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.
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Read the Tea Leaves |
| Canonical URL | https://nolanlawson.com/2026/05/25/using-ai-to-write-better-code-more-slowly/ |
| Publication time | Mon, 25 May 2026 11:19:14 -0500 |
| Retrieval time | 2026-05-25T16:37:38.283Z |
| Last seen | 2026-05-25T16:37:38.283Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | aeiQG5OzZmGH |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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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