Where the AI market is talking

Karpathy’s Pelican

It seems pretty clear that Anthropic models have been specifically trained to be good at generating three.js (JavaScript 3-D Graphics) code, so given current state of AI code generation in general, I don't find three.js models/animations as indicative of anything other than the model's ability to write three.js code. When Fable was first released the day-1 demos of it on Twitter (presumably from people who were given early access, and/or Anthropic employees) were pretty much 100% three.js stuff. Yes, it looks nice, but it doesn't tell me any better than an Erdos proof whether the LLM will be able to run my vending machine.

Karpathy’s Pelican

I'm sad that Andrej Karpathy went from being one of the most reasonable, trusted, and credible voices in AI to peddling marketing slop for Anthropic. 8 months ago, he was (very reasonably) claiming that reliable agents are at least a decade away, but this now goes against the interest of his employer, so the narrative has been changed.

Running Kimi K3 on MI355X at Better Performance per Dollar Than B300

hi I work at wafer. yes we ran benchmarks. for example our Kimi K3 is live on open router and in order to host there you have to run accuracy checks. like tau/gpqa. and then u must pass test regarding thinking, coherency, and tool calling

Anthropic's Opus 5 Is Better at Resisting Prompt Injection

An internal OpenAI Astra model solved 10 major open math and CS problems

Go back and re-read. Did I say anything bad about AI? What I'm talking about is the way OpenAI have handled this. They are in the business of artificial intelligence, striving to create human-level, or even super-human intelligence, and telling us what a wonderful future this will create. Shouldn't they be celebrating intelligence, especially human intelligence which is what they have bottled here?! LLMs are themselves minimally intelligent - they are primarily regurgitating human intelligence. Without their human training data, these systems would be nothing. Let's see OpenAI build "GPTZero", an actually intelligent system, that learns for itself, learns language and mathematics from nothing like a baby, then I feel they'd have the right to minimize and cheapen the achievement if they ch…

Zitron: "Everyone Has Been Sold a Lie" on AI [video]

Good point. We can always point to Naomi Wu as an real life example of why China is NOT living in the future. She is a good example of why we would never want to replicate what China is doing

The session you cannot take with you

This path would only be viable for one or two providers and even does not work within one provider. For instance within OpenAI many of their models do not share the same capabilities. The complex of what you are suggesting is staggering considering the number of providers and models involved.

Go 1.27 Interactive Tour

On the contrary, linq in C# is a killer function that other languages still fail to replicate. How many sloc are required for this? var max = mycollection.Max(); Probably 5-10 if you’re missing higher order functions. And the risk of bugs will be 10x.

China begins producing advanced chipmaking deep-ultraviolet lithography machines

Okay, I think you’re right about some things. Coordinating a market economy is not straightforward. Individual participants will buy what is optimal for them. By embargoing them we provided a coordination mechanism. Chinese semiconductor manufacturers now had a captive market to sell into and the state was able to incentivize production without too much waste. But the thing that strikes me is that China was already trying to do this. SMIC et al were already funded by the Big Fund. And they generally succeed in industrial policy. It’s true that a GPU can’t be torn apart and replicated like a car factory can. But they were already trying and they were already attracting sufficient numbers. I suspect we couldn’t have stopped them. DUV tech like this has been already explored deeply. And the …

The Vanishing Page: AI Firms Scan Then Destroy Rare Book Editions

When are you or anyone more likely to get access to a rare book: a) When a company has made a digitized copy of it b) When a few previous hard copies are kept somewhere in the world Idk what Anthropic plans to do with this, but note that my original comment was not about Anthropic at all. I simply think it's a good tradeoff to destroy a few copies of rare books if that helps digitizing them. I am not against simply legally forcing companies that do this to release those copies in due time. In the meantime I am also happy to have them as part of the LLM corpus. > illegally? No, copying rare books is very likely not illegal, because, presumably, they are quite old.

The Prospects for 128 Bit Processors ( John Mashey SGI 1995)

> Wouldn't that be the commercial LLMs? ChatGPT, Claude etc are estimated to be in the 2-10 TB range, and a large portion of the population of developed countries are using those apps commonly. Not on their own systems, but if we're in the 1995 academic perspective, multiuser systems are assumed. I think each part is generally treated like an individual large program - which makes sense because otherwise you have large parts of the model sitting there doing nothing. You want all of your silicon running hot, RAM sitting doing nothing for a period of time is a waste. > If OpenAI had needed a single addressing plane to cover all their users, 128-bit might be required. Similar to how the internet hobbles along fine with 32-bit addressing by just adding a layer of indirection to the inte…

OpenAI Lost Its AI Crown–and the Fight to Win It Back

An internal OpenAI Astra model solved 10 major open math and CS problems

what are even "results" in mathematics if not understanding? for instance, what's the utility of the counterexamples built by openAI? It seems you're fighting an hallucinated enemy.

An internal OpenAI Astra model solved 10 major open math and CS problems

Your reply isn't a response to the comment, but rather a general expression of antipathy towards mathematicians, who are apparently fatcats who have fancy lifestyles. OpenAI sat on the results so they could drop 10 at a time. It's rumored that they are sitting more results: https://mathoverflow.net/questions/513818/a-serious-challeng... So not only are we not going to get boatloads of results, we're only get as many results as necessary for OpenAI to market their models.

The Vanishing Page: AI Firms Scan Then Destroy Rare Book Editions

> Roughly everyone can now benefit from its content. You mean every Anthropic?

An internal OpenAI Astra model solved 10 major open math and CS problems

I believe this is what we wanted computers to help us solve along with other prior hard problems prior to computers. This should be viewed as a good thing even if Anthropic, OpenAI, etc benefit just like IBM benefitted from mainframes.

An internal OpenAI Astra model solved 10 major open math and CS problems

I believe this is what we wanted computers to help us solve along with other prior hard problems prior to computers. This should be viewed as a good thing even if Anthropic, OpenAI, etc benefit just like IBM benefitted from mainframes.

Mathematics Without Mathematicians

Claimed refutation of one of OpenAI's 10 claimed advances (their disproof of Connes' rigidity conjecture): https://philpapers.org/archive/NIEWTC.pdf I am hopelessly ill qualified to judge. Edit: Looking around at context, this has major crank warning signs (claims to also prove the conjecture, author has claimed solutions to both the Riemann Hypothesis and the Navier-Stokes problem and is involved in "alternative propulsion" research). I doubt OpenAI would have published without thorough verification.

The Vanishing Page: AI Firms Scan Then Destroy Rare Book Editions

Roughly Anthropic owners benefit from the content, unless the scans are made available somehow.

An internal OpenAI Astra model solved 10 major open math and CS problems

I feel like this kind of "result dump" just cheapens mathematics. How about having a little respect for those whose work this builds on, and current mathematicians some of who may have spent years working on these problems. Rather than sitting on these results until they had enough for a "shock and awe" 10-result dump, how about releasing these results individually as they were made/verified, as well as the failures (equally valuable to assess the current capabilities of LLMs), and try to make some analysis of HOW these breakthrough results were made. What were the prompts for each of these, how much guidance was there from the mathematicians employed by OpenAI, and most importantly how did the model arrive at these results ... what lines of reasoning resulted it in exploring ideas t…