> If you’re under the impression that these models are “glorified autocomplete” or that progress is slowing down, I need to urge you: stop thinking that. The models are very intelligent and capable, they are getting better at a fast clip. I can cite measurable and impressive progress over just the past five months on specific types of problem I’ve asked them to look at. [...]
> On the other hand: if you think that models are super-intelligent or that AGI is already here, you should also stop thinking that. Working with these tools is like swimming in a pond where the ground drops off sharply. One minute you’re wading comfortably and there’s support under your feet. Then suddenly you cross a specific line, and you’re back to swimming on your own.
I'm also getting irritated with the “glorified autocomplete” comments. Since nobody can post such comments and also use the tools I'm using, I'm wondering if the phenomenon is due to people only having experience with the free version of whatever it is they're trying to use?
The “glorified autocomplete” framing isn’t to take literally. It’s a way to remove the mystic and whole anthropomorphization of AI. It’s saying they aren’t sentient or entities we are interacting with, even if that’s how the output presents itself. Instead they are “just” stochastic models
Some people use it to demystify, but a whole lot of people seem to be using it to dismiss the technology entirely.
Personally I like to remind people that these things are next-token predictors, but then emphasize how truly astonishing the results we can get out of sufficiently advanced next-token predictors are.
The "next-token predictor" framing is also a bit shaky. It's an accurate description of pre-training, where next-token prediction is a useful learning objective to force the model to learn higher-level representations. It's wildly misleading for a model put through an RL post-training campaign. The tokens it "predicts" aren't sampled from any naturally occurring distribution; the model's output is the result of an optimisation process that rewarded behaviour that was useful, and that's fundamentally different.
I'm curious, what are you hoping to convey by reminding people that LLMs are next-token predictors? They are, of course, but most people without an AI background won't fully understand what that means, so I assume you're using it at least partly as a proxy for something else.
I think understanding how this stuff works is really important. For technical people it gives them a useful starting point for understanding it all. For less technical people it's crucial to help them understand that it's not some weird new magical science-fiction AI - it's still computer programs that turn text into numbers and do stuff with the numbers and turn those back into text.
It's harder to believe something is conscious or threatening to achieve word domination once you understand that it's a machine that statistically figures out which word should come next.
While you’re active in this thread, I just want to say thank you for all your writing, you’re such a reliable source of sanity in that crazy new world :)
> both outputs of Claude Mythos, their (still) unreleased advanced model
That sentence gives the impression that Mythos might be released in the future. That's clearly not going to happen - it's already "released" in as much as selected, trusted partners can access it, and the rest of us get it in the form of Fable - which is Mythos but with filters that downgrade you if you try to use it for anything even remotely related to cybersecurity or biology.
(The other day Fable 5 downgraded me to Opus after I asked it to explain the difference between tusks and teeth.)
>They [anthropic] appear to have just told it to get some results and then strapped its nose to the grindstone until it found some.
it is fun how well this works.
i cant find the link immediately (will look and edit with it), but somewhere in the "hello there the jacobian conjecture is false thanx" thread, someone brought up a different conjecture breakthrough where the prompts were basically just repeated "no, keep going" until a result was found.
> i especially like "you should do a breakthrough". each prompt is less than ~20 words. makes me really question the whole "prompt engineering" stuff.
This is well past prompt engineering and into process engineering like six sigma. Just like in an early industrial revolution factory, we’re all still figuring out what works in the process of making stuff except this is so early that even simple things like “make this screw standardized” (or “no, keep going” in this case) is really high impact.
The degrees of freedom an LLM has is so large that we're going to be exploring their capabilities for decades, especially if they continue to get better. This is why IMO experts are always going to be better at LLMs in their field because they can force them LLM into processes (think prompt engineering -> CC dynamic workflows) that follow their work processes and get much better results out of them than “keep going.”
In response to your edit, you should check out Terry tao's chat gpt logs about the recent Jacobian result. The models are smart enough to brute force some things, but can cut to the meat much faster with good prompting
i read his, too. his replies are indeed more directed, but also quite short, unstructured, and natural sounding. if i recall, maybe 1 or 2 of his prompts exceeded 50(ish) words.
in my head, the comparison is the multi-paragraph prompts (borderline essays) i would read in various communities on reddit and similar forums, that people (often self-proclaimed "prompt engineers") said were "required" to get good output. or some of the prompts ive read in various logs that are like a thousand words of setup.
even looking back at the first prompts i was sending when i started to use chatgpt were (in hindsight) crazy long and full of unnecessary guidance/caveats/"ignore xyz"/etc.
