AI code incident playbook

Turn the bugs AI agents sneak into your codebase into a searchable avoidance list

Problem

Developers are amazed at how good frontier models are at coding, yet keep feeling gaslit when something breaks in ways they can't articulate. HM posters describe handling AI code with a mix of FOMO and doubt, unsure whether review is still needed and with nowhere to record the specific failure modes they hit. Those lessons stay trapped in personal memory and group chats.

Solution

A structured public incident library where users submit model/agent, task context, exact failure mode, how it was caught, and how it was fixed, searchable by model, language, framework, and error type. It also generates a weekly personal digest of what your agents got wrong for retrospectives.

Users

Developers who lean heavily on AI coding, tech leads, and code reviewers

MVP

Submission form with fixed fields; public list filterable by model and error type; private vs. public entries; export to Markdown for team wikis

Revenue

Free public library for growth, paid private team instances ($19–49/mo), or anonymized incident data API for model vendors

Why now

AI coding is mainstream but incident knowledge is scattered with no canonical venue; a community-contributed format lets an indie start it without building models