A collection of articles, protocols, and best practices for effective AI-driven software development.
As AI coding assistants become integral to development workflows, teams need structured approaches to maintain code quality, testing discipline, and engineering rigor. This repository gathers proven practices that help developers and AI agents collaborate effectively.
An 8-step protocol for systematic bug fixing that treats every production bug as two failures: the code defect itself and the testing system's failure to catch it. The protocol enforces a disciplined workflow — reproduce with a failing test, trace the root cause, apply a minimal fix, verify green, and then audit the testing system to close the gap that let the bug through. Over time, this approach drives the testing system toward its ideal state: a self-improving safety net where no class of bug can slip through twice.
Contributions of articles, protocols, and practices for AI-driven development are welcome.