Using AI to Write Better Code More Slowly, Not Faster
Original: Using AI to write better code more slowly
Why This Matters
Demonstrates alternative AI development approach focused on quality over speed
Software engineer Nolan Lawson argues against using AI to write low-quality code quickly, instead advocating for using multiple LLM models to find bugs and improve code quality through slower, more thorough development processes.
Nolan Lawson challenges the common perception that AI coding tools should be used to generate code quickly. Instead, he proposes using multiple AI models to create higher-quality code through extensive bug detection. His workflow involves running Claude sub-agent, Codex, and Cursor Bugbot to find bugs ranked by severity, then having agents research and rule out false positives. This multi-model approach produces near-zero false positives and finds numerous bugs ranging from critical security issues to minor comment improvements. The process often reveals pre-existing bugs, leading to tangential fixes that improve overall codebase health. While this doesn't increase development velocity, Lawson finds it satisfying as it teaches developers about system failure modes and architectural assumptions.