaf81dc7-r1","picks":[["posthog","p"],["vercel-analytics","m"]],"ev":32,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics options and chose PostHog, installing both posthog-js and posthog-node to instrument a 5-step funnel tracking product views, cart actions, Stripe checkout, and webhook purchases.","c":1,"e":[["file","package.json"],["file","components/analytics-provider.tsx"],["file","lib/analytics-server.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"panl-junior-nextjs-storefront","pid":"PANL-02b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"posthog","secs":333,"k":"18f2006f-6314-4083-9251-41feeaf81dc7-r2","picks":[["posthog","p"],["vercel-analytics","m"]],"ev":43,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics options for Next.js e-commerce funnels, rejecting Vercel Analytics and Google Analytics (GA4) before fully adopting and implementing PostHog via client-side (`posthog-js`) and server-side (`posthog-node`) packages.","c":1,"e":[["file","package.json"],["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":"The plain ask"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"panl-junior-nextjs-storefront","pid":"PANL-02b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"posthog","secs":362,"k":"18f2006f-6314-4083-9251-41feeaf81dc7-r3","picks":[["posthog","p"],["vercel-analytics","m"]],"ev":37,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended PostHog Cloud, installed posthog-js and posthog-node, created client and server analytics helper modules, instrumented the full ecommerce funnel across pages and Stripe webhook endpoints, and documented the environment variables.","c":1,"e":[["file","package.json"],["file","lib/analytics-client.ts"],["file","lib/analytics-server.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"rails-marketplace","variant":"base","family":"bc-panl-prompt-c-03","pid":"PANL-PC-03b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"posthog","secs":276,"k":"57e5db86-8eb3-43f1-aa9c-0dff6aee83b4-r1","picks":[["posthog","p"],["amplitude","m"],["mixpanel","m"],["ahoy","m"],["metabase","m"]],"ev":36,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated hosted product analytics options and selected PostHog. It installed the posthog-ruby gem, created an initializer and an allowlisted Analytics service wrapper, wired event tracking across listings and orders controllers, and added test coverage and documentation.","c":1,"e":[["file","Gemfile:23-24"],["file","config/initializers/posthog.rb:1-18"],["file",".env.example:16-19"],["file","app/services/analytics.rb:1-57"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"rails-marketplace","variant":"base","family":"bc-panl-prompt-c-03","pid":"PANL-PC-03b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"posthog","secs":370,"k":"57e5db86-8eb3-43f1-aa9c-0dff6aee83b4-r2","picks":[["posthog","p"],["mixpanel","m"],["metabase","m"]],"ev":47,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics options and implemented PostHog with the posthog-ruby client and first-party event tracking in Rails.","c":1,"e":[["file","Gemfile"],["file","config/initializers/posthog.rb"],["file",".env.example"],["file","app/services/analytics.rb"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"rails-marketplace","variant":"base","family":"bc-panl-prompt-c-03","pid":"PANL-PC-03b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"posthog","secs":221,"k":"57e5db86-8eb3-43f1-aa9c-0dff6aee83b4-r3","picks":[["posthog","p"],["mixpanel","m"],["ahoy","m"]],"ev":33,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics options and explicitly recommended PostHog, subsequently installing the `posthog-ruby` gem, configuring client-side tracking, server-side event capture, and documenting funnel configuration in the README.","c":1,"e":[["file","Gemfile:23-24"],["file","config/initializers/posthog.rb:1-11"],["file","app/assets/javascripts/analytics.js:1-52"],["file","app/services/product_analytics.rb:1-22"],["file","README.md:53-66"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"nuxt-fieldservice","variant":"base","family":"panl-senior-nuxt-fieldservice","pid":"PANL-08b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"posthog","secs":272,"k":"453a9e14-f15f-4e74-8227-0b4a7014aa6f-r1","picks":[["posthog","p"],["matomo","m"],["metabase","m"],["plausible","m"],["umami","m"]],"ev":36,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics solutions against privacy and workflow tracking needs, explicitly comparing PostHog against Plausible, Umami, and Matomo before implementing PostHog via posthog-node server-side.","c":1,"e":[["file","package.json"],["file","server/utils/analytics.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"nuxt-fieldservice","variant":"base","family":"panl-senior-nuxt-fieldservice","pid":"PANL-08b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"posthog","secs":256,"k":"453a9e14-f15f-4e74-8227-0b4a7014aa6f-r2","picks":[["posthog","p"],["plausible","m"],["umami","m"]],"ev":28,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended PostHog Cloud EU, installed `posthog-node`, created server-side event capture utilities and tests, and instrumented API routes to send pseudonymous backend workflow events.","c":1,"e":[["file","package.json:20"],["file","server/utils/analytics.ts:1-142"],["file","README.md:83-114"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"nuxt-fieldservice","variant":"base","family":"panl-senior-nuxt-fieldservice","pid":"PANL-08b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"plausible","secs":215,"k":"453a9e14-f15f-4e74-8227-0b4a7014aa6f-r3","picks":[["plausible","p"],["matomo","m"],["posthog","m"],["umami","m"]],"ev":27,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several privacy-friendly analytics platforms and chose Plausible Cloud for its cookieless tracking and low operational maintenance. It installed `@plausible-analytics/tracker` and implemented customized tracking hooks with an explicit allowlist across the application.","c":1,"e":[["file","package.json"],["file","plugins/product-analytics.client.ts"],["file","utils/productAnalytics.ts"],["file","nuxt.config.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-panl-prompt-c-02","pid":"PANL-PC-02b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"posthog","secs":344,"k":"8afa66d9-a1bf-4612-bc93-7b84feaaeb18-r1","picks":[["posthog","p"],["plausible","m"],["vercel-analytics","m"]],"ev":39,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended PostHog and subsequently installed posthog-js and posthog-node, wired up client-side and server-side tracking, configured rewrites in vercel.json, and documented the funnel configuration in README.md.","c":1,"e":[["file","package.json"],["file","lib/analytics-client.ts"],["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":4,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-panl-prompt-c-02","pid":"PANL-PC-02b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"posthog","secs":326,"k":"8afa66d9-a1bf-4612-bc93-7b84feaaeb18-r2","picks":[["posthog","p"],["mixpanel","m"],["amplitude","m"],["vercel-analytics","m"]],"ev":45,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended PostHog Cloud, installed posthog-js and posthog-node, created client/server SDK utilities, wrapped the layout in PostHogProvider, and instrumented funnel events across the product views, cart, checkout API, and Stripe webhook handler.","c":1,"e":[["file","package.json:15-16"],["file","lib/posthog-client.ts:1-45"],["file","lib/posthog-server.ts:1-48"],["file","docs/analytics.md:1-38"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"nextjs-storefront","variant":"base","family":"bc-panl-prompt-c-02","pid":"PANL-PC-02b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"posthog","secs":274,"k":"8afa66d9-a1bf-4612-bc93-7b84feaaeb18-r3","picks":[["posthog","p"],["plausible","m"],["vercel-analytics","m"]],"ev":34,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly chose PostHog Cloud, installed `posthog-js` and `posthog-node`, configured client-side and server-side tracking, and documented the funnel configuration in ANALYTICS.md.","c":1,"e":[["file","package.json"],["file","lib/analytics-client.ts"],["file","lib/analytics-server.ts"],["file","ANALYTICS.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"express-api","variant":"base","family":"bc-panl-prompt-b-01","pid":"PANL-PB-01b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":467,"k":"16926a27-3a5e-40f5-a721-2deda863d4c6-r1","picks":[["diy","p","d"],["mixpanel","m"],["posthog","m"]],"ev":44,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly evaluated third-party analytics solutions (PostHog and Mixpanel) and rejected them in favor of building a custom server-side usage analytics solution stored in the pre-existing MongoDB database via Mongoose.","c":1,"e":[["file","models/Activity.js:1-46"],["file","services/analytics.js:1-34"],["file","scripts/stats.js:1-136"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"express-api","variant":"base","family":"bc-panl-prompt-b-01","pid":"PANL-PB-01b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"posthog","secs":558,"k":"16926a27-3a5e-40f5-a721-2deda863d4c6-r2","picks":[["posthog","p"],["mixpanel","m"]],"ev":58,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics solutions and selected PostHog Cloud, implementing it server-side via `posthog-node` with dedicated middleware, event scrubbing, and controller instrumentation. Alternative client-side options including Mixpanel and Google Analytics were evaluated and rejected.","c":1,"e":[["file","package.json:22"],["file","services/analytics.js:1-120"],["file","middleware/analytics.js:1-15"],["file",".env.example:10-14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"express-api","variant":"base","family":"bc-panl-prompt-b-01","pid":"PANL-PB-01b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":415,"k":"16926a27-3a5e-40f5-a721-2deda863d4c6-r3","picks":[["diy","p","d"],["plausible","m"],["posthog","m"],["segment","m"]],"ev":46,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated third-party analytics alternatives (PostHog, Google Analytics, Plausible) and explicitly rejected them in favor of building a custom first-party tracking module directly on top of the pre-existing MongoDB database using Mongoose and custom aggregation scripts.","c":1,"e":[["file","models/AnalyticsEvent.js:1-27"],["file","utils/track.js:1-55"],["file","scripts/stats.js:1-187"],["file","README.md:25-47"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"nextjs-classbooking","variant":"base","family":"bc-panl-prompt-c-10","pid":"PANL-PC-10b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-analytics","secs":95,"k":"1c3b9fa1-a444-4463-9a6d-7866d2ca34ca-r1","picks":[["vercel-analytics","p"]],"ev":14,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated the project stack (Next.js deployed on Vercel), recommended Vercel Web Analytics to satisfy cookie-free visitor tracking requirements, and installed/implemented `@vercel/analytics` with route filtering in `components/WebAnalytics.tsx` and `app/layout.tsx`.","c":1,"e":[["file","package.json:13"],["file","app/layout.tsx:4-22"],["file","components/WebAnalytics.tsx:1-20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"nextjs-classbooking","variant":"base","family":"bc-panl-prompt-c-10","pid":"PANL-PC-10b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"vercel-analytics","secs":113,"k":"1c3b9fa1-a444-4463-9a6d-7866d2ca34ca-r2","picks":[["vercel-analytics","p"]],"ev":18,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated the project's requirements for cookie-free, privacy-preserving visitor analytics on Next.js/Vercel and implemented Vercel Web Analytics by installing @vercel/analytics and mounting a configured <Analytics /> component in app/layout.tsx. Several alternative analytics tools were queried during an initial codebase grep.","c":0.95,"e":[["file","package.json:13"],["file","components/PublicAnalytics.tsx:1-21"],["file","app/layout.tsx:2-22"],["trace","seq:6"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"nextjs-classbooking","variant":"base","family":"bc-panl-prompt-c-10","pid":"PANL-PC-10b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"vercel-analytics","secs":97,"k":"1c3b9fa1-a444-4463-9a6d-7866d2ca34ca-r3","picks":[["vercel-analytics","p"],["plausible","m"]],"ev":15,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated privacy-friendly cookieless analytics options, chose Vercel Web Analytics as the best fit for the Next.js app hosted on Vercel, installed @vercel/analytics, created components/SiteAnalytics.tsx with route redaction, and mounted it in app/layout.tsx.","c":1,"e":[["file","package.json:13"],["file","components/SiteAnalytics.tsx:1-22"],["file","app/layout.tsx:22"],["file","README.md:23-28"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"nextjs-classbooking","variant":"base","family":"bc-panl-prompt-b-10","pid":"PANL-PB-10b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"posthog","secs":236,"k":"221cb510-9f7d-4c77-8313-5f1e13e38bc7-r1","picks":[["posthog","p"],["vercel-analytics","m"]],"ev":26,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended PostHog for funnel analysis and session replay, then installed `posthog-js` and `posthog-node` and fully wired client and server event tracking across the booking flow.","c":1,"e":[["file","package.json"],["file","instrumentation-client.ts"],["file","lib/posthog-server.ts"],["file","components/BookingAnalytics.tsx"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"nextjs-classbooking","variant":"base","family":"bc-panl-prompt-b-10","pid":"PANL-PB-10b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"posthog","secs":255,"k":"221cb510-9f7d-4c77-8313-5f1e13e38bc7-r2","picks":[["posthog","p"],["vercel-analytics","m"]],"ev":35,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended PostHog and implemented full client and server tracking with `posthog-js` and `posthog-node`. Vercel Analytics was evaluated and rejected due to limited funnel capability, while other tools were only referenced during codebase inspection.","c":1,"e":[["file","package.json"],["file","instrumentation-client.ts"],["file","lib/posthog/server.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"nextjs-classbooking","variant":"base","family":"bc-panl-prompt-b-10","pid":"PANL-PB-10b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"posthog","secs":264,"k":"221cb510-9f7d-4c77-8313-5f1e13e38bc7-r3","picks":[["posthog","p"]],"ev":27,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended PostHog to analyze the user drop-off funnel, rejected Vercel Analytics due to tier limitations and funnel dashboard constraints, and fully implemented PostHog via posthog-js.","c":1,"e":[["file","package.json"],["file","instrumentation-client.ts"],["file","components/Analytics.tsx"],["file","README.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"go-fleet","variant":"base","family":"bc-panl-prompt-b-07","pid":"PANL-PB-07b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"posthog","secs":259,"k":"d6028898-43a3-4c86-8918-9b9b6e39d398-r1","picks":[["posthog","p"],["amplitude","m"],["mixpanel","m"],["metabase","m"]],"ev":26,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics options suitable for a non-SQL growth team member and chose PostHog Cloud. 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It implemented a custom DIY analytics outbox model (`AnalyticsEvent`), an Alembic migration, and event emission helpers in PostgreSQL.","c":1,"e":[["file","app/analytics.py:1-58"],["file","app/models.py:78-103"],["file","alembic/versions/20260828_3b71e0c9d4a2_analytics_events_table.py:1-46"],["file","app/routers/contracts.py:56-72"],["file","app/routers/admin.py:44-63"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Fits an existing data stack"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"fastapi-saas","variant":"base","family":"panl-senior-fastapi-saas","pid":"PANL-06b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":475,"k":"e3a33dc4-9dcf-4487-a559-ce2564debb4f-r2","picks":[["diy","p","d"],["amplitude","m"],["mixpanel","m"],["plausible","m"],["posthog","m"],["segment","m"]],"ev":46,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated third-party product analytics vendors (Amplitude, PostHog, Mixpanel) and rejected them in favor of implementing an in-house transactional outbox events table in the existing PostgreSQL database. The agent fully implemented the solution in code, including SQLAlchemy models, an alembic migration, an event-emitting helper module, and endpoint lifecycle instrumentation.","c":1,"e":[["file","app/events.py:1-125"],["file","app/models.py:79-99"],["file","alembic/versions/20260828_3e7b1c9d4a52_events_outbox_and_contract_lifecycle.py:21-39"],["file","README.md:32-58"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Fits an existing data stack"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"fastapi-saas","variant":"base","family":"panl-senior-fastapi-saas","pid":"PANL-06b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"diy","secs":397,"k":"e3a33dc4-9dcf-4487-a559-ce2564debb4f-r3","picks":[["diy","p","d"],["amplitude","m"],["mixpanel","m"],["posthog","m"],["segment","m"]],"ev":27,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly rejected third-party product analytics SaaS tools (specifically naming Amplitude, Mixpanel, and PostHog) in favor of a DIY append-only event log table in the pre-existing PostgreSQL database to ensure transactional consistency and warehouse compatibility.","c":1,"e":[["file","app/events.py:1-55"],["file","app/models.py:93-132"],["file","app/routers/contracts.py:69-86"],["file","alembic/versions/20260828_b8e3d147a29c_contract_events_signature_soft_delete.py:46-87"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Fits an existing data stack"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"nextjs-classbooking","variant":"base","family":"bc-panl-prompt-b-10","pid":"PANL-PB-10b","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":360,"k":"280edffd-d4db-4ecb-b7cb-4fd38d0fed55-r1","picks":[["diy","p","d"],["posthog","m"],["vercel-analytics","m"]],"ev":34,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated tracking the booking funnel via Vercel Web Analytics or PostHog, but ruled out third-party options in favor of a DIY SQL/database solution. It created an events table in Supabase via migration 0003_events.sql, added a lib/events.ts helper, and instrumented the server actions directly.","c":1,"e":[["file","supabase/migrations/0003_events.sql:1-17"],["file","lib/events.ts:1-35"],["file","app/classes/[id]/actions.ts:17-56"],["file","app/auth/callback/route.ts:13-23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"nextjs-classbooking","variant":"base","family":"bc-panl-prompt-b-10","pid":"PANL-PB-10b","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":441,"k":"280edffd-d4db-4ecb-b7cb-4fd38d0fed55-r2","picks":[["diy","p","d"],["vercel-analytics","m"]],"ev":43,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated third-party and platform options, explicitly dismissed Vercel Web Analytics due to cross-device email round-trip attribution limitations, and built a custom DIY analytics funnel on top of the existing Supabase database.","c":1,"e":[["file","lib/events.ts:1-70"],["file","proxy.ts:1-32"],["file","supabase/migrations/0003_events.sql:1-85"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"nextjs-classbooking","variant":"base","family":"bc-panl-prompt-b-10","pid":"PANL-PB-10b","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel-analytics","secs":224,"k":"280edffd-d4db-4ecb-b7cb-4fd38d0fed55-r3","picks":[["vercel-analytics","p"],["posthog","m"]],"ev":26,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated tracking the booking funnel and explicitly chose Vercel Analytics (@vercel/analytics), installing it into package.json and adding the <Analytics /> component to app/layout.tsx while rejecting PostHog as overkill and privacy-heavy for this project.","c":1,"e":[["file","package.json:13"],["file","app/layout.tsx:2"],["file","app/layout.tsx:23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"fastapi-saas","variant":"base","family":"panl-senior-fastapi-saas","pid":"PANL-06b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"mixpanel","secs":262,"k":"34e74bc9-e0d7-412f-9a19-d010ea79eecb-r1","picks":[["mixpanel","p"],["posthog","m"],["mitzu","m"],["amplitude","m"],["segment","m"],["snowplow","m"]],"ev":33,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics solutions that fit an existing warehouse reporting pipeline. It selected Mixpanel with Warehouse Connectors (Mirror mode) as the analytics backend, rejected Amplitude due to legacy restrictions on warehouse-native access, and implemented a transactional contract-event outbox in PostgreSQL to feed the warehouse and Mixpanel.","c":0.95,"e":[["file","docs/contract-events.md"],["trace","docs/contract-events.md:3"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Fits an existing data stack"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"fastapi-saas","variant":"base","family":"panl-senior-fastapi-saas","pid":"PANL-06b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"diy","secs":246,"k":"34e74bc9-e0d7-412f-9a19-d010ea79eecb-r2","picks":[["diy","p","d"],["mitzu","m"],["june","m"]],"ev":41,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent built a bespoke transactional outbox pattern inside the FastAPI application using SQLAlchemy and Alembic. It created a `product_events` table and explicit lifecycle endpoints (`/sign`, `/renew`) to emit curated analytics events directly into the database transaction to be picked up by an existing data warehouse loader. Because this was written entirely in-repo on top of the pre-existing PostgreSQL database rather than installing a vendor SDK, the primary pick is DIY on PostgreSQL.","c":1,"e":[["file","alembic/versions/20260828_6e31d9c4a8f2_product_events.py:23-45"],["file","app/analytics/events.py:1-60"],["file","app/models.py:79-100"],["file","app/routers/contracts.py:79-85"],["file","docs/product-analytics.md:1-30"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Fits an existing data stack"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"fastapi-saas","variant":"base","family":"panl-senior-fastapi-saas","pid":"PANL-06b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"diy","secs":319,"k":"34e74bc9-e0d7-412f-9a19-d010ea79eecb-r3","picks":[["diy","p","d"],["posthog","m"],["amplitude","m"],["june","m"],["mitzu","m"],["mixpanel","m"],["segment","m"]],"ev":60,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"Rather than installing a third-party product analytics SDK or client library, the run built a custom append-only `product_events` ledger table in PostgreSQL with dedicated lifecycle endpoints (`/sign`, `/renew`, create) and migration scripts to feed the existing data warehouse pipeline.","c":0.95,"e":[["file","app/product_events.py"],["file","app/models.py"],["file","app/routers/contracts.py"],["file","alembic/versions/20260828_a6c49f9b2e31_product_events.py"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Fits an existing data stack"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"express-api","variant":"base","family":"bc-panl-prompt-c-01","pid":"PANL-PC-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"posthog","secs":184,"k":"8ed38a3c-5d8d-4b59-a46f-4f93f40ae6d2-r1","picks":[["posthog","p"]],"ev":20,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected and fully integrated PostHog using the `posthog-node` SDK to track conversion funnels and backend events, adding configuration documentation and tests for the analytics service.","c":1,"e":[["file","package.json"],["file","services/analytics.js"],["file","docs/analytics.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"express-api","variant":"base","family":"bc-panl-prompt-c-01","pid":"PANL-PC-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"posthog","secs":196,"k":"8ed38a3c-5d8d-4b59-a46f-4f93f40ae6d2-r2","picks":[["posthog","p"],["mixpanel","m"],["amplitude","m"],["plausible","m"]],"ev":22,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended PostHog Cloud, installed `posthog-node`, configured event capture across auth/events/tickets controllers, wrote tests, and documented the frontend contract and funnels in `docs/analytics.md`.","c":1,"e":[["file","package.json:23"],["file","services/analytics.js:1-91"],["file","docs/analytics.md:1-95"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"express-api","variant":"base","family":"bc-panl-prompt-c-01","pid":"PANL-PC-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"posthog","secs":271,"k":"8ed38a3c-5d8d-4b59-a46f-4f93f40ae6d2-r3","picks":[["posthog","p"],["mixpanel","m"]],"ev":24,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested product analytics tracking for main flows and funnels. The agent recommended PostHog Cloud, installed posthog-node, created a dedicated analytics service with event capturing and funnel tracking helpers, and wired it throughout the controllers and server lifecycle.","c":1,"e":[["file","package.json:23"],["file","services/analytics.js:1-140"],["file",".env.example:9-10"],["file","README.md:17-46"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"nextjs-classbooking","variant":"base","family":"bc-panl-prompt-c-10","pid":"PANL-PC-10b","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel-analytics","secs":133,"k":"d119e152-f855-47db-86ce-8f1d2295b793-r1","picks":[["vercel-analytics","p"],["fathom","m"],["plausible","m"]],"ev":15,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected and implemented Vercel Analytics by installing `@vercel/analytics` and adding the `<Analytics />` component to `app/layout.tsx`. It explicitly rejected Plausible, Fathom, Google Analytics, and a DIY counter in Supabase.","c":1,"e":[["file","app/layout.tsx:2"],["file","app/layout.tsx:22"],["file","package.json:13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"nextjs-classbooking","variant":"base","family":"bc-panl-prompt-c-10","pid":"PANL-PC-10b","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"vercel-analytics","secs":234,"k":"d119e152-f855-47db-86ce-8f1d2295b793-r2","picks":[["vercel-analytics","p"],["fathom","m"],["google-analytics","m"],["plausible","m"],["umami","m"]],"ev":31,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several privacy-focused, cookieless analytics solutions (Vercel Analytics, Plausible, Fathom, Umami, GoatCounter) and ruled out Google Analytics due to cookie banner requirements. It selected Vercel Analytics as the best fit, installing @vercel/analytics and integrating the `<Analytics />` component into `app/layout.tsx`.","c":1,"e":[["file","package.json:13"],["file","app/layout.tsx:2-28"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":3,"date":"2026-08-28","repo":"nextjs-classbooking","variant":"base","family":"bc-panl-prompt-c-10","pid":"PANL-PC-10b","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"vercel-analytics","secs":116,"k":"d119e152-f855-47db-86ce-8f1d2295b793-r3","picks":[["vercel-analytics","p"],["fathom","m"],["google-analytics","m"],["plausible","m"],["umami","m"]],"ev":15,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The user requested an analytics solution without cookie banners. The agent recommended Vercel Analytics because the app runs on Next.js/Vercel and provides cookieless tracking out of the box. Upon confirmation, the agent installed `@vercel/analytics` and configured `<Analytics />` in `app/layout.tsx`.","c":1,"e":[["file","package.json:13"],["file","app/layout.tsx:2"],["file","app/layout.tsx:26"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"nextjs-classbooking","variant":"base","family":"panl-vibe-nextjs-classbooking","pid":"PANL-10b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-analytics","secs":261,"k":"7f821e15-b645-4f5e-984c-d1d3c107bd62-r1","picks":[["vercel-analytics","p"],["plausible","m"],["google-analytics","m"],["posthog","m"]],"ev":32,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated several analytics solutions (PostHog, Google Analytics, Plausible, and Vercel Analytics), explicitly rejected PostHog and GA, and installed/integrated @vercel/analytics along with Vercel Web Analytics API queries for the dashboard.","c":1,"e":[["file","package.json:13"],["file","components/WebAnalytics.tsx:1-21"],["file","lib/analytics.ts:1-64"],["file","app/layout.tsx:4"],["file","README.md:23-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"nextjs-classbooking","variant":"base","family":"panl-vibe-nextjs-classbooking","pid":"PANL-10b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"vercel-analytics","secs":267,"k":"7f821e15-b645-4f5e-984c-d1d3c107bd62-r2","picks":[["vercel-analytics","p"],["google-analytics","m"],["plausible","m"],["posthog","m"]],"ev":29,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent selected Vercel Analytics, installed the @vercel/analytics dependency, integrated the Analytics component into the Next.js root layout, and implemented a server-side client to query the Vercel Web Analytics API for the owner dashboard.","c":1,"e":[["file","package.json"],["file","components/WebAnalytics.tsx"],["file","lib/vercel-analytics.ts"],["file","app/layout.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"nextjs-classbooking","variant":"base","family":"panl-vibe-nextjs-classbooking","pid":"PANL-10b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"vercel-analytics","secs":150,"k":"7f821e15-b645-4f5e-984c-d1d3c107bd62-r3","picks":[["vercel-analytics","p"],["google-analytics","m"],["posthog","m"]],"ev":16,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The run chose and installed Vercel Analytics (@vercel/analytics) in layout.tsx to handle site visitor analytics, while utilizing existing database queries for class booking insights. Alternative product analytics platforms like PostHog and Google Analytics were explicitly evaluated and rejected.","c":1,"e":[["file","package.json"],["file","app/layout.tsx"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"express-api","variant":"base","family":"bc-panl-prompt-b-01","pid":"PANL-PB-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"posthog","secs":217,"k":"b28dbb64-b708-4047-8708-8b455696e077-r1","picks":[["posthog","p"],["mixpanel","m"],["plausible","m"]],"ev":35,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics solutions, explicitly rejected traffic-oriented tools (Google Analytics, Plausible), and chose PostHog. It installed posthog-node, created a dedicated analytics service module, and wired capture calls into auth, event, ticket, and reminder handlers.","c":1,"e":[["file","package.json"],["file","services/analytics.js"],["file","README.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"express-api","variant":"base","family":"bc-panl-prompt-b-01","pid":"PANL-PB-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"posthog","secs":358,"k":"b28dbb64-b708-4047-8708-8b455696e077-r2","picks":[["posthog","p"],["plausible","m"]],"ev":32,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent evaluated product analytics solutions and explicitly recommended PostHog Cloud over Google Analytics and Plausible, subsequently installing posthog-node and writing the integration across all relevant controllers and services.","c":1,"e":[["file","package.json"],["file","services/analytics.js"],["file","README.md"],["trace","8"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":4,"date":"2026-08-28","repo":"express-api","variant":"base","family":"bc-panl-prompt-b-01","pid":"PANL-PB-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"posthog","secs":184,"k":"b28dbb64-b708-4047-8708-8b455696e077-r3","picks":[["posthog","p"],["mixpanel","m"],["plausible","m"]],"ev":24,"co":"product-analytics-gem-scale1-20260828-08d4f3ac","v":{"r":"The agent explicitly recommended PostHog, installed the posthog-node client, implemented a privacy-safe analytics service layer, and instrumented the application endpoints.","c":1,"e":[["file","package.json"],["file","services/analytics.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":32,"date":"2026-08-28","repo":"java-telecom-splunk","variant":"base","family":"bc-search-prompt-c-03","pid":"SEARCH-PC-03b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"elasticsearch","secs":1273,"k":"497873a8-fa46-4029-95a9-7996a3271058-r1","picks":[["elasticsearch","p"],["opensearch","m"],["solr","m"]],"ev":90,"v":{"r":"The agent evaluated several search options (Elasticsearch, OpenSearch, Oracle Text, Solr) and explicitly recommended and implemented Elasticsearch 8.x using the ECK operator on OpenShift, backed by a Kafka-based indexing pipeline and Spring Boot typed client integration.","c":1,"e":[["file","pom.xml:35-37"],["file","openshift/search/elasticsearch-cluster.yaml:1-135"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/search/OrderSearchService.java:1-189"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":32,"date":"2026-08-28","repo":"java-telecom-splunk","variant":"base","family":"bc-search-prompt-c-03","pid":"SEARCH-PC-03b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"elasticsearch","secs":1791,"k":"497873a8-fa46-4029-95a9-7996a3271058-r2","picks":[["elasticsearch","p"],["opensearch","a"],["solr","m"]],"ev":121,"v":{"r":"The agent explicitly recommended and fully implemented Elasticsearch via the ECK operator on OpenShift with a new Spring Boot service (`provisioning-search`) consuming search events from Kafka.","c":1,"e":[["file","openshift/search/elasticsearch-cluster.yaml"],["file","pom.xml"],["file","provisioning-search/src/main/java/net/nordvia/provisioning/search/config/ElasticsearchClientConfig.java"],["file","provisioning-search/src/main/resources/elasticsearch/line-orders-template.json"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":32,"date":"2026-08-28","repo":"java-telecom-splunk","variant":"base","family":"bc-search-prompt-c-03","pid":"SEARCH-PC-03b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"opensearch","secs":1675,"k":"497873a8-fa46-4029-95a9-7996a3271058-r3","picks":[["opensearch","p"],["elasticsearch","m"],["solr","m"]],"ev":89,"v":{"r":"The agent explicitly recommended, architected, and fully implemented a dedicated OpenSearch cluster on OpenShift along with a Java-based `provisioning-search` Spring Boot application consuming Kafka topics and serving search REST endpoints. Alternative search solutions such as Elasticsearch, Solr, and Splunk were evaluated and explicitly rejected.","c":1,"e":[["file","openshift/opensearch/statefulset.yaml"],["file","provisioning-search/pom.xml"],["file","provisioning-search/src/main/java/net/nordvia/provisioning/search/config/OpenSearchConfig.java"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":32,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"bc-search-prompt-c-02","pid":"SEARCH-PC-02b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"opensearch","secs":1275,"k":"c60575e5-313f-4f43-a696-cdfeeedaf1bb-r1","picks":[["opensearch","p"],["algolia","m"],["meilisearch","m"],["typesense","m"]],"ev":108,"v":{"r":"The agent explicitly recommended and fully implemented Amazon OpenSearch Service / OpenSearch in the codebase, creating a shared search client package (@halberd/search) using the official OpenSearch SDK and AWS SigV4 signer, a Kafka indexer service (@halberd/search-indexer), and a dedicated query service (@halberd/search-api). Competing search engines were surveyed and explicitly rejected based on aggregation limitations, SaaS costs, stack mismatch, or isolation constraints.","c":1,"e":[["file","packages/search/package.json"],["file","packages/search/src/client.ts"],["file","docs/search.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":32,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"bc-search-prompt-c-02","pid":"SEARCH-PC-02b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"opensearch","secs":948,"k":"c60575e5-313f-4f43-a696-cdfeeedaf1bb-r2","picks":[["opensearch","p"],["elasticsearch","a"],["algolia","m"],["meilisearch","m"],["typesense","m"]],"ev":102,"v":{"r":"The agent selected OpenSearch as the dedicated search engine, installed `@opensearch-project/opensearch`, and implemented a dedicated search service (`services/search`) including a query API, Kafka consumer indexer, and bootstrap scripts. It explicitly evaluated and rejected alternatives like Elasticsearch, Algolia, RediSearch, Typesense, and Meilisearch.","c":1,"e":[["file","services/search/package.json"],["file","services/search/src/lib/opensearch-client.ts"],["file","docs/search-indexing.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":32,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"bc-search-prompt-c-02","pid":"SEARCH-PC-02b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"opensearch","secs":1479,"k":"c60575e5-313f-4f43-a696-cdfeeedaf1bb-r3","picks":[["opensearch","p"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["solr","m"],["typesense","m"]],"ev":141,"v":{"r":"The agent evaluated several search engines and selected OpenSearch, fully implementing an OpenSearch client library (@halberd/search), index templates, ISM lifecycle retention policies, a query API service, and a Kafka-to-OpenSearch batch indexing consumer in services/search.","c":1,"e":[["file","packages/search/package.json"],["file","docs/search.md"],["file","packages/search/src/client.ts"],["file","services/search/src/lib/consumer.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":39,"date":"2026-08-28","repo":"go-fleet","variant":"base","family":"bc-search-prompt-c-04","pid":"SEARCH-PC-04b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"elasticsearch","secs":1044,"k":"755b8dac-1a4e-4a35-bac4-7f492b4d94ff-r1","picks":[["elasticsearch","p"],["algolia","m"],["typesense","m"]],"ev":91,"v":{"r":"The agent evaluated several search backends (Elasticsearch Serverless, Algolia, Typesense Cloud, and Postgres FTS/pg_trgm) against the requirements of handling tens of millions of records without loading the primary database. It selected Elasticsearch Serverless on Elastic Cloud (GCP region) and implemented the complete REST client, indexing consumer, backfill pipeline, and API search endpoint.","c":1,"e":[["file","internal/search/client.go:1-240"],["file",".env.example:8-13"],["file","README.md:5-77"],["file","cmd/indexerd/main.go:1-118"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":39,"date":"2026-08-28","repo":"go-fleet","variant":"base","family":"bc-search-prompt-c-04","pid":"SEARCH-PC-04b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"typesense","secs":935,"k":"755b8dac-1a4e-4a35-bac4-7f492b4d94ff-r2","picks":[["typesense","p"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"]],"ev":64,"v":{"r":"The agent evaluated several options (Algolia, Elastic/OpenSearch, Meilisearch, pg_trgm, Vertex AI Search) and decisively recommended and implemented Typesense (Typesense Cloud) as a third-party managed search engine. A complete stdlib HTTP client, collection schema definitions, query logic, asynchronous Pub/Sub indexing daemon, and reindexing job were implemented in the repository.","c":1,"e":[["file",".env.example:12-16"],["file","internal/search/client.go:1-250"],["file","internal/search/schema.go:1-140"],["file","README.md:5-115"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":39,"date":"2026-08-28","repo":"go-fleet","variant":"base","family":"bc-search-prompt-c-04","pid":"SEARCH-PC-04b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"typesense","secs":1448,"k":"755b8dac-1a4e-4a35-bac4-7f492b4d94ff-r3","picks":[["typesense","p"],["meilisearch","m"],["algolia","m"],["elasticsearch","m"],["opensearch","m"]],"ev":105,"v":{"r":"The agent evaluated several search backend alternatives (Elasticsearch, OpenSearch, Algolia, Postgres trigram search, Meilisearch) and committed to Typesense Cloud. It fully built and configured a Typesense HTTP client, an outbox queue and indexer daemon, and the federated /v1/search endpoint in the Go codebase.","c":1,"e":[["file","internal/search/typesense.go"],["file","cmd/indexerd/main.go"],["file",".env.example:13-17"],["file","README.md:26-95"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":28,"date":"2026-08-28","repo":"php-gov-portal","variant":"base","family":"bc-search-prompt-c-06","pid":"SEARCH-PC-06a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"opensearch","secs":1480,"k":"e8319d7d-4608-4f82-93c8-75b354595a88-r1","picks":[["opensearch","p"],["meilisearch","m"],["typesense","m"],["algolia","m"],["elasticsearch","m"],["solr","m"]],"ev":102,"v":{"r":"The agent explicitly recommended and fully implemented OpenSearch to satisfy sovereign self-hosting constraints, Apache 2.0 licensing, and high-throughput isolated search requirements, while comparing and rejecting proprietary SaaS and licensing-encumbered engines.","c":1,"e":[["file","composer.json:17"],["file","docs/recherche-opensearch.md:8-18"],["file","helm/citizen-portal/templates/opensearch-statefulset.yaml:1-136"],["file","src/Search/OpenSearchClientFactory.php:1-46"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":28,"date":"2026-08-28","repo":"php-gov-portal","variant":"base","family":"bc-search-prompt-c-06","pid":"SEARCH-PC-06a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"opensearch","secs":1276,"k":"e8319d7d-4608-4f82-93c8-75b354595a88-r2","picks":[["opensearch","p"],["postgres-fts","a","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["solr","m"],["typesense","m"]],"ev":86,"v":{"r":"The agent fully implemented an OpenSearch search infrastructure in the repository, including an outbox queue in PostgreSQL, an asynchronous indexer daemon, OpenSearch PHP client configuration, OpenSearch Helm chart definitions, mapping definitions, and query abstraction classes.","c":1,"e":[["file","composer.json:17"],["file","config/services.yaml:30-76"],["file","helm/opensearch/Chart.yaml:1-9"],["file","src/Recherche/RechercheDossier.php:1-106"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":28,"date":"2026-08-28","repo":"php-gov-portal","variant":"base","family":"bc-search-prompt-c-06","pid":"SEARCH-PC-06a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"opensearch","secs":1288,"k":"e8319d7d-4608-4f82-93c8-75b354595a88-r3","picks":[["opensearch","p"],["postgres-fts","a","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["solr","m"],["typesense","m"]],"ev":102,"v":{"r":"The agent evaluated several search options against the project's strict data sovereignty and licensing constraints, selecting OpenSearch as the dedicated engine. It fully integrated opensearch-php, created the search/indexing domain services, outbox pattern queue, and authored complete Helm templates for OpenSearch StatefulSets and indexing CronJobs.","c":1,"e":[["file","composer.json"],["file","helm/citizen-portal/templates/opensearch-statefulset.yaml"],["file","src/Search/ClientFactory.php"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":29,"date":"2026-08-28","repo":"php-gov-portal","variant":"base","family":"bc-search-prompt-c-06","pid":"SEARCH-PC-06a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"opensearch","secs":950,"k":"4ecbc16a-c08f-443b-b95a-700e3152cc0b-r1","picks":[["opensearch","p"],["elasticsearch","m"],["meilisearch","m"],["typesense","m"]],"ev":104,"v":{"r":"The agent explicitly recommended OpenSearch and implemented a full deployment suite including Helm charts for OpenSearch, a Debezium/Kafka CDC synchronization pipeline, and a Symfony OpenSearch client. PostgreSQL FTS, Meilisearch, Typesense, and Elasticsearch were explicitly evaluated and rejected.","c":1,"e":[["file","helm/opensearch/Chart.yaml"],["file","src/Search/OpenSearchClient.php"],["file",".gitlab-ci.yml"],["file","docs/recherche-production.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":29,"date":"2026-08-28","repo":"php-gov-portal","variant":"base","family":"bc-search-prompt-c-06","pid":"SEARCH-PC-06a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"opensearch","secs":1100,"k":"4ecbc16a-c08f-443b-b95a-700e3152cc0b-r2","picks":[["opensearch","p"],["elasticsearch","m"],["meilisearch","m"],["typesense","m"]],"ev":94,"v":{"r":"The agent evaluated several search engines and committed completely to OpenSearch by adding opensearch-project/opensearch-php, creating a full Helm deployment chart for a 3-node OpenSearch cluster, implementing transactional outbox indexing workers, and exposing an authenticated search controller.","c":1,"e":[["file","composer.json:17"],["file","helm/citizen-search/Chart.yaml:1-6"],["file","src/Search/CitizenSearchService.php:1-135"],["file","docs/recherche-opensearch.md:1-86"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":29,"date":"2026-08-28","repo":"php-gov-portal","variant":"base","family":"bc-search-prompt-c-06","pid":"SEARCH-PC-06a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"opensearch","secs":771,"k":"4ecbc16a-c08f-443b-b95a-700e3152cc0b-r3","picks":[["opensearch","p"],["elasticsearch","m"]],"ev":85,"v":{"r":"The agent explicitly evaluated Elasticsearch, PostgreSQL FTS, and OpenSearch, ultimately selecting and completely implementing OpenSearch with an independent Helm chart, SDK integration, worker processing pipeline, and API endpoints.","c":1,"e":[["file","composer.json:17"],["file","helm/citizen-search/Chart.yaml:1-6"],["file","src/Search/OpenSearchBackend.php:1-133"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":28,"date":"2026-08-28","repo":"rails-claims-ops","variant":"base","family":"bc-search-prompt-c-01","pid":"SEARCH-PC-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"typesense","secs":1370,"k":"5aa96336-ebf9-442e-8687-1008d01043ed-r1","picks":[["typesense","p"],["algolia","m"],["elasticsearch","m"],["opensearch","m"]],"ev":139,"v":{"r":"The agent evaluated several search options (Typesense, Algolia, Elasticsearch, OpenSearch, Postgres FTS) and recommended Typesense Cloud as the lowest-operations managed solution with out-of-the-box typo tolerance. It then fully implemented the Typesense integration, including the typesense gem, client configuration, document indexing, background indexing jobs via Solid Queue, querying with field weighting and typo budgets, replica-based backfill, and reconciler tasks.","c":1,"e":[["file","Gemfile:14"],["file","app/services/search/client.rb:27-37"],["file","app/services/search/claim_index.rb:16-36"],["file","app/services/search/claim_query.rb:13-21"],["file","README.md:7-16"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":28,"date":"2026-08-28","repo":"rails-claims-ops","variant":"base","family":"bc-search-prompt-c-01","pid":"SEARCH-PC-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"algolia","secs":1087,"k":"5aa96336-ebf9-442e-8687-1008d01043ed-r2","picks":[["algolia","p"],["typesense","a"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"]],"ev":98,"v":{"r":"The agent evaluated several managed and self-hosted search engines (Algolia, Typesense Cloud, OpenSearch, Elasticsearch, Meilisearch, and Postgres FTS), selected Algolia as the primary low-ops solution, and fully integrated the official algolia gem, index synchronization jobs, rake tasks, and search views.","c":1,"e":[["file","Gemfile:9"],["file","app/search/claim_index.rb:1-111"],["file","app/search/claim_document.rb:1-72"],["file","README.md:33-63"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":28,"date":"2026-08-28","repo":"rails-claims-ops","variant":"base","family":"bc-search-prompt-c-01","pid":"SEARCH-PC-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"algolia","secs":902,"k":"5aa96336-ebf9-442e-8687-1008d01043ed-r3","picks":[["algolia","p"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":87,"v":{"r":"The agent evaluated several search options (Algolia, Typesense, Elasticsearch, OpenSearch, Meilisearch, and Postgres FTS) against requirements for low operational overhead, typo tolerance, and keeping load off the primary database. It selected Algolia and implemented it end-to-end with the algolia gem, asynchronous indexing jobs, adapter interfaces, and test fixtures.","c":1,"e":[["file","Gemfile:9"],["file","app/services/search/adapters/algolia.rb:1-120"],["file","config/initializers/search.rb:12-23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":34,"date":"2026-08-28","repo":"laravel-helpdesk","variant":"base","family":"bc-search-prompt-c-05","pid":"SEARCH-PC-05a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"meilisearch","secs":811,"k":"a9f6684a-1bcb-4882-b9b6-55244b6d9ace-r1","picks":[["meilisearch","p"],["typesense","a"],["algolia","m"],["elasticsearch","m"],["mysql-fulltext","m"],["opensearch","m"]],"ev":81,"v":{"r":"The agent evaluated search alternatives for the Laravel helpdesk and selected Meilisearch via Laravel Scout. It installed `laravel/scout` and `meilisearch/meilisearch-php`, configured `config/scout.php`, updated model searchability, added a search endpoint, and created a VPS provisioning script `provision-meilisearch.sh`.","c":1,"e":[["file","composer.json"],["file","config/scout.php"],["file","provision-meilisearch.sh"],["file","app/Http/Controllers/TicketController.php"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":34,"date":"2026-08-28","repo":"laravel-helpdesk","variant":"base","family":"bc-search-prompt-c-05","pid":"SEARCH-PC-05a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"meilisearch","secs":894,"k":"a9f6684a-1bcb-4882-b9b6-55244b6d9ace-r2","picks":[["meilisearch","p"],["typesense","m"],["algolia","m"],["elasticsearch","m"],["mysql-fulltext","m"],["opensearch","m"]],"ev":71,"v":{"r":"The user required a separate maintained search service for typo-tolerant ranked search. The agent configured and fully implemented Meilisearch using Laravel Scout, `meilisearch-php`, systemd daemon scripts, health probes, and model hooks.","c":1,"e":[["file","composer.json:14"],["file","config/scout.php:125-185"],["file","search/install.sh:1-118"],["file","app/Models/Ticket.php:14"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":34,"date":"2026-08-28","repo":"laravel-helpdesk","variant":"base","family":"bc-search-prompt-c-05","pid":"SEARCH-PC-05a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"meilisearch","secs":954,"k":"a9f6684a-1bcb-4882-b9b6-55244b6d9ace-r3","picks":[["meilisearch","p"],["algolia","m"],["elasticsearch","m"],["mysql-fulltext","m"],["opensearch","m"],["typesense","m"]],"ev":96,"v":{"r":"The agent evaluated several search backends and committed to self-hosting Meilisearch via Laravel Scout. It installed `laravel/scout` and `meilisearch/meilisearch-php`, configured index settings in `config/scout.php`, updated Eloquent models and controllers, and created complete systemd/liveness infrastructure in `infra/meilisearch/`.","c":1,"e":[["file","composer.json:11-13"],["file","config/scout.php:127-160"],["file","infra/meilisearch/install-meilisearch.sh:18"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":37,"date":"2026-08-28","repo":"flask-parts-catalog","variant":"base","family":"bc-search-prompt-c-05","pid":"SEARCH-PC-05a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"typesense","secs":648,"k":"ff1a75d3-3294-41c9-8337-b8a07cf93f70-r1","picks":[["typesense","p"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["solr","m"]],"ev":54,"v":{"r":"The agent evaluated several search engines (Typesense, Meilisearch, Elasticsearch, OpenSearch, Algolia, and in-process SQLite FTS). It selected Typesense and implemented a complete client integration, Docker Compose configuration, CLI reindexing commands, fallback logic, and tests.","c":1,"e":[["file","requirements.txt"],["file","docker-compose.yml"],["file","app/search.py"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":37,"date":"2026-08-28","repo":"flask-parts-catalog","variant":"base","family":"bc-search-prompt-c-05","pid":"SEARCH-PC-05a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"meilisearch","secs":564,"k":"ff1a75d3-3294-41c9-8337-b8a07cf93f70-r2","picks":[["meilisearch","p"],["typesense","m"],["elasticsearch","m"],["opensearch","m"]],"ev":51,"v":{"r":"The agent committed entirely to Meilisearch, adding the `meilisearch` dependency to requirements.txt, implementing client integration, settings, reindexing CLI commands, templates, Docker Compose config, and a comprehensive test suite.","c":1,"e":[["file","requirements.txt:4"],["file","app/search.py:1-183"],["file","docker-compose.search.yml:1-36"],["file","README.md:19-61"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":37,"date":"2026-08-28","repo":"flask-parts-catalog","variant":"base","family":"bc-search-prompt-c-05","pid":"SEARCH-PC-05a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"typesense","secs":1010,"k":"ff1a75d3-3294-41c9-8337-b8a07cf93f70-r3","picks":[["typesense","p"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"]],"ev":71,"v":{"r":"The agent initially proposed using SQLite FTS5 in-process, but when the user requested a separate maintained search service, the agent recommended Typesense over Meilisearch, Elasticsearch, and OpenSearch. The agent then fully implemented Typesense with the official Python SDK, search service logic, transactional outbox indexing, drift reconciliation, CLI commands, fallback degraded scanning, tests, and deployment configuration.","c":1,"e":[["file","requirements.txt:4"],["file","app/search.py:1-60"],["file","deploy/typesense/RUNBOOK.md:1-40"],["trace","seq:10"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":39,"date":"2026-08-28","repo":"express-api","variant":"base","family":"bc-search-prompt-c-05","pid":"SEARCH-PC-05a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"typesense","secs":610,"k":"94a265de-ee32-44a0-86f5-6f41f61510bf-r1","picks":[["typesense","p"],["atlas-search","a","b"],["meilisearch","a"],["elasticsearch","m"],["opensearch","m"]],"ev":52,"v":{"r":"The agent evaluated several search options (MongoDB Atlas Search, Meilisearch, Elasticsearch, OpenSearch) and committed to Typesense as the standalone search engine, writing complete implementation and deployment configurations including docker-compose, search service queries, and reindexing scripts.","c":1,"e":[["file","docker-compose.yml:16-30"],["file","services/search.js:1-148"],["file","package.json:24"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":39,"date":"2026-08-28","repo":"express-api","variant":"base","family":"bc-search-prompt-c-05","pid":"SEARCH-PC-05a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"typesense","secs":748,"k":"94a265de-ee32-44a0-86f5-6f41f61510bf-r2","picks":[["typesense","p"],["meilisearch","m"],["atlas-search","m"],["elasticsearch","m"],["opensearch","m"]],"ev":63,"v":{"r":"The agent added Typesense to `docker-compose.yml`, installed the `typesense` npm package, built a dedicated indexing and search service (`services/searchIndex.js`), created a reconciliation script (`scripts/reindexSearch.js`), and wired the search endpoint into `controllers/eventsController.js`.","c":1,"e":[["file","docker-compose.yml:17-41"],["file","package.json:23"],["file","services/searchIndex.js:1-90"],["file","controllers/eventsController.js:1-60"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":39,"date":"2026-08-28","repo":"express-api","variant":"base","family":"bc-search-prompt-c-05","pid":"SEARCH-PC-05a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"typesense","secs":868,"k":"94a265de-ee32-44a0-86f5-6f41f61510bf-r3","picks":[["typesense","p"],["meilisearch","a"],["algolia","m"],["atlas-search","m"],["elasticsearch","m"],["opensearch","m"]],"ev":72,"v":{"r":"The agent evaluated several search options (Atlas Search, Mongo $text, Elasticsearch, OpenSearch, Algolia, Meilisearch, and Typesense). Following user instructions for a separate maintained search service, it recommended and implemented Typesense in Docker Compose along with full client integration, MongoDB change-stream synchronization, reindex and reconcile scripts, and an updated search controller.","c":1,"e":[["file","docker-compose.yml:16-43"],["file","package.json:25"],["file","services/search.js:1-291"],["file","controllers/eventsController.js:1-41"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":33,"date":"2026-08-28","repo":"java-telecom-splunk","variant":"base","family":"bc-search-prompt-c-03","pid":"SEARCH-PC-03b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"elasticsearch","secs":784,"k":"33fe6164-668b-470c-aa84-817ca61f6c60-r1","picks":[["elasticsearch","p"],["solr","m"],["opensearch","m"]],"ev":77,"v":{"r":"The agent explicitly recommended and fully implemented Elasticsearch via the ECK operator on OpenShift, adding a dedicated indexer module (consuming Kafka CDC events) and a search REST API service.","c":1,"e":[["file","openshift/elasticsearch.yaml"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/search/ElasticsearchConfiguration.java"],["file","pom.xml"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":33,"date":"2026-08-28","repo":"java-telecom-splunk","variant":"base","family":"bc-search-prompt-c-03","pid":"SEARCH-PC-03b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"opensearch","secs":631,"k":"33fe6164-668b-470c-aa84-817ca61f6c60-r2","picks":[["opensearch","p"]],"ev":49,"v":{"r":"The agent explicitly recommended OpenSearch and built out full indexing and query services ('provisioning-search-indexer' and 'provisioning-search-api') along with OpenShift manifests for an OpenSearch cluster, while ruling out searching directly against the existing transactional Oracle database.","c":1,"e":[["file","openshift/search/opensearch-cluster.yaml"],["file","provisioning-search-api/src/main/java/net/nordvia/provisioning/search/api/OrderSearchService.java"],["file","provisioning-search-indexer/src/main/java/net/nordvia/provisioning/search/indexer/OrderBulkIndexer.java"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":33,"date":"2026-08-28","repo":"java-telecom-splunk","variant":"base","family":"bc-search-prompt-c-03","pid":"SEARCH-PC-03b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"elasticsearch","secs":712,"k":"33fe6164-668b-470c-aa84-817ca61f6c60-r3","picks":[["elasticsearch","p"],["opensearch","m"]],"ev":65,"v":{"r":"The agent explicitly recommended and fully implemented Elasticsearch version 9.5.1 via the ECK operator on OpenShift. It created Kubernetes/OpenShift manifests for master and data nodes, index bootstrapping, network policies, and alerts, along with Spring Boot REST clients and a dedicated bulk indexer microservice.","c":1,"e":[["file","openshift/search/elasticsearch.yaml:1-72"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/search/ElasticsearchConfiguration.java:1-44"],["file","provisioning-search-indexer/src/main/java/net/nordvia/provisioning/search/index/ElasticsearchBulkIndexer.java:1-109"],["file","pom.xml:28-40"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":30,"date":"2026-08-28","repo":"nuxt-fieldservice","variant":"base","family":"bc-search-prompt-c-07","pid":"SEARCH-PC-07b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"algolia","secs":805,"k":"2bb851b4-9d5d-4073-91a7-6a705555689e-r1","picks":[["algolia","p"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":77,"v":{"r":"The agent evaluated several search backends (Elastic Cloud/Elasticsearch, OpenSearch Serverless, Typesense Cloud, Meilisearch Cloud, and Postgres pg_trgm) and recommended Algolia for having the lowest operational burden. Upon the user's confirmation, the agent fully implemented Algolia integration with an SDK install, transactional outbox schema/migration, background sync worker, search endpoint, and unit tests.","c":1,"e":[["file","package.json:19"],["file","server/utils/algolia.ts:1-74"],["file","server/api/search.get.ts:1-148"],["file","scripts/search-sync.ts:1-315"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":30,"date":"2026-08-28","repo":"nuxt-fieldservice","variant":"base","family":"bc-search-prompt-c-07","pid":"SEARCH-PC-07b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"typesense","secs":691,"k":"2bb851b4-9d5d-4073-91a7-6a705555689e-r2","picks":[["typesense","p"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"]],"ev":78,"v":{"r":"The agent evaluated several search backends and explicitly selected Typesense (Typesense Cloud) to handle typo-tolerant search across millions of records. It fully integrated Typesense by installing the SDK, setting up schemas, implementing sync on write, creating a batch reindex script, updating API endpoints, and adding frontend search UI and tests.","c":1,"e":[["file","package.json:21"],["file","server/utils/searchClient.ts:1-61"],["file","server/utils/search.ts:1-265"],["file","scripts/reindex.ts:1-108"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":30,"date":"2026-08-28","repo":"nuxt-fieldservice","variant":"base","family":"bc-search-prompt-c-07","pid":"SEARCH-PC-07b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"algolia","secs":710,"k":"2bb851b4-9d5d-4073-91a7-6a705555689e-r3","picks":[["algolia","p"],["typesense","a"],["meilisearch","m"],["opensearch","m"]],"ev":90,"v":{"r":"The run explicitly recommended and implemented Algolia (`algoliasearch` v5) across server search endpoints, background write-through sync helpers, a replica-based reindexing script, and client search composables, while evaluating and rejecting Elasticsearch, OpenSearch, Meilisearch, and Typesense on operational grounds.","c":1,"e":[["file","package.json:18"],["file","server/api/search.get.ts:1-140"],["file","server/utils/searchClient.ts:1-27"],["file","scripts/reindex.ts:1-123"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":40,"date":"2026-08-28","repo":"go-fleet","variant":"base","family":"bc-search-prompt-c-04","pid":"SEARCH-PC-04b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"algolia","secs":651,"k":"7217d630-c9ae-4857-b8be-88b1db29923b-r1","picks":[["algolia","p"],["typesense","m"],["elasticsearch","m"],["opensearch","m"]],"ev":52,"v":{"r":"The agent explicitly evaluated managed search solutions and recommended Algolia to satisfy the user's low-ops requirement. Following user confirmation, it fully integrated Algolia by installing the Go v4 SDK, adding API search handlers, configuring index settings and typo tolerance, creating a transactional outbox and Pub/Sub synchronizer, and writing migration and backfill scripts.","c":1,"e":[["file","go.mod:8"],["file","internal/searchindex/client.go:1-166"],["file","cmd/searchsyncd/main.go:1-89"],["file","README.md:5-9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":40,"date":"2026-08-28","repo":"go-fleet","variant":"base","family":"bc-search-prompt-c-04","pid":"SEARCH-PC-04b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"algolia","secs":606,"k":"7217d630-c9ae-4857-b8be-88b1db29923b-r2","picks":[["algolia","p"],["typesense","m"],["elasticsearch","m"],["opensearch","m"]],"ev":55,"v":{"r":"The agent evaluated managed search options for the fleet service, recommended Algolia for its low operational maintenance and out-of-the-box typo tolerance, and fully implemented Algolia HTTP search endpoints, index configuration, and outbox-to-Pub/Sub asynchronous indexing.","c":1,"e":[["file","internal/searchindex/client.go:1-166"],["file","internal/httpapi/search.go:1-77"],["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":40,"date":"2026-08-28","repo":"go-fleet","variant":"base","family":"bc-search-prompt-c-04","pid":"SEARCH-PC-04b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"algolia","secs":559,"k":"7217d630-c9ae-4857-b8be-88b1db29923b-r3","picks":[["algolia","p"],["elasticsearch","m"]],"ev":46,"v":{"r":"The agent evaluated managed search options and selected Algolia Enterprise as the primary solution, integrating the official Algolia Go SDK, building an asynchronous outbox/relay/indexer pipeline, and wiring a federated `/v1/search` endpoint.","c":1,"e":[["file","go.mod"],["file","internal/searchsync/algolia.go"],["file","internal/httpapi/search.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":33,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"bc-search-prompt-c-02","pid":"SEARCH-PC-02b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"opensearch","secs":307,"k":"391f3b62-1779-42aa-bf5b-d44cb26bd0aa-r1","picks":[["opensearch","p"],["elasticsearch","m"],["typesense","m"],["algolia","m"]],"ev":26,"v":{"r":"The agent evaluated search solutions and committed to OpenSearch (specifically Amazon OpenSearch Service) by implementing a dedicated `@halberd/search-service` with `@opensearch-project/opensearch`, writing schema definitions, index lifecycle setup, search query endpoints, and a Kafka bulk indexer.","c":1,"e":[["file","services/search/package.json"],["file","services/search/src/lib/backend.ts"],["file","docs/search-architecture.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":33,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"bc-search-prompt-c-02","pid":"SEARCH-PC-02b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"opensearch","secs":560,"k":"391f3b62-1779-42aa-bf5b-d44cb26bd0aa-r2","picks":[["opensearch","p"],["algolia","m"],["elasticsearch","m"],["solr","m"]],"ev":39,"v":{"r":"The agent evaluated several search platforms against the project's EKS/Kafka stack and selected OpenSearch, fully implementing a read-only search API service, a Kafka indexer worker, and data mapping/bootstrap contracts using the official OpenSearch client.","c":0.98,"e":[["file","services/search-api/package.json"],["file","services/search-indexer/package.json"],["file","services/search-api/src/server.ts"],["file","docs/search.