I would bet a significant amount of money that many LLM users don’t check the output. And as tools improve, this will only increase.

The number of users actually checking the output of a compiler is nonexistent. You just trust it.

LLMs are moving that direction, whether we like it or not

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Jensson 5 days ago | parent | next [-]

> The number of users actually checking the output of a compiler is nonexistent. You just trust it.

Quite a few who work on low level systems do this. I have done this a few times to debug build issues: this one time a single file suddenly made compile times go up by orders of magnitude, the compiler inlined a big sort procedure in an unrolled loop, so it added the sorting code hundreds of times over in a single function and created a gigantic binary that took ages to compile since it tried to optimize that giant function.

That is slow both in runtime and compile time, so I added a tag to not inline the sort there, and all the issues disappeared. The sort didn't have a tag to inline it, so the compiler just made an error here, it shouldn't have inlined such a large function in an unrolled loop.

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aprilthird2021 5 days ago | parent | prev | next [-]

Of course they don't. That's why things like the NX breach happen. That's also why they don't learn anything when they use these tools and their brains stagnate.

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__loam 5 days ago | parent | prev [-]

Well they're not improving that much anymore. That's why Sam Altman is out there saying it's a bubble.

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CuriouslyC 5 days ago | parent [-]

This is incorrect, they are improving, you just don't understand how to measure and evaluate it.

The Chinese models are getting hyper efficient and really good at agentic tasks. They're going to overtake Claude as the agentic workhorses soon for sure, Anthropic is slow rolling their research and the Chinese labs are smoking. Speed/agentic ability don't show big headlines, but they really matter.

GPT5 might not impress you with its responses to pedestrian prompts, but it is a science/algorithm beast. I understand what Sam Altman was saying about how unnerving its responses can be, it can synthesize advanced experiments and pull in research from diverse areas to improve algorithms/optimize in a way that's far beyond the other LLMs. It's like having a myopic autistic savant postdoc to help me design experiments, I have to keep it on target/focused but the depth of its suggestions are pretty jaw dropping.

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