How to Use AI to Write Good Code (Slowly)
The article argues that AI coding agents require a disciplined, human-led approach to ensure the production of robust and maintainable software. It outlines a development cycle where engineers retain responsibility for planning, design, and critical review rather than relying on autonomous code generation. This method prioritizes system understanding and long-term reliability over the speed of initial code creation.
- ▪AI coding agents represent a significant shift in software development but are not yet capable of autonomously managing entire systems without supervision.
- ▪Engineers should personally write plans and ticket descriptions to maintain a clear understanding of how changes fit into the broader system architecture.
- ▪The recommended workflow involves using AI for research and code generation, followed by rigorous human review and iterative bug hunting.
- ▪Experimental branches can be used to rapidly test AI-generated concepts before committing to changes intended for production merge.
- ▪Maintaining the code, documentation, and team understanding as a cohesive unit is essential for long-term system maintainability.
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Story provenance
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Record
| Original publisher | Github |
| Canonical URL | https://jdagostino.github.io/slowly/index.html |
| Publication time | Fri, 02 Oct 2026 16:10:59 +0000 |
| Retrieval time | 2026-10-02T16:22:21.261Z |
| Last seen | 2026-10-02T16:22:21.261Z |
| 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 | _-NZmHFLXAAe · 1 stories |
| 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
How To Use AI To Write Good Code (Slowly) October 2 2026 You're doing software engineering at an interesting time The new AI coding agent tools have led to the biggest change in the practice of software development since the invention of the compiler. We've all seen examples of how to use these tools to write rough, sloppy code quickly, code that runs without any human being having ever read it, and while there are plenty of legitimate uses for that (experimental prototypes, UI mockups, internal tools, one-off test harnessing) it's clear that development of highly reliabile systems (such as satellite flight software, where I spend most of my professional time) need a different approach.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Github.
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