I put the examples he gave into Claude 4(Sonnet) purely asking to eval the code, it pointed out every single issue about the code snippets (N+1 Query, race condition, memory leak). The article doesn;t mention which model was used, or how exactly it was used, or in which environment/IDE it was used.

The rest of the advice in there is sound, but without more specifics I don't know how actionable the section "The spectrum of AI-appropriate tasks" really is.

I initially thought that layout of the sections was an odd and terrible poem.

I think this is correct, and I also think it holds for reviewing human-authored code: it's hard to do the job well without first having your own idea in your head of what the correct solution looks like [even if that idea is itself flawed].

This isn’t new. Have we not already seen this everywhere already? The example at the top of the article (in a completely different field, no less) just goes to show humans had this particular sin nailed well before AI came along.

Bloated software and unstable code bases abound. This is especially prevalent in legacy code whose maintenance is handed down from one developer to the next, where their understanding of the code base differs from their predecessor’s. Combine that with pressures to ship now vs. getting it right, and you have the perfect recipe for an insipid form of technical debt.