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Comment by booty | Hacker News Reader

    but my anecdotal experience is that LLM written 
    software is very reliable and easy to extend and 
    develop.

Extremely refreshing to read. The whole thing, not just this statement.

Too much discourse creates a false dichotomy of "awesome, wonderful, hand-crafted code" vs "shitty AI slop."

A lot of hand-crafted code is pretty bad. This is true even with talented engineers: there are often edge cases they did not think of... and in many cases, could not have thought of.

And AI code is pretty good if it's steered and vetted by a knowledgeable engineer. It is clear to me, and has been clear for a long time, that "talented engineer plus AI" is the winning combination and will remain that way for at least the near future.

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*"talented engineer plus AI" is the winning combination and will remain that way for at least the near future.

Talented programmers will retire and Father Time takes care of the rest. Where will the new wave of talented programmers come from? I use AI, but I am a hobbyist.

I still review PRs, all of which are written by agents. I almost never have meaningful feedback to offer unless it's feedback on the architectural approach. And even then, a lot of architectural/coding patterns that I've been a stickler for in the past mean less to me now because those patterns served to create maintainable code for human beings, which just isn't a priority anymore.

At our company (big Rails/React monolith) all PRs do get a human review. Now, I use LLMs to accelerate my reviews, but there's still a lot of space for humans.

- LLMs struggle, or at least burn oodles of tokens, on big complex codebases. Managing tech debt helps the agents as well IMO. - If you ask an LLM to review a non-trivial pull request, it nearly always seems to find at least 10 issues. Many of which are not worth pursuing. Often, they would introduce a lot of extra complexity to harden the code against scenarios we don't care about or can never happen. So there's a lot of room for human judgement there. Typical scenario - the LLM reviewer finds 10 issues and I judge that 2-3 are worth pursuing - As you said, often the architectural approach sucks even with frontier LLMs. Usually from a lack of problem space / usage scenarios more than a lack of technical chops - LLMs tend to err on the side of overengineering the shit out of everything

I do not see how LLMs will be able to manage huge, hairy polygot codebases without humans-in-the-loop in the near future.

Given the pace of progress, I'd be a fool to bet against LLMs in the medium term future. But I think it's far from a given. LLMs themselves, and large codebases, are essentially many-to-many problems approaching something like O(n^n).

Most companies would rather have AI without talented engineer.

Maybe. The market will figure out if this is viable, I guess.

For smaller projects or short-lived projects, "AI without talented engineer" is a viable choice already.

But a lot of projects are big and long lived.

The difference in complexity between 1K lines of code and 10K lines of code isn't merely 10x. It's usually more like 100x.

So even if AI becomes 10x better than today (which seems reasonable) there's going to be a level of complexity beyond which you still need some kind of talented engineer-type(s) steering the AI.

Yeah let’s be real there was a ton of slop around before AI

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