711–720 of 944 posts
Well, o3-mini-high just successfully found the root cause of a seg fault that o1 missed: mistakenly using _mm512_store_si512 for an unaligned store that should have been _mm512_storeu_si512.
why is this impressive at all? It effectively amounts to correcting a typo.
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And another way to rephrase it is that almost half of the users prefer the older model, which is terrible PR.
Not if the goal is to claim that the models deliver comparable quality, but with the new one excelling at something else (here: inferrence cost).
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If the comments say it's worth it I read it, but often (especially on HN) the top comment starts with a summary of errors/inconsistencies in the article and then I don't really feel the need.
You do you, but hivemind thinking is a real thing. I have seen highly upvoted comments seemingly "debunk" an article where on closer examination it becomes clear they actually didn't read the article either. It quickly becomes this weird bubble of people just acting on what everything "thinks" the content is about without ever having looked at the content. I get that is easier, but intellectually you are doing yourse…
> It quickly becomes this weird bubble of people just acting on what everything "thinks" the content is about without ever having looked at the content.
That isn't an issue though since the important part is what you learn or not, not whether you think an imaginary article is true or not. If you learn something from someone debunking an imaginary article, that is just as good as learning something from debunking a real article.
The only issue here is attribution, but why should a reader care about that?
Edit: And it isn't an issue that people will think it actually debunks the linked article, since there will always be a sub comment stating that the commenter didn't read the article and therefore missed the mark.
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LLMs are the new compilers. As a student, you should continue to focus on fundamentals, but also adapt LLMs into your workflow where you can. Skip writing the assembly (now curly braces and semicolons), and focus on what the software you’re building actually does, who it serves, and how it works. Programming is both changing a lot, and not at all. The mechanics may look different, but the purpose is still the same: e…
> LLMs are the new compilers. This shows a grave misunderstanding of what compilers and LLMs are. They're fundamentally opposite concepts. Compilers are about optimizing abstract code down to the most efficient representation possible for some hardware. LLMs are about wasting petaflops (made possible by compiler engineers) to produce random statements that don't have any static guarantees.
Jokes aside, I understand your point.
In the history of computing, LLMs and compilers are closer than one might think.
Compilers weren’t first created to optimize “abstract code down to the most efficient” assembly as possible, even if that is the goal of a compiler writer today.
Compilers were created to enable the use of higher-level languages. Abstraction, efficiency, portability, error reduction, and most importantly: saving time.
They allowed humans to create more software, faster.
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Is it possible people are voting for speed of responsiveness too?
I suspect people on LLM Arena don't ask complex questions too often, and reasoning models seem to perform worse than simple models when the goal is just casual conversation or retrieving embedded knowledge. Reasoning models probably 'overthink' in such cases. And slower, too.
It's easy to pin this on the users, but that website is hostile to putting in any effort.
This is something I've noticed a lot actually. A lot of AI projects just give you an input field and call it a day. Expecting the user to do the heavy lifting.
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You do you, but hivemind thinking is a real thing. I have seen highly upvoted comments seemingly "debunk" an article where on closer examination it becomes clear they actually didn't read the article either. It quickly becomes this weird bubble of people just acting on what everything "thinks" the content is about without ever having looked at the content. I get that is easier, but intellectually you are doing yourse…
But often the top comment debunks what you think is true, and then you learn something. > It quickly becomes this weird bubble of people just acting on what everything "thinks" the content is about without ever having looked at the content. That isn't an issue though since the important part is what you learn or not, not whether you think an imaginary article is true or not. If you learn something from someone debunk…
An argument for synthetic corpi (plural of corpus..esses?) - AI ingesting AI.
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I noticed that it thought that GoatInGrey wrote “openai is no longer relevant.” However, they were just quoting a different user (buyucu) who was the person who first wrote that.
Good catch. That's likely an artifact of the way I flatten the nested JSON from the comments API. I originally did that to save on tokens but modern models have much larger input windows so I may not need to do that any more.
IMO, (Strict)YAML is a very good alternative, it has even been suggested to me by multiple LLMs when I asked them what they thought the best format for presenting conversations to an LLM would be. It is very easy to chunk simple YAML and present it to an LLM directly off the wire: you only need to remember to repeat the indentation and names of all higher level keys (properties) pertaining to the current chunk at the top of the chunk, then start a text block containing the remaining text in the chunk, and the LLM will happily take it from there:
topic:
subtopic:
text: |
Subtopic text for this chunk.
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That would be an actual improvement. Reading the comments section usually just leads to personal energy waste.
On both HN & Reddit, I find the comments more informative and less frustrating than reading the article usually. But I guess YMMV.
Though at this point it's a habit I cannot quite bring myself to break...