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Comment by naasking | Hacker News Reader

> LMs generate text output that appears to be useful, but regularly is not.

No, they are empirically useful, and only getting more useful. This is not even a debate anymore.

What is useful about nearly starting a war based on incorrect output?

Yes it is. I find them empirically not useful. You may not wish to debate it, but the fact remains that there are a great many people who are not convinced of their usefulness.

LLMs are very good at writing code. The reality is that they are able to write code faster, at higher quality and with fewer bugs, with correct prompting. They are also really good at code analysis, penetration testing and discovery, and adjacent computer science disciplines.

No they are not perfect, nor do they produce the best code. But the undeniable reality is that any good engineer will produce more code, at higher quality, using an LLM.

So, that’s not really up for debate. The debatable part is if all that code is a good idea or has as much value as we think. The conversation has long moved passed “can LLMs write code?”. Yes, they can, very well, particularly if they’re steered by trained engineers.

We have good engineers that gave us curl, ffmpeg, the 4.4BSD, tmux, vim and emacs, x11,… To this day, no one can show any particular important software that comes from LLM assistance at scale. It’s all slop.

llama.cpp has significant AI contributions. The Linux kernel too is receiving AI patches. You are seriously out of touch.

Well it's been barely a year, but basically all commercial software, and most open-source software, is engineered with AI assistance. It's not all slop, you just aren't looking around. Ask any engineer at any company - are they using LLMs to assist in writing code? All of them will say yes. Source: I'm a software engineer working on commercial software at a company, I also talk to many engineers at other companies.

Building software takes time. It remains a difficult craft, with or without the help of LLMs. You can't simply build a piece of "important software" in just a few months, and the good models capable of reasoning on complexity came out just a few months ago. And no, it's not "all slop". If you're incapable of using tools to build something meaningful with them, it's on you - don't blame the tools. LLMs are qualified to help with developing software, and if you're focusing on just the code-generation aspect of it, you're using it wrong; that's not how it's done.

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