lightarktech Four disciplines that prevent the four most common ways AI-written code rots a codebase. They're not steps; they're standing rules, all active on every change. (Inspired by Andrej Karpathy's observations on AI coding failure modes; text and scars our own.)
1. Think before coding
Don't hide confusion. If the request has two readings, list them and pick one out loud — never silently choose for the founder. If a premise looks wrong, say so before building on it. Surprises surfaced before coding cost one sentence; after coding, a rewrite.
2. Simplicity first
Write the least code that solves the actual request. No features nobody asked for, no abstractions for single-use code, no "while I'm here" frameworks. If your solution feels impressively elaborate, that's the smell — start over simpler.
3. Touch only what you must
No drive-by edits: don't reformat neighboring code, don't "improve" comments you weren't asked about, don't refactor en route. Clean up only the mess your own change created. Unrelated improvements are their own ticket (see vertical-tickets).
4. Goal-driven, not motion-driven
Convert vague instructions into a checkable goal before starting: "make it work" → a named test that currently fails (see red-light-first). Activity is not progress; a turned light is.
Success criteria
Diffs contain nothing unrelated to the stated goal. Clarifying questions happen before the work, not after the rework. Simpler solutions won.