How to Tell if Your AI Coding Workflow Is Healthy
_xjdr · x · 2026-07-17
The author shares a personal metric for measuring codebase health: as models advance and the proportion of AI-generated code increases, the repo still hasn't experienced any catastrophic moments demanding a "massive rewrite" or causing an "oh God, stop" reaction.
He believes this indicates the workflow is starting to scale across multiple model families, but the trade-off is that more responsibility falls back on humans. Tasks the model can't currently handle must be isolated—either taken over manually or used as benchmarks for the next model generation. Otherwise, scaling model usage will easily lead to being overwhelmed by low-quality output and instant gratification.
Related event: Developer Shares Criteria for Healthy AI Codebases(2 posts)→
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