alt Hacker NewsI tried a lot of tools. Claude code, deepseek with kilocode and OMP, codex... I still use claude quite a bit. But frankly, all of them produce some absolutely godawful code. Review load went way up with AI, and it's not just the volume that caused it, but also the quality. It's extremely verbose, hard to read, often repeats code instead of factoring it into reusable components. And yes, sometimes it's also buggy. Except now, you have to debug a problem that's in code you didn't write yourself, and is awful to read.
LLM is incredibly valuable for debugging complex problems, codebase exploration, and planning large changes. But the writing code part itself, I find, LLMs are just not very good at it yet.
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I’ve had some luck prompting them to be concise, both in writing and in code, and with code doing an approach where they get it working, write tons of tests, and then refactor for conciseness and readability. All the tests prevent regressions doing this.
Without such prompting and a conciseness and clarity pass you get a slop grenade.
They overall work better with tests, and Rust is a great language for them. Overall they do better with lots of walls and alarms that go off if they mess up. I don’t need nearly as much of this, can mentally simulate it, which is a good “are we superintelligence yet” reality check. Still not even as good as my wet meat brain. But impressive given what was possible even two years ago!
The result is still not as clean as a good programmer but it’s better than the slop grenade you get first pass.
I don't have to debug anything.
Vaguely telling the agent what the issue is and what behavior I expect solves the issue with a fraction of the effort.
Some claim that the tech debt only keeps increasing and that the result will be unmaintainable. This is not my experience, and I don't think it is theirs either. These claims are often entirely speculative.