#code-review

A short post on a simple framework for AI-assisted coding, credited to an engineer on the author's team — brain, then AI, then brain — so engineers delegate implementation without letting their judgment atrophy.

The first "brain" step is understanding the problem well enough to judge the agent's output, since without it AI can steer toward the wrong problem entirely. The last step is making the work reviewable before it reaches anyone else: read the diff, question whether the complexity is earned, and write a real PR description. The rule of thumb is that if you can't explain what changed and why, it isn't ready for review.

Geoffrey Litt, a design engineer at Notion, argues that as AI agents write code faster than humans can absorb it, understanding still matters — not to verify correctness, but to remain a creative participant in the project. He walks through three techniques for building that understanding efficiently — structured "explainer" docs with embedded quizzes, interactive micro-worlds for stepping through changes, and shared team spaces for building common mental models.

Three months into AI-assisted development, the author finds the fatigue has shifted from wrestling with implementation details to constant architecture-level decision-making, since the AI is ready to implement whatever is chosen the moment it's chosen. Also flags a code-review blind spot: you can't interrogate an AI's reasoning after the fact the way you can ask a human collaborator why they made a choice.

Referenced in George Hotz's "I love LLMs, I hate hype" as a caution about AI-assisted coding increasing cognitive fatigue.