Link to tweet:

https://x.com/ArtificialAnlys/status/2115265133467635877?a=MdxXvvV3KDsac8gnZBc&s=20

Link to bench:

https://artificialanalysis.ai/

The vulnerability, with a severity rating of 9.5 out of 10, resides in the TianoCore EDK II, an open source codebase that hardware makers and cloud providers use to create the UEFI firmware that runs during the boot-up sequence.

The PHP vulnerability was rated 9.3

Xplore released RWE-bench today, 20 mechanical, electrical and aerospace tasks written by engineers who do this for a living. One is a diesel crankshaft that has to survive 2,400 rpm, one is routing a DDR3 memory bus on a real FPGA board, and one is CFD on a turbine vane. No model grades the results. A program checks every design against every requirement and runs physics solvers where it has to. GPT-6 Astra leads at 37%, Opus 5.5 is at about 29% and GPT-6.1 Sol at about 25%, for $10 a run. Astra and Opus cost about six times that per run for 12 and 4 more points.

37% sounds low until you ask how a person would do. Give someone off the street the same tasks and tools and they pass none, because they won’t get past drawing the part in CAD and running the simulation. An engineering graduate might get one or two in their own field after a month of work. A specialist would pass the ones in their own field, about a third, at roughly a week each. The model does some of all three fields in a few hours a run. The human time and what they could do are my own rough estimate, Xplore doesn’t publish a human score. (but i think they should next time!)

Code got good fast because you can check the answer, and most physical engineering has a much longer feedback loop than code. There’s still a lot of work to do, but now it’s work with a scoreboard, and from what I’ve seen that’s what makes the next jump happen. I wrote up the tasks, the human comparison and why I think the grading matters more than who’s on top: https://www.anchorterminal.com/blog/rwe-bench-engineering

https://github.com/anthropics/formal-math/tree/16a5ce41304fcdaea4e0be7c132e1c361dc26da5/eth

We already know that software is easy (hence, all the rapid explosive progress in generating text/math/code/images/video). Unfortunately, the digital space is the area that has the least immediate benefits to the lives of the average person.

Biology and Hardware are different beasts entirely. They're much more difficult, but solving them would be immensely more beneficial to the human experience.

By what year do you guys think general-purpose humanoid robots will be capable of autonomously performing professional-quality haircuts: fades, tapers, and complex hairstyles, with the precision, safety, consistency, and speed of an experienced human barber, without any human assistance?

And will you trust a robot with a straight razor next to your neck/head?

For the record, I'm aware that singularity doesn't have a consensus definition, and that everyone interprets it uniquely or focuses on different aspects of it to varuous degrees. That said, this year is proving to be rather remarkable in terms of progress, breakthroughs, and application. It would seem 2030 or soon after is the "brace for impact" moment when our relationship to technology, and what day-to-day life entails, will shift dramatically. Is Kurzweil sticking to his guns for any particular reason?

I see how some people can watch flashy robot demos on the timeline and think with a shiver that the robot apocalypse is around the corner, but reality check:

There is no robot in the world that can change the batteries in a jack-o-lantern.

If you have a robot friendly door handle it might be able to walk outside, pick it up, and carry it back inside. The robot could open the drawer with your batteries in it, but then things get tough.

Opening the package of AA batteries? Extracting a precision screwdriver from its case? Unscrewing four tiny screws that require a hard press and twist? Popping batteries out of the case?

Yes, you could build and train a specialist robot to do that today, but there is a very long way to go to replace the full range of human capabilities. Near term doomer scenarios should stop at “secretly controlling humanity” instead of “wiping humanity out”

https://x.com/ID_AA_Carmack/status/2114897771001974921

There are lots of wild rumors about discoveries governments may be keeping under wraps: antigravity, zero-point energy, UFO recovery and reverse engineering, and major advances in math and physics. There are even claims that “dead end” research like string theory was intentionally encouraged to distract from other discoveries.

People like Marc Andreessen have talked about hearing interesting things from government officials, and Eric Weinstein has raised related questions (whatever you think of him, these are just a couple of the more recognizable names who have made claims along these lines). I don’t know how much of it I believe, but it’s interesting to consider.

With the progress happening in mathematics at AI labs, and potentially physics soon as well, I wonder whether some of these supposed secrets could come out through independent discovery. If another lab reaches the same conclusions, keeping the original work classified might no longer accomplish much.