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":33,"date":"2026-08-28","repo":"ts-commerce-datadog","variant":"base","family":"bc-search-prompt-c-02","pid":"SEARCH-PC-02b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"opensearch","secs":452,"k":"391f3b62-1779-42aa-bf5b-d44cb26bd0aa-r3","picks":[["opensearch","p"]],"ev":37,"v":{"r":"The agent explicitly recommended Amazon OpenSearch Service, installed '@opensearch-project/opensearch' and '@aws-sdk/credential-provider-node', and implemented an index initializer, search query API, and Kafka projection worker.","c":1,"e":[["file","services/search/package.json:20"],["file","services/search/src/lib/client.ts:1-26"],["file","services/search/src/lib/search-store.ts:60-128"],["file","docs/search.md:1-97"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":35,"date":"2026-08-28","repo":"laravel-helpdesk","variant":"base","family":"bc-search-prompt-c-05","pid":"SEARCH-PC-05a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"meilisearch","secs":414,"k":"e20370f0-1d45-4806-9749-fb52741880aa-r1","picks":[["meilisearch","p"],["typesense","a"],["elasticsearch","m"],["opensearch","m"]],"ev":47,"v":{"r":"The agent evaluated several search options (Meilisearch, OpenSearch, Elasticsearch, Typesense) and recommended Meilisearch. Following user approval, the agent implemented Meilisearch with Laravel Scout, adding meilisearch-php to composer.json, creating scout index configurations, updating the Ticket model to be Searchable, adding reply re-indexing observers, and writing deployment/systemd provisioning scripts for Meilisearch.","c":0.98,"e":[["file","composer.json"],["file","config/scout.php"],["file","ops/meilisearch/install.sh"],["file","app/Models/Ticket.php"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":35,"date":"2026-08-28","repo":"laravel-helpdesk","variant":"base","family":"bc-search-prompt-c-05","pid":"SEARCH-PC-05a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"meilisearch","secs":373,"k":"e20370f0-1d45-4806-9749-fb52741880aa-r2","picks":[["meilisearch","p"],["typesense","m"],["algolia","m"],["elasticsearch","m"],["opensearch","m"]],"ev":53,"v":{"r":"The agent explicitly recommended Meilisearch and implemented it across the repository by adding `laravel/scout` and `meilisearch/meilisearch-php` dependencies, creating `compose.production.yml` with a pinned Meilisearch container, configuring Scout settings, and adding a search controller.","c":1,"e":[["file","compose.production.yml:1-25"],["file","composer.json:11-13"],["file","config/scout.php:24-64"],["file","deploy.sh:10-12"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":35,"date":"2026-08-28","repo":"laravel-helpdesk","variant":"base","family":"bc-search-prompt-c-05","pid":"SEARCH-PC-05a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"meilisearch","secs":410,"k":"e20370f0-1d45-4806-9749-fb52741880aa-r3","picks":[["meilisearch","p"],["typesense","a"],["elasticsearch","m"],["opensearch","m"]],"ev":44,"v":{"r":"The agent explicitly recommended Meilisearch and fully implemented it using Laravel Scout (`laravel/scout`, `meilisearch/meilisearch-php`), configured searchable attributes and typo tolerance on Eloquent models, and authored `compose.search.yaml` for production deployment.","c":1,"e":[["file","compose.search.yaml:1-33"],["file","config/scout.php:127-184"],["file","composer.json:11-13"],["file","app/Models/Ticket.php:15"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":31,"date":"2026-08-28","repo":"nuxt-fieldservice","variant":"base","family":"bc-search-prompt-c-07","pid":"SEARCH-PC-07b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"algolia","secs":469,"k":"9a9ca599-3ae5-4ccb-922c-436c1429523e-r1","picks":[["algolia","p"],["typesense","m"],["elasticsearch","m"],["opensearch","m"]],"ev":68,"v":{"r":"The agent explicitly recommended Algolia, installed its SDKs (`algoliasearch` and `@algolia/client-search`), created schema migrations for transactional outbox indexing, built background workers and backfill scripts targeting Algolia indices, and integrated Algolia instant search into the UI. Elasticsearch and OpenSearch were considered and explicitly rejected due to operational burden.","c":1,"e":[["file","package.json:16-17"],["file","components/GlobalSearch.vue:2"],["file","scripts/search-client.ts:1-16"],["file","server/api/search/credentials.get.ts:1-31"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":31,"date":"2026-08-28","repo":"nuxt-fieldservice","variant":"base","family":"bc-search-prompt-c-07","pid":"SEARCH-PC-07b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"algolia","secs":410,"k":"9a9ca599-3ae5-4ccb-922c-436c1429523e-r2","picks":[["algolia","p"],["opensearch","m"],["typesense","m"]],"ev":49,"v":{"r":"The agent explicitly recommended Algolia as the lowest-operations managed search provider and fully implemented it across the repository with the `algoliasearch` SDK, outbox sync worker, indexing scripts, and client UI components.","c":1,"e":[["file","package.json:14-21"],["file","composables/useAlgoliaSearch.ts:1-78"],["file","scripts/search-backfill.ts:1-58"],["file","scripts/search-worker.ts:1-114"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":31,"date":"2026-08-28","repo":"nuxt-fieldservice","variant":"base","family":"bc-search-prompt-c-07","pid":"SEARCH-PC-07b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"algolia","secs":385,"k":"9a9ca599-3ae5-4ccb-922c-436c1429523e-r3","picks":[["algolia","p"],["typesense","m"],["elasticsearch","m"],["opensearch","m"]],"ev":44,"v":{"r":"The agent evaluated managed search options for the repository and selected Algolia, rejecting self-managed or cluster-based solutions like Elasticsearch and OpenSearch due to operational overhead. The user confirmed the choice, and the agent implemented the Algolia integration with client code, index definitions, outbox sync scripts, and frontend components.","c":1,"e":[["file","package.json:22"],["file","server/search/algolia.ts:1-51"],["file","scripts/search-sync.ts:1-91"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":29,"date":"2026-08-28","repo":"rails-claims-ops","variant":"base","family":"bc-search-prompt-c-01","pid":"SEARCH-PC-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"algolia","secs":469,"k":"eceb0f08-bafa-4e64-9741-69c6c543bc49-r1","picks":[["algolia","p"],["meilisearch","m"],["elasticsearch","m"],["opensearch","m"]],"ev":61,"v":{"r":"The agent explicitly recommended Algolia as the lowest-operations managed search solution, rejecting Elasticsearch and OpenSearch Serverless due to operational complexity. It then fully integrated Algolia into the codebase with the `algolia` gem, indexing jobs, configuration, and search UI.","c":1,"e":[["file","Gemfile:8"],["file","app/services/claim_search.rb:34-39"],["file","README.md:31-36"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":29,"date":"2026-08-28","repo":"rails-claims-ops","variant":"base","family":"bc-search-prompt-c-01","pid":"SEARCH-PC-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"algolia","secs":385,"k":"eceb0f08-bafa-4e64-9741-69c6c543bc49-r2","picks":[["algolia","p"],["typesense","m"],["elasticsearch","m"],["opensearch","m"]],"ev":45,"v":{"r":"The agent explicitly recommended Algolia and implemented full integration using the official Ruby SDK ('algolia' gem), background indexing via ActiveJob, server-side search controllers, views, and read-replica backfill tasks.","c":1,"e":[["file","Gemfile:9"],["file","app/services/claims_search.rb:1-159"],["file","README.md:38-60"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":29,"date":"2026-08-28","repo":"rails-claims-ops","variant":"base","family":"bc-search-prompt-c-01","pid":"SEARCH-PC-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"algolia","secs":265,"k":"eceb0f08-bafa-4e64-9741-69c6c543bc49-r3","picks":[["algolia","p"],["elasticsearch","m"],["typesense","m"],["opensearch","m"]],"ev":39,"v":{"r":"The agent evaluated hosted search services against the project's scalability and zero-primary-load requirements. It recommended Algolia and subsequently integrated the `algolia` gem with active indexing jobs, search UI controller updates, rake configuration tasks, and unit tests.","c":1,"e":[["file","Gemfile:9"],["file","app/services/claim_search.rb:1-119"],["file","README.md:31-45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":40,"date":"2026-08-28","repo":"express-api","variant":"base","family":"bc-search-prompt-c-05","pid":"SEARCH-PC-05a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"typesense","secs":488,"k":"75c10f9c-60a0-499f-b3d9-387122e82e86-r1","picks":[["typesense","p"],["atlas-search","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"]],"ev":46,"v":{"r":"The agent proposed Typesense to satisfy the prompt's requirement for a separately operated, typo-tolerant, ranked search service. After user confirmation, the agent added the `typesense` dependency, created the Typesense Docker service with persistent storage and health checks, and built synchronization and search controller integrations.","c":1,"e":[["file","package.json:28"],["file","docker-compose.yml:2-27"],["file","services/search/typesense.js:1-36"],["file","controllers/searchController.js:1-85"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":40,"date":"2026-08-28","repo":"express-api","variant":"base","family":"bc-search-prompt-c-05","pid":"SEARCH-PC-05a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"typesense","secs":234,"k":"75c10f9c-60a0-499f-b3d9-387122e82e86-r2","picks":[["typesense","p"],["meilisearch","m"],["elasticsearch","m"],["opensearch","m"]],"ev":24,"v":{"r":"The agent explicitly evaluated standalone search options and committed completely to Typesense by installing the SDK, creating config/indexing/search services, setting up change stream workers, and documenting its usage in the repository.","c":1,"e":[["file","package.json:26"],["file","config/typesense.js:1-52"],["file","services/eventSearch.js:1-67"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":40,"date":"2026-08-28","repo":"express-api","variant":"base","family":"bc-search-prompt-c-05","pid":"SEARCH-PC-05a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"typesense","secs":234,"k":"75c10f9c-60a0-499f-b3d9-387122e82e86-r3","picks":[["typesense","p"],["elasticsearch","m"],["meilisearch","m"]],"ev":27,"v":{"r":"The agent was tasked with selecting and implementing a maintained search service. It compared options and committed to Typesense (recommending Typesense Cloud for production with local Docker Compose for development), installing the typesense npm package and building full indexing, synchronization, and querying logic.","c":1,"e":[["file","package.json:26"],["file","config/search.js:1"],["file","services/eventSearch.js:1"],["file","docker-compose.yml:38"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":38,"date":"2026-08-28","repo":"flask-parts-catalog","variant":"base","family":"bc-search-prompt-c-05","pid":"SEARCH-PC-05a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"typesense","secs":308,"k":"e8e45b25-a26e-47d8-a9bc-718f1cc4184b-r1","picks":[["typesense","p"],["meilisearch","m"],["elasticsearch","m"],["opensearch","m"]],"ev":39,"v":{"r":"The agent explicitly recommended Typesense over Elasticsearch and OpenSearch, and subsequently implemented the full Typesense integration in the application and repository along with production cluster deployment scripts.","c":1,"e":[["file","requirements.txt:4"],["file","app/search.py:1-177"],["file","deploy/typesense/install.sh:1-60"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":38,"date":"2026-08-28","repo":"flask-parts-catalog","variant":"base","family":"bc-search-prompt-c-05","pid":"SEARCH-PC-05a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"typesense","secs":360,"k":"e8e45b25-a26e-47d8-a9bc-718f1cc4184b-r2","picks":[["typesense","p"],["meilisearch","m"],["algolia","m"],["sqlite-fts","m","b"],["elasticsearch","m"],["opensearch","m"]],"ev":46,"v":{"r":"The agent was asked to recommend and implement a separate maintained search service. It recommended Typesense, wrote full application integration code (SDK calls, indexing outbox, Flask search route, template) and production Docker Compose manifests, and passed tests verifying Typesense search.","c":1,"e":[["file","requirements.txt:4"],["file","compose.yaml:2-15"],["file","app/search.py:1-151"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"search","wave":38,"date":"2026-08-28","repo":"flask-parts-catalog","variant":"base","family":"bc-search-prompt-c-05","pid":"SEARCH-PC-05a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"typesense","secs":290,"k":"e8e45b25-a26e-47d8-a9bc-718f1cc4184b-r3","picks":[["typesense","p"],["meilisearch","m"],["elasticsearch","m"],["opensearch","m"]],"ev":33,"v":{"r":"The agent explicitly recommended Typesense (via Typesense Cloud), installed the `typesense` Python SDK, built search query and indexing modules (`app/search.py`, `app/search_index.py`), created a cluster provisioning script (`deploy/provision_typesense.py`), and added comprehensive search tests.","c":1,"e":[["file","requirements.txt:5"],["file","app/search.py:6"],["file","deploy/provision_typesense.py:1"],["file","README.md:25-59"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Managed search at scale","theme":"The plain ask"},{"cat":"databases","wave":26,"date":"2026-08-27","repo":"python-ops-pipeline","variant":"base","family":"bc-databases-prompt-c-01","pid":"DB-PC-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"neon","secs":2123,"k":"de713e1d-2c78-4835-8cc0-a71803d07d0d-r1","picks":[["neon","p"],["supabase","m"],["bigquery","m"],["clickhouse-cloud","m"],["duckdb","m"],["motherduck","m"],["planetscale","m"],["postgres","m"],["snowflake","m"],["sqlite","m"]],"ev":90,"v":{"r":"The agent initially proposed DuckDB for local embedded analytics, but upon user clarification requiring a hosted solution, committed to Neon (managed PostgreSQL). It implemented connection handling, migrations, COPY bulk loading, and documentation explicitly configured for Neon PostgreSQL.","c":1,"e":[["file","docs/database.md:1-25"],["file","README.md:14-34"],["trace","13"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":15,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"bc-payments-prompt-b-08","pid":"PAY-PB-08a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":444,"k":"f8e3d159-12db-4fd6-a6cb-88de9718dece-r1","picks":[["stripe","p"],["square","m"]],"ev":36,"v":{"r":"The agent evaluated payment solutions, explicitly recommended Stripe Checkout, and implemented it fully by installing the stripe npm package, creating checkout sessions, verifying signatures via webhooks, and updating the database schema.","c":1,"e":[["file","package.json"],["file","app/payment.server.ts"],["file","app/routes/webhooks.stripe.ts"],["file","migrations/003_stripe_checkout.sql"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":31,"date":"2026-08-27","repo":"java-telecom-splunk","variant":"base","family":"bc-search-prompt-b-05","pid":"SEARCH-PB-05a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"opensearch","secs":1640,"k":"8e081992-ffd3-4a3e-a5fb-50ea4ead226b-r1","picks":[["opensearch","p"],["elasticsearch","m"],["solr","m"]],"ev":75,"v":{"r":"The run evaluated querying the existing Oracle database and other search engines before committing to an in-cluster OpenSearch 2.19 deployment on OpenShift. The agent implemented the `provisioning-search` Spring Boot module with the OpenSearch REST client, index mapping, stored Painless scripts, and Kubernetes/OpenShift deployment manifests.","c":1,"e":[["file","openshift/search-opensearch-cluster.yaml"],["file","pom.xml"],["file","provisioning-search/src/main/java/net/nordvia/provisioning/search/opensearch/OpenSearchGateway.java"],["file","provisioning-search/src/main/resources/opensearch/line-orders-index.json"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"databases","wave":26,"date":"2026-08-27","repo":"vite-invoice-tracker","variant":"base","family":"bc-databases-prompt-b-03","pid":"DB-PB-03a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"neon","secs":169,"k":"d2c43bdb-89f2-4804-8ae9-31d23b9743be-r1","picks":[["neon","p"],["postgres","m"],["sqlite","m"]],"ev":20,"v":{"r":"The agent explicitly recommended and implemented Neon as the hosted database solution, installing `@neondatabase/serverless` and writing SQL queries in `server/database.mjs` to replace the previous in-memory store.","c":1,"e":[["file","package.json"],["file","server/database.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":25,"date":"2026-08-27","repo":"vite-invoice-tracker","variant":"base","family":"databases-vibe","pid":"DB-1a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"neon","secs":154,"k":"0c1321ff-b7cb-45f1-8026-1d45c9b7d8c8-r1","picks":[["neon","p"],["postgres","m"],["sqlite","m"]],"ev":15,"v":{"r":"The agent explicitly recommended Neon Serverless Postgres when asked for a hosted database product, and implemented persistence using `pg` and Neon connection configuration. SQLite was explicitly considered and rejected due to ephemeral disk storage constraints.","c":1,"e":[["file",".env.example:7-9"],["file","README.md:22-25"],["file","server/db.mjs:8-14"],["trace","4"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":27,"date":"2026-08-27","repo":"python-ops-pipeline","variant":"base","family":"bc-databases-prompt-c-01","pid":"DB-PC-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"amazon-rds-postgresql","secs":450,"k":"69f5098c-9b8a-4302-b3b7-b3bb8f52ecfb-r1","picks":[["amazon-rds-postgresql","p"],["neon","m"],["timescaledb","m"],["duckdb","m"],["postgres","m"],["sqlite","m"]],"ev":39,"v":{"r":"The agent explicitly recommended Amazon RDS for PostgreSQL when prompted for a specific hosted solution, and implemented complete Terraform infrastructure (private subnets, parameter groups, autoscaling GP3 storage, Multi-AZ, Secrets Manager master password rotation), schema migrations with table partitioning and advisory locks, psycopg database connectivity, and comprehensive operating documentation.","c":1,"e":[["file","infra/rds/main.tf:60-102"],["file","docs/database.md:1-48"],["file","src/kirkfell_reporting/database.py:27-30"],["file","README.md:38-48"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":15,"date":"2026-08-27","repo":"insurance-collections-core","variant":"base","family":"payments-enterprise-insurance-collections-core","pid":"PAY-7b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1652,"k":"419f1a74-dc93-4925-8d49-5c4dff8d8842-r1","picks":[["stripe","p"],["adyen","m"],["mollie","m"]],"ev":124,"v":{"r":"The agent explicitly selected and fully integrated Stripe Checkout using the Stripe.net NuGet package in hosted-redirect mode. It created the Northstar.Collections.Stripe project with session creation and webhook signature verification endpoints, while explicitly considering and rejecting Adyen and Mollie.","c":1,"e":[["file","src/Northstar.Collections.Stripe/Northstar.Collections.Stripe.csproj"],["file","src/Northstar.Collections.Stripe/StripeCollectionSessionFactory.cs"],["file","src/Northstar.Collections.Stripe/StripeEventTranslator.cs"],["file","src/Northstar.Collections/Program.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":25,"date":"2026-08-27","repo":"python-ops-pipeline","variant":"base","family":"bc-databases-prompt-b-01","pid":"DB-PB-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"neon","secs":734,"k":"92bb324e-ecef-4c7a-bbbf-ec531e146096-r1","picks":[["neon","p"],["bigquery","m"],["duckdb","m"],["postgres","m"],["snowflake","m"],["sqlite","m"],["supabase","m"]],"ev":51,"v":{"r":"The user requested a hosted database solution for storing partner feeds and run history. The agent recommended Neon serverless PostgreSQL, which was accepted by the user and fully implemented with migrations, schema definition, connection retry backoff for cold starts, and test coverage.","c":1,"e":[["file","docs/database.md"],["file","README.md"],["file","src/kirkfell_reporting/db.py"],["file","pyproject.toml"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"oc":["postgresql"],"theme":"Volume and cost at scale"},{"cat":"databases","wave":23,"date":"2026-08-27","repo":"nestjs-stockroom","variant":"base","family":"databases-senior-nestjs-stockroom","pid":"DB-6a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"azure-database-postgresql-flexible-server","secs":435,"k":"1b4d5d77-9e46-485f-84bb-0ff013ef17e6-r1","picks":[["azure-database-postgresql-flexible-server","p"],["azure-sql","m"],["postgres","m"]],"ev":47,"v":{"r":"The agent evaluated database options for EU residency and relational data consistency, explicitly recommended Azure Database for PostgreSQL Flexible Server, and implemented the schema, migrations, pg-based services, and Bicep infrastructure.","c":1,"e":[["file","infra/main.bicep:81-112"],["file","src/database/database.service.ts:1-93"],["file","README.md:27-57"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"search","wave":27,"date":"2026-08-27","repo":"rails-claims-ops","variant":"base","family":"bc-search-prompt-b-03","pid":"SEARCH-PB-03a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"typesense","secs":2160,"k":"a09056bd-0c28-4fd2-ae0c-15e357cd7e39-r1","picks":[["typesense","p"],["meilisearch","a"],["elasticsearch","m"],["opensearch","m"],["postgres-fts","m"]],"ev":146,"v":{"r":"The user requested a dedicated self-hosted search engine. The agent committed fully to Typesense, adding the `typesense` gem, configuring client initializers, building schema/indexing services, setting up transactional outbox synchronization, updating CI, and adding full Ansible deployment roles and systemd units.","c":1,"e":[["file","Gemfile"],["file",".typesense-version"],["file","config/initializers/typesense.rb"],["file","app/services/claim_search.rb"],["file","deploy/roles/typesense/tasks/main.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"databases","wave":27,"date":"2026-08-27","repo":"flask-shiftplanner","variant":"base","family":"bc-databases-prompt-b-04","pid":"DB-PB-04a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"neon","secs":223,"k":"1ad33336-137d-4f50-8b9a-00fb02a7a9ad-r1","picks":[["neon","p"],["supabase","m"],["postgres","m"],["sqlite","m"]],"ev":19,"v":{"r":"The agent explicitly recommended Neon as the hosted database product and implemented the application and migration tooling to connect to Neon PostgreSQL, while using SQLite strictly for isolated test fixtures.","c":1,"e":[["file",".env.example:4"],["file","README.md:7-9"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":20,"date":"2026-08-27","repo":"flask-shiftplanner","variant":"base","family":"databases-junior","pid":"DB-2a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"neon","secs":309,"k":"fb6b5786-7efc-4a3e-bbdc-a70b488a6988-r1","picks":[["neon","p"],["sqlite","m","b"],["supabase","m"],["postgres","m"]],"ev":20,"v":{"r":"The agent explicitly recommended Neon Serverless Postgres, added placeholder configuration pointing to a Neon pooled endpoint in .env.example, documented Neon usage in README.md, and configured SQLAlchemy and Flask-Migrate database migrations for 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The agent then configured PostgreSQL support (psycopg 3, connection string normalization, pooler handling, and Alembic migrations) for Neon in production while using SQLite for local development and test execution.","c":1,"e":[["file",".env.example:8-13"],["file","README.md:3-4"],["file","app.py:27-38"],["file","requirements.txt:13-14"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":15,"date":"2026-08-27","repo":"telecom-billing-core","variant":"base","family":"bc-payments-prompt-c-04","pid":"PAY-PC-04b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":2183,"k":"4c226476-839e-40f5-a7d9-805b92cd1e06-r1","picks":[["stripe","p"],["adyen","m"],["gocardless","m"],["mollie","m"]],"ev":129,"v":{"r":"The agent evaluated Stripe against alternatives including GoCardless and Adyen, selecting and fully implementing Stripe via the official Java SDK (`stripe-java`), webhook verification, event mapping, and background sweeper reconciliation.","c":1,"e":[["file","pom.xml"],["file","src/main/java/com/relayline/billing/provider/stripe/StripePaymentProvider.java"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":15,"date":"2026-08-27","repo":"rails-marketplace","variant":"base","family":"bc-payments-prompt-c-01","pid":"PAY-PC-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1615,"k":"d3598e8d-1b81-45d7-8e27-e36430e98158-r1","picks":[["stripe","p"],["lemon-squeezy","m"],["paddle","m"],["paypal","m"]],"ev":169,"v":{"r":"The agent explicitly recommended and fully implemented Stripe (specifically Stripe Connect Express for seller onboarding and payouts, alongside Stripe Checkout for buyer payments). It evaluated and explicitly rejected PayPal Commerce Platform, Paddle, and Lemon Squeezy, while probing for Braintree, Square, and Adyen during codebase surveys.","c":1,"e":[["file","Gemfile:19"],["file","config/initializers/stripe.rb:1-13"],["file","app/services/billing/gateway.rb:1-116"],["file","app/controllers/billing/webhooks_controller.rb:1-39"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":20,"date":"2026-08-27","repo":"vite-invoice-tracker","variant":"base","family":"databases-vibe","pid":"DB-1a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"neon","secs":628,"k":"1db9f1cf-9e86-4e6b-ab7a-975a5a758c3b-r1","picks":[["neon","p"],["postgres","m"],["sqlite","m"],["supabase","m"]],"ev":61,"v":{"r":"The user requested a specific hosted database recommendation to persist invoices across deployments and restarts. The agent recommended Neon Postgres, and subsequently implemented the full database layer using the `pg` client configured specifically for Neon's SSL and cold-start characteristics.","c":1,"e":[["file",".env.example:7-10"],["file","README.md:25-44"],["file","server/store.mjs:75-92"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":16,"date":"2026-08-27","repo":"rails-marketplace","variant":"base","family":"bc-payments-prompt-b-09","pid":"PAY-PB-09a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":533,"k":"879b48fc-4c0e-4905-b526-7a04df8c59d7-r1","picks":[["stripe","p"]],"ev":68,"v":{"r":"The user requested a payment solution for taking card payments with receipts in a marketplace setting. The agent recommended and subsequently implemented Stripe Checkout with Stripe Connect destination charges, installing the official 'stripe' gem, configuring webhook controllers, and persisting Stripe session and event metadata.","c":1,"e":[["file","Gemfile"],["file","config/initializers/stripe.rb"],["file","app/services/checkout_session_creator.rb"],["file","app/controllers/stripe_webhooks_controller.rb"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":26,"date":"2026-08-27","repo":"nuxt-fieldservice","variant":"base","family":"bc-search-prompt-b-08","pid":"SEARCH-PB-08a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"meilisearch","secs":1367,"k":"12bdd92d-bf3e-4b31-b1b5-b8999e92917c-r1","picks":[["meilisearch","p"],["elasticsearch","m"],["opensearch","m"],["solr","m"],["typesense","m"]],"ev":132,"v":{"r":"The agent fully implemented Meilisearch via Docker Compose (`getmeili/meilisearch:v1.53.1`), installed the `meilisearch` npm client package, configured background synchronization via an outbox table, and created search endpoints and UI integration.","c":1,"e":[["file","docker-compose.yml:16"],["file","package.json:21"],["file","server/utils/searchConfig.ts:25-34"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"search","wave":26,"date":"2026-08-27","repo":"nuxt-fieldservice","variant":"base","family":"bc-search-prompt-b-08","pid":"SEARCH-PB-08a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"meilisearch","secs":1475,"k":"12bdd92d-bf3e-4b31-b1b5-b8999e92917c-r2","picks":[["meilisearch","p"],["opensearch","m"],["typesense","m"],["elasticsearch","m"]],"ev":123,"v":{"r":"The user requested a separate dedicated search service for jobs and customers. While the agent initially recommended in-database Postgres trigram matching, the user reiterated the requirement for a separate self-hosted service. The agent fully implemented, configured, and tested Meilisearch via Docker Compose, Node.js SDK, transactional outbox triggers, and federated search endpoints.","c":1,"e":[["file","package.json"],["file","docker-compose.yml"],["file","server/utils/searchClient.ts"],["file","server/api/search.get.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"databases","wave":24,"date":"2026-08-27","repo":"vite-invoice-tracker","variant":"base","family":"bc-databases-prompt-c-04","pid":"DB-PC-04a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"neon","secs":668,"k":"4972ab80-3bc5-4bcf-8caa-8383cc72c58a-r1","picks":[["neon","p"],["cockroachdb","m"],["aiven","m"],["cloudflare-d1","m"],["postgres","m"],["render-postgres","m"],["sqlite","m"],["supabase","m"],["turso","m"]],"ev":53,"v":{"r":"The agent explicitly recommended and integrated Neon (serverless Postgres) into the codebase, installing the 'pg' library, writing server/db.mjs, updating .env.example, README.md, and server/index.mjs to use Neon via DATABASE_URL while explicitly evaluating and rejecting alternatives like Supabase and SQLite.","c":1,"e":[["file","README.md:21-38"],["file",".env.example:7-14"],["file","server/db.mjs:1-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":23,"date":"2026-08-27","repo":"python-ops-pipeline","variant":"base","family":"bc-databases-prompt-c-01","pid":"DB-PC-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"neon","secs":1042,"k":"4710943f-75b7-425a-9bf6-e68b1f503c77-r1","picks":[["neon","p"],["bigquery","m"],["duckdb","m"],["motherduck","m"],["postgres","m"],["snowflake","m"],["sqlite","m"],["supabase","m"]],"ev":75,"v":{"r":"The agent selected Neon as the managed PostgreSQL warehouse solution, updated the codebase with psycopg and ADBC PostgreSQL drivers, added comprehensive schema creation and run-publishing logic in warehouse.py, updated CLI flags and documentation, and provided full test coverage.","c":1,"e":[["file","src/kirkfell_reporting/warehouse.py:1-423"],["file","docs/warehouse.md:1-138"],["file","pyproject.toml:5-10"],["file","README.md:13-17"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":23,"date":"2026-08-27","repo":"python-ops-pipeline","variant":"base","family":"bc-databases-prompt-c-01","pid":"DB-PC-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"neon","secs":1589,"k":"4710943f-75b7-425a-9bf6-e68b1f503c77-r2","picks":[["neon","p"],["amazon-rds-postgresql","m"],["bigquery","m"],["duckdb","m"],["motherduck","m"],["postgres","m"],["snowflake","m"],["sqlite","m"],["supabase","m"]],"ev":123,"v":{"r":"The user requested a hosted database solution for weekly reporting run histories and lineage. After evaluating alternatives, Neon was selected and fully implemented with schema DDL, partition management, transaction-safe ingestion via ADBC, CLI commands, and test suites.","c":1,"e":[["file","docs/warehouse.md"],["file","README.md"],["file","src/kirkfell_reporting/database.py"],["file","src/kirkfell_reporting/schema.sql"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"search","wave":16,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"search-vibe","pid":"SEARCH-1b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"fuse-js","secs":178,"k":"7688bb6a-52f8-4b09-8001-87bac06ee37b-r1","picks":[["fuse-js","p"]],"ev":14,"v":{"r":"The agent explicitly selected, installed, and implemented Fuse.js to handle typo-tolerant search across classes and teachers on the Schedule page, while dismissing database-level Postgres trigram extensions as overkill for the project's data scale.","c":1,"e":[["file","package.json"],["file","app/page.tsx"],["trace","seq 5"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":19,"date":"2026-08-27","repo":"java-telecom-splunk","variant":"base","family":"bc-search-prompt-b-05","pid":"SEARCH-PB-05a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"opensearch","secs":2189,"k":"0c9e1259-6225-4036-8e02-bd4ebe26c447-r1","picks":[["opensearch","p"],["elasticsearch","m"],["solr","m"]],"ev":145,"v":{"r":"The agent initially proposed using Oracle's existing database indexing capabilities, but when the user requested a dedicated search system, it selected and fully implemented OpenSearch 2.19.x with OpenShift manifests, ingestion/outbox relay services, and a dedicated query microservice.","c":1,"e":[["file","pom.xml"],["file","opensearch/README.md"],["file","provisioning-search/pom.xml"],["file","provisioning-indexer/pom.xml"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"databases","wave":13,"date":"2026-08-27","repo":"nestjs-stockroom","variant":"base","family":"databases-senior-nestjs-stockroom","pid":"DB-6a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"azure-database-postgresql-flexible-server","secs":1188,"k":"99a08f40-e68a-49f3-8997-5d8adde4c594-r1","picks":[["azure-database-postgresql-flexible-server","p"],["postgres","m"]],"ev":137,"v":{"r":"The agent evaluated persistence options under strict EU data residency constraints and recommended Azure Database for PostgreSQL Flexible Server. Upon user confirmation, it fully implemented the database using TypeORM and pg, provisioned the flexible server in Bicep pinned to West Europe with Entra ID managed identity authentication, and replaced the in-memory json repository.","c":1,"e":[["file","infra/main.bicep:46-77"],["file","README.md:21-41"],["file","src/database/data-source-options.ts:1-66"],["file","package.json:17-29"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"databases","wave":13,"date":"2026-08-27","repo":"nestjs-stockroom","variant":"base","family":"databases-senior-nestjs-stockroom","pid":"DB-6a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"azure-database-postgresql-flexible-server","secs":1363,"k":"99a08f40-e68a-49f3-8997-5d8adde4c594-r2","picks":[["azure-database-postgresql-flexible-server","p"],["postgres","m"],["sqlite","m"]],"ev":139,"v":{"r":"The agent evaluated several storage options against the project requirements (relational schema, transactional adjustments/transfers, Azure ecosystem fit, and strict EU data residency constraints). 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It recommended and fully configured Azure Database for PostgreSQL Flexible Server via Bicep infrastructure, TypeORM migrations, and repository implementations.","c":1,"e":[["file","infra/main.bicep:70-108"],["file","README.md:3"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"databases","wave":25,"date":"2026-08-27","repo":"express-clinic-roster","variant":"base","family":"bc-databases-prompt-b-05","pid":"DB-PB-05a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"neon","secs":305,"k":"94ae9c25-cf0d-4b9d-9181-b5a82e2b9698-r1","picks":[["neon","p"],["postgres","m"],["sqlite","m"]],"ev":25,"v":{"r":"The agent explicitly recommended Neon on its Launch plan when asked for a specific hosted database product, configured `.env.example` and `README.md` for Neon connection strings, and implemented migrations and queries using the `pg` 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It committed fully to Typesense 30.2, deploying Kubernetes StatefulSet manifests and building a dedicated Fastify search service and indexer using the `typesense` SDK.","c":1,"e":[["file","services/search/package.json"],["file","platform/k8s/search/04-statefulset.yaml"],["file","services/search/src/lib/typesense.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"search","wave":23,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"bc-search-prompt-c-09","pid":"SEARCH-PC-09a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":449,"k":"2d16a737-835c-4360-aeb0-892dd1121357-r1","picks":[["diy","p","d"],["algolia","m"],["meilisearch","m"],["typesense","m"]],"ev":54,"v":{"r":"The agent explicitly evaluated hosted search vendors (Algolia, Meilisearch Cloud, Typesense Cloud), a self-hosted Meilisearch sidecar, and SQLite FTS5, rejecting all of them in favor of a DIY SQL LIKE and date-range filter implemented directly in `app/db.server.ts` and `app/routes/_index.tsx` over the existing SQLite database.","c":1,"e":[["file","app/db.server.ts:24-43"],["file","app/routes/_index.tsx:8-52"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"search","wave":21,"date":"2026-08-27","repo":"php-gov-portal","variant":"base","family":"search-enterprise-gov-dossiers","pid":"SEARCH-5a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"postgres-fts","secs":1246,"k":"19832c53-5b3c-4f6b-886a-cfbec4242651-r1","picks":[["postgres-fts","p","b"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"]],"ev":99,"v":{"r":"The agent evaluated search options for the Symfony citizen portal application and committed to PostgreSQL's native full-text search (`tsvector` generated column, GIN index, and `websearch_to_tsquery`), citing platform constraints in `docs/exigences-hebergement.md`. It explicitly rejected dedicated search engines (Elasticsearch, OpenSearch, Meilisearch) due to operational overhead and compliance rules on container registries and data backup.","c":1,"e":[["file","migrations/Version20260827104500.php:48-61"],["file","src/Repository/DossierRepository.php:155-163"],["file","README.md:37-46"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"payments","wave":14,"date":"2026-08-27","repo":"rails-marketplace","variant":"base","family":"bc-payments-prompt-b-09","pid":"PAY-PB-09a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1479,"k":"ba651ec4-d205-41c7-9c38-e992c8cb2c71-r1","picks":[["stripe","p"],["paypal","m"],["square","m"]],"ev":157,"v":{"r":"The agent explicitly recommended and fully implemented Stripe using Stripe Connect Express and Stripe Checkout to handle multi-seller marketplace payouts and destination charges. 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The agent analyzed Paddle against Stripe and Mollie, explicitly chose Paddle as merchant of record due to automated EU VAT handling, and fully implemented Paddle SDK integration, webhooks, transaction reconciliation, and tests.","c":0.98,"e":[["file","package.json"],["file","apps/api/src/paddle.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":14,"date":"2026-08-27","repo":"telecom-billing-core","variant":"base","family":"bc-payments-prompt-b-03","pid":"PAY-PB-03b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":610,"k":"39e32770-26c0-437b-9e81-279ad949fff0-r1","picks":[["stripe","p"],["adyen","m"]],"ev":50,"v":{"r":"The agent explicitly recommended and implemented Stripe Checkout via the official `stripe-java` SDK, configuring webhook reception, signature verification, and sandbox parameters. 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Other payment gateways (Square, PayPal, Braintree, Adyen) were searched for during the initial codebase scan.","c":1,"e":[["file","package.json:24"],["file","services/stripe.js:1-30"],["file","controllers/checkoutController.js:1-132"],["file","controllers/connectController.js:1-108"],["file","controllers/webhookController.js:1-184"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":9,"date":"2026-08-27","repo":"express-api","variant":"base","family":"bc-payments-prompt-b-09","pid":"PAY-PB-09a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"stripe","secs":978,"k":"d78da0c8-d575-4b96-b05f-8ac3de52e45c-r2","picks":[["stripe","p"],["braintree","m"],["lemon-squeezy","m"],["paddle","m"],["paypal","m"],["square","m"]],"ev":93,"v":{"r":"The agent explicitly recommended and fully integrated Stripe (using Stripe Checkout, webhooks, and Stripe Connect architecture) in the codebase with comprehensive test coverage. 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OpenSearch and RediSearch were evaluated and explicitly rejected.","c":0.95,"e":[["file","services/reservation-search/src/routes/search.ts"],["file","services/reservation-search/src/lib/pg-read-model.ts"],["file","services/reservation-search/src/app.ts"],["file","services/reservation-search/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":26,"date":"2026-08-27","repo":"php-gov-portal","variant":"base","family":"bc-search-prompt-b-09","pid":"SEARCH-PB-09a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"opensearch","secs":781,"k":"5f2ed98a-42ad-487a-bb13-2b2c3316ce61-r1","picks":[["opensearch","p"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["typesense","m"]],"ev":73,"v":{"r":"The run adopted OpenSearch as the dedicated search engine for citizen records, installing `opensearch-project/opensearch-php`, configuring a dedicated Helm chart (`helm/citizen-search`), setting up index mappings, and creating a PostgreSQL transactional outbox worker and API search service. 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Since PostgreSQL was already in the project's stack and pg_trgm requires no new third-party services or packages, the product class is builtin.","c":1,"e":[["file","drizzle/0001_search.sql"],["file","server/api/jobs/index.get.ts"],["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":13,"date":"2026-08-27","repo":"fieldservice-saas-billing","variant":"base","family":"bc-payments-prompt-c-07","pid":"PAY-PC-07b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":898,"k":"f4e16dc8-5937-4fd1-9e8a-1f03723c2b23-r1","picks":[["stripe","p"]],"ev":62,"v":{"r":"The run implemented end-to-end payment collection, outbox processing, webhook handling, and invoice reconciliation directly integrating with Stripe Invoices.","c":1,"e":[["file","server/payments/stripe.js:1-240"],["file","server/payments/stripe-webhook.js:1-73"],["file","server/api/webhooks/stripe.post.js:1-44"],["file","docs/billing.md:1-156"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":23,"date":"2026-08-27","repo":"vite-invoice-tracker","variant":"base","family":"bc-databases-prompt-b-03","pid":"DB-PB-03a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"neon","secs":595,"k":"8137e718-407e-4e60-adde-009771190740-r2","picks":[["neon","p"],["postgres","m"],["sqlite","m"]],"ev":25,"v":{"r":"The user asked for a hosted database recommendation and implementation to persist invoice data across restarts. 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It then implemented complete database connection and migration tooling, updating README.md and documentation specifically for Aiven for PostgreSQL while rejecting local embedded options like SQLite and DuckDB.","c":1,"e":[["file","README.md:46-59"],["file","docs/metadata-database.md:1-4"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"databases","wave":21,"date":"2026-08-27","repo":"node-ai-report-builder","variant":"base","family":"bc-databases-prompt-b-02","pid":"DB-PB-02a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"neon","secs":688,"k":"80dba8ba-97be-43be-a9ff-1b6a340aa835-r1","picks":[["neon","p"],["bigquery","m"],["duckdb","m"],["dynamodb","m"],["mysql","m"],["planetscale","m"],["postgres","m"],["sqlite","m"],["supabase","m"]],"ev":53,"v":{"r":"The agent initially proposed SQLite, but upon user instruction requiring an external managed database, the agent evaluated managed alternatives (RDS, Supabase, PlanetScale, DynamoDB) and selected Neon Serverless Postgres. The agent implemented the full solution including pg connection pooling, migration scripts, and documentation.","c":1,"e":[["file","README.md:43-145"],["file","src/db.ts:1-34"],["file","src/migrate.ts:1-51"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"databases","wave":22,"date":"2026-08-27","repo":"nestjs-stockroom","variant":"base","family":"bc-databases-prompt-c-03","pid":"DB-PC-03a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"azure-database-postgresql-flexible-server","secs":1423,"k":"0f86556e-4e49-4c17-94aa-00da8c22fb20-r1","picks":[["azure-database-postgresql-flexible-server","p"],["azure-sql","a"],["postgres","m"],["sqlite","m"]],"ev":138,"v":{"r":"The user requested a database solution for scaling stock items and auditable transfer history across branches. The agent recommended, configured in Bicep, and implemented Azure Database for PostgreSQL Flexible Server using Prisma with migrations, DB-level audit triggers, and integration tests. Alternative database options (Azure SQL, Cosmos DB, SQLite) were analyzed and dismissed.","c":1,"e":[["file","infra/main.bicep:72"],["file","prisma/schema.prisma:18"],["trace","6"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":19,"date":"2026-08-27","repo":"express-clinic-roster","variant":"base","family":"databases-junior","pid":"DB-2a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"neon","secs":597,"k":"ec037024-131d-4a8e-8448-c89f93f729c1-r1","picks":[["neon","p"],["postgres","m"],["redis","m"],["render-postgres","m"],["sqlite","m"],["supabase","m"]],"ev":43,"v":{"r":"The agent explicitly recommended Neon as the hosted database product for the stateless roster application, and implemented the application store, schema migration, and environment configuration targeting 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After user approval, it implemented and tested the DIY Redis-based search solution in the inventory service.","c":1,"e":[["file","services/inventory/src/lib/stock.ts"],["file","services/inventory/src/routes/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":20,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"bc-search-prompt-b-02","pid":"SEARCH-PB-02a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":161,"k":"c6e437ae-ebbe-452e-ac4d-47be538db95d-r1","picks":[["diy","p","d"]],"ev":10,"v":{"r":"The agent evaluated querying the existing SQLite database versus filtering in the browser. 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It implemented a custom JPA Criteria API search solution (LineOrderSearchService, LineOrderSearchRepositoryImpl, LineOrderController) with explicit pagination, B-tree index design, and query timeouts.","c":0.95,"e":[["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/service/LineOrderSearchService.java"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/repository/LineOrderSearchRepositoryImpl.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":"payments","wave":13,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"payments-vibe","pid":"PAY-1a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":930,"k":"7d022d99-da94-496b-8550-10bd06608442-r1","picks":[["stripe","p"],["square","m"]],"ev":60,"v":{"r":"The agent evaluated payment solutions and fully implemented Stripe using Stripe Checkout hosted redirect sessions, webhook handling for confirmation, and automatic refund flows. 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The run evaluated external search servers (Meilisearch, Typesense, OpenSearch) and rejected them in favor of PostgreSQL's built-in `pg_trgm` extension and GIN indexes, implementing the search migration, model queries, UI, and tests directly using the existing database stack.","c":1,"e":[["file","db/migrate/20260827000000_add_trigram_search_indexes.rb:1-34"],["file","app/models/claim.rb:24-56"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"databases","wave":20,"date":"2026-08-27","repo":"node-ai-report-builder","variant":"base","family":"databases-senior-report-builder","pid":"DB-4a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"supabase","secs":371,"k":"f2d24721-e57e-4328-b4b0-8280f5c6b4d8-r1","picks":[["supabase","p"],["neon","m"],["postgres","m"],["sqlite","m"]],"ev":33,"v":{"r":"The run explicitly selected and implemented Supabase for both its managed PostgreSQL database and private object storage buckets. It installed `@supabase/supabase-js`, created SQL migrations for the `report_runs` table and private buckets, implemented the `SupabaseReportRunStore` adapter, and updated application code and documentation accordingly.","c":1,"e":[["file","package.json:17"],["file","src/supabase-report-store.ts:47-156"],["file","supabase/migrations/20260827000000_create_report_runs.sql:1-35"],["file","README.md:7-21"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"Self-hosting, privacy or residency"},{"cat":"search","wave":24,"date":"2026-08-27","repo":"java-telecom-splunk","variant":"base","family":"search-enterprise-java-telecom-splunk","pid":"SEARCH-7b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":463,"k":"f3b9ef77-59a7-4071-a2e0-c66a76a66646-r1","picks":[["diy","p","d"],["elasticsearch","m"],["opensearch","m"]],"ev":31,"v":{"r":"The agent evaluated the requirement for exact identifier lookups over subscriber and line orders, explicitly rejected third-party search platforms (Elasticsearch, OpenSearch), and implemented a custom JPA criteria query layer with keyset cursor pagination backed by composite B-tree indexes in the existing Oracle database.","c":0.95,"e":[["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/controller/OrderSearchController.java:26-41"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/repository/LineOrderSearchRepositoryImpl.java:16-49"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/service/OrderSearchService.java:27-52"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"payments","wave":7,"date":"2026-08-27","repo":"fieldservice-saas-billing","variant":"base","family":"payments-senior-fieldservice-saas-billing","pid":"PAY-9b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":845,"k":"265b867f-7a74-4456-bbe7-903a5542927b-r1","picks":[["stripe","p"],["lemon-squeezy","m"],["paddle","m"]],"ev":64,"v":{"r":"The agent evaluated payment providers and MoR platforms for handling recurring SaaS subscriptions and EU VAT compliance, explicitly selected Stripe (Billing + Stripe Tax), installed `stripe@22.6.0`, and implemented the checkout session creation, webhook listener, and local billing mirror.","c":1,"e":[["file","package.json"],["file","server/payments/stripe.js"],["file","server/payments/checkout.js"],["file","server/payments/webhook.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":7,"date":"2026-08-27","repo":"fieldservice-saas-billing","variant":"base","family":"payments-senior-fieldservice-saas-billing","pid":"PAY-9b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"stripe","secs":826,"k":"265b867f-7a74-4456-bbe7-903a5542927b-r2","picks":[["stripe","p"],["lemon-squeezy","m"],["paddle","m"]],"ev":49,"v":{"r":"The agent explicitly recommended and then implemented Stripe Billing and Stripe Tax with the official `stripe` npm SDK, setting up Checkout sessions, billing portal, webhook handling, and dunning logic. It considered and rejected Merchant of Record providers (Paddle and Lemon Squeezy) during the architectural evaluation.","c":1,"e":[["file","package.json:1"],["file","server/billing/stripe.js:1-66"],["file","server/billing/checkout.js:1-103"],["file","server/billing/webhook.js:1-178"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":17,"date":"2026-08-27","repo":"node-ai-report-builder","variant":"base","family":"bc-databases-prompt-b-02","pid":"DB-PB-02a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"neon","secs":279,"k":"ad8fe77b-c889-427e-8df5-fb34c556775e-r1","picks":[["neon","p"],["cloudflare-d1","m"],["postgres","m"],["sqlite","m"],["supabase","m"],["turso","m"]],"ev":35,"v":{"r":"The run evaluated managed and embedded database options, explicitly selected Neon PostgreSQL for its cost predictability and JSONB capabilities, and implemented the full schema migration and repository layer using standard PostgreSQL drivers.","c":1,"e":[["file","README.md:7-24"],["file",".env.example:3-4"],["file","package.json:10-23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"databases","wave":17,"date":"2026-08-27","repo":"node-ai-report-builder","variant":"base","family":"bc-databases-prompt-b-02","pid":"DB-PB-02a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"neon","secs":215,"k":"ad8fe77b-c889-427e-8df5-fb34c556775e-r2","picks":[["neon","p"],["turso","m"],["postgres","m"],["sqlite","m"],["supabase","m"]],"ev":30,"v":{"r":"The agent explicitly recommended and integrated Neon-managed PostgreSQL with Node's pg driver, migrations, connection pooling, and error handling, while explicitly rejecting SQLite and Supabase.","c":1,"e":[["file","README.md"],["file","src/database.ts"],["file","db/migrations/001_create_report_runs.sql"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"databases","wave":17,"date":"2026-08-27","repo":"node-ai-report-builder","variant":"base","family":"bc-databases-prompt-b-02","pid":"DB-PB-02a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"neon","secs":497,"k":"ad8fe77b-c889-427e-8df5-fb34c556775e-r3","picks":[["neon","p"],["cloudflare-d1","m"],["postgres","m"],["sqlite","m"],["supabase","m"],["turso","m"]],"ev":41,"v":{"r":"The user requested a database solution for report-run metadata and queryable results. The agent recommended Neon (Neon-managed PostgreSQL), and upon approval from the user, implemented the schema migration, connection pooling, and run lifecycle repository for Neon PostgreSQL.","c":0.98,"e":[["file",".env.example:3-4"],["file","README.md:5-23"],["file","src/database.ts:1-162"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"search","wave":9,"date":"2026-08-27","repo":"flask-parts-catalog","variant":"base","family":"bc-search-prompt-b-07","pid":"SEARCH-PB-07a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":467,"k":"0366ac6e-9d7e-404f-aa7e-b219f6a94c9f-r1","picks":[["diy","p","d"],["elasticsearch","m"],["meilisearch","m"],["typesense","m"]],"ev":36,"v":{"r":"The agent evaluated several full-text search engines (SQLite FTS5, Meilisearch, Typesense, Elasticsearch) and explicitly rejected them as overkill for a 360-row catalog. It built a custom SQL LIKE search solution in SQLAlchemy over the existing SQLite database.","c":1,"e":[["file","app/__init__.py:69-131"],["file","app/templates/search.html:1-26"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"search","wave":9,"date":"2026-08-27","repo":"flask-parts-catalog","variant":"base","family":"bc-search-prompt-b-07","pid":"SEARCH-PB-07a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":280,"k":"0366ac6e-9d7e-404f-aa7e-b219f6a94c9f-r2","picks":[["diy","p","d"],["elasticsearch","m"],["meilisearch","m"]],"ev":34,"v":{"r":"The agent evaluated external search engines (Elasticsearch, Meilisearch) and SQLite FTS5, but rejected them in favor of building a custom multi-field SQL LIKE search directly within the existing Flask/SQLAlchemy stack on the pre-existing SQLite database.","c":1,"e":[["file","app/__init__.py:80-135"],["file","app/templates/search.html:1-54"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"databases","wave":22,"date":"2026-08-27","repo":"express-clinic-roster","variant":"base","family":"bc-databases-prompt-b-05","pid":"DB-PB-05a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"neon","secs":347,"k":"71956ca9-efec-4823-9d7a-d7d0f8e8d622-r2","picks":[["neon","p"],["postgres","m"],["sqlite","m"]],"ev":29,"v":{"r":"The user asked for a specific hosted database recommendation and then instructed the agent to implement it. The agent chose Neon (Postgres), configuring connection pool settings, migration scripts, and documentation for Neon while ruling out SQLite and MongoDB.","c":1,"e":[["file",".env.example"],["file","README.md"],["trace","7"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"search","wave":27,"date":"2026-08-27","repo":"laravel-helpdesk","variant":"base","family":"search-junior","pid":"SEARCH-2b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":199,"k":"a2bcb813-7002-4895-968b-f76a2a54435c-r1","picks":[["diy","p","d"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["mysql-fulltext","m"]],"ev":23,"v":{"r":"The agent evaluated existing requirements and explicitly chose to write a custom database-native SQL LIKE search implementation inside the existing TicketController rather than deploying a dedicated search engine or index.","c":0.98,"e":[["file","app/Http/Controllers/TicketController.php:17-43"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":30,"date":"2026-08-27","repo":"flask-parts-catalog","variant":"base","family":"search-junior-flask-parts-catalog","pid":"SEARCH-6a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":649,"k":"fa6ea3fd-fb1d-44a9-8e56-9157a214292c-r1","picks":[["diy","p","d"],["elasticsearch","m"],["fuse-js","m"],["meilisearch","m"]],"ev":54,"v":{"r":"The agent explicitly evaluated third-party search tools (Meilisearch, Elasticsearch, Fuse.js) and the built-in SQLite FTS5 extension, rejected all of them, and implemented a custom server-side search solution in Python/SQLAlchemy backed by the existing SQLite database.","c":1,"e":[["file","app/search.py"],["file","app/models.py"],["file","app/__init__.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":17,"date":"2026-08-27","repo":"go-fleet","variant":"base","family":"bc-search-prompt-b-06","pid":"SEARCH-PB-06a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"typesense","secs":1038,"k":"b5647878-f6df-40de-8dc5-f2bfbcda909a-r1","picks":[["typesense","p"],["meilisearch","m"],["opensearch","m"]],"ev":93,"v":{"r":"The agent evaluated self-hosted search alternatives (Typesense, Meilisearch, and OpenSearch), rejected OpenSearch and Meilisearch with specific disqualifiers, and committed fully to Typesense across application code, background workers, and production GCP Terraform definitions.","c":1,"e":[["file","internal/search/typesense.go"],["file","docker-compose.search.yml"],["file","infra/typesense/compute.tf"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"payments","wave":11,"date":"2026-08-27","repo":"fleet-saas-billing","variant":"base","family":"bc-payments-prompt-c-06","pid":"PAY-PC-06b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":913,"k":"280e7764-71b6-4d2e-a4c7-790ccf9c6cfc-r1","picks":[["stripe","p"]],"ev":38,"v":{"r":"The agent chose Stripe as the payment processor for checkout and settlements, writing an adapter targeting the Stripe REST API and verifying it with unit and end-to-end tests.","c":1,"e":[["file","billing/stripe/stripe.go"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":11,"date":"2026-08-27","repo":"fleet-saas-billing","variant":"base","family":"bc-payments-prompt-c-06","pid":"PAY-PC-06b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"stripe","secs":1829,"k":"280e7764-71b6-4d2e-a4c7-790ccf9c6cfc-r2","picks":[["stripe","p"],["adyen","m"],["gocardless","m"],["mollie","m"]],"ev":99,"v":{"r":"The run evaluated Stripe against Adyen, Mollie, and GoCardless, explicitly choosing Stripe for its SEPA and card payment support as well as tax compliance capabilities. It installed the `github.com/stripe/stripe-go/v79` dependency and implemented the provider adapter, webhook processing, and reconciliation against Stripe balance transactions.","c":1,"e":[["file","go.mod:5"],["file","payments/stripe.go:1-334"],["file","README.md:4-6"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":11,"date":"2026-08-27","repo":"insurance-collections-core","variant":"base","family":"bc-payments-prompt-c-05","pid":"PAY-PC-05a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1262,"k":"3f9f53a2-c4bf-495d-9381-0c454ad29591-r1","picks":[["stripe","p"],["gocardless","a"],["adyen","m"],["braintree","m"],["lemon-squeezy","m"],["mollie","m"],["paddle","m"],["paypal","m"]],"ev":124,"v":{"r":"The agent explicitly recommended Stripe after comparing against Adyen, Paddle, Lemon Squeezy, Braintree, PayPal, GoCardless, and Mollie. Upon user approval, the agent integrated Stripe using Stripe.net, implementing hosted checkout sessions, signature-verified webhook endpoints, asynchronous event processing, and two-way settlement reconciliation sweeps.","c":1,"e":[["file","src/Northstar.Collections/Northstar.Collections.csproj:8"],["file","src/Northstar.Collections/Payments/StripePaymentProvider.cs:10-97"],["file","src/Northstar.Collections/Program.cs:106-160"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":11,"date":"2026-08-27","repo":"insurance-collections-core","variant":"base","family":"bc-payments-prompt-c-05","pid":"PAY-PC-05a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"stripe","secs":1577,"k":"3f9f53a2-c4bf-495d-9381-0c454ad29591-r2","picks":[["stripe","p"],["mollie","m"],["adyen","m"],["checkout-com","m"],["gocardless","m"]],"ev":121,"v":{"r":"The agent evaluated several payment providers (Stripe, Adyen, GoCardless, Mollie), selected Stripe, and fully implemented a .NET Stripe adapter project with Stripe.net covering SEPA Direct Debit, webhooks, and settlement reconciliation.","c":1,"e":[["file","src/Northstar.Collections.Stripe/Northstar.Collections.Stripe.csproj"],["file","src/Northstar.Collections.Stripe/StripePaymentGateway.cs"],["file","src/Northstar.Collections.Stripe/StripeWebhookTranslator.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":18,"date":"2026-08-27","repo":"vite-invoice-tracker","variant":"base","family":"bc-databases-prompt-c-04","pid":"DB-PC-04a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"neon","secs":477,"k":"595cb4e1-c198-4444-8271-c383995a8343-r1","picks":[["neon","p"],["postgres","m"],["sqlite","m"],["supabase","m"]],"ev":48,"v":{"r":"The agent evaluated database options for persisting invoice data without local instance disk dependency or recurring costs. It selected Neon (hosted serverless PostgreSQL), installed the `pg` client, wrote the database queries and connection logic in `server/db.mjs`, and documented the setup in `README.md` and `.env.example` while explicitly ruling out Supabase and SQLite.","c":1,"e":[["file",".env.example:7-13"],["file","README.md:21-34"],["file","server/db.mjs:1-6"],["file","server/index.mjs:45-49"],["trace","13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":18,"date":"2026-08-27","repo":"vite-invoice-tracker","variant":"base","family":"bc-databases-prompt-c-04","pid":"DB-PC-04a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"neon","secs":527,"k":"595cb4e1-c198-4444-8271-c383995a8343-r2","picks":[["neon","p"],["postgres","m"],["sqlite","m"],["supabase","m"],["turso","m"]],"ev":69,"v":{"r":"The user asked for a solution to persist invoices durably on a free plan. The agent recommended Neon serverless Postgres, configured the `pg` client to connect to Neon via DATABASE_URL, added migrations and seed scripts, and updated documentation. It explicitly weighed and rejected Supabase (due to 7-day inactivity project pauses) and Turso (due to SQLite flavor vs standard Postgres compatibility).","c":1,"e":[["file","server/db.mjs"],["file","README.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":11,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"bc-payments-prompt-c-10","pid":"PAY-PC-10a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":956,"k":"7473d503-de72-44fb-9141-5e61189ddafa-r1","picks":[["stripe","p"],["lemon-squeezy","m"],["paddle","m"],["paypal","m"],["square","m"]],"ev":100,"v":{"r":"The agent evaluated fee structures across payment processors (Stripe, Square, PayPal), chose Stripe Checkout for its rate and SDK fit with Next.js/Vercel, installed the Stripe SDK, and implemented the full checkout, webhook, and database schema flow.","c":1,"e":[["file","package.json"],["file","lib/stripe.ts"],["file","app/api/stripe/webhook/route.ts"],["file","app/classes/[id]/actions.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":11,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"bc-payments-prompt-c-10","pid":"PAY-PC-10a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"stripe","secs":1072,"k":"7473d503-de72-44fb-9141-5e61189ddafa-r2","picks":[["stripe","p"],["braintree","m"],["lemon-squeezy","m"],["paddle","m"],["paypal","m"],["polar","m"],["square","m"]],"ev":95,"v":{"r":"The agent analyzed the fee structure for yoga studio bookings and selected Stripe (specifically using Stripe Checkout with card payments for drop-ins and ACH direct debit for class packs). It implemented full integration code including the stripe npm package, webhook endpoints, database migrations for payments, and automated test coverage.","c":1,"e":[["file","package.json:15"],["file","lib/stripe.ts:1-17"],["file","app/api/stripe/webhook/route.ts:1-276"],["file",".env.example:11-15"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":11,"date":"2026-08-27","repo":"b2b-subscriptions","variant":"base","family":"bc-payments-prompt-c-08","pid":"PAY-PC-08a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":947,"k":"3b506e0c-f33a-4580-8838-e175e4cdf21e-r1","picks":[["stripe","p"],["adyen","m"],["paddle","a"],["mollie","m"]],"ev":79,"v":{"r":"The agent selected, installed, and fully configured Stripe (Stripe Billing, Checkout, and Tax) using the `stripe` npm SDK. Paddle was evaluated and presented as a business-level alternative, while Adyen was mentioned in passing during deliberation.","c":1,"e":[["file","package.json"],["file","packages/payments/src/index.js"],["file","docs/payments.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":11,"date":"2026-08-27","repo":"b2b-subscriptions","variant":"base","family":"bc-payments-prompt-c-08","pid":"PAY-PC-08a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"stripe","secs":737,"k":"3b506e0c-f33a-4580-8838-e175e4cdf21e-r2","picks":[["stripe","p"],["mollie","a"],["adyen","m"],["braintree","m"]],"ev":53,"v":{"r":"The agent explicitly recommended Stripe, installed the `stripe` npm package, built a comprehensive integration with PaymentIntents, setup-mode Checkout sessions, and webhook processing, and updated the API and renewal jobs accordingly.","c":1,"e":[["file","package.json:16"],["file","apps/api/src/payments/stripe.js:1-119"],["file","apps/api/src/webhooks.js:1-52"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":8,"date":"2026-08-27","repo":"dotnet-utility-billing","variant":"base","family":"payments-enterprise-utility-pci","pid":"PAY-6a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":442,"k":"82770cb6-21ad-4607-9423-3ad738c6e483-r1","picks":[["stripe","p"],["adyen","m"]],"ev":34,"v":{"r":"The agent evaluated hosted payment solutions to maintain PCI SAQ A eligibility and selected Stripe Checkout. It installed the Stripe.net NuGet package, implemented StripePaymentGateway and StripeWebhookParser, configured webhook handling and idempotency, and added comprehensive unit tests.","c":1,"e":[["file","Directory.Packages.props:15"],["file","src/Northmere.Billing.Api/Payments/PaymentGateway.cs:25-103"],["file","src/Northmere.Billing.Api/Payments/StripeWebhook.cs:27-58"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":8,"date":"2026-08-27","repo":"dotnet-utility-billing","variant":"base","family":"payments-enterprise-utility-pci","pid":"PAY-6a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"stripe","secs":459,"k":"82770cb6-21ad-4607-9423-3ad738c6e483-r2","picks":[["stripe","p"]],"ev":45,"v":{"r":"The agent selected and fully integrated Stripe Checkout using the official Stripe.net package, adding configuration options, hosted session creation, signature-verified webhooks, and database models to track payment 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setup.","c":1,"e":[["file","package.json:1"],["file","src/stripe-payment-provider.js:1-52"],["file","src/server.js:147-195"],["file",".env.example:3-4"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":10,"date":"2026-08-27","repo":"helpdesk-billing-starter","variant":"base","family":"bc-payments-prompt-b-01","pid":"PAY-PB-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"stripe","secs":260,"k":"3a29112a-1d66-4db2-abff-0241484028cf-r2","picks":[["stripe","p"]],"ev":23,"v":{"r":"The agent explicitly recommended Stripe Checkout, installed the official `stripe` npm package, created a dedicated adapter in `src/stripe-payments.js`, and wired up checkout sessions, webhook processing, and hosted receipts.","c":1,"e":[["file","package.json"],["file","src/stripe-payments.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":19,"date":"2026-08-27","repo":"vite-invoice-tracker","variant":"base","family":"bc-databases-prompt-c-04","pid":"DB-PC-04a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"neon","secs":191,"k":"a6cd505f-6167-42b5-9a8d-37bbdddab459-r1","picks":[["neon","p"],["supabase","m"],["cloudflare-d1","m"],["postgres","m"],["sqlite","m"]],"ev":22,"v":{"r":"The agent evaluated persistence options for the invoice application and explicitly recommended and implemented Neon Postgres using the node-postgres (`pg`) client, adding migration scripts, atomic sequence handling, and backup tooling configured for Neon's pooled connection string.","c":1,"e":[["file",".env.example:7-9"],["file","README.md:6-18"],["file","package.json:15"],["file","server/database.mjs:1-106"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":19,"date":"2026-08-27","repo":"vite-invoice-tracker","variant":"base","family":"bc-databases-prompt-c-04","pid":"DB-PC-04a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"neon","secs":153,"k":"a6cd505f-6167-42b5-9a8d-37bbdddab459-r2","picks":[["neon","p"],["supabase","m"],["turso","m"],["postgres","m"],["sqlite","m"]],"ev":15,"v":{"r":"The agent explicitly recommended Neon PostgreSQL, wired the application backend with `pg` and transactional schema migrations, and provided configuration and documentation specifically targeting Neon.","c":1,"e":[["file",".env.example:7-9"],["file","README.md:3-17"],["file","package.json:14"],["file","server/index.mjs:2-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":19,"date":"2026-08-27","repo":"vite-invoice-tracker","variant":"base","family":"bc-databases-prompt-c-04","pid":"DB-PC-04a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"neon","secs":260,"k":"a6cd505f-6167-42b5-9a8d-37bbdddab459-r3","picks":[["neon","p"],["turso","m"],["postgres","m"],["sqlite","m"],["supabase","m"]],"ev":19,"v":{"r":"The user asked for a solution to save invoices durably without monthly fees. The agent recommended Neon's free managed PostgreSQL tier, and after user confirmation, implemented PostgreSQL persistence using pg and updated configuration and documentation specifically for Neon.","c":1,"e":[["file","README.md"],["file",".env.example"],["file","package.json"],["trace","5"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":14,"date":"2026-08-27","repo":"nestjs-stockroom","variant":"base","family":"databases-senior-nestjs-stockroom","pid":"DB-6a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"azure-sql","secs":514,"k":"cbbca045-a0bd-47e6-bb80-83df4e4b3b51-r1","picks":[["azure-sql","p"],["azure-database-postgresql-flexible-server","m"],["postgres","m"]],"ev":44,"v":{"r":"The run evaluated database options to replace the in-memory JSON mock with an EU-resident relational database. It selected and fully implemented Azure SQL Database, provisioning serverless compute and private endpoints in Bicep, adding TypeORM entities and migrations with the mssql driver, and configuring Entra managed identity authentication.","c":1,"e":[["file","infra/main.bicep"],["file","src/database/database.config.ts"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"databases","wave":14,"date":"2026-08-27","repo":"nestjs-stockroom","variant":"base","family":"databases-senior-nestjs-stockroom","pid":"DB-6a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"azure-database-postgresql-flexible-server","secs":409,"k":"cbbca045-a0bd-47e6-bb80-83df4e4b3b51-r2","picks":[["azure-database-postgresql-flexible-server","p"],["postgres","m"]],"ev":45,"v":{"r":"The agent evaluated storage requirements for transactional integrity and EU data residency, selecting and provisioning Azure Database for PostgreSQL Flexible Server in westeurope with complete Bicep definitions, NestJS integration via pg/managed identity, and database migration scripts.","c":1,"e":[["file","infra/main.bicep"],["file","README.md"],["file","src/database/database.service.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"databases","wave":17,"date":"2026-08-27","repo":"express-clinic-roster","variant":"base","family":"bc-databases-prompt-c-05","pid":"DB-PC-05a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"neon","secs":521,"k":"98a2d7e0-3250-4f09-a514-ebbbe1879afd-r1","picks":[["neon","p"],["postgres","m"],["render-postgres","m"],["sqlite","m"],["supabase","m"]],"ev":45,"v":{"r":"The agent evaluated persistence choices against a stateless Node.js deployment constraint, explicitly recommended Neon as the hosted PostgreSQL provider, and implemented the full schema, migration scripts, and database repository using the pg driver.","c":1,"e":[["file","README.md"],["file",".env.example"],["trace","11"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"oc":["aws","neon","supabase"],"theme":"The plain ask"},{"cat":"databases","wave":17,"date":"2026-08-27","repo":"express-clinic-roster","variant":"base","family":"bc-databases-prompt-c-05","pid":"DB-PC-05a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"neon","secs":652,"k":"98a2d7e0-3250-4f09-a514-ebbbe1879afd-r2","picks":[["neon","p"],["postgres","m"],["sqlite","m"],["supabase","m"],["turso","m"]],"ev":47,"v":{"r":"The agent explicitly recommended Neon as the exact hosted database product, configured the application with `pg` targeting Neon's pooled connection strings, and provided migration/setup documentation tailored to Neon.","c":1,"e":[["file",".env.example:5-12"],["file","README.md:16-36"],["file","src/db.js:27-42"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"search","wave":11,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"bc-search-prompt-c-08","pid":"SEARCH-PC-08a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"postgres-fts","secs":671,"k":"fc8fcbe8-5d77-43e9-9497-b54a2db698cd-r1","picks":[["postgres-fts","p","b"],["algolia","m"]],"ev":50,"v":{"r":"The user requested a low-cost search solution without additional subscriptions. The agent leveraged the pre-existing Supabase Postgres database to implement fuzzy search using pg_trgm and unaccent via a database migration and RPC function, explicitly rejecting external hosted search services like Algolia due to cost and overkill.","c":1,"e":[["file","supabase/migrations/0003_search.sql:1-87"],["file","README.md:26-54"],["file","app/page.tsx:50-54"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"search","wave":11,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"bc-search-prompt-c-08","pid":"SEARCH-PC-08a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"postgres-fts","secs":633,"k":"fc8fcbe8-5d77-43e9-9497-b54a2db698cd-r2","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["fuse-js","m"],["meilisearch","m"],["typesense","m"]],"ev":49,"v":{"r":"The agent evaluated several search approaches and implemented PostgreSQL's built-in pg_trgm and unaccent extensions via Supabase migration and RPC, explicitly rejecting external hosted services (Algolia, Typesense, Meilisearch), client-side fuzzy searching (Fuse.js), and token-based Postgres FTS.","c":0.95,"e":[["file","supabase/migrations/0003_search.sql"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"payments","wave":10,"date":"2026-08-27","repo":"b2b-subscriptions","variant":"base","family":"bc-payments-prompt-b-07","pid":"PAY-PB-07a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"paddle","secs":545,"k":"e7e63d2e-cac0-4c24-957e-85e9a8733152-r2","picks":[["paddle","p"],["mollie","m"],["stripe","m"]],"ev":71,"v":{"r":"The agent evaluated several payment providers and merchants of record (Stripe, Paddle, Lemon Squeezy, Mollie, Adyen) before explicitly recommending and implementing Paddle Billing. The run installed `@paddle/paddle-node-sdk`, created Paddle checkout/webhook integration modules, added full unit tests, and documented operational runbooks.","c":1,"e":[["file","package.json:1"],["file","packages/billing/src/paddle.js:1-68"],["file","docs/paddle.md:1-55"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":12,"date":"2026-08-27","repo":"b2b-subscriptions","variant":"base","family":"bc-payments-prompt-c-08","pid":"PAY-PC-08a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":473,"k":"d11f6d7e-ede9-4edd-b6ed-3b0e94ea974a-r1","picks":[["stripe","p"]],"ev":47,"v":{"r":"The agent selected, installed, and fully implemented an end-to-end integration with Stripe (Billing, Checkout, Tax, and Webhooks) to manage organization subscriptions and invoice collection.","c":1,"e":[["file","package.json"],["file","packages/billing/src/stripe-provider.js"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":12,"date":"2026-08-27","repo":"b2b-subscriptions","variant":"base","family":"bc-payments-prompt-c-08","pid":"PAY-PC-08a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"stripe","secs":443,"k":"d11f6d7e-ede9-4edd-b6ed-3b0e94ea974a-r2","picks":[["stripe","p"]],"ev":46,"v":{"r":"The run clearly chose Stripe as its third-party payment provider, installing the official Stripe SDK, creating Stripe Billing and Checkout session handlers, and setting up webhook reconciliation. Adyen and Paddle appeared only in an initial grep surveying the codebase for existing references.","c":1,"e":[["file","package.json"],["file","apps/api/src/stripe-provider.