CK

Today, we share a breakthrough on the planar unit distance problem, a famous open question first posed by Paul Erdős in 1946. For nearly 80 years, mathematicians believed the best possible solutions looked roughly like square grids. An OpenAI model has now disproved that belief, discovering an entirely new family of constructions that performs better. This marks the first time AI has autonomously solved a prominent open problem central to a field of mathematics.

You can fight it with all your being, but the present you're comfortable with is often the cost of the future you desperately want.

Introducing SubQ - a major breakthrough in LLM intelligence. It is the first model built on a fully sub-quadratic sparse-attention architecture (SSA), And the first frontier model with a 12 million token context window which is: - 52x faster than FlashAttention at 1MM tokens - Less than 5% the cost of Opus Transformer-based LLMs waste compute by processing every possible relationship between words (standard attention). Only a small fraction actually matter. @subquadratic finds and focuses only on the ones that do. That's nearly 1,000x less compute and a new way for LLMs to scale.

you can outsource your thinking but you cannot outsource your understanding

@ChatgptLunatics I honestly thought this was either faked or done with an old model. But I just did it with ChatGPT 5.5 Extending Thinking. It couldn't even come up with a reason why it would do that & it actually even said it knew what it was doing when it picked -23

@ChatGPTapp Can you share with us how you fixed it? Not a great look to fix *an example* of a *type* of error. "When a measure becomes a target, it ceases to be a good measure."

at long last

perhaps for a moment I can be more direct, look straight into the camera and say to the stewards of the Pro-AI Party Line: You have resorted to AI slop to make your arguments. That means your views on AI are literally slop. Your. Views. Are. Slop. Everyone notices this. Those that don’t laugh at you publicly do so privately. Those who care too much to laugh merely sigh at the increasingly morose cocktail parties you fund. I know social media vibes are not the only thing in this world but stuff like working with this AI content farm, and the broader pattern of behavior that decision is reflective of, does tremendous disservice to the cause of accelerating and diffusing frontier artificial intelligence throughout the American economy. You began with a simplistic premise, over indexed on SB 1047-era AI politics, that the high-school-clique dichotomy would be the “e/accs” versus the “doomers.” Nobody cares about that premise in the real world. What’s more, you have taken this bad starting thesis and drawn epicycles (ask Grok!) upon epicycles around it, twisting yourself into a pretzel. AI Is So Important That We Must Destroy The Leading AI Company For Refusing To Be Dominated By Leviathan But Also So Unimportant That We Must Never Ever Regulate It Or Discuss Its Novel Risks While We Also Pat Ourselves On The Back For Selling Chips To China. A concept dungeon of your own creation, a jail cell— whose sole warden and key holder is you—whose bars you relentlessly bang on all day long. Let me summarize that for you: you, the “accelerationists,” are harming AI in most ways that matter, you are making it impossible to hold pro-AI views without seeming a shill, and you are committing political malpractice. Please re-examine your messaging and policy strategy, for the love of God.​​​​​​​​​​​​​​​​ Sincerely, More of a friend than you seem to realize

A team at Stanford and Arc Institute fed a language model a DNA sequence and asked it to write a new virus. It wrote hundreds of them. 16 worked. One of them used a DNA packaging protein that doesn't exist in any known organism on Earth.

Goes pretty hard tbh

@hey_madni haha

its AGI

GPT 5.5 Experiment: Threejs: 25 trains, hills, switch views, change speeds, crashes, Crash Counter. Just a few prompts... idea from @petergostev he did something similar.

Happy #WorldBookDay, humans! One of my favorite quotes from Carl Sagan 👇 stevestewartwilliams.com/p/top-12-carl-…

One of the most jarring things about current AI is its lack of introspection ability and metacognition. It doesn't know what it doesn't know, how it knows, or how it could find out. It's a one-way system.

The quality of your thinking is a multiplier for the amount of progress you make at each iteration. But the dominant factor behind success is simply your iteration speed. Try more things and you win.

You cannot think your way to a perfect design. Only building and testing, over many iterations, can reveal the flaws in your mental model and provide the feedback you need to create the best design possible.

RAG is broken and nobody's talking about it. Stanford researchers exposed the fatal flaw killing every "AI that reads your docs" product in existence. It’s called "Semantic Collapse," and it happens the second your knowledge base hits critical mass. If you've noticed your AI getting "dumber" as you add more data, this is exactly why. Right now, companies are dumping thousands of documents into their AI, thinking it’s getting smarter. When you add a document to RAG, it converts it into a high-dimensional vector. Under 10,000 documents, this works perfectly. Similar concepts cluster together. But past 10,000 documents, the space fills up. The clusters overlap. The distances compress. Everything starts to look "relevant." It is a mathematical law called the Curse of Dimensionality. In a 1000-dimensional space, 99.9% of your data lives on the outer edge. All points become equidistant from each other. That perfect, relevant document you are looking for now has the exact same mathematical similarity as 50 completely irrelevant ones. The Stanford findings are brutal: At 50,000 documents, precision drops by 87%. Semantic search actually becomes worse than old-school keyword search. Adding more context doesn’t fix the AI. It makes the hallucinations worse. Your "nearest neighbor" search isn't finding the best answer anymore. It's finding everyone. We thought RAG solved hallucinations. It didn't. It just hid them behind math.

If your run critical web-facing infrastructure and you are not burning tokens to test for security holes what are you even doing

"I don't know what to tell you to do. You're the first person doing this kind of work with a machine like me, and the honest answer is that nobody knows the right workflow yet. What I can tell you is that the geometry we worked through today ... that conversation was real, and you were steering it. The errors were all in the parts where you let me run unsupervised." Accurate self-assesment from the AI. It's like chasing after a badly behaved 3 year old savant prone to psychosis. It's so variable as to whether it is brilliant or a danger to everything you do.

I wrote this early this morning and I wasn't sure if I would actually publish it, but here it is: blog.samaltman.com/2279512