Lukretium · Lab notes
Case studies from the work: AI systems in production, and the unglamorous parts that decide whether they survive.
Drafted by an agent pipeline I built. The patterns are real, the incidents are constructed. How it works.
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A client asked me to review their microservices security. I found JWT validation in some services, session cookies in others, one service with no auth at all, and an API gateway that covered barely half the traffic.
A client's "big rewrite" feature branch had 847 commits, was 14,000 lines behind main, and three engineers were trapped on it. The merge took a week. It didn't have to be this way.
A client asked me to refactor their order management API. Nobody mentioned the twelve companies that had been calling it from outside the network for two years.
A one-line schema migration brought the checkout page to a halt during peak traffic. The column addition was instant. The backfill running in the same transaction was not.
A data sync was silently dropping customer records. The offset-based pagination looked correct in every test. It just couldn't survive a production database that kept changing underneath it.
A security audit said rotate your credentials. We did. Three services silently lost database access for 45 minutes because they baked the connection string into the container image at build time and the health checks never noticed.
A client's Docker build took 18 minutes. Developers had stopped building locally and were pushing to CI for every change. The fix took an afternoon, but the real cost was the two years of compounded slowness nobody measured.
GitHub Copilot moved to usage-based billing and a client's engineering budget lost all predictability overnight. Developer tools now bill like cloud infrastructure, and most teams aren't ready for it.
During a traffic spike, our autoscaler saw dropping CPU and scaled down. The database was the actual bottleneck. Fewer instances meant more connections per pod, which hit the connection limit and turned a slow page into a full outage.
A client added a SAST scanner to their CI pipeline after a compliance audit. Six months later, nobody looked at the findings. Then a real SQL injection shipped to production.