The Empty Middle of AI Coding
a day ago
- #Productivity
- #LLM Pipeline
- #AI Coding
- The AI coding community is polarized between skeptics who doubt AI's utility and 'vibe coders' who claim significant productivity gains.
- Personal experience shifted from skepticism to practical use, starting with mundane tasks and progressing with structured prompts and new model releases.
- Key challenges include 'comprehension debt' from rapid LLM use and failure modes like wrong gap-filling, confident incorrectness, and forgotten requirements.
- A structured pipeline—frame, research, design, spec, code—addresses issues by maintaining understanding and reducing LLM errors.
- The 'ai-behaviors' tool enforces phase boundaries using hashtags, improving feature development and uncovering overlooked interactions.
- This approach balances increased velocity with deeper comprehension, extending beyond coding to tasks like writing blog posts.
- Similar frameworks exist, but lighter-weight methods are preferred for structured LLM conversations.