AI code review tools are everywhere now. After rolling one out on a real project, I learned more about our team than about the tool itself.
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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.
- How a 45-minute end-to-end test suite trained an entire team to ignore test failures — and what we did about it.
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- How 340 feature flags turned a codebase into a minefield — and the boring process that dug us out.
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- A consulting story about a minor field rename in an internal API that cascaded into a production incident, and what we put in place to stop it from happening again.
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- The latest DORA report confirms what I've seen on consulting engagements — AI tools amplify existing conditions. If your processes are solid, AI accelerates you. If they're not, you just create technical debt faster.
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