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One thing I'm really starting to notice is that the theoretical physics community is responding to the disruption created by AI in a completely different way than the mathematics community has.
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Bend 2 is here!
It is a new programming language that blocks AI mistakes via *proof checking* - the same technique big AI labs used to solve open math problems, like Navier-Stokes.
It is also very fast, and runs on GPUs.
Watch the video. Link in the comments.
RELEASE DAY
After almost 10 years of hard work, tireless research, and a dive deep into the kernels of computer science, I finally realized a dream: running a high-level language on GPUs. And I'm giving it to the world!
Bend compiles modern programming features, including:
- Lambdas with full closure support
- Unrestricted recursion and loops
- Fast object allocations of all kinds
- Folds, ADTs, continuations and much more
To HVM2, a new runtime capable of spreading that workload across 1000's of cores, in a thread-safe, low-overhead fashion. As a result, we finally have a true high-level language that runs natively on GPUs!
Here's a quick demo:
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Anthropic’s Economics team is sharing a new model of how AI might affect economic growth, jobs, wages, and more by 2030.
Explore the scenarios, tell us what you think will happen, and see how your answers compare to more than 10,000 Americans. anthropic.com/institute/econ…
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As a PhD in mathematics who now works in AI/ML, the recent Navier–Stokes news may bring both excitement and unease to the math community. My PhD advisor, Yizhao T. Hou, has spent more than two decades studying blowup in the Euler equations with his students and collaborators.
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Physicists should put together a list of mathematically well-defined problems that could advance the field. Such as, eg, what constitutes a low-temperature superconductor, what are the lattice conditions for cold fusion (or why do they not exist), what is a local completion of quantum mechanics, what is the simplest (in terms of computational complexity) way to obtain the masses and coupling constants of the standard model, what is the UV-completion of MOND or why isn't there any, is there any provably UV-finite version of quantum gravity, etc etc
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China published the most uncomfortable paper on vibe coding.
ETH Zurich tested 100 developers in a controlled, commercial-grade vibe coding environment to see who actually succeeds.
The findings are brutal.
The researchers tracked computer science achievement, written communication skills, and general cognitive reasoning.
They wanted to see what actually predicts vibe coding proficiency when you never touch a line of source code yourself.
Two major predictors emerged.
Written communication proficiency mattered. The ability to structure thoughts and articulate intent unambiguously in text directly impacts what the AI builds.
But that wasn't even the main takeaway.
Computer science achievement was a massive, dominant predictor of success.
Even when researchers controlled for general intelligence and reasoning skills, CS background still heavily dictated who built working software and who completely crashed.
In fact, CS knowledge contributed roughly twice the unique predictive variance of writing skills alone.
Why? Because vibe coding isn't about writing code. It’s about debugging logic.
When an AI agent builds a complex application and quietly breaks an edge case under the hood, a non-technical user looks at the glowing UI and assumes it works.
They don't know what questions to ask. They don't know what logic to challenge. They lack the mental models to recognize architectural catastrophe.
You can prompt your way past syntax.
You cannot prompt your way past a fundamental lack of engineering intuition.
The hype told us that learning to code is dead because language is all you need.
The data just proved the opposite.
To truly master the vibe, you still need to understand how the machine thinks.
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D. E. Shaw Research AI/ML Roles Applications are now open!
13 roles spanning LLM research, agentic AI, drug discovery & infrastructure with transparent salary ranges from $115K to $1M base.
We're hiring researchers, engineers and scientists for hybrid roles in NYC, Durham or Pittsburgh. Selected candidates get variable comp (sign-on + year-end bonuses), relocation/immigration assistance and access to cutting-edge DESRES infrastructure.
Apply at the link here: deshawresearch.com/current-o…
Applications reviewed on rolling basis • Hybrid work
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We are now poised to:
Formalize all known Human Mathematics.
This is the modern equivalent of the Human Genome Project. or AlphaFold more recently, whereby all data of its kind is fully classified.
With sufficient funding and compute/tokens, a coordinated effort among academia, frontier labs, philanthropic organizations, and the federal government can formalize all Human Mathematics in the next year.
