Shipping Quality AI Code

AI-assisted engineering raises the ceiling when the surrounding system forces useful discipline. The model can explore options quickly, but the repository still deserves the same care: read the local patterns, preserve user work, write tests where behavior can drift, and verify before claiming the work is finished.

The difference between a demo and a useful system is usually not model cleverness. It is the boring loop around it: typecheck, lint, unit tests, browser checks, Docker checks, and honest notes about what was not covered.

I want AI code to feel less like a magic trick and more like a very fast teammate who keeps receipts.