Using AI to write better code more slowly
The article discusses the potential of AI coding tools to produce high-quality code at a slower pace, countering the notion that they only generate low-quality output quickly. It emphasizes the effectiveness of using multiple AI models to identify and prioritize bugs in code. The author advocates for a more methodical approach to coding that enhances code quality and understanding, rather than focusing solely on speed and volume.
- ▪Many believe AI coding tools are only useful for quickly generating low-quality code.
- ▪Using multiple AI models can help find and prioritize bugs effectively.
- ▪The author suggests a slower, more careful coding approach can lead to better overall code quality.
Opening excerpt (first ~120 words) tap to expand
A lot of people seem convinced that the point of AI coding is to write low-quality code as fast as possible. Spew out barely-passable slop, open massive PRs, and merge them unvetted. Ship it! But the thing is, LLMs are very flexible. And you can use them just as effectively to write high-quality code more slowly. This statement seems completely obvious to me at this point, and I almost didn’t want to write this post for that reason. But there seem to be enough people convinced that LLMs are only good as slop cannons that it’s worth making the opposite case. If Mythos taught us anything, it’s that LLM agents are really good at finding bugs. Throw them at a codebase enough times, and they will find so many bugs that you’ll barely know what to do with them.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Read the Tea Leaves.
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