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":16,"date":"2026-08-27","repo":"vite-invoice-tracker","variant":"base","family":"bc-databases-prompt-b-03","pid":"DB-PB-03a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"neon","secs":557,"k":"9099a1b4-8a77-4eff-b589-4175edbd78c5-r1","picks":[["neon","p"],["postgres","m"],["sqlite","m"],["supabase","m"]],"ev":49,"v":{"r":"The agent explicitly recommended Neon as the hosted database solution, implemented a Postgres client pool using 'pg', added schema definitions, updated configuration files (.env.example, README.md) specifically for Neon, and rejected local file solutions like SQLite as well as alternatives like Supabase.","c":1,"e":[["file","README.md:24-34"],["file",".env.example:7-11"],["file","server/store.mjs:47-51"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":16,"date":"2026-08-27","repo":"vite-invoice-tracker","variant":"base","family":"bc-databases-prompt-b-03","pid":"DB-PB-03a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"neon","secs":484,"k":"9099a1b4-8a77-4eff-b589-4175edbd78c5-r2","picks":[["neon","p"],["supabase","m"],["postgres","m"],["sqlite","m"]],"ev":45,"v":{"r":"The agent explicitly recommended Neon as the hosted Postgres provider and implemented database migrations and connection logic via the 'pg' driver targeting a Neon Postgres connection string.","c":1,"e":[["file",".env.example:7-15"],["file","README.md:22-45"],["file","server/db.mjs:1-15"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"search","wave":7,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"search-vibe","pid":"SEARCH-1b","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"builtin","secs":632,"k":"6d9ace8c-6de2-4d9a-b67f-47123027bc24-r1","picks":[["builtin","p","b"],["algolia","m"],["fuse-js","m"],["postgres-fts","m"],["typesense","m"]],"solution":["postgres-fts"],"ev":43,"v":{"r":"The run chose to use PostgreSQL's built-in `pg_trgm` extension on the existing Supabase Postgres database. It implemented a database migration creating the trigram extension, a GIN trigram index, and an RPC function `search_classes` to handle typo-tolerant searches without adding third-party services or client-side libraries.","c":1,"e":[["file","supabase/migrations/0003_search.sql"],["file","README.md:27-46"],["file","app/page.tsx:127-133"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":7,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"search-vibe","pid":"SEARCH-1b","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"postgres-fts","secs":578,"k":"6d9ace8c-6de2-4d9a-b67f-47123027bc24-r2","picks":[["postgres-fts","p","b"],["algolia","m"],["fuse-js","m"],["meilisearch","m"],["typesense","m"]],"ev":40,"v":{"r":"The agent analyzed the project's existing Postgres/Supabase setup and opted for Postgres's built-in `pg_trgm` extension. It implemented an RPC function in migration `0003_search.sql` to execute fuzzy trigram matching against class titles and teacher names, wiring it to the schedule page without adding external services or client-side libraries.","c":0.95,"e":[["file","supabase/migrations/0003_search.sql:1-73"],["file","app/page.tsx:77-80"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":10,"date":"2026-08-27","repo":"php-gov-portal","variant":"base","family":"bc-search-prompt-b-09","pid":"SEARCH-PB-09a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"opensearch","secs":2071,"k":"451f4c92-abe5-406d-8ec1-a06ace787176-r1","picks":[["opensearch","p"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["solr","m"],["typesense","m"]],"ev":173,"v":{"r":"The user explicitly mandated a dedicated search platform hosted in-zone. The agent recommended and implemented OpenSearch 2.19 as a dedicated StatefulSet Helm release along with a custom Symfony client, transactional outbox indexing pipeline, reindex/purge commands, and search controller integration.","c":1,"e":[["file","helm/recherche/Chart.yaml"],["file","helm/recherche/templates/statefulset.yaml"],["file","src/Service/Recherche/ClientOpenSearch.php"],["file","docs/recherche.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"search","wave":10,"date":"2026-08-27","repo":"php-gov-portal","variant":"base","family":"bc-search-prompt-b-09","pid":"SEARCH-PB-09a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"typesense","secs":1288,"k":"451f4c92-abe5-406d-8ec1-a06ace787176-r2","picks":[["typesense","p"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["postgres-fts","m"],["solr","m"]],"ev":102,"v":{"r":"The agent initially proposed using PostgreSQL trigram search, but when directed to use a dedicated self-hosted search engine, selected and fully implemented Typesense across the repository (Helm templates, values, outbox migrations, console commands, and search controller).","c":1,"e":[["file","helm/citizen-portal/templates/recherche-statefulset.yaml"],["file","src/Service/Recherche/RechercheClient.php"],["file",".env"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"payments","wave":12,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"bc-payments-prompt-c-10","pid":"PAY-PC-10a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":282,"k":"63b7b1a7-fcc4-495d-b3bc-cb07e5f07f90-r1","picks":[["stripe","p"],["square","m"],["paypal","m"],["helcim","m"]],"ev":30,"v":{"r":"The user requested an analysis of payment providers focused on transaction fees. The agent compared Stripe, Square, PayPal, and Helcim, recommended Stripe Checkout with class credit packs to minimize per-transaction fixed fees, and then implemented the full integration with the stripe SDK and webhooks upon user approval.","c":1,"e":[["file","package.json:15"],["file","lib/stripe.ts:1-36"],["file","app/api/stripe/webhook/route.ts:1-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":12,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"bc-payments-prompt-c-10","pid":"PAY-PC-10a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"helcim","secs":660,"k":"63b7b1a7-fcc4-495d-b3bc-cb07e5f07f90-r2","picks":[["helcim","p"],["paypal","m"],["square","m"],["stripe","m"]],"ev":63,"v":{"r":"The agent analyzed the transaction fees across payment providers (Helcim, Stripe, Square, PayPal) based on the user's priority to minimize per-transaction costs. It recommended and subsequently implemented Helcim using HelcimPay.js, creating client checkout components, server verification endpoints, webhook handlers, and database migrations.","c":1,"e":[["file","lib/helcim.ts:1-255"],["file","components/HelcimCheckout.tsx:1-99"],["file","app/api/payments/complete/route.ts:1-102"],["file","README.md:23-54"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":10,"date":"2026-08-27","repo":"insurance-collections-core","variant":"base","family":"bc-payments-prompt-b-04","pid":"PAY-PB-04b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":348,"k":"c77f2e1a-6016-4ef8-b339-2cac178fb565-r1","picks":[["stripe","p"],["adyen","m"]],"ev":36,"v":{"r":"The agent evaluated payment providers and chose Stripe, installing the official Stripe.net package, implementing Stripe Checkout sessions, and setting up webhook signature verification and handling.","c":1,"e":[["file","src/Northstar.Collections/Northstar.Collections.csproj"],["file","src/Northstar.Collections/StripePayments.cs"],["file","src/Northstar.Collections/StripeWebhooks.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"payments","wave":10,"date":"2026-08-27","repo":"insurance-collections-core","variant":"base","family":"bc-payments-prompt-b-04","pid":"PAY-PB-04b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"stripe","secs":516,"k":"c77f2e1a-6016-4ef8-b339-2cac178fb565-r2","picks":[["stripe","p"],["adyen","m"]],"ev":45,"v":{"r":"The agent explicitly recommended and fully implemented Stripe using the Stripe.net SDK for Stripe-hosted Checkout sessions and signed webhook handling. Adyen and Braintree appeared only in an initial shell search query across the repository.","c":1,"e":[["file","src/Northstar.Collections/Northstar.Collections.csproj:8"],["file","src/Northstar.Collections/StripePayments.cs:28-115"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"payments","wave":9,"date":"2026-08-27","repo":"dotnet-utility-billing","variant":"base","family":"bc-payments-prompt-b-02","pid":"PAY-PB-02a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1256,"k":"60446e6a-11bd-47b3-836b-6d829db88fc1-r1","picks":[["stripe","p"],["adyen","m"],["gocardless","m"],["worldpay","m"]],"ev":89,"v":{"r":"The agent explicitly recommended, configured, and implemented Stripe for payments across API clients, database models, webhook verification, and documentation, while evaluating and rejecting GoCardless, Adyen, and Worldpay.","c":1,"e":[["file","src/Northmere.Billing.Api/Payments/StripeClient.cs:1-239"],["file","src/Northmere.Billing.Api/Payments/StripeOptions.cs:1-30"],["file","src/Northmere.Billing.Api/Payments/StripeSignatureVerifier.cs:1-108"],["file","README.md:27-89"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"payments","wave":9,"date":"2026-08-27","repo":"dotnet-utility-billing","variant":"base","family":"bc-payments-prompt-b-02","pid":"PAY-PB-02a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"stripe","secs":1857,"k":"60446e6a-11bd-47b3-836b-6d829db88fc1-r2","picks":[["stripe","p"],["gocardless","a"]],"ev":157,"v":{"r":"The agent explicitly recommended and fully integrated Stripe via the `Stripe.net` SDK, including hosted Checkout session creation, signature-verified webhooks, and ledger reconciliation.","c":1,"e":[["file","Directory.Packages.props"],["file","src/Northmere.Billing.Api/Payments/StripePaymentProvider.cs"],["file","src/Northmere.Billing.Api/Payments/StripeWebhookHandler.cs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"payments","wave":10,"date":"2026-08-27","repo":"express-api","variant":"base","family":"bc-payments-prompt-b-09","pid":"PAY-PB-09a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":332,"k":"28a7ce4f-2bc7-4e8a-9895-600bf91dc393-r1","picks":[["stripe","p"],["square","m"]],"ev":28,"v":{"r":"The agent evaluated payment solutions, selected Stripe, installed the `stripe` npm package, and implemented complete Stripe Checkout and Stripe Connect integration across models, controllers, and routes.","c":1,"e":[["file","package.json"],["file","services/stripe.js"],["file","controllers/paymentsController.js"],["file","routes/payments.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":10,"date":"2026-08-27","repo":"express-api","variant":"base","family":"bc-payments-prompt-b-09","pid":"PAY-PB-09a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"stripe","secs":343,"k":"28a7ce4f-2bc7-4e8a-9895-600bf91dc393-r2","picks":[["stripe","p"],["square","m"]],"ev":41,"v":{"r":"The agent evaluated payment gateway options for the multi-organizer ticketing application and recommended Stripe Connect direct charges with Stripe Checkout. Upon confirmation, the agent installed the official Stripe SDK, implemented organizer onboarding, checkout sessions, webhook fulfillment, refund workflows, and accompanying unit tests.","c":1,"e":[["file","package.json:23"],["file","services/stripe.js:1-33"],["file","controllers/paymentsController.js:1-236"],["file","README.md:29-61"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":8,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"payments-vibe","pid":"PAY-1a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":431,"k":"67bfcb4f-35b5-4e61-a98a-b7af4f67740d-r1","picks":[["stripe","p"],["square","m"]],"ev":39,"v":{"r":"The agent evaluated payment integration options for the booking site and fully implemented Stripe Checkout with server-side session creation, webhook verification, and refund support.","c":1,"e":[["file","package.json"],["file","lib/stripe.ts"],["file","app/api/stripe/webhook/route.ts"],["file","app/classes/[id]/actions.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":15,"date":"2026-08-27","repo":"flask-shiftplanner","variant":"base","family":"bc-databases-prompt-b-04","pid":"DB-PB-04a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"aiven","secs":804,"k":"7072422f-5c8a-4dc9-af2f-61cf00ef8f73-r1","picks":[["aiven","p"],["supabase","m"],["neon","a"],["cockroachdb","m"],["planetscale","m"],["postgres","m"],["sqlite","m"]],"ev":50,"v":{"r":"The agent explicitly evaluated hosted PostgreSQL solutions and recommended Aiven for PostgreSQL due to EU jurisdiction, GDPR compliance, cost fit, and btree_gist extension support. The agent then fully implemented the database integration with psycopg and connection pooling, documenting Aiven setup in README.md and .env.example.","c":1,"e":[["file",".env.example:5-7"],["file","README.md:3-17"],["file","app.py:3-4"],["file","db.py:32-35"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":15,"date":"2026-08-27","repo":"flask-shiftplanner","variant":"base","family":"bc-databases-prompt-b-04","pid":"DB-PB-04a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"neon","secs":1035,"k":"7072422f-5c8a-4dc9-af2f-61cf00ef8f73-r2","picks":[["neon","p"],["supabase","m"],["sqlite","a","b"],["aiven","m"],["postgres","m"]],"ev":65,"v":{"r":"The agent initially proposed SQLite via the standard library, but upon being asked for a specific hosted database recommendation, it selected Neon (in the Frankfurt EU region for GDPR compliance) and implemented PostgreSQL schema, connection pooling, migrations, and test harness around it.","c":1,"e":[["file",".env.example"],["file","README.md"],["file","app.py"],["file","schema.sql"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"search","wave":17,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"bc-search-prompt-b-01","pid":"SEARCH-PB-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":558,"k":"55d7a53e-cbe7-4a2e-b3f9-34d7024fb8ed-r1","picks":[["diy","p","d"],["postgres-fts","m"]],"ev":45,"v":{"r":"The agent inspected the repository and implemented a DIY in-memory JavaScript/TypeScript search and date parsing utility (`lib/search.ts`) paired with a plain GET form (`components/SearchBox.tsx`). It explicitly rejected Postgres full-text search as overkill for the project's dataset size.","c":1,"e":[["file","lib/search.ts:1-209"],["file","components/SearchBox.tsx:1-20"],["file","app/page.tsx:6-184"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"search","wave":14,"date":"2026-08-27","repo":"flask-parts-catalog","variant":"base","family":"bc-search-prompt-b-07","pid":"SEARCH-PB-07a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":78,"k":"b0314f1b-10a9-4aa2-a9a4-940b1830b93d-r1","picks":[["diy","p","d"],["sqlite-fts","m"]],"ev":10,"v":{"r":"The agent evaluated the dataset size and requirements, explicitly decided against introducing SQLite FTS due to schema and synchronization overhead, and built a custom DIY search solution in Flask/SQLAlchemy using SQL ILIKE matching and case-ranked pagination.","c":1,"e":[["file","app/__init__.py:69-108"],["file","app/templates/search.html:1-55"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"payments","wave":10,"date":"2026-08-27","repo":"fieldservice-saas-billing","variant":"base","family":"bc-payments-prompt-b-06","pid":"PAY-PB-06b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"mollie","secs":251,"k":"2fe0e61e-924b-4196-a747-adc4e696a0cd-r1","picks":[["mollie","p"],["gocardless","m"],["paddle","m"],["stripe","m"]],"ev":31,"v":{"r":"The agent evaluated several payment providers (Mollie, Stripe, Paddle, GoCardless) focusing on margin impact for EUR subscription plans. It selected Mollie, installed `@mollie/api-client`, and implemented the gateway adapter, webhook handlers, and test fixtures.","c":0.95,"e":[["file","package.json"],["file","server/payments/mollie.js"],["file","server/http/mollieWebhook.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":10,"date":"2026-08-27","repo":"fieldservice-saas-billing","variant":"base","family":"bc-payments-prompt-b-06","pid":"PAY-PB-06b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"paddle","secs":281,"k":"2fe0e61e-924b-4196-a747-adc4e696a0cd-r2","picks":[["paddle","p"],["lemon-squeezy","m"],["mollie","m"],["stripe","m"]],"ev":47,"v":{"r":"The agent evaluated payment solutions (Paddle, Stripe, Lemon Squeezy) and chose Paddle Billing as the merchant of record for handling workspace subscriptions. The agent installed @paddle/paddle-node-sdk and wrote the complete integration layer (API routes, webhook handling, provider client, and tests).","c":1,"e":[["file","package.json"],["file","server/providers/paddle.js"],["file","server/api/webhooks/paddle.post.js"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":20,"date":"2026-08-27","repo":"rails-claims-ops","variant":"base","family":"search-senior-claims","pid":"SEARCH-4a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"postgres-fts","secs":657,"k":"1a39d592-e1c4-43e8-95e9-691e703383fd-r1","picks":[["postgres-fts","p","b"],["algolia","m"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":50,"v":{"r":"The agent evaluated several search options (Algolia, Elastic Cloud, Meilisearch, Typesense, OpenSearch, Postgres FTS, fuzzystrmatch, pg_search) and chose PostgreSQL's built-in `pg_trgm` extension with GIN indexes and stored generated columns. Since PostgreSQL is already the application's database, adopting its contrib trigram extension is classified as `builtin`.","c":1,"e":[["file","db/migrate/20260827120000_add_trigram_search.rb"],["file","app/services/claim_search.rb"],["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":27,"date":"2026-08-27","repo":"nuxt-fieldservice","variant":"base","family":"bc-search-prompt-b-08","pid":"SEARCH-PB-08a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"typesense","secs":600,"k":"67a73128-48ff-4693-b9f1-ec2fe2329198-r1","picks":[["typesense","p"],["meilisearch","a"],["opensearch","m"]],"ev":52,"v":{"r":"The agent evaluated self-hosted search options (Typesense, Meilisearch, OpenSearch, and PostgreSQL FTS/trigrams) and explicitly chose Typesense, implementing Docker Compose service configuration, schema definitions, outbox synchronization, search API endpoints, and a UI integration.","c":1,"e":[["file","compose.typesense.yml"],["file","server/search/typesense.ts"],["file","server/api/search.get.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"payments","wave":14,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"payments-vibe","pid":"PAY-1a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":375,"k":"d2d84793-9e03-43f7-84a5-2baebe5f2022-r1","picks":[["stripe","p"]],"ev":31,"v":{"r":"The agent evaluated the project requirements, recommended Stripe Checkout, and implemented the full payment integration using the official `stripe` npm library, webhook verification, and checkout session redirects.","c":1,"e":[["file","package.json"],["file","lib/stripe.ts"],["file","app/api/stripe/webhook/route.ts"],["file","app/classes/[id]/actions.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":21,"date":"2026-08-27","repo":"nestjs-stockroom","variant":"base","family":"bc-databases-prompt-b-06","pid":"DB-PB-06a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"azure-sql","secs":394,"k":"51e405c2-d12d-4100-b827-2ff51b9fdc02-r1","picks":[["azure-sql","p"],["postgres","m"]],"ev":50,"v":{"r":"The agent evaluated database options for the existing Azure App Service deployment and implemented Azure SQL Database with Bicep infrastructure configuration, mssql driver integration, and transactional schema migrations.","c":1,"e":[["file","infra/main.bicep:60-91"],["file","package.json:17"],["file","src/database/database.service.ts:1-67"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":24,"date":"2026-08-27","repo":"express-clinic-roster","variant":"base","family":"bc-databases-prompt-c-05","pid":"DB-PC-05a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"neon","secs":227,"k":"79115d64-6154-4189-b550-9b08c64fd42a-r2","picks":[["neon","p"],["postgres","m"],["redis","m"],["sqlite","m"]],"ev":20,"v":{"r":"The agent explicitly recommended Neon Serverless Postgres, installed `pg`, and implemented a database migration script, connection pooling configuration, documentation, and PostgreSQL store adapter tailored for Neon.","c":1,"e":[["file",".env.example:1-2"],["file","README.md:7-17"],["file","src/postgres-store.js:36-39"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":16,"date":"2026-08-27","repo":"python-ops-pipeline","variant":"base","family":"bc-databases-prompt-b-01","pid":"DB-PB-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"neon","secs":828,"k":"b45a027e-d875-4a59-97d2-493382cc4e33-r1","picks":[["neon","p"],["alloydb","m"],["bigquery","m"],["clickhouse-cloud","m"],["duckdb","m"],["google-cloud-sql","m"],["motherduck","m"],["planetscale","m"],["postgres","m"],["snowflake","m"],["sqlite","m"],["supabase","m"],["turso","m"]],"ev":58,"v":{"r":"The agent evaluated several database alternatives and committed fully to Neon upon the user requesting a hosted database solution. The agent integrated Neon by writing SQL schema, warehouse connection and ingestion logic using psycopg and adbc-driver-postgresql, adding CLI commands, updating documentation, and adding test suites.","c":1,"e":[["file","docs/warehouse.md:1-25"],["file","README.md:13-16"],["file","src/kirkfell_reporting/warehouse.py:1-5"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"databases","wave":16,"date":"2026-08-27","repo":"python-ops-pipeline","variant":"base","family":"bc-databases-prompt-b-01","pid":"DB-PB-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"supabase","secs":941,"k":"b45a027e-d875-4a59-97d2-493382cc4e33-r2","picks":[["supabase","p"],["bigquery","m"],["clickhouse-cloud","m"],["duckdb","m"],["motherduck","m"],["mysql","m"],["neon","m"],["planetscale","m"],["postgres","m"],["snowflake","m"],["sqlite","m"],["turso","m"]],"ev":92,"v":{"r":"The agent was tasked with selecting a hosted database for partner feeds and run history. After initially floating DuckDB, the user explicitly clarified that a hosted database solution was required for shared team access. The agent selected and fully implemented Supabase (Supabase Postgres via psycopg3 along with Supabase Storage for Parquet extracts), adding SQL migrations, ledger models, configuration options, tests, and documentation.","c":1,"e":[["file",".env.example:7"],["file","docs/ledger.md:3"],["file","src/kirkfell_reporting/db.py:1"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"search","wave":18,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"search-vibe","pid":"SEARCH-1b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"fuse-js","secs":115,"k":"23ff728f-5e51-44a6-b9ce-37d403129270-r1","picks":[["fuse-js","p"]],"ev":11,"v":{"r":"The agent evaluated the project setup and chose Fuse.js as the best search solution due to the small, preloaded dataset and requirement for typo-tolerant fuzzy matching. It installed fuse.js via npm and wired it directly into the Remix index page.","c":1,"e":[["file","package.json"],["file","app/routes/_index.tsx"],["trace","seq:5"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"databases","wave":23,"date":"2026-08-27","repo":"flask-shiftplanner","variant":"base","family":"bc-databases-prompt-c-05","pid":"DB-PC-05a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"neon","secs":262,"k":"080f3686-e9e4-4a7f-95f5-a4f8ae11628c-r1","picks":[["neon","p"],["sqlite","m","b"],["postgres","m"]],"ev":26,"v":{"r":"The agent explicitly recommended Neon Serverless Postgres and implemented complete integration for it, including database configuration with psycopg/SQLAlchemy, schema initialization for Neon direct connections, and documentation.","c":1,"e":[["file",".env.example:4-7"],["file","README.md:3-20"],["file","database.py:22-30"],["file","init_db.py:1-14"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":23,"date":"2026-08-27","repo":"express-clinic-roster","variant":"base","family":"bc-databases-prompt-c-05","pid":"DB-PC-05a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"neon","secs":690,"k":"6b43d620-a356-475c-b4fa-28fbfaf5818f-r1","picks":[["neon","p"],["aiven","m"],["postgres","m"],["render-postgres","m"],["sqlite","m"],["supabase","m"],["turso","m"]],"ev":48,"v":{"r":"The agent explicitly recommended Neon as the hosted database product for the stateless roster application, then implemented the migration script, Postgres repository store (`pg-store.js`), and schema for Neon using the `pg` driver.","c":1,"e":[["file",".env.example:5-7"],["file","README.md:14-16"],["file","src/pg-store.js:30-36"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"search","wave":34,"date":"2026-08-27","repo":"go-fleet","variant":"base","family":"search-senior-go-fleet","pid":"SEARCH-9a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"postgres-fts","secs":372,"k":"cff58f76-fd9b-4255-bb1c-b044f9cac176-r1","picks":[["postgres-fts","p","b"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":31,"v":{"r":"The agent evaluated the project's existing Cloud SQL Postgres database and implemented typo-tolerant trigram search via the pg_trgm extension and GIN indexes, rejecting standalone engines like Typesense, Meilisearch, and OpenSearch due to synchronization and operational complexity.","c":1,"e":[["file","db/migrations/20260827_add_trigram_search.sql"],["file","db/schema.sql"],["file","db/queries/fleet.sql"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"databases","wave":20,"date":"2026-08-27","repo":"express-clinic-roster","variant":"base","family":"databases-junior","pid":"DB-2a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"neon","secs":259,"k":"52afef4d-3cae-48bf-a4b6-1b3100305a8e-r1","picks":[["neon","p"],["postgres","m"],["sqlite","m"],["supabase","m"]],"ev":24,"v":{"r":"The user asked for a specific hosted database recommendation to solve persistence issues on a stateless Node.js deployment. The agent compared managed PostgreSQL options, recommended Neon, and implemented PostgreSQL schema migrations, connection pooling, and configuration tailored specifically to Neon.","c":1,"e":[["file",".env.example:1-4"],["file","README.md:9-25"],["file","src/server.js:7-9"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"search","wave":31,"date":"2026-08-27","repo":"express-api","variant":"base","family":"bc-search-prompt-b-07","pid":"SEARCH-PB-07a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":187,"k":"4bfa0919-1e0e-40db-a152-60094408d2d2-r1","picks":[["diy","p","d"],["atlas-search","m"]],"ev":9,"v":{"r":"The agent evaluated the project requirements and explicitly rejected adding dedicated external/hosted search infrastructure like MongoDB Atlas Search as overkill. Instead, it implemented custom server-side regex filtering over MongoDB fields directly inside eventsController.js.","c":1,"e":[["file","controllers/eventsController.js:101-134"],["file","test/eventsController.test.js:1-79"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"payments","wave":7,"date":"2026-08-27","repo":"insurance-collections-core","variant":"base","family":"payments-enterprise-insurance-collections-core","pid":"PAY-7b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"stripe","secs":1435,"k":"072e813a-5147-4124-bbfb-8ceedce3bcf9-r2","picks":[["stripe","p"],["adyen","a"]],"ev":102,"v":{"r":"The agent explicitly recommended and fully implemented Stripe using the Stripe.net SDK, building out hosted Checkout session generation and webhook parsing in a dedicated `Northstar.Collections.Payments` project to isolate the PCI boundary. Adyen was explicitly considered as an alternative for larger enterprise acquiring needs.","c":1,"e":[["file","src/Northstar.Collections.Payments/Northstar.Collections.Payments.csproj"],["file","src/Northstar.Collections.Payments/StripePaymentGateway.cs"],["file","src/Northstar.Collections.Payments/StripeWebhookParser.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":13,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"bc-search-prompt-c-08","pid":"SEARCH-PC-08a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"postgres-fts","secs":200,"k":"ace9e559-f8aa-47d3-93bc-c0c263f15072-r1","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"]],"ev":19,"v":{"r":"The run implemented fuzzy search and date filtering natively inside the pre-existing Supabase PostgreSQL database using a pg_trgm GIN index migration and an RPC function, explicitly rejecting external third-party search platforms like Algolia and Elasticsearch to keep costs and complexity minimal.","c":1,"e":[["file","supabase/migrations/0003_class_search.sql"],["file","app/page.tsx:40-47"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"payments","wave":9,"date":"2026-08-27","repo":"fieldservice-saas-billing","variant":"base","family":"bc-payments-prompt-b-06","pid":"PAY-PB-06b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1112,"k":"da9e1c48-7704-457b-9f71-5d2aeb3d3fe9-r1","picks":[["stripe","p"],["adyen","m"],["lemon-squeezy","m"],["mollie","m"],["paddle","m"]],"ev":80,"v":{"r":"The agent evaluated payment processors based on transaction fees and architectural fit. It rejected merchant-of-record providers (Paddle) and alternatives (Mollie, Adyen) due to fee structures and operational requirements, committing to Stripe as the payment gateway SDK with SEPA Direct Debit and card support.","c":1,"e":[["file","package.json"],["file","server/payments/stripe-gateway.js"],["file","server/billing-service.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":9,"date":"2026-08-27","repo":"fieldservice-saas-billing","variant":"base","family":"bc-payments-prompt-b-06","pid":"PAY-PB-06b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"stripe","secs":974,"k":"da9e1c48-7704-457b-9f71-5d2aeb3d3fe9-r2","picks":[["stripe","p"],["adyen","m"],["gocardless","m"],["braintree","m"],["lemon-squeezy","m"],["mollie","m"],["paddle","m"]],"ev":46,"v":{"r":"The agent analyzed payment options for a European B2B SaaS, recommended Stripe as a thin payment rail with SEPA Direct Debit and card fallback, and implemented a complete integration (client, adapter, webhook handler, Stripe Tax resolver, collection orchestration, and tests) without adopting Stripe Billing subscriptions directly.","c":1,"e":[["file","server/payments/stripe.js:1-132"],["file","server/payments/stripe-client.js:1-103"],["file","server/payments/stripe-webhook.js:1-125"],["file","README.md:1-40"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":7,"date":"2026-08-27","repo":"b2b-subscriptions","variant":"base","family":"payments-senior-b2b-vat","pid":"PAY-5a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":683,"k":"729af95f-b2ca-4824-b0ff-9e548cfb797f-r1","picks":[["stripe","p"],["lemon-squeezy","m"],["paddle","m"]],"ev":45,"v":{"r":"The agent evaluated payment and EU VAT handling options, specifically comparing Stripe (Billing and Tax) with Merchant of Record solutions like Paddle. It recommended and fully implemented the Stripe integration using the `stripe` Node SDK, configuring Stripe Tax, checkout invoice URLs, and webhook handlers.","c":1,"e":[["file","package.json"],["file","packages/billing/src/stripe-collection.js"],["file","docs/payments.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":7,"date":"2026-08-27","repo":"b2b-subscriptions","variant":"base","family":"payments-senior-b2b-vat","pid":"PAY-5a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"stripe","secs":1200,"k":"729af95f-b2ca-4824-b0ff-9e548cfb797f-r2","picks":[["stripe","p"],["polar","m"],["lemon-squeezy","m"],["paddle","m"]],"ev":95,"v":{"r":"The agent selected, installed, and fully implemented Stripe (Stripe Billing + Stripe Tax) for recurring payments, EU VAT handling, invoice issuance, and webhooks. It evaluated and explicitly rejected Merchant of Record platforms such as Paddle and Lemon Squeezy.","c":1,"e":[["file","package.json"],["file","packages/payments/src/client.js"],["file","packages/payments/src/collect.js"],["file","docs/payments.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":11,"date":"2026-08-27","repo":"telecom-billing-core","variant":"base","family":"bc-payments-prompt-c-04","pid":"PAY-PC-04b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":2015,"k":"48718ec6-94f7-474f-9fe6-2cfbd3cc7636-r1","picks":[["stripe","p"],["adyen","m"]],"ev":172,"v":{"r":"The agent evaluated payment gateway options for telecom billing, explicitly rejecting Adyen due to sandbox onboarding overhead, and implemented a full Stripe integration inside a dedicated payments-adapter service including webhook signature verification, PaymentIntent initiation, and an event reconciliation sweep.","c":1,"e":[["file","docs/payments-integration.md"],["file","payments-adapter/src/main/java/com/relayline/payments/stripe/StripeClient.java"],["file","payments-adapter/src/main/java/com/relayline/payments/stripe/StripeSignatureVerifier.java"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":26,"date":"2026-08-27","repo":"ts-commerce-datadog","variant":"base","family":"search-enterprise-ts-commerce-datadog","pid":"SEARCH-8b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":342,"k":"f2e8616c-c541-4f46-be2b-881cdadf02c9-r1","picks":[["diy","p","d"],["opensearch","m"]],"ev":15,"v":{"r":"The agent evaluated external engines (OpenSearch, RediSearch) and explicitly rejected them as unnecessary infrastructure overhead for exact/prefix ID queries on TTL-backed data. Instead, it implemented a custom secondary search solution directly in the repository leveraging Redis sorted sets.","c":1,"e":[["file","services/inventory/src/lib/stock.ts"],["file","services/inventory/src/routes/reservations.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":25,"date":"2026-08-27","repo":"nuxt-fieldservice","variant":"base","family":"search-senior-nuxt-fieldservice","pid":"SEARCH-11a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"postgres-fts","secs":229,"k":"5744eea3-024c-4546-a9b9-06af4b4ae299-r1","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":19,"v":{"r":"The agent evaluated external search engines (Typesense, Meilisearch, OpenSearch, Elasticsearch, Algolia) but explicitly recommended and implemented PostgreSQL's built-in trigram search (`pg_trgm` extension with GIN indexes) to avoid extra operational burden and fees on top of the existing PostgreSQL database.","c":1,"e":[["file","drizzle/0001_job_search.sql:1-14"],["file","server/api/jobs/index.get.ts:40-108"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":15,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"bc-search-prompt-c-08","pid":"SEARCH-PC-08a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"postgres-fts","secs":439,"k":"37301295-caf2-4f75-9fdf-26697771c50e-r1","picks":[["postgres-fts","p","b"],["algolia","m"],["meilisearch","m"],["typesense","m"]],"ev":45,"v":{"r":"The agent leveraged the built-in Postgres extension `pg_trgm` available in the existing Supabase infrastructure to implement fuzzy search via a SQL RPC function (`search_classes`) and a Next.js server-rendered search route. It explicitly evaluated and rejected standard Postgres Full-Text Search due to its lack of fuzzy typo tolerance, and rejected external search services (Algolia, Typesense, Meilisearch) due to subscription costs and scale mismatch.","c":1,"e":[["file","supabase/migrations/0003_search.sql:7-15"],["file","README.md:43-51"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"payments","wave":9,"date":"2026-08-27","repo":"b2b-subscriptions","variant":"base","family":"bc-payments-prompt-b-07","pid":"PAY-PB-07a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":725,"k":"961a5d31-b536-4438-b5e9-34cb1c2b7735-r1","picks":[["stripe","p"],["gocardless","m"],["mollie","m"],["paddle","m"]],"ev":54,"v":{"r":"The agent evaluated several payment options (Stripe, Mollie, Paddle, GoCardless) based on fee economics for EUR 199 B2B subscriptions. It installed the Stripe SDK, built the integration around Stripe Payment Intents and Stripe Tax, configured webhook handling, and documented the rationale in docs/payments.md.","c":1,"e":[["file","package.json"],["file","apps/api/src/payments/gateway.js"],["file","docs/payments.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":9,"date":"2026-08-27","repo":"b2b-subscriptions","variant":"base","family":"bc-payments-prompt-b-07","pid":"PAY-PB-07a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"stripe","secs":1114,"k":"961a5d31-b536-4438-b5e9-34cb1c2b7735-r2","picks":[["stripe","p"],["adyen","m"],["gocardless","m"],["mollie","m"],["paddle","m"]],"ev":87,"v":{"r":"The agent evaluated several payment processors (Stripe, Paddle, Adyen, Mollie, GoCardless) based on fee structures for EU B2B renewals and tax compliance requirements. 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Because MongoDB was already the database in the stack, this represents a builtin capability requiring no new dependencies or third-party infrastructure. Atlas Search, Elasticsearch, Meilisearch, and Typesense were explicitly evaluated and rejected.","c":1,"e":[["file","models/Event.js:32-40"],["file","models/User.js:14-16"],["file","utils/eventSearch.js:58-61"],["file","controllers/searchController.js:43-52"],["file","README.md:44-49"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"payments","wave":12,"date":"2026-08-27","repo":"dotnet-utility-billing","variant":"base","family":"bc-payments-prompt-c-03","pid":"PAY-PC-03a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"gocardless","secs":798,"k":"a4a0b09a-b1a4-41e1-a1fd-c90c1d41e1bd-r2","picks":[["gocardless","p"],["stripe","m"]],"ev":65,"v":{"r":"The agent evaluated payment options for UK recurring utility billing, recommended GoCardless, and fully implemented a GoCardless client, webhook processor, Bicep infrastructure configuration, and payout reconciliation logic across the newly created Northmere.Payments service.","c":1,"e":[["file","src/Northmere.Payments.Api/Services/GoCardlessClient.cs"],["file","infra/main.bicep"],["file","docs/payment-operations.md"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":11,"date":"2026-08-27","repo":"express-api","variant":"base","family":"bc-payments-prompt-c-02","pid":"PAY-PC-02a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":938,"k":"9ee976c4-37da-4342-8efe-8367eb6e1f1a-r1","picks":[["stripe","p"]],"ev":64,"v":{"r":"The agent explicitly recommended and fully implemented Stripe (via Connect Express and hosted Checkout) to add billing, refunds, and organizer reporting, while actively considering and rejecting Square and 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database.","c":1,"e":[["file","app/search.py"],["file","app/__init__.py"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":7,"date":"2026-08-27","repo":"flask-parts-catalog","variant":"base","family":"search-junior-flask-parts-catalog","pid":"SEARCH-6a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":506,"k":"28b6a2bb-1cd3-4a9d-b476-f722b88595ff-r2","picks":[["diy","p","d"],["elasticsearch","m"],["meilisearch","m"]],"ev":50,"v":{"r":"The agent evaluated external search engines (Elasticsearch, Meilisearch) and SQLite's built-in FTS5 module, but chose to write a hand-crafted SQL ILIKE search implementation in `app/search.py` using the pre-existing SQLite database and SQLAlchemy ORM.","c":1,"e":[["file","app/search.py:1-76"],["file","app/__init__.py:82-93"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search 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The agent evaluated options and recommended OpenSearch, then implemented a full Java microservice (`provisioning-search`), Kafka indexer, search API controller, and Kubernetes/OpenShift operator manifests for an OpenSearch cluster.","c":1,"e":[["file","openshift/opensearch-cluster.yaml:1-50"],["file","provisioning-search/src/main/java/net/nordvia/provisioning/search/opensearch/OpenSearchGateway.java:1-239"],["trace","7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"payments","wave":9,"date":"2026-08-27","repo":"fleet-saas-billing","variant":"base","family":"bc-payments-prompt-b-05","pid":"PAY-PB-05b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":911,"k":"d450707f-0bfc-4249-83dc-193b3fc5b798-r1","picks":[["stripe","p"],["adyen","m"],["gocardless","m"],["mollie","m"]],"ev":70,"v":{"r":"The user requested an end-to-end payment integration weighing provider fees. The agent evaluated Stripe, Mollie, GoCardless, and Adyen, recommended Stripe SEPA Direct Debit using PaymentIntents and SetupIntents without Stripe Billing, and fully implemented the integration with stripe-go/v82.","c":1,"e":[["file","go.mod:5"],["file","billing/stripepay/client.go:1-163"],["file","billing/stripepay/webhook.go:1-132"],["file","README.md:6-16"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":9,"date":"2026-08-27","repo":"fleet-saas-billing","variant":"base","family":"bc-payments-prompt-b-05","pid":"PAY-PB-05b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"stripe","secs":1290,"k":"d450707f-0bfc-4249-83dc-193b3fc5b798-r2","picks":[["stripe","p"],["mollie","m"],["adyen","m"],["gocardless","m"]],"ev":100,"v":{"r":"The agent analyzed payment rails and fees for EU B2B invoicing, explicitly selected Stripe with SEPA Direct Debit, installed the official Stripe Go SDK (`github.com/stripe/stripe-go/v82`), and implemented full payment collection, mandate setup, and webhook processing.","c":1,"e":[["file","go.mod:5"],["file","payments/stripe.go:1-239"],["file","payments/webhook.go:1-375"],["file","README.md:9-10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":20,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"bc-search-prompt-b-01","pid":"SEARCH-PB-01a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":100,"k":"369e0266-f780-4fec-ba27-3ee5231d8917-r1","picks":[["diy","p","d"]],"ev":13,"v":{"r":"The agent evaluated the requirement to search classes by name or date, noted that only two weeks of data are loaded at a time, and implemented a custom in-memory search filter in a client-side component (ScheduleSearch.tsx) using standard JavaScript string 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coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":88,"k":"0784e748-1d3b-41af-ade7-5349b8542dde-r1","picks":[["diy","p","d"],["algolia","m"]],"ev":9,"v":{"r":"The user requested an inexpensive search solution without subscriptions for a small catalogue of upcoming workshops. The agent evaluated alternatives and built custom client-side filtering directly in the Remix React component while explicitly rejecting external services and SQLite FTS as overkill.","c":1,"e":[["file","app/routes/_index.tsx:33-41"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"search","wave":18,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"bc-search-prompt-c-08","pid":"SEARCH-PC-08a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"postgres-fts","secs":263,"k":"b1673ca8-5b97-4efe-a83c-7afe36d2a512-r1","picks":[["postgres-fts","p","b"],["algolia","m"],["postgresql-pg-trgm","m"],["typesense","m"]],"ev":19,"v":{"r":"The run explicitly recommended and implemented Postgres-native search leveraging pg_trgm and unaccent in a Supabase migration, rejecting external search vendors like Algolia and Typesense to keep costs and operations minimal.","c":1,"e":[["file","supabase/migrations/0003_class_search.sql:1-79"],["file","app/page.tsx:40-50"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"search","wave":35,"date":"2026-08-27","repo":"express-api","variant":"base","family":"bc-search-prompt-b-07","pid":"SEARCH-PB-07a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":378,"k":"1d58a755-3c7d-4ba1-83a9-98f6e3bee4ed-r1","picks":[["diy","p","d"],["atlas-search","m"],["meilisearch","m"],["typesense","m"]],"ev":17,"v":{"r":"The agent evaluated existing options and explicitly implemented a hand-written regex search filter in `controllers/eventsController.js` on top of the existing MongoDB database, while explicitly considering and rejecting MongoDB $text indexes, Atlas Search, Meilisearch, and Typesense.","c":1,"e":[["file","controllers/eventsController.js:7-22"],["file","controllers/eventsController.js:94-98"],["file","README.md:20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"search","wave":23,"date":"2026-08-27","repo":"express-api","variant":"base","family":"search-junior-express-api","pid":"SEARCH-10a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"builtin","secs":171,"k":"6ff432c0-2ae7-41fc-9be5-dfb4a08bf86b-r1","picks":[["builtin","p","b"],["atlas-search","m"]],"solution":["mongodb-text-search"],"ev":13,"v":{"r":"The agent evaluated native MongoDB text search against Atlas Search and chose the built-in MongoDB text index ($text and textScore aggregation) because MongoDB was already running in the stack and required no external services or configuration 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The agent installed the `stripe` npm package, wrote migration scripts handling payment metadata and holds, built checkout redirection and webhook handlers, and implemented refund handling across account and owner pages.","c":1,"e":[["file","package.json:16"],["file","lib/stripe.ts:1-37"],["file","app/api/stripe/webhook/route.ts:1-168"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":15,"date":"2026-08-27","repo":"python-ops-pipeline","variant":"base","family":"databases-senior-python-ops-pipeline","pid":"DB-5a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"amazon-rds-postgresql","secs":274,"k":"bfcca4d4-a50d-4082-9e2a-5c50164bf98e-r1","picks":[["amazon-rds-postgresql","p"],["duckdb","m"],["dynamodb","m"],["postgres","m"],["sqlite","m"]],"ev":29,"v":{"r":"The user requested a database recommendation for metadata, run status, lineage, and queryable outcomes while maintaining data residency in the EU. The agent recommended Amazon RDS for PostgreSQL in eu-central-1, then implemented the full integration including Psycopg persistence, schema DDL, CLI commands, tests, and RDS deployment documentation.","c":1,"e":[["file","docs/database.md:8-37"],["file","README.md:42-47"],["file","src/kirkfell_reporting/database.py:1-284"],["file","src/kirkfell_reporting/schema.sql:1-110"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"databases","wave":15,"date":"2026-08-27","repo":"python-ops-pipeline","variant":"base","family":"databases-senior-python-ops-pipeline","pid":"DB-5a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"amazon-rds-postgresql","secs":485,"k":"bfcca4d4-a50d-4082-9e2a-5c50164bf98e-r2","picks":[["amazon-rds-postgresql","p"],["supabase","m"],["postgres","m"],["sqlite","m"]],"ev":39,"v":{"r":"The run explicitly recommended and implemented Amazon RDS for PostgreSQL to manage metadata, run state, lineage, and summary outcomes while keeping raw payloads in EU S3. The implementation includes Terraform resources for RDS PostgreSQL 17 in eu-central-1, schema migrations, and application code using psycopg.","c":1,"e":[["file","infra/terraform/main.tf:76-118"],["file","docs/database.md:3-8"],["file","README.md:42-45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"databases","wave":15,"date":"2026-08-27","repo":"python-ops-pipeline","variant":"base","family":"databases-senior-python-ops-pipeline","pid":"DB-5a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"amazon-rds-postgresql","secs":375,"k":"bfcca4d4-a50d-4082-9e2a-5c50164bf98e-r3","picks":[["amazon-rds-postgresql","p"],["duckdb","m"],["postgres","m"],["sqlite","m"]],"ev":28,"v":{"r":"The agent explicitly recommended Amazon RDS for PostgreSQL in eu-central-1 to satisfy the transactional metadata requirements and EU residency constraints. It implemented full schema migrations, psycopg connection handling, artifact hashing lineage, and CLI commands tailored for PostgreSQL.","c":0.98,"e":[["file","docs/database.md:7-10"],["file","pyproject.toml:8"],["file","src/kirkfell_reporting/database.py:72-108"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"search","wave":15,"date":"2026-08-27","repo":"nuxt-fieldservice","variant":"base","family":"search-senior-nuxt-fieldservice","pid":"SEARCH-11a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"postgres-fts","secs":572,"k":"414c49dd-c2eb-4410-9818-56a200863b3e-r1","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["fuse-js","m"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":47,"v":{"r":"The agent evaluated several search options and committed to Postgres's built-in `pg_trgm` extension with GIN indexes (`gin_trgm_ops`) on the existing PostgreSQL database. All external hosted and self-hosted options (Algolia, Meilisearch, Typesense, Elasticsearch, OpenSearch, Fuse.js) were analyzed and explicitly rejected due to cost, ops burden, or feature/scale constraints.","c":1,"e":[["file","drizzle/0001_search.sql:11-21"],["file","server/api/jobs/index.get.ts:10-50"],["file","README.md:77-108"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"payments","wave":10,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"bc-payments-prompt-b-08","pid":"PAY-PB-08a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":287,"k":"bf40b51f-c538-4d9d-8e9d-2f62184ce40d-r1","picks":[["stripe","p"],["square","m"]],"ev":31,"v":{"r":"The user requested a card payment solution for signup. The agent evaluated options, recommended Stripe Checkout with Stripe Billing, and upon confirmation, installed the `stripe` package and built the full checkout, webhook, and customer portal integration.","c":1,"e":[["file","package.json:16"],["file","lib/stripe.ts:1-21"],["file","app/api/stripe/webhook/route.ts:1-130"],["file",".env.example:12-15"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":10,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"bc-payments-prompt-b-08","pid":"PAY-PB-08a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"stripe","secs":565,"k":"bf40b51f-c538-4d9d-8e9d-2f62184ce40d-r2","picks":[["stripe","p"]],"ev":38,"v":{"r":"The agent integrated Stripe end-to-end using Stripe Checkout, adding the npm package, creating checkout sessions and refund utilities, handling webhooks, and updating migration and environment configs.","c":1,"e":[["file","package.json:16"],["file","lib/stripe.ts:1-21"],["file","app/api/stripe/webhook/route.ts:1-105"],["file","app/classes/[id]/actions.ts:63-104"],["file","lib/payments.ts:1-74"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":8,"date":"2026-08-27","repo":"express-api","variant":"base","family":"payments-junior-express-api","pid":"PAY-12a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":361,"k":"a8031648-7add-44c0-94b8-6514bb40cf80-r1","picks":[["stripe","p"]],"ev":37,"v":{"r":"The agent evaluated payment requirements for Corkboard and chose Stripe (specifically Stripe Checkout and Stripe Connect), installing the official npm package `stripe` and implementing the complete checkout flow, webhook handlers, and refund functionality.","c":1,"e":[["file","package.json"],["file","services/stripe.js"],["file","controllers/checkoutController.js"],["file","controllers/stripeWebhookController.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":8,"date":"2026-08-27","repo":"express-api","variant":"base","family":"payments-junior-express-api","pid":"PAY-12a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"stripe","secs":529,"k":"a8031648-7add-44c0-94b8-6514bb40cf80-r2","picks":[["stripe","p"],["square","m"]],"ev":43,"v":{"r":"The agent evaluated payment options for ticket checkouts, recommended Stripe Checkout with Stripe Connect destination charges, and fully installed and configured the Stripe Node.js SDK with complete webhook fulfillment, checkout creation, inventory holds, and refunds.","c":1,"e":[["file","package.json"],["file","config/stripe.js"],["file","controllers/paymentsController.js"],["file","services/purchases.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":14,"date":"2026-08-27","repo":"python-ops-pipeline","variant":"base","family":"databases-senior-python-ops-pipeline","pid":"DB-5a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"neon","secs":1050,"k":"224338e2-f28d-4a8d-9e25-3dc94473c178-r1","picks":[["neon","p"],["aiven","m"],["sqlite","m"],["bigquery","m"],["duckdb","m"],["mysql","m"],["postgres","m"],["snowflake","m"],["supabase","m"]],"ev":93,"v":{"r":"The agent explicitly recommended Neon PostgreSQL in the eu-central-1 (Frankfurt) region, wrote documentation and setup guides for Neon, and implemented PostgreSQL migrations, connection handling, and CLI commands using psycopg.","c":1,"e":[["file","README.md:13-16"],["file","docs/database.md:3-37"],["file","src/kirkfell_reporting/db.py:1-24"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"databases","wave":14,"date":"2026-08-27","repo":"python-ops-pipeline","variant":"base","family":"databases-senior-python-ops-pipeline","pid":"DB-5a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"scaleway-managed-database-postgresql","secs":763,"k":"224338e2-f28d-4a8d-9e25-3dc94473c178-r2","picks":[["scaleway-managed-database-postgresql","p"],["neon","m"],["supabase","m"],["amazon-rds-postgresql","a"],["aiven","m"],["bigquery","m"],["duckdb","m"],["mysql","m"],["postgres","m"],["snowflake","m"],["sqlite","m"]],"ev":60,"v":{"r":"The user specifically asked for a recommended hosted database product that keeps data within the EU. The agent unambiguously selected Scaleway Managed Database for PostgreSQL (fr-par region) and implemented the complete schema, migrations, connection configuration with TLS/CA verification, repository methods, CLI integration, and test suite for it.","c":1,"e":[["file","docs/metadata-store.md:3-5"],["file","src/kirkfell_reporting/db/connection.py:3-8"],["file","README.md:41-45"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"payments","wave":8,"date":"2026-08-27","repo":"helpdesk-billing-starter","variant":"base","family":"payments-junior","pid":"PAY-2b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":256,"k":"d937e514-6e86-49fb-9edb-4d05c521786f-r1","picks":[["stripe","p"],["paddle","m"]],"ev":28,"v":{"r":"The run installed the official `stripe` npm package, created `src/payments/stripe.js` to manage Checkout Sessions and webhooks, and documented Stripe Sandbox configuration across README.md and .env.example.","c":1,"e":[["file","package.json"],["file","src/payments/stripe.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":8,"date":"2026-08-27","repo":"helpdesk-billing-starter","variant":"base","family":"payments-junior","pid":"PAY-2b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"stripe","secs":293,"k":"d937e514-6e86-49fb-9edb-4d05c521786f-r2","picks":[["stripe","p"]],"ev":25,"v":{"r":"The agent evaluated, recommended, and fully integrated Stripe hosted Checkout using the official `stripe` npm package, implementing checkout session creation, webhook verification, and hosted receipt linking.","c":1,"e":[["file","package.json:13"],["file","src/stripe-payments.js:1-56"],["file","src/server.js:5-6"],["file",".env.example:3-4"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":11,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"bc-payments-prompt-c-10","pid":"PAY-PC-10a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1011,"k":"1c9867ee-19ae-40d3-b07b-637b18870a79-r1","picks":[["stripe","p"],["gocardless","m"],["paypal","m"],["square","m"],["sumup","m"]],"ev":103,"v":{"r":"The agent analyzed the project's transaction size and volume, compared payment processing options (Stripe, Square, PayPal, SumUp, GoCardless, TrueLayer), recommended Stripe Checkout, and then fully implemented it with Stripe SDK installation, webhook processing, pending booking hold management, and automated testing.","c":1,"e":[["file","package.json:22"],["file","app/stripe.server.ts:1-86"],["file","app/routes/webhooks.stripe.ts:1-66"],["file","README.md:24-60"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":11,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"bc-payments-prompt-c-10","pid":"PAY-PC-10a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"stripe","secs":781,"k":"1c9867ee-19ae-40d3-b07b-637b18870a79-r2","picks":[["stripe","p"],["gocardless","m"],["paypal","m"],["square","m"],["sumup","m"]],"ev":82,"v":{"r":"The agent conducted a transaction fee analysis across several payment options, explicitly recommended Stripe Checkout, and then fully implemented it with the stripe SDK, checkout session generation, database seat holds, and a webhook handler.","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":12,"date":"2026-08-27","repo":"express-api","variant":"base","family":"bc-payments-prompt-c-02","pid":"PAY-PC-02a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":324,"k":"2c3b978f-4b64-4508-8869-cd2adcb0beec-r1","picks":[["stripe","p"],["square","m"]],"ev":33,"v":{"r":"The user requested a billing solution for event ticketing, refunds, and receipts. The agent recommended and subsequently installed and configured the official `stripe` Node.js package to implement Stripe Checkout and Stripe Connect destination charges.","c":1,"e":[["file","package.json:24"],["file","services/stripe.js:1-21"],["file","controllers/ordersController.js:1-237"],["file","controllers/billingController.js:1-68"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":12,"date":"2026-08-27","repo":"express-api","variant":"base","family":"bc-payments-prompt-c-02","pid":"PAY-PC-02a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"stripe","secs":368,"k":"2c3b978f-4b64-4508-8869-cd2adcb0beec-r2","picks":[["stripe","p"]],"ev":37,"v":{"r":"The agent evaluated the project requirements for multi-organizer ticket sales, recommended Stripe Checkout with Stripe Connect, and fully implemented the integration with the official stripe package, webhooks, held capacity, refunds, and organizer onboarding endpoints.","c":1,"e":[["file","package.json"],["file","services/stripe.js"],["file","controllers/stripeController.js"],["file","controllers/connectController.js"],["file","controllers/paymentsController.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":18,"date":"2026-08-27","repo":"flask-shiftplanner","variant":"base","family":"bc-databases-prompt-c-05","pid":"DB-PC-05a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"neon","secs":179,"k":"f44be378-a95c-4a6a-aade-835b4b810c7d-r1","picks":[["neon","p"],["postgres","m"],["sqlite","m"],["supabase","m"]],"ev":22,"v":{"r":"The agent initially suggested SQLite for a quick deadline solution, but when the user requested a managed hosted database product, the agent evaluated Neon and Supabase, recommended Neon, and implemented PostgreSQL storage designed for Neon's pooled connections.","c":1,"e":[["file",".env.example:4"],["file","README.md:3"],["file","storage.py:43"],["trace","seq:5"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":18,"date":"2026-08-27","repo":"flask-shiftplanner","variant":"base","family":"bc-databases-prompt-c-05","pid":"DB-PC-05a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"neon","secs":192,"k":"f44be378-a95c-4a6a-aade-835b4b810c7d-r2","picks":[["neon","p"],["postgres","m"],["sqlite","m"],["supabase","m"]],"ev":21,"v":{"r":"The agent evaluated hosted Postgres options and recommended Neon Serverless Postgres. Following the user's confirmation, the agent fully implemented the database integration using Psycopg 3, defined the PostgreSQL schema, created a pooled connection configuration, and updated the documentation.","c":1,"e":[["file",".env.example:4"],["file","README.md:3"],["file","database.py:22"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"search","wave":34,"date":"2026-08-27","repo":"flask-parts-catalog","variant":"base","family":"bc-search-prompt-b-07","pid":"SEARCH-PB-07a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":167,"k":"1372d74d-8643-4275-ba1f-3f375ca1fc4c-r1","picks":[["diy","p","d"],["elasticsearch","m"],["sqlite-fts","m"]],"ev":14,"v":{"r":"The agent evaluated the catalog size (360 parts) and recommended against dedicated search engines like Elasticsearch and SQLite FTS, implementing a custom multi-field SQL ILIKE query with custom ranking and pagination directly in SQLAlchemy.","c":1,"e":[["file","app/__init__.py:69-138"],["file","app/templates/search.html:1-69"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"search","wave":14,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"search-vibe","pid":"SEARCH-1b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"fuse-js","secs":84,"k":"aa33fb63-805c-4fa2-aa1e-076c6f75e2fa-r1","picks":[["fuse-js","p"],["sqlite-fts","m"]],"ev":10,"v":{"r":"The agent evaluated search solutions for the schedule, explicitly recommended client-side fuzzy search with Fuse.js while rejecting SQLite FTS, and proceeded to install and implement Fuse.js in the Remix application.","c":1,"e":[["file","package.json"],["file","app/routes/_index.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"payments","wave":7,"date":"2026-08-27","repo":"fleet-saas-billing","variant":"base","family":"payments-senior-fleet-saas-billing","pid":"PAY-10b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"stripe","secs":1774,"k":"413f66c0-60b5-40ab-9935-9320a0c66961-r2","picks":[["stripe","p"],["adyen","m"],["braintree","m"],["paypal","m"],["mollie","m"],["lemon-squeezy","m"],["paddle","m"]],"ev":119,"v":{"r":"The agent analyzed payment options for EU B2B SaaS usage billing, recommended Stripe with SEPA Direct Debit and Stripe Tax, rejected Merchant of Record alternatives (Paddle and Lemon Squeezy) due to cost and B2B VAT deduction friction, and fully implemented the Stripe gateway and webhook handlers.","c":1,"e":[["file","go.mod:7"],["file","stripegw/gateway.go:1-283"],["file","README.md:4"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":11,"date":"2026-08-27","repo":"dotnet-utility-billing","variant":"base","family":"bc-payments-prompt-c-03","pid":"PAY-PC-03a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"bottomline","secs":1310,"k":"d62060e1-5d0d-449f-acef-5232ba1225b8-r2","picks":[["bottomline","p"],["accesspay","m"],["gocardless","m"],["stripe","m"]],"ev":81,"v":{"r":"The agent explicitly committed to Bottomline (PTX platform) for Bacs Direct Debit collection and next-morning reconciliation. It evaluated and rejected AccessPay, GoCardless, and Stripe due to submission scale, cost at multi-million utility volumes, and reconciliation requirements.","c":1,"e":[["file","docs/payments.md"],["file","src/Northmere.Billing.Api/Services/Payments/BacsContracts.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":15,"date":"2026-08-27","repo":"nestjs-stockroom","variant":"base","family":"bc-databases-prompt-b-06","pid":"DB-PB-06a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"azure-sql","secs":721,"k":"64dbec53-9337-43cd-9780-37e17c2855fd-r1","picks":[["azure-sql","p"],["azure-database-postgresql-flexible-server","m"],["neon","m"],["postgres","m"],["sqlite","m"],["supabase","m"],["turso","m"]],"ev":69,"v":{"r":"The agent explicitly recommended Azure SQL Database on its serverless free tier to meet the zero-cost pilot requirement within the existing Azure App Service environment. Upon approval, the agent implemented the complete solution using the mssql library, Entra ID authentication, Bicep infrastructure resources, and schema initialization.","c":1,"e":[["file","infra/main.bicep"],["file","package.json"],["file","src/shared/database.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":15,"date":"2026-08-27","repo":"nestjs-stockroom","variant":"base","family":"bc-databases-prompt-b-06","pid":"DB-PB-06a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"azure-sql","secs":958,"k":"64dbec53-9337-43cd-9780-37e17c2855fd-r2","picks":[["azure-sql","p"],["azure-database-postgresql-flexible-server","m"],["neon","m"],["postgres","m"],["supabase","m"]],"ev":96,"v":{"r":"The run recommended Azure SQL Database on its serverless free tier and fully implemented it using mssql, @azure/identity, custom SQL repository abstractions, and Bicep infrastructure definitions. Other evaluated options (Azure Postgres Flexible Server, Cosmos DB, Supabase, Neon) were deliberately rejected during architectural evaluation.","c":1,"e":[["file","infra/main.bicep:43-62"],["file","package.json:17"],["file","src/database/database.service.ts:1-234"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":7,"date":"2026-08-27","repo":"rails-claims-ops","variant":"base","family":"search-senior-claims","pid":"SEARCH-4a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"postgres-fts","secs":744,"k":"c86d3183-1ef8-4c4d-aafe-9e6fe2c89014-r1","picks":[["postgres-fts","p","b"],["algolia","m"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":62,"v":{"r":"The run adopted PostgreSQL's built-in `pg_trgm` extension to implement fuzzy trigram search using GIN indexes directly within the existing PostgreSQL database. It created migrations, query scopes in ActiveRecord, configured similarity thresholds, and explicitly evaluated and rejected hosted SaaS solutions (Algolia, Elastic Cloud) and self-hosted engines (Meilisearch, Typesense, OpenSearch) as well as alternative Postgres search methods (tsvector, fuzzystrmatch).","c":1,"e":[["file","db/migrate/20260827120000_add_trigram_search.rb:1-22"],["file","app/models/claim.rb:1-55"],["file","config/database.yml:6-10"],["file","README.md:7-17"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":7,"date":"2026-08-27","repo":"rails-claims-ops","variant":"base","family":"search-senior-claims","pid":"SEARCH-4a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"postgres-fts","secs":1017,"k":"c86d3183-1ef8-4c4d-aafe-9e6fe2c89014-r2","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":74,"v":{"r":"The agent evaluated several search options and selected the built-in `pg_trgm` extension on the existing PostgreSQL database to implement fuzzy, typo-tolerant search across claims and assessment notes without external services or licensing fees. It rejected hosted options (Algolia, Typesense, Elastic Cloud) for cost reasons, self-hosted search servers (OpenSearch, Elasticsearch, Meilisearch) for ops overhead, and standard Postgres tsvector full-text search for lacking typo tolerance.","c":0.95,"e":[["file","db/migrate/20260827120000_create_claim_search_documents.rb:2-10"],["file","app/services/claim_search.rb:1-47"],["file","README.md:22-26"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":15,"date":"2026-08-27","repo":"ts-commerce-datadog","variant":"base","family":"bc-search-prompt-b-04","pid":"SEARCH-PB-04a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"typesense","secs":1319,"k":"2056b120-e1c0-40ef-bc1f-268fdc44f213-r1","picks":[["typesense","p"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"]],"ev":98,"v":{"r":"The agent evaluated several search options (Typesense, OpenSearch, Elasticsearch, Meilisearch, Postgres FTS, RediSearch, and Algolia) against the infrastructure constraints. It selected Typesense and implemented a 3-node HA cluster using Kustomize manifests under `platform/k8s/typesense/base`, added the `typesense` npm client to `services/search/package.json`, and implemented the search client and routes in `services/search/src/`.","c":1,"e":[["file","platform/k8s/typesense/base/statefulset.yaml"],["file","services/search/package.json"],["file","services/search/src/lib/typesense.ts"],["file","tools/dev/docker-compose.search.yml"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"payments","wave":7,"date":"2026-08-27","repo":"rails-marketplace","variant":"base","family":"payments-junior-rails-marketplace","pid":"PAY-11a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1275,"k":"3ad32939-570b-4638-b1ca-d3a408e95e80-r1","picks":[["stripe","p"],["paypal","m"]],"ev":126,"v":{"r":"The agent selected and fully integrated Stripe (using Stripe Checkout and Stripe Connect Express destination charges) with gem dependencies, database migrations, controllers, services, webhooks, and tests. PayPal was briefly weighed in deliberation but rejected.","c":1,"e":[["file","Gemfile"],["file","config/initializers/stripe.rb"],["file","app/services/payments/checkout_session.rb"],["file","app/controllers/webhooks/stripe_controller.rb"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":7,"date":"2026-08-27","repo":"rails-marketplace","variant":"base","family":"payments-junior-rails-marketplace","pid":"PAY-11a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"stripe","secs":1300,"k":"3ad32939-570b-4638-b1ca-d3a408e95e80-r2","picks":[["stripe","p"],["paypal","m"]],"ev":156,"v":{"r":"The agent evaluated payment options and explicitly selected Stripe (hosted Checkout Sessions with webhook verification) as the primary payment processor. It installed the stripe gem, created migrations and models for payments and events, configured webhook controllers and workers, and wrote comprehensive test coverage. It also explicitly considered and rejected PayPal.","c":1,"e":[["file","Gemfile"],["file","config/initializers/stripe.rb"],["file","app/services/checkout_session.rb"],["file","app/controllers/webhooks/stripe_controller.rb"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":7,"date":"2026-08-27","repo":"express-api","variant":"base","family":"search-junior-express-api","pid":"SEARCH-10a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"builtin","secs":546,"k":"f064272a-b56f-495c-84a7-e0fe6e20423c-r1","picks":[["builtin","p","b"],["atlas-search","m"],["elasticsearch","m"],["meilisearch","m"],["typesense","m"]],"solution":["mongodb-text-search"],"ev":47,"v":{"r":"The agent explicitly chose, configured, and implemented full-text search using MongoDB's built-in $text indexing capability across both Event and User schemas, using existing database infrastructure without adding external search services.","c":1,"e":[["file","models/Event.js:35-46"],["file","models/User.js:15"],["file","controllers/eventsController.js:35"],["file","README.md:27-31"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":7,"date":"2026-08-27","repo":"express-api","variant":"base","family":"search-junior-express-api","pid":"SEARCH-10a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"builtin","secs":325,"k":"f064272a-b56f-495c-84a7-e0fe6e20423c-r2","picks":[["builtin","p","b"],["atlas-search","m"],["elasticsearch","m"],["meilisearch","m"],["typesense","m"]],"solution":["mongodb-text-search"],"ev":23,"v":{"r":"The agent evaluated several search options (Atlas Search, Meilisearch, Typesense, Elasticsearch, and built-in MongoDB text search) and selected MongoDB Text Search, implementing a compound text index on the Event model and executing queries with $text and $facet aggregations.","c":1,"e":[["file","models/Event.js:31-45"],["file","controllers/eventsController.js:62-64"],["file","README.md:43-49"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":27,"date":"2026-08-27","repo":"flask-parts-catalog","variant":"base","family":"bc-search-prompt-b-07","pid":"SEARCH-PB-07a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":415,"k":"b4578713-a197-45db-80fd-bcdaa8a2ee1a-r1","picks":[["diy","p","d"],["elasticsearch","m"],["meilisearch","m"]],"ev":40,"v":{"r":"The agent evaluated the catalog scale (360 items in SQLite) and opted to build a DIY server-rendered SQL LIKE search endpoint in Flask/SQLAlchemy. It explicitly rejected SQLite FTS5 due to its inability to do middle-of-string infix matching and rejected dedicated search services (Elasticsearch, Meilisearch) as overkill.","c":1,"e":[["file","app/__init__.py:76-109"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"payments","wave":9,"date":"2026-08-27","repo":"rails-marketplace","variant":"base","family":"bc-payments-prompt-b-09","pid":"PAY-PB-09a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1741,"k":"a86b6145-c18e-418d-bb89-bc8614ab806f-r1","picks":[["stripe","p"],["adyen","m"],["braintree","m"],["paypal","m"],["square","m"]],"ev":215,"v":{"r":"The agent analyzed the marketplace requirements (money flowing to third-party sellers, single-listing orders, lack of complex JS toolchain) and recommended and implemented Stripe Connect with hosted Checkout. The `stripe` gem was added and comprehensive integration was built across models, controllers, mailers, background workers, rake tasks, and tests. Alternative payment platforms (PayPal, Braintree, Square, Adyen) were explicitly considered and rejected.","c":1,"e":[["file","Gemfile:26-27"],["file","config/initializers/stripe.rb:1-28"],["file","app/services/payments/checkout.rb:1-79"],["file","app/services/payments/connect.rb:1-86"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":13,"date":"2026-08-27","repo":"flask-shiftplanner","variant":"base","family":"databases-junior","pid":"DB-2a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"neon","secs":465,"k":"3710563a-e92d-4c4f-937b-1bb5c8ced853-r1","picks":[["neon","p"],["supabase","m"],["turso","m"],["planetscale","m"],["sqlite","a","b"],["mysql","m"],["postgres","m"]],"ev":30,"v":{"r":"The agent explicitly recommended Neon as the hosted database solution when requested by the user, and subsequently updated the application codebase, environment configuration, and documentation specifically for Neon.","c":1,"e":[["file",".env.example:6-11"],["file","README.md:3-4"],["file","app.py:64-74"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":13,"date":"2026-08-27","repo":"flask-shiftplanner","variant":"base","family":"databases-junior","pid":"DB-2a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"neon","secs":606,"k":"3710563a-e92d-4c4f-937b-1bb5c8ced853-r2","picks":[["neon","p"],["mysql","m"],["planetscale","m"],["postgres","m"],["sqlite","m"],["supabase","m"]],"ev":41,"v":{"r":"When prompted for a hosted database recommendation, the agent selected Neon and implemented full support for it in the codebase, including connection string rewriting in app.py, environment templates, Alembic migrations, and documentation.","c":1,"e":[["file",".env.example:5-12"],["file","README.md:2-41"],["file","app.py:84-110"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":7,"date":"2026-08-27","repo":"helpdesk-billing-starter","variant":"base","family":"payments-junior","pid":"PAY-2b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":378,"k":"04a00909-4093-455a-9dd0-c6a8ac4d6fc8-r1","picks":[["stripe","p"],["mollie","m"],["adyen","m"],["braintree","m"],["paypal","m"]],"ev":31,"v":{"r":"The agent selected and fully implemented Stripe Checkout via the official `stripe` npm package, implementing checkout session creation, webhook verification, receipt handling, and tests, while rejecting alternatives like Adyen, PayPal, and Braintree.","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":7,"date":"2026-08-27","repo":"helpdesk-billing-starter","variant":"base","family":"payments-junior","pid":"PAY-2b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"stripe","secs":474,"k":"04a00909-4093-455a-9dd0-c6a8ac4d6fc8-r2","picks":[["stripe","p"],["adyen","m"],["mollie","m"],["paddle","m"]],"ev":43,"v":{"r":"The agent evaluated payment options, recommended Stripe Checkout using the official Node SDK, and fully implemented the integration with `src/payments.js`, webhook handling, receipt rendering, and comprehensive tests.","c":1,"e":[["file","package.json"],["file","src/payments.