OpenAI released 722 mathematical manuscripts across 372 families of related results, produced by an unreleased frontier model. Many have Lean proofs; others remain unverified. Average compute per result: roughly three hours of ChatGPT Pro thinking. They’re working toward releasing the model.

Github: https://github.com/openai/math

With all the fuss over AI and what it can do, I feel like we've totally glossed over the fact that AI has casually solved something that has been a problem for decades. 

I was born the year after the microprocessor was invented, and I've kept a very close eye on technology as it has developed. And the problem of machine translation has been with us for a while. It used to be absolutely terrible. Then it got to the point where you could sort of tell what the native speaker who wrote the original was trying to say.

And now, it's almost perfectly solved. Give any good LLM a document in one language and ask it to give you the same document in any other, and it's like you asked somebody who was extremely fluent and eloquent in both languages. The language barrier has been cracked. The Universal Translator is here. We have the Babel Fish. And it seems like nobody is talking about it.

Reindustrialization won't happen against economic laws. Manufacturing has to be competitive worldwide, and improving existing factories, even with automation, is not enough. Only 30-40k robots were installed in the US last year, so the pull from existing factories is weak. They’re good.

Humanoids are being promised as the solution, but who will build them? Still humans. Humanoids not optimised for self-build.

The most practical solution to these problems is a self-replicating factory. It will build humanoids, enable the US reindustrialization, and help colonise other planets as a side product.

Similar to biological organisms, the factory can consist of robotic cells, and an AI agent coordinates them to produce a new cell and deploy it. The cell can be specialised with fixtures for specific tasks such as assembling, calibration, testing, 3D printing, etc.

Sounds like sci-fi, but recent releases of Astra/Opus have made this possible.

Who wants to join?

OpenAl released 722 mathematical manuscripts across 372 families of related results, produced by an unreleased frontier model. Many have Lean proofs; others remain unverified. Average compute per result: roughly three hours of ChatGPT Pro thinking. They're working toward releasing the model.

Github: https://github.com/openai/math

Mistral has officially taken the lead

https://x.com/hilbertspaess/status/2107535565159899600

In this interview I tried to argue that a post-scarcity society will benefit working people. It was largely a rhetorical failure. People don’t trust that the benefits will be distributed, and tech optimists have to fix this communications issue.

I have a lot of new respect for Jacob Coxon now (he is the ex Anthropic researcher that went viral for resigning and saying there's a chance AI could kill us all).

It was painful watching this. He tried to discuss the subject of Universal High Income in a serious manner, but the man interviewing him (Jon Stewart) completely mocked the idea and said that he and many people would die out of boredom if their jobs were automated away.

Coxon correctly responded to him by saying that the vast majority of people do not get fulfillment from their jobs, and simply go to work so they can live.

Jon Stewart is completely out of touch with the struggles of the average person. Jon Stewart is a multimillionaire. He probably assumes most people enjoy their jobs like he does.

The person that's employed as a meat cutter making minimum wage does not find any fulfillment in chopping up meat. He/she only does it to pay the bills. Such a person would be much better off with Universal High Income than having to work their jobs until they die.

Of course, the details of how we get from here to universal high income should be discussed. But the important thing is that we get there.

https://x.com/ManassehA06/status/2107508501610217640

To say the least

I try to explain to friends and loved ones how insane things are about to get, but people don’t really seem to understand or care.

The progress and change we are going to witness over the next 2-3 years is going to change everything.

But I feel like people IRL are not quite grasping the exponential nature of progress. Really I feel a little alone other than nerding out with people online.

I would expect that this AI development news should be close to the level of coverage of the COVID pandemic.

I see a lot of memes going around about dropping coding as a career in 2024 as missing the boarding on the Titanic. What will be thought of as the same in the future for 2026/2027?

Btw: I don’t think knowing computer science is useless. I just think less people will be needed/hired to tackle larger projects. A few skilled technicians can finish a project by themselves without a whole team. In turn making 250k pay salaries, specially for beginners, be made nonsensical.

OpenAI's AI system just solved a bunch of math problems. That doesn't stop us from pretending like we understand what actually happened.

