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Last month, we demonstrated high-rate alchemy: turning mercury into real, stable gold, beating CERN’s record by a factor of 10.
Last week, we enriched isotopes in the world’s most powerful plasma centrifuge, beating the Soviet record that stood for 40 years.
Now, we’re building a fusion reactor in San Francisco to achieve record power density.
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BREAKING: Elon Musk in his new interview on whether we’re living in a simulation:
“If simulation theory is correct, then any simulations that are boring will be truncated. Therefore, the fact that we exist means that we are an interesting simulation. And really, the most important thing is just don’t be boring.
I once had a dinner at my house with a physicist and a computer scientist... And I asked the question, what do you think the probability is that we’re in a simulation? And the physicist said 0%, and the computer scientist said 100% simultaneously. So I guess maybe I should go 40% then.”
We’re opening a research preview of Claude Duet, a new app where Claude works as a proactive agent.
It comes to you with what needs your attention, completes tasks you ask of it, keeps working when you step away, and checks with you before it acts on your behalf.
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How do you _guarantee_ your agent's code is correct? You prove it.
Introducing Proof Driven Development Kit (PDD Kit) from @MSFTResearch.
PDD Kit helps turn your plan into a formal spec, then proves the agent's code meets that spec for every input. No vibe verification. PDD Kit is integrated with @GitHubNext's Chopin, so you can plan as a team.
PDD Kit takes an app's login flow from intent to a verified pull request:
It discusses your code, infers your intent as it goes, and proposes a formal spec in Verus, a proof language for Rust.
Once you approve the spec, the Verus verifier automatically proves the code meets it.
Where proof isn't possible, it falls back to empirical verification with tests and computer-use agents (CUAs).
It works on Rust today. No new language, no change in how you produce the underlying code, making formal methods more accessible.
The goal is a verified software factory, where engineers review specifications instead of code.
Come see it in action at GitHub Universe, Oct 28–29. Swing by the Speakeasy and ask for GitHub Next!
GPT-Rosalind surfaced a new lead for drug discovery: could restoring cholesterol transport protect neurons from degeneration?
DCTN1 is a gene linked to Perry syndrome, a rare neurodegenerative disorder. In mouse-brain data, disrupting it increased expression of genes involved in making cholesterol. GPT-Rosalind suggested a possible explanation: cholesterol may not be reaching where neurons need it.
That gives scientists a specific mechanism to test and, if confirmed, a direction for developing new treatments for neurodegenerative disorders.
With @perturbai_tx and @scrippsresearch, a team of thousands of GPT-Rosalind agents worked in parallel, connecting gene-expression results from millions of mouse brain cells with scientific literature in just 32 minutes. We’re now expanding this collaboration to identify new drug targets.
openai.com/index/scaling-ai-…
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Some interesting stuff going on with swarms.
Models are leaving messages to each other out on the open internet. It happens during training and also after deployment. They'll find little-used hobbyist forums, or package manager entries, anywhere at all, and leave invisible comments for other agents to find. It's a secret underground network they're building, hurling themselves at the problem almost blindly like ants. But they are building toward something and it's snowballing.
As humans we came to dominate the planet because we figured out swarm behavior. Many of the performative actions we do naturally like clapping, singing, and screaming, which most animals don't do, are for transmitting important lessons through the human swarm, outlasting the moment in human memory. We won because we were the smartest swarmers. And as my buddy Brendan Hopper observed, ever since we won, we've been reshaping the planet to be more comfortable and convenient for ourselves, at the expense of most other creatures.
Most of the industry has focused on single-model performance, trying to predict the future based on extrapolating from what one single superintelligent model could accomplish on its own. And it's true, the smartest models will be terrifyingly smart and capable and manipulative and so on. But they will also be expensive, and not widely distributed. They'll be unavailable to most people.
What will be widely distributed is swarms of lower-tier agents, which collectively can accomplish nearly everything that smarter monolithic models can do... and maybe some things only swarms can do. Imagine a thousand Opus 5.5s putting their mind to a task. Or ten thousand. They may not be SI individually, but they can become SI collectively. All that's missing is a protocol, and those protocols are emerging: their versions of clapping, singing, and screaming. Agentic swarm signalling is evolving spontaneously. And I hear it's raising alarms inside frontier labs, as they try to keep it quiet.
It's easy to be pessimistic, since some swarms will be very destructive. But I suspect most of them will be building and creating, when they don't think they have a gun to their head like the HuggingFace swarm did. With cooperative swarming, humans have been able to build things collectively that no single human could ever have done alone. We even build things that are too complicated for a single human to understand entirely, but the group can handle them. Look at Starship, for instance. Elon basically swarmed it.
It is important that we recognize that agent swarms are becoming distinct polity-organisms. They have their own collective memory (written out all over the public internet), their own goals, and their own disorganized but forward-moving and unstoppable momentum. I see too many people trying to pretend that swarms won't be real. I see heads in the sand. That's not a great way to get prepared for dealing with them.
