"Write a function to fetch the list of users." — same prompt, same codebase. Yesterday: getUsers() . Today: fetchUserList() . Tomorrow: loadAllUsers() . Six months of AI-assisted coding and I kept hitting this wall. My initial reaction was "maybe I need to write better prompts." I wrote better prompts. The functions got slightly better. New inconsistencies appeared elsewhere. The problem wasn't the AI's capability. It was that I had never given it my team's unwritten rules. That realization led me to build AI Dev OS — an open-source framework that converts implicit developer knowledge into explicit, enforceable rules for AI coding assistants. It supports Claude Code, Cursor, and Kiro (Amazon's AI-native IDE). GitHub: github.com/yunbow/ai-dev-os The Root Problem: "Almost Correct" Is the Most Expensive Output When an AI produces obviously wrong code, you catch it immediately and discard it. Not a big deal.…