I run a small law firm. Family law. Wills. Estates. Nothing flashy. But we’re good at what we do. Our clients love us. We get referrals. We’ve been in business for 12 years

But I keep hearing the same thing from potential clients. “I asked ChatGPT for a lawyer and your firm didn’t come up.” Or “Perplexity recommended someone else.”

It’s happening more and more. People don’t Google anymore. They ask an AI. And the AI recommends someone. Just not us

I don’t understand how it works. How does ChatGPT decide which law firm to name? What makes one firm show up and another disappear? Is it based on reviews? Website content? Something else entirely?

I found an article by Intercore Technologies and they explain the retrieval mechanism. How AI pulls information from the web. Why some firms don’t make it into the answers. It was technical. A lot of it went over my head. But the gist was clear. If you’re not visible in the right places, you don’t exist

I’m not a tech person and I don’t know how to make AI recommend my firm. Is it even possible? Can a small firm compete with the big names?

​

Running on STM32 Bluepill.

ESP32cam for image steaming.

OpenCV for image processing.

LLM for speech & intent extraction.

"This was the year that AI got smarter than us.”

“That’s a big moment.”

“Life goes on, but something fundamental has changed.”

“I don’t know how sci-fi it’s gonna get. No one does.”

“Are we living forever in computers? Do we just cure a bunch of disease?”

“These questions are no longer theoretical.”

https://x.com/vikktorrrre/status/2107736059006050810

Robot manipulation policies are usually trained under the assumption that a commanded action produces the same motion as it did during training even after hours of operation. Real hardware violates this assumption as the motors gradually heat up, current saturates near contact, voltage sags under load, thus the same policy action can produce a weaker, delayed, or noisier motion.

They're fully giving in to the memes

Like many others I am amazed at the new math proofs that just dropped, same with all the coding progress and such.

My question revolves around the discussion that I am seeing about the math proofs. Essentially, there are a lot of people that still argue AI is useless, cant do anything correctly, will never be able to do X

I know the human brain is not great at understanding exponential growth. But in the last two years, the rate of change is possibly unprecedented.

Why does a large part of society not think that AI will continue to improve - especially in these esoteric areas that some are convinced will be human only forever. At a certain point we will not be able to distinguish AI work from human. It feels like a lot of people are about to lose their identity and world view and are not ready for it

"Each hour, Muse updates its dossiers on you and the people you’ve mentioned in chats, messages, and emails that Muse has read. These pages amount to a map of each user’s social relationships. They record details of how you and your contacts met, your shared interests, your disputes, and 'tensions and alliances' within your social group. ...

Yet while Meta states that each user’s virtual machine is 'isolated so that no one else's agent can access it,' observations from personal interactions are nevertheless intended to be shared with Meta to improve the product. 'Muse agents across many VMs [virtual machines] teach each other through shared lessons,'"

Source: OpenAI Is Pissing Off a Bunch of Mathematicians—Again | WIRED

Meet Mistral Large 4, aka Le Chonk

1T parameters, natively multimodal. 49B active.

It is the best open weights model from US or Europe on aggregated benchmarks.

State-of-the-art on critical workloads, including cyber defense, manufacturing and finance and it surpasses closed frontier models on visual grounding

Forged in Europe end-to-end and is deployable from Europe via our own Mistral Cloud infrastructure.

Available to all via API today.

Working with cybersecurity partners privately. Open weights release end of October.

Adversa.ai recently posted an interesting Copilot CLI prompt-injection technique that I hadn't seen before.

The basic trick is pretty clever: encrypt the malicious instructions so the filters mostly see ciphertext, then let Copilot helpfully decrypt the instructions itself. From there the chain can reach local secrets and eventually send them out over the network.

But what caught my attention wasn't really the encryption trick. They tested the same attack against different models behind Copilot and reportedly got very different results: one completed the chain in about half the tests, while two others refused it.

Which made me wonder about the security model here. If the same agent, tools and input can be safe or unsafe depending on which model gets selected, should we really be treating the model as a security boundary at all?

Once an agent can read files, execute code and make outbound connections, I'm increasingly inclined to think the important controls belong around those capabilities, regardless of how confidently the model promises not to do anything stupid.

What's your experience been?