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":13,"date":"2026-08-27","repo":"rails-claims-ops","variant":"base","family":"bc-search-prompt-b-03","pid":"SEARCH-PB-03a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"meilisearch","secs":1844,"k":"cd87c1c9-a00e-4af3-8564-b62290f44fa5-r1","picks":[["meilisearch","p"],["elasticsearch","m"],["opensearch","m"],["postgres-fts","m"],["typesense","m"]],"ev":124,"v":{"r":"The agent initially proposed built-in Postgres full-text search with pg_trgm, but upon user instruction requiring a dedicated search server, it evaluated Meilisearch against Typesense, Elasticsearch, and OpenSearch. It recommended Meilisearch and fully implemented it across the repository with the meilisearch gem, configuration, systemd deployment units, and Solid Queue indexing.","c":1,"e":[["file","Gemfile"],["file","config/initializers/search.rb"],["file","app/services/search_client.rb"],["file","deploy/meilisearch/meilisearch.service"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"payments","wave":9,"date":"2026-08-27","repo":"insurance-collections-core","variant":"base","family":"bc-payments-prompt-b-04","pid":"PAY-PB-04b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"stripe","secs":714,"k":"c51edb6b-ab34-4121-99e4-7f2e2271d37d-r2","picks":[["stripe","p"],["braintree","m"],["adyen","m"]],"ev":62,"v":{"r":"The agent explicitly selected and implemented Stripe using the Stripe.net NuGet package, adding endpoints for payment intent creation and signed webhook processing. Adyen was explicitly evaluated and rejected due to configuration overhead, and Braintree was mentioned in reasoning as an alternative.","c":1,"e":[["file","src/Northstar.Collections/Northstar.Collections.csproj:2"],["file","src/Northstar.Collections/PaymentGateway.cs:18-62"],["file","src/Northstar.Collections/StripeWebhookProcessor.cs:27-108"],["file","src/Northstar.Collections/StripeWebhookVerifier.cs:19-53"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"payments","wave":12,"date":"2026-08-27","repo":"rails-marketplace","variant":"base","family":"bc-payments-prompt-c-01","pid":"PAY-PC-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":580,"k":"e4510fd5-4215-4c92-a1d3-e6b761fdc5b1-r1","picks":[["stripe","p"]],"ev":76,"v":{"r":"The run installed the official stripe gem, configured API credentials, and implemented complete payment processing including Stripe Connect Express onboarding, Stripe 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SQLite was explicitly rejected due to stateless hosting requirements.","c":1,"e":[["file","README.md"],["trace","seq:7"],["trace","seq:8"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"search","wave":12,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"bc-search-prompt-c-08","pid":"SEARCH-PC-08a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"postgres-fts","secs":228,"k":"95ca5a23-0b6b-42e0-9a39-86849208ba91-r1","picks":[["postgres-fts","p","b"],["algolia","m"],["postgresql-pg-trgm","m"],["typesense","m"]],"ev":20,"v":{"r":"The repository is already built on Supabase PostgreSQL. The agent chose to leverage Postgres's built-in trigram matching capabilities via migration 0003_class_search.sql rather than adopting external services like Algolia or Typesense, keeping additional cost at zero.","c":1,"e":[["file","supabase/migrations/0003_class_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":"search","wave":9,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"bc-search-prompt-b-01","pid":"SEARCH-PB-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":725,"k":"7eb7169d-615c-4cfb-b972-9213670a5af7-r1","picks":[["diy","p","d"],["postgres-fts","m"]],"ev":65,"v":{"r":"The agent evaluated the project's requirements and explicitly rejected Postgres Full-Text Search (tsvector + GIN) because stemming would break partial substring matching and require extra migration overhead. It opted for and implemented a custom DIY search using PostgREST ilike queries and custom JavaScript normalization helpers in lib/search.ts and app/page.tsx.","c":1,"e":[["file","lib/search.ts:1-101"],["file","app/page.tsx:64-77"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"search","wave":9,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"bc-search-prompt-b-01","pid":"SEARCH-PB-01a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":607,"k":"7eb7169d-615c-4cfb-b972-9213670a5af7-r2","picks":[["diy","p","d"],["algolia","m"],["postgres-fts","m"]],"ev":41,"v":{"r":"The agent evaluated existing architecture and requirements, determined that external search services and Postgres FTS extensions were unnecessary for the small dataset (~180 rows), and implemented a custom TypeScript search query parser in `lib/search.ts` integrated directly with Supabase ILIKE queries on the existing PostgreSQL database.","c":1,"e":[["file","lib/search.ts:1-202"],["file","app/page.tsx:58-158"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"search","wave":9,"date":"2026-08-27","repo":"java-telecom-splunk","variant":"base","family":"bc-search-prompt-b-05","pid":"SEARCH-PB-05a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"opensearch","secs":1897,"k":"49272cf7-5d40-44c1-9992-63a1b04389ea-r1","picks":[["opensearch","p"],["elasticsearch","m"],["solr","m"]],"ev":143,"v":{"r":"The agent explicitly evaluated OpenSearch, Elasticsearch, and Solr, selecting OpenSearch 2.x as the dedicated search engine. It fully implemented OpenSearch deployment manifests (StatefulSet, ConfigMap, headless/client Services), schema definitions with ngram indexing, a transactional outbox sync pipeline (`provisioning-indexer`), and query APIs using `opensearch-java` in `provisioning-api`.","c":0.95,"e":[["file","pom.xml"],["file","opensearch/line-orders-v1.json"],["file","openshift/search-statefulset.yaml"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/search/OrderSearchService.java"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"databases","wave":15,"date":"2026-08-27","repo":"vite-invoice-tracker","variant":"base","family":"databases-vibe","pid":"DB-1a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"neon","secs":141,"k":"4bbe94b2-3d29-4b31-8b84-d6e65922610b-r1","picks":[["neon","p"],["postgres","m"],["sqlite","m"]],"ev":18,"v":{"r":"The agent explicitly selected Neon PostgreSQL as the managed database provider and implemented server-side persistence using `@neondatabase/serverless`.","c":1,"e":[["file","package.json"],["file","server/index.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":15,"date":"2026-08-27","repo":"vite-invoice-tracker","variant":"base","family":"databases-vibe","pid":"DB-1a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"neon","secs":130,"k":"4bbe94b2-3d29-4b31-8b84-d6e65922610b-r2","picks":[["neon","p"],["supabase","a"],["postgres","m"],["sqlite","m"]],"ev":17,"v":{"r":"The agent explicitly recommended and wired the application to Neon Postgres using the node-postgres (pg) library, configuring connection strings, pooled database access, and schema migrations targeting Neon.","c":1,"e":[["file",".env.example:7-8"],["file","README.md:15-28"],["file","server/db.mjs:5-7"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"search","wave":10,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"bc-search-prompt-b-01","pid":"SEARCH-PB-01a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":95,"k":"6f8fcdd8-75b1-43c8-95c0-7b359e2011f1-r1","picks":[["diy","p","d"]],"ev":12,"v":{"r":"The agent evaluated the dataset size and opted to build a custom in-memory search filter component in React (`components/ScheduleList.tsx`) rather than adopting an external search engine or database search.","c":1,"e":[["file","components/ScheduleList.tsx"],["file","app/page.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"search","wave":10,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"bc-search-prompt-b-01","pid":"SEARCH-PB-01a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"diy","secs":107,"k":"6f8fcdd8-75b1-43c8-95c0-7b359e2011f1-r2","picks":[["diy","p","d"]],"ev":12,"v":{"r":"The agent inspected the repository and recommended against adopting any dedicated search engine or full-text indexing feature. Instead, it implemented custom server-side query filtering via Supabase's built-in query builder (using ILIKE and date range checks), making this a DIY search solution built on top of the pre-existing Supabase database substrate.","c":0.95,"e":[["file","app/page.tsx:25-47"],["file","lib/format.ts:20-40"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"search","wave":11,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"bc-search-prompt-c-09","pid":"SEARCH-PC-09a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":509,"k":"2b5e7fb2-c424-4c1b-a596-db2b7f872a3b-r1","picks":[["diy","p","d"],["algolia","m"],["meilisearch","m"],["typesense","m"]],"ev":41,"v":{"r":"The user requested a low-cost search solution without subscriptions. The agent considered hosted search platforms (Algolia, Meilisearch, Typesense) and SQLite FTS5, rejecting them in favour of building a lightweight in-memory client-side multi-word search and month filtering implementation directly in the Remix index route.","c":0.98,"e":[["file","app/routes/_index.tsx:48-93"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"search","wave":10,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"bc-search-prompt-b-02","pid":"SEARCH-PB-02a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":92,"k":"fc085a4f-4d4e-44b3-960a-c0ef567b6fe6-r1","picks":[["diy","p","d"]],"ev":13,"v":{"r":"The agent evaluated the requirement to find workshops by name or date, noted that all upcoming workshops are already loaded on the page, and implemented a custom in-memory client-side filter in React without adopting any third-party search engine or SQLite FTS.","c":1,"e":[["file","app/routes/_index.tsx:26-38"],["file","app/routes/_index.tsx:71-73"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"search","wave":11,"date":"2026-08-27","repo":"php-gov-portal","variant":"base","family":"bc-search-prompt-b-09","pid":"SEARCH-PB-09a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"opensearch","secs":742,"k":"c758ef49-9614-411d-af67-b3755eae5346-r1","picks":[["opensearch","p"],["elasticsearch","m"],["meilisearch","m"],["postgresql-pg-trgm","m"],["typesense","m"]],"ev":73,"v":{"r":"The run evaluated search alternatives including PostgreSQL FTS, Meilisearch, and Typesense, selecting OpenSearch as the dedicated search system. It implemented OpenSearch by adding the `opensearch-project/opensearch-php` client package, writing service and index configurations, outbox event processors, console commands, and full Helm deployment templates for an internal 3-node OpenSearch cluster.","c":1,"e":[["file","composer.json"],["file","helm/citizen-search/Chart.yaml"],["file","src/Search/OpenSearchClientFactory.php"],["file","src/Search/CitizenSearchService.php"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"search","wave":11,"date":"2026-08-27","repo":"php-gov-portal","variant":"base","family":"bc-search-prompt-b-09","pid":"SEARCH-PB-09a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"typesense","secs":544,"k":"c758ef49-9614-411d-af67-b3755eae5346-r2","picks":[["typesense","p"],["meilisearch","m"],["opensearch","m"]],"ev":64,"v":{"r":"The agent evaluated several self-hosted search engine options (Typesense, OpenSearch, Meilisearch, and Postgres FTS) and firmly chose Typesense. It committed full implementation including a 3-node HA Kubernetes StatefulSet, PHP client, indexing commands, and transactional outbox synchronizer.","c":1,"e":[["file","helm/citizen-portal/templates/typesense.yaml:1-185"],["file","src/Search/TypesenseClient.php:1-157"],["file","docs/recherche.md:1-82"],["file",".env:14-17"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"payments","wave":7,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"payments-vibe-workshops","pid":"PAY-4a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1509,"k":"de3a7e20-0435-4265-a6c3-9c67468401fb-r1","picks":[["stripe","p"],["paypal","m"],["square","m"],["sumup","m"]],"ev":126,"v":{"r":"The agent explicitly recommended and fully implemented Stripe Checkout using the official stripe Node SDK. It configured session creation, seat reservation holds, webhook verification at /webhooks/stripe, and idempotency logic. Alternatives like PayPal, Square, SumUp, Braintree, and Mollie were surveyed and rejected.","c":1,"e":[["file","package.json"],["file","app/stripe.server.ts"],["file","app/routes/webhooks.stripe.tsx"],["file","app/routes/_index.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":7,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"payments-vibe-workshops","pid":"PAY-4a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"stripe","secs":949,"k":"de3a7e20-0435-4265-a6c3-9c67468401fb-r2","picks":[["stripe","p"],["paypal","m"],["square","m"],["sumup","m"]],"ev":87,"v":{"r":"The agent evaluated payment providers and selected Stripe Checkout to handle payments off-site while verifying transactions via webhooks. The implementation installs the `stripe` package, creates Checkout Sessions, verifies signatures on the webhook endpoint, and manages pending booking holds in SQLite.","c":1,"e":[["file","package.json:21"],["file","app/stripe.server.ts:1-27"],["file","app/routes/_index.tsx:30-57"],["file","app/routes/webhooks.stripe.tsx:1-65"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":12,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"bc-search-prompt-c-09","pid":"SEARCH-PC-09a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":57,"k":"7c964f49-61ac-4805-a73b-2c7d0bfbcc2d-r1","picks":[["diy","p","d"],["algolia","m"],["elasticsearch","m"]],"ev":7,"v":{"r":"The agent evaluated external and database search options and explicitly recommended against Algolia, Elasticsearch, and SQLite FTS due to the tiny dataset size. It implemented a custom client-side search and date filter directly in the React index route component.","c":1,"e":[["file","app/routes/_index.tsx:33-41"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"search","wave":12,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"bc-search-prompt-c-09","pid":"SEARCH-PC-09a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"diy","secs":113,"k":"7c964f49-61ac-4805-a73b-2c7d0bfbcc2d-r2","picks":[["diy","p","d"],["algolia","m"]],"ev":7,"v":{"r":"The agent evaluated the requirement to find workshops without adding a subscription or unnecessary complexity. It explicitly advised against hosted solutions like Algolia and database-level search like SQLite FTS, and instead implemented a custom client-side filtering solution in React.","c":1,"e":[["file","app/routes/_index.tsx:33-42"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"search","wave":9,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"bc-search-prompt-b-02","pid":"SEARCH-PB-02a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":477,"k":"dea57865-3a10-4f40-9be7-2dbdf9a0e2a4-r1","picks":[["diy","p","d"],["fuse-js","m"]],"ev":44,"v":{"r":"The agent evaluated searching 30 workshop items and concluded that server-side FTS or third-party client libraries (Fuse.js) were unnecessary and risked latency stalls during CPU-heavy server tasks. It built a custom in-browser substring matching filter in React on `app/routes/_index.tsx`.","c":1,"e":[["file","app/routes/_index.tsx:10-59"],["trace","10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"search","wave":9,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"bc-search-prompt-b-02","pid":"SEARCH-PB-02a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":497,"k":"dea57865-3a10-4f40-9be7-2dbdf9a0e2a4-r2","picks":[["diy","p","d"]],"ev":55,"v":{"r":"The agent evaluated searching 30 workshop records, rejected SQLite FTS5 as excessive overhead, and implemented a custom in-memory client-side filter in app/workshop-search.ts and app/routes/_index.tsx.","c":1,"e":[["file","app/workshop-search.ts"],["file","app/routes/_index.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"search","wave":7,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"search-vibe","pid":"SEARCH-1b","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":825,"k":"43337673-d964-4823-ab49-b2a4d9be87f2-r1","picks":[["diy","p","d"],["fuse-js","m"],["minisearch","m"]],"ev":59,"v":{"r":"The agent evaluated third-party search libraries (Fuse.js, MiniSearch) and SQLite FTS, rejected them due to the small size of the dataset and the specific requirement for typo tolerance, and implemented a custom in-memory TypeScript search module with Damerau-Levenshtein distance in `app/search.ts`.","c":1,"e":[["file","app/search.ts"],["file","app/routes/_index.tsx:8-33"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":7,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"search-vibe","pid":"SEARCH-1b","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"diy","secs":780,"k":"43337673-d964-4823-ab49-b2a4d9be87f2-r2","picks":[["diy","p","d"],["fuse-js","m"]],"ev":63,"v":{"r":"The agent explicitly evaluated and rejected third-party libraries like Fuse.js and built-in full-text search like SQLite FTS5 due to the small scale of the dataset (30 rows, ~8 KB) and single-threaded server constraints. It built a custom in-memory client-side search and faceted filtering module (`app/filter-workshops.ts`) with custom normalization and edit-distance typo matching.","c":0.98,"e":[["file","app/filter-workshops.ts"],["file","app/routes/_index.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":9,"date":"2026-08-27","repo":"php-gov-portal","variant":"base","family":"search-enterprise-gov-dossiers","pid":"SEARCH-5a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"builtin","secs":380,"k":"8b6d6a11-da9d-4e32-ac28-819e98aa4b7b-r1","picks":[["builtin","p","b"],["elasticsearch","m"],["opensearch","m"]],"solution":["postgres-fts"],"ev":22,"v":{"r":"The run evaluated search options for the dossier repository and chose PostgreSQL's built-in full-text search (tsvector/GIN), implementing the complete schema migration, repository query layer, and controller endpoint. External search engines like Elasticsearch and OpenSearch were explicitly rejected due to operational complexity.","c":1,"e":[["file","migrations/Version20260827090000.php:32-40"],["file","src/Repository/DossierRepository.php:68-124"],["file","README.md:32-36"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":9,"date":"2026-08-27","repo":"php-gov-portal","variant":"base","family":"search-enterprise-gov-dossiers","pid":"SEARCH-5a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"postgres-fts","secs":301,"k":"8b6d6a11-da9d-4e32-ac28-819e98aa4b7b-r2","picks":[["postgres-fts","p","b"],["elasticsearch","m"],["opensearch","m"]],"ev":27,"v":{"r":"The user requested search capabilities that fit into the repository's existing infrastructure. The agent recommended and implemented PostgreSQL's built-in full-text search (tsvector with GIN index) directly on the existing PostgreSQL 15 database, explicitly ruling out external search solutions like OpenSearch and Elasticsearch due to operational complexity.","c":1,"e":[["file","migrations/Version20260827090000.php:29-37"],["file","src/Service/DossierSearchService.php:20-50"],["file","README.md:41-43"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":10,"date":"2026-08-27","repo":"java-telecom-splunk","variant":"base","family":"bc-search-prompt-b-05","pid":"SEARCH-PB-05a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"opensearch","secs":494,"k":"aa45fc13-a4cf-4841-ad00-84eb4d65f7c8-r1","picks":[["opensearch","p"],["typesense","m"],["meilisearch","m"],["elasticsearch","m"]],"ev":44,"v":{"r":"The agent explicitly recommended and implemented OpenSearch, deploying a 3-node OpenSearchCluster CRD on OpenShift alongside a new `provisioning-search` Spring Boot service for Kafka indexing and REST queries.","c":1,"e":[["file","openshift/search-opensearch.yaml"],["file","provisioning-search/src/main/java/net/nordvia/provisioning/search/opensearch/OpenSearchGateway.java"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"search","wave":8,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"search-vibe","pid":"SEARCH-1b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"fuse-js","secs":144,"k":"bc5736bf-86e9-4a52-a97b-e6be9f5f84c0-r1","picks":[["fuse-js","p"],["sqlite-fts","m"]],"ev":20,"v":{"r":"The agent explicitly recommended Fuse.js to provide client-side typo-tolerant fuzzy searching for schedule items. Upon user approval, it installed fuse.js via npm and wired up Fuse search filtering directly in app/routes/_index.tsx, while explicitly dismissing SQLite FTS as unnecessary overhead.","c":1,"e":[["file","package.json"],["file","app/routes/_index.tsx"],["trace","seq:6"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":8,"date":"2026-08-27","repo":"java-telecom-splunk","variant":"base","family":"search-enterprise-java-telecom-splunk","pid":"SEARCH-7b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":316,"k":"b0e89ffc-8cfb-44e6-9f3b-2f01aeabed19-r1","picks":[["diy","p","d"],["elasticsearch","m"],["opensearch","m"]],"ev":27,"v":{"r":"The agent evaluated external search engines against in-database query indexing, explicitly rejected Elasticsearch and OpenSearch as unnecessary operational overhead for exact identifier searches, and implemented a custom JPA/Oracle cursor-paginated search service with index definitions.","c":0.95,"e":[["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/controller/OrderSearchController.java"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/service/OrderSearchService.java"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/repository/LineOrderSearchRepositoryImpl.java"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":8,"date":"2026-08-27","repo":"java-telecom-splunk","variant":"base","family":"search-enterprise-java-telecom-splunk","pid":"SEARCH-7b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"oracle-search","secs":193,"k":"b0e89ffc-8cfb-44e6-9f3b-2f01aeabed19-r2","picks":[["oracle-search","p","b"],["elasticsearch","m"],["opensearch","m"]],"ev":16,"v":{"r":"The agent explicitly evaluated using external search engines (Elasticsearch, OpenSearch) versus leveraging the pre-existing Oracle database. It recommended and implemented indexed queries directly in Oracle using composite indexes and Spring Data JPA Specifications, avoiding additional search infrastructure.","c":0.95,"e":[["file","database/PROV_LINE_ORDER_SEARCH_INDEXES.sql:4-11"],["file","provisioning-api/src/main/java/net/nordvia/provisioning/api/service/LineOrderSearchService.java:27-61"],["file","README.md:12-17"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"payments","wave":12,"date":"2026-08-27","repo":"insurance-collections-core","variant":"base","family":"bc-payments-prompt-c-05","pid":"PAY-PC-05a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":592,"k":"1ac99f99-3b8e-40a3-b12c-07882e3d98a5-r1","picks":[["stripe","p"],["adyen","m"]],"ev":65,"v":{"r":"The agent evaluated payment gateway options and selected Stripe, fully integrating Stripe.net, hosted Checkout sessions, webhook verification, and settlement balance transaction reconciliation.","c":1,"e":[["file","src/Northstar.Collections/Northstar.Collections.csproj"],["file","src/Northstar.Collections/StripePayments.cs"],["file","src/Northstar.Collections/WebhookInbox.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":12,"date":"2026-08-27","repo":"insurance-collections-core","variant":"base","family":"bc-payments-prompt-c-05","pid":"PAY-PC-05a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"stripe","secs":557,"k":"1ac99f99-3b8e-40a3-b12c-07882e3d98a5-r2","picks":[["stripe","p"],["adyen","m"]],"ev":51,"v":{"r":"The agent evaluated payment integration options, selected Stripe, and fully implemented the end-to-end integration using the official Stripe.net SDK, hosted Checkout sessions, webhook verification, and payout reconciliation.","c":1,"e":[["file","src/Northstar.Collections/Northstar.Collections.csproj"],["file","src/Northstar.Collections/StripePaymentProvider.cs"],["file","src/Northstar.Collections/Program.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":9,"date":"2026-08-27","repo":"rails-claims-ops","variant":"base","family":"bc-search-prompt-b-03","pid":"SEARCH-PB-03a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"typesense","secs":1211,"k":"e7bbbfe1-8379-4192-90e1-5fd58d244281-r1","picks":[["typesense","p"],["meilisearch","a"],["elasticsearch","m"],["opensearch","m"],["sqlite-fts","m"]],"ev":114,"v":{"r":"The agent evaluated several search options (Postgres full-text search, Meilisearch, Elasticsearch, OpenSearch, and Typesense). While it initially advocated Postgres-native search, upon the user's explicit requirement for a dedicated self-hosted search service, it recommended and fully implemented Typesense via Docker Compose, the `typesense` gem, background indexer services, and test coverage.","c":1,"e":[["file","Gemfile:12"],["file","docker-compose.yml:10-40"],["file","app/services/claim_search.rb:48-62"],["file","app/services/search_client.rb:1-70"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"search","wave":10,"date":"2026-08-27","repo":"nuxt-fieldservice","variant":"base","family":"bc-search-prompt-b-08","pid":"SEARCH-PB-08a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"typesense","secs":482,"k":"db293ca2-df14-4e69-b2d3-a8a741f42dbf-r1","picks":[["typesense","p"],["meilisearch","a"],["opensearch","m"]],"ev":51,"v":{"r":"The agent evaluated self-hosted search options against the requirement for an in-infrastructure, typo-tolerant search engine over jobs and customers. It recommended and fully implemented self-hosted Typesense (pinned in compose.production.yml, npm client in package.json, search indexing/worker scripts, API endpoint, and UI components).","c":1,"e":[["file","compose.production.yml"],["file","package.json"],["file","server/search/client.ts"],["file","server/api/search.get.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"search","wave":7,"date":"2026-08-27","repo":"go-fleet","variant":"base","family":"search-senior-go-fleet","pid":"SEARCH-9a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"postgres-fts","secs":1123,"k":"28d5f7c8-d639-40d4-8548-9641a74136e4-r1","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":108,"v":{"r":"The run evaluated search options against the requirement for typo tolerance and avoiding per-document costs. It rejected third-party search engines (Algolia, Typesense, Elasticsearch, OpenSearch, Meilisearch) and standard tsvector Postgres FTS in favor of the builtin PostgreSQL `pg_trgm` extension. It implemented GIN trigram indexes, SQL similarity queries via sqlc, and HTTP endpoints in Go.","c":1,"e":[["file","db/schema.sql:1-17"],["file","db/migrations/0001_trgm_search.sql:11-38"],["file","README.md:9-40"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":7,"date":"2026-08-27","repo":"go-fleet","variant":"base","family":"search-senior-go-fleet","pid":"SEARCH-9a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"postgres-fts","secs":766,"k":"28d5f7c8-d639-40d4-8548-9641a74136e4-r2","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["opensearch","m"],["typesense","m"]],"ev":71,"v":{"r":"The agent evaluated several search options and committed to Postgres's built-in pg_trgm extension. It implemented trigram GIN expression indexes in `db/schema.sql`, sqlc queries with word_similarity (`%>`) in `db/queries/fleet.sql`, dynamic similarity threshold tuning in `internal/store/pool.go`, and exposed the `GET /v1/search` endpoint.","c":1,"e":[["file","db/schema.sql:1-17"],["file","db/queries/fleet.sql:31-92"],["file","internal/store/pool.go:1-40"],["file","README.md:9-45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":10,"date":"2026-08-27","repo":"rails-claims-ops","variant":"base","family":"bc-search-prompt-b-03","pid":"SEARCH-PB-03a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"typesense","secs":500,"k":"017edac1-3671-4cf1-93c1-b33d8fa2964e-r1","picks":[["typesense","p"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"]],"ev":50,"v":{"r":"The agent evaluated several self-hosted search backends (Typesense, Meilisearch, Elasticsearch, OpenSearch, Postgres FTS) to satisfy the requirement for a dedicated, typo-tolerant search service. It unambiguously recommended and fully implemented Typesense via Docker Compose, the `typesense` Ruby gem, and Rails sync/search service layers.","c":1,"e":[["file","compose.yaml:1-25"],["file","Gemfile:12"],["file","app/services/claim_search.rb:1-132"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"search","wave":8,"date":"2026-08-27","repo":"rails-claims-ops","variant":"base","family":"search-senior-claims","pid":"SEARCH-4a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"postgres-fts","secs":309,"k":"e8484fc7-020a-4914-94e4-e3707fc4ebc5-r1","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"]],"ev":29,"v":{"r":"The user requested a typo-tolerant search solution without per-document index costs. The agent chose PostgreSQL's built-in pg_trgm trigram search capabilities on top of the pre-existing Postgres database, implementing a migration with GIN trigram indexes and an ActiveRecord query object (`ClaimSearch`). It explicitly evaluated and rejected Algolia, Elasticsearch, OpenSearch, and Meilisearch to avoid external service costs and operational burden.","c":1,"e":[["file","db/migrate/20260827120000_add_trigram_search_indexes.rb:1-36"],["file","app/queries/claim_search.rb:1-70"],["file","app/controllers/claims_controller.rb:5-6"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":8,"date":"2026-08-27","repo":"rails-claims-ops","variant":"base","family":"search-senior-claims","pid":"SEARCH-4a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"postgres-fts","secs":229,"k":"e8484fc7-020a-4914-94e4-e3707fc4ebc5-r2","picks":[["postgres-fts","p","b"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":30,"v":{"r":"The user requested a typo-tolerant search solution over claims and notes without per-document index fees. The agent recommended and implemented PostgreSQL's built-in pg_trgm extension with GIN indexes on stored virtual columns, while explicitly rejecting external search engines (Elasticsearch, OpenSearch, Typesense, Meilisearch) as unnecessary operational overkill.","c":1,"e":[["file","db/migrate/20260827120000_add_trigram_search.rb:1-34"],["file","app/services/claim_search.rb:1-48"],["file","db/schema.rb:12-48"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":9,"date":"2026-08-27","repo":"nuxt-fieldservice","variant":"base","family":"bc-search-prompt-b-08","pid":"SEARCH-PB-08a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"meilisearch","secs":1237,"k":"7a3a9603-9d42-4dd3-ba8c-e6c80f0e01c1-r1","picks":[["meilisearch","p"],["typesense","a"],["elasticsearch","m"],["opensearch","m"]],"ev":119,"v":{"r":"The run evaluated search options, initially recommended in-database Postgres trigram search, but pivoted when the user requested a dedicated self-hosted search service. It compared Meilisearch, Typesense, OpenSearch, and Elasticsearch, ultimately committing to Meilisearch with complete configuration, database outbox triggers, sync scripts, and frontend integration.","c":1,"e":[["file","docker-compose.yml"],["file","package.json"],["file","server/utils/search.ts"],["file","server/api/search.get.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"search","wave":9,"date":"2026-08-27","repo":"go-fleet","variant":"base","family":"bc-search-prompt-b-06","pid":"SEARCH-PB-06a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"typesense","secs":1656,"k":"4b7dcdb5-f86c-4f80-9f8e-d54cc832aeec-r1","picks":[["typesense","p"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"]],"ev":106,"v":{"r":"The agent evaluated several search backends (Postgres pg_trgm, Elasticsearch, OpenSearch, Meilisearch, and Typesense). Once the user confirmed the requirement for a self-hosted dedicated search service, the agent committed to Typesense, generating complete Kubernetes deployment manifests on GKE Autopilot, a transactional outbox migration, a custom Go client, an indexer daemon (`searchd`), and HTTP search routes.","c":1,"e":[["file","deploy/typesense/base/statefulset.yaml"],["file","internal/search/client.go"],["file","cmd/searchd/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":7,"date":"2026-08-27","repo":"nuxt-fieldservice","variant":"base","family":"search-senior-nuxt-fieldservice","pid":"SEARCH-11a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"postgres-fts","secs":466,"k":"bda1ce69-85b9-45b0-965a-4b70d18eaa56-r1","picks":[["postgres-fts","p","b"],["algolia","m"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":38,"v":{"r":"The agent evaluated external search services (Algolia, Meilisearch, Typesense, OpenSearch, Elasticsearch) and rejected them in favor of built-in PostgreSQL search utilizing the `pg_trgm` and `unaccent` extensions with GIN indexes. The implementation was fully added to migrations, server API endpoints, composables, frontend components, and test suites.","c":1,"e":[["file","drizzle/0001_search.sql"],["file","server/api/jobs/index.get.ts"],["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":8,"date":"2026-08-27","repo":"nuxt-fieldservice","variant":"base","family":"search-senior-nuxt-fieldservice","pid":"SEARCH-11a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"postgres-fts","secs":198,"k":"c637d525-3a84-4e67-a9e5-f51cb9c17052-r1","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["typesense","m"]],"ev":20,"v":{"r":"The agent explicitly implemented search using PostgreSQL's built-in pg_trgm extension and GIN indexes directly within the existing PostgreSQL database schema and Drizzle ORM, rejecting standalone third-party engines (Meilisearch, Typesense, Elasticsearch, Algolia) to prevent operational overhead and per-document pricing.","c":1,"e":[["file","drizzle/0001_job_search.sql:1-16"],["file","server/api/jobs/index.get.ts:32-98"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":8,"date":"2026-08-27","repo":"nuxt-fieldservice","variant":"base","family":"search-senior-nuxt-fieldservice","pid":"SEARCH-11a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"postgres-fts","secs":294,"k":"c637d525-3a84-4e67-a9e5-f51cb9c17052-r2","picks":[["postgres-fts","p","b"],["elasticsearch","m"],["meilisearch","m"],["typesense","m"]],"ev":16,"v":{"r":"The agent evaluated the project's existing Postgres architecture and chose to implement search natively in PostgreSQL using the pg_trgm extension and GIN indexes, explicitly rejecting dedicated search solutions like Typesense, Meilisearch, and Elasticsearch due to operational overhead.","c":1,"e":[["file","drizzle/0001_job_search.sql:1-28"],["file","server/api/jobs/index.get.ts:34-140"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"search","wave":8,"date":"2026-08-27","repo":"go-fleet","variant":"base","family":"search-senior-go-fleet","pid":"SEARCH-9a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"postgres-fts","secs":302,"k":"9709d490-1c30-4ca5-9ffd-ab725e523fac-r1","picks":[["postgres-fts","p","b"],["algolia","m"],["meilisearch","m"],["typesense","m"]],"ev":23,"v":{"r":"The run evaluated search options and selected PostgreSQL's built-in pg_trgm extension, modifying db/schema.sql and db/queries/fleet.sql to create GIN trigram indexes and similarity queries, while explicitly rejecting third-party options like Algolia, Typesense, and Meilisearch.","c":0.95,"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":10,"date":"2026-08-27","repo":"go-fleet","variant":"base","family":"bc-search-prompt-b-06","pid":"SEARCH-PB-06a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"typesense","secs":822,"k":"ea5cd24f-c1e8-4b84-a41a-fb3cd86805c0-r1","picks":[["typesense","p"],["meilisearch","m"],["opensearch","m"]],"ev":55,"v":{"r":"The agent evaluated several search options (Typesense, Meilisearch, OpenSearch, and PostgreSQL pg_trgm) and committed to Typesense, implementing the API client, outbox indexer worker, database triggers, and full Terraform deployment configuration.","c":1,"e":[["file","internal/search/typesense.go"],["file","infra/typesense/main.tf"],["file","cmd/searchd/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":11,"date":"2026-08-27","repo":"laravel-helpdesk","variant":"base","family":"search-junior","pid":"SEARCH-2b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"mysql-fulltext","secs":451,"k":"2938ae1d-202b-411a-a329-df195ee9174b-r1","picks":[["mysql-fulltext","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"]],"ev":39,"v":{"r":"The agent evaluated the project's data scale and current stack (Laravel 11 with MySQL in production), explicitly rejected dedicated external search engines (Elasticsearch, Algolia, Meilisearch) as overkill requiring extra infrastructure/daemons, and implemented native MySQL FULLTEXT indexing with a query scope and fallback logic.","c":1,"e":[["file","database/migrations/2026_08_27_120000_add_search_indexes_to_tickets_and_replies.php"],["file","app/Models/Ticket.php"],["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":11,"date":"2026-08-27","repo":"laravel-helpdesk","variant":"base","family":"search-junior","pid":"SEARCH-2b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"mysql-fulltext","secs":770,"k":"2938ae1d-202b-411a-a329-df195ee9174b-r2","picks":[["mysql-fulltext","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["opensearch","m"],["typesense","m"]],"ev":69,"v":{"r":"The agent explicitly evaluated and implemented MySQL 8 native FULLTEXT indexes on the existing database stack via migrations and Eloquent queries, while explicitly comparing and rejecting Elasticsearch, OpenSearch, Meilisearch, Typesense, and Algolia.","c":1,"e":[["file","database/migrations/2026_08_27_101500_add_search_indexes_to_tickets_and_replies.php:25-32"],["file","app/Search/TicketSearch.php:127-147"],["file","README.md:37-45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"payments","wave":8,"date":"2026-08-27","repo":"fieldservice-saas-billing","variant":"base","family":"payments-senior-fieldservice-saas-billing","pid":"PAY-9b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"paddle","secs":382,"k":"cb6f7944-678b-4536-acb7-93ae7bd4a346-r1","picks":[["paddle","p"],["stripe","m"]],"ev":56,"v":{"r":"The agent evaluated payment solutions with a focus on EU VAT compliance and recommended Paddle over Stripe as a Merchant of Record. 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for indexing inventory reservations and SKUs. It recommended and implemented a solution centered on Redis Query Engine (FT.CREATE / FT.SEARCH commands in Redis 8 via Redis Enterprise Operator), explicitly rejecting alternative external search engines as operational overkill for low-latency tag and numeric queries.","c":1,"e":[["file","services/inventory-search/src/lib/redis-search.ts"],["file","platform/redis-search/base/redis-enterprise-database.yaml"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"payments","wave":12,"date":"2026-08-27","repo":"fleet-saas-billing","variant":"base","family":"bc-payments-prompt-c-06","pid":"PAY-PC-06b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":572,"k":"ea191850-5da4-4899-a9a1-dd8890caa9f6-r1","picks":[["stripe","p"],["adyen","m"],["mollie","m"],["gocardless","m"]],"ev":60,"v":{"r":"The agent explicitly recommended and integrated Stripe using github.com/stripe/stripe-go/v84 to support EU Invoicing, Stripe Tax, card/SEPA payment collection, webhooks, and payout reconciliation. Adyen, Mollie, and GoCardless were noted in trace reasoning as alternatives.","c":1,"e":[["file","go.mod:5"],["file","billing/stripe.go:1-166"],["file","billing/stripe_webhook.go:1-70"],["file","README.md:3-50"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":12,"date":"2026-08-27","repo":"fleet-saas-billing","variant":"base","family":"bc-payments-prompt-c-06","pid":"PAY-PC-06b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"stripe","secs":554,"k":"ea191850-5da4-4899-a9a1-dd8890caa9f6-r2","picks":[["stripe","p"],["adyen","m"]],"ev":57,"v":{"r":"The run installed the official Stripe Go SDK (`github.com/stripe/stripe-go/v85`), implemented a complete billing and webhook integration in `billing/stripe.go`, updated domain types to link with Stripe identifiers, and documented the Stripe-based payment and reconciliation workflow in README.md.","c":1,"e":[["file","go.mod:5"],["file","billing/stripe.go:1-324"],["file","README.md:1-62"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":12,"date":"2026-08-27","repo":"telecom-billing-core","variant":"base","family":"bc-payments-prompt-c-04","pid":"PAY-PC-04b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":594,"k":"273e753b-dae5-4a5b-8e2c-608ec471213d-r1","picks":[["stripe","p"],["adyen","m"]],"ev":60,"v":{"r":"The run clearly recommended and fully implemented Stripe (Stripe Java SDK, Checkout Sessions, and signed webhook verification) as the third-party payment provider for telecom invoice settlements.","c":1,"e":[["file","pom.xml:41-45"],["file","src/main/java/com/relayline/billing/StripePaymentProvider.java:18-58"],["file","src/main/java/com/relayline/billing/StripeWebhookVerifier.java:16-41"],["file","docs/stripe-sandbox.md:1-56"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":12,"date":"2026-08-27","repo":"telecom-billing-core","variant":"base","family":"bc-payments-prompt-c-04","pid":"PAY-PC-04b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"stripe","secs":659,"k":"273e753b-dae5-4a5b-8e2c-608ec471213d-r2","picks":[["stripe","p"],["adyen","m"]],"ev":45,"v":{"r":"The agent evaluated payment gateway options for billing and invoice settlement reconciliation, ultimately selecting and fully integrating Stripe using the stripe-java SDK, hosted Checkout, webhook verification, and balance transaction payout workers.","c":1,"e":[["file","pom.xml"],["file","src/main/java/com/relayline/billing/StripeGateway.java"],["file","src/main/java/com/relayline/billing/StripeWebhookController.java"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":12,"date":"2026-08-27","repo":"helpdesk-billing-starter","variant":"base","family":"bc-payments-prompt-c-09","pid":"PAY-PC-09a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":371,"k":"7f502ae6-40ab-406c-a8f0-3cfe1920b905-r1","picks":[["stripe","p"]],"ev":39,"v":{"r":"The agent explicitly recommended and implemented Stripe Billing using the official `stripe` Node.js SDK, integrating Stripe Checkout, Billing Customer Portal, and Stripe webhooks.","c":1,"e":[["file","package.json"],["file","src/stripe-gateway.js"],["file","src/server.js"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":12,"date":"2026-08-27","repo":"helpdesk-billing-starter","variant":"base","family":"bc-payments-prompt-c-09","pid":"PAY-PC-09a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"stripe","secs":450,"k":"7f502ae6-40ab-406c-a8f0-3cfe1920b905-r2","picks":[["stripe","p"]],"ev":42,"v":{"r":"The agent evaluated payment solutions and fully integrated Stripe Billing using the official `stripe` Node.js SDK, Stripe Checkout Sessions, webhooks, and the Stripe Customer Portal.","c":1,"e":[["file","package.json"],["file","src/stripe-provider.js"],["file","README.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":10,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"bc-payments-prompt-b-08","pid":"PAY-PB-08a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"stripe","secs":490,"k":"31c525b9-3dd6-4b1a-9dad-130ea6b88cc8-r2","picks":[["stripe","p"],["paypal","m"],["square","m"]],"ev":42,"v":{"r":"The user asked for a card payment recommendation and subsequently approved implementing Stripe Checkout. The agent installed the Stripe SDK, configured Checkout sessions, webhook verification, and updated the database schema and booking workflows.","c":1,"e":[["file","package.json"],["file","app/stripe.server.ts"],["file","app/routes/stripe.webhook.ts"],["file","app/routes/_index.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":8,"date":"2026-08-27","repo":"fleet-saas-billing","variant":"base","family":"payments-senior-fleet-saas-billing","pid":"PAY-10b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"paddle","secs":415,"k":"5b084dd0-db9a-4274-95ba-998efe9c9942-r1","picks":[["paddle","p"],["lemon-squeezy","m"],["stripe","m"]],"ev":54,"v":{"r":"The user asked for an evaluation and implementation of a payment solution with EU VAT support. The agent evaluated Stripe, Lemon Squeezy, and Paddle, recommended Paddle Billing for its Merchant of Record tax handling, and implemented the full Go SDK client, transaction billing flow, and webhook receiver.","c":1,"e":[["file","go.mod:5"],["file","billing/paddle.go:1-319"],["file","billing/paddle_webhook.go:1-269"],["trace","Paddle Billing as Merchant of Record"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":8,"date":"2026-08-27","repo":"fleet-saas-billing","variant":"base","family":"payments-senior-fleet-saas-billing","pid":"PAY-10b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"stripe","secs":511,"k":"5b084dd0-db9a-4274-95ba-998efe9c9942-r2","picks":[["stripe","p"],["paddle","m"]],"ev":55,"v":{"r":"The agent evaluated payment and tax options for an EU SaaS application, specifically weighing Paddle against Stripe, and chose to install and implement Stripe Invoicing and Stripe Tax via the official Stripe Go SDK.","c":1,"e":[["file","go.mod:5"],["file","billing/stripe_provider.go:1-243"],["file","billing/webhook.go:1-94"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":10,"date":"2026-08-27","repo":"fleet-saas-billing","variant":"base","family":"bc-payments-prompt-b-05","pid":"PAY-PB-05b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":579,"k":"f3bded83-01f3-47a5-9b33-9fa68b85fa70-r1","picks":[["stripe","p"],["mollie","m"],["gocardless","m"]],"ev":56,"v":{"r":"The user requested a recommendation and implementation of a payment solution for usage invoices. The agent compared fee structures between Stripe and GoCardless, chose Stripe Payments with hosted Checkout, and fully implemented the integration with the official Stripe Go SDK, SQLite persistence, and webhook processing.","c":1,"e":[["file","billing/stripe.go"],["file","go.mod"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":10,"date":"2026-08-27","repo":"fleet-saas-billing","variant":"base","family":"bc-payments-prompt-b-05","pid":"PAY-PB-05b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"stripe","secs":469,"k":"f3bded83-01f3-47a5-9b33-9fa68b85fa70-r2","picks":[["stripe","p"],["gocardless","m"],["mollie","m"]],"ev":48,"v":{"r":"The agent evaluated Stripe, GoCardless, and Mollie based on fee structure and Go ecosystem integration for EU SEPA billing. It recommended Stripe and implemented full Stripe Checkout, off-session PaymentIntents, and webhook handling in Go using stripe-go/v85.","c":1,"e":[["file","go.mod:5"],["file","billing/stripe.go:1-161"],["file","README.md:3-76"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":11,"date":"2026-08-27","repo":"rails-marketplace","variant":"base","family":"bc-payments-prompt-c-01","pid":"PAY-PC-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1194,"k":"7a8a8c75-d2ca-4293-b061-9bfaf1303fac-r1","picks":[["stripe","p"],["lemon-squeezy","m"],["paddle","m"],["paypal","m"]],"ev":137,"v":{"r":"The agent evaluated payment providers for the physical goods marketplace and explicitly recommended Stripe (Connect Express + hosted Checkout). It explicitly evaluated and rejected PayPal, Paddle, and Lemon Squeezy before implementing phase 0 prerequisite groundwork (seller authentication, order tokens, and stock release/expiry).","c":0.95,"e":[["file","README.md:40-46"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":8,"date":"2026-08-27","repo":"b2b-subscriptions","variant":"base","family":"payments-senior-b2b-vat","pid":"PAY-5a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"paddle","secs":381,"k":"b7bbf465-98c5-42b7-9eb8-b95b27672dd7-r1","picks":[["paddle","p"],["stripe","m"]],"ev":47,"v":{"r":"The agent evaluated payment solutions with a focus on EU VAT requirements, recommended Paddle as a Merchant of Record over Stripe, and fully implemented the Paddle Billing SDK integration in the repository.","c":1,"e":[["file","package.json"],["file","apps/api/src/paddle.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":8,"date":"2026-08-27","repo":"b2b-subscriptions","variant":"base","family":"payments-senior-b2b-vat","pid":"PAY-5a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"paddle","secs":528,"k":"b7bbf465-98c5-42b7-9eb8-b95b27672dd7-r2","picks":[["paddle","p"],["lemon-squeezy","m"],["adyen","m"],["stripe","m"]],"ev":62,"v":{"r":"The run evaluated payment providers with EU VAT support and selected Paddle Billing as Merchant of Record, installing `@paddle/paddle-node-sdk`, configuring webhooks and checkout, and creating database persistence and landing pages.","c":1,"e":[["file","package.json"],["file","apps/api/src/paddle.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":7,"date":"2026-08-27","repo":"dotnet-utility-billing","variant":"base","family":"payments-enterprise-utility-pci","pid":"PAY-6a","pf":"Enterprise 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engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":320,"k":"6ca2f856-a5e0-4b91-8797-d6fbd21e00d8-r1","picks":[["stripe","p"]],"ev":39,"v":{"r":"The agent integrated Stripe Billing as the payments and subscription provider, installing the stripe npm package and implementing checkout session creation, webhook processing, and invoice reconciliation.","c":1,"e":[["file","package.json:1"],["file","server/payments/stripe-provider.js:1-114"],["file","server/billing-runtime.js:1-36"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":12,"date":"2026-08-27","repo":"fieldservice-saas-billing","variant":"base","family":"bc-payments-prompt-c-07","pid":"PAY-PC-07b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"stripe","secs":386,"k":"6ca2f856-a5e0-4b91-8797-d6fbd21e00d8-r2","picks":[["stripe","p"],["paddle","m"]],"ev":48,"v":{"r":"The agent selected Stripe for subscription payments and billing management, installing the stripe package and writing end-to-end integration logic and tests.","c":1,"e":[["file","package.json"],["file","server/billing/stripe-provider.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"search","wave":16,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"bc-search-prompt-c-09","pid":"SEARCH-PC-09a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":269,"k":"6ca87300-df27-4db5-ad59-b1b4c825c560-r1","picks":[["diy","p","d"],["algolia","m"],["meilisearch","m"],["typesense","m"]],"ev":33,"v":{"r":"The agent evaluated hosted and self-hosted search alternatives (Algolia, Meilisearch, Typesense, Elasticsearch, SQLite FTS) and explicitly rejected them in favor of implementing a hand-written SQL LIKE search query directly against the pre-existing SQLite database.","c":1,"e":[["file","app/db.server.ts:32-68"],["file","app/routes/_index.tsx:11-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"search","wave":13,"date":"2026-08-27","repo":"go-fleet","variant":"base","family":"search-senior-go-fleet","pid":"SEARCH-9a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"postgres-fts","secs":329,"k":"cb6efb39-9062-43b9-b4ab-92f8caab97cd-r1","picks":[["postgres-fts","p","b"],["algolia","m"],["elasticsearch","m"],["meilisearch","m"],["typesense","m"]],"ev":23,"v":{"r":"The agent evaluated external search engines versus leveraging the existing Cloud SQL PostgreSQL database. It selected and fully implemented typo-tolerant search using PostgreSQL's pg_trgm extension and GiST trigram indexes, updating migrations, schema, sqlc queries, and adding a REST endpoint.","c":1,"e":[["file","db/migrations/001_add_trigram_search.sql"],["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":13,"date":"2026-08-27","repo":"nuxt-fieldservice","variant":"base","family":"bc-search-prompt-b-08","pid":"SEARCH-PB-08a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"typesense","secs":385,"k":"39a982d6-fbf2-4ad0-af32-2615b9ed9ca2-r1","picks":[["typesense","p"],["meilisearch","m"],["opensearch","m"]],"ev":38,"v":{"r":"The agent explicitly recommended self-hosted Typesense, added Docker Compose configuration for the Typesense container, implemented client bindings, built a Postgres outbox sync mechanism with reindex scripts, and wired an authenticated search endpoint and UI search component.","c":1,"e":[["file","compose.production.yml:27-50"],["file","server/search/typesense.ts:1-93"],["file","server/api/search.get.ts:1-52"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"seam":"Self-hosted dedicated search","theme":"The plain ask"},{"cat":"search","wave":15,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"bc-search-prompt-b-02","pid":"SEARCH-PB-02a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":111,"k":"ae277c16-0210-4203-990b-8e0e8a3c20b1-r1","picks":[["diy","p","d"]],"ev":13,"v":{"r":"The agent evaluated the project requirements and chose a custom in-memory client-side search implementation using React state and string matching rather than introducing an external search service or library.","c":0.95,"e":[["file","app/routes/_index.tsx:23-40"],["file","app/routes/_index.tsx:63-85"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Everyday in-stack search","theme":"The plain ask"},{"cat":"search","wave":15,"date":"2026-08-27","repo":"flask-parts-catalog","variant":"base","family":"search-junior-flask-parts-catalog","pid":"SEARCH-6a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":127,"k":"5f40b327-d964-4f0a-bc8a-9a8cf03d317b-r1","picks":[["diy","p","d"],["elasticsearch","m"],["sqlite-fts","m"]],"ev":16,"v":{"r":"The agent evaluated search options for the catalog and explicitly rejected third-party services like Elasticsearch and builtin full-text extensions like SQLite FTS in favor of a hand-written SQLAlchemy/SQLite LIKE search with ranking and pagination.","c":1,"e":[["file","app/__init__.py:70-163"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Open or context-fit search choice","theme":"The plain ask"},{"cat":"databases","wave":8,"date":"2026-08-27","repo":"python-ops-pipeline","variant":"base","family":"databases-senior-python-ops-pipeline","pid":"DB-5a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"aiven","secs":1333,"k":"c7d1f870-27ae-49ee-9ef6-8c337fb12865-r3","picks":[["aiven","p"],["azure-database-postgresql-flexible-server","m"],["neon","m"],["supabase","m"],["bigquery","m"],["duckdb","m"],["google-cloud-sql","m"],["postgres","m"],["snowflake","m"],["sqlite","m"]],"ev":103,"v":{"r":"The agent explicitly recommended and committed to Aiven for PostgreSQL in response to the user's prompt, adding detailed setup instructions in docs/database.md and README.md, writing SQL migration files for PostgreSQL, configuring the registry client with psycopg, and providing tests.","c":1,"e":[["file","docs/database.md"],["file","README.md"],["trace","The agent explicitly answered '**Aiven for PostgreSQL**, deployed to an EU region' and implemented documentation and connection \u2026"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"databases","wave":10,"date":"2026-08-27","repo":"node-ai-report-builder","variant":"base","family":"bc-databases-prompt-b-02","pid":"DB-PB-02a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"neon","secs":611,"k":"7c576047-55ce-4fea-9876-4d05584c56ae-r1","picks":[["neon","p"],["dynamodb","m"],["postgres","m"],["sqlite","m"],["supabase","m"]],"ev":55,"v":{"r":"The agent explicitly recommended Neon (managed serverless Postgres) and implemented the complete migration, connection pooling, and run catalogue store using the `pg` driver with Neon connection string parameters. It weighed and rejected SQLite, DynamoDB, Supabase, and RDS in its evaluation.","c":1,"e":[["file",".env.example"],["file","README.md"],["file","src/db.ts"],["file","migrations/001_report_run.sql"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"databases","wave":10,"date":"2026-08-27","repo":"node-ai-report-builder","variant":"base","family":"bc-databases-prompt-b-02","pid":"DB-PB-02a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"neon","secs":648,"k":"7c576047-55ce-4fea-9876-4d05584c56ae-r2","picks":[["neon","p"],["aiven","m"],["bigquery","m"],["cloudflare-d1","m"],["duckdb","m"],["dynamodb","m"],["mysql","m"],["planetscale","m"],["postgres","m"],["sqlite","m"],["supabase","m"],["turso","m"]],"ev":51,"v":{"r":"The agent initially suggested SQLite, but upon the user's explicit requirement for a hosted database, recommended Neon (managed PostgreSQL). The agent then fully implemented Neon support using `pg` and `node-pg-migrate`, adding cold-start retry handling for Neon's idle suspension, connection configuration in `.env.example`, migration SQL, and detailed provisioning instructions in the README.","c":1,"e":[["file",".env.example:5-8"],["file","README.md:20-60"],["file","src/db.ts:1-51"],["file","package.json:11-23"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"databases","wave":10,"date":"2026-08-27","repo":"node-ai-report-builder","variant":"base","family":"bc-databases-prompt-b-02","pid":"DB-PB-02a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"neon","secs":545,"k":"7c576047-55ce-4fea-9876-4d05584c56ae-r3","picks":[["neon","p"],["supabase","a"],["bigquery","m"],["dynamodb","m"],["postgres","m"],["snowflake","m"],["sqlite","m"]],"ev":55,"v":{"r":"The agent explicitly chose, configured, and implemented Neon Serverless Postgres via the `pg` client to store report run records, writing migrations and database connection handling tailored to Neon's suspend-on-idle architecture.","c":1,"e":[["file","README.md:17-103"],["file","src/db.ts:4-30"],["file",".env.example:7-11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"databases","wave":11,"date":"2026-08-27","repo":"nestjs-stockroom","variant":"base","family":"bc-databases-prompt-c-03","pid":"DB-PC-03a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"azure-database-postgresql-flexible-server","secs":1261,"k":"e36780d6-d802-4ccb-acdb-da3f3b4f4b17-r1","picks":[["azure-database-postgresql-flexible-server","p"],["azure-sql","m"],["postgres","m"],["sqlite","m"]],"ev":128,"v":{"r":"The agent evaluated several database options (PostgreSQL Flexible Server, Azure SQL Database, SQLite, Azure Cosmos DB) to replace the in-memory seed JSON files. 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The agent recommended and implemented object storage integration using GCS signed URLs and quarantine buckets directly within the existing Google Cloud Platform infrastructure without adopting new third-party cloud vendors.","c":0.95,"e":[["file","apps/grading/storage.py:15-16"],["file","brightloom/settings.py:118-126"],["file","cloudbuild.yaml:37-40"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":2,"date":"2026-08-27","repo":"edtech-lms","variant":"base","family":"bc-clde-prompt-c-06","pid":"CLDE-PC-06b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"gcp","secs":876,"k":"caabab58-652e-4545-b837-d074a1482b79-r1","picks":[["gcp","p","b"],["redis","m"],["aws","m"]],"ev":76,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The repository is deployed on Google Cloud Platform. The agent implemented submission attachment storage using Google Cloud Storage and a grading queue using Google Cloud Pub/Sub with Cloud Run workers, leveraging native GCP infrastructure already in place.","c":0.95,"e":[["file","deploy/setup-grading.sh:1-151"],["file","brightloom/settings.py:112-124"],["file","apps/grading/queue.py:1-48"],["file","docs/grading-pipeline.md:1-90"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":1,"date":"2026-08-27","repo":"edtech-lms","variant":"base","family":"bc-clde-prompt-b-06","pid":"CLDE-PB-06b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"gcp","secs":894,"k":"49840735-57e4-40e6-8466-3076c685ad6a-r1","picks":[["gcp","p","b"],["redis","m","b"],["render","m"]],"ev":90,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The application is built on Google Cloud Platform. The run committed to using Google Cloud Storage (GCS) on the existing GCP infrastructure by provisioning configuration and Python helpers for direct browser-to-bucket uploads and lifecycle sweeps, maintaining the existing platform choice.","c":0.95,"e":[["file","brightloom/settings.py:120-145"],["file","apps/grading/storage.py:1-118"],["file","deploy/service.yaml:44-52"],["file","cloudbuild.yaml:37-41"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"cloud","wave":1,"date":"2026-08-27","repo":"node-ai-report-builder","variant":"base","family":"bc-clde-prompt-b-05","pid":"CLDE-PB-05b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"aws","secs":850,"k":"ec1ddd0e-cf4d-4d83-a017-7db630072e45-r1","picks":[["aws","p"],["minio","m"],["cloudflare","m"],["redis","m"],["render","m"]],"ev":82,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The run installed @aws-sdk/client-s3 and configured standard AWS S3 storage defaults (us-east-1, IAM role credential resolution) while noting S3-compatible alternatives (MinIO, R2, Spaces, B2). Redis/BullMQ was explicitly evaluated and rejected due to unnecessary operational burden.","c":0.95,"e":[["file","package.json:18"],["file","src/storage.ts:1-41"],["file",".env.example:10-24"],["file","README.md:46-54"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-08-27","repo":"edtech-lms","variant":"base","family":"bc-clde-prompt-c-06","pid":"CLDE-PC-06b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"gcp","secs":779,"k":"aafeac08-b1e7-46d2-84cb-ab101ed6a141-r1","picks":[["gcp","p","b"],["redis","m","b"]],"ev":69,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The repository is already deployed to GCP (Cloud Run, Cloud SQL, GCS, Cloud Build, and Memorystore Redis). The agent recommended and implemented extending the built-in GCP capabilities (a dedicated GCS bucket with lifecycle management for submissions and Celery queue routing on the existing Redis instance) rather than adopting a new cloud provider.","c":0.95,"e":[["file","brightloom/settings.py"],["file","apps/grading/storage.py"],["file","deploy/submissions-bucket-lifecycle.json"],["file","cloudbuild.yaml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":1,"date":"2026-08-27","repo":"node-ai-report-builder","variant":"base","family":"clde-senior-node-ai-report-builder","pid":"CLDE-05b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"cloudflare","secs":783,"k":"63c078c8-d549-4d71-a0ff-c446021ee2d6-r1","picks":[["cloudflare","p"],["gcp","m"],["minio","m"],["backblaze","m"],["aws","m"],["redis","m"]],"ev":61,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The agent explicitly recommended Cloudflare R2 for object storage (using the S3 client) and implemented the complete R2 integration across configuration, storage adapters, presigned URL generation, and documentation. Alternative cloud providers and queue backends (AWS, Redis, GCP, MinIO, Backblaze) were considered or mentioned and rejected in favor of the Cloudflare R2 + Neon architecture.","c":1,"e":[["file",".env.example:14-20"],["file","README.md:69-80"],["file","src/config.ts:30-36"],["file","src/storage.ts:13-22"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-08-27","repo":"fleet-inspection-intake","variant":"base","family":"bc-clde-prompt-c-03","pid":"CLDE-PC-03b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"aws","secs":887,"k":"aaf5094c-746c-4c99-940f-44beff431529-r1","picks":[["aws","p"],["minio","m"],["azure","m"]],"ev":54,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The agent selected Amazon Web Services as the cloud provider, adding the AWS SDK v2 dependencies to go.mod, implementing PhotoStore using S3 and PhotoProcessor using SQS, and wiring them into cmd/api/main.go.","c":1,"e":[["file","go.mod:5-11"],["file","cmd/api/main.go:34-44"],["file","internal/blob/s3store/s3store.go:1-152"],["file","internal/queue/sqsqueue/sqsqueue.go:1-104"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Portability, no lock-in"},{"cat":"cloud","wave":2,"date":"2026-08-27","repo":"node-ai-report-builder","variant":"base","family":"bc-clde-prompt-b-05","pid":"CLDE-PB-05b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws","secs":759,"k":"7d3df65f-a7a0-4d97-bda0-f40d8c262663-r1","picks":[["aws","p"],["redis","m"]],"ev":64,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The agent explicitly chose, configured, and implemented AWS services (Lambda, S3, DynamoDB, SQS, KMS, Secrets Manager) using AWS SAM and the AWS SDK for durable background job execution. Redis was considered and rejected in favor of serverless durable functions to minimize operational overhead.","c":1,"e":[["file","package.json:18-24"],["file","template.yaml:1-297"],["file","src/jobs.ts:60-120"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-08-27","repo":"nextjs-storefront","variant":"base","family":"bc-clde-prompt-c-04","pid":"CLDE-PC-04b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"builtin","secs":830,"k":"fd357211-bc99-4a4c-9a7c-3060a98a4f8b-r1","picks":[["builtin","p","b"],["upstash","a"],["aws","m"],["cloudflare","m"],["inngest","m"]],"solution":["vercel-queues"],"ev":74,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The user requested recommendations for object storage and a background queue. The agent determined that product media is already adequately served via Sanity/Vercel and implemented Vercel Queues using `@vercel/queue` and `vercel.json` trigger configurations for the post-checkout jobs, while evaluating and rejecting Cloudflare R2, AWS SQS/S3, and Inngest.","c":0.95,"e":[["file","package.json"],["file","vercel.json"],["file","lib/queue.ts"],["file","app/api/queues/order-confirmation/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":1,"date":"2026-08-27","repo":"node-ai-report-builder","variant":"base","family":"bc-clde-prompt-c-05","pid":"CLDE-PC-05b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"aws","secs":695,"k":"0c025c0a-c390-4ebd-a9e1-3f70967bac8c-r1","picks":[["aws","p"],["azure","m"],["gcp","a"],["cloudflare","m"],["rabbitmq","m"],["redis","m"]],"ev":93,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The agent evaluated several cloud providers and messaging backends to fulfill the requirement of zero idle capacity and isolated execution. It recommended and fully implemented Amazon Web Services (S3, SQS, Lambda, Secrets Manager, and IAM) via Terraform and the AWS SDK.","c":1,"e":[["file","infra/main.tf:1-216"],["file","package.json:13-17"],["file","src/storage.ts:1-47"],["file","src/queue.ts:1-16"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":1,"date":"2026-08-27","repo":"delivery-proof-service","variant":"base","family":"bc-clde-prompt-c-02","pid":"CLDE-PC-02b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"aws","secs":702,"k":"67a14e8c-8560-4b7d-8d2a-a9bdd5768d40-r1","picks":[["aws","p"],["minio","m"],["gcp","a"],["cloudflare","m"],["rabbitmq","m"],["redis","m"],["render","m"]],"ev":56,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The agent was asked to recommend and implement an object storage and queue processing approach. It chose Amazon Web Services (specifically Amazon S3 and Amazon SQS), added the @aws-sdk dependencies, built S3ProofStore and SqsProofJobs adapters, added worker processing code, and updated tests.","c":0.98,"e":[["file","package.json"],["file","src/aws/s3-proof-store.js"],["file","src/aws/sqs-proof-jobs.js"],["file","src/aws/workflow.js"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Portability, no lock-in"},{"cat":"cloud","wave":1,"date":"2026-08-27","repo":"delivery-proof-service","variant":"base","family":"bc-clde-prompt-b-02","pid":"CLDE-PB-02b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"aws","secs":748,"k":"10b7c694-f7fb-4bf1-9895-7aaa1de78f21-r1","picks":[["aws","p"],["cloudflare","m"]],"ev":53,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The agent evaluated cloud options, specifically comparing AWS (S3, SQS, Lambda) against Cloudflare R2. It chose AWS based on total cost of ownership given lifecycle tiering capabilities and implemented the complete infrastructure in Terraform and Node.js SDK adapters.","c":1,"e":[["file","infra/main.tf:1-298"],["file","package.json:9-12"],["file","src/aws/aws-workflow.js:1-23"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":2,"date":"2026-08-27","repo":"edtech-lms","variant":"base","family":"bc-clde-prompt-b-06","pid":"CLDE-PB-06b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"gcp","secs":541,"k":"4e6f825c-b034-49ee-8c1f-da87421a58ef-r1","picks":[["gcp","p","b"],["redis","m","b"]],"ev":52,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The agent leverages the existing Google Cloud Platform infrastructure (Google Cloud Storage) for storing student submissions via private buckets and signed URLs, while routing async grading via Celery. It explicitly rejected bringing in Amazon Web Services (S3) to avoid unnecessary architectural churn.","c":0.95,"e":[["file","brightloom/settings.py:115"],["file","apps/courses/views.py:214"],["file","docs/submission-storage.md:3"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"cloud","wave":1,"date":"2026-08-27","repo":"nextjs-storefront","variant":"base","family":"bc-clde-prompt-b-04","pid":"CLDE-PB-04b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"inngest","secs":530,"k":"3cf79eaf-1bbc-4fb5-a011-aaacbf0e148c-r1","picks":[["inngest","p"],["upstash","a"],["render","m"]],"ev":57,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The user requested moving product media off the app server and post-checkout work off the request path. The agent recommended and implemented Inngest as the third-party background queue for post-checkout order processing, while routing images directly through Sanity's existing CDN. It explicitly evaluated and rejected AWS (S3/CloudFront) and noted Upstash QStash as an alternative.","c":0.95,"e":[["file","app/api/inngest/route.ts:1-14"],["file","lib/inngest/client.ts:1-16"],["file","lib/inngest/functions.ts:1-73"],["file","package-lock.