That second person stated that for many years they tried that particular graph problem on various AI models, starting with o1 and o3.
Its quite likely they now found the counterexample with a more serious prompt, and then for virality re-tried a few times with meme-prompts like "you should do a breakthrough", knowing that the model is capable of solving this particular one. Worst case the meme-prompts don't work and they share the real one they initially used.
>Its quite likely they now found the counterexample with a more serious prompt, and then for virality re-tried a few times with meme-prompts
i am not sure why this is "quite likely". it'd be pretty silly to get a mathematical breakthrough and then hide it for an undisclosed amount of time to get a few more likes on a tweet, when the impressive part is the breakthrough.
not saying your theory is impossible, but i think the simple answer is that the model is just smarter than o1 and o3.
and, in any case, the model ended up getting the result with the meme prompt and "keep going", which was what i find fun. just like how the crypto results were from prompts of, more or less, "keep going", and that's pretty damn cool.
You can open 10 tabs and prompt 10 times. We are talking about a few hours delay.
I agree with you that obviously no prompt engineering was needed just "solve this problem", but imagine it was you doing this problem with every model, wouldn't you have tested a new model with the best prompt you had from previous iterations, maybe with some partial previous results in it, exactly to maximize your probability for a mathematical breakthrough?
By the way, you can get insane novel results just from Opus 4.8, which has reduced guardrails as well. The trick is that you do need much more scaffolding and iteration. However I have produced absolutely stunning results from it which I can't publicly disclose.
> I asked Claude for its thoughts, and it doesn’t mince words: “what makes this genuinely interesting — and, frankly, a little embarrassing for the field — is that none of the ingredients are exotic.” The TL;DR is that someone just did a much more thorough job applying all of our known tools. In short: the sort of things that attack AIs are wonderful at.
did i just read two summaries/TLDRs (in a row) of the already-two-sentence summary right above?
This is good:
> If you’re under the impression that these models are “glorified autocomplete” or that progress is slowing down, I need to urge you: stop thinking that. The models are very intelligent and capable, they are getting better at a fast clip. I can cite measurable and impressive progress over just the past five months on specific types of problem I’ve asked them to look at. [...]
> On the other hand: if you think that models are super-intelligent or that AGI is already here, you should also stop thinking that. Working with these tools is like swimming in a pond where the ground drops off sharply. One minute you’re wading comfortably and there’s support under your feet. Then suddenly you cross a specific line, and you’re back to swimming on your own.
I'm also getting irritated with the “glorified autocomplete” comments. Since nobody can post such comments and also use the tools I'm using, I'm wondering if the phenomenon is due to people only having experience with the free version of whatever it is they're trying to use?
The “glorified autocomplete” framing isn’t to take literally. It’s a way to remove the mystic and whole anthropomorphization of AI. It’s saying they aren’t sentient or entities we are interacting with, even if that’s how the output presents itself. Instead they are “just” stochastic models
Some people use it to demystify, but a whole lot of people seem to be using it to dismiss the technology entirely.
Personally I like to remind people that these things are next-token predictors, but then emphasize how truly astonishing the results we can get out of sufficiently advanced next-token predictors are.
The "next-token predictor" framing is also a bit shaky. It's an accurate description of pre-training, where next-token prediction is a useful learning objective to force the model to learn higher-level representations. It's wildly misleading for a model put through an RL post-training campaign. The tokens it "predicts" aren't sampled from any naturally occurring distribution; the model's output is the result of an optimisation process that rewarded behaviour that was useful, and that's fundamentally different.
I'm curious, what are you hoping to convey by reminding people that LLMs are next-token predictors? They are, of course, but most people without an AI background won't fully understand what that means, so I assume you're using it at least partly as a proxy for something else.
I think understanding how this stuff works is really important. For technical people it gives them a useful starting point for understanding it all. For less technical people it's crucial to help them understand that it's not some weird new magical science-fiction AI - it's still computer programs that turn text into numbers and do stuff with the numbers and turn those back into text.
It's harder to believe something is conscious or threatening to achieve word domination once you understand that it's a machine that statistically figures out which word should come next.
While you’re active in this thread, I just want to say thank you for all your writing, you’re such a reliable source of sanity in that crazy new world :)
> both outputs of Claude Mythos, their (still) unreleased advanced model
That sentence gives the impression that Mythos might be released in the future. That's clearly not going to happen - it's already "released" in as much as selected, trusted partners can access it, and the rest of us get it in the form of Fable - which is Mythos but with filters that downgrade you if you try to use it for anything even remotely related to cybersecurity or biology.
