271–280 of 291 posts
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Huh? Is ABNF a "natural language"? Is the Go language spec a "natural language"?
How is ABNF itself specified? Yes, via natural language. And the Go language spec is written in natural language, too, you can check for yourself: https://go.dev/ref/spec
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Temperature > 0 isn’t a problem as long as you can specify/save the random seed and everything else is deterministic. Of course, “as long as” is still a tall order here.
My understanding is that the implementation of modern hosted LLMs is nondeterministic even with known seed because the generated results are sensitive to a number of other factors including, but not limited to, other prompts running in the same batch .
> Now, when you send a request to one of the Gemini 2.5 models, if the request shares a common prefix as one of previous requests, then it’s eligible for a cache hit. We will dynamically pass cost savings back to you, providing the same 75% token discount.
> In order to increase the chance that your request contains a cache hit, you should keep the content at the beginning of the request the same and add things like a user's question or other additional context that might change from request to request at the end of the prompt.
From https://news.ycombinator.com/item?id=43939774 re: same:
> Does this make it appear that the LLM's responses converge on one answer when actually it's just caching?
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I don't even think anybody expects reliably-correct code. They expect code that can be made as reliably as they themselves could make code, with some minimal amount of effort. Which clearly is the case.
Forget about reliably-correct. The code that any current-gen LLM generates, no matter how precise the prompt it's given, is never even close to the quality standards expected of any senior-level engineer, in any organization I've been a part of, at any point in my career. They very much never produce code that is as good as what I can create. If the LLM-generated code you're seeing passes this level of muster, in you…
You are just making assertions here with no evidence.
If you prompt the LLM for code, and then you review the code, identify specific problems, and direct the LLM to fix those problems, and repeat, you can, in fact, end up with production-ready code -- in less time than it would take to write by hand.
Proof: My project. I did this. It worked. It's in production.
It seems like you believe this code is not production-ready because it was produced using an LLM which, you believe, cannot produce production-ready code. This is a cyclic argument.
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If you can 100% reproduce the same generated code from the same prompts, even 5 years later, given the same versions and everything then I'd say "Sure, go ahead and don't saved the generated code, we can always regenerate it". As someone who spent some time in frontend development, we've been doing it like that for a long time with (MB+) generated code, keeping it in scm just isn't feasible long-term. But given this…
Temperature > 0 isn’t a problem as long as you can specify/save the random seed and everything else is deterministic. Of course, “as long as” is still a tall order here.
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Forget about reliably-correct. The code that any current-gen LLM generates, no matter how precise the prompt it's given, is never even close to the quality standards expected of any senior-level engineer, in any organization I've been a part of, at any point in my career. They very much never produce code that is as good as what I can create. If the LLM-generated code you're seeing passes this level of muster, in you…
> The code that any current-gen LLM generates, no matter how precise the prompt it's given, is never even close to the quality standards expected of any senior-level engineer, in any organization I've been a part of, at any point in my career. You are just making assertions here with no evidence. If you prompt the LLM for code, and then you review the code, identify specific problems, and direct the LLM to fix those…
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Forget about reliably-correct. The code that any current-gen LLM generates, no matter how precise the prompt it's given, is never even close to the quality standards expected of any senior-level engineer, in any organization I've been a part of, at any point in my career. They very much never produce code that is as good as what I can create. If the LLM-generated code you're seeing passes this level of muster, in you…
> The code that any current-gen LLM generates, no matter how precise the prompt it's given, is never even close to the quality standards expected of any senior-level engineer, in any organization I've been a part of, at any point in my career. You are just making assertions here with no evidence. If you prompt the LLM for code, and then you review the code, identify specific problems, and direct the LLM to fix those…
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Most LLM software isn’t deterministic, sure. But LLMs are just doing a bunch of arithmetic. They can be 100% deterministic if you want them to be.
In practice, they definitely are not.
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The argument is that this stuff will so radically improve senior engineer productivity that the demand for junior engineers will crater. And without a pipeline of junior engineers, the junior-to-senior trajectory will radically atrophy Essentially, the field will get frozen where existing senior engineers will be able to utilize AI to outship traditional senior-junior teams, even as junior engineers fail to secure em…
I don't know why people don't give more credence to the argument that the exact opposite thing will happen.
> Almost every feature required multiple iterations and refinements. This isn't a limitation—it's how the collaboration works. I guess that's where a big miss in understanding so much of the messaging about generative AI in coding happens for me, and why the Fly.io skepticism blog post irritated me so much as well. It _is_ how collaboration with a person works, but the when you have to fix the issues that the tool cr…
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Forget about reliably-correct. The code that any current-gen LLM generates, no matter how precise the prompt it's given, is never even close to the quality standards expected of any senior-level engineer, in any organization I've been a part of, at any point in my career. They very much never produce code that is as good as what I can create. If the LLM-generated code you're seeing passes this level of muster, in you…
> The code that any current-gen LLM generates, no matter how precise the prompt it's given, is never even close to the quality standards expected of any senior-level engineer, in any organization I've been a part of, at any point in my career. You are just making assertions here with no evidence. If you prompt the LLM for code, and then you review the code, identify specific problems, and direct the LLM to fix those…
Out of interest: How much did the entire process take and how much would you estimate it to take without the LLM in the loop?