I Let AI Write 100% of My Code for 30 Days — Here's What Broke

This is a retrospective essay about a 30-day experiment in fully AI-generated code. The value is in the failure taxonomy — what categories of problems showed up, where the AI tooling fell apart, and what had to be fixed by hand. That framing is more useful than a generic hot-take on AI coding tools, assuming the author gets specific about the failure modes rather than staying at the level of 'it sometimes hallucinates.' The description in the source is empty, so there is no preview of how deep the technical detail goes. The experiment format is well-worn at this point — a dozen similar pieces ran last year — so the bar for this one to add signal over the existing corpus is whether it surfaces genuinely novel breakage patterns rather than the usual suspects. Worth a skim if you are calibrating how much to trust AI-generated code in a production context. -> Best for: solo founder or SaaS team of 2-5 evaluating AI coding assistant workflows