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this rumor that Anthropic has solved a Millennium Prize Problem is getting very interesting.
Terence Tao a few hours ago:
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Checking that a major mathematical proof is correct can take years. Formalization—converting the mathematical reasoning into a form computer proof assistants like Lean can verify—can help.
Last month, Claude completed the first formalized proof of Fermat’s Last Theorem, one of the most famous theorems of all time. This was a project experts thought would take many years. It is the largest Lean proof ever written.
Fermat’s Last Theorem was first proven in 1995 by Sir Andrew Wiles, more than 350 years after it was conjectured. Our proof, which totals over 13 million lines of code, provides machine verification. More importantly, it proves over 29,000 other theorems that the proof requires, across many areas of math which had never before been formalized.
We see this as a major step in the long process of firming up the core of mathematical knowledge, building on work from three centuries of mathematicians and hundreds of contributors to Lean and Mathlib. We are optimistic that AI-assisted verification of mathematical proofs will help reduce the burden of refereeing mathematics in an era where more proofs are being produced than ever before.
You can read about the process on our Science Blog: anthropic.com/research/forma…
And see the complete proof on GitHub: github.com/anthropics/fermat…
Announcing Discovery Loop!
I am very excited to announce that, along with my longtime friends and collaborators @Sanjay_Ghemawat, @OriolVinyalsML and @quocleix, we are founding Discovery Loop (@DiscoLoopAI), a Public Benefit Corporation whose mission is to automate machine learning, science, and engineering to accelerate discoveries and progress. The four of us have worked together for 14 to 30 years, and have helped build some of the world’s most used products, infrastructure and AI models, and we’re excited to turn our attention to this ambitious endeavor.
♾
Learn more at: discoveryloop.com
Tomorrow will be my last day at Google after 27 years, and watching it grow from 25 people to 190,000+ has been an amazing journey. Below is a note I shared with many people internally at Google today. An excerpt is:
It has been an absolute pleasure to work with you and to help build some of the most widely used and impactful products of all time. As a kid, I dreamed of helping build software that would be used by many people, and Google now has thirteen products used by more than a billion people (amazing!). Our work has had a tremendous impact in the world, and I have been lucky enough to collaborate and form friendships with many colleagues that I deeply admire, respect, and enjoy. It still brings me joy every time I see people out in the world using our products to find information, handle email, translate documents, watch videos, learn new things, navigate and understand the physical world, browse the web, use their phone, run large-scale computations on our infrastructure, ride in an autonomous vehicle, or perform complex tasks with the help of our AI systems. I hope you all share this sense of joy, because it is a shared accomplishment! Thank you to all of my colleagues at Google over many years!
Now I'm excited to go start @DiscoLoopAI with my longtime friends and colleagues @Sanjay_Ghemawat, @OriolVinyalsML, and @quocleix.
(Updated post: slightly redacted to not have some personal info)
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Scribble is live for @AlphaSchoolATX. Families and schools can join our waitlist at:
usescribble.com/
Our learning scientists and developers ship new programs to students throughout the year. We don't hold updates until fall.
At a traditional school, kids wait for the teacher to get trained on new software before anyone can use it. That's not how we work. When our team builds something better, students get it immediately.
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10 weeks. Cohort 4 shipped.
Live demos. Real talks. Spotlights with grads now in the field.
The proof was in the build.
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Right now we have three sets of Gauntlet AI spouses on stage (both spouses attended/graduated from Gauntlet).
I don't know what it says about Gauntlet that people later encourage their spouse to come, but it makes me really happy.
teapaw.org/i/broadcasts/1yxBeMDkP…
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Cohort 4 – Showcase Day teapaw.org/i/broadcasts/1yxBeMDkP…
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The astronauts. Their ride around the Moon.
The Artemis II astronauts pose for a group photo after viewing their Orion spacecraft — which they named Integrity — in the well deck of USS John P. Murtha following their splashdown.

ALT NASA astronauts Reid Wiseman, commander; left, Christina Koch, mission specialist; CSA (Canadian Space Agency) astronaut Jeremy Hansen, mission specialist; and NASA astronaut Victor Glover, Artemis II pilