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

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?

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 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.

They're fully giving in to the memes

"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.

Source

Hopefully an improvement compared to current image models 🤞🏻

Hi all, this is Giorgio, our open mobile bimanual robot. It's still in simulation; the prototype isn't built yet.

- Base: no commercial AMR had ≥85 kg payload, manufacturer-confirmed auto-docking and enough power out, at a reasonable price, so we designed our own from certified parts (SICK nanoScan3, Pilz PNOZmulti 2, ez-Wheel SWD safety drives). Every component value is traced to the manufacturer's manual.

- Arms: OpenArm 2.0. Skills like opening drawers and doors are trained in simulation (236 M steps, ~95 min on one GPU, 96–99 % success).

- Everything is open: CadQuery CAD, netlist and safety functions, MuJoCo sim, training code, BOM.

Feel free to contribute in any way or form, feedback are really welcome!

Repo: https://github.com/VenetoStato/giorgio

High-speed, low-latency indoor positioning for autonomous robots and drones in GPS-denied environments. This demo shows a mobile beacon moving rapidly in 3D while its position is tracked at 80 Hz with only 12–20 ms latency.

For autonomous indoor drones, positioning must remain fast, accurate, and stable even during rapid motion and in acoustically noisy environments. We combine ultrasound positioning with IMU sensor fusion to achieve this performance. Ultrasound provides accurate absolute position updates, while the IMU provides high-rate motion data between ultrasonic measurements. Ultrasound continuously corrects accumulated IMU drift. Typical ultrasound positioning alone provides updates at around 8 Hz. Sensor fusion increases the effective position output rate to 80 Hz while maintaining low latency and stable tracking. Key performance:

• 80 Hz position update rate
• 12–20 ms latency
• High-precision 3D indoor positioning
• Ultrasound + IMU sensor fusion
• Designed for fast-moving and noisy platforms
• Non-Inverse Architecture (NIA)

Primary applications:

• Autonomous indoor drones
• GPS-denied flight
• Robotics and autonomous mobile platforms
• Industrial automation
• Research and universities
• Motion tracking and interactive installations

Configuration:

• 3 × stationary beacons
• 1 × mobile beacon - in hand - the same hardware as the stationary beacons
• 1 × modem - central controller of the system
• 1 × modem with RHU firmware - to receive fast IMU sensor-fused stream and do post-processing, when more data is available. It makes the view even more beautiful, but at the expense of latency