A client's engineering team adopted AI coding tools across the board. Individual velocity soared. But their DORA metrics flatlined — and the reasons had nothing to do with the tools themselves.
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Blog
Engineering case studies from the work I do: 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.
- 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.
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- 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.
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- 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.
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- 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.
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