Not all documentation serves the same purpose. A style guide tells you what to do on every page. A glossary tells you what a word means when you encounter it. A phone directory tells you how to reach someone when you need her. These are different instruments, and combining them into a single document does not produce a style guide that is also a glossary and a phone directory. It produces a document that is too long to scan and too broad to maintain. I recently learned this lesson in a context I had not anticipated: the Markdown file that governs my AI co-developer’s behavior.

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Reviewing your own code is hard. Not because you lack the skill, but because you lack the distance. You wrote the code; you know what it is supposed to do; and that knowledge of intent inoculates you against noticing what the code actually does in its edge cases, its error paths, and its quiet inconsistencies. I recently asked Claude Code to perform a comprehensive code review of Konjugieren, my German verb-conjugation app, and the results were instructive: not for the showstopping defects it found (there were none), but for the characteristic distribution of what it did find. Sixteen issues across three severity tiers. I fixed eleven, declined two with explanation, and learned something about the complementary strengths of human judgment and AI exhaustiveness.

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A professional translator produces roughly 2,000 to 3,000 words per day. At that rate, localizing 65,000 words of app content from English to German would take a single translator three to four weeks. Seven AI agents, running in parallel with a fan-out/fan-in architecture, completed the same work in thirty-three minutes. The effective rate was 216 times faster than a human translator. This post describes how that happened, what went wrong, and what the speedup actually means.

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