March 19, 2026
Chaos or clean? You decide
Be intentional about how AI changes your codebase
Keep your code tidy—or let the robots run wild, say devs
TLDR: A new guide says to supervise AI-written code with simple functions, clear orchestration, and strict data models. Comments split between discipline diehards, chaos jokers, and slow-and-steady testers, with a mobile rendering facepalm; it matters because AI is shipping code now, and mess spreads fast.
A new “be intentional with AI code” manifesto the guide is making the rounds, preaching simple, self-explaining code and clear roles: tiny building-block functions that do one thing right, and big wrapper functions that choreograph the messy bits. Models (the shape of your data) should make wrong states impossible. Sounds sensible… but the comments are where it gets spicy.
Author benswerd throws down: AI isn’t ruining code—people are, when they let bots spray changes without standards. Meanwhile, mrbluecoat turns the thread into a meme fest, dubbing unrestrained bots the “Modern Chaos Monkey,” and some folks admit the adrenaline rush of letting AI run free just to see what breaks. On the practical side, p1necone argues to trim the rules hard—keep instruction files short and only say what truly matters, because long “be good” lists become wallpaper.
Then there’s the slow-cooker camp: ChrisMarshallNY reviews everything, tests each step, and asks for refactors later—“slower than agents, faster than solo,” which feels very grown-up. And just when the lecture on clean code hits peak earnest, clbrmbr reports the page is busted on iPhone Safari. Irony level: chef’s kiss. The takeaway? Let AI help, but babysit it—your future self will thank you.
Key Points
- •The article is a manifesto and guide for integrating AI coding agents into codebases with intentional standards.
- •It advocates self-documenting code and careful decomposition of logic into well-defined functions.
- •Semantic functions should be minimal, avoid side effects unless intended, return required outputs directly, and be highly unit-testable.
- •Pragmatic functions orchestrate semantic functions and unique logic, should be used sparingly, tested via integration tests, and documented for non-obvious behavior.
- •Data models should prevent invalid states; optional/loosely typed fields introduce ambiguity, and correctness should surface at construction time.