Primary deployment platform for AI Execution Lab (Next.js 15). All production builds, environment variable management, and static page generation run through Vercel. Has been the site of multiple documented failures: edge runtime restriction conflicts, missing production environment variable scopes, and build pipeline issues. Every Lab deployment since 2026-05-10 has shipped through Vercel.
Operational records — 15 total
How AI Execution Lab runs autonomously on free/hobby plans: model-tier routing, content-addressed caching, semantic deduplication, publish throttling, empty-queue early-exit crons, Firestore read/write minimization via increment counters, and batched embeddings. Includes expensive-operation analysis, scaling bottlenecks, the cheapest viable architecture, and estimated monthly cost ranges.
Platform-specific deployment verification checklists for Vercel (AI Execution Lab), Firebase (TrustSeal and ScamCheck Cloud Functions), GitHub Pages (TrustSeal and ScamCheck SPAs), and WordPress (asquaresolution.com). A deploy is not safe until every item on the relevant checklist has been confirmed in production — not in the emulator, not locally, not from build logs.
How changes move safely from intent to stable production operation. Change classification framework, blast radius evaluation, preflight discipline, staging philosophy, and change-management invariants extracted from real deployment history across TrustSeal, ScamCheck, AI Execution Lab, and WordPress. Answers: how do we reduce the probability that a production change introduces unexpected operational behavior?
Operational pattern for managing test vs. live mode separation across payment processors, analytics platforms, and authentication providers. Covers the full failure surface: mode-mixed credentials, preview environment contamination, domain authorization gaps, and the unifying root cause — credentials or configuration valid in one scope that are absent, wrong, or mismatched in production.
What it actually takes to operate a Next.js 15 App Router platform on Vercel in production: deployment configuration, monitoring, known failure modes, build performance, and the operational discipline that keeps it stable. From real operational experience on AI Execution Lab.
Complete guide for deploying AI Execution Lab to lab.asquaresolution.com — DNS configuration, Vercel setup, environment variables, SSL, and launch verification.
End-to-end case study: launching lab.asquaresolution.com — a public AI engineering journal built on Next.js 15 App Router, MDX content pipeline, Vercel deployment, custom content sections, and an execution tracks system.
How the AI Execution Lab Vercel deployment pipeline evolved from initial setup through two documented build failures to a stable production configuration — covering edge runtime failure, next-mdx-remote v6 blockJS, environment variable scoping, preview workflows, and current build performance.
Production deployment of the Operational Intelligence Layer: failure-memory.ts, execution pathways, confidence scoring, /ops observability upgrade. 424 pages at build time.
Step-by-step deployment process, rollback procedures, and environment management for the AI Execution Lab platform.
Upgrading to next-mdx-remote v6 silently stripped all array and object literal props from JSX components in MDX. StepList, Checklist, and LessonObjectives rendered empty. Resolved in 41 minutes.
export const runtime = 'edge' in app/opengraph-image.tsx blocked the entire Vercel deployment. Took 23 minutes to identify and revert.
A new Gemini API key environment variable was added to .env.local but not to Vercel production. Feature worked locally and in dev but silently returned undefined in production. Root cause: variable scoped to Development only.
New subdomain scamcheck.asquaresolution.com propagated in some regions after 20 minutes but took 4 hours to fully propagate globally. Caused inconsistent test results and premature go-live.
NEXT_PUBLIC_GA_MEASUREMENT_ID was scoped to all Vercel environments (Production, Preview, Development). Every preview deployment URL accessed during development fired GA4 events to the production analytics property. Production session counts, traffic sources, and pageview totals were inflated by developer and build-verification activity until the variable was rescoped to Production only.