Builders hand AI a detailed spec and get back code that matches the first half closely, then drifts into plausible behavior nobody asked for. The cause is not carelessness. As generation goes on, the…
When a human writes code against a library, they usually check the docs for the version they actually installed. When an AI writes it, the code reflects whatever mix of versions showed up in its…
Ask an AI assistant to make code decoupled or testable and it reliably produces callbacks, event emitters, hooks, and handler registries. Control flow gets inverted, but the source code dependencies…
An AI assistant asked to refactor a module treats the code as text to restructure, not as a system with a cost profile. Memoization, lookup tables, precomputed indexes, and hoisted queries tend to get…
Every time AI generates code from a loose prompt, it makes dozens of quiet decisions to fill the gaps: a timeout of 30 seconds, a retry count of three, a timezone of UTC, a sort order, a default…
AI-generated code tends to perform well on a developer laptop, where every call hits a local database, a mocked API, or an in-memory fixture and costs close to nothing. The same code carries loops…
AI coding tools put new code wherever it is easiest to reach. They import whatever is already in scope, add a parameter to a function that already exists, and read shared state directly because it is…
Builders who string multiple Claude Code agents together usually hit the same wall. Each agent is capable, but the system as a whole forgets decisions, repeats mistakes, and drifts from the…
AI-generated code handles failure as if it were a clean switch: the call either works or it throws, and a try/except with a fallback takes care of the rest. Real production failures are partial and…
AI-generated code almost always ships with error handling that looks responsible: try/except blocks, logged messages, graceful fallbacks, retry loops. But much of it is theater. It swallows the…