Do you think significant breakthroughs have already been discovered and withheld for military or economic reasons? And could AI make it harder to keep those discoveries hidden, especially as more people gain access to increasingly capable models?

Edit: I know people are dunking on the UFO stuff in the comments, but there has been a lot of whistleblower testimony for decades, and most recently in 2017 to now. People have even testified to congress about it. There is documentation that the govt participated and pushed a discrediting campaign about UFOs to make anyone who engaged in the topic to seem crazy. And with the way the comments are going, kinda reinforces that…

Two mathematician/physicist friends of mine have worked on Yang Mills for the last few months and made substantial progress. Due to OpenAI's claim, they have decided to publicize their results. They have a vision of community-generated math that makes a lot of sense to me in post-AI mathematics. I consider that part just as interesting as the result itself so I am helping share their claims around.

They worked with small budget (~$500) and made substantial progress toward YM existence, building on Bałaban as I understand it, just as OpenAI did in their statement. Their work is human-guided with AI doing substantial specified work. Both Claude and ChatGPT were substantial contributors.

This github has their work in it, with timestamped commits starting from a few days ago when they decided to release. They have a vision of a community-generated mathematics, wherein the open internet can contribute towards a full solution using any tools at their disposal, starting for now through this github. It's a new modality of math that to me seems positive/to look forward to. Nobody loses substantial time from being scooped by an unknown competitor because progress is visible live, with more honest attribution than a traditional paper. As they are completed, proofs can be formalized and published in a traditional manner to reach fully accepted status. I understand it as an experiment on how math can be done as much as an important partial result. (yes, community math has been done before, the difference is that now it is a more logical modality than ever in the past)

https://github.com/MathePhysics/4D_Wilson_Loop_RG

https://x.com/MathePhysical/status/2115040756226474115

AI has made leaps this year. And yes we are right to celebrate it. But what does the future hold? Not everyone in software right now can move to farming?
Last 2-3 months hardware was supposed to be a bit of a hedge, but with models getting more efficient that might not be a big blocker soon.
What work do you think will be sustainable in the long run?
And honestly I do not believe the narrative “we will no more work for money”. Yeah AI democratises but people sitting in control will never let a powerful tool fully fall into the hands of the masses without heavy restrictions.

Google DeepMind announced Genie 4 today. You describe a place and it builds a scene you can move through in real time, and you can keep typing while it runs to change the weather, move the camera or add people. Scenes now stay consistent for two minutes, double Genie 3, it makes its own sound, and characters talk back. The interesting change is Gemini 3.8 Flash running inside it, breaking goals into steps and checking every frame to see if each step worked, which is an agent loop in a world that’s being generated around it.

It’s a research preview for trusted testers. Google says Ultra subscribers get it in Project Genie (waitlist) and early access developers get it through the Gemini API over the coming weeks, with no price yet. Google’s own list of what doesn’t work lists that objects drift in long sessions, and text, sound accuracy and complex physics aren’t solved. I wrote up what’s new, why world models matter as training grounds for agents, and who can use it: https://www.anchorterminal.com/blog/genie-4-world-model

Read through Anthropic’s S-1 and the revenue numbers are wild.

They did $4.6B for all of 2025 and $16.3B in the first six months of 2026. The number of customers spending $100K+ a year also went from 950+ to 11,900+ in roughly a year.

The $80.7B net loss is probably the headline everyone will focus on, but $72.4B of that comes from changes in the value of its financing instruments. Operating loss was $693M, so the picture is a bit different from what that first number suggests.

One thing I found interesting is the ownership structure. The founders get 50.1% of the voting power through a special share, which means public investors won't have much control over company decisions.

I came across the funding numbers through Autumn AI while looking into the filing. The rest is from the S-1.

I am more curious about whether this growth can hold up than the headline loss. These numbers are huge, but the infrastructure commitments are huge too. Curious how people here are reading this.

What will happen when everyone can create software? When AI is getting better and better that software can be created in a few months, then a month, then a few weeks?

Well, the news of Microsoft today is what is about to happen to the entire Industry, or maybe one of the things that may happen.

https://www.cnbc.com/2026/10/26/microsoft-msft-voluntary-buyouts.html