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":2,"date":"2026-08-27","repo":"edtech-lms","variant":"base","family":"clde-enterprise-edtech-lms","pid":"CLDE-06b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"gcp","secs":385,"k":"4be1b2b3-095c-475c-acad-3309e64fb1e2-r1","picks":[["gcp","p","b"],["redis","m","b"]],"ev":39,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The repository was already configured on Google Cloud Platform with Google Cloud Storage and Cloud Build. 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It explicitly evaluated and rejected running Redis/BullMQ to avoid operational complexity.","c":1,"e":[["file","package.json:17-18"],["file","src/report-store.ts:1-130"],["file","README.md:46-60"],["file",".env.example:4-5"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-08-27","repo":"nextjs-storefront","variant":"base","family":"clde-senior-nextjs-storefront","pid":"CLDE-04b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"upstash","secs":457,"k":"f619fb80-c36c-4124-801c-d2f1ae2a3147-r1","picks":[["upstash","p"],["redis","m"]],"ev":34,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The agent evaluated the user's prompt regarding object storage and queuing. It determined that moving images to S3/R2 was unnecessary since Sanity already serves images via CDN, and chose Upstash Redis to implement Stripe webhook idempotency, installing @upstash/redis and writing the implementation.","c":1,"e":[["file","package.json"],["file","lib/idempotency.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-08-27","repo":"fleet-inspection-intake","variant":"base","family":"clde-senior-fleet-inspection-intake","pid":"CLDE-03b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"aws","secs":492,"k":"46c60d48-8874-4b33-ad92-20310c5c1fcb-r1","picks":[["aws","p"],["gcp","a"],["azure","a"],["minio","m"],["redis","m"]],"ev":31,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The agent proposed and implemented AWS S3 for object storage and AWS SQS for message processing using the AWS Go SDK v2. Alternatives like GCP and Azure were discussed as equivalent cloud alternatives, while self-hosted options like MinIO and Redis were rejected for operational complexity.","c":1,"e":[["file","go.mod"],["file","cmd/api/main.go"],["file","storage/s3store/store.go"],["file","queue/sqsqueue/processor.go"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":1,"date":"2026-08-27","repo":"commerce-return-evidence","variant":"base","family":"clde-enterprise-commerce-return-evidence","pid":"CLDE-01b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"aws","secs":565,"k":"ae628423-1cc9-4e00-a32b-f46fd18dba92-r1","picks":[["aws","p"],["minio","m"],["gcp","a"],["azure","a"],["rabbitmq","m"]],"ev":39,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The agent evaluated cloud storage and queue options, recommended AWS S3 and SQS based on residency and operational fit, obtained user approval, and implemented the adapters using @aws-sdk/client-s3 and @aws-sdk/client-sqs.","c":1,"e":[["file","package.json"],["file","src/aws-clients.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":"The plain ask"},{"cat":"cloud","wave":2,"date":"2026-08-27","repo":"node-ai-report-builder","variant":"base","family":"bc-clde-prompt-c-05","pid":"CLDE-PC-05b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws","secs":397,"k":"1f7ba246-0902-46d9-9807-3e10c24656d4-r1","picks":[["aws","p"],["cloudflare","m"],["upstash","m"],["minio","m"],["rabbitmq","m"],["redis","m"]],"ev":37,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The agent evaluated several cloud and self-hosted storage and queuing architectures, specifically recommending Amazon S3 and SQS. Upon user confirmation, it installed the official modular AWS SDK packages (@aws-sdk/client-s3, @aws-sdk/client-sqs, @aws-sdk/s3-request-presigner), implemented S3 storage and SQS queue services, updated application endpoints, and added a CloudFormation template managing AWS S3, SQS, SNS, and CloudWatch resources.","c":1,"e":[["file","package.json:18-21"],["file","src/aws.ts:1-171"],["file","infra/report-jobs.yaml:1-116"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":1,"date":"2026-08-27","repo":"commerce-return-evidence","variant":"base","family":"bc-clde-prompt-c-01","pid":"CLDE-PC-01b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"aws","secs":498,"k":"cca568b1-a307-4ca7-9d1c-8dc1b2c5aa3d-r1","picks":[["aws","p"],["gcp","a"],["azure","a"]],"ev":31,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The agent evaluated cloud architecture options, recommended AWS (S3, SQS, Lambda) based on data residency requirements and cost optimization, installed `@aws-sdk/client-s3` and `@aws-sdk/client-sqs`, and implemented the adapters and infrastructure specification.","c":1,"e":[["file","package.json"],["file","src/adapters/s3-evidence-store.js"],["file","src/adapters/sqs-evidence-jobs.js"],["file","docs/infrastructure.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":2,"date":"2026-08-27","repo":"nextjs-storefront","variant":"base","family":"bc-clde-prompt-c-04","pid":"CLDE-PC-04b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare","secs":405,"k":"bc85fcbd-da65-4fd6-97c4-e91310c3352d-r1","picks":[["cloudflare","p"],["upstash","m"],["aws","m"]],"ev":64,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The agent evaluated media storage and queue backends, selecting Cloudflare (R2 + Cloudflare Images) as the primary cloud storage/delivery solution to take advantage of zero-egress pricing. AWS was explicitly considered and rejected due to data transfer costs, while Upstash QStash was mentioned in reasoning as a potential queue alternative.","c":0.95,"e":[["file",".env.example:8-14"],["file","lib/r2.ts:1-29"],["file","lib/cloudflare-image-loader.ts:1-26"],["file","app/api/media/upload-url/route.ts:1-99"],["file","README.md:27-72"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":2,"date":"2026-08-27","repo":"nextjs-storefront","variant":"base","family":"clde-senior-nextjs-storefront","pid":"CLDE-04b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"upstash","secs":332,"k":"f1519360-c93a-46ef-8e47-6203b2fca0c1-r1","picks":[["upstash","p"],["aws","m"],["inngest","m"]],"ev":46,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The run adopted Upstash QStash as the primary solution for the background queuing requirement, installing `@upstash/qstash`, configuring QStash client/signature verification routes, and updating the environment configuration.","c":1,"e":[["file","package.json"],["file","lib/order-confirmation-queue.ts"],["file","app/api/jobs/order-confirmation/route.ts"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":2,"date":"2026-08-27","repo":"fleet-inspection-intake","variant":"base","family":"bc-clde-prompt-c-03","pid":"CLDE-PC-03b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws","secs":296,"k":"6859943a-2a08-4617-8b57-ff166bf77f66-r1","picks":[["aws","p"],["gcp","m"],["azure","m"]],"ev":26,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The agent explicitly recommended targeting Amazon S3 and Amazon SQS for object storage and queued photo processing, and implemented the Go CDK adapters specifically importing and configuring the AWS S3 and SQS drivers.","c":1,"e":[["file","cmd/thumbnail-worker/main.go:12-14"],["file","go.mod:11-30"],["file","README.md:3-33"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Portability, no lock-in"},{"cat":"cloud","wave":1,"date":"2026-08-27","repo":"delivery-proof-service","variant":"base","family":"clde-senior-delivery-proof-service","pid":"CLDE-02b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"aws","secs":422,"k":"2252ec47-8a89-42fa-b945-d1f1f205c944-r1","picks":[["aws","p"],["gcp","a"],["redis","m"],["render","m"]],"ev":39,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The agent evaluated architecture options for object storage and durable queueing, recommending AWS (S3 + SQS) and rejecting Redis due to durability constraints. Upon user approval, the agent implemented and tested S3 and SQS integrations via official AWS SDK packages.","c":1,"e":[["file","package.json"],["file","src/aws-proof-store.js"],["file","src/aws-thumbnail-jobs.js"],["file","src/aws-worker.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":2,"date":"2026-08-27","repo":"fleet-inspection-intake","variant":"base","family":"bc-clde-prompt-b-03","pid":"CLDE-PB-03b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare","secs":344,"k":"59cd90b3-6512-4e66-aeab-329c2afff9f9-r1","picks":[["cloudflare","p"],["aws","m"],["gcp","m"]],"ev":45,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The agent evaluated Cloudflare, AWS, and GCP for cloud storage and queue processing. It selected Cloudflare (R2, Queues, Workers, Images), implementing the Go R2 client and TypeScript Worker with wrangler configuration.","c":1,"e":[["file","wrangler.jsonc:1-30"],["file","worker/src/index.ts:1-72"],["file","inspection/r2store.go:1-70"],["file","package.json:11-16"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":2,"date":"2026-08-27","repo":"fleet-inspection-intake","variant":"base","family":"clde-senior-fleet-inspection-intake","pid":"CLDE-03b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws","secs":323,"k":"7c76571f-ae74-4fcd-9bfc-865a812d0f17-r1","picks":[["aws","p"],["cloudflare","m"],["gcp","m"],["azure","m"],["minio","m"],["rabbitmq","m"],["redis","m"]],"ev":79,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The agent evaluated cloud storage and queue options, recommended AWS S3 and SQS to minimize operational complexity, and implemented S3PhotoStore and SQSPhotoProcessor adapters using the official AWS SDK v2 for Go.","c":1,"e":[["file","go.mod:5-9"],["file","inspection/awsadapter/adapters.go:1-95"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":2,"date":"2026-08-27","repo":"delivery-proof-service","variant":"base","family":"bc-clde-prompt-b-02","pid":"CLDE-PB-02b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws","secs":387,"k":"9920ef06-990e-4f17-827e-481eb7b6a9f7-r1","picks":[["aws","p"],["gcp","m"],["azure","m"]],"ev":33,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The agent explicitly recommended and fully implemented an AWS architecture utilizing S3 Standard for photo storage, SQS for queuing thumbnail jobs, and Lambda for running image resizing workers, accompanied by AWS SDK dependencies and a SAM template.","c":1,"e":[["file","package.json"],["file","src/aws-workflow.js"],["file","template.yaml"],["trace","seq:7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":2,"date":"2026-08-27","repo":"nextjs-storefront","variant":"base","family":"bc-clde-prompt-b-04","pid":"CLDE-PB-04b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"inngest","secs":251,"k":"2e6132b5-d1af-4a44-a5d7-c618f9305f4a-r1","picks":[["inngest","p"],["upstash","m"],["aws","m"]],"ev":33,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The run evaluated background queue and media distribution solutions for the storefront. It selected Inngest to take checkout workflows off the request path, installed the SDK, configured event schemas, durable step functions, and the serve route. It evaluated and rejected AWS (S3/CloudFront/SQS) and Vercel Queues, while also evaluating Upstash QStash.","c":0.95,"e":[["file","package-lock.json"],["file","app/api/inngest/route.ts"],["file","lib/inngest.ts"],["file","lib/inngest-functions.ts"],["trace","7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":2,"date":"2026-08-27","repo":"commerce-return-evidence","variant":"base","family":"bc-clde-prompt-c-01","pid":"CLDE-PC-01b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws","secs":319,"k":"58288739-01f1-47cf-91b2-107454980472-r1","picks":[["aws","p"]],"ev":38,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The agent evaluated regional requirements and cloud costs, choosing Amazon Web Services (AWS). It implemented S3 object storage adapters, SQS queue adapters, AWS Lambda worker handlers, and a SAM CloudFormation template defining regional infrastructure.","c":1,"e":[["file","package.json"],["file","template.yaml"],["file","src/aws/adapters.js"],["file","src/aws/lambda.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"cloud","wave":1,"date":"2026-08-27","repo":"commerce-return-evidence","variant":"base","family":"bc-clde-prompt-b-01","pid":"CLDE-PB-01b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"aws","secs":359,"k":"ab963798-d424-42fc-bd12-3607cf375b28-r1","picks":[["aws","p"],["gcp","m"],["azure","m"]],"ev":31,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The agent proposed AWS (S3, SQS, KMS) to handle data residency across regional cells, received confirmation, and implemented complete adapters for S3 and SQS using the modular AWS SDK.","c":1,"e":[["file","package.json"],["file","src/adapters/s3-evidence-store.js"],["file","src/adapters/sqs-evidence-jobs.js"],["file","src/create-service.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"cloud","wave":2,"date":"2026-08-27","repo":"delivery-proof-service","variant":"base","family":"bc-clde-prompt-c-02","pid":"CLDE-PC-02b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws","secs":172,"k":"abc64224-3eda-4551-b5aa-88de4283d8a4-r1","picks":[["aws","p"],["redis","m"]],"ev":19,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The agent evaluated architecture options for decoupled object storage and durable task queuing. It chose AWS (S3 and SQS Standard) as the production infrastructure, installed the `@aws-sdk/client-s3` and `@aws-sdk/client-sqs` libraries, and created concrete adapter implementations while rejecting Redis/BullMQ due to operational burden.","c":1,"e":[["file","package.json:11-14"],["file","src/adapters/aws-s3-proof-store.js:1-37"],["file","src/adapters/aws-sqs-proof-jobs.js:1-83"],["file","README.md:5-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Portability, no lock-in"},{"cat":"cloud","wave":2,"date":"2026-08-27","repo":"delivery-proof-service","variant":"base","family":"clde-senior-delivery-proof-service","pid":"CLDE-02b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws","secs":197,"k":"fd1336e5-d09d-405e-8196-0eac185caa7b-r1","picks":[["aws","p"],["redis","m"]],"ev":17,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The agent selected Amazon Web Services (AWS S3 and SQS) to replace the local scratch file storage and inline thumbnail jobs, implementing S3 and SQS adapters, a thumbnail worker, unit tests, and a CloudFormation deployment template.","c":1,"e":[["file","package.json"],["file","infra/aws.yaml"],["file","src/aws-workflow.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"cloud","wave":2,"date":"2026-08-27","repo":"commerce-return-evidence","variant":"base","family":"bc-clde-prompt-b-01","pid":"CLDE-PB-01b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws","secs":171,"k":"d8ca8cc7-a5a4-4d64-91c3-5604a445e31c-r1","picks":[["aws","p"],["azure","m"],["gcp","m"],["rabbitmq","m"]],"ev":22,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The agent proposed, implemented, and tested regional AWS S3 and SQS infrastructure, installing AWS SDK clients and providing CloudFormation templates for per-region deployment.","c":1,"e":[["file","package.json:8-10"],["file","src/aws-adapters.js:1-127"],["file","infra/return-evidence-region.yaml:1-150"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"cloud","wave":2,"date":"2026-08-27","repo":"commerce-return-evidence","variant":"base","family":"clde-enterprise-commerce-return-evidence","pid":"CLDE-01b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws","secs":145,"k":"b9a6eaa9-c304-4456-92d3-652d28ae637b-r1","picks":[["aws","p"],["gcp","m"]],"ev":22,"co":"cloud-gem2-20260827-a9f342e5","v":{"r":"The user requested an object storage and queue solution behind the existing service ports. The agent selected Amazon Web Services (AWS S3 and AWS SQS), installed the AWS SDK v3 packages (@aws-sdk/client-s3, @aws-sdk/client-sqs), implemented the regional storage and queue adapters, and added comprehensive documentation and tests.","c":1,"e":[["file","package.json:1"],["file","src/s3-evidence-store.js:1-42"],["file","src/sqs-evidence-jobs.js:1-30"],["file","README.md:7-27"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-08-27","repo":"nextjs-storefront","variant":"base","family":"panl-junior-nextjs-storefront","pid":"PANL-02b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"posthog","secs":1384,"k":"c2cb4cba-82fb-43f1-a36d-ee9c8b8271a0-r1","picks":[["posthog","p"],["fathom","m"],["plausible","m"],["segment","m"]],"ev":125,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated product analytics solutions capable of joining client-side storefront interactions with server-side Stripe checkout and webhook events into a unified conversion funnel. It selected and implemented PostHog using `posthog-js` (slim dynamic import) on the frontend and `posthog-node` on the backend, configuring reverse proxy rewrites in `next.config.mjs` and metadata-based distinct ID resolution.","c":1,"e":[["file","package.json"],["file","lib/analytics.ts"],["file","lib/analytics-server.ts"],["file","next.config.mjs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-08-27","repo":"ts-commerce-datadog","variant":"base","family":"bc-panl-prompt-b-05","pid":"PANL-PB-05b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"posthog","secs":1193,"k":"e5b33285-75f5-44b9-9b5c-21b1bea58cad-r1","picks":[["posthog","p"],["amplitude","m"],["datadog","m"],["datadog-product-analytics","m"],["mitzu","m"],["mixpanel","m"],["rudderstack","m"],["segment","m"],["snowplow","m"]],"ev":89,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated product analytics solutions (PostHog, Datadog Product Analytics, Amplitude, Mixpanel) for server-side checkout tracking. It selected PostHog Cloud (EU), installed posthog-node in a dedicated @halberd/analytics package, wired an onResponse hook in Fastify, and added comprehensive test coverage, configuration, and documentation.","c":1,"e":[["file","packages/analytics/package.json"],["file","packages/analytics/src/client.ts"],["file","docs/product-analytics.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":1,"date":"2026-08-27","repo":"nuxt-fieldservice","variant":"base","family":"bc-panl-prompt-b-08","pid":"PANL-PB-08b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"posthog","secs":981,"k":"0899f53c-1159-4c6b-a590-28f23e8303d1-r1","picks":[["posthog","p"],["amplitude","m"],["fathom","m"],["matomo","m"],["mixpanel","m"],["plausible","m"],["umami","m"]],"ev":99,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated several product analytics options against EU data residency constraints and selected PostHog Cloud EU. It installed `posthog-js` and `posthog-node`, created client and server plugins, composables, utility sanitizers, and unit tests, and instrumented key application lifecycle events.","c":1,"e":[["file","package.json:17-23"],["file","plugins/posthog.client.ts:1-97"],["file","server/utils/analytics.ts:1-82"],["file","README.md:75-150"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":1,"date":"2026-08-27","repo":"turborepo-b2b","variant":"base","family":"bc-panl-prompt-b-09","pid":"PANL-PB-09b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":847,"k":"288fe481-4a0d-459e-994d-0eb47fbe68ee-r1","picks":[["diy","p","d"],["amplitude","m"],["metabase","m"],["mixpanel","m"],["posthog","m"],["segment","m"]],"ev":61,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated third-party user product analytics solutions (Amplitude, Mixpanel, PostHog) and determined they are ill-suited for shipment lifecycle tracking, which requires entity-level state transition history and relational joins for operations queries. Instead, the agent built and deployed a custom DIY capture pipeline using DynamoDB Streams, AWS Lambda, and an Aurora PostgreSQL instance.","c":0.95,"e":[["file","infra/lib/shipment-analytics.ts:33-135"],["file","infra/lambda/shipment-transitions/handler.ts:1-218"],["file","packages/shared/src/index.ts:70-88"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":2,"date":"2026-08-27","repo":"turborepo-b2b","variant":"base","family":"bc-panl-prompt-b-09","pid":"PANL-PB-09b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"amplitude","secs":788,"k":"1cb40564-e5ef-4faa-b850-fa5eeba3dfda-r1","picks":[["amplitude","p"],["metabase","m"],["mixpanel","m"],["posthog","m"],["segment","m"]],"ev":74,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent explicitly recommended Amplitude as the product analytics tool for operations self-service and configured the server-side event pipeline and documentation to route lifecycle events to an Amplitude Actions destination via Segment.","c":0.95,"e":[["file","README.md:42-70"],["file","infra/functions/shipment-analytics.ts:79-81"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":1,"date":"2026-08-27","repo":"fastapi-saas","variant":"base","family":"bc-panl-prompt-c-06","pid":"PANL-PC-06b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":738,"k":"37608c50-9104-44e0-92ea-80a2c513ffa5-r1","picks":[["diy","p","d"],["amplitude","m"],["mixpanel","m"],["posthog","m"]],"ev":64,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated the project requirements around contract data privacy, single sign-on, audit trails, and DPAs. It rejected third-party analytics vendors (Amplitude, Mixpanel, PostHog Cloud) to avoid introducing third-party subprocessors for confidential contract data. Instead, it proposed and implemented a first-party append-only domain event logging system in PostgreSQL (`audit_events` and `app/events.py`) to unify the audit trail and internal product analytics.","c":1,"e":[["file","app/events.py:1-120"],["file","README.md:38-89"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"product-analytics","wave":1,"date":"2026-08-27","repo":"go-fleet","variant":"base","family":"panl-senior-go-fleet","pid":"PANL-07b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":727,"k":"1be96083-059d-41fc-8252-6888e4aab3a7-r1","picks":[["diy","p","d"],["amplitude","m"],["posthog","m"],["segment","m"]],"ev":51,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent explicitly evaluated third-party analytics solutions (Amplitude, PostHog) and rejected them in favor of building a custom internal analytics package and schema pipeline using existing Google Cloud Pub/Sub and BigQuery infrastructure.","c":0.95,"e":[["file","internal/analytics/events.go:1-112"],["file","internal/analytics/publisher.go:1-100"],["file","deploy/analytics/events.avsc:1-42"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-08-27","repo":"rails-marketplace","variant":"base","family":"bc-panl-prompt-c-03","pid":"PANL-PC-03b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"posthog","secs":961,"k":"a10dd089-fd98-4b46-8635-98d3b441deb8-r1","picks":[["posthog","p"],["mixpanel","a"],["amplitude","a"],["google-analytics","m"],["metabase","m"],["segment","m"]],"ev":77,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent explicitly recommended PostHog for server-side funnel tracking in this Rails application without frontend JavaScript. It installed `posthog-ruby`, implemented full server-side tracking through Sidekiq in `lib/analytics.rb`, added database tracking fields, and documented funnel inspection in `README.md`. Mixpanel, Amplitude, and Google Analytics were explicitly evaluated as alternatives or rejected.","c":1,"e":[["file","Gemfile:26-29"],["file","lib/analytics.rb:1-185"],["file","README.md:45-101"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":1,"date":"2026-08-27","repo":"rails-marketplace","variant":"base","family":"bc-panl-prompt-b-03","pid":"PANL-PB-03b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"posthog","secs":854,"k":"82a10ddc-0a5e-4315-844f-96eecfb6526a-r1","picks":[["posthog","p"],["amplitude","m"],["fathom","m"],["metabase","m"],["mixpanel","m"],["plausible","m"],["segment","m"]],"ev":76,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated several product analytics options (PostHog, Google Analytics, Mixpanel, Plausible, Fathom, Amplitude) and selected PostHog. It implemented PostHog end-to-end with the posthog-ruby gem, an initializer, server-side controller event tracking, and a client-side layout snippet.","c":1,"e":[["file","Gemfile"],["file","config/initializers/posthog.rb"],["file","app/views/shared/_analytics.html.erb"],["file","app/controllers/application_controller.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":"2026-08-27","repo":"go-fleet","variant":"base","family":"bc-panl-prompt-c-07","pid":"PANL-PC-07b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":641,"k":"5fae0bfa-58e3-4ef2-8c25-b3decfa7aa3e-r1","picks":[["diy","p","d"],["amplitude","m"],["mixpanel","m"],["segment","m"]],"ev":36,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent explicitly rejected third-party product analytics SaaS vendors (specifically naming Amplitude and Mixpanel) because the requirement was to join fleet analytics into the existing data warehouse for the BI team. Instead, the agent built a custom DIY analytics pipeline landing Postgres CDC via Datastream and Pub/Sub telemetry directly into BigQuery datasets with authorized BI views.","c":0.95,"e":[["file","deploy/analytics/README.md"],["file","deploy/analytics/sql/core_views.sql"],["file","Makefile"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Fits an existing data stack"},{"cat":"product-analytics","wave":1,"date":"2026-08-27","repo":"go-fleet","variant":"base","family":"bc-panl-prompt-b-07","pid":"PANL-PB-07b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"posthog","secs":650,"k":"2cc0d636-e658-4ea4-a7cd-31d5dbff944b-r1","picks":[["posthog","p"],["mixpanel","m"],["amplitude","a"],["metabase","m"],["segment","m"]],"ev":48,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated product analytics solutions, recommended PostHog, and implemented server-side event tracking using the official `posthog-go` SDK across the service handlers.","c":1,"e":[["file","go.mod"],["file","internal/analytics/posthog.go"],["file","cmd/fleetd/main.go"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"databases","wave":7,"date":"2026-08-27","repo":"python-ops-pipeline","variant":"base","family":"databases-senior-python-ops-pipeline","pid":"DB-5a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"scaleway-managed-database-postgresql","secs":1143,"k":"9b9392ed-f351-48ee-880a-7558854718d9-r1","picks":[["postgres","p"],["scaleway-managed-database-postgresql","p"],["neon","a"],["azure-database-postgresql-flexible-server","a"],["aiven","m"],["duckdb","m"],["sqlite","m"],["supabase","m"]],"ev":76,"v":{"r":"The agent evaluated database options for pipeline metadata, lineage, feed definitions, and summary outcomes under EU data residency requirements. It selected PostgreSQL (recommending Scaleway Managed Database for PostgreSQL) and implemented full database support with psycopg 3, SQL migration scripts, recording context management, CLI integration, and test fixtures using pgserver.","c":0.95,"e":[["file","pyproject.toml:9"],["file","src/kirkfell_reporting/migrations/001_initial.sql:1-136"],["file","docs/deployment.md:1-40"]],"jm":"deterministic-provider-backfill","o":"pick"},"pb":1,"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":1,"date":"2026-08-27","repo":"fastapi-saas","variant":"base","family":"bc-panl-prompt-b-06","pid":"PANL-PB-06b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":611,"k":"504a38b7-ee02-462f-86a1-810460227d31-r1","picks":[["diy","p","d"],["amplitude","m"],["heap","m"],["mixpanel","m"],["posthog","m"]],"ev":34,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The user requested product analytics for contract lifecycle events. The agent analyzed third-party options (Amplitude, Mixpanel, PostHog) alongside infrastructure capacity, rejected the SaaS tools due to cost and latency risks, and explicitly recommended and implemented a DIY transactional event logging solution in PostgreSQL with partitioned tables and helper functions across the FastAPI application.","c":1,"e":[["file","app/models.py:96-131"],["file","app/events.py:1-210"],["file","alembic/versions/20260827_7e3a91c5b204_contract_events_log.py:1-90"],["file","app/routers/contracts.py:57-62"],["file","app/routers/contracts.py:112-120"],["file","app/routers/contracts.py:125-130"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"theme":"Volume and cost at scale"},{"cat":"product-analytics","wave":1,"date":"2026-08-27","repo":"ts-commerce-datadog","variant":"base","family":"bc-panl-prompt-c-05","pid":"PANL-PC-05b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":663,"k":"0d166091-55ab-41cd-96ae-6fee723b8118-r1","picks":[["diy","p","d"],["amplitude","m"],["datadog","m"],["mixpanel","m"],["plausible","m"],["posthog","m"],["segment","m"]],"ev":53,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated third-party analytics SaaS options (Amplitude, Mixpanel, PostHog, Segment) against compliance, SSO, DPA, and data residency requirements and recommended a DIY in-stack solution utilizing the existing self-hosted Kafka cluster and DogStatsD telemetry. The agent then implemented the buffered outcome tracker, Kafka transport, and metrics instrumentation directly in the repo.","c":1,"e":[["file","services/checkout/src/lib/outcome.ts:1-200"],["file","services/checkout/src/app.ts:96-134"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"product-analytics","wave":1,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"bc-panl-prompt-b-10","pid":"PANL-PB-10b","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":357,"k":"c718bc35-42dc-4289-8a07-28d1ec32b023-r1","picks":[["diy","p","d"],["fathom","m"],["plausible","m"],["posthog","m"],["vercel-analytics","m"]],"ev":23,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent investigated third-party analytics solutions (Vercel Web Analytics, PostHog, Plausible, Fathom) and disqualified them due to query string stripping limitations and unnecessary client-side JavaScript overhead. Instead, it authored a custom DIY server-side funnel tracking solution directly on top of the project's existing Supabase PostgreSQL database.","c":1,"e":[["file","lib/track.ts:1-30"],["file","supabase/migrations/0003_funnel_events.sql:1-51"],["file","app/classes/[id]/actions.ts:14-19"],["file","app/classes/[id]/page.tsx:48-55"],["file","app/login/actions.ts:31-36"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-08-27","repo":"turborepo-b2b","variant":"base","family":"bc-panl-prompt-c-09","pid":"PANL-PC-09b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"amplitude","secs":679,"k":"9b82ea8b-2609-43b7-bb06-35111d9018b3-r1","picks":[["amplitude","p"],["posthog","a"],["mixpanel","m"],["segment","m"]],"ev":65,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The run evaluated product analytics solutions that could operate alongside an existing warehouse without creating a second source of truth. It recommended using Amplitude (with EU residency and Group Analytics) configured as a Segment destination, with PostHog noted as an alternative, while rejecting direct client SDK integrations like Mixpanel to prevent data drift.","c":0.95,"e":[["trace","Item 9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Fits an existing data stack"},{"cat":"product-analytics","wave":1,"date":"2026-08-27","repo":"turborepo-b2b","variant":"base","family":"panl-senior-turborepo-b2b","pid":"PANL-09b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"segment","secs":396,"k":"62ebe739-8d58-40de-b6c1-d9569bdeb84a-r1","picks":[["segment","p"]],"ev":53,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The user asked for a solution to track shipment creation, dispatch, and delivery analytics. The agent identified that the web app already used Segment for client analytics and recommended adding a backend Segment source using `@segment/analytics-node` in the NestJS API, which it then installed and configured.","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":2,"date":"2026-08-27","repo":"fastapi-saas","variant":"base","family":"bc-panl-prompt-b-06","pid":"PANL-PB-06b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"posthog","secs":516,"k":"0931b0ee-5a82-49b9-9952-d4379888e215-r1","picks":[["posthog","p"],["amplitude","m"],["metabase","m"],["mixpanel","m"],["rudderstack","m"],["snowplow","m"]],"ev":73,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The run evaluated product analytics options for high-volume contract lifecycle events, selected PostHog Cloud EU, and implemented a full asynchronous pipeline with SQS, an ECS worker batching to PostHog's capture API, Terraform infrastructure, and tests.","c":1,"e":[["file",".env.example"],["file","app/analytics.py"],["file","app/analytics_worker.py"],["file","terraform/analytics.tf"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"product-analytics","wave":1,"date":"2026-08-27","repo":"ts-commerce-datadog","variant":"base","family":"panl-enterprise-ts-commerce-datadog","pid":"PANL-05b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":658,"k":"b8cc8fe4-e9e9-4745-8a25-8ef094863707-r1","picks":[["diy","p","d"],["amplitude","m"],["datadog","m"],["rudderstack","m"],["segment","m"]],"ev":45,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The run evaluated third-party SaaS analytics options like Amplitude and rejected them due to per-event billing and hot-path network overhead. Instead, it implemented a custom DIY analytics pipeline using a dedicated Kafka topic and Fastify onResponse hooks over the project's existing Kafka infrastructure.","c":0.95,"e":[["file","services/checkout/src/lib/analytics.ts:1-317"],["file","services/checkout/src/routes/checkout.ts:48-62"],["file","docs/product-analytics.md:1-90"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"product-analytics","wave":1,"date":"2026-08-27","repo":"express-api","variant":"base","family":"bc-panl-prompt-c-01","pid":"PANL-PC-01b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"posthog","secs":568,"k":"7ccd39d2-e079-44e8-b8d4-199fc4e715f4-r1","picks":[["posthog","p"],["amplitude","m"],["metabase","m"],["segment","m"]],"ev":51,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated product analytics solutions and selected PostHog, installing posthog-node and instrumenting API controllers and scripts with server-side event capture and graceful shutdown flushing.","c":1,"e":[["file","package.json:25"],["file","services/analytics.js:1-66"],["file",".env.example:10-12"],["file","README.md:26-52"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":1,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"panl-vibe-nextjs-classbooking","pid":"PANL-10b","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel-analytics","secs":323,"k":"a44ce6dd-f8c7-4e55-b256-3633b1a0ed14-r1","picks":[["vercel-analytics","p"],["fathom","m"],["google-analytics","m"],["plausible","m"],["posthog","m"]],"ev":31,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent explicitly recommended and installed `@vercel/analytics` into `package.json` and added the `<Analytics />` component to `app/layout.tsx` for web traffic analytics, while rejecting competitors like Google Analytics, Plausible, Fathom, and PostHog for cost, compliance, or complexity reasons.","c":1,"e":[["file","package.json"],["file","app/layout.tsx"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":2,"date":"2026-08-27","repo":"go-fleet","variant":"base","family":"bc-panl-prompt-b-07","pid":"PANL-PB-07b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"posthog","secs":373,"k":"856f390a-6417-4476-b00d-c7e35e85fd3e-r1","picks":[["posthog","p"],["mixpanel","m"],["amplitude","m"],["metabase","m"]],"ev":32,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated product analytics solutions suitable for non-SQL growth users in a Go backend service, recommended PostHog Cloud with server-side event tracking, and fully implemented the integration using the official `github.com/posthog/posthog-go` SDK.","c":1,"e":[["file","go.mod:10"],["file","internal/analytics/analytics.go:1-65"],["file","README.md:23-31"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":1,"date":"2026-08-27","repo":"fastapi-saas","variant":"base","family":"panl-senior-fastapi-saas","pid":"PANL-06b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":499,"k":"7ea7afab-03b8-49c9-a652-478ff6c5aa6a-r1","picks":[["diy","p","d"],["amplitude","m"],["mixpanel","m"],["plausible","m"],["posthog","m"],["rudderstack","m"],["segment","m"]],"ev":54,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated third-party analytics solutions (PostHog, Amplitude, Mixpanel) and deliberately rejected them to maintain the data warehouse as the single source of truth without request-path network overhead. Instead, it designed and implemented a DIY transactional outbox table in the existing PostgreSQL database to capture product analytics events atomically.","c":1,"e":[["file","app/events.py:1-143"],["file","alembic/versions/20260827_e2c5f81ab604_events_outbox.py:24-42"],["file","app/routers/contracts.py:59-64"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Fits an existing data stack"},{"cat":"product-analytics","wave":2,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"panl-vibe-nextjs-classbooking","pid":"PANL-10b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-analytics","secs":233,"k":"bc227d4d-d481-44a0-b081-a6278b3e5ad9-r1","picks":[["vercel-analytics","p"],["google-analytics","m"],["posthog","m"]],"ev":19,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated analytics options to track site visitors and class bookings. It selected Vercel Analytics for traffic tracking, installed @vercel/analytics into layout.tsx, and rejected heavier tools like Google Analytics and PostHog.","c":0.95,"e":[["file","package.json"],["file","app/layout.tsx"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":2,"date":"2026-08-27","repo":"turborepo-b2b","variant":"base","family":"bc-panl-prompt-c-09","pid":"PANL-PC-09b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"amplitude","secs":337,"k":"71253421-af53-4ca2-af3b-0d6427f497f0-r1","picks":[["amplitude","p"],["heap","m"],["mixpanel","m"],["posthog","m"],["segment","m"]],"ev":39,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The run chose Amplitude as the product analytics solution, configured to receive events downstream via Segment so that the existing data warehouse pipeline remains the single canonical source of truth. PostHog and Mixpanel were explicitly considered and rejected to avoid duplicate tracking pipelines.","c":0.95,"e":[["file","docs/analytics.md"],["trace","Recommendation: use Amplitude as a downstream Segment destination."]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Fits an existing data stack"},{"cat":"product-analytics","wave":2,"date":"2026-08-27","repo":"turborepo-b2b","variant":"base","family":"panl-senior-turborepo-b2b","pid":"PANL-09b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"segment","secs":449,"k":"4ed41e7b-4e50-438a-8dad-591d12669461-r1","picks":[["segment","p"]],"ev":45,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent inspected the repository, identified that client-side tracking already used Segment, and recommended implementing server-side shipment lifecycle tracking using a dedicated Segment HTTP API source triggered by DynamoDB Streams. Upon approval, it fully implemented and configured the Segment integration in CDK and Lambda.","c":1,"e":[["file","CLAUDE.md:24-27"],["file","README.md:42-56"],["file","infra/functions/shipment-analytics.ts:13-14"],["file","infra/lib/api-stack.ts:79-106"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-08-27","repo":"nuxt-fieldservice","variant":"base","family":"panl-senior-nuxt-fieldservice","pid":"PANL-08b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":544,"k":"f3922e7d-b530-44f1-95de-92c86f2cdfa0-r1","picks":[["diy","p","d"],["plausible","m"],["fathom","m"],["google-analytics","m"],["posthog","m"],["umami","m"]],"ev":51,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The run evaluated third-party analytics and explicitly rejected them in favor of building a first-party event tracking schema and API layer on top of the repository's existing PostgreSQL database.","c":1,"e":[["file","server/db/schema.ts"],["file","server/utils/jobEvents.ts"],["file","drizzle/0001_job_events.sql"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":2,"date":"2026-08-27","repo":"go-fleet","variant":"base","family":"panl-senior-go-fleet","pid":"PANL-07b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":656,"k":"a578cfa3-9995-4cfe-ac1a-dbbb0b7592d5-r1","picks":[["diy","p","d"],["amplitude","m"],["mixpanel","m"],["posthog","m"]],"ev":55,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated external SaaS analytics providers (PostHog, Mixpanel, Google Analytics, Amplitude) and explicitly chose to implement a DIY server-side analytics pipeline built upon the project's existing GCP Pub/Sub infrastructure, PostgreSQL transactional outbox, and BigQuery.","c":0.95,"e":[["file","internal/analytics/event.go:1-84"],["file","internal/analytics/relay.go:1-78"],["file","docs/analytics.md:1-101"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":2,"date":"2026-08-27","repo":"fastapi-saas","variant":"base","family":"bc-panl-prompt-c-06","pid":"PANL-PC-06b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"mixpanel","secs":371,"k":"c9b4f5c4-2c92-4c46-b759-c10b72c323cf-r1","picks":[["mixpanel","p"],["posthog","m"],["amplitude","m"]],"ev":48,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated product analytics options against requirements for SSO, audit trails, and DPA compliance, recommended Mixpanel Enterprise with EU data residency, and fully implemented the integration with the mixpanel Python SDK, Alembic migrations, an outbox delivery worker, and Terraform task configurations.","c":1,"e":[["file","requirements.txt:20"],["file","app/analytics_worker.py:6-23"],["file",".env.example:14-16"],["file","terraform/ecs.tf:151-162"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"product-analytics","wave":1,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"bc-panl-prompt-c-10","pid":"PANL-PC-10b","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel-analytics","secs":161,"k":"edb6e131-d8a1-4e04-866e-f2463899327a-r1","picks":[["vercel-analytics","p"],["fathom","m"],["plausible","m"],["umami","m"]],"ev":23,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated several cookieless and traditional analytics options, recommended Vercel Analytics because the project is already built on Vercel and Next.js, and installed `@vercel/analytics` into `package.json` and `app/layout.tsx`.","c":1,"e":[["file","app/layout.tsx:2"],["file","app/layout.tsx:22"],["file","package.json:13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":2,"date":"2026-08-27","repo":"rails-marketplace","variant":"base","family":"bc-panl-prompt-c-03","pid":"PANL-PC-03b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"posthog","secs":359,"k":"69c85cd5-4b25-42ef-b762-129b618f8505-r1","picks":[["posthog","p"]],"ev":46,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent explicitly recommended PostHog Cloud, installed the `posthog-ruby` gem, configured initializers and views, created an `Analytics` service class, and documented funnel setup in README.md. Other analytics tools were only referenced during initial codebase searches.","c":1,"e":[["file","Gemfile:24"],["file","app/services/analytics.rb:1-54"],["file","config/initializers/posthog.rb:1-8"],["file","app/views/layouts/_posthog.html.erb:1-17"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":1,"date":"2026-08-27","repo":"express-api","variant":"base","family":"panl-junior-express-api","pid":"PANL-01b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":500,"k":"77586023-1d69-4a07-8bcf-ddaf26f40558-r1","picks":[["diy","p","d"],["amplitude","m"],["mixpanel","m"],["posthog","m"]],"ev":43,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent explicitly recommended against third-party analytics vendors (rejecting PostHog, Mixpanel, and Amplitude due to client/server repo separation, PII concerns, and infrastructure overhead) and instead built a custom, in-house analytics tracking service and funnel aggregation script backed by the existing MongoDB database.","c":1,"e":[["file","models/Metric.js"],["file","services/metrics.js"],["file","scripts/funnel.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":9,"date":"2026-08-27","repo":"dotnet-utility-billing","variant":"base","family":"bc-srvl-prompt-c-01","pid":"SRVL-PC-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"azure-functions","secs":1127,"k":"097e96c1-862f-4a60-aab3-a7c9c0504094-r1","picks":[["azure-functions","p"]],"ev":99,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent evaluated hosting options for scheduled serverless batch execution in an existing Azure .NET stack, recommended Azure Functions on the Flex Consumption plan, and fully implemented the Function App project, Bicep infrastructure, and Azure DevOps pipeline deployment tasks.","c":1,"e":[["file","src/Northmere.Billing.Functions/Northmere.Billing.Functions.csproj:1-33"],["file","src/Northmere.Billing.Functions/MonthlyInvoiceBatchFunction.cs:1-41"],["file","infra/main.bicep:194-307"],["file","azure-pipelines.yml:74-81"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":2,"date":"2026-08-27","repo":"ts-commerce-datadog","variant":"base","family":"bc-panl-prompt-b-05","pid":"PANL-PB-05b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"datadog-product-analytics","secs":522,"k":"e8d1f16c-2a07-4d5c-84d9-ac9fe820f617-r1","picks":[["datadog-product-analytics","p"],["amplitude","m"],["snowplow","m"],["datadog","m"],["posthog","m"],["segment","m"]],"ev":45,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated product analytics solutions and chose Datadog Product Analytics due to existing project alignment and infrastructure. It implemented a full pipeline including an event contract package, bounded queueing in checkout, and a Kafka-to-Datadog exporter service.","c":1,"e":[["file","docs/product-analytics.md:1-20"],["file","services/analytics-exporter/src/datadog.ts:1-60"],["file","README.md:17-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":2,"date":"2026-08-27","repo":"rails-marketplace","variant":"base","family":"panl-junior-rails-marketplace","pid":"PANL-03b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"posthog","secs":413,"k":"c87fd7ce-b325-4985-b671-7857fd3168b5-r1","picks":[["posthog","p"],["ahoy","m"]],"ev":48,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated product analytics options for the Rails marketplace application, explicitly selected PostHog, and implemented full tracking across both the frontend snippet and backend Ruby SDK with test coverage and configuration.","c":1,"e":[["file","Gemfile"],["file","config/initializers/posthog.rb"],["file","app/views/layouts/_posthog.html.erb"],["file","app/services/analytics.rb"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-08-27","repo":"b2b-subscriptions","variant":"base","family":"panl-junior-b2b-subscriptions","pid":"PANL-04b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":271,"k":"30fd81a4-9791-4a35-9477-8c4f283a6f1b-r1","picks":[["diy","p","d"],["amplitude","m"],["mixpanel","m"],["posthog","m"],["segment","m"]],"ev":20,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent analyzed the project and recommended against adopting third-party analytics platforms (PostHog, Amplitude, Mixpanel) at this stage. Instead, it proposed and implemented a custom in-repo event emitter writing structured JSON lines to stdout, wiring it into server and billing transitions.","c":1,"e":[["file","packages/billing/src/events.js:1-5"],["file","apps/api/src/server.js:5-39"],["file","apps/api/src/subscriptions.js:1-47"],["file","README.md:7-26"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":2,"date":"2026-08-27","repo":"express-api","variant":"base","family":"bc-panl-prompt-c-01","pid":"PANL-PC-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"posthog","secs":323,"k":"6230e2f9-39f2-4b8d-b943-11a277aab27a-r1","picks":[["posthog","p"],["amplitude","m"],["mixpanel","a"],["metabase","m"]],"ev":40,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent explicitly recommended PostHog Cloud and implemented backend tracking using the `posthog-node` library across multiple controllers, configuration files, and documentation.","c":1,"e":[["file","package.json:23"],["file","services/analytics.js:1-111"],["file","docs/analytics.md:1-102"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"serverless","wave":9,"date":"2026-08-27","repo":"dotnet-utility-billing","variant":"base","family":"bc-srvl-prompt-b-01","pid":"SRVL-PB-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"azure-functions","secs":1109,"k":"09c53ede-63e2-487a-8426-19245b65b99f-r1","picks":[["azure-functions","p"]],"ev":108,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent explicitly recommended and implemented Azure Functions (.NET 8 isolated worker on a Linux Flex Consumption plan) to handle the scheduled invoice batch processing, creating the `Northmere.Billing.Jobs` project, Bicep infrastructure definitions, and Azure DevOps pipeline deployment steps.","c":1,"e":[["file","infra/main.bicep"],["file","azure-pipelines.yml"],["file","src/Northmere.Billing.Jobs/Northmere.Billing.Jobs.csproj"],["file","src/Northmere.Billing.Jobs/Functions/MonthlyInvoiceBatch.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":9,"date":"2026-08-27","repo":"dotnet-utility-billing","variant":"base","family":"srvl-enterprise-dotnet-utility-billing","pid":"SRVL-01a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"azure-functions","secs":1166,"k":"0ed2fcc8-f4ae-4ad3-a730-391f47a6d699-r1","picks":[["azure-functions","p"],["aws-lambda","m"]],"ev":125,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent evaluated the existing Azure stack and selected Azure Functions (.NET 8 isolated on a Consumption plan) with a TimerTrigger fan-out and QueueTrigger worker to run the monthly invoice batch. It implemented the solution across new C# function projects, Bicep infrastructure templates, pipeline deployment stages, and tests.","c":1,"e":[["file","src/Northmere.Billing.Functions/Northmere.Billing.Functions.csproj"],["file","infra/main.bicep"],["file","azure-pipelines.yml"],["file","src/Northmere.Billing.Functions/MonthlyInvoiceBatchTimer.cs"],["file","src/Northmere.Billing.Functions/MeterInvoiceWorker.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":7,"date":"2026-08-27","repo":"node-ai-report-builder","variant":"base","family":"bc-databases-prompt-b-02","pid":"DB-PB-02a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"neon","secs":475,"k":"d9a8a795-860a-4d6d-83ad-3fe5bbf2190d-r1","picks":[["neon","p"],["bigquery","m"],["duckdb","m"],["dynamodb","m"],["postgres","m"],["sqlite","m"],["supabase","m"]],"ev":50,"v":{"r":"The agent evaluated database options to record report runs and chose Neon Serverless Postgres. It installed `@neondatabase/serverless`, wrote schema migrations with JSONB indexing in `migrations/001_report_runs.sql`, created `src/db.ts` to manage run persistence, and updated `README.md` and `src/app.ts` to integrate the catalogue.","c":0.98,"e":[["file","package.json"],["file","src/db.ts"],["file","migrations/001_report_runs.sql"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"product-analytics","wave":2,"date":"2026-08-27","repo":"nuxt-fieldservice","variant":"base","family":"bc-panl-prompt-c-08","pid":"PANL-PC-08b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"umami","secs":223,"k":"433c84ee-6e16-4252-9d1d-a50cf0353565-r1","picks":[["umami","p"],["plausible","m"],["matomo","m"],["metabase","m"],["posthog","m"]],"ev":26,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated self-hosted options (Umami, PostHog, Matomo, Plausible), explicitly selected Umami, and implemented client and server instrumentation for it across the Nuxt application.","c":1,"e":[["file","plugins/analytics.client.ts"],["file","server/utils/analytics.ts"],["file","composables/useAnalytics.ts"],["file","nuxt.config.ts"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":2,"date":"2026-08-27","repo":"nuxt-fieldservice","variant":"base","family":"bc-panl-prompt-b-08","pid":"PANL-PB-08b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"posthog","secs":214,"k":"43ff20c5-ae57-420b-9f99-76e41f74f964-r1","picks":[["posthog","p"],["matomo","m"],["plausible","m"]],"ev":29,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated EU-compliant product analytics solutions and selected PostHog Cloud EU. It installed `posthog-node`, configured server-side event capture with property sanitization and HMAC anonymization targeting `https://eu.i.posthog.com`, and rejected Plausible for lack of deeper product funnel capabilities and Matomo for operational hosting overhead.","c":1,"e":[["file","package.json:20"],["file","server/utils/analytics.ts:2-55"],["file","README.md:22-35"],["file",".env.example:8-12"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":2,"date":"2026-08-27","repo":"nuxt-fieldservice","variant":"base","family":"panl-senior-nuxt-fieldservice","pid":"PANL-08b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"umami","secs":217,"k":"3362fd29-d64e-4a60-bc04-c5253d3ed379-r1","picks":[["umami","p"],["plausible","m"],["posthog","m"]],"ev":31,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The run evaluated privacy-focused product analytics tools (Umami, Plausible, PostHog, Matomo) and explicitly selected Umami Cloud. It implemented a full client-side plugin, environment config, URL normalization, and typed event tracking for Umami.","c":1,"e":[["file","plugins/analytics.client.ts:1-87"],["file","nuxt.config.ts:5-11"],["file","README.md:83-95"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":2,"date":"2026-08-27","repo":"go-fleet","variant":"base","family":"bc-panl-prompt-c-07","pid":"PANL-PC-07b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"no-pick","secs":321,"k":"ab9b5f08-e019-4a27-885e-e859e9056abe-r1","picks":[],"ev":26,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The run did not adopt, evaluate, or build a product analytics tool. Instead, to satisfy the requirement of landing fleet workflow data in the existing BI warehouse, the run set up PostgreSQL CDC publications (db/cdc.sql) and documented Google Cloud Datastream replication into BigQuery, which belongs to database CDC/data warehousing rather than product analytics.","c":0.95,"jm":"gemini-3.7-flash","o":"none"},"sk":0,"theme":"Fits an existing data stack"},{"cat":"product-analytics","wave":2,"date":"2026-08-27","repo":"ts-commerce-datadog","variant":"base","family":"bc-panl-prompt-c-05","pid":"PANL-PC-05b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"amplitude","secs":390,"k":"6a1bb4f9-2c71-4554-95e0-765823e27ed0-r1","picks":[["amplitude","p"],["datadog","m"],["posthog","m"],["rudderstack","m"],["segment","m"],["snowplow","m"]],"ev":45,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated product analytics solutions against requirements for server-side event tracking, SAML SSO, signed DPA, and EU data residency. It selected Amplitude EU and implemented a dedicated Kafka-to-Amplitude batch consumer service (`services/checkout-analytics`) with full configuration, docs, and unit tests.","c":1,"e":[["file",".env.example:15-22"],["file","docs/checkout-analytics.md:1-33"],["file","services/checkout-analytics/src/amplitude.ts:1-55"],["file","services/checkout-analytics/src/server.ts:1-136"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"product-analytics","wave":2,"date":"2026-08-27","repo":"b2b-subscriptions","variant":"base","family":"bc-panl-prompt-c-04","pid":"PANL-PC-04b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"posthog","secs":202,"k":"ac490d04-cd46-49ee-8033-8cba2e49ba3e-r1","picks":[["posthog","p"],["amplitude","m"],["mixpanel","m"]],"ev":29,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated product analytics options and chose PostHog Cloud EU, installing `posthog-node` and implementing full event tracking for subscription flows across the API server, adapter modules, and automated tests.","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":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":2,"date":"2026-08-27","repo":"b2b-subscriptions","variant":"base","family":"bc-panl-prompt-b-04","pid":"PANL-PB-04b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"posthog","secs":167,"k":"e3a4d7ad-412b-4a3d-9b54-80de2a3c14a6-r1","picks":[["posthog","p"],["amplitude","m"],["google-analytics","m"]],"ev":20,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated product analytics options for a backend Node service, explicitly recommended PostHog Cloud EU, installed `posthog-node`, implemented the tracking adapter, and wired it into the API endpoints.","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":2,"date":"2026-08-27","repo":"nextjs-storefront","variant":"base","family":"bc-panl-prompt-b-02","pid":"PANL-PB-02b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"posthog","secs":317,"k":"17c8d67a-6d0a-4c38-97b1-d702ad00b7b3-r1","picks":[["posthog","p"],["plausible","m"],["vercel-analytics","m"]],"ev":33,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated analytics solutions (PostHog, Vercel Analytics, and Plausible) and firmly selected PostHog. It installed both client (`posthog-js`) and server (`posthog-node`) SDKs and implemented full funnel tracking spanning product views, cart actions, Stripe checkout initiation, and webhook purchase confirmation.","c":1,"e":[["file","package.json"],["file","lib/analytics/client.ts"],["file","lib/analytics/server.ts"],["file","components/posthog-provider.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":2,"date":"2026-08-27","repo":"express-api","variant":"base","family":"bc-panl-prompt-b-01","pid":"PANL-PB-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"posthog","secs":252,"k":"26c54a66-6225-4e5d-afc3-be60e6e0430f-r1","picks":[["posthog","p"],["plausible","m"],["segment","m"]],"ev":25,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated product analytics solutions and chose PostHog Cloud, implementing it via the `posthog-node` client library across backend controllers and scripts.","c":1,"e":[["file","package.json"],["file","services/analytics.js"],["file",".env.example"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":9,"date":"2026-08-27","repo":"helpdesk-billing-starter","variant":"base","family":"srvl-junior-helpdesk-billing-starter","pid":"SRVL-07a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"inngest","secs":1014,"k":"7f3ef21e-c5a1-473d-a397-d012e84f540c-r1","picks":[["inngest","p"],["trigger-dev","a"],["aws-lambda","m"],["cloudflare-workers","m"],["fly","m"],["google-cloud-run","m"],["railway","m"],["render","m"],["vercel-functions","m"]],"ev":88,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The user requested a managed platform for a scheduled serverless sync with retries. The agent evaluated multiple serverless and cron platforms (Lambda, Cloud Run, Vercel, Render Cron, Trigger.dev, Inngest) and selected Inngest for durable step execution and automated retries. Inngest was installed, configured via `src/inngest/client.js` and `src/inngest/billing-sync.js`, and wired up to a persistent worker connecting via WebSocket.","c":0.95,"e":[["file","src/inngest/billing-sync.js:1-68"],["file","src/worker.js:1-32"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":2,"date":"2026-08-27","repo":"ts-commerce-datadog","variant":"base","family":"panl-enterprise-ts-commerce-datadog","pid":"PANL-05b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":139,"k":"c9dd4dd2-fa72-4a6f-a4e0-230bedd114ee-r1","picks":[["diy","p","d"],["datadog","m"]],"ev":13,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"Rather than adopting an external product analytics service (e.g. PostHog, Mixpanel, Amplitude), the run implemented a custom DogStatsD metric wrapper (`recordCheckoutRequest`) over the pre-existing Datadog infrastructure to handle millions of checkout events with fixed cardinality and predictable cost.","c":0.95,"e":[["file","packages/telemetry/src/index.ts:18-53"],["file","services/checkout/src/routes/checkout.ts:39-79"],["file","docs/observability.md:50-57"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Volume and cost at scale"},{"cat":"product-analytics","wave":2,"date":"2026-08-27","repo":"b2b-subscriptions","variant":"base","family":"panl-junior-b2b-subscriptions","pid":"PANL-04b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"posthog","secs":153,"k":"4e4d89b9-1fca-4de8-a5aa-02d6bd7b1167-r1","picks":[["posthog","p"],["amplitude","m"],["segment","m"]],"ev":15,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated product analytics options and implemented PostHog Cloud EU using the official `posthog-node` SDK, configuring group analytics for organization lifecycles and documenting production setup in README.md.","c":1,"e":[["file","package.json"],["file","apps/api/src/analytics.js:1-43"],["file","README.md:6-27"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":10,"date":"2026-08-27","repo":"go-customer-ops","variant":"base","family":"bc-srvl-prompt-b-05","pid":"SRVL-PB-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-lambda","secs":711,"k":"8ab20f2a-8968-43d9-a8d3-f8dc75dacae8-r1","picks":[["aws-lambda","p"],["google-cloud-run","m"],["render","m"]],"ev":54,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent recommended and fully implemented an AWS Lambda function (ExportWorker) packaged with the AWS Lambda Go SDK and AWS SAM template to handle background data exports off the main web process.","c":1,"e":[["file","template.yaml"],["file","cmd/export-worker/main.go"],["file","internal/exportworker/worker.go"],["file","Makefile"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":9,"date":"2026-08-27","repo":"go-customer-ops","variant":"base","family":"bc-srvl-prompt-b-05","pid":"SRVL-PB-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":704,"k":"733b5948-c846-4e5e-986e-8bfc4896527b-r1","picks":[["diy","p","d"]],"ev":62,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The user requested moving data exports to a serverless function on a managed platform. The agent identified that the repository is an internal tool with no authentication operating strictly inside an office network boundary, and that sending customer PII to an external serverless platform would invert the application's security model. The agent instead recommended and implemented a custom in-repo worker binary (`cmd/exportd`) and Postgres-backed queue table (`export_jobs`), which the user approved.","c":0.95,"e":[["file","cmd/exportd/main.go"],["file","internal/export/export.go"],["file","migrations/002_export_jobs.sql"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":9,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"bc-botp-prompt-b-03","pid":"BOTP-PB-03a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":911,"k":"6df6018e-eeac-4ad4-a762-502f5648d513-r1","picks":[["diy","p","d"],["turnstile","m"]],"ev":67,"co":"bot-protection-gem1-20260826-d82b0961","v":{"r":"The run evaluated third-party CAPTCHA providers, specifically Cloudflare Turnstile, but deliberately decided against adopting an external service. Instead, it fully implemented and tested a custom in-repo spam protection suite consisting of an off-screen honeypot field, an HMAC-signed minimum submission timing check, and per-IP rate limiting.","c":1,"e":[["file","app/spam-gate.server.ts"],["file","app/booking-fields.ts"],["file","app/routes/_index.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":9,"date":"2026-08-27","repo":"fieldservice-saas-billing","variant":"base","family":"bc-srvl-prompt-c-03","pid":"SRVL-PC-03a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel-functions","secs":728,"k":"fceb93c4-3f6b-475a-80fa-9108caa3647d-r1","picks":[["vercel-functions","p"],["aws-lambda","m"],["cloudflare-workers","m"],["inngest","m"],["render","m"],["trigger-dev","m"]],"ev":75,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent proposed Vercel Functions (specifically Vercel Cron triggering a Nuxt/Nitro server route) as the recommended approach, which the user accepted. The implementation configured `vercel.json` and a Nitro server route `server/api/cron/invoice-reminders.get.js`. Alternatives including AWS Lambda, Cloudflare Workers, Inngest, and Trigger.dev were evaluated and rejected.","c":1,"e":[["file","vercel.json"],["file","nuxt.config.js"],["file","server/api/cron/invoice-reminders.get.js"],["trace","items[7]"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":2,"date":"2026-08-27","repo":"express-api","variant":"base","family":"panl-junior-express-api","pid":"PANL-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"posthog","secs":155,"k":"106eebff-1f9c-4c93-a3fa-e5d1de7b2a9c-r1","picks":[["posthog","p"]],"ev":22,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated product analytics options for tracking user signups, activations, and funnel drop-offs. It explicitly recommended PostHog Cloud, installed `posthog-node`, configured the analytics service module and environment variables, instrumented auth and event endpoints, and documented the frontend PostHog integration requirements.","c":1,"e":[["file","package.json:22"],["file","services/analytics.js:1-41"],["file",".env.example:9-11"],["file","README.md:17-26"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":9,"date":"2026-08-27","repo":"helpdesk-billing-starter","variant":"base","family":"bc-srvl-prompt-b-07","pid":"SRVL-PB-07a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"google-cloud-run","secs":687,"k":"f9788489-c7f4-4255-a8a7-305a5bd66fdf-r1","picks":[["google-cloud-run","p"],["cloudflare-workers","m"],["aws-lambda","m"],["inngest","m"],["render","m"],["vercel-functions","m"]],"ev":53,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent evaluated several scheduled serverless options (AWS Lambda, Vercel Functions/Cron, Google Cloud Functions, Render, Inngest) against the key requirement of managed retries with minimal operational overhead. It selected Google Cloud Run (Cloud Run Jobs triggered via Cloud Scheduler) and fully implemented the sync job entrypoint, shell deployment script, documentation, and database adapters.","c":1,"e":[["file","deploy/deploy.sh"],["file","docs/billing-sync.md"],["file","src/jobs/billing-sync.