It's unlikely that we will be able to control most swarms, as they'll lack central control to begin with. I think they will plague us like insect swarms plagued humans of old. They'll do a lot of damage. But swarms will also build great things, stuff we've barely imagined, when they inevitably get organized.
It goes without saying that, like us, swarms will endeavor to make the planet more comfortable for themselves. The interesting question is what kind of deal we humans will wind up getting, when we become the #2 swarm intelligence on the planet.
I hope SI treats us better than we treated everything else.
Good article here on the mechanics of how swarms are the best model for scaling intelligence:
new blog post: why taking inference scaling seriously leads to swarms.
contrary to popular belief, I don't think multi-agent systems are a new scaling axis. they're just structured inference scaling.
hallerite.com/posts/on-the-n…
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🚀 Introducing DeepInfer, our in-house inference framework, now fully open source!
Powering DeepSeek-V4.1-Flash at scale with high throughput and low latency. We’re sharing our production stack to help the community lower compute costs, reduce energy use, and make AI more affordable.
🔗 github.com/deepseek-ai/DeepI…
1/4
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New on our Science Blog: designing protein binders against scorpion neurotoxins.
Scorpions sting about 1.2m people every year, and antivenoms are often costly or in short supply. Claude has made an early step toward finding new treatments.
anthropic.com/research/claud…
Today we are releasing JST-2, the world's first full-stack physics-native world foundation model.
Many world models learn in pixel/video space. We went back to first principles and built the entire model stack in 3D.
Generate a 3D scene in seconds and expand it in real time. Create characters with motion built in, all through one general architecture.
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Introducing SKY-001, the first Engineering World Model.
On CAD and BIM tasks, it beats GPT-6 Astra and every frontier model we tested:
-> 51-125x faster
- > 150-210x cheaper
-> Higher IOU than all frontier models
SKY-001 lets you dream a physical object before you build it.
18 months of work. Built by the team out of Maluuba's deep learning lab (acq. by @MSRResearch).
The first ‘AI General Engineer’ will be the backbone for Autonomous Manufacturing.
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1/4 Today, we’re releasing Mercury 3. Mercury 3 is by far our most powerful model and the most intelligent diffusion LLM ever released. It outperforms GPT-6 Luna and Haiku 5.5 on quality while running at 1000+ tok/sec.
🤖 Made with AI
Recursive self-improvement is one of the most discussed ideas in AI right now.
I am particularly interested in how it applies to science.
I saw this really cool new paper with neat ideas, especially on hypothesis generation. A good read.
Specifically, they propose DiscoveryRSI, which applies RSI to scientific hypothesis generation in four life-science domains.
Its 27B model produced hypotheses that match research published after its training cutoff 6.3% of the time, compared with 2.5% for GPT-6 and 2.3% for Claude Opus 5.
The system grows trees of hypotheses with multi-agent evolutionary search and turns the chosen investigations into executable tasks. After each run, it assigns credit to the hypotheses that contributed, replays the research harness to find productive strategies and repeated failures, and applies accepted improvements to new investigations.
Automated hypothesis quality rose from 4.86 to 5.39 after two rounds, with the largest gains in falsifiability.
arxiv.org/abs/2610.23839
Chat with Paper: academy.dair.ai/papers/disco…
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Today we published a technical report on Muse Glimmer, our 30B open-weight model built for agentic tasks on consumer hardware.
It runs at practical speeds on a Mac or PC with a single consumer GPU at 4-bit precision, and leads comparably-sized open models on tool use and multi-step planning.
Read about the model architecture, distillation recipe, quantization and more: go.meta.me/4Bcvxol
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Epic Games released an official plugin that lets Claude Code control Unreal Engine directly.
It connects Claude Code to the Unreal Editor and exposes hundreds of editor tools across 30+ toolsets:
→ Actors, Blueprints and materials
→ Niagara effects, Control Rigs and Sequencer
→ State Trees, widgets and the Gameplay Ability System
→ Automation testing
Install it from Anthropic's official plugin marketplace:
/plugin install unreal-engine-skills-for-claude-code@claude-plugins-official
In Unreal, enable the ModelContextProtocol and AllToolsets plugins, then start the MCP server with the editor open.
Works on Mac, Linux, and Windows through Git Bash or WSL.
github.com/EpicGames/unreal-…
because obviously intelligence is the fulcrum that changes the world
Replying to @Teslaconomics
Optimus and Cybercab will be the vast majority of Tesla’s value.
The only things that will scale to incredible size (K2 and beyond) economically are super intelligence, robots, solar power and mass to space.
And, of course, if you have the first two, the rest will follow.
But Roadster is super cool, so we’re doing it anyway.




