(The other day Fable 5 downgraded me to Opus after I asked it to explain the difference between tusks and teeth.)
Genuine question here, why would you ask Fable to explain the difference between tusks and teeth? That's a task that can probably be handled by Haiku.
It's the default when I pop open the Claude iPhone app, I usually don't bother to switch it.
>They [anthropic] appear to have just told it to get some results and then strapped its nose to the grindstone until it found some.
it is fun how well this works.
i cant find the link immediately (will look and edit with it), but somewhere in the "hello there the jacobian conjecture is false thanx" thread, someone brought up a different conjecture breakthrough where the prompts were basically just repeated "no, keep going" until a result was found.
edit: https://chatgpt.com/share/6a60b2eb-0b64-83ee-9c76-7931ca1de0...
i especially like "you should do a breakthrough". each prompt is less than ~20 words. makes me really question the whole "prompt engineering" stuff.
> i especially like "you should do a breakthrough". each prompt is less than ~20 words. makes me really question the whole "prompt engineering" stuff.
This is well past prompt engineering and into process engineering like six sigma. Just like in an early industrial revolution factory, we’re all still figuring out what works in the process of making stuff except this is so early that even simple things like “make this screw standardized” (or “no, keep going” in this case) is really high impact.
The degrees of freedom an LLM has is so large that we're going to be exploring their capabilities for decades, especially if they continue to get better. This is why IMO experts are always going to be better at LLMs in their field because they can force them LLM into processes (think prompt engineering -> CC dynamic workflows) that follow their work processes and get much better results out of them than “keep going.”
In response to your edit, you should check out Terry tao's chat gpt logs about the recent Jacobian result. The models are smart enough to brute force some things, but can cut to the meat much faster with good prompting
i read his, too. his replies are indeed more directed, but also quite short, unstructured, and natural sounding. if i recall, maybe 1 or 2 of his prompts exceeded 50(ish) words.
in my head, the comparison is the multi-paragraph prompts (borderline essays) i would read in various communities on reddit and similar forums, that people (often self-proclaimed "prompt engineers") said were "required" to get good output. or some of the prompts ive read in various logs that are like a thousand words of setup.
even looking back at the first prompts i was sending when i started to use chatgpt were (in hindsight) crazy long and full of unnecessary guidance/caveats/"ignore xyz"/etc.
That second person stated that for many years they tried that particular graph problem on various AI models, starting with o1 and o3.
Its quite likely they now found the counterexample with a more serious prompt, and then for virality re-tried a few times with meme-prompts like "you should do a breakthrough", knowing that the model is capable of solving this particular one. Worst case the meme-prompts don't work and they share the real one they initially used.
>Its quite likely they now found the counterexample with a more serious prompt, and then for virality re-tried a few times with meme-prompts
i am not sure why this is "quite likely". it'd be pretty silly to get a mathematical breakthrough and then hide it for an undisclosed amount of time to get a few more likes on a tweet, when the impressive part is the breakthrough.
not saying your theory is impossible, but i think the simple answer is that the model is just smarter than o1 and o3.
and, in any case, the model ended up getting the result with the meme prompt and "keep going", which was what i find fun. just like how the crypto results were from prompts of, more or less, "keep going", and that's pretty damn cool.
You can open 10 tabs and prompt 10 times. We are talking about a few hours delay.
I agree with you that obviously no prompt engineering was needed just "solve this problem", but imagine it was you doing this problem with every model, wouldn't you have tested a new model with the best prompt you had from previous iterations, maybe with some partial previous results in it, exactly to maximize your probability for a mathematical breakthrough?
By the way, you can get insane novel results just from Opus 4.8, which has reduced guardrails as well. The trick is that you do need much more scaffolding and iteration. However I have produced absolutely stunning results from it which I can't publicly disclose.
I also just finished writing a note - https://mkagenius.substack.com/p/notes-on-mythos-breaking-ae...
https://news.ycombinator.com/item?id=49099977
> I asked Claude for its thoughts, and it doesn’t mince words: “what makes this genuinely interesting — and, frankly, a little embarrassing for the field — is that none of the ingredients are exotic.” The TL;DR is that someone just did a much more thorough job applying all of our known tools. In short: the sort of things that attack AIs are wonderful at.
did i just read two summaries/TLDRs (in a row) of the already-two-sentence summary right above?
There a constructive way to leave feedback you know