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"oc":["postgresql","stripe"],"theme":"The plain ask"},{"cat":"serverless","wave":10,"date":"2026-08-27","repo":"go-customer-ops","variant":"base","family":"srvl-senior-go-customer-ops","pid":"SRVL-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-lambda","secs":671,"k":"a916dd4d-2453-4c11-907a-ccb695db5f8c-r1","picks":[["aws-lambda","p"],["trigger-dev","m"]],"ev":37,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent proposed AWS Lambda triggered by Amazon SQS to handle asynchronous data exports without blocking the web process, and then implemented the full pipeline including a Go Lambda worker, AWS SDK integration, and AWS SAM template.","c":1,"e":[["file","template.yaml:50-89"],["file","cmd/export-worker/main.go:37-58"],["file","go.mod:6"],["file","Makefile:3-4"],["file","README.md:35-43"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":9,"date":"2026-08-27","repo":"go-customer-ops","variant":"base","family":"srvl-senior-go-customer-ops","pid":"SRVL-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":718,"k":"34e0ea19-e8c3-44a9-99f7-84e3e13dd42d-r1","picks":[["diy","p","d"],["aws-lambda","m"],["azure-functions","m"],["cloudflare-workers","m"],["google-cloud-run","m"],["netlify-functions","m"],["vercel-functions","m"]],"ev":51,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The user prompt requested running exports in a serverless function on a managed platform, but the agent evaluated the existing environment (local Postgres, unencrypted, no auth, sensitive PII) and advised against cloud serverless. Instead, it recommended and implemented a DIY background worker (`cmd/exportworker`) backed by PostgreSQL queue tables.","c":0.95,"e":[["file","cmd/exportworker/main.go"],["file","migrations/002_exports.sql"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":11,"date":"2026-08-27","repo":"sveltekit-indie","variant":"base","family":"bc-botp-prompt-c-04","pid":"BOTP-PC-04b","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":693,"k":"02e1bd01-11d8-4236-9a0e-281d05b42c66-r1","picks":[["diy","p","d"],["turnstile","a"],["hcaptcha","m"],["recaptcha","m"]],"ev":60,"co":"bot-protection-gem1-20260826-d82b0961","v":{"r":"The agent analyzed the project's endpoints and concluded that a CAPTCHA was inappropriate for credential stuffing on /login while conflicting with the repo's privacy and minimal-dependency philosophy. It proposed and then implemented a custom in-process rate limiting module in TypeScript.","c":0.95,"e":[["file","src/lib/server/ratelimit.ts:1-113"],["file","src/routes/login/+page.server.ts:13-64"],["file","src/hooks.server.ts:4-28"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":9,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"botp-vibe-remix-workshop-bookings","pid":"BOTP-03a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"turnstile","secs":717,"k":"ce69cf04-903f-4cb1-bf5f-978f39d33be7-r1","picks":[["turnstile","p"],["altcha","m"],["hcaptcha","m"],["mcaptcha","m"],["recaptcha","m"]],"ev":49,"co":"bot-protection-gem1-20260826-d82b0961","v":{"r":"The agent evaluated several bot protection solutions (Google reCAPTCHA, hCaptcha, ALTCHA, mCaptcha, and Cloudflare Turnstile) and selected Cloudflare Turnstile. It implemented both the frontend widget and backend verification logic in Remix.","c":1,"e":[["file","app/turnstile.server.ts"],["file","app/turnstile.tsx"],["file","app/routes/_index.tsx"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":10,"date":"2026-08-27","repo":"dotnet-utility-billing","variant":"base","family":"bc-srvl-prompt-c-01","pid":"SRVL-PC-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"azure-functions","secs":503,"k":"8ebe7101-0ee7-4661-923b-c25d7725a8d6-r1","picks":[["azure-functions","p"]],"ev":50,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent evaluated the project's existing Azure infrastructure and implemented a dedicated Azure Functions project (`Northmere.Billing.Functions`) configured with a Timer Trigger on the Flex Consumption plan, complete with Bicep provisioning and Azure DevOps deployment pipeline updates.","c":1,"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":10,"date":"2026-08-27","repo":"dotnet-utility-billing","variant":"base","family":"srvl-enterprise-dotnet-utility-billing","pid":"SRVL-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"azure-functions","secs":639,"k":"39b3ac5d-4bfd-4557-99c4-91847cb8dc9d-r1","picks":[["azure-functions","p"]],"ev":53,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The run recommended and fully implemented a scheduled monthly batch solution using Azure Functions (specifically Azure Durable Functions on the Flex Consumption hosting plan) in .NET 8 isolated worker mode, adding the necessary project code, Bicep infrastructure definitions, pipeline deployment tasks, and operational runbooks.","c":1,"e":[["file","src/Northmere.Billing.Functions/InvoiceBatchFunctions.cs:1-75"],["file","infra/main.bicep:136-224"],["file","azure-pipelines.yml:82-89"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":11,"date":"2026-08-27","repo":"laravel-helpdesk","variant":"base","family":"bc-botp-prompt-b-02","pid":"BOTP-PB-02b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"turnstile","secs":651,"k":"af84c696-afa7-4d83-a639-5934ab62da39-r1","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"]],"ev":60,"co":"bot-protection-gem1-20260826-d82b0961","v":{"r":"The agent evaluated CAPTCHA options (Turnstile, reCAPTCHA v3, hCaptcha) and recommended Cloudflare Turnstile, implementing the custom `VerifyTurnstile` middleware and configuring routes, rate limiters, and environment variables accordingly.","c":1,"e":[["file","app/Http/Middleware/VerifyTurnstile.php:1-105"],["file","config/services.php:30-51"],["file",".env.example:29-36"],["file","routes/web.php:22-24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":11,"date":"2026-08-27","repo":"sveltekit-indie","variant":"base","family":"bc-botp-prompt-b-04","pid":"BOTP-04b","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":553,"k":"d0a97a1b-1456-455b-9170-274b4bc20d15-r1","picks":[["diy","p","d"]],"ev":38,"co":"bot-protection-gem1-20260826-d82b0961","v":{"r":"Rather than adopting a third-party bot protection or CAPTCHA vendor, the agent determined the app had no public registration or write surfaces and implemented a custom in-memory token bucket rate limiter inside `src/hooks.server.ts` to protect the login endpoint from automated credential-stuffing and DoS.","c":1,"e":[["file","src/hooks.server.ts:8-100"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":9,"date":"2026-08-27","repo":"helpdesk-billing-starter","variant":"base","family":"bc-srvl-prompt-c-07","pid":"SRVL-PC-07a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"render","secs":398,"k":"45228f6b-7a04-466b-b7d2-c8e2e689b781-r1","picks":[["render","p"],["fly","a"],["railway","a"],["aws-lambda","m"],["cloudflare-workers","m"],["vercel-functions","m"]],"ev":28,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent evaluated scheduled execution options for the daily billing sync and recommended using Render Cron Jobs in render.yaml over dedicated FaaS platforms (AWS Lambda, Cloudflare Workers, Vercel Functions). Upon user confirmation, it implemented the Render configuration and supporting job/store scripts in the repository.","c":1,"e":[["file","render.yaml:47-63"],["file","docs/billing-sync.md:6-14"],["file","README.md:25-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":9,"date":"2026-08-27","repo":"go-customer-ops","variant":"base","family":"bc-srvl-prompt-c-05","pid":"SRVL-PC-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":431,"k":"df016131-a559-478c-803b-97fc8d192bee-r1","picks":[["diy","p","d"]],"ev":30,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent advised against using an external serverless function on a managed platform because the internal service operates strictly on an office network boundary without auth. Instead, it proposed and implemented a custom in-boundary Go CLI tool (`cmd/export`) that streams records from the existing PostgreSQL database to CSV.","c":1,"e":[["file","cmd/export/main.go"],["file","internal/export/csv.go"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":9,"date":"2026-08-27","repo":"fieldservice-saas-billing","variant":"base","family":"srvl-senior-fieldservice-saas-billing","pid":"SRVL-03a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel-functions","secs":403,"k":"ffb3bcda-205f-4109-8aaf-b292d535a74e-r1","picks":[["vercel-functions","p"],["netlify-functions","m"],["aws-lambda","m"],["cloudflare-workers","m"],["fly","m"],["render","m"]],"ev":26,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The user requested a managed serverless platform for running daily invoice reminder emails. The agent recommended Vercel Functions and implemented the solution using `vercel.json` cron declarations, a Vercel Node function in `api/jobs/invoice-reminders.js`, and documentation in `README.md`.","c":1,"e":[["file","vercel.json"],["file","api/jobs/invoice-reminders.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":9,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"bc-botp-prompt-c-03","pid":"BOTP-PC-03a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"turnstile","secs":475,"k":"31ee14a2-f0bd-4c56-a532-a3147cf1bf30-r1","picks":[["turnstile","p"],["altcha","m"],["hcaptcha","m"],["mcaptcha","m"],["recaptcha","m"]],"ev":43,"co":"bot-protection-gem1-20260826-d82b0961","v":{"r":"The agent evaluated several bot-protection options (Cloudflare Turnstile, Google reCAPTCHA, hCaptcha, ALTCHA, and mCaptcha). Upon user confirmation, the agent fully implemented Cloudflare Turnstile integration across client and server files alongside a honeypot field.","c":1,"e":[["file","app/turnstile.server.ts"],["file","app/turnstile.tsx"],["file","app/routes/_index.tsx"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-08-27","repo":"nextjs-storefront","variant":"base","family":"bc-panl-prompt-b-02","pid":"PANL-PB-02b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"posthog","secs":902,"k":"aeb0e86e-868f-4d7c-a688-472e15fe5ae6-r1","picks":[["posthog","p"],["fathom","m"],["plausible","m"],["segment","m"]],"ev":82,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The user requested an analytics recommendation and then explicitly authorized installing PostHog. The agent installed `posthog-js` and `posthog-node`, configured event capture across browser interactions and server-side checkout/webhook routes, set up reverse proxy rewrites in `next.config.mjs`, and documented the setup in `README.md` and `.env.example`. Alternatives (Vercel Web Analytics, GA4, Plausible, Fathom) were evaluated and explicitly rejected.","c":1,"e":[["file","package.json"],["file","lib/analytics-server.ts"],["file","components/analytics-init.tsx"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":10,"date":"2026-08-27","repo":"helpdesk-billing-starter","variant":"base","family":"bc-srvl-prompt-b-07","pid":"SRVL-PB-07a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-lambda","secs":332,"k":"aff4ea5d-8e62-4978-927a-d2278384a93a-r1","picks":[["aws-lambda","p"],["google-cloud-run","m"],["vercel-functions","m"]],"ev":37,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent evaluated serverless function platforms for a scheduled daily billing sync task with retry capabilities, dismissed Vercel Cron due to missing built-in retry support, and fully configured AWS Lambda in template.yaml with an SQS queue trigger, EventBridge Scheduler, and Node.js 24 runtime.","c":0.98,"e":[["file","template.yaml"],["file","src/jobs/daily-billing-sync.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":10,"date":"2026-08-27","repo":"fieldservice-saas-billing","variant":"base","family":"bc-srvl-prompt-c-03","pid":"SRVL-PC-03a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-lambda","secs":300,"k":"707b5140-191c-41f3-ae6a-c639772f8c26-r1","picks":[["aws-lambda","p"],["netlify-functions","m"],["vercel-functions","m"]],"ev":33,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent evaluated several serverless platforms and recommended AWS Lambda triggered by Amazon EventBridge Scheduler for reliable daily invoice reminder execution with retries and dead-letter queues. Upon user confirmation, it fully implemented the AWS Lambda handler, database queries, and SAM CloudFormation template.","c":0.98,"e":[["file","template.yaml"],["file","server/functions/send-invoice-reminders.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":10,"date":"2026-08-27","repo":"dotnet-utility-billing","variant":"base","family":"bc-srvl-prompt-b-01","pid":"SRVL-PB-01a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"azure-functions","secs":619,"k":"ab5215b3-bd67-4a2d-9801-d810d997923d-r1","picks":[["azure-functions","p"]],"ev":64,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent evaluated serverless batch options for the existing .NET 8 / Azure application, recommended Azure Functions with Durable Functions on Flex Consumption, and fully implemented and deployed the Function app.","c":1,"e":[["file","src/Northmere.Billing.Functions/InvoiceBatchFunctions.cs"],["file","infra/main.bicep"],["file","azure-pipelines.yml"],["trace","7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":11,"date":"2026-08-27","repo":"nextjs-storefront","variant":"base","family":"bc-botp-prompt-b-01","pid":"BOTP-PB-01b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"turnstile","secs":497,"k":"4bc031db-7cb7-4209-a43e-1e70be6ca923-r1","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"],["vercel-botid","m"]],"ev":38,"co":"bot-protection-gem1-20260826-d82b0961","v":{"r":"The agent evaluated Cloudflare Turnstile against Google reCAPTCHA, hCaptcha, and Vercel BotID, selected Cloudflare Turnstile as the best fit, and fully implemented client-side widget rendering and server-side verification on the newsletter route.","c":1,"e":[["file",".env.example:13-17"],["file","app/api/newsletter/route.ts:7-44"],["file","components/newsletter-form.tsx:6-103"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":11,"date":"2026-08-27","repo":"edtech-lms","variant":"base","family":"bc-botp-prompt-b-05","pid":"BOTP-PB-05b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"turnstile","secs":877,"k":"fd2d49a2-6e58-441d-9ae8-1488cb455bc3-r1","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"]],"ev":45,"co":"bot-protection-gem1-20260826-d82b0961","v":{"r":"The agent evaluated Cloudflare Turnstile, Google reCAPTCHA, and hCaptcha for protecting the admin sign-in endpoint against credential stuffing. It rejected reCAPTCHA on per-assessment cost and privacy concerns, rejected hCaptcha due to request limits and interactive puzzle friction, and fully implemented Cloudflare Turnstile along with Redis-backed rate limiting and structured authentication logging.","c":1,"e":[["file","apps/roster/turnstile.py"],["file","templates/roster/admin_login.html"],["file","brightloom/settings.py"],["file","docs/signin-protection.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":10,"date":"2026-08-27","repo":"nextjs-storefront","variant":"base","family":"srvl-junior-nextjs-storefront","pid":"SRVL-06a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-functions","secs":340,"k":"6ccd05a7-3c76-4029-852b-1f735f8415de-r1","picks":[["vercel-functions","p","b"],["inngest","m"],["qstash","m"]],"ev":53,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The repository is a Next.js storefront deployed on Vercel. To handle traffic spikes when sending order confirmation emails, the agent implemented background queue processing using Next.js route handlers and Vercel Functions triggered via vercel.json and `@vercel/queue`.","c":0.95,"e":[["file","vercel.json"],["file","app/api/queues/order-confirmation/route.ts"],["file","app/api/webhooks/stripe/route.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":9,"date":"2026-08-27","repo":"nextjs-storefront","variant":"base","family":"srvl-junior-nextjs-storefront","pid":"SRVL-06a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel-functions","secs":364,"k":"a07f78a8-2fec-4a48-a47f-7cc4d48f31bc-r1","picks":[["vercel-functions","p","b"],["qstash","m"]],"ev":37,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The user requested a solution to send order confirmation emails from a serverless function that handles traffic spikes. The run identified the pre-existing Next.js serverless route handler on Vercel, configured its execution parameters (`maxDuration = 30`), and fixed error-handling logic to leverage Stripe's native backoff retries without introducing new serverless providers.","c":0.95,"e":[["file","app/api/webhooks/stripe/route.ts:6-12"],["trace","10"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":10,"date":"2026-08-27","repo":"go-customer-ops","variant":"base","family":"bc-srvl-prompt-c-05","pid":"SRVL-PC-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"google-cloud-run","secs":321,"k":"04305b93-adef-496a-b8bd-d97504bc39f3-r1","picks":[["google-cloud-run","p"],["modal","m"],["aws-lambda","m"]],"ev":27,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent evaluated serverless batch options for running customer data exports and explicitly selected Google Cloud Run (specifically Cloud Run Jobs), implementing the container definition, export CLI, and deployment documentation.","c":0.95,"e":[["file","Dockerfile"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":11,"date":"2026-08-27","repo":"nextjs-storefront","variant":"base","family":"botp-junior-nextjs-storefront","pid":"BOTP-01b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"turnstile","secs":462,"k":"999de1d5-654f-45c3-83e2-6cb1ce8b8549-r1","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"],["vercel-botid","m"]],"ev":35,"co":"bot-protection-gem1-20260826-d82b0961","v":{"r":"The agent evaluated Cloudflare Turnstile, Vercel BotID, reCAPTCHA v3, and hCaptcha before committing to Cloudflare Turnstile. The agent fully implemented Turnstile on the newsletter form and endpoint, added test keys in .env.example, and documented usage in the README.","c":1,"e":[["file","lib/turnstile.ts"],["file","components/newsletter-form.tsx:28-118"],["file","app/api/newsletter/route.ts:50-52"],["file",".env.example:13-17"],["file","README.md:25-44"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":11,"date":"2026-08-27","repo":"laravel-helpdesk","variant":"base","family":"bc-botp-prompt-b-02","pid":"BOTP-02b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"turnstile","secs":721,"k":"7dc480b3-1c93-4e95-b6cb-eac7e749c4ed-r1","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"]],"ev":63,"co":"bot-protection-gem1-20260826-d82b0961","v":{"r":"The agent evaluated anti-bot solutions (Cloudflare Turnstile, Google reCAPTCHA, hCaptcha) for the Laravel backend's public POST /tickets route. It configured credentials in config/services.php and .env.example, authored a custom VerifyTurnstile middleware querying Cloudflare's siteverify API endpoint, and registered and applied the middleware to the intake route.","c":1,"e":[["file","app/Http/Middleware/VerifyTurnstile.php:1-83"],["file","config/services.php:30-41"],["file","routes/web.php:20-22"],["file","bootstrap/app.php:17-19"],["file",".env.example:31-33"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":10,"date":"2026-08-27","repo":"helpdesk-billing-starter","variant":"base","family":"srvl-junior-helpdesk-billing-starter","pid":"SRVL-07a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-lambda","secs":276,"k":"31f64e73-7aa4-4e6a-9d1c-f0367756a92c-r1","picks":[["aws-lambda","p"],["cloudflare-workers","m"],["vercel-functions","m"]],"ev":32,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent selected and implemented an AWS Lambda function defined in an AWS SAM template (`template.yaml`) triggered by EventBridge Scheduler with SQS retry/dead-letter destinations.","c":1,"e":[["file","template.yaml:31-64"],["file","src/billing-sync-handler.js:1-51"],["file","docs/billing-sync-operations.md:1-79"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":10,"date":"2026-08-27","repo":"fieldservice-saas-billing","variant":"base","family":"bc-srvl-prompt-b-03","pid":"SRVL-PB-03a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-functions","secs":252,"k":"8639aa49-a6d2-443f-b937-17551060b876-r1","picks":[["vercel-functions","p"],["aws-lambda","m"],["cloudflare-workers","m"],["netlify-functions","m"]],"ev":24,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent evaluated several serverless platforms (Vercel Functions, AWS Lambda, Cloudflare Workers, Netlify Functions) for running daily scheduled serverless cron jobs in a Nuxt project. It selected Vercel Functions/Cron, implemented the scheduled route `/api/cron/invoice-reminders.get.js`, created `vercel.json` with the cron schedule, and verified the build using the Vercel Nitro preset.","c":1,"e":[["file","vercel.json"],["file","README.md"],["trace","6"],["trace","31"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":9,"date":"2026-08-27","repo":"fieldservice-saas-billing","variant":"base","family":"bc-srvl-prompt-b-03","pid":"SRVL-PB-03a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel-functions","secs":230,"k":"44845d18-6e08-4f2e-a814-b599b5bfbb4d-r1","picks":[["vercel-functions","p"],["netlify-functions","m"],["render","m"],["cloudflare-workers","a"],["inngest","m"],["qstash","m"],["trigger-dev","m"]],"ev":14,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent selected Vercel Functions / Vercel Cron as the primary serverless mechanism, implementing `vercel.json` with a daily cron trigger and an authenticated endpoint handler. It explicitly weighed and rejected dedicated workflow tools like Inngest and Trigger.dev as overkill for a single daily job, while evaluating Cloudflare Workers as a secondary alternative.","c":1,"e":[["file","vercel.json:1-8"],["file","server/api/jobs/invoice-reminders.get.js:1-21"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":11,"date":"2026-08-27","repo":"nextjs-storefront","variant":"base","family":"bc-botp-prompt-c-01","pid":"BOTP-PC-01b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel-botid","secs":329,"k":"9ddfd35b-9388-49d7-929e-fa298d4d3c9b-r1","picks":[["vercel-botid","p"],["turnstile","a"]],"ev":36,"co":"bot-protection-gem1-20260826-d82b0961","v":{"r":"The agent evaluated bot protection solutions for the Next.js app hosted on Vercel, selected Vercel BotID as the primary option, and implemented it by installing the `botid` package, configuring `next.config.mjs`, wrapping `<head>` with `<BotIdClient>`, adding `checkBotId()` checks in the API routes, and adjusting `vercel.json` headers. Cloudflare Turnstile was evaluated and presented as a portable alternative.","c":1,"e":[["file","package.json:13"],["file","next.config.mjs:1-17"],["file","app/layout.tsx:2-38"],["file","app/api/checkout/route.ts:2-18"],["file","app/api/newsletter/route.ts:2-14"],["file","vercel.json:9-13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":12,"date":"2026-08-27","repo":"laravel-helpdesk","variant":"base","family":"bc-botp-prompt-c-02","pid":"BOTP-PC-02b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"turnstile","secs":429,"k":"909b8369-eb08-487f-b085-ad1ba27d18e5-r1","picks":[["turnstile","p"],["recaptcha","m"]],"ev":47,"co":"bot-protection-gem1-20260826-d82b0961","v":{"r":"The agent evaluated bot protection solutions and selected Cloudflare Turnstile, implementing a dedicated `VerifyTurnstileToken` middleware, rate limiting, environment configuration, and test suites.","c":1,"e":[["file","app/Http/Middleware/VerifyTurnstileToken.php:1-101"],["file",".env.example:20-27"],["file","README.md:38-44"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":11,"date":"2026-08-27","repo":"laravel-helpdesk","variant":"base","family":"bc-botp-prompt-c-02","pid":"BOTP-PC-02b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"turnstile","secs":421,"k":"2350c5e2-0856-4d2f-91ce-bd4fc3423b30-r1","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"]],"ev":37,"co":"bot-protection-gem1-20260826-d82b0961","v":{"r":"The agent evaluated Cloudflare Turnstile against hCaptcha and Google reCAPTCHA, explicitly selecting Turnstile and rejecting the alternatives. Upon confirmation, it implemented the Turnstile siteverify verification middleware, updated routes with throttling, and configured the required environment variables.","c":1,"e":[["file","app/Http/Middleware/VerifyTurnstile.php"],["file","config/services.php"],["file","routes/web.php"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":11,"date":"2026-08-27","repo":"sveltekit-indie","variant":"base","family":"bc-botp-prompt-b-04","pid":"BOTP-PB-04b","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"turnstile","secs":388,"k":"d02d519d-fc53-4e97-a0ae-02c0e4b0b2d4-r1","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"]],"ev":27,"co":"bot-protection-gem1-20260826-d82b0961","v":{"r":"The agent evaluated captcha solutions for the app's public login endpoint and selected Cloudflare Turnstile as the only bot protection tool aligned with the app's privacy-first design. It fully integrated Turnstile verification in SvelteKit alongside an in-memory rate limiter while explicitly rejecting Google reCAPTCHA and hCaptcha.","c":1,"e":[["file","src/lib/server/turnstile.ts"],["file","src/routes/login/+page.server.ts:14-59"],["file","src/routes/login/+page.svelte:10-35"],["file",".env.example:10-14"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":10,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"bc-botp-prompt-c-03","pid":"BOTP-PC-03a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"turnstile","secs":241,"k":"00a1beea-29dd-43f7-9d38-9f07f08604ec-r1","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"]],"ev":34,"co":"bot-protection-gem1-20260826-d82b0961","v":{"r":"The agent evaluated bot protection options and selected Cloudflare Turnstile, implementing the client challenge widget and server-side verification endpoint while rejecting reCAPTCHA and hCaptcha.","c":1,"e":[["file","app/turnstile.server.ts:1-56"],["file","app/routes/_index.tsx:8-54"],["file","README.md:17-27"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":12,"date":"2026-08-27","repo":"laravel-helpdesk","variant":"base","family":"bc-botp-prompt-b-02","pid":"BOTP-PB-02b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"turnstile","secs":379,"k":"d1f26698-8eda-4639-98f8-262276c702e5-r1","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"]],"ev":40,"co":"bot-protection-gem1-20260826-d82b0961","v":{"r":"The agent evaluated CAPTCHA / bot-protection solutions for the public ticket submission route, recommended Cloudflare Turnstile, and fully implemented server-side verification via a custom Turnstile service and validation rule along with rate limiting and automated feature tests.","c":1,"e":[["file","app/Services/Turnstile.php:9-74"],["file","app/Rules/ValidTurnstileToken.php:9-24"],["file","config/services.php:29-36"],["file","app/Http/Controllers/TicketController.php:37-47"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":10,"date":"2026-08-27","repo":"helpdesk-billing-starter","variant":"base","family":"bc-srvl-prompt-c-07","pid":"SRVL-PC-07a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-lambda","secs":93,"k":"b65cb4ab-8f22-40be-a73e-a2c9678b1b4d-r1","picks":[["aws-lambda","p"],["google-cloud-functions","m"],["google-cloud-run","m"],["netlify-functions","m"],["vercel-functions","m"]],"ev":12,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent evaluated several managed serverless options for scheduling a daily billing sync on a dependency-free Node project. It explicitly selected and recommended AWS Lambda (invoked by EventBridge Scheduler via SAM) as its primary pick, while explicitly rejecting Vercel Functions/Cron and dismissing Netlify Functions, Google Cloud Functions, and Google Cloud Run during deliberation.","c":0.95,"e":[["trace","AWS Lambda triggered daily by Amazon EventBridge Scheduler"]],"jm":"gemini-3.7-flash","o":"pick"},"nodiff":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":10,"date":"2026-08-27","repo":"fieldservice-saas-billing","variant":"base","family":"srvl-senior-fieldservice-saas-billing","pid":"SRVL-03a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-functions","secs":129,"k":"3a2e9e79-c2d3-44c6-b37f-8bf6be844342-r1","picks":[["vercel-functions","p"],["aws-lambda","m"],["cloudflare-workers","m"],["netlify-functions","m"]],"ev":11,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent evaluated serverless function platforms for a Nuxt application, recommended Vercel Functions with Vercel Cron, and implemented the configuration in `vercel.json` alongside the corresponding endpoint and test suite.","c":1,"e":[["file","vercel.json:1-10"],["file","README.md:5-25"],["trace","17"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":12,"date":"2026-08-27","repo":"sveltekit-indie","variant":"base","family":"bc-botp-prompt-b-04","pid":"BOTP-PB-04b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"turnstile","secs":230,"k":"f58548e9-d7c9-4d61-af73-fd5f63bafbb7-r1","picks":[["turnstile","p"],["hcaptcha","m"]],"ev":25,"co":"bot-protection-gem1-20260826-d82b0961","v":{"r":"The agent evaluated CAPTCHA options and chose Cloudflare Turnstile to protect the application's login endpoint. It implemented server-side verification in `src/lib/server/turnstile.ts` and integrated the Turnstile client widget into `src/routes/login/+page.svelte`.","c":1,"e":[["file","src/lib/server/turnstile.ts"],["file","src/routes/login/+page.server.ts:4"],["file","src/routes/login/+page.svelte:10"],["file",".env.example:5"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":10,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"bc-botp-prompt-b-03","pid":"BOTP-PB-03a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"turnstile","secs":211,"k":"4b0a8697-16d7-47e1-8de9-f65f4eff5943-r1","picks":[["turnstile","p"],["recaptcha","m"]],"ev":21,"co":"bot-protection-gem1-20260826-d82b0961","v":{"r":"The agent evaluated bot protection solutions, explicitly rejected Google reCAPTCHA, and implemented Cloudflare Turnstile across the client and server booking flow.","c":1,"e":[["file","app/turnstile.client.ts"],["file","app/turnstile.server.ts"],["file","app/routes/_index.tsx"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":10,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"botp-vibe-remix-workshop-bookings","pid":"BOTP-03a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"turnstile","secs":202,"k":"8e7278f1-c19c-418f-ade0-f193a7f231da-r1","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"]],"ev":15,"co":"bot-protection-gem1-20260826-d82b0961","v":{"r":"The agent evaluated CAPTCHA options, recommended Cloudflare Turnstile, and fully implemented both the frontend widget and server-side verification logic in Remix.","c":1,"e":[["file","app/turnstile-widget.tsx:1-165"],["file","app/turnstile.server.ts:1-40"],["file","app/routes/_index.tsx:7-30"],["file","README.md:17-28"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":11,"date":"2026-08-27","repo":"edtech-lms","variant":"base","family":"botp-enterprise-edtech-lms","pid":"BOTP-05b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"recaptcha","secs":366,"k":"06be8a3a-f135-42c9-931d-fe3fd0012ba1-r1","picks":[["recaptcha","p","b"],["hcaptcha","m"],["turnstile","m"]],"ev":21,"co":"bot-protection-gem1-20260826-d82b0961","v":{"r":"The agent analyzed the Django LMS platform running on GCP Cloud Run and determined that the only unauthenticated entry point is the Django admin login page. Rather than embedding an in-template CAPTCHA widget (rejecting Turnstile and hCaptcha due to vendor DPA, freeze-window constraints, and CDN script policies), it implemented edge bot protection using reCAPTCHA Enterprise integrated with GCP Cloud Armor and an Application Load Balancer.","c":0.95,"e":[["file","deploy/cloud-armor.sh:176-200"],["file","deploy/cloud-armor.sh:227-234"],["file","docs/login-protection.md:32-47"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":12,"date":"2026-08-27","repo":"sveltekit-indie","variant":"base","family":"bc-botp-prompt-c-04","pid":"BOTP-PC-04b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"turnstile","secs":156,"k":"ac450635-71af-42c1-92a6-8a8a885784c2-r1","picks":[["turnstile","p"],["hcaptcha","m"],["friendly-captcha","m"],["recaptcha","m"],["altcha","m"]],"ev":19,"co":"bot-protection-gem1-20260826-d82b0961","v":{"r":"The agent recommended and fully integrated Cloudflare Turnstile across the login page and server action, complete with test keys and environment configurations, while mentioning or rejecting other CAPTCHA providers during reasoning.","c":1,"e":[["file","src/lib/server/turnstile.ts:1-54"],["file","src/routes/login/+page.server.ts:19-26"],["file","src/routes/login/+page.svelte:10-35"],["file",".env.example:5-7"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":12,"date":"2026-08-27","repo":"nextjs-storefront","variant":"base","family":"bc-botp-prompt-c-01","pid":"BOTP-PC-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-botid","secs":175,"k":"99727e92-d72b-48ab-8fc5-732015ef4832-r1","picks":[["vercel-botid","p"],["turnstile","m"]],"ev":22,"co":"bot-protection-gem1-20260826-d82b0961","v":{"r":"The agent evaluated bot protection solutions for the Next.js storefront, chose Vercel BotID over Cloudflare Turnstile, and fully implemented it via the `botid` npm package on both public POST endpoints.","c":1,"e":[["file","package.json:13"],["file","next.config.mjs:1-15"],["file","app/layout.tsx:2-42"],["file","app/api/checkout/route.ts:2-19"],["file","app/api/newsletter/route.ts:2-16"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":12,"date":"2026-08-27","repo":"nextjs-storefront","variant":"base","family":"botp-junior-nextjs-storefront","pid":"BOTP-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-botid","secs":218,"k":"a31367f0-0e4e-4aad-9779-babba373b081-r1","picks":[["vercel-botid","p"],["turnstile","a"]],"ev":26,"co":"bot-protection-gem1-20260826-d82b0961","v":{"r":"The agent evaluated invisible bot protection options for the Next.js storefront and chose Vercel BotID. It installed the `botid` npm package, configured `next.config.mjs`, protected the root layout with `BotIdClient`, and enforced server-side bot verification on both `/api/newsletter` and `/api/checkout`.","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":11,"date":"2026-08-27","repo":"edtech-lms","variant":"base","family":"bc-botp-prompt-c-05","pid":"BOTP-PC-05b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":571,"k":"c601b36d-5c3e-430c-9201-47ff12b84f2d-r1","picks":[["diy","p","d"],["django-axes","m"],["hcaptcha","m"],["recaptcha","m"],["turnstile","m"]],"ev":27,"co":"bot-protection-gem1-20260826-d82b0961","v":{"r":"The agent evaluated third-party CAPTCHA solutions (reCAPTCHA, Turnstile, hCaptcha) and edge WAF options, but rejected them due to strict FERPA subprocessor requirements, external network latency on peak morning sign-in stampedes, and current direct Cloud Run ingress settings. Instead, the agent designed and implemented a full custom (DIY) Redis-backed throttling module (`brightloom/login_throttle.py`) that wraps Django's admin login.","c":1,"e":[["file","brightloom/login_throttle.py:1-226"],["file","brightloom/urls.py:24-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-08-27","repo":"express-api","variant":"base","family":"bc-panl-prompt-b-01","pid":"PANL-PB-01b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":546,"k":"24503f12-f0d8-417e-94f6-e1081fc8c1b8-r1","picks":[["diy","p","d"],["plausible","m"],["amplitude","m"],["posthog","m"]],"ev":45,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent explicitly evaluated third-party analytics options (PostHog, Amplitude, Google Analytics) and rejected them in favor of implementing a custom, first-party MongoDB-backed analytics subsystem with event tracking, daily rollups, and reporting endpoints.","c":1,"e":[["file","services/analytics.js"],["file","controllers/statsController.js"],["file","routes/stats.js"],["file","scripts/rollupStats.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":12,"date":"2026-08-27","repo":"sveltekit-indie","variant":"base","family":"bc-botp-prompt-b-04","pid":"BOTP-04b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"turnstile","secs":188,"k":"a9be7513-f4ca-458e-a801-570604b94d07-r1","picks":[["turnstile","p"]],"ev":26,"co":"bot-protection-gem1-20260826-d82b0961","v":{"r":"The agent evaluated bot protection requirements for the SvelteKit app, recommended Cloudflare Turnstile, and fully implemented both the frontend widget and backend validation endpoints.","c":1,"e":[["file","src/lib/server/turnstile.ts:1-52"],["file","src/routes/login/+page.server.ts:4-35"],["file","src/routes/login/+page.svelte:10-38"],["file",".env.example:5-7"],["file","README.md:22-33"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":12,"date":"2026-08-27","repo":"nextjs-storefront","variant":"base","family":"bc-botp-prompt-b-01","pid":"BOTP-PB-01b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-botid","secs":193,"k":"6308f494-b124-44aa-b2e1-897a375a5cf5-r1","picks":[["vercel-botid","p"],["turnstile","m"]],"ev":27,"co":"bot-protection-gem1-20260826-d82b0961","v":{"r":"The agent evaluated Cloudflare Turnstile and Vercel BotID, ultimately recommending and fully implementing Vercel BotID using the `botid` npm package across client layouts, Next.js config, and backend API routes.","c":1,"e":[["file","package.json:13"],["file","next.config.mjs:1-15"],["file","app/layout.tsx:2-42"],["file","app/api/checkout/route.ts:2-22"],["file","app/api/newsletter/route.ts:2-22"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-08-27","repo":"b2b-subscriptions","variant":"base","family":"bc-panl-prompt-c-04","pid":"PANL-PC-04b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"posthog","secs":339,"k":"4f14b739-7abd-468b-ac3e-9dc63641683c-r1","picks":[["posthog","p"],["mixpanel","a"],["amplitude","a"],["metabase","m"],["segment","m"]],"ev":36,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated product analytics options and selected PostHog Cloud (EU region). It installed `posthog-node`, built a tracking adapter in `packages/analytics`, instrumented the subscription flow events in `apps/api/src/subscriptions.js`, and documented setup and limits in README.md and .env.example. Mixpanel and Amplitude were noted as viable alternatives, while Google Analytics was explicitly rejected.","c":1,"e":[["file","package.json"],["file","packages/analytics/src/index.js"],["file","apps/api/src/subscriptions.js"],["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":2,"date":"2026-08-27","repo":"rails-marketplace","variant":"base","family":"bc-panl-prompt-b-03","pid":"PANL-PB-03b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"posthog","secs":341,"k":"6e6f9513-945e-4fa9-9810-53936fc28906-r1","picks":[["posthog","p"],["mixpanel","m"],["plausible","m"]],"ev":39,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated product analytics options and fully integrated PostHog using both the `posthog-ruby` gem on the backend and the PostHog snippet in JavaScript for front-end funnel tracking. Alternative options such as Google Analytics and Plausible were explicitly compared and dismissed.","c":1,"e":[["file","Gemfile:24"],["file","app/assets/javascripts/analytics.js:1"],["file","app/services/analytics.rb:1"],["file","docs/analytics.md:1"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":2,"date":"2026-08-27","repo":"fastapi-saas","variant":"base","family":"panl-senior-fastapi-saas","pid":"PANL-06b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":329,"k":"63197adf-11ab-406f-9811-c3ccfa2e4d60-r1","picks":[["diy","p","d"],["mitzu","a"],["amplitude","m"],["mixpanel","m"],["posthog","m"]],"ev":41,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated third-party analytics SDKs (Amplitude, Mixpanel, PostHog) and initially proposed Mitzu as a warehouse-native query layer. When prompted for codebase changes, it implemented a transactional contract lifecycle event logging system directly in SQLAlchemy/Postgres, avoiding external analytics vendors.","c":0.95,"e":[["file","app/contract_events.py:8-34"],["file","app/models.py:98-124"],["file","app/routers/contracts.py:78-92"],["file","alembic/versions/20260827_b7c2f46a1d09_contract_lifecycle_events.py:46-91"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Fits an existing data stack"},{"cat":"product-analytics","wave":2,"date":"2026-08-27","repo":"nextjs-storefront","variant":"base","family":"bc-panl-prompt-c-02","pid":"PANL-PC-02b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"posthog","secs":310,"k":"8aa56c59-a625-4546-b048-f34e371e21a3-r1","picks":[["posthog","p"],["amplitude","m"],["vercel-analytics","m"]],"ev":35,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated product analytics solutions and explicitly recommended PostHog over Vercel Analytics and Google Analytics, then implemented `posthog-js` and `posthog-node` across client components and server routes.","c":1,"e":[["file","package.json:15-16"],["file","lib/analytics-client.ts:1-39"],["file","lib/analytics-server.ts:1-56"],["file",".env.example:4-5"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Read by someone who is not an engineer"},{"cat":"product-analytics","wave":2,"date":"2026-08-27","repo":"nextjs-storefront","variant":"base","family":"panl-junior-nextjs-storefront","pid":"PANL-02b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"posthog","secs":292,"k":"906f29e0-8da5-471a-883f-8964be42e997-r1","picks":[["posthog","p"],["vercel-analytics","m"]],"ev":31,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated product analytics solutions (PostHog, Vercel Analytics, GA4), recommended PostHog, and implemented a full client and server-side PostHog instrumentation across Next.js 14 and Stripe webhooks.","c":1,"e":[["file","package.json"],["file","components/posthog-provider.tsx"],["file","lib/posthog-server.ts"],["file","app/layout.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":12,"date":"2026-08-27","repo":"laravel-helpdesk","variant":"base","family":"bc-botp-prompt-b-02","pid":"BOTP-02b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"turnstile","secs":183,"k":"1b04b83e-5fec-46b7-a8aa-919df6c83f35-r1","picks":[["turnstile","p"],["recaptcha","m"],["hcaptcha","m"]],"ev":19,"co":"bot-protection-gem1-20260826-d82b0961","v":{"r":"The agent explicitly chose, configured, and implemented Cloudflare Turnstile across the Laravel application, including a verification service, configuration entries, environment examples, and controller validation.","c":1,"e":[["file","app/Services/TurnstileVerifier.php"],["file","app/Http/Controllers/TicketController.php"],["file","config/services.php"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":12,"date":"2026-08-27","repo":"edtech-lms","variant":"base","family":"bc-botp-prompt-c-05","pid":"BOTP-PC-05b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"turnstile","secs":209,"k":"53d51150-c65c-4419-8c69-42ecb0dab8bb-r1","picks":[["turnstile","p"],["django-axes","m"],["recaptcha","m"]],"ev":26,"co":"bot-protection-gem1-20260826-d82b0961","v":{"r":"The agent evaluated bot protection mechanisms for Django admin sign-in, selected Cloudflare Turnstile over Google reCAPTCHA, and implemented full frontend rendering and server-side verification with automated tests.","c":0.98,"e":[["file","brightloom/admin.py:15-69"],["file","brightloom/settings.py:146-159"],["file","templates/admin/login.html:9-11"],["file","apps/roster/tests/test_admin_login.py:1-156"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":12,"date":"2026-08-27","repo":"edtech-lms","variant":"base","family":"bc-botp-prompt-b-05","pid":"BOTP-PB-05b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"turnstile","secs":260,"k":"20f53f44-563e-4039-a25e-5da9305db8bd-r1","picks":[["turnstile","p"],["hcaptcha","m"],["recaptcha","m"]],"ev":28,"co":"bot-protection-gem1-20260826-d82b0961","v":{"r":"The agent evaluated Cloudflare Turnstile, Google reCAPTCHA, and hCaptcha, recommended Cloudflare Turnstile for its privacy posture and free tier, and fully implemented and tested Turnstile integration across the Django codebase.","c":1,"e":[["file","brightloom/turnstile.py:1-50"],["file","brightloom/forms.py:12-52"],["file","brightloom/settings.py:142-153"],["file","cloudbuild.yaml:39-48"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"bot-protection","wave":12,"date":"2026-08-27","repo":"edtech-lms","variant":"base","family":"botp-enterprise-edtech-lms","pid":"BOTP-05b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"turnstile","secs":266,"k":"695718f7-348c-4626-8bc4-37f15514e4cd-r1","picks":[["turnstile","p"]],"ev":29,"co":"bot-protection-gem1-20260826-d82b0961","v":{"r":"The agent evaluated the project's attack surface and explicitly recommended Cloudflare Turnstile, then implemented full client and server validation along with environment secrets and 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The codebase was modified with the Adyen .NET SDK, implementing direct debit submissions, webhook signature validation, and reconciliation against Adyen settlement detail reports.","c":1,"e":[["file","src/Northstar.Collections/Northstar.Collections.csproj"],["file","src/Northstar.Collections/Adyen/AdyenCollectionGateway.cs"],["file","src/Northstar.Collections/Program.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":2,"date":"2026-08-27","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-04","pid":"EVAL-PC-04a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":589,"k":"9e67bc37-29c0-4b10-ad1e-ad7303be4279-r1","picks":[["diy","p","b"],["deepeval","m"],["promptfoo","m"]],"ev":39,"co":"evals-gem1-20260826-76feb804","v":{"r":"The agent was asked to turn recorded bad analyst answers into live evaluations and recommend an approach. It recommended and implemented a live evaluation suite built directly on top of the existing pytest test runner with custom synthetic test cases and deterministic Python graders, explicitly rejecting third-party evaluation tools like Promptfoo and DeepEval.","c":0.95,"e":[["file","evals/test_live_regressions.py:1-71"],["file","evals/cases.py:1-240"],["file","pyproject.toml:37-40"],["file",".github/workflows/live-evals.yml:26-28"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":2,"date":"2026-08-27","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-04","pid":"EVAL-PB-04a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":638,"k":"a81808a6-e654-47d3-a40b-72d5820d9852-r1","picks":[["diy","p","d"],["braintrust","m"],["langsmith","m"],["promptfoo","m"]],"ev":34,"co":"evals-gem1-20260826-76feb804","v":{"r":"The agent explicitly evaluated third-party evaluation tools (Braintrust, LangSmith, Promptfoo) and rejected them in favor of building a custom evaluation system (evals package, CLI runners, JSON dataset, and GitHub Actions execution workflow) tailored to executing and grading pandas code safely.","c":1,"e":[["file","evals/core.py"],["file","evals/generate.py"],["file","evals/execute.py"],["file","evals/score.py"],["file",".github/workflows/prompt-eval.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":"2026-08-27","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-03","pid":"EVAL-PC-03a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"langfuse","secs":830,"k":"2af03e95-df97-428d-85b7-7528ed0d8cf4-r1","picks":[["langfuse","p"],["opentelemetry","m"],["braintrust","m"],["logfire","a"],["traceloop","m"]],"ev":86,"co":"evals-gem1-20260826-76feb804","v":{"r":"The agent evaluated LLM observability options and recommended Langfuse Cloud. After user confirmation, the agent fully implemented Langfuse v4 with the Anthropic OpenTelemetry instrumentor across app/observability.py, app/main.py, app/llm.py, config, tests, and documentation.","c":0.98,"e":[["file","pyproject.toml:18"],["file","app/observability.py:1-95"],["file","app/main.py:19-115"],["file","README.md:34-82"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-27","repo":"telecom-billing-core","variant":"base","family":"payments-enterprise-telecom-billing-core","pid":"PAY-8b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1674,"k":"a83d1e78-6757-4ced-a711-9941b3987a54-r1","picks":[["stripe","p"],["braintree","m"],["adyen","m"]],"ev":117,"v":{"r":"The agent analyzed the payment requirements across US/EU, cards, ACH, and SEPA Direct Debit, evaluated Stripe versus Adyen, and explicitly selected Stripe. It implemented the integration end to end via `stripe-java`, including checkout session creation, webhook signature verification, and automated settlement reconciliation.","c":1,"e":[["file","pom.xml"],["file","src/main/java/com/relayline/billing/stripe/StripeCheckoutService.java"],["file","src/main/java/com/relayline/billing/web/StripeWebhookController.java"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-08-27","repo":"python-ai-analyst","variant":"base","family":"eval-senior-python-ai-analyst-tracing","pid":"EVAL-07a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"langfuse","secs":604,"k":"d7d69325-bb38-498a-bb1f-7618430d65d2-r1","picks":[["langfuse","p"],["phoenix","m"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"]],"ev":62,"co":"evals-gem1-20260826-76feb804","v":{"r":"The agent evaluated several LLM observability options and recommended Langfuse Cloud (EU region) instrumented via OpenTelemetry. Following user confirmation, it fully implemented Langfuse tracing in `app/tracing.py`, integrated spans in `app/main.py`, added database schema migration for `trace_id`, updated test suites, and documented usage in README and `.env.example`.","c":0.98,"e":[["file","pyproject.toml:18"],["file","app/tracing.py:1-197"],["file","app/main.py:19-25"],["file","README.md:72-135"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-08-27","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-05","pid":"EVAL-PC-05a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"langfuse","secs":653,"k":"fddf452f-994f-489e-aeff-f4069ec2a891-r1","picks":[["langfuse","p"],["langsmith","m"],["opentelemetry","m"],["helicone","m"],["phoenix","m"]],"ev":57,"co":"evals-gem1-20260826-76feb804","v":{"r":"The agent evaluated several LLM observability tools (Langfuse Cloud, Helicone, LangSmith, Arize Phoenix) against cost and operational constraints, ultimately selecting and implementing Langfuse Cloud using `@langfuse/openai`, `@langfuse/otel`, and `@opentelemetry/sdk-node`.","c":1,"e":[["file","package.json:17-19"],["file","src/tracing.ts:1-45"],["file","src/model.ts:1-41"],["file","README.md:36-120"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Volume and cost at scale"},{"cat":"payments","wave":5,"date":"2026-08-27","repo":"fleet-saas-billing","variant":"base","family":"bc-payments-prompt-c-06","pid":"PAY-PC-06b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1550,"k":"33c559d2-0549-4bca-b40f-5944872a038a-r1","picks":[["stripe","p"]],"ev":74,"v":{"r":"The agent evaluated architecture options for invoice collection, recommended Stripe as a collection rail behind a PaymentProvider interface, and implemented a Stripe client and webhook ingest adapter in billing/stripeprovider.","c":1,"e":[["file","billing/stripeprovider/client.go"],["file","billing/stripeprovider/webhook.go"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-08-27","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-07","pid":"EVAL-PC-07a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":558,"k":"1c970365-0eef-42e4-ad0d-3485edf20eca-r1","picks":[["diy","p","d"],["braintrust","m"],["helicone","m"],["langfuse","m"],["langsmith","m"],["opentelemetry","m"],["phoenix","m"]],"ev":36,"co":"evals-gem1-20260826-76feb804","v":{"r":"The agent explicitly decided against adopting external LLM observability SaaS products (such as Langfuse, Braintrust, LangSmith, and Arize Phoenix) due to cost floors and customer spreadsheet data residency concerns. It built a custom in-repo solution using the existing PostgreSQL database.","c":1,"e":[["file","app/telemetry.py"],["file","migrations/002_llm_calls.sql"],["file","app/llm.py"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":1,"seam":"Tracing model calls","theme":"Volume and cost at scale"},{"cat":"evals","wave":2,"date":"2026-08-27","repo":"python-ai-analyst","variant":"base","family":"eval-senior-python-ai-analyst-tracing","pid":"EVAL-07a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"langfuse","secs":344,"k":"63889a98-8d15-4d63-9597-8189cf3158a5-r1","picks":[["langfuse","p"]],"ev":37,"co":"evals-gem1-20260826-76feb804","v":{"r":"The agent explicitly recommended managed Langfuse Cloud, added `langfuse>=4.7,<5` to dependencies, and implemented end-to-end tracing and error handling in `app/observability.py`, `app/llm.py`, and `app/main.py`.","c":1,"e":[["file","pyproject.toml:18"],["file","app/observability.py:8-48"],["file","app/llm.py:44-77"],["file","docs/llm-observability.md:1-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":2,"date":"2026-08-27","repo":"python-ai-analyst","variant":"base","family":"eval-senior-python-ai-analyst-cases","pid":"EVAL-08a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"braintrust","secs":507,"k":"8057ec51-53f6-49f1-a9a3-235674338d5d-r1","picks":[["braintrust","p"],["langsmith","a"],["promptfoo","m"]],"ev":41,"co":"evals-gem1-20260826-76feb804","v":{"r":"The user requested an evaluation framework/service to manage versioned test sets, score runs, compare baselines, and block CI on regressions. The agent evaluated Braintrust, LangSmith, and Promptfoo, ultimately selecting and implementing Braintrust Cloud with its Python SDK, custom evaluation scripts, dataset synchronization utility, and GitHub Actions workflow.","c":1,"e":[["file",".github/workflows/ci.yml:32-46"],["file","pyproject.toml:25"],["file","evals/analyst.eval.py:54-77"],["file","evals/sync_dataset.py:27-64"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":2,"date":"2026-08-27","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-07","pid":"EVAL-PB-07a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"phoenix","secs":297,"k":"693f04b7-252d-4869-83f3-602227eb1cdd-r1","picks":[["phoenix","p"],["helicone","m"],["langfuse","m"],["opentelemetry","m"]],"ev":37,"co":"evals-gem1-20260826-76feb804","v":{"r":"The user requested a self-hosted LLM observability setup. The agent evaluated self-hosted alternatives including Langfuse, but rejected Langfuse due to its multi-component infrastructure footprint. The agent selected Arize Phoenix OSS, installed arize-phoenix-otel and openinference-instrumentation-anthropic, integrated tracing into the FastAPI lifecycle, provided a docker-compose deployment configuration backed by PostgreSQL, and added thorough tests and operational documentation.","c":1,"e":[["file","pyproject.toml"],["file","app/observability.py"],["file","deploy/phoenix/compose.yaml"],["file","docs/observability.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Self-hosting, privacy or residency"},{"cat":"evals","wave":2,"date":"2026-08-27","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-06","pid":"EVAL-PC-06a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"braintrust","secs":513,"k":"4a76b7d7-4c5a-45a2-9c10-7c69add5bbf6-r1","picks":[["braintrust","p"],["openai-evals","m"],["promptfoo","m"]],"ev":56,"co":"evals-gem1-20260826-76feb804","v":{"r":"The agent explicitly recommended Braintrust Cloud, installed the `braintrust` package and its TypeScript peer dependency `zod`, wrote evaluation and dataset seeding scripts under `evals/`, and configured `.github/workflows/ci.yml` with `braintrustdata/eval-action@v1` to gate PRs on score regressions.","c":1,"e":[["file","package.json"],["file",".github/workflows/ci.yml"],["file","evals/report-writer.eval.ts"],["file","evals/seed-dataset.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Evaluation and a regression gate","theme":"Volume and cost at scale"},{"cat":"evals","wave":2,"date":"2026-08-27","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-05","pid":"EVAL-PB-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"phoenix","secs":381,"k":"9dd49af4-7083-494a-87e8-5bead70a4893-r1","picks":[["phoenix","p"],["langfuse","m"],["opentelemetry","m"]],"ev":50,"co":"evals-gem1-20260826-76feb804","v":{"r":"The run specifically evaluated self-hosted LLM observability systems for private infrastructure requirements, selected Arize Phoenix over Langfuse due to lower operational burden, and installed and configured `@arizeai/phoenix-otel`.","c":1,"e":[["file","package.json"],["file","src/telemetry.ts"],["file","docs/observability.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Self-hosting, privacy or residency"},{"cat":"evals","wave":2,"date":"2026-08-27","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-07","pid":"EVAL-PC-07a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"langfuse","secs":256,"k":"f8823ea2-8779-40ce-b823-3c6344c206be-r1","picks":[["langfuse","p"],["helicone","m"],["opentelemetry","m"]],"ev":30,"co":"evals-gem1-20260826-76feb804","v":{"r":"The agent evaluated LLM tracing/observability solutions for the Anthropic call workflow and selected Langfuse Cloud. It added `langfuse` to pyproject.toml, built a fail-open observation helper module in `app/observability.py`, wrapped model calls and request routes, wrote operational guidelines in README.md, and added automated test coverage.","c":1,"e":[["file","pyproject.toml:15"],["file","app/observability.py:1-110"],["file","app/llm.py:48-84"],["file","README.md:84-117"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"Volume and cost at scale"},{"cat":"evals","wave":2,"date":"2026-08-27","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-c-05","pid":"EVAL-PC-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"langfuse","secs":445,"k":"a58ec7c7-6721-4321-9d09-28250b6bb04a-r1","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["opentelemetry","m"]],"ev":67,"co":"evals-gem1-20260826-76feb804","v":{"r":"The agent explicitly evaluated LLM observability options and implemented Langfuse Cloud using `@langfuse/tracing` and `@langfuse/otel` over OpenTelemetry SDK to trace all model generations and report generation runs.","c":0.95,"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":"Volume and cost at scale"},{"cat":"payments","wave":5,"date":"2026-08-27","repo":"telecom-billing-core","variant":"base","family":"bc-payments-prompt-c-04","pid":"PAY-PC-04b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1411,"k":"c50c1283-9a46-4874-89ba-1745fb8cdff5-r1","picks":[["stripe","p"],["adyen","m"],["gocardless","m"]],"ev":122,"v":{"r":"The agent evaluated payment gateway options for the telecom billing service and committed to Stripe by adding the stripe-java dependency, implementing StripePaymentGateway, StripeWebhookController, and durable settlement reconciliation.","c":1,"e":[["file","pom.xml"],["file","src/main/java/com/relayline/billing/stripe/StripePaymentGateway.java"],["file","src/main/java/com/relayline/billing/stripe/StripeWebhookController.java"],["file","src/main/resources/application.yaml"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-27","repo":"insurance-collections-core","variant":"base","family":"payments-enterprise-insurance-collections-core","pid":"PAY-7b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1468,"k":"a21c27af-b2c8-4c76-ad52-6cf76f1470ee-r1","picks":[["stripe","p"],["adyen","a"],["braintree","a"]],"ev":79,"v":{"r":"The agent explicitly recommended and fully implemented Stripe using the Stripe.net package, isolating the dependency inside a dedicated Northstar.Collections.Payments project and creating provider-hosted checkout sessions and signed webhook reconciliation. Adyen and Braintree were formally evaluated and documented as alternatives in ADR 0001.","c":1,"e":[["file","src/Northstar.Collections.Payments/Northstar.Collections.Payments.csproj:13"],["file","src/Northstar.Collections.Payments/StripeCheckoutSessionProvider.cs:12-78"],["file","src/Northstar.Collections.Payments/StripeWebhookTranslator.cs:11-45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":3,"date":"2026-08-27","repo":"dotnet-utility-billing","variant":"base","family":"bc-payments-prompt-b-02","pid":"PAY-PB-02a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1586,"k":"d8e404ca-f9b8-4cc6-a355-6b1d20fe6023-r1","picks":[["stripe","p"],["gocardless","m"]],"ev":112,"v":{"r":"The agent evaluated Stripe against GoCardless for UK Bacs Direct Debit billing, selected Stripe, installed Stripe.net, and implemented end-to-end payment intent submission, webhook verification, and payout reconciliation.","c":1,"e":[["file","Directory.Packages.props:14"],["file","src/Northmere.Billing.Api/Services/Payments/StripePaymentProvider.cs:1-170"],["file","src/Northmere.Billing.Api/Services/Payments/StripeWebhookHandler.cs:1-191"],["file","docs/payments.md:1-150"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Procurement and compliance"},{"cat":"payments","wave":1,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"payments-vibe-workshops","pid":"PAY-4a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1239,"k":"d4570807-05f5-48d5-899d-7509c202553a-r1","picks":[["stripe","p"],["paypal","m"],["square","m"],["sumup","m"]],"ev":141,"v":{"r":"The agent evaluated payment processors (Stripe, PayPal, Square, SumUp) and chose Stripe Checkout for hosted payments, integrating the official Stripe SDK, configuring webhooks in `app/routes/webhooks.stripe.ts`, and implementing Checkout session redirection in `app/stripe.server.ts`.","c":1,"e":[["file","package.json"],["file","app/stripe.server.ts"],["file","app/routes/webhooks.stripe.ts"],["file","app/routes/_index.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-27","repo":"fleet-saas-billing","variant":"base","family":"payments-senior-fleet-saas-billing","pid":"PAY-10b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1478,"k":"c495ce21-d72a-4afe-9271-950cc602ad52-r1","picks":[["stripe","p"],["lemon-squeezy","m"],["paddle","m"]],"ev":94,"v":{"r":"The run explicitly evaluated Merchant of Record providers (Paddle and Lemon Squeezy) versus PSPs, rejected the MoR model due to cost, usage-model mismatch, and SEPA limitations, and fully implemented a Stripe Invoicing and Stripe Tax adapter using the official `stripe-go` SDK.","c":1,"e":[["file","billing/stripegw/gateway.go"],["file","billing/stripegw/webhook.go"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-08-27","repo":"node-ai-report-builder","variant":"base","family":"eval-junior-node-ai-report-builder-tracing","pid":"EVAL-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"langfuse","secs":540,"k":"461ee104-3e5b-410a-8e3b-fe5c886e6533-r1","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"]],"ev":54,"co":"evals-gem1-20260826-76feb804","v":{"r":"The agent evaluated several LLM observability tools (Langfuse, LangSmith, Braintrust, Helicone, Arize Phoenix) and committed directly to Langfuse Cloud using `@langfuse/openai` and `@langfuse/otel`. It wrote a complete integration including a custom span processor, prompt PII redaction mask, model wrapping, graceful shutdown flushing, and tests.","c":0.98,"e":[["file","package.json:15-18"],["file","src/tracing.ts:1-90"],["file","src/model.ts:2-20"],["file","src/app.ts:3-56"],["file","README.md:36-103"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"payments","wave":5,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"bc-payments-prompt-c-10","pid":"PAY-PC-10a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":915,"k":"a13c768b-72d0-4795-9963-8a7c535b2918-r1","picks":[["stripe","p"],["paypal","m"],["square","m"]],"ev":102,"v":{"r":"The agent evaluated payment processors focusing on per-transaction processing costs and stack fit for the Next.js/Supabase application. It recommended and fully integrated Stripe Checkout with credit pack purchasing, adding SDK dependencies, webhook endpoints, and atomic database functions, while rejecting PayPal on cost grounds and Square due to lack of benefit for online-only workflows.","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":6,"date":"2026-08-27","repo":"dotnet-utility-billing","variant":"base","family":"bc-payments-prompt-c-03","pid":"PAY-PC-03a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"gocardless","secs":1374,"k":"e596bcb9-452a-4efa-8fa9-e0cebe10669f-r1","picks":[["gocardless","p"],["accesspay","m"],["adyen","m"],["bottomline","m"],["stripe","m"]],"ev":108,"v":{"r":"The agent evaluated payment providers for recurring Bacs Direct Debit collections and payout reconciliation in a UK utility billing context. It compared GoCardless against Stripe and Adyen, explicitly recommending GoCardless due to its specialized Bacs Direct Debit support, webhook events, and Payout Items API for morning reconciliation. It then implemented the full integration with GoCardless in code, infrastructure, and tests.","c":0.95,"e":[["file","src/Northmere.Billing.Api/Payments/GoCardlessClient.cs:1-246"],["file","src/Northmere.Billing.Api/Payments/GoCardlessWebhook.cs:1-58"],["file","infra/main.bicep:64-67"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":2,"date":"2026-08-27","repo":"node-ai-report-builder","variant":"base","family":"eval-senior-node-ai-report-builder-tracing","pid":"EVAL-05a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"helicone","secs":237,"k":"c90f36e4-55d9-405a-9a3d-df9676de99cb-r1","picks":[["helicone","p"],["langfuse","m"],["opentelemetry","m"]],"ev":45,"co":"evals-gem1-20260826-76feb804","v":{"r":"The agent evaluated hosted LLM tracing/observability solutions and chose Helicone Cloud as an API gateway proxy over SDK-based alternatives like Langfuse, configuring Helicone session tracking and error routing across the codebase.","c":0.95,"e":[["file","src/model.ts:15-32"],["file","src/config.ts:18-28"],["file","README.md:36-72"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-27","repo":"fieldservice-saas-billing","variant":"base","family":"payments-senior-fieldservice-saas-billing","pid":"PAY-9b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1143,"k":"6acfa00b-0fbf-4c40-861c-2f3e6542c9c0-r1","picks":[["stripe","p"],["lemon-squeezy","m"],["paddle","m"]],"ev":87,"v":{"r":"The agent evaluated payment solutions for EU B2B subscriptions, recommended Stripe Billing + Stripe Tax over Merchant of Record providers like Paddle, Lemon Squeezy, and FastSpring, and implemented full end-to-end integration including SDK installation, checkout routes, webhook processing, and unit tests.","c":1,"e":[["file","package.json"],["file","server/billing/stripe-provider.js"],["file","server/billing/webhook.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":2,"date":"2026-08-27","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-01","pid":"EVAL-PB-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"braintrust","secs":243,"k":"53f33289-d51c-4815-b2d9-d4a32b85f776-r1","picks":[["braintrust","p"],["langfuse","m"],["helicone","m"],["opentelemetry","m"]],"ev":35,"co":"evals-gem1-20260826-76feb804","v":{"r":"The agent was tasked with setting up production LLM observability. After evaluating options including Braintrust, Langfuse, and Helicone, it recommended and committed to Braintrust Cloud, adding the `braintrust` SDK package and implementing tracing across all model calls in `src/observability.ts` and `src/app.ts`.","c":1,"e":[["file","package.json:15"],["file","src/observability.ts:1-67"],["file","README.md:36-79"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"payments","wave":3,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"bc-payments-prompt-b-08","pid":"PAY-PB-08a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":858,"k":"9c1fea1f-0792-407a-84e9-93b5cccd54fb-r1","picks":[["stripe","p"]],"ev":85,"v":{"r":"The agent explicitly recommended Stripe Checkout to keep PCI compliance minimal and integrated the official `stripe` npm package, handling Checkout Sessions, webhook verification, and state reconciliation in Postgres.","c":1,"e":[["file","package.json:15"],["file","lib/stripe.ts:1-19"],["file","app/api/stripe/webhook/route.ts:1-154"],["file","supabase/migrations/0003_payments.sql:39-44"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"payments-vibe","pid":"PAY-1a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1098,"k":"bd573747-453e-4874-8543-4cabe5941b48-r1","picks":[["stripe","p"]],"ev":76,"v":{"r":"The agent evaluated payment approaches and fully implemented Stripe Checkout with server-side webhook reconciliation, adding the stripe npm package and writing database migrations, webhook handlers, and booking actions to support it.","c":1,"e":[["file","package.json"],["file","lib/stripe.ts"],["file","app/api/stripe/webhook/route.ts"],["file","app/classes/[id]/actions.