AI coding assistants are rewriting the developer experience. Code appears in seconds, entire modules materialize from a prompt, and the temptation to ship first and test later has never been stronger. But speed without discipline is just faster failure.
This session tells the story of what happened when a practitioner refused to let AI bypass the fundamentals. Over the course of shipping four open-source packages to PyPI, building multiple frameworks, and building enterprise-grade systems, a pattern emerged: Test-Driven Development doesn't just survive AI-assisted workflows; it becomes the single most important practice keeping the human in control.
Through live demonstrations and real project walkthroughs, you'll see what a disciplined AI-assisted TDD workflow actually looks like in practice: the red-green-refactor cycle with a non-deterministic collaborator, the failures that taught hard lessons, the guardrails that had to be formalized into repeatable process, and the pre-push checklists that saved production more than once. This isn't a talk about testing AI models. It's about what happens to software quality when the fastest code generator in the room has no concept of accountability, and how TDD gives that accountability back to the human.
You'll walk away with a concrete, battle-tested framework for maintaining engineering rigor in AI-assisted development without sacrificing the speed advantage that makes these tools worth using in the first place.