Your AI Coding Tool Just Got Acquired. Now What?
The SpaceX-Cursor deal forced a lot of teams to rethink their AI tooling strategy overnight. But the real problem isn't the acquisition — it's that most teams never had a strategy to begin with.
Lukretium · Lab notes
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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The Node.js Upgrade We Put Off Until a CVE Forced Our Hand
A client's API had been running Node 16 for two years past end-of-life. When a critical OpenSSL vulnerability dropped, the "we'll upgrade next quarter" plan collapsed into a three-week fire drill. The upgrade itself wasn't hard. Undoing two years of drift was.
The Rate Limiter That Let Everything Through
A client's API had rate limiting configured and enforced. It still couldn't prevent a single customer from tanking performance for everyone else. The problem wasn't the limiter — it was what we were counting.
Reviewing AI Code Is the New Bottleneck
Last quarter I tracked my time across three consulting engagements. I spent 11 hours a week reviewing AI-generated code and under 8 writing my own. The ratio surprised me, and it changed how I think about developer productivity.
The Memory Leak That Only Showed Up on Tuesdays
A Node.js service kept getting OOMKilled, but only on Tuesdays. The batch job was a red herring. The real problem was a lazy cache eviction strategy that nobody thought to question.
The Sixty-Seven Environment Variables Nobody Documented
A client's deployment kept failing in staging but not locally. The root cause wasn't code — it was sixty-seven environment variables spread across five files with no documentation and no single source of truth.
The Rollback That Didn't Roll Back
We practiced deployments religiously but never tested a rollback. When a release broke checkout and we hit the big red button, we found out half the system couldn't actually go backward.
The Team That Had More AI Agents Than Engineers
A client's platform had 23 AI agents built by a team of 8. Nobody could tell me what half of them did. Agent sprawl is the new microservices sprawl, and the cleanup looks depressingly similar.
Every Exception Was Caught. No Error Was Handled.
A client's codebase had try-catch blocks wrapped around everything. Nothing ever crashed. Nothing ever worked correctly either. The error handling strategy was actually an error hiding strategy.
The Deploy That Dropped Requests in Silence
Every deploy was losing a handful of HTTP requests, but nobody noticed until a payment callback disappeared. The fix wasn't in the deployment pipeline — it was in the application code that never learned how to shut down.