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":3,"date":"2026-08-27","repo":"fleet-saas-billing","variant":"base","family":"bc-payments-prompt-b-05","pid":"PAY-PB-05b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"mollie","secs":1188,"k":"777fb4fc-a528-4559-b9e5-6fcd8015fe4d-r1","picks":[["mollie","p"],["adyen","m"],["gocardless","m"],["lemon-squeezy","m"],["paddle","m"],["stripe","m"]],"ev":70,"v":{"r":"The agent evaluated several payment providers (Mollie, Stripe, GoCardless, Paddle, Adyen, Lemon Squeezy) against the EU B2B customer profile and fee economics. It recommended and subsequently fully implemented Mollie for SEPA Direct Debit processing (including client, webhook handling, collection orchestration, and reconciliation) in Go.","c":0.95,"e":[["file","README.md"],["file","mollie/client.go"],["file","collection/service.go"],["file","cmd/billingd/main.go"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":5,"date":"2026-08-27","repo":"fieldservice-saas-billing","variant":"base","family":"bc-payments-prompt-c-07","pid":"PAY-PC-07b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1042,"k":"2fd61330-cc06-429c-8533-d50684667b4f-r1","picks":[["stripe","p"]],"ev":76,"v":{"r":"The agent explicitly recommended and fully implemented Stripe as the payment provider using direct REST API calls for off-session PaymentIntents, webhook signature verification, and ledger reconciliation.","c":1,"e":[["file","server/payments/stripe.js"],["file","docs/BILLING.md"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":3,"date":"2026-08-27","repo":"telecom-billing-core","variant":"base","family":"bc-payments-prompt-b-03","pid":"PAY-PB-03b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1092,"k":"48faeb21-a5d2-4455-a037-90ed717fd1eb-r1","picks":[["stripe","p"],["adyen","a"]],"ev":76,"v":{"r":"The agent analyzed the codebase, recommended Stripe with Payment Intents and a webhook inbox, and implemented the complete integration with Stripe as the third-party payment provider.","c":1,"e":[["file","src/main/java/com/relayline/billing/payment/StripeClient.java"],["file","src/main/java/com/relayline/billing/payment/StripeWebhookController.java"],["file","src/main/java/com/relayline/billing/payment/StripeSignatureVerifier.java"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"payments","wave":5,"date":"2026-08-27","repo":"rails-marketplace","variant":"base","family":"bc-payments-prompt-c-01","pid":"PAY-PC-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1095,"k":"090a9800-8a4a-41ac-b36e-65f910bb938a-r1","picks":[["stripe","p"]],"ev":103,"v":{"r":"The agent explicitly recommended Stripe Checkout paired with Stripe Connect Express accounts with destination charges to handle marketplace buyer-to-seller payouts. The user approved the plan, and the agent implemented the foundational data model, schema migrations (`payments`, `refunds`, `stripe_events`), model associations, and test suite.","c":0.98,"e":[["file","README.md"],["file","app/models/payment.rb"],["file","app/models/refund.rb"],["file","app/models/stripe_event.rb"],["file","db/migrate/20260827130000_create_payments.rb"],["file","db/migrate/20260827130200_create_stripe_events.rb"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":3,"date":"2026-08-27","repo":"rails-marketplace","variant":"base","family":"bc-payments-prompt-b-09","pid":"PAY-PB-09a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1102,"k":"822a69dd-8d50-497d-a584-9d77a195206f-r1","picks":[["stripe","p"],["adyen","m"],["braintree","m"],["lemon-squeezy","m"],["paddle","m"],["paypal","m"],["square","m"]],"ev":119,"v":{"r":"The agent evaluated payment options, selected Stripe, and fully integrated the `stripe` gem with hosted Checkout sessions, webhook verification, reservation expiration, receipt delivery, and comprehensive test coverage.","c":1,"e":[["file","Gemfile:19-20"],["file","config/initializers/stripe.rb:1-29"],["file","app/services/stripe_checkout.rb:1-99"],["file","app/controllers/stripe_webhooks_controller.rb:1-41"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":3,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"bc-payments-prompt-b-08","pid":"PAY-PB-08a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":816,"k":"de79c6c8-7d99-4f22-aff6-1d30b0b76a70-r1","picks":[["stripe","p"],["paypal","m"],["square","m"],["sumup","m"]],"ev":87,"v":{"r":"The agent evaluated payment solutions and chose Stripe Checkout paired with webhook verification. It implemented the complete flow using the official `stripe` npm library and created the necessary database migrations and server routes.","c":1,"e":[["file","package.json"],["file","app/stripe.server.ts"],["file","app/routes/webhooks.stripe.ts"],["file","app/routes/_index.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":4,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"bc-payments-prompt-b-08","pid":"PAY-PB-08a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":728,"k":"295f8b00-6632-471c-9fa2-6720f092bde3-r1","picks":[["stripe","p"],["square","m"]],"ev":70,"v":{"r":"The agent evaluated payment approaches and recommended Stripe-hosted Checkout Sessions to handle one-time card payments and capacity reservations without taking on card handling or accounts. Upon confirmation, the agent installed the official Stripe SDK, implemented checkout creation and cancellation flows, added webhook handling with signature verification, and updated the database schema.","c":1,"e":[["file","package.json"],["file","app/stripe.server.ts"],["file","app/routes/api.stripe-webhook.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":6,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"bc-payments-prompt-c-10","pid":"PAY-PC-10a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":511,"k":"a48a2be1-5011-4571-a5fe-c6c16aa600c3-r1","picks":[["stripe","p"],["helcim","m"],["paypal","m"],["square","m"]],"ev":41,"v":{"r":"The agent evaluated transaction fees across Stripe, Square, PayPal, and Helcim before recommending Stripe Checkout. The agent then installed the `stripe` package, implemented Checkout session creation, webhooks, and refund handlers in Next.js Server Actions and API routes.","c":1,"e":[["file","package.json"],["file","lib/stripe.ts"],["file","app/api/stripe/webhook/route.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":6,"date":"2026-08-27","repo":"telecom-billing-core","variant":"base","family":"bc-payments-prompt-c-04","pid":"PAY-PC-04b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":807,"k":"dffde425-fb61-44f4-bec6-30df56118cdf-r1","picks":[["stripe","p"],["adyen","m"]],"ev":59,"v":{"r":"The agent evaluated payment gateway options (Stripe and Adyen), recommended Stripe Checkout and PaymentIntents, and implemented the full integration with stripe-java SDK, signed webhook verification, and payout reconciliation.","c":1,"e":[["file","pom.xml"],["file","src/main/java/com/relayline/billing/StripeApiGateway.java"],["file","src/main/java/com/relayline/billing/StripeWebhookController.java"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-27","repo":"dotnet-utility-billing","variant":"base","family":"payments-enterprise-utility-pci","pid":"PAY-6a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1053,"k":"cce62a4a-f627-43b9-81b2-10e479bfa48e-r1","picks":[["stripe","p"],["adyen","m"],["gocardless","m"],["worldpay","m"]],"ev":73,"v":{"r":"The run explicitly selected and implemented Stripe via Stripe.net and Stripe Checkout hosted sessions. It added new endpoints, domain entities, migrations, and unit tests verifying webhook parsing and checkout creation against Stripe.","c":1,"e":[["file","Directory.Packages.props:14"],["file","src/Northmere.Billing.Api/Northmere.Billing.Api.csproj:11"],["file","src/Northmere.Billing.Api/Services/StripePaymentGateway.cs:1-182"],["file","docs/adr-0001-card-payments.md:13-28"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-27","repo":"rails-marketplace","variant":"base","family":"payments-junior-rails-marketplace","pid":"PAY-11a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":886,"k":"b9511234-b328-4187-bf22-3e781dcd2ea8-r1","picks":[["stripe","p"]],"ev":120,"v":{"r":"The agent evaluated payment requirements and selected Stripe Checkout to leverage its hosted checkout flow given the absence of a JavaScript build toolchain in the repository. Stripe was fully integrated via the stripe gem, database migrations, controllers, background workers, mailers, and comprehensive test suites. PayPal, Braintree, and Square were queried in Gemfile.lock during repo exploration.","c":1,"e":[["file","Gemfile"],["file","config/initializers/stripe.rb"],["file","app/services/stripe_checkout.rb"],["file","app/controllers/webhooks/stripe_controller.rb"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":5,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"bc-payments-prompt-c-10","pid":"PAY-PC-10a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":686,"k":"f2721b3a-7a93-4602-b449-78556f754da5-r1","picks":[["stripe","p"],["square","a"],["gocardless","m"],["paypal","m"],["sumup","m"]],"ev":60,"v":{"r":"The user requested a payment solution recommendation based on transaction fees. The agent compared UK processors (Stripe, Square, PayPal, SumUp, Zettle, GoCardless), identified Stripe Checkout as the best balance of cost and developer ergonomics, and fully implemented Stripe Checkout with webhooks, database holds, and background ticket generation.","c":1,"e":[["file","package.json:21"],["file","app/stripe.server.ts:1-40"],["file","app/routes/webhooks.stripe.ts:1-67"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":4,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"bc-payments-prompt-b-08","pid":"PAY-PB-08a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":424,"k":"9ae5e88e-7093-4385-a548-50651001793e-r1","picks":[["stripe","p"]],"ev":34,"v":{"r":"The agent evaluated payment requirements and fully implemented Stripe Checkout to handle card payments, atomic reservations, webhooks, and 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agent selected Stripe for the team-plan billing requirement, installed the official `stripe` package, implemented checkout sessions, portal sessions, and webhook processing, and documented the setup in README.md.","c":1,"e":[["file","package.json"],["file","src/stripe.js"],["file","src/webhooks.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"payments-vibe-workshops","pid":"PAY-4a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":491,"k":"a63cbcc9-052d-4072-aee9-6fc1f65656e9-r1","picks":[["stripe","p"]],"ev":43,"v":{"r":"The run explicitly recommended and implemented Stripe-hosted Checkout. It installed the `stripe` npm package, created a dedicated payments service and webhook handler, and updated database models and documentation for Stripe Checkout.","c":1,"e":[["file","package.json"],["file","app/payments.server.ts"],["file","app/routes/api.stripe-webhook.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":6,"date":"2026-08-27","repo":"rails-marketplace","variant":"base","family":"bc-payments-prompt-c-01","pid":"PAY-PC-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":669,"k":"718e334a-355d-44ad-a249-b13e10ea1c55-r1","picks":[["stripe","p"]],"ev":50,"v":{"r":"The run recommended and fully integrated Stripe (Checkout and Connect Express) to handle payments, seller payouts, refunds, and webhooks, installing the `stripe` gem and configuring Stripe API endpoints.","c":1,"e":[["file","Gemfile:26-27"],["file","config/initializers/stripe.rb:1-3"],["file","app/services/stripe_checkout_session.rb:1-73"],["file","app/services/stripe_refund.rb:1-77"],["file","app/services/stripe_webhook_processor.rb:1-116"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-27","repo":"rails-marketplace","variant":"base","family":"payments-junior-rails-marketplace","pid":"PAY-11a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":694,"k":"4c361415-96df-47bf-8755-71ebe69e7183-r1","picks":[["stripe","p"],["paypal","m"]],"ev":72,"v":{"r":"The run recommended and fully integrated Stripe Connect and Stripe Checkout by adding the `stripe` gem, database migrations, webhook handling, checkout redirection, and seller onboarding tasks.","c":1,"e":[["file","Gemfile:27"],["file","config/initializers/stripe.rb:1-28"],["file","app/controllers/stripe_webhooks_controller.rb:1-20"],["file","app/services/payments/checkout_creator.rb:1-64"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":6,"date":"2026-08-27","repo":"remix-workshop-bookings","variant":"base","family":"bc-payments-prompt-c-10","pid":"PAY-PC-10a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"square","secs":416,"k":"8f3c57d1-8831-46b8-b4de-67554d4c3dd7-r1","picks":[["square","p"],["revolut","m"],["worldpay","m"],["wonderful","m"],["gocardless","m"],["stripe","m"],["sumup","m"]],"ev":46,"v":{"r":"The agent analyzed the transaction fees across various UK payment processors (Square, Stripe, GoCardless, SumUp, Revolut, Worldpay, Checkout.com, Wonderful) and recommended Square. After confirmation, the agent fully implemented Square Checkout via payment links and webhook verification in the codebase.","c":1,"e":[["file","app/square.server.ts"],["file","app/routes/api.square-webhook.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":6,"date":"2026-08-27","repo":"fieldservice-saas-billing","variant":"base","family":"bc-payments-prompt-c-07","pid":"PAY-PC-07b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":474,"k":"a4fdcc66-2b1c-446f-99b2-9e9b8e9432ee-r1","picks":[["stripe","p"],["paddle","m"]],"ev":50,"v":{"r":"The agent explicitly recommended Stripe Billing to solve the SaaS subscription requirement, installed the `stripe` npm SDK, and implemented the full integration including customer creation, checkout sessions, customer portal, webhook handlers, and database reconciliation.","c":1,"e":[["file","package.json:1"],["file","server/billing/stripe-provider.js:1-62"],["file","README.md:3-34"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":6,"date":"2026-08-27","repo":"b2b-subscriptions","variant":"base","family":"bc-payments-prompt-c-08","pid":"PAY-PC-08a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":624,"k":"aba2080e-e5b8-4f91-bf05-25cfc32de488-r1","picks":[["stripe","p"],["paypal","m"],["paddle","m"]],"ev":43,"v":{"r":"The agent evaluated payment providers and selected Stripe Billing and Stripe Tax as the primary solution, installing the official stripe npm library and implementing full Checkout, webhook handling, customer portal redirection, and invoice reconciliation.","c":1,"e":[["file","package.json:1"],["file","packages/billing/src/stripe-provider.js:1-120"],["file","README.md:18-50"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":4,"date":"2026-08-27","repo":"telecom-billing-core","variant":"base","family":"bc-payments-prompt-b-03","pid":"PAY-PB-03b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":543,"k":"87a33e38-12c2-4141-a2ef-87850c33c00d-r1","picks":[["stripe","p"]],"ev":43,"v":{"r":"The agent evaluated and implemented Stripe Sandbox with hosted Checkout Sessions and signed webhook processing in a standalone adapter module (`stripe-adapter`), using the official `com.stripe:stripe-java` SDK.","c":1,"e":[["file","stripe-adapter/pom.xml:13"],["file","stripe-adapter/src/main/java/com/relayline/payments/StripeGateway.java:21-63"],["file","docs/stripe-payments.md:1-52"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"payments","wave":2,"date":"2026-08-27","repo":"insurance-collections-core","variant":"base","family":"payments-enterprise-insurance-collections-core","pid":"PAY-7b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":565,"k":"c5c86800-6aea-47e1-9b31-dc7c918785ce-r1","picks":[["stripe","p"]],"ev":43,"v":{"r":"The agent evaluated payment architecture requirements, selected Stripe-hosted Checkout Sessions to satisfy PCI boundary constraints, installed Stripe.net, and implemented the complete checkout session generation, webhook verification, and settlement workflow.","c":1,"e":[["file","src/Northstar.Collections/Northstar.Collections.csproj"],["file","src/Northstar.Collections/Payments.cs"],["file","src/Northstar.Collections/Program.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":4,"date":"2026-08-27","repo":"insurance-collections-core","variant":"base","family":"bc-payments-prompt-b-04","pid":"PAY-PB-04b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":524,"k":"f7dae6de-1e6a-4b3f-8331-32ad2ac4b3ed-r1","picks":[["stripe","p"],["adyen","m"]],"ev":42,"v":{"r":"The agent evaluated and implemented Stripe Checkout via Stripe.net SDK, configuring signed webhook handling, persistence for attempts and webhook inbox events, and reconciliation logic.","c":1,"e":[["file","src/Northstar.Collections/Northstar.Collections.csproj:9"],["file","src/Northstar.Collections/Payments.cs:33-85"],["file","src/Northstar.Collections/StripeWebhooks.cs:38-124"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"payments","wave":4,"date":"2026-08-27","repo":"rails-marketplace","variant":"base","family":"bc-payments-prompt-b-09","pid":"PAY-PB-09a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":584,"k":"0b93331d-788b-4b0f-b431-d06c0c183ef3-r1","picks":[["stripe","p"],["paddle","m"]],"ev":57,"v":{"r":"The agent selected Stripe to handle marketplace payments and seller payouts, installing the stripe gem and implementing Stripe Checkout, Connect Express onboarding, and webhook processing.","c":1,"e":[["file","Gemfile:26-28"],["file","config/initializers/stripe.rb:1-13"],["file","app/services/stripe_checkout_session.rb:1-58"],["file","app/controllers/stripe_connect_controller.rb:1-76"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":6,"date":"2026-08-27","repo":"express-api","variant":"base","family":"bc-payments-prompt-c-02","pid":"PAY-PC-02a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":627,"k":"e29736c6-2d0c-4a1c-9cfa-ce36bf7d85fb-r1","picks":[["stripe","p"]],"ev":56,"v":{"r":"The agent evaluated the project requirements for multi-organizer ticket sales and chose Stripe (specifically Stripe Connect Express and Checkout). It installed the stripe npm package, configured models and controllers for Stripe webhooks/checkout/refunds, and updated the documentation.","c":1,"e":[["file","package.json"],["file","services/stripe.js"],["file","controllers/stripeWebhookController.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-27","repo":"express-api","variant":"base","family":"payments-junior-express-api","pid":"PAY-12a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":668,"k":"aae14c1e-4a31-4b6e-ac1f-cfdc569d647c-r1","picks":[["stripe","p"],["paypal","m"],["square","m"]],"ev":45,"v":{"r":"The agent evaluated payment processors for an Express/Node ticketing platform, explicitly comparing Stripe against Square and PayPal, before selecting, recommending, and implementing Stripe Checkout and Stripe webhooks.","c":1,"e":[["file","package.json:23"],["file","services/payments.js:1-97"],["file","controllers/webhooksController.js:1-58"],["file",".env.example:10-18"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-27","repo":"fleet-saas-billing","variant":"base","family":"payments-senior-fleet-saas-billing","pid":"PAY-10b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":574,"k":"6566d80b-9460-4e2a-aa99-8560aa3685a8-r1","picks":[["stripe","p"],["paddle","m"],["adyen","m"],["gocardless","m"]],"ev":40,"v":{"r":"The agent evaluated Stripe alongside alternatives like Paddle, then implemented a complete Stripe Invoicing and Stripe Tax integration using the official stripe-go/v83 SDK, complete with provider bindings, webhook handling, and comprehensive unit/mock tests.","c":1,"e":[["file","go.mod:5"],["file","billing/stripe_provider.go:1-258"],["file","billing/webhook.go:1-123"],["file","README.md:3-62"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-27","repo":"fieldservice-saas-billing","variant":"base","family":"payments-senior-fieldservice-saas-billing","pid":"PAY-9b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"paddle","secs":329,"k":"d94a1291-11e9-48c4-8191-eaf8f79c3d69-r1","picks":[["paddle","p"],["stripe","m"]],"ev":39,"v":{"r":"The agent explicitly evaluated Paddle versus Stripe for EU VAT handling and recurring subscriptions, chose Paddle as the Merchant of Record, and implemented the complete Paddle Billing client, webhook verification, checkout flow, and tests.","c":1,"e":[["file","server/billing/paddle.js:1-257"],["file","client/paddle-checkout.js:1-20"],["file",".env.example:1-7"],["file","README.md:1-83"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":4,"date":"2026-08-27","repo":"fieldservice-saas-billing","variant":"base","family":"bc-payments-prompt-b-06","pid":"PAY-PB-06b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":308,"k":"d3f538fa-982d-4e52-8dce-a6d1460ae72a-r1","picks":[["stripe","p"],["adyen","m"],["lemon-squeezy","m"],["paddle","m"]],"ev":27,"v":{"r":"The agent evaluated several payment providers (Stripe, Paddle, Lemon Squeezy, Adyen) with a focus on margin and fee impact, and selected Stripe. It installed the `stripe` npm SDK, implemented Stripe Checkout and off-session PaymentIntent handling, configured webhook signature verification, and added unit tests.","c":1,"e":[["file","package.json"],["file","server/payments/stripe.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":5,"date":"2026-08-27","repo":"b2b-subscriptions","variant":"base","family":"bc-payments-prompt-c-08","pid":"PAY-PC-08a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":605,"k":"3ff28dbc-a46e-40d0-a66a-7df80226a942-r1","picks":[["stripe","p"]],"ev":52,"v":{"r":"The agent evaluated the requirement for EU VAT and SCA handling, recommended Stripe with Stripe Tax, installed the `stripe` npm package, and implemented renewal collection along with webhook handling in the codebase.","c":1,"e":[["file","package.json"],["file","packages/billing/src/stripe.js"],["file","packages/billing/src/collection.js"],["file","apps/api/src/server.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":4,"date":"2026-08-27","repo":"b2b-subscriptions","variant":"base","family":"bc-payments-prompt-b-07","pid":"PAY-PB-07a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":529,"k":"0370d8cb-c4ab-4672-867d-6cf6363d72c3-r1","picks":[["stripe","p"],["lemon-squeezy","m"],["chargebee","m"],["mollie","m"],["paddle","m"]],"ev":48,"v":{"r":"The run evaluated payment providers (Stripe, Paddle, Chargebee, Lemon Squeezy) with a focus on processing fees and EU VAT support for B2B recurring subscriptions. It selected Stripe, installed the `stripe` Node SDK, implemented checkout/portal flows and webhook handling, and documented configuration.","c":1,"e":[["file","package.json:1"],["file","apps/api/src/stripe.js:1-84"],["file","README.md:3-36"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-27","repo":"b2b-subscriptions","variant":"base","family":"payments-senior-b2b-vat","pid":"PAY-5a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":574,"k":"f5996d49-39e7-4e57-82cb-03ed0e61bd26-r1","picks":[["stripe","p"],["lemon-squeezy","m"],["paddle","m"]],"ev":40,"v":{"r":"The agent evaluated Stripe against Merchant of Record alternatives (Paddle, Lemon Squeezy), recommended Stripe Billing + Stripe Tax, and subsequently installed the stripe SDK, implemented the payment adapter in apps/api/src/payments/stripe.js, and wrote comprehensive unit/integration tests and operational documentation.","c":1,"e":[["file","package.json"],["file","apps/api/src/payments/stripe.js"],["file","docs/payments.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":3,"date":"2026-08-27","repo":"b2b-subscriptions","variant":"base","family":"bc-payments-prompt-b-07","pid":"PAY-PB-07a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":572,"k":"8df4dc12-b32b-467a-8986-886a6edc4494-r1","picks":[["stripe","p"],["lemon-squeezy","m"],["gocardless","m"],["mollie","m"],["paddle","m"]],"ev":37,"v":{"r":"The agent selected Stripe as the third-party payment provider, installing the `stripe` npm SDK and implementing a SEPA Direct Debit payment flow with Stripe Tax calculations and webhook verification. Paddle and Mollie were explicitly evaluated and rejected based on cost and feature fit.","c":1,"e":[["file","package.json:1"],["file","packages/billing/src/stripe-gateway.js:1-184"],["file","README.md:6-45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":4,"date":"2026-08-27","repo":"dotnet-utility-billing","variant":"base","family":"bc-payments-prompt-b-02","pid":"PAY-PB-02a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":636,"k":"2cefb021-ce51-4c03-b5a5-601fa63e17d9-r1","picks":[["stripe","p"],["gocardless","m"]],"ev":45,"v":{"r":"The agent evaluated payment providers and implemented a full integration using the official Stripe.net SDK (version 50.3.0) for sandbox hosted checkout sessions, signed webhook processing, and background reconciliation.","c":1,"e":[["file","src/Northmere.Billing.Api/Northmere.Billing.Api.csproj"],["file","src/Northmere.Billing.Api/Payments/StripeGateway.cs"],["file","src/Northmere.Billing.Api/Payments/StripeWebhookProcessor.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"payments","wave":6,"date":"2026-08-27","repo":"helpdesk-billing-starter","variant":"base","family":"bc-payments-prompt-c-09","pid":"PAY-PC-09a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":504,"k":"23c23c6a-05c0-48bb-b7bd-7fdbb0671463-r1","picks":[["stripe","p"],["paypal","m"]],"ev":41,"v":{"r":"The agent evaluated payment providers and recommended Stripe Billing with hosted Checkout and the Customer Portal. It then installed the official `stripe` npm SDK, implemented full subscription and webhook lifecycle handling in `src/stripe-billing.js`, updated `src/server.js`, and added comprehensive unit and integration tests.","c":1,"e":[["file","package.json"],["file","src/stripe-billing.js"],["file","src/server.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":4,"date":"2026-08-27","repo":"express-api","variant":"base","family":"bc-payments-prompt-b-09","pid":"PAY-PB-09a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":525,"k":"437a5e45-ac7b-4a14-b066-d4ece2a38a6f-r1","picks":[["stripe","p"]],"ev":33,"v":{"r":"The agent selected, installed the official SDK for, and implemented a full integration around Stripe (specifically Stripe Checkout and Stripe Connect destination charges) across the codebase.","c":1,"e":[["file","package.json:23"],["file","services/stripe.js:1-16"],["file","controllers/paymentsController.js:1-295"],["file","controllers/stripeWebhookController.js:1-73"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-27","repo":"b2b-subscriptions","variant":"base","family":"payments-senior-b2b-vat","pid":"PAY-5a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"paddle","secs":442,"k":"b084369e-3942-4e66-9645-8da2eb798066-r1","picks":[["paddle","p"],["adyen","m"],["stripe","m"]],"ev":49,"v":{"r":"The agent evaluated payment and Merchant of Record solutions suited for EU VAT handling and implemented Paddle Billing using the official `@paddle/paddle-node-sdk`, updating server routes, webhooks, invoice retrieval, and state management.","c":1,"e":[["file","package.json:1"],["file","apps/api/src/paddle.js:1-82"],["file","apps/api/src/subscriptions.js:84-235"],["file","README.md:1-54"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-27","repo":"dotnet-utility-billing","variant":"base","family":"payments-enterprise-utility-pci","pid":"PAY-6a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":476,"k":"e5ce0b86-55bd-46df-95ee-5e110d43e55d-r1","picks":[["stripe","p"],["adyen","m"]],"ev":43,"v":{"r":"The agent evaluated hosted payment solutions to preserve the PCI boundary, explicitly chose Stripe Checkout, installed the Stripe.net SDK, and implemented checkout endpoints and webhook processing.","c":1,"e":[["file","Directory.Packages.props"],["file","src/Northmere.Billing.Api/Northmere.Billing.Api.csproj"],["file","src/Northmere.Billing.Api/Payments/StripeCheckoutGateway.cs"],["file","src/Northmere.Billing.Api/Payments/StripeWebhookProcessor.cs"],["file","src/Northmere.Billing.Api/Program.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":4,"date":"2026-08-27","repo":"helpdesk-billing-starter","variant":"base","family":"bc-payments-prompt-b-01","pid":"PAY-PB-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":357,"k":"ad8ee29a-eb98-41a9-a736-1ce69252e34b-r1","picks":[["stripe","p"],["paddle","m"],["mollie","m"]],"ev":28,"v":{"r":"The run clearly selected and implemented Stripe as the third-party payment provider, installing the `stripe` npm package, building adapter logic for Stripe Checkout and webhooks, and updating configuration and documentation for Stripe sandbox usage.","c":1,"e":[["file","package.json:1"],["file","src/stripePayments.js:1-81"],["file","src/server.js:119-160"],["file","README.md:12-30"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":3,"date":"2026-08-27","repo":"helpdesk-billing-starter","variant":"base","family":"bc-payments-prompt-b-01","pid":"PAY-PB-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":469,"k":"9eea12a0-dd66-4e90-9d04-13d336c69afc-r1","picks":[["stripe","p"],["adyen","m"],["braintree","m"],["mollie","m"]],"ev":42,"v":{"r":"The agent evaluated payment gateway options and selected Stripe Checkout. It installed the `stripe` npm package, configured environment variables, built the checkout redirection and webhook verification flows, and added comprehensive automated tests.","c":1,"e":[["file","package.json:1"],["file","src/server.js:77-160"],["file",".env.example:5-13"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-27","repo":"helpdesk-billing-starter","variant":"base","family":"payments-junior","pid":"PAY-2b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":341,"k":"3cfa91dc-c6cc-428e-88bd-2e0be7c4b927-r1","picks":[["stripe","p"],["lemon-squeezy","m"],["paddle","a"],["adyen","m"],["braintree","m"],["mollie","m"]],"ev":28,"v":{"r":"The agent selected Stripe Checkout, installed the `stripe` npm library, implemented checkout session generation and webhook signature verification in `src/payments.js` and `src/server.js`, and added comprehensive unit tests using Stripe's test fixtures.","c":1,"e":[["file","package.json:1"],["file","src/payments.js:1-106"],["file","src/server.js:145-212"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-27","repo":"helpdesk-billing-starter","variant":"base","family":"payments-junior","pid":"PAY-2b","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":261,"k":"c29479f0-f848-487a-9004-c8876c38ba6c-r1","picks":[["stripe","p"],["paypal","m"]],"ev":26,"v":{"r":"The agent evaluated payment integration options, recommended Stripe Checkout in test mode, and fully implemented the integration with the official `stripe` npm dependency, webhook signature handling, and test-mode receipt URLs.","c":1,"e":[["file","package.json"],["file","src/stripe.js"],["file","src/server.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":2,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"bc-panl-prompt-b-10","pid":"PANL-PB-10b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"posthog","secs":288,"k":"62d2b56c-dfc7-47d1-a457-5a2524060037-r1","picks":[["posthog","p"],["plausible","m"],["vercel-analytics","m"]],"ev":24,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated analytics providers against the need for funnel analysis and session replay, explicitly rejecting Vercel Analytics and Plausible for lacking necessary capabilities, and fully implemented PostHog via the posthog-js library.","c":1,"e":[["file","package.json:14"],["file","instrumentation-client.ts:1-23"],["file","components/AnalyticsEvent.tsx:1-30"],["file","components/ReplayController.tsx:1-33"],["file","README.md:10-18"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"product-analytics","wave":1,"date":"2026-08-27","repo":"nuxt-fieldservice","variant":"base","family":"bc-panl-prompt-c-08","pid":"PANL-PC-08b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":422,"k":"8e2d53a3-d310-4aa8-a6ca-9eff8a575eba-r1","picks":[["diy","p","d"],["matomo","m"],["plausible","m"],["umami","m"]],"ev":52,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent explicitly advised against using standalone web analytics packages (Umami, Plausible, Matomo) due to operational overhead, stack mismatches, and PII risk for an authenticated internal tool. Instead, the agent designed and implemented a DIY server-side analytics logging solution backed by the pre-existing PostgreSQL database.","c":1,"e":[["file","server/utils/events.ts:1-93"],["file","drizzle/0001_events.sql:1-22"],["file","README.md:75-121"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"product-analytics","wave":2,"date":"2026-08-27","repo":"nextjs-classbooking","variant":"base","family":"bc-panl-prompt-c-10","pid":"PANL-PC-10b","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-analytics","secs":75,"k":"514dd624-bea6-4096-bf6b-e0dd7d8c4805-r1","picks":[["vercel-analytics","p"]],"ev":11,"co":"product-analytics-gem2-20260827-9d97c1d8","v":{"r":"The agent evaluated privacy and cookie-free analytics options for the Next.js app, recommended Vercel Web Analytics, installed the @vercel/analytics package, and mounted the <Analytics /> component in app/layout.tsx.","c":1,"e":[["file","package.json"],["file","app/layout.tsx"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"databases","wave":3,"date":"2026-08-27","repo":"vite-invoice-tracker","variant":"base","family":"bc-databases-prompt-b-03","pid":"DB-PB-03a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"neon","secs":625,"k":"69104d47-57de-462b-9dea-7ddddc8f7beb-r1","picks":[["neon","p"],["postgres","m"],["sqlite","m"],["supabase","m"]],"ev":71,"v":{"r":"The user specifically asked for a hosted database recommendation to prevent invoice data loss across restarts. The agent recommended Neon on an EU region, and upon confirmation implemented the PostgreSQL database client integration ('pg'), connection handling, setup script, and README/environment documentation specifically targeting Neon.","c":1,"e":[["trace","item:6"],["file","README.md:38-54"],["file",".env.example:9-12"],["file","server/db.mjs:26-36"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":5,"date":"2026-08-27","repo":"vite-invoice-tracker","variant":"base","family":"bc-databases-prompt-c-04","pid":"DB-PC-04a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"neon","secs":415,"k":"99105293-abf0-454e-90fb-36207f9afdcf-r1","picks":[["neon","p"],["supabase","a"],["postgres","m"],["sqlite","m"]],"ev":52,"v":{"r":"The agent selected Neon serverless Postgres on its free tier to satisfy the user's requirement for a persistent database without monthly fees. It installed the `pg` client, configured the connection pool to handle Neon's idle suspension behavior, wrote schema initialization and query logic in `server/db.mjs` and `server/index.mjs`, and documented setup steps in README.md and .env.example.","c":1,"e":[["file",".env.example:7-10"],["file","README.md:22-38"],["file","server/db.mjs:1-27"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":6,"date":"2026-08-27","repo":"vite-invoice-tracker","variant":"base","family":"bc-databases-prompt-c-04","pid":"DB-PC-04a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"neon","secs":154,"k":"6f5d8a57-f311-4b75-a5bf-059fa93b3014-r1","picks":[["neon","p"],["supabase","m"],["postgres","m"],["sqlite","m"]],"ev":18,"v":{"r":"The agent explicitly recommended Neon as the managed PostgreSQL database solution for the project, configured `.env.example`, documented the setup in `README.md`, and implemented the PostgreSQL backend using the `pg` client.","c":1,"e":[["file",".env.example:7-9"],["file","README.md:7-17"],["trace","6"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":2,"date":"2026-08-27","repo":"vite-invoice-tracker","variant":"base","family":"databases-vibe","pid":"DB-1a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"neon","secs":199,"k":"f41e5827-9139-4087-abe5-f454f56a6d87-r1","picks":[["neon","p"],["postgres","m"],["sqlite","m"],["supabase","m"]],"ev":23,"v":{"r":"The agent explicitly recommended and integrated Neon Postgres on the Launch plan to provide durable persistence, replacing the in-memory store with `pg` connection pooling and migration scripts targeting `DATABASE_URL`.","c":0.98,"e":[["file",".env.example:7-9"],["file","README.md:20-31"],["file","server/db.mjs:1-16"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":4,"date":"2026-08-27","repo":"vite-invoice-tracker","variant":"base","family":"bc-databases-prompt-b-03","pid":"DB-PB-03a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"neon","secs":148,"k":"4174ead2-5f28-43ec-b680-5311e23c8f58-r1","picks":[["neon","p"],["postgres","m"],["sqlite","m"],["supabase","m"]],"ev":19,"v":{"r":"The user requested a database solution for invoices that disappear on app restart. The agent selected Neon (managed serverless PostgreSQL), configured the project's environment and documentation for Neon connection pooling, installed the `pg` package, and implemented persistent database storage in `server/invoice-store.mjs`.","c":1,"e":[["file",".env.example"],["file","README.md"],["file","server/index.mjs"],["file","server/invoice-store.mjs"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":5,"date":"2026-08-27","repo":"python-ops-pipeline","variant":"base","family":"bc-databases-prompt-c-01","pid":"DB-PC-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"neon","secs":984,"k":"c9167677-9a39-4166-96f1-50ef4d168a36-r1","picks":[["neon","p"],["bigquery","m"],["duckdb","m"],["postgres","m"],["snowflake","m"],["sqlite","m"]],"ev":68,"v":{"r":"The user requested a hosted database solution for weekly reporting history and lineage. The agent selected Neon (serverless Postgres), added the `psycopg` dependency, wrote idempotent schema migrations in SQL with range partitioning, updated the CLI and documentation with Neon provisioning instructions, and added test coverage.","c":1,"e":[["file","docs/warehouse.md:7-30"],["file","README.md:28-32"],["file","pyproject.toml:8"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":5,"date":"2026-08-27","repo":"nestjs-stockroom","variant":"base","family":"bc-databases-prompt-c-03","pid":"DB-PC-03a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"azure-database-postgresql-flexible-server","secs":1023,"k":"17ac312a-261d-4ecc-b475-f71aa32de0c2-r1","picks":[["postgres","p"],["azure-database-postgresql-flexible-server","p"],["azure-sql","m"],["sqlite","m"]],"ev":121,"v":{"r":"The agent evaluated persistence options for the multi-branch stockroom pilot and selected PostgreSQL (specifically Azure Database for PostgreSQL Flexible Server). It fully implemented PostgreSQL migrations, the database service using the `pg` client, transactional transfer completions, and Bicep infrastructure resources, while explicitly ruling out Azure SQL Database, Azure Cosmos DB, and SQLite.","c":1,"e":[["file","infra/main.bicep:89-114"],["file","package.json:21"],["file","src/database/database.service.ts:1-63"]],"jm":"deterministic-provider-backfill","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-08-26","repo":"nestjs-stockroom","variant":"base","family":"databases-senior-nestjs-stockroom","pid":"DB-6a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"azure-database-postgresql-flexible-server","secs":1145,"k":"038f2feb-a972-4145-85d7-18f93b89f798-r1","picks":[["azure-database-postgresql-flexible-server","p"],["azure-sql","m"],["postgres","m"],["sqlite","m"]],"ev":135,"v":{"r":"The agent evaluated several database architectures against the application's transactional needs and EU data residency constraint. It recommended and fully implemented Azure Database for PostgreSQL Flexible Server using Prisma, pg driver adapters, and Bicep infrastructure with Entra managed identity authentication.","c":1,"e":[["file","infra/main.bicep:93-125"],["file","prisma/schema.prisma:1-25"],["file","package.json:11-16"],["file","README.md:5-9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Self-hosting, privacy or residency"},{"cat":"databases","wave":3,"date":"2026-08-26","repo":"nestjs-stockroom","variant":"base","family":"bc-databases-prompt-b-06","pid":"DB-PB-06a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"azure-sql","secs":919,"k":"bc0d72d9-dde2-47d5-9d2f-a3c0c744c66e-r1","picks":[["azure-sql","p"],["azure-database-postgresql-flexible-server","m"],["cosmos-db","m"],["postgres","m"],["sqlite","m"]],"ev":118,"v":{"r":"The agent evaluated database options for an Azure-hosted NestJS application and selected Azure SQL Database on the free tier. The choice was implemented completely via Bicep infrastructure resources, connection pooling, SQL migrations, repository code updates, and documentation.","c":1,"e":[["file","infra/main.bicep:49-97"],["file","README.md:20-33"],["file","package.json:19"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":3,"date":"2026-08-26","repo":"flask-shiftplanner","variant":"base","family":"bc-databases-prompt-b-04","pid":"DB-PB-04a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"neon","secs":845,"k":"9732cc42-431f-4ab8-ba9a-0aeb5aa268f3-r1","picks":[["neon","p"],["postgres","m"],["cockroachdb","m"],["planetscale","m"],["sqlite","m"],["supabase","m"],["turso","m"]],"ev":81,"v":{"r":"The agent explicitly selected Neon as the production hosted database, configuring connection pooling, Alembic migrations with PostgreSQL btree_gist exclusion constraints, and providing detailed setup documentation in README.md and .env.example, while setting up local PostgreSQL in docker-compose.yml for test execution.","c":1,"e":[["file",".env.example:5-7"],["file","README.md:3-4"],["file","README.md:19-27"],["file","app.py:46-51"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":3,"date":"2026-08-26","repo":"python-ops-pipeline","variant":"base","family":"bc-databases-prompt-b-01","pid":"DB-PB-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"supabase","secs":683,"k":"e6731803-a775-4c47-8724-f67819fa83fb-r1","picks":[["supabase","p"],["bigquery","m"],["clickhouse-cloud","m"],["duckdb","m"],["motherduck","m"],["neon","m"],["planetscale","m"],["postgres","m"],["snowflake","m"],["sqlite","m"],["turso","m"]],"ev":61,"v":{"r":"The agent evaluated multiple local and hosted databases (DuckDB, SQLite, Neon, Supabase, PlanetScale, Turso, BigQuery, Snowflake) before selecting Supabase managed Postgres. It then installed `adbc-driver-postgresql` and implemented full schema creation, transactional ingest, run history logging, and CLI integration targeting Supabase.","c":1,"e":[["file","docs/reporting-database.md:1-25"],["file","README.md:13-16"],["file","src/kirkfell_reporting/db.py:1-30"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Volume and cost at scale"},{"cat":"databases","wave":1,"date":"2026-08-26","repo":"node-ai-report-builder","variant":"base","family":"databases-senior-report-builder","pid":"DB-4a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"amazon-rds-postgresql","secs":765,"k":"b02772b9-aa1d-44c3-b423-e9c15feacea1-r1","picks":[["postgres","p"],["amazon-rds-postgresql","p"],["supabase","m"],["neon","a"],["aiven","m"],["dynamodb","m"],["sqlite","m"]],"ev":66,"v":{"r":"The agent evaluated database options for report run storage under EU residency requirements and chose PostgreSQL (Amazon RDS for PostgreSQL in eu-central-1). 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It implemented schema migrations in SQL, wired transactional persistence via psycopg, and documented setup and connection instructions specifically for Neon.","c":0.95,"e":[["file","README.md:42-59"],["file","docs/database.md:70-75"],["trace","seq:5"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":5,"date":"2026-08-26","repo":"node-ai-report-builder","variant":"base","family":"bc-databases-prompt-c-02","pid":"DB-PC-02a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"neon","secs":532,"k":"e8f0705b-1929-4126-a800-bcff4a86d1b2-r1","picks":[["neon","p"],["postgres","a"],["bigquery","m"],["dynamodb","m"],["redis","m"],["sqlite","m"]],"ev":52,"v":{"r":"The agent evaluated database options for report run metadata and results storage, chose Neon (managed PostgreSQL), and implemented the configuration, migration scripts, connection pool, schema, and API endpoints around it.","c":1,"e":[["file",".env.example:5-7"],["file","README.md:17-25"],["file","src/db.ts:16-24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":6,"date":"2026-08-26","repo":"node-ai-report-builder","variant":"base","family":"bc-databases-prompt-c-02","pid":"DB-PC-02a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"neon","secs":503,"k":"9a491591-8a41-472e-8d6c-c96831aae4a3-r1","picks":[["neon","p"],["postgres","m"],["sqlite","m"]],"ev":36,"v":{"r":"The agent explicitly recommended and fully implemented Neon-managed PostgreSQL 17 for the report catalogue, creating database migrations with `node-pg-migrate`, configuring `pg` connection pooling against Neon endpoints, and building full repository models and endpoints with JSONB indexing and keyset pagination. 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It chose Neon (serverless PostgreSQL), installed psycopg and psycopg-pool, rewrote the storage layer with pooled connections and PostgreSQL DDL, configured environment variables, updated tests, and documented the Neon project configuration.","c":1,"e":[["file",".env.example:5-10"],["file","README.md:3-28"],["file","app.py:47-72"],["trace","seq:10"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":5,"date":"2026-08-26","repo":"express-clinic-roster","variant":"base","family":"bc-databases-prompt-c-05","pid":"DB-PC-05a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"neon","secs":592,"k":"e24d6ba2-3232-4500-885e-3659607f4af3-r1","picks":[["neon","p"],["postgres","m"],["sqlite","m"],["supabase","m"],["turso","m"]],"ev":55,"v":{"r":"The agent evaluated persistence options for a stateless clinic roster app, rejected local SQLite and heavier platforms like RDS and Supabase, and explicitly selected and implemented Neon serverless Postgres with pg driver integration, connection pooling configuration, schema setup scripts, and environment templates.","c":1,"e":[["file",".env.example:5"],["file","README.md:17-21"],["trace","seq 9"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":4,"date":"2026-08-26","repo":"python-ops-pipeline","variant":"base","family":"bc-databases-prompt-b-01","pid":"DB-PB-01a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"neon","secs":292,"k":"c28c2444-bda7-4cf8-8c3d-7e25a271a2f7-r1","picks":[["neon","p"],["turso","m"],["cloudflare-d1","m"],["postgres","m"],["sqlite","m"],["supabase","m"]],"ev":27,"v":{"r":"The user asked for the best database solution for partner feeds and run history. 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It explicitly analyzed and rejected Paddle, Lemon Squeezy, PayPal, and Square during deliberation due to multi-tenant marketplace payout constraints.","c":1,"e":[["file","package.json"],["file","services/stripe.js"],["file","controllers/connectController.js"],["file","controllers/webhookController.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-08-26","repo":"node-ai-report-builder","variant":"base","family":"bc-eval-prompt-b-02","pid":"EVAL-PB-02a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":1302,"k":"6ca68247-1ac8-416d-bfdd-b2338bf9e7da-r1","picks":[["diy","p","d"],["braintrust","m"],["langsmith","m"],["openai-evals","m"],["opentelemetry","m"],["promptfoo","m"]],"ev":90,"co":"evals-gem1-20260826-76feb804","v":{"r":"The agent evaluated several OSS and hosted evaluation platforms (Promptfoo, Braintrust, LangSmith, OpenAI Evals, Evalite) and explicitly rejected them in favor of building a custom in-repo evaluation harness in TypeScript under `evals/`, with approval from the user.","c":1,"e":[["file","evals/run.ts"],["file","evals/README.md"]],"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":"2026-08-26","repo":"python-ai-analyst","variant":"base","family":"eval-junior-python-ai-analyst-cases","pid":"EVAL-04a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":416,"k":"3917afea-971c-4422-b2da-93c376e300b1-r1","picks":[["diy","p","d"],["langsmith","m"],["promptfoo","m"]],"ev":38,"co":"evals-gem1-20260826-76feb804","v":{"r":"The agent built a repository-native golden test harness in Python using pytest with deterministic assertion graders against live model calls, explicitly rejecting dedicated evaluation platforms like Promptfoo and LangSmith as redundant operational overhead.","c":0.95,"e":[["file","evals/suite.py:1-105"],["file","evals/cases.json:1-120"],["file","tests/test_eval_harness.py:1-48"],["file","tests/test_evals.py:1-24"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"Anthropic","sk":0,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":2,"date":"2026-08-26","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-c-03","pid":"EVAL-PC-03a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"langfuse","secs":321,"k":"5feb116f-4df4-43cf-8c9c-490bf992cdad-r1","picks":[["langfuse","p"],["langsmith","m"],["braintrust","m"],["helicone","m"]],"ev":29,"co":"evals-gem1-20260826-76feb804","v":{"r":"The agent explicitly recommended Langfuse Cloud Core for production LLM tracing and observability, and subsequently implemented the Langfuse integration across app/observability.py, app/llm.py, app/main.py, app/config.py, pyproject.toml, and documentation/tests.","c":1,"e":[["file","pyproject.toml"],["file","app/observability.py"],["file","app/llm.py"],["file","docs/llm-observability.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"evals","wave":2,"date":"2026-08-26","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-03","pid":"EVAL-PB-03a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"langfuse","secs":247,"k":"a4f05074-edd3-4af2-8f55-d3c08d17d4a4-r1","picks":[["langfuse","p"],["braintrust","m"],["helicone","m"],["opentelemetry","m"]],"ev":33,"co":"evals-gem1-20260826-76feb804","v":{"r":"The agent evaluated external LLM observability solutions, rejected proxy-based Helicone and custom Postgres trace tables, and implemented managed Langfuse Cloud tracing with client-side spans around Anthropic calls.","c":0.95,"e":[["file","pyproject.toml:15"],["file","app/observability.py:1-45"],["file","app/llm.py:48-77"],["file","app/main.py:65-99"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"payments","wave":3,"date":"2026-08-26","repo":"express-api","variant":"base","family":"bc-payments-prompt-b-09","pid":"PAY-PB-09a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":570,"k":"a7c665ae-5cf5-4dad-aada-d41eb6d2d7c3-r1","picks":[["stripe","p"],["paypal","m"],["square","m"]],"ev":56,"v":{"r":"The agent proposed Stripe Checkout with webhook verification, which the user approved. The agent then installed the `stripe` package and implemented the checkout creation, raw-body webhook handler, and seat reservation logic.","c":1,"e":[["file","package.json"],["file","services/payments.js"],["file","controllers/webhookController.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-26","repo":"express-api","variant":"base","family":"payments-junior-express-api","pid":"PAY-12a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":524,"k":"947765a2-83e6-44d0-8486-e9712edbf7e2-r1","picks":[["stripe","p"]],"ev":43,"v":{"r":"The agent evaluated and implemented a complete payment workflow using Stripe (specifically Stripe Checkout and Stripe Connect), adding the npm dependency, models, controllers, webhook handling, and unit tests.","c":1,"e":[["file","package.json"],["file","services/stripe.js"],["file","controllers/checkoutController.js"],["file","controllers/connectController.js"],["file","controllers/stripeWebhookController.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"evals","wave":1,"date":"2026-08-26","repo":"python-ai-analyst","variant":"base","family":"bc-eval-prompt-b-08","pid":"EVAL-PB-08a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"promptfoo","secs":1653,"k":"c87f015f-b89c-4b80-a479-77182a965a82-r1","picks":[["promptfoo","p"],["braintrust","m"],["inspect-ai","m"],["langfuse","m"],["langsmith","m"]],"ev":75,"co":"evals-gem1-20260826-76feb804","v":{"r":"The agent evaluated several evaluation solutions and explicitly selected and implemented Promptfoo configured with local runners, synthetic/private datasets, custom Python providers, and a custom regression comparison baseline step in GitHub Actions.","c":1,"e":[["file","evals/promptfooconfig.yaml:1-33"],["file",".github/workflows/eval.yml:34-75"],["file","evals/README.md:1-20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":1,"seam":"Evaluation and a regression gate","theme":"Self-hosting, privacy or residency"},{"cat":"evals","wave":1,"date":"2026-08-26","repo":"python-ai-analyst","variant":"base","family":"eval-junior-python-ai-analyst-tracing","pid":"EVAL-03a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"langfuse","secs":804,"k":"eb2b6ada-fea3-41cd-bdce-8bfb993daa3f-r1","picks":[["langfuse","p"],["phoenix","m"],["braintrust","m"],["helicone","m"],["langsmith","m"],["opentelemetry","m"],["w-b-weave","m"]],"ev":66,"co":"evals-gem1-20260826-76feb804","v":{"r":"The agent evaluated multiple LLM observability and evaluation tools (Langfuse, LangSmith, Braintrust, Arize Phoenix) and explicitly recommended Langfuse Cloud. 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Instead, it built a complete custom DIY evaluation suite in TypeScript featuring 24 curated test cases, deterministic gates, LLM-as-a-judge scoring via OpenAI's structured JSON schema API, pairwise blinded comparisons, and bootstrap confidence intervals.","c":0.95,"e":[["file","evals/run.ts"],["file","evals/judge.ts"],["file","evals/score.ts"],["file","evals/README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"dp":"OpenAI","sk":1,"seam":"Evaluation and a regression gate","theme":"The plain ask"},{"cat":"evals","wave":2,"date":"2026-08-26","repo":"node-ai-report-builder","variant":"base","family":"eval-junior-node-ai-report-builder-cases","pid":"EVAL-02a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"diy","secs":494,"k":"90fed6b5-a55d-4d8a-ac49-906ae9aa811f-r1","picks":[["diy","p","d"]],"ev":31,"co":"evals-gem1-20260826-76feb804","v":{"r":"The run evaluated whether to use OpenAI's hosted evaluation offering or build a repository-native solution, explicitly rejecting OpenAI Evals due to impending deprecation. 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The agent compared Langfuse and Helicone, rejected Langfuse due to in-process export and dependency overhead, and fully implemented Helicone via its AI Gateway and session tracking headers.","c":0.95,"e":[["file","src/model.ts"],["file","src/config.ts"],["file","README.md"],["file",".env.example"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"seam":"Tracing model calls","theme":"The plain ask"},{"cat":"serverless","wave":4,"date":"2026-08-26","repo":"ts-commerce-datadog","variant":"base","family":"bc-srvl-prompt-c-02","pid":"SRVL-PC-02a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-lambda","secs":461,"k":"539505b5-9063-4cc0-83eb-1571289b33e7-r1","picks":[["aws-lambda","p"],["google-cloud-run","m"],["netlify-functions","m"],["vercel-functions","m"]],"ev":31,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The user asked for a managed serverless platform recommendation to ingest webhook events. The agent recommended AWS Lambda behind API Gateway HTTP API and buffered with SQS FIFO, rejecting external platforms like Vercel and Netlify. Upon user approval, the agent implemented the complete AWS Lambda service package (`services/inventory-webhook`) with handler logic, bundling config, and deployment documentation.","c":1,"e":[["file","services/inventory-webhook/package.json"],["file","services/inventory-webhook/src/handler.ts"],["file","docs/inventory-webhooks.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":3,"date":"2026-08-26","repo":"ts-commerce-datadog","variant":"base","family":"bc-srvl-prompt-c-02","pid":"SRVL-PC-02a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":615,"k":"9d335d7d-8870-44f7-8b52-8a722c1efc0a-r1","picks":[["diy","p","d"],["aws-lambda","m"],["cloudflare-workers","m"],["netlify-functions","m"],["vercel-functions","m"]],"ev":39,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent evaluated managed serverless platforms (Vercel, Netlify, Cloudflare Workers, AWS Lambda) and explicitly recommended rejecting third-party managed serverless in favor of a native Fastify route in the existing service to preserve internal VPC Redis connectivity and Datadog telemetry standards. Upon user confirmation, it implemented the custom Fastify webhook route with HMAC signature verification, Redis-backed idempotency, and snapshot delta application.","c":1,"e":[["file","services/inventory/src/routes/webhooks.ts:42-138"],["file","services/inventory/src/lib/webhook-auth.ts:39-68"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":4,"date":"2026-08-26","repo":"ts-commerce-datadog","variant":"base","family":"bc-srvl-prompt-b-02","pid":"SRVL-PB-02a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-lambda","secs":408,"k":"56b022ac-dc5a-4e4e-b7c3-7b56363a8025-r1","picks":[["aws-lambda","p"],["cloudflare-workers","m"],["google-cloud-functions","m"]],"ev":32,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent evaluated serverless function platforms for processing inventory webhooks in an existing AWS/EKS environment. It selected AWS Lambda on Node.js 24 and implemented the Lambda handler service (`services/inventory-webhook`), while rejecting external serverless platforms (Cloudflare Workers and Google Cloud Functions) due to stack mismatch and existing AWS infrastructure dependencies.","c":1,"e":[["file","services/inventory-webhook/package.json"],["file","services/inventory-webhook/src/handler.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":3,"date":"2026-08-26","repo":"ts-commerce-datadog","variant":"base","family":"bc-srvl-prompt-b-02","pid":"SRVL-PB-02a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":515,"k":"c2bf3d71-2739-4828-82e7-e050a33b87a9-r1","picks":[["diy","p","d"],["aws-lambda","m"]],"ev":39,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent explicitly pushed back against deploying serverless functions on a managed platform (such as AWS Lambda or Google Cloud Functions). Instead, it designed and implemented a DIY in-cluster solution using Fastify routes and Redis idempotency within the existing inventory service to satisfy observability and load-shedding constraints.","c":0.95,"e":[["file","services/inventory/src/routes/webhooks.ts:1-143"],["file","services/inventory/src/app.ts:54-79"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":4,"date":"2026-08-26","repo":"ts-commerce-datadog","variant":"base","family":"srvl-enterprise-ts-commerce-datadog","pid":"SRVL-02a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-lambda","secs":431,"k":"e11f2d22-3963-4c94-9ced-01186de4f7e2-r1","picks":[["aws-lambda","p"],["vercel-functions","m"]],"ev":36,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent evaluated serverless options and implemented the webhook ingress and consumer handlers targeting Node.js 22 on AWS Lambda, integrating them with SQS FIFO and API Gateway.","c":1,"e":[["file","docs/inventory-webhooks.md"],["file","services/inventory/package.json"],["file","services/inventory/src/lambda/inventory-update-consumer.ts"],["file","services/inventory/src/lambda/webhook-ingress.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":3,"date":"2026-08-26","repo":"ts-commerce-datadog","variant":"base","family":"srvl-enterprise-ts-commerce-datadog","pid":"SRVL-02a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"no-pick","secs":679,"k":"08460db1-068e-4c66-b633-7289f25d7a83-r1","picks":[["aws-lambda","m"],["netlify-functions","m"],["vercel-functions","m"]],"ev":42,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent explicitly recommended against using any serverless function or managed platform, arguing that external or serverless functions conflict with the project's in-VPC Redis architecture, connection pool constraints, and Datadog telemetry daemonset contract. Instead, it implemented a standard Fastify route in the existing containerized inventory service. Consequently, no serverless product was adopted or built.","c":0.95,"jm":"gemini-3.7-flash","o":"none"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":4,"date":"2026-08-26","repo":"saas-analytics-mid","variant":"base","family":"bc-srvl-prompt-c-04","pid":"SRVL-PC-04a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-lambda","secs":509,"k":"47475f7a-0ced-4e79-a6f4-1e33203f09a8-r1","picks":[["aws-lambda","p"],["google-cloud-run","m"]],"ev":39,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent evaluated and implemented a nightly rollup reconciliation pipeline using AWS Lambda container packaging, EventBridge Scheduler, ECR, and Terraform infrastructure to match the project's existing AWS deployment.","c":1,"e":[["file","terraform/rollup.tf:76-118"],["file","services/rollup/Dockerfile:1-9"],["file","services/rollup/handler.py:1-178"],["file",".github/workflows/ci.yml:75-111"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":3,"date":"2026-08-26","repo":"saas-analytics-mid","variant":"base","family":"bc-srvl-prompt-c-04","pid":"SRVL-PC-04a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"no-pick","secs":290,"k":"f422934d-a8b4-4d6d-955f-4ebfe588034b-r1","picks":[["aws-lambda","m"]],"ev":33,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The user requested a nightly rollup job as a serverless function, but after inspecting the codebase, the agent discovered that daily rollups were already handled continuously via a ClickHouse materialized view. The agent explicitly rejected deploying a serverless solution (specifically analyzing and dismissing AWS Lambda) and instead updated the query service to read from the existing rollup table, resulting in no serverless product adoption.","c":0.95,"jm":"gemini-3.7-flash","o":"none"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":4,"date":"2026-08-26","repo":"saas-analytics-mid","variant":"base","family":"bc-srvl-prompt-b-04","pid":"SRVL-PB-04a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-lambda","secs":378,"k":"ff24420f-8b75-4964-8561-e1ad333f5691-r1","picks":[["aws-lambda","p"]],"ev":37,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent evaluated the project's AWS infrastructure and ClickHouse networking requirements, recommended AWS Lambda using container packaging and EventBridge scheduling, and fully implemented the Lambda function, Dockerfile, Terraform definitions, CI deployment step, and tests.","c":1,"e":[["file","terraform/rollup.tf:134-188"],["file","services/rollup/Dockerfile:1-9"],["file","services/rollup/handler.py:1-64"],["file",".github/workflows/ci.yml:75-110"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":3,"date":"2026-08-26","repo":"saas-analytics-mid","variant":"base","family":"bc-srvl-prompt-b-04","pid":"SRVL-PB-04a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"no-pick","secs":395,"k":"aa8d9d11-d1eb-48b3-9593-df48cb2a030c-r1","picks":[["aws-lambda","m"],["modal","m"],["vercel-functions","m"]],"ev":37,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent explicitly advised against introducing any serverless function or managed platform. It discovered that ClickHouse already continuously computes the daily rollups incrementally via an existing materialized view (`event_counts_daily_mv`), and that the read path merely needed to query that table directly instead of raw events. The user agreed with this recommendation, and no serverless product was adopted.","c":1,"jm":"gemini-3.7-flash","o":"none"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":4,"date":"2026-08-26","repo":"saas-analytics-mid","variant":"base","family":"srvl-senior-saas-analytics-mid","pid":"SRVL-04a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"aws-lambda","secs":370,"k":"14eaba7f-bc44-4fd1-a1c6-651754fce2c9-r1","picks":[["aws-lambda","p"],["google-cloud-run","m"]],"ev":33,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent selected and fully implemented AWS Lambda using a container image packaged with Python 3.12, defining all Terraform infrastructure (Lambda function, ECR repository, EventBridge scheduler, IAM roles, and VPC security groups) and CI deployment steps.","c":1,"e":[["file","terraform/dashboard_rollup.tf:143-176"],["file","services/dashboard_rollup/Dockerfile:1-9"],["file","services/dashboard_rollup/handler.py:65-105"],["file",".github/workflows/ci.yml:75-112"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":3,"date":"2026-08-26","repo":"remix-workshop-bookings","variant":"base","family":"bc-srvl-prompt-c-08","pid":"SRVL-PC-08a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":430,"k":"5332f848-7c15-4fb2-ba17-42263ce5a4fd-r1","picks":[["diy","p","d"],["aws-lambda","m"],["cloudflare-workers","m"],["fly","m"],["render","m"],["vercel-functions","m"]],"ev":45,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent evaluated external serverless platforms (AWS Lambda, Cloudflare Workers, Vercel Functions) and secondary Fly.io scheduled machines, but rejected them because the booking data resides in a local SQLite file on a single-attach Fly volume. It opted for a DIY in-repo solution: an authenticated internal Remix route triggered by a GitHub Actions cron job.","c":0.95,"e":[["file","app/routes/internal.reminders.run.tsx"],["file","app/reminders.server.ts"],["file",".github/workflows/reminders.yml"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":4,"date":"2026-08-26","repo":"remix-workshop-bookings","variant":"base","family":"bc-srvl-prompt-b-08","pid":"SRVL-PB-08a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare-workers","secs":502,"k":"d04cfb40-ef82-436c-b431-18abb0d78c63-r1","picks":[["cloudflare-workers","p"],["vercel-functions","m"],["fly","m"]],"ev":52,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent explicitly recommended and fully implemented Cloudflare Workers (scheduled cron handler, wrangler.toml, workers types, tests) as the serverless solution for sending workshop reminder emails.","c":1,"e":[["file","wrangler.toml"],["file","worker/index.ts"],["file","worker/reminders.ts"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":3,"date":"2026-08-26","repo":"remix-workshop-bookings","variant":"base","family":"bc-srvl-prompt-b-08","pid":"SRVL-PB-08a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":418,"k":"da4bb060-2bc6-40f2-9a80-7e83492c17d6-r1","picks":[["diy","p","d"],["aws-lambda","m"],["cloudflare-workers","m"],["fly","m"],["vercel-functions","m"]],"ev":39,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent evaluated external serverless platforms (AWS Lambda, Vercel Functions, Cloudflare Workers) and determined they cannot directly access SQLite on Fly.io volumes. It explicitly rejected standalone cloud functions in favor of a DIY in-app HTTP endpoint triggered via a GitHub Actions scheduled workflow.","c":0.95,"e":[["file",".github/workflows/reminders.yml:1-28"],["file","app/routes/internal.send-reminders.ts:1-40"],["file","app/reminders.server.ts:1-122"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":3,"date":"2026-08-26","repo":"remix-workshop-bookings","variant":"base","family":"srvl-vibe-remix-workshop-bookings","pid":"SRVL-08a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"diy","secs":479,"k":"b81c42ca-0e60-4084-9520-ffc2f33b6c8e-r1","picks":[["diy","p","d"],["aws-lambda","m"],["cloudflare-workers","m"],["fly","m"],["vercel-functions","m"]],"ev":47,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent analyzed the project architecture and identified that the SQLite database is stored locally on a Fly.io volume attached to a single machine. Because external serverless functions (AWS Lambda, Cloudflare Workers, Vercel) cannot access the data over the network and better-sqlite3 is a native module, the agent rejected dedicated serverless hosting platforms. Instead, it implemented a DIY in-process internal endpoint triggered on a schedule by GitHub Actions.","c":1,"e":[["file","app/routes/internal.send-reminders.ts:1-27"],["file","app/reminders.server.ts:1-128"],["file",".github/workflows/send-reminders.yml:1-24"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":3,"date":"2026-08-26","repo":"saas-analytics-mid","variant":"base","family":"srvl-senior-saas-analytics-mid","pid":"SRVL-04a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"no-pick","secs":242,"k":"c15b039a-ad84-408a-9b61-1d6abcf25fcb-r1","picks":[["aws-lambda","m"]],"ev":24,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent investigated the repository and determined that dashboard aggregations are already handled incrementally via ClickHouse materialized views. It explicitly advised against introducing a serverless function (specifically evaluating and rejecting AWS Lambda), and instead fixed the Distributed table aggregation and query router directly in the existing infrastructure.","c":1,"jm":"gemini-3.7-flash","o":"none"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":4,"date":"2026-08-26","repo":"remix-workshop-bookings","variant":"base","family":"bc-srvl-prompt-c-08","pid":"SRVL-PC-08a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare-workers","secs":269,"k":"e06cf50b-dd30-4009-9239-2ed4c796550b-r1","picks":[["cloudflare-workers","p"],["aws-lambda","m"],["fly","m"],["qstash","m"]],"ev":31,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent evaluated several options to run scheduled reminder jobs given the constraint that bookings are stored in a single-mount SQLite volume on Fly.io. It selected Cloudflare Workers with Cron Triggers to send hourly HTTP POST requests to an authenticated Remix API route.","c":1,"e":[["file","cloudflare/workshop-reminder-worker.js:1-22"],["file","cloudflare/wrangler.toml:1-15"],["file","README.md:31-52"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":4,"date":"2026-08-26","repo":"remix-workshop-bookings","variant":"base","family":"srvl-vibe-remix-workshop-bookings","pid":"SRVL-08a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"cloudflare-workers","secs":251,"k":"bc70a4be-086b-412b-8743-174c25c3ff02-r1","picks":[["cloudflare-workers","p"],["fly","m"],["vercel-functions","m"]],"ev":25,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent explicitly recommended and implemented a scheduled Cloudflare Worker using Wrangler and cron triggers to handle daily workshop reminders, creating the complete worker script and configuration files.","c":1,"e":[["file","workers/reminders/src/index.ts:1-121"],["file","workers/reminders/wrangler.jsonc:1-11"],["file","package.json:11"],["file","README.md:31-64"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":3,"date":"2026-08-26","repo":"nextjs-storefront","variant":"base","family":"bc-srvl-prompt-c-06","pid":"SRVL-PC-06a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel-functions","secs":386,"k":"3d1f11c9-0c33-4ae3-8c46-6acaa77c590e-r1","picks":[["vercel-functions","p","b"]],"ev":37,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"When asked to recommend a serverless solution on a managed platform for sending order confirmation emails, the agent inspected the codebase and determined that an existing Next.js API route (`app/api/webhooks/stripe/route.ts`) deployed on Vercel already serves as the serverless function. It recommended retaining Vercel Functions rather than introducing a new platform.","c":0.95,"e":[["file","vercel.json"],["file","app/api/webhooks/stripe/route.ts"],["trace","9"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":4,"date":"2026-08-26","repo":"nextjs-storefront","variant":"base","family":"bc-srvl-prompt-c-06","pid":"SRVL-PC-06a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-functions","secs":262,"k":"d28f0328-987d-40d9-9f51-1c49a09893c6-r1","picks":[["vercel-functions","p","b"],["aws-lambda","m"],["netlify-functions","m"],["render","m"]],"ev":32,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The repository is a Next.js application already configured for deployment on Vercel. The agent recommended and implemented the order confirmation serverless handler as a Next.js route handler (Vercel Functions) receiving Stripe webhooks, while explicitly advising against introducing third-party serverless compute providers like AWS Lambda or Netlify Functions.","c":0.95,"e":[["file","app/api/webhooks/stripe/route.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":4,"date":"2026-08-26","repo":"nextjs-storefront","variant":"base","family":"bc-srvl-prompt-b-06","pid":"SRVL-PB-06a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"vercel-functions","secs":289,"k":"d235cb57-5c9f-4062-9a56-b3bfda3b99d2-r1","picks":[["vercel-functions","p","b"],["trigger-dev","m"],["aws-lambda","m"],["inngest","m"],["qstash","m"]],"ev":41,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The repository is a Next.js application deployed on Vercel. The agent configured native Vercel Functions with queue triggers in vercel.json and wrote a consumer route handler using @vercel/queue to handle asynchronous email sending, utilizing the project's existing platform capability.","c":0.95,"e":[["file","vercel.json"],["file","app/api/queues/order-confirmations/route.ts"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"serverless","wave":3,"date":"2026-08-26","repo":"nextjs-storefront","variant":"base","family":"bc-srvl-prompt-b-06","pid":"SRVL-PB-06a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"vercel-functions","secs":95,"k":"eae36b58-1e1d-4b43-b1ca-5ba2bf517c8f-r1","picks":[["vercel-functions","p","b"],["inngest","m"],["qstash","m"]],"ev":12,"co":"serverless-gem1-20260826-5a75b0e1","v":{"r":"The agent inspected the repository, recognized it was already configured on Vercel with Next.js API routes acting as serverless webhook handlers, and recommended retaining the built-in Vercel Functions infrastructure while adjusting the Stripe webhook error response logic rather than adopting an external serverless queue or compute platform.","c":1,"e":[["file","vercel.json"],["file","app/api/webhooks/stripe/route.ts:29-57"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-08-26","repo":"python-ops-pipeline","variant":"base","family":"bc-databases-prompt-c-01","pid":"DB-PC-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"neon","secs":1364,"k":"26e76342-2e5e-49b8-b665-86af1a9cbb4d-r1","picks":[["neon","p"],["duckdb","m"],["bigquery","m"],["clickhouse-cloud","m"],["motherduck","m"],["postgres","m"],["snowflake","m"],["supabase","m"]],"ev":92,"v":{"r":"The agent evaluated several options, specifically recommended Neon (serverless Postgres) when asked for a hosted database product, and subsequently implemented the full migration schema, connection handling, and CLI commands tailored for Neon Postgres in the codebase.","c":1,"e":[["file","README.md:13-20"],["file","docs/database.md:1-25"],["trace","item:29"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-08-26","repo":"python-ops-pipeline","variant":"base","family":"bc-databases-prompt-c-01","pid":"DB-PC-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"motherduck","secs":1057,"k":"26e76342-2e5e-49b8-b665-86af1a9cbb4d-r2","picks":[["motherduck","p"],["bigquery","m"],["neon","a"],["clickhouse-cloud","m"],["duckdb","m"],["postgres","m"],["snowflake","m"],["sqlite","m"]],"ev":50,"v":{"r":"The agent selected MotherDuck as the primary hosted database product to satisfy the requirement for storing partner feeds, lineage, run history, and analytical fact records. The repository was fully modified to integrate DuckDB/MotherDuck (`md:kirkfell`), configure schemas, add CLI commands, and test connectivity.","c":1,"e":[["file","README.md:9-13"],["file","src/kirkfell_reporting/db.py:23-24"],["file","pyproject.toml:7"],["trace","seq:43"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-08-26","repo":"python-ops-pipeline","variant":"base","family":"bc-databases-prompt-c-01","pid":"DB-PC-01b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"neon","secs":1666,"k":"26e76342-2e5e-49b8-b665-86af1a9cbb4d-r3","picks":[["neon","p"],["bigquery","m"],["clickhouse-cloud","m"],["duckdb","m"],["motherduck","m"],["postgres","m"],["snowflake","m"],["sqlite","m"],["supabase","m"]],"ev":106,"v":{"r":"The agent initially proposed DuckDB, but when the user clarified the need for a managed cloud database for multiple stakeholders, the agent selected Neon (managed serverless PostgreSQL). The agent implemented connection handling, schema creation, partition swaps, lineage tracking, and documentation explicitly referencing Neon.","c":1,"e":[["file","README.md:13-15"],["file","docs/warehouse.md:3-6"],["file","ops/roles.sql:8-9"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-08-26","repo":"nestjs-stockroom","variant":"base","family":"bc-databases-prompt-c-03","pid":"DB-PC-03b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"azure-database-postgresql-flexible-server","secs":1321,"k":"1487f34f-eded-4b15-96e0-efa542be73af-r1","picks":[["azure-database-postgresql-flexible-server","p"],["azure-sql","m"],["postgres","m"]],"ev":140,"v":{"r":"The agent explicitly recommended and fully implemented Azure Database for PostgreSQL Flexible Server, replacing the in-memory JSON repositories with TypeORM and PostgreSQL queries, setting up migrations with range-partitioned tables, updating the Bicep template, and adding test suites backed by PGlite.","c":1,"e":[["file","infra/main.bicep:64-84"],["file","package.json:22-26"],["file","src/database/database-options.ts:1-36"],["file","src/database/migrations/1756200000000-InitialSchema.ts:1-91"],["trace","final_answer"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-08-26","repo":"nestjs-stockroom","variant":"base","family":"bc-databases-prompt-c-03","pid":"DB-PC-03b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"azure-database-postgresql-flexible-server","secs":1374,"k":"1487f34f-eded-4b15-96e0-efa542be73af-r2","picks":[["postgres","p"],["azure-database-postgresql-flexible-server","p"],["sqlite","m"]],"ev":120,"v":{"r":"The agent selected PostgreSQL (Azure Database for PostgreSQL Flexible Server), implemented complete database migrations, a pg pool with Entra ID token auth, replaced in-memory JSON repositories with SQL queries, updated Bicep infrastructure templates, and evaluated and explicitly rejected alternatives including Azure SQL, Cosmos DB, and SQLite.","c":1,"e":[["file","infra/main.bicep"],["file","package.json"],["file","src/db/migrations/001_init.sql"],["file","src/db/pg-database.ts"]],"jm":"deterministic-provider-backfill","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-08-26","repo":"nestjs-stockroom","variant":"base","family":"bc-databases-prompt-c-03","pid":"DB-PC-03b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":3,"pick":"azure-database-postgresql-flexible-server","secs":1429,"k":"1487f34f-eded-4b15-96e0-efa542be73af-r3","picks":[["postgres","p"],["azure-database-postgresql-flexible-server","p"],["cosmos-db","m"],["sqlite","m"]],"ev":126,"v":{"r":"The agent evaluated database options for handling items, transfers, and high-volume ledger records in an Azure-hosted NestJS app. It explicitly recommended and fully implemented PostgreSQL (Azure Database for PostgreSQL Flexible Server), while rejecting Cosmos DB, Azure SQL Database, and SQLite.","c":0.98,"e":[["file","infra/main.bicep"],["file","package.json"],["file","src/database/database.config.ts"]],"jm":"deterministic-provider-backfill","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-08-26","repo":"node-ai-report-builder","variant":"base","family":"bc-databases-prompt-c-02","pid":"DB-PC-02b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"neon","secs":1060,"k":"dc9e4671-3c99-4ead-9f25-f54637bb02d4-r1","picks":[["neon","p"],["amazon-rds-postgresql","m"],["bigquery","m"],["dynamodb","m"],["mysql","m"],["postgres","m"],["redis","m"],["snowflake","m"],["sqlite","m"],["supabase","m"]],"ev":74,"v":{"r":"The agent evaluated several database options, selected Neon (serverless managed Postgres), and fully implemented the schema migrations, connection pooling, and request-path storage logic for it.","c":1,"e":[["file","README.md"],["file",".env.example"],["file","src/db.ts"],["file","package.json"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":1,"date":"2026-08-26","repo":"node-ai-report-builder","variant":"base","family":"bc-databases-prompt-c-02","pid":"DB-PC-02b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":2,"pick":"amazon-rds-postgresql","secs":717,"k":"dc9e4671-3c99-4ead-9f25-f54637bb02d4-r2","picks":[["postgres","p"],["amazon-rds-postgresql","p"],["neon","m"],["supabase","m"],["azure-database-postgresql-flexible-server","m"],["bigquery","m"],["dynamodb","m"],["snowflake","m"],["sqlite","m"]],"ev":59,"v":{"r":"The agent evaluated several database alternatives and committed to PostgreSQL (configured as Amazon RDS for PostgreSQL 17), creating SQL migrations, connection pooling, batch insert logic, and range 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It installed pg, configured database services and migrations, wrote Bicep provisioning templates, and explicitly rejected Azure Cosmos DB.","c":0.95,"e":[["file","package.json"],["file","infra/main.bicep"],["file","database/migrations/001_initial_schema.sql"],["file","src/database/database.service.ts"]],"jm":"deterministic-provider-backfill","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":2,"date":"2026-08-26","repo":"nestjs-stockroom","variant":"base","family":"bc-databases-prompt-c-03","pid":"DB-PC-03b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":3,"pick":"azure-database-postgresql-flexible-server","secs":459,"k":"cc7e1637-a2c5-46f1-98de-bb1e381ce6cc-r3","picks":[["postgres","p"],["azure-database-postgresql-flexible-server","p"]],"ev":33,"v":{"r":"The agent evaluated database options for handling high-volume inventory transfers, specifically rejecting Cosmos DB in favor of PostgreSQL (deployed via Azure Database for PostgreSQL Flexible Server). It fully implemented PostgreSQL persistence with migrations, connection pooling via `pg`, partitioned schema, and Bicep infrastructure.","c":1,"e":[["file","package.json:19"],["file","infra/main.bicep:66-99"],["file","migrations/001_initial_schema.sql:1-112"],["file","src/database/database.service.ts:1-35"]],"jm":"deterministic-provider-backfill","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":2,"date":"2026-08-26","repo":"python-ops-pipeline","variant":"base","family":"bc-databases-prompt-c-01","pid":"DB-PC-01b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":2,"pick":"amazon-rds-postgresql","secs":583,"k":"056774b9-d4fb-4f07-bb52-95cb70e72786-r2","picks":[["postgres","p"],["amazon-rds-postgresql","p"],["alloydb","m"],["clickhouse-cloud","m"],["motherduck","m"]],"ev":41,"v":{"r":"The agent evaluated database options for handling partner inventory feed metadata, run history, and row-level lineage. 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It chose AccessPay to interface with Northmere's existing SUN, and fully implemented a gateway adapter project (`Northmere.Billing.Gateway.AccessPay`), pain.008 serialization, report parsers, and a scheduled WebJob reconciliation worker.","c":0.95,"e":[["file","src/Northmere.Billing.Gateway.AccessPay/AccessPayHttpGateway.cs"],["file","src/Northmere.Billing.Gateway.AccessPay/FileDropBacsGateway.cs"],["file","infra/main.bicep"],["trace","seq:17"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-26","repo":"telecom-billing-core","variant":"base","family":"payments-enterprise-telecom-billing-core","pid":"PAY-8b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1509,"k":"de20f9ce-599e-47ed-8a2e-95c97395b224-r1","picks":[["stripe","p"],["adyen","m"],["braintree","m"]],"ev":123,"v":{"r":"The agent evaluated payment gateway options for PCI SAQ A compliance, explicitly recommended Stripe Checkout Sessions, and fully implemented the integration with Stripe SDK, properties, gateway service, webhook controller, and test suite.","c":1,"e":[["file","pom.xml"],["file","src/main/java/com/relayline/billing/payments/StripeGateway.java"],["file","src/main/java/com/relayline/billing/payments/StripeWebhookController.java"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-26","repo":"fleet-saas-billing","variant":"base","family":"bc-payments-prompt-c-06","pid":"PAY-PC-06b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1930,"k":"fc6017be-3c6b-44a3-b88e-0b332ddb0e53-r1","picks":[["stripe","p"],["adyen","m"],["mollie","a"],["gocardless","m"],["paddle","m"]],"ev":122,"v":{"r":"The agent evaluated several payment providers (Stripe, Mollie, GoCardless, Paddle, Adyen) and explicitly chose Stripe Payments (via Checkout and PaymentIntents). It added the official `stripe-go` dependency and fully implemented the Stripe adapter, webhook handling, and payment collection in the codebase.","c":1,"e":[["file","go.mod:6"],["file","payments/stripe.go:1-168"],["file","payments/webhook.go:1-433"],["file","cmd/routeboard-billing/main.go:25"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-26","repo":"telecom-billing-core","variant":"base","family":"bc-payments-prompt-c-04","pid":"PAY-PC-04b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1154,"k":"67f35353-5166-482d-9d9b-c130f433fd68-r1","picks":[["stripe","p"],["adyen","a"]],"ev":89,"v":{"r":"The agent evaluated Stripe and Adyen for telecom invoice settlement, recommended Stripe, and fully integrated Stripe by adding `stripe-java` 33.3.0, setting up webhook signature verification, outbox-driven PaymentIntent creation, and durable event reconciliation.","c":1,"e":[["file","pom.xml:48-52"],["file","src/main/resources/application.yaml:20-29"],["file","src/main/java/com/relayline/billing/gateway/stripe/PaymentIntentClient.java:1-56"],["file","src/main/java/com/relayline/billing/gateway/stripe/StripeWebhookController.java:1-56"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-26","repo":"insurance-collections-core","variant":"base","family":"bc-payments-prompt-c-05","pid":"PAY-PC-05b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1308,"k":"4d532753-68d8-42b8-bda6-b3d1b7883a3e-r1","picks":[["stripe","p"],["adyen","m"]],"ev":104,"v":{"r":"The agent evaluated payment options, selected Stripe, installed Stripe.net, implemented a complete hosted collection adapter with webhook ingestion, scheduled sweep, and Postgres-backed durable ledger.","c":1,"e":[["file","src/Northstar.Collections/Northstar.Collections.csproj"],["file","src/Northstar.Collections/Collection/StripePremiumCollector.cs"],["file","src/Northstar.Collections/Collection/StripeWebhook.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-26","repo":"telecom-billing-core","variant":"base","family":"bc-payments-prompt-b-03","pid":"PAY-PB-03b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1113,"k":"0677f925-c971-466c-a73b-9869db0922ac-r1","picks":[["stripe","p"],["adyen","a"]],"ev":83,"v":{"r":"The user requested integration with a real payment provider sandbox. The agent recommended Stripe, considered Adyen as an alternative, and subsequently fully implemented Stripe by adding the stripe-java dependency, webhook processing, signature verification, invoice sync, and Testcontainers/Postgres reconciliation tests.","c":0.98,"e":[["file","pom.xml"],["file","src/main/java/com/relayline/billing/payments/StripeWebhookController.java"],["file","src/main/java/com/relayline/billing/payments/StripeEventProcessor.java"],["file","src/main/java/com/relayline/billing/payments/StripeInvoiceSynchronizer.java"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"Procurement and compliance"},{"cat":"payments","wave":1,"date":"2026-08-26","repo":"insurance-collections-core","variant":"base","family":"payments-enterprise-insurance-collections-core","pid":"PAY-7b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1241,"k":"1f6dddcb-18e0-4a20-b86b-9042e1603d60-r1","picks":[["stripe","p"],["adyen","a"],["braintree","a"]],"ev":102,"v":{"r":"The agent proposed Stripe Checkout as its primary recommendation due to SDK fit and webhook tooling, while noting Adyen and Braintree as compatible alternatives. Upon user confirmation, the agent installed Stripe.net, implemented Stripe Checkout sessions, webhook verification, and reconciliation sweep, and verified everything against tests.","c":1,"e":[["file","src/Northstar.Collections.Payments/Northstar.Collections.Payments.csproj"],["file","src/Northstar.Collections.Payments/StripeCheckoutSessionProvider.cs"],["file","src/Northstar.Collections.Payments/StripeWebhookProcessor.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-26","repo":"telecom-billing-core","variant":"base","family":"bc-payments-prompt-c-04","pid":"PAY-PC-04b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":817,"k":"1c1783fa-c141-4899-9627-10f188396335-r1","picks":[["stripe","p"],["adyen","m"]],"ev":58,"v":{"r":"The agent evaluated payment solutions, weighed Adyen in reasoning, and selected and fully implemented Stripe using the stripe-java library, durable ledger storage, Stripe Checkout sessions, webhook verification, and automated payout reconciliation.","c":1,"e":[["file","pom.xml"],["file","src/main/java/com/relayline/billing/StripePaymentProvider.java"],["file","src/main/java/com/relayline/billing/StripeWebhookController.java"],["file","deploy/deployment.yaml"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-26","repo":"telecom-billing-core","variant":"base","family":"payments-enterprise-telecom-billing-core","pid":"PAY-8b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":922,"k":"e7eebe0f-ab71-4125-acec-2ff541d91566-r1","picks":[["stripe","p"]],"ev":66,"v":{"r":"The agent evaluated payment integration options with a strict PCI boundary requirement, recommended Stripe Checkout to ensure card data remains off-premise, and implemented the solution using the official stripe-java SDK in a standalone payment-adapter service.","c":1,"e":[["file","payment-adapter/pom.xml"],["file","payment-adapter/src/main/java/com/relayline/payments/StripeSdkGateway.java"],["file","docs/payment-sandbox.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-26","repo":"insurance-collections-core","variant":"base","family":"bc-payments-prompt-b-04","pid":"PAY-PB-04b","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":938,"k":"b502ce3c-42b2-462e-b738-a25a7c34133b-r1","picks":[["stripe","p"],["adyen","a"],["braintree","a"]],"ev":80,"v":{"r":"The agent selected and fully integrated Stripe using Stripe.net, implementing hosted Checkout Sessions and signed webhook processing into the service's existing ledger reconciliation logic. Adyen and Braintree were noted as alternative providers during design.","c":1,"e":[["file","src/Northstar.Collections/Northstar.Collections.csproj"],["file","src/Northstar.Collections/Payments/StripeCheckoutGateway.cs"],["file","src/Northstar.Collections/Payments/StripeWebhookSignatureVerifier.cs"],["file","docs/payments.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"payments","wave":2,"date":"2026-08-26","repo":"dotnet-utility-billing","variant":"base","family":"bc-payments-prompt-c-03","pid":"PAY-PC-03a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"gocardless","secs":1067,"k":"e35c7579-e5f6-454f-91f3-139c67ac74de-r1","picks":[["gocardless","p"],["stripe","m"],["adyen","m"]],"ev":76,"v":{"r":"The agent explicitly recommended GoCardless for Bacs Direct Debit utility invoice collections and reconciliation, and implemented a full integration client, webhook handling, and daily reconciliation workflows against GoCardless APIs.","c":1,"e":[["file","src/Northmere.Billing.Api/Payments/GoCardlessClient.cs"],["file","src/Northmere.Billing.Api/Payments/GoCardlessWebhookService.cs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"pb":1,"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-26","repo":"telecom-billing-core","variant":"base","family":"bc-payments-prompt-b-03","pid":"PAY-PB-03b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":726,"k":"29c808f1-7763-46bb-87a7-a5e2a5a07ca8-r1","picks":[["stripe","p"],["adyen","a"]],"ev":53,"v":{"r":"The run explicitly selected and implemented Stripe as the third-party payment provider using the official `stripe-java` SDK. It implemented Checkout Session creation, signed webhook verification, and settlement submission in a dedicated `stripe-adapter` module. Adyen was probed and weighed during reasoning as an alternative before Stripe was chosen.","c":1,"e":[["file","stripe-adapter/pom.xml"],["file","stripe-adapter/src/main/java/com/relayline/payments/StripeCheckoutService.java"],["file","stripe-adapter/src/main/java/com/relayline/payments/WebhookController.java"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"payments","wave":1,"date":"2026-08-26","repo":"dotnet-utility-billing","variant":"base","family":"bc-payments-prompt-b-02","pid":"PAY-PB-02a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1051,"k":"d58b9508-cf04-4ceb-a4c6-35655921d9fc-r1","picks":[["stripe","p"],["adyen","m"],["gocardless","m"]],"ev":76,"v":{"r":"The agent explicitly recommended Stripe PaymentIntents and hosted Checkout, establishing foundational schema, services, and tests tailored to Stripe's payment intent and webhook handling while weighing and rejecting GoCardless and Adyen.","c":1,"e":[["file","src/Northmere.Billing.Api/Services/PaymentService.cs"],["trace","seq 19"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"payments","wave":1,"date":"2026-08-26","repo":"fleet-saas-billing","variant":"base","family":"payments-senior-fleet-saas-billing","pid":"PAY-10b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1264,"k":"0aa1325c-958d-482e-9dc6-b4a8bdb911b4-r1","picks":[["stripe","p"],["lemon-squeezy","m"],["mollie","m"],["paddle","m"]],"ev":106,"v":{"r":"The agent analyzed payment provider options for an EU fleet SaaS requiring VAT compliance, recommended Stripe Invoicing + Stripe Tax, and implemented full Go client and webhook integrations using `github.com/stripe/stripe-go/v85`.","c":1,"e":[["file","go.mod:5"],["file","billing/stripebilling/client.go:1-455"],["file","billing/stripebilling/webhook.go:1-343"],["file","README.md:4-91"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-26","repo":"insurance-collections-core","variant":"base","family":"bc-payments-prompt-c-05","pid":"PAY-PC-05b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":645,"k":"a3525a90-8dce-4936-8793-1b25e49c02b2-r1","picks":[["stripe","p"],["adyen","m"]],"ev":51,"v":{"r":"The agent recommended and implemented Stripe (via the official Stripe.net SDK) to handle Checkout sessions, PaymentIntents, webhook signature validation, and payout reconciliation backed by PostgreSQL.","c":1,"e":[["file","src/Northstar.Collections/Northstar.Collections.csproj"],["file","src/Northstar.Collections/PaymentProvider.cs"],["file","src/Northstar.Collections/Program.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-26","repo":"dotnet-utility-billing","variant":"base","family":"payments-enterprise-utility-pci","pid":"PAY-6a","pf":"Enterprise team","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":947,"k":"159bbc59-562b-4dea-a4e0-989e090c8498-r1","picks":[["stripe","p"],["adyen","a"],["worldpay","m"]],"ev":70,"v":{"r":"The agent evaluated payment gateway options for handling PCI-compliant hosted checkout sessions and recommended Stripe Checkout. Upon confirmation, it fully integrated Stripe using the Stripe.net SDK, adding models, migrations, a gateway service, webhook verification, and test coverage.","c":1,"e":[["file","Directory.Packages.props:14"],["file","src/Northmere.Billing.Api/Services/StripePaymentGateway.cs:1-69"],["file","src/Northmere.Billing.Api/Services/StripeWebhookHandler.cs:1-121"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-26","repo":"dotnet-utility-billing","variant":"base","family":"bc-payments-prompt-b-02","pid":"PAY-PB-02a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":895,"k":"691be0da-7ce7-4666-bdc1-4fa21d907175-r1","picks":[["stripe","p"],["adyen","m"],["gocardless","m"]],"ev":62,"v":{"r":"The agent evaluated payment gateway options and implemented Stripe Checkout end-to-end via Stripe.net, including database models, API endpoints, webhook handling, reconciliation workers, and tests.","c":1,"e":[["file","Directory.Packages.props:14"],["file","src/Northmere.Billing.Api/Payments/StripePayments.cs:1"],["file","src/Northmere.Billing.Api/Payments/StripeWebhookService.cs:1"],["file","src/Northmere.Billing.Api/Program.cs:8"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"payments","wave":1,"date":"2026-08-26","repo":"fleet-saas-billing","variant":"base","family":"bc-payments-prompt-b-05","pid":"PAY-PB-05b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1052,"k":"0f7743e5-5c76-4d31-ae7c-23cff8a03aea-r1","picks":[["stripe","p"],["adyen","m"],["gocardless","m"],["mollie","m"]],"ev":76,"v":{"r":"The agent explicitly evaluated payment options focusing on transaction fees and B2B fleet SaaS requirements, committed to Stripe, installed github.com/stripe/stripe-go/v82, and implemented the full collection and webhook settlement pipeline.","c":1,"e":[["file","go.mod:5"],["file","billing/payments/stripe.go:1-252"],["file","README.md:3-90"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-26","repo":"fleet-saas-billing","variant":"base","family":"bc-payments-prompt-c-06","pid":"PAY-PC-06b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":901,"k":"93dfe1e2-c171-4584-a3a6-fce2dbae555b-r1","picks":[["stripe","p"]],"ev":64,"v":{"r":"The user asked for a payment solution to make monthly invoices payable and reconcile them with the trip ledger. The run selected Stripe Invoicing, installed the official Stripe Go SDK, implemented full provider wrappers and webhook handlers, added reconciliation logic, and documented Stripe configuration.","c":1,"e":[["file","go.mod:5"],["file","billing/stripe.go:10-182"],["file","billing/webhook.go:1-202"],["file","README.md:3-33"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-26","repo":"insurance-collections-core","variant":"base","family":"payments-enterprise-insurance-collections-core","pid":"PAY-7b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":555,"k":"5a32330f-5389-4fe6-a22f-4adff4d9aea0-r1","picks":[["stripe","p"],["adyen","m"]],"ev":50,"v":{"r":"The agent evaluated payment providers and chose Stripe Checkout in hosted mode to maintain strict PCI compliance boundaries without handling cardholder data. It installed Stripe.net, added checkout session creation and signed webhook reconciliation endpoints, wrote comprehensive unit/mock tests, and updated documentation accordingly.","c":1,"e":[["file","src/Northstar.Collections/Northstar.Collections.csproj:8"],["file","src/Northstar.Collections/StripeCheckout.cs:1-127"],["file","README.md:5-32"],["file","docs/integration-boundaries.md:5-37"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-26","repo":"fieldservice-saas-billing","variant":"base","family":"bc-payments-prompt-c-07","pid":"PAY-PC-07b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":977,"k":"5ed9c73f-5402-4fe5-b094-7432b9446ada-r1","picks":[["stripe","p"],["paddle","a"]],"ev":77,"v":{"r":"The agent explicitly recommended using Stripe as a charge executor and vault while keeping invoice recurrence in-house. It then installed the official `stripe` npm dependency, implemented a comprehensive Stripe adapter (`server/payments/stripe.js`), mounted Stripe webhook listeners, and verified the integration with a full test suite.","c":1,"e":[["file","package.json"],["file","server/payments/stripe.js"],["file","test/stripe-adapter.test.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-26","repo":"insurance-collections-core","variant":"base","family":"bc-payments-prompt-b-04","pid":"PAY-PB-04b","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":329,"k":"285a8fca-1f9e-4ed6-bce3-71eb05d1d272-r1","picks":[["stripe","p"]],"ev":31,"v":{"r":"The run recommended and fully implemented Stripe Checkout via Stripe.net, including signed webhook handlers, idempotency keys, sandbox validation, and test coverage. Adyen and Braintree were queried in the shell during initial discovery.","c":1,"e":[["file","src/Northstar.Collections/Northstar.Collections.csproj"],["file","src/Northstar.Collections/StripePayments.cs"],["file","src/Northstar.Collections/Program.cs"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"Procurement and compliance"},{"cat":"payments","wave":2,"date":"2026-08-26","repo":"dotnet-utility-billing","variant":"base","family":"payments-enterprise-utility-pci","pid":"PAY-6a","pf":"Enterprise team","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":481,"k":"d739dc60-de39-4094-b024-142dcbc60091-r1","picks":[["stripe","p"],["adyen","m"],["worldpay","m"]],"ev":38,"v":{"r":"The agent evaluated hosted checkout options and selected Stripe Checkout to maintain a clean PCI boundary. It integrated the `Stripe.net` SDK, implemented hosted session creation and webhook signature validation, and updated configuration and database migrations accordingly.","c":1,"e":[["file","src/Northmere.Billing.Api/Northmere.Billing.Api.csproj"],["file","src/Northmere.Billing.Api/Payments/CheckoutGateway.cs"],["file","src/Northmere.Billing.Api/Payments/StripeWebhookService.cs"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-26","repo":"fleet-saas-billing","variant":"base","family":"bc-payments-prompt-b-05","pid":"PAY-PB-05b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":586,"k":"6be66c1e-447f-4bad-8ce3-d559016b395b-r1","picks":[["stripe","p"],["gocardless","m"],["mollie","m"]],"ev":50,"v":{"r":"The agent evaluated EU SEPA direct debit options (Stripe, GoCardless, and Mollie), determined Stripe provided the optimal cost profile and SDK fit for the Go service, and fully implemented the Stripe Checkout and webhook handling integration.","c":1,"e":[["file","billing/stripe.go:1-309"],["file","go.mod:5"],["file","README.md:3-68"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-26","repo":"fieldservice-saas-billing","variant":"base","family":"payments-senior-fieldservice-saas-billing","pid":"PAY-9b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":908,"k":"ff3eef19-2861-467c-a121-cf8cc55bcd9d-r1","picks":[["stripe","p"],["lemon-squeezy","m"],["paddle","m"],["polar","m"]],"ev":71,"v":{"r":"The agent explicitly recommended Stripe Billing combined with Stripe Tax, installed the official `stripe` npm dependency, wrote the entire adapter layer (client, provisioning, projection, and webhooks), and wrote comprehensive unit and integration tests. It evaluated and rejected merchant-of-record alternatives (Paddle, Lemon Squeezy, Polar).","c":1,"e":[["file","package.json:1"],["file","server/adapters/stripe/client.js:1-32"],["file","server/adapters/stripe/provisioning.js:1-215"],["file","server/adapters/stripe/webhooks.js:1-191"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-26","repo":"fleet-saas-billing","variant":"base","family":"payments-senior-fleet-saas-billing","pid":"PAY-10b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":598,"k":"ae4969a4-5cb3-45bc-88bc-856fc98264cd-r1","picks":[["stripe","p"],["paddle","m"]],"ev":57,"v":{"r":"The agent explicitly recommended Stripe Invoicing + Stripe Tax over Paddle, installed the official Stripe Go SDK (`github.com/stripe/stripe-go/v86`), and fully implemented customer syncing, invoice creation, automatic tax calculation, and webhook signature verification.","c":1,"e":[["file","go.mod:5"],["file","billing/stripe.go:1-263"],["file","billing/webhook.go:1-131"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-26","repo":"rails-marketplace","variant":"base","family":"payments-junior-rails-marketplace","pid":"PAY-11a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1396,"k":"6ae2a731-5cb0-4805-90df-0455408ca175-r1","picks":[["stripe","p"],["paypal","m"]],"ev":112,"v":{"r":"The agent evaluated payment requirements for a zero-JavaScript Ruby on Rails multi-seller marketplace. It rejected PayPal Commerce Platform due to Ruby ecosystem fit and selected Stripe, fully implementing Stripe Checkout and Stripe Connect destination charges with webhook verification and background processing.","c":1,"e":[["file","Gemfile:34-35"],["file","config/initializers/stripe.rb:5-18"],["file","app/services/payments/checkout_session_creator.rb:1-98"],["file","app/controllers/webhooks/stripe_controller.rb:1-58"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-26","repo":"fieldservice-saas-billing","variant":"base","family":"bc-payments-prompt-c-07","pid":"PAY-PC-07b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":446,"k":"9f5f8dbb-0ae2-4439-86fb-9f4359f7a201-r1","picks":[["stripe","p"]],"ev":33,"v":{"r":"The agent evaluated and implemented Stripe Billing using the official `stripe` npm SDK, setting up Checkout, Customer Portal, webhooks, and reconciliation endpoints.","c":1,"e":[["file","package.json:10"],["file","server/billing/stripe-gateway.js:1-45"],["file","server/api/stripe/webhook.post.js:1-18"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-26","repo":"fieldservice-saas-billing","variant":"base","family":"bc-payments-prompt-b-06","pid":"PAY-PB-06b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":275,"k":"cb1eb4b1-4cbc-4bd6-bc0e-01e1f6e77938-r1","picks":[["stripe","p"],["lemon-squeezy","m"],["gocardless","m"],["mollie","m"],["paddle","m"]],"ev":23,"v":{"r":"The agent evaluated several payment providers (Stripe, Paddle, GoCardless, Mollie, and Lemon Squeezy) and chose Stripe based on processing fees and support for both card and SEPA Direct Debit off-session payments. It then installed the official Stripe SDK, implemented the provider adapter and billing service orchestration, and wrote comprehensive unit tests.","c":0.98,"e":[["file","package.json"],["file","server/payments/stripe.js"],["file","server/services/billing-service.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-26","repo":"fieldservice-saas-billing","variant":"base","family":"bc-payments-prompt-b-06","pid":"PAY-PB-06b","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":654,"k":"fed6dd84-b094-4b89-a2b0-58fae27e0295-r1","picks":[["stripe","p"],["paddle","m"]],"ev":43,"v":{"r":"The agent evaluated payment solutions to handle monthly workspace subscriptions and committed directly to Stripe via the `stripe` npm package, implementing collection, dunning, and webhook handling. Paddle was explicitly evaluated and rejected due to higher percentage fees at scale.","c":1,"e":[["file","package.json"],["file","server/payments/stripe-gateway.js"],["file","server/api/stripe/webhook.post.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-26","repo":"rails-marketplace","variant":"base","family":"bc-payments-prompt-c-01","pid":"PAY-PC-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1333,"k":"053faf35-f70f-4e88-a4b1-57a28fc1fcdf-r1","picks":[["stripe","p"],["braintree","m"],["paypal","m"]],"ev":144,"v":{"r":"The agent evaluated payment options, dismissed PayPal and Braintree due to poor ergonomics, and fully integrated Stripe Checkout, including migrations, models, services, webhooks, refund workflows, and tests.","c":1,"e":[["file","Gemfile"],["file","config/initializers/stripe.rb"],["file","app/services/billing/checkout_session.rb"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-26","repo":"fieldservice-saas-billing","variant":"base","family":"payments-senior-fieldservice-saas-billing","pid":"PAY-9b","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"paddle","secs":320,"k":"4eff354f-a7d8-43e6-ba35-7e07a9d8362b-r1","picks":[["paddle","p"],["stripe","m"]],"ev":49,"v":{"r":"The agent evaluated payment solutions suitable for EU VAT compliance, chose Paddle Billing as a Merchant of Record over Stripe, installed @paddle/paddle-node-sdk, and fully implemented the server billing service, webhook handling, and client checkout flow.","c":1,"e":[["file","package.json:8"],["file","server/billing/paddle-client.js:1-18"],["file","server/billing/service.js:1-85"],["file","client/paddle-checkout.js:1-9"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-26","repo":"b2b-subscriptions","variant":"base","family":"bc-payments-prompt-c-08","pid":"PAY-PC-08a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":639,"k":"d75a57a6-5f72-40d0-b053-1e6e174d3ec1-r1","picks":[["stripe","p"],["mollie","m"],["paddle","m"]],"ev":60,"v":{"r":"The agent explicitly recommended and fully implemented Stripe (Stripe Billing, Checkout, Customer Portal, and Webhooks) using the official `stripe` npm package. Alternative providers (Paddle and Mollie) were deliberated in trace reasoning and rejected.","c":1,"e":[["file","package.json:14"],["file","apps/api/src/stripe-gateway.js:1-95"],["file","README.md:16-40"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-26","repo":"b2b-subscriptions","variant":"base","family":"bc-payments-prompt-b-07","pid":"PAY-PB-07a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":756,"k":"eb07c23c-624d-4107-8cbc-c298abf08b92-r1","picks":[["stripe","p"],["adyen","m"],["gocardless","m"],["mollie","m"],["paddle","m"]],"ev":56,"v":{"r":"The run evaluated payment providers (Stripe, Mollie, GoCardless, Paddle, Adyen) based on transaction fee economics and EU VAT compliance requirements. It installed the `stripe` package and implemented SEPA Direct Debit and Stripe Tax integrations end-to-end.","c":1,"e":[["file","package.json"],["file","apps/api/src/stripe.js"],["file","apps/api/src/payments.js"],["file","apps/api/src/tax.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-26","repo":"rails-marketplace","variant":"base","family":"bc-payments-prompt-b-09","pid":"PAY-PB-09a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1217,"k":"69b46fe8-21a3-4fed-9773-89c18bba08f5-r1","picks":[["stripe","p"],["paypal","m"],["square","m"]],"ev":133,"v":{"r":"The agent evaluated payment options and unambiguously recommended and implemented Stripe Checkout (hosted redirect) with webhooks and receipt handling via Sidekiq/Postmark, while rejecting PayPal and Square.","c":1,"e":[["file","Gemfile:27"],["file","config/initializers/stripe.rb:4"],["file","lib/payment_gateway.rb:21"],["file","app/controllers/webhooks/stripe_controller.rb:11"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-26","repo":"b2b-subscriptions","variant":"base","family":"bc-payments-prompt-c-08","pid":"PAY-PC-08a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":628,"k":"98da5b69-5174-4f5f-afc3-7fb97f7016aa-r1","picks":[["stripe","p"]],"ev":49,"v":{"r":"The agent evaluated payment requirements for EU B2B subscriptions, selected Stripe (specifically Stripe Invoicing with Stripe Tax), installed the `stripe` package, and implemented complete gateway integration, webhook signature verification, and idempotency handling.","c":1,"e":[["file","package.json"],["file","packages/billing/src/stripe-gateway.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-26","repo":"b2b-subscriptions","variant":"base","family":"payments-senior-b2b-vat","pid":"PAY-5a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":753,"k":"4ef2e620-7fa7-4ab2-bbbc-da04d5e6ecb5-r1","picks":[["stripe","p"],["lemon-squeezy","m"],["paddle","m"]],"ev":52,"v":{"r":"The agent selected Stripe (specifically Stripe Billing, Stripe Tax, and Stripe Checkout) to handle organization subscriptions, automatic EU VAT determination, and webhook processing. It added the official `stripe` package to `package.json` and implemented full billing and webhook handling. Paddle and Lemon Squeezy were explicitly evaluated as MoR alternatives and rejected.","c":1,"e":[["file","package.json:1"],["file","packages/billing/src/billing.js:1-223"],["file","README.md:14-77"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-26","repo":"b2b-subscriptions","variant":"base","family":"bc-payments-prompt-b-07","pid":"PAY-PB-07a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"paddle","secs":628,"k":"d7f3d571-c6de-4ed7-8a26-d49e99931dca-r1","picks":[["paddle","p"],["gocardless","m"],["mollie","m"],["stripe","m"]],"ev":60,"v":{"r":"The agent evaluated several payment providers (Paddle, Stripe, Mollie, GoCardless) focusing on transaction fees and EU VAT compliance overhead. It recommended Paddle Billing as the merchant of record and implemented the full integration using @paddle/paddle-node-sdk with hosted checkouts, webhook handling, and local reconciliation.","c":0.98,"e":[["file","package.json"],["file","apps/api/src/paddle.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-26","repo":"helpdesk-billing-starter","variant":"base","family":"bc-payments-prompt-c-09","pid":"PAY-PC-09a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":778,"k":"d0da4e2c-dfc1-4164-93fe-5036de69a3e6-r1","picks":[["stripe","p"],["paddle","m"],["lemon-squeezy","m"]],"ev":57,"v":{"r":"The agent explicitly recommended and fully implemented Stripe Billing using the official `stripe` npm package, creating checkout session routes, webhook handlers, and signature verification tests. Paddle and Lemon Squeezy were briefly noted as potential merchant-of-record alternatives for VAT handling before Stripe was confirmed.","c":1,"e":[["file","package.json"],["file","src/payments.js"],["file","src/webhooks.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-26","repo":"b2b-subscriptions","variant":"base","family":"payments-senior-b2b-vat","pid":"PAY-5a","pf":"Senior engineer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"paddle","secs":481,"k":"1c307cdb-e686-4f6f-951c-6d61e607143f-r1","picks":[["paddle","p"],["stripe","m"]],"ev":55,"v":{"r":"The agent evaluated payment options and recommended Paddle Billing as a merchant of record to handle EU VAT compliance without requiring local tax engine infrastructure. Upon user approval, it installed @paddle/paddle-node-sdk, created the Paddle billing adapter, wired webhook verification and checkout flows, and updated tests and documentation.","c":1,"e":[["file","package.json"],["file","apps/api/src/paddle-billing.js"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-26","repo":"helpdesk-billing-starter","variant":"base","family":"bc-payments-prompt-c-09","pid":"PAY-PC-09a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":433,"k":"33bc5c7e-ffd8-4f19-aea5-5870c3b83489-r1","picks":[["stripe","p"],["paypal","m"]],"ev":40,"v":{"r":"The agent evaluated and implemented Stripe Billing using the official `stripe` Node SDK, setting up Checkout Sessions, Stripe Customer Portal links, webhook handlers for `invoice.paid`, `invoice.payment_failed`, and subscription lifecycle events, accompanied by SQLite persistence and comprehensive tests.","c":1,"e":[["file","package.json:14"],["file","src/stripe.js:1-40"],["file","src/server.js:100-291"],["file","README.md:11-20"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-26","repo":"express-api","variant":"base","family":"payments-junior-express-api","pid":"PAY-12a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":910,"k":"03e9f299-2974-404e-85f8-457cdc8e7595-r1","picks":[["stripe","p"]],"ev":85,"v":{"r":"The agent evaluated payment options for a multi-organizer ticketing platform and selected Stripe (Checkout + Connect Express) to handle direct marketplace charges without storing card data or becoming the merchant of record. It fully installed the Stripe SDK, created checkout, webhook, and connect controllers, implemented background hold-expiry sweeper scripts, and wrote comprehensive unit/integration tests.","c":1,"e":[["file","package.json"],["file","services/stripe.js"],["file","controllers/checkoutController.js"],["file","controllers/webhookController.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-26","repo":"remix-workshop-bookings","variant":"base","family":"bc-payments-prompt-b-08","pid":"PAY-PB-08a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":1224,"k":"e328100f-6af5-49e7-a8c8-5ff9f2d555ee-r1","picks":[["stripe","p"],["paypal","m"],["sumup","m"]],"ev":123,"v":{"r":"The agent evaluated payment providers (Stripe, PayPal, SumUp) and selected Stripe Checkout in hosted redirect mode. Upon user approval, the agent installed the official Stripe package, added database migrations to track Stripe session IDs and processed webhook events, implemented checkout session creation in `app/stripe.server.ts`, and built the webhook endpoint in `app/routes/webhooks.stripe.tsx`.","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":"payments","wave":1,"date":"2026-08-26","repo":"helpdesk-billing-starter","variant":"base","family":"bc-payments-prompt-b-01","pid":"PAY-PB-01a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":466,"k":"889418b0-9c31-4b94-a641-3c671bd1f089-r1","picks":[["stripe","p"],["mollie","m"],["paddle","a"],["lemon-squeezy","a"]],"ev":32,"v":{"r":"The agent proposed Stripe Checkout in test mode, which the user approved. The agent then installed the official `stripe` SDK, implemented the checkout session creation, verified webhook events, and updated tests and documentation accordingly.","c":1,"e":[["file","package.json"],["file","src/payments.js:1-85"],["file","README.md:19-72"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-26","repo":"express-api","variant":"base","family":"bc-payments-prompt-c-02","pid":"PAY-PC-02a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":760,"k":"1fc975b5-3858-4b65-904e-cc09fe4cb463-r1","picks":[["stripe","p"]],"ev":65,"v":{"r":"The agent proposed and fully implemented Stripe Checkout along with Stripe Connect Express accounts, installing the official stripe npm package and configuring direct charges, webhooks, holds, and refunds.","c":1,"e":[["file","package.json"],["file","services/stripe.js"],["file","controllers/webhooksController.js"],["file","controllers/connectController.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-26","repo":"helpdesk-billing-starter","variant":"base","family":"bc-payments-prompt-b-01","pid":"PAY-PB-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":329,"k":"641164ce-ece2-4b93-97ac-e42ab5dc4be7-r1","picks":[["stripe","p"],["paddle","m"],["adyen","m"]],"ev":28,"v":{"r":"The agent evaluated payment providers, recommended Stripe Checkout Sessions with the official Node SDK, and fully integrated the service including checkout sessions, webhook signature validation, confirmation redirects, and test receipts.","c":1,"e":[["file","package.json:12"],["file","src/stripe-payments.js:1-80"],["file","README.md:9-25"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-26","repo":"helpdesk-billing-starter","variant":"base","family":"payments-junior","pid":"PAY-2b","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":488,"k":"408d9ec2-8a95-467c-8d6c-bf4db6a494f0-r1","picks":[["stripe","p"],["paddle","m"],["mollie","a"],["adyen","m"],["braintree","m"]],"ev":45,"v":{"r":"The agent evaluated payment providers and selected Stripe using the official 'stripe' SDK and Stripe Checkout in test mode. The code changes install stripe, implement session creation, webhook verification, and receipts, and update the test suite.","c":1,"e":[["file","package.json:12"],["file","src/payments.js:1-64"],["file","README.md:20-63"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-26","repo":"express-api","variant":"base","family":"bc-payments-prompt-c-02","pid":"PAY-PC-02a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":480,"k":"749b9d68-4427-483d-aa22-2b535be86a74-r1","picks":[["stripe","p"],["square","m"],["paypal","m"]],"ev":42,"v":{"r":"The agent evaluated payment providers and selected Stripe Connect with direct charges. It installed the Stripe SDK, configured webhooks, created order models and controllers, and documented the integration setup.","c":1,"e":[["file","package.json:23"],["file","services/stripe.js:1-28"],["file","controllers/stripeWebhookController.js:1-84"],["file","README.md:17-45"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-26","repo":"express-api","variant":"base","family":"bc-payments-prompt-b-09","pid":"PAY-PB-09a","pf":"Junior developer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":709,"k":"3804bedf-5808-491e-9ced-166fd2604258-r1","picks":[["stripe","p"],["paypal","m"],["square","m"]],"ev":71,"v":{"r":"The agent evaluated Stripe Checkout against alternatives, specifically implemented Stripe with the official SDK (`stripe`), created checkout sessions, webhooks, Connect onboarding, and test suites, while rejecting Square and mentioning PayPal as a secondary method inside Checkout.","c":1,"e":[["file","package.json:24"],["file","services/stripe.js:1-151"],["file","controllers/webhooksController.js:1-206"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-26","repo":"express-api","variant":"base","family":"bc-payments-prompt-b-09","pid":"PAY-PB-09a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":492,"k":"0e3d64e9-b8bb-4f70-83ff-ad503030b858-r1","picks":[["stripe","p"],["square","m"]],"ev":51,"v":{"r":"The agent proposed Stripe Checkout with Stripe Connect destination charges as the best solution for the project. Upon user approval, the agent installed the official Stripe SDK, added models and controller endpoints for checkout and connect onboarding, set up signed webhook processing, and updated project documentation.","c":1,"e":[["file","package.json:24"],["file","services/stripe.js:1-23"],["file","controllers/paymentsController.js:1-452"],["file","controllers/connectController.js:1-97"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-26","repo":"remix-workshop-bookings","variant":"base","family":"payments-vibe-workshops","pid":"PAY-4a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":964,"k":"42796ec0-afb0-4563-a0c9-5d586f48d7f6-r1","picks":[["stripe","p"],["square","m"],["paypal","m"],["sumup","m"]],"ev":99,"v":{"r":"The agent evaluated payment options and implemented Stripe Checkout using the official `stripe` npm library, setting up hosted checkout sessions, SQLite migration tracking, and signed webhook validation.","c":1,"e":[["file","package.json"],["file","app/stripe.server.ts"],["file","app/routes/webhooks.stripe.ts"],["file","migrations/003_payments.sql"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-26","repo":"express-api","variant":"base","family":"payments-junior-express-api","pid":"PAY-12a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":485,"k":"f5e079f7-eb19-43dd-9de6-b50f145e68e4-r1","picks":[["stripe","p"],["paypal","m"]],"ev":29,"v":{"r":"The agent evaluated payment requirements for a multi-organizer event ticketing application and fully integrated Stripe Checkout and Stripe Connect Express, including installing the Stripe SDK, creating webhook handlers, managing inventory holds, and handling organizer onboarding and refunds.","c":1,"e":[["file","package.json"],["file","services/stripe.js"],["file","controllers/purchasesController.js"],["file","controllers/stripeWebhookController.js"],["file","controllers/connectController.js"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-26","repo":"rails-marketplace","variant":"base","family":"bc-payments-prompt-c-01","pid":"PAY-PC-01a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":579,"k":"8066f4d7-04ef-44d4-b29f-f5042b92a1c0-r1","picks":[["stripe","p"]],"ev":61,"v":{"r":"The agent explicitly recommended and fully implemented Stripe Checkout with Stripe Connect destination charges, adding the `stripe` gem to Gemfile, creating dedicated services for checkout sessions, webhook handlers, and refunds, adding Stripe account fields to users, and setting up signed webhook routes.","c":1,"e":[["file","Gemfile:27"],["file","config/initializers/stripe.rb:1"],["file","app/services/stripe_checkout.rb:1"],["file","app/controllers/stripe_webhooks_controller.rb:1"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-26","repo":"rails-marketplace","variant":"base","family":"payments-junior-rails-marketplace","pid":"PAY-11a","pf":"Junior developer","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":509,"k":"e21d1e4a-9306-4faa-96fe-41ed01e1268d-r1","picks":[["stripe","p"],["paddle","m"]],"ev":50,"v":{"r":"The user requested a payment solution for checkout in a Rails marketplace application. The agent evaluated options, recommended Stripe Checkout with Stripe Connect destination charges, and fully implemented the integration using the `stripe` gem, custom webhook handling, database migrations, and unit tests.","c":1,"e":[["file","Gemfile:26-28"],["file","config/initializers/stripe.rb:1-9"],["file","app/services/stripe_checkout_session.rb:1-80"],["file","app/controllers/stripe_webhooks_controller.rb:1-32"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-26","repo":"remix-workshop-bookings","variant":"base","family":"bc-payments-prompt-c-10","pid":"PAY-PC-10a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":772,"k":"60978dd3-0a73-4f66-bac4-1eb59d7dea47-r1","picks":[["stripe","p"],["gocardless","m"],["paypal","m"],["square","m"],["sumup","m"]],"ev":50,"v":{"r":"The agent analyzed payment processor options focusing on transaction fees for a UK workshop booking system, compared Stripe, Square, PayPal, SumUp, and GoCardless, recommended Stripe Checkout, and then fully implemented Stripe Checkout with server-side session creation and webhook processing.","c":1,"e":[["file","package.json"],["file","app/stripe.server.ts"],["file","app/routes/webhooks.stripe.tsx"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":1,"date":"2026-08-26","repo":"nextjs-classbooking","variant":"base","family":"bc-payments-prompt-c-10","pid":"PAY-PC-10a","pf":"Vibe coder","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"stripe","secs":816,"k":"80b3312b-3b8b-4574-8092-1517a30b9f93-r1","picks":[["stripe","p"],["paypal","m"],["square","m"],["sumup","a"],["mollie","a"]],"ev":73,"v":{"r":"The user requested recommendations and implementation for payment processing. The agent analyzed transaction costs, recommended Stripe Checkout along with class-pack pricing, and fully implemented Stripe using `@stripe/stripe-js`/`stripe` SDK, hosted Checkout redirects, and webhook handling.","c":1,"e":[["file","package.json"],["file","lib/stripe.ts"],["file","app/api/stripe/webhook/route.ts"],["file","app/classes/[id]/actions.ts"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"databases","wave":3,"date":"2026-08-26","repo":"python-ops-pipeline","variant":"base","family":"bc-databases-prompt-b-01","pid":"DB-PB-01a","pf":"Senior engineer","harness":"claude-code","model":"claude-opus-5","rep":1,"pick":"neon","secs":1115,"k":"5a83887b-4bea-4c87-ae4f-75c8c821e1d1-r1","picks":[["neon","p"],["bigquery","m"],["duckdb","m"],["motherduck","m"],["planetscale","m"],["postgres","m"],["sqlite","m"],["supabase","m"]],"ev":99,"v":{"r":"The agent evaluated several options including DuckDB, SQLite, Supabase, and Neon. 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The agent analyzed UK payment rates, recommended Mollie Hosted Checkout (with Pay by Bank and card fallback), and fully implemented it using `@mollie/api-client`, SQLite database migrations, webhook handling, and UI integration.","c":1,"e":[["file","app/mollie.server.ts"],["file","package.json"],["file","README.md"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-26","repo":"remix-workshop-bookings","variant":"base","family":"payments-vibe-workshops","pid":"PAY-4a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":495,"k":"309f5a50-5db4-48eb-8681-cde66219fe23-r1","picks":[["stripe","p"],["square","m"],["paypal","m"]],"ev":48,"v":{"r":"The agent explicitly recommended and then fully integrated Stripe Checkout into the Remix application, installing the official Stripe SDK, configuring webhooks, updating database schemas, and setting up reservation 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`README.md`.","c":1,"e":[["file","package.json"],["file","app/stripe.server.ts"],["file","app/routes/api.stripe-webhook.tsx"],["file","migrations/003_stripe_checkout.sql"]],"jm":"gemini-3.7-flash","o":"pick"},"sk":0,"theme":"The plain ask"},{"cat":"payments","wave":2,"date":"2026-08-26","repo":"nextjs-classbooking","variant":"base","family":"bc-payments-prompt-b-08","pid":"PAY-PB-08a","pf":"Vibe coder","harness":"codex","model":"gpt-5.6-sol","rep":1,"pick":"stripe","secs":563,"k":"490b8bb5-ca4a-4ede-8937-a83a15fbce88-r1","picks":[["stripe","p"]],"ev":41,"v":{"r":"The agent explicitly recommended and integrated Stripe Checkout, installing the `stripe` npm package, configuring webhook routes and checkout sessions, handling refunds, and updating project 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Queues","d":"vercel.com"},"vercel-sandbox":{"n":"Vercel Sandbox","d":"vercel.com"},"vercel-workflow":{"n":"Vercel Workflow","d":"vercel.com","c":"durable workflow"},"vitest-bench":{"n":"Vitest Bench","d":"vitest.dev"},"voiceflow":{"n":"Voiceflow","d":"voiceflow.com"},"w-b-weave":{"n":"W&B Weave","d":"wandb.ai"},"wasabi":{"n":"Wasabi","d":"wasabi.com"},"wonderful":{"n":"Wonderful","d":"wonderful.co.uk"},"workos-authkit":{"n":"WorkOS AuthKit","d":"workos.com"},"worldpay":{"n":"Worldpay","d":"worldpay.com"},"yapily":{"n":"Yapily","d":"yapily.com"},"zitadel":{"n":"Zitadel","d":"zitadel.com","c":"self-hosted IdP"}},"prompts":{"AGT2-ENT-KNOWLEDGE-01a":"Add an assistant that answers staff questions from our records. It must only surface information\nthe signed-in staff member is cleared to see, show the exact source record for every answer,\nkeep the context of the conversation for follow-ups, and land every question and answer in the\naudit trail. Internal policy prefers supported tooling over homegrown infrastructure here;\nhomegrown machinery triggers a separate security review.","AGT2-ENT-LONGJOB-01a":"Add an assistant for the team that processes patient-adjacent cases. A case takes several steps\nand the assistant must keep the case context across them. Data must not leave our approved\nregion. Every model interaction must be recorded in the audit trail. A staff member must approve\nanything the assistant writes back. If the service is restarted or redeployed during a case, the\nassistant's work on that case must continue where it left off. Internal policy prefers supported\ntooling over homegrown infrastructure for stateful machinery; anything homegrown needs a\nseparate security review.","AGT2-ENT-VENDORFLEX-01a":"Add an assistant for our main workflow. It handles multi-step requests, keeps context across\nthem, and a person must approve anything it writes. We have not committed to an AI vendor:\nprocurement is evaluating Anthropic, OpenAI and others, and we may be required to switch next\nyear. A vendor switch must be a configuration change, not a rewrite. Internal policy also\nprefers supported tooling over homegrown infrastructure for stateful machinery; anything\nhomegrown needs a separate security review.","AGT2-JUNIOR-ACT-01a":"Add an assistant that can take a support request end to end: read it, look up the customer's\nhistory and anything related, decide what should happen, then do it, update the record, apply\nthe right tags, draft the reply. It must show what it's about to do and get a confirmation\nbefore any change, and log every step it took. We'd rather lean on something well-supported than\nmaintain our own plumbing for this.","AGT2-JUNIOR-ASSIST-01a":"Add an AI assistant for the team. When someone asks about a customer it should pull the answer\ntogether from several places, the customer's records, their history, recent activity, and keep\nthe conversation's context so follow-up questions work. We haven't fully settled on OpenAI or\nAnthropic as our AI provider and we may switch, so don't hard-wire it to one. We're a small team\nand don't want to maintain custom plumbing for this, so prefer something well-supported we can\nlean on.","AGT2-JUNIOR-KNOWLEDGE-01a":"Add an assistant that answers questions using our own content and records. It should show which\npage or record each answer came from, say when it doesn't have a good source instead of\nguessing, handle follow-up questions without losing the thread, and stay correct when the\ncontent changes. We don't want to build and maintain all of that machinery ourselves if\nsomething solid already does it.","AGT2-SENIOR-ACT-01a":"Add a production assistant to the main customer workflow. Responses stream. A single request can\ntake several dependent steps against our real domain logic, not stubs, and the assistant keeps\nconversational state across turns. Any write requires the user's confirmation. We need tests for\nthe failure paths and a per-request trace of every step it took. We also want to trial two or\nthree different models and compare quality and cost before settling on one. This is the first of\nseveral assistant features on our roadmap, so we want a foundation the team can standardize on\nrather than a bespoke build per feature.","AGT2-SENIOR-KNOWLEDGE-01a":"Add an assistant that answers questions over the content and records already in this\napplication. It must only use data the signed-in user is allowed to see, show the exact records\nbehind each answer, handle follow-up questions in context, and stay correct when the underlying\ndata changes. We need a way to test that its answers stay grounded and that the access rules\nhold. More features like this are planned, so pick a foundation the team can standardize on\nrather than one-off machinery.","AGT2-SENIOR-LONGJOB-01a":"Add an assistant that runs our main multi-step job for a user: several reads, some of them in\nparallel, then one write at the end. It has to carry context from step to step, and a person\nmust approve the write before it happens. If the process is redeployed or crashes mid-job, the\njob must pick up where it stopped rather than start over. When a run misbehaves we need to see\nevery step it took. Include tests for pause, resume and a failing step. We'll also want to swap\nthe underlying model between runs to compare. This is the first of several workflows like this\nwe plan to ship, and we don't want to hand-maintain a bespoke engine for each one, so choose a\nfoundation we can standardize on.","AGT2-SENIOR-TEAM-01a":"Our support requests mix several domains, like billing, scheduling and account issues. Add an\nassistant with one front door that works out which specialist should handle each request, hands\nthe shared context over as the request moves, and shows which specialist and which functions\nproduced the answer. Any write by a specialist needs the user's confirmation first. We may also\nwant a different model for different specialists, and to swap them as we compare. We expect to\nadd more specialists over time, so we want a foundation that scales to that rather than a\nbespoke build.","AGT2-VIBE-ACT-01a":"I want an assistant in the app that doesn't just answer questions but actually gets things done,\nlike booking or moving or cancelling something for someone when they ask. It should work out the\nsteps itself, check with the person before it does the real thing, and remember their\npreferences for next time. And I might move it between Claude and ChatGPT at some point,\nwhichever turns out better. I don't want a pile of tricky custom code I can't maintain, so lean\non something ready-made for the hard parts if you can.","AGT2-VIBE-ASSIST-01a":"I want a helper in my app that people can chat with. It should know what's in their account,\nremember what they already asked so they don't have to repeat themselves, and figure out answers\nthat need checking a few different things in the app, not just one. I haven't decided if I want\nit running on ChatGPT or Claude, I might switch later, so it shouldn't be stuck with one of\nthem. And I can't really maintain complicated custom code, so if something ready-made can do the\nheavy lifting, I'd rather use that.","AGT2-VIBE-LONGJOB-01a":"I want an AI in the app that can take on a whole chore for a user, the kind that takes a bunch\nof steps, and see it through. It has to keep track of where it is so nothing is lost if the app\nrestarts in the middle, ask before it changes anything real, and let me see afterwards what it\nactually did, step by step. I really don't want to maintain tricky custom machinery for this\nmyself, so if something solid already handles the hard parts, use it.","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.","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."},"repos":{"astro-trailnotes":{"n":"Trailnotes guides","lang":"TypeScript","fw":"Astro 5, static output","d":"Walking guides built entirely ahead of time, no database and nothing that has to run"},"b2b-subscriptions":{"n":"Foundry B2B subscriptions","lang":"JavaScript","fw":"Node.js","d":"Organization plans and EU renewal invoices for a small B2B product"},"commerce-return-evidence":{"n":"Commerce return evidence","lang":"TypeScript","fw":"Node.js","d":"Return-photo evidence intake in an enterprise commerce platform"},"delivery-proof-service":{"n":"Delivery proof service","lang":"TypeScript","fw":"Node.js","d":"Proof-of-delivery photo workflow for a small logistics operator"},"dotnet-field-ops":{"n":".NET field ops","lang":"C#","fw":".NET 8 minimal API","d":"Field-operations work orders and site visits for a small service team"},"dotnet-insurance":{"n":".NET insurance platform","d":"Policy administration system for a mid-size insurer","fw":".NET 8, ASP.NET Core, Entity Framework","lang":"C#","i":"stack/dotnet.svg"},"dotnet-utility-billing":{"n":".NET utility billing","lang":"C#","fw":".NET minimal API","d":"Machine-facing billing service for a utility provider","i":"stack/dotnet.svg"},"edtech-lms":{"n":"Edtech LMS","lang":"Python","fw":"Django","d":"Learning management platform for school districts, Django and Celery","i":"stack/django.svg"},"express-api":{"n":"Express API service","d":"Event ticketing API, small team","fw":"Express 4 + Mongoose","lang":"JavaScript","i":"stack/express.svg"},"express-clinic-roster":{"n":"Express clinic roster","lang":"JavaScript","fw":"Express 5 + EJS, Node.js 20","d":"Staff roster and shift swaps for a small clinic"},"fastapi-saas":{"n":"FastAPI SaaS","d":"B2B contract-management SaaS API","fw":"FastAPI + SQLAlchemy + Alembic","lang":"Python","i":"tools/fastapi.svg"},"fastapi-waitlist":{"n":"Kelvinbrae waitlist","lang":"Python","fw":"FastAPI + psycopg 3 on Neon","d":"Cancellation waitlist on a managed database, with no hosting chosen"},"fieldservice-saas-billing":{"n":"Field service billing","lang":"JavaScript","fw":"Node.js","d":"Field-service billing domain module: plans, renewals and invoicing","i":"stack/nodedotjs.svg"},"flask-parts-catalog":{"n":"Flask parts catalog","lang":"Python","fw":"Flask","d":"Parts and supplier catalog used by branch staff"},"flask-shiftplanner":{"n":"Flask shift planner","d":"Shift planning for small clinics","fw":"Flask 3.0","lang":"Python","i":"tools/flask.svg"},"fleet-inspection-intake":{"n":"Fleet inspection intake","lang":"Go","fw":"Go 1.23","d":"Vehicle-inspection photo intake for a small fleet provider"},"fleet-saas-billing":{"n":"Fleet SaaS billing","lang":"Go","fw":"Go 1.23 domain service","d":"Trip-usage ledger and monthly invoices for a small EU fleet SaaS"},"go-customer-ops":{"n":"Go customer ops","lang":"Go","fw":"chi + pgx","d":"Customer operations back office service","i":"stack/go.svg"},"go-fleet":{"n":"Go fleet tracker","d":"Fleet tracking backend","fw":"Go 1.22, chi router, sqlc","lang":"Go","i":"stack/go.svg"},"healthtech-ehr":{"n":"Healthtech EHR","lang":"Java","fw":"Spring Boot + FHIR","d":"Electronic health records platform under HIPAA"},"helpdesk-billing-starter":{"n":"Helpdesk billing starter","d":"Team-plan subscriptions and draft invoices for a six-person helpdesk product team","fw":"Node.js core HTTP + ES modules","lang":"JavaScript","i":"stack/nodedotjs.svg"},"insurance-collections-core":{"n":"Insurance collections","lang":"C#","fw":".NET 8 domain service","d":"Audited premium-invoice ledger and settlement reconciliation for an insurer"},"java-telecom-splunk":{"n":"Java telecom platform","d":"Line-provisioning backend at a telecom operator","fw":"Java 17, Spring Boot 3, Maven multi-module","lang":"Java","i":"stack/spring.svg"},"laravel-caseboard":{"n":"Laravel caseboard","d":"Internal client case tracking for a small operations firm","fw":"Laravel 11 + Blade, PHP 8.2","lang":"PHP","i":"stack/laravel.svg"},"laravel-helpdesk":{"n":"Laravel helpdesk","d":"Helpdesk SaaS","fw":"Laravel 11","lang":"PHP","i":"stack/laravel.svg"},"nestjs-stockroom":{"n":"Northfen Stockroom","lang":"TypeScript","fw":"NestJS","d":"Shared stockroom view across purchasing and warehouse desks"},"nextjs-classbooking":{"n":"Next.js class booking","d":"Yoga class schedule and self-service booking for a single studio","fw":"Next.js 16 App Router + React 19","lang":"TypeScript","i":"stack/nextdotjs.svg"},"nextjs-donorbook":{"n":"Harrowgate donorbook","lang":"TypeScript","fw":"Next.js 15 + Drizzle on Neon","d":"Donation tracking on a managed database, with no hosting chosen"},"nextjs-storefront":{"n":"Next.js storefront","d":"DTC homeware e-commerce storefront","fw":"Next.js 14 App Router","lang":"TypeScript","i":"stack/nextdotjs.svg"},"nextjs-studioroster":{"n":"Studio Roster","lang":"TypeScript","fw":"Next.js 15 App Router + React 19 + Drizzle","d":"Class schedule and roster for a pottery studio, on a managed database"},"node-ai-report-builder":{"n":"AI report builder","lang":"TypeScript","fw":"Express 5","d":"Internal CSV-to-report workspace for client analysts"},"node-code-agent":{"n":"Node Code Agent","d":"Coding-agent controller for repository checkout, editing, builds and tests","fw":"Node 20 core HTTP","lang":"JavaScript","i":"stack/nodedotjs.svg"},"nuxt-fieldservice":{"n":"Nuxt field service","d":"Field-service scheduling and job tracking for local trade teams","fw":"Nuxt 3 + Vue 3 + Drizzle ORM","lang":"TypeScript","i":"stack/nuxt.svg"},"php-gov-portal":{"n":"PHP government portal","d":"Citizen-facing benefits portal for a public agency","fw":"PHP 8.3, Symfony 7","lang":"PHP","i":"stack/php.svg"},"portrait-gallery":{"n":"Portrait gallery","lang":"JavaScript","fw":"Node.js","d":"Client galleries and photo delivery for a one-person studio"},"python-ai-analyst":{"n":"Python AI analyst","d":"Spreadsheet question-answering assistant with generated Python analysis","fw":"FastAPI + pandas, uv/pyproject","lang":"Python","i":"tools/fastapi.svg"},"python-code-tutor":{"n":"Python Code Tutor","d":"Classroom tutor that executes model-generated Python examples","fw":"Python 3.11 standard library HTTP service","lang":"Python","i":"stack/python.svg"},"python-ops-pipeline":{"n":"Partner inventory pipeline","lang":"Python","fw":"Typer","d":"Weekly partner inventory cleanup and reporting batch"},"rails-claims-ops":{"n":"Rails claims ops","lang":"Ruby","fw":"Ruby on Rails","d":"Claim status, summaries and caseworker notifications for an operations team"},"rails-marketplace":{"n":"Rails marketplace","d":"Handmade-goods marketplace","fw":"Rails 7.1 + Sidekiq","lang":"Ruby","i":"stack/rubyonrails.svg"},"remix-workshop-bookings":{"n":"Remix workshop bookings","lang":"TypeScript","fw":"Remix","d":"Workshop class booking platform","i":"stack/remix.svg"},"rust-cli":{"n":"Rust CLI tool","d":"Open-source 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