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Thoughts on AI engineering, responsible automation, and building systems that matter.
I used to start every morning with three browser tabs, three inboxes, and a growing sense of dread. Now an AI agent reads them all before I wake up. The journey from inbox chaos to automated calm took six months and changed how I think about attention.
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At my first startup, documentation meant a README with three bullet points and a prayer. Then I joined a German enterprise and discovered that documentation is not overhead - it is infrastructure. That shift changed how I build everything.
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My first week at the new company, I tried to paste a code snippet into ChatGPT. My colleague physically reached over and closed my laptop. "We do not do that here." That was my introduction to real AI governance.
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I had a production hotfix, two feature branches, and a code review all due by end of day. Stashing was not going to cut it.
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The bug crashed production every Tuesday at 2 AM. Only Tuesday. Only 2 AM. The logs showed nothing. The monitoring showed nothing. Three days later I found it - and the fix was not code.
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AI can write code quickly. That does not make the code safe, maintainable, or production-ready. Over time I built a testing workflow that treats AI output like a fast junior teammate: useful, productive, and never above verification.
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I needed to build a CLI tool that could orchestrate Docker containers, manage Git worktrees, allocate ports, and run a 9-stage pipeline in under a minute. I evaluated five options. The winner surprised me.
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The first time a lawyer asked me "but who is liable when the AI makes a wrong decision?" I realized I had been building systems without understanding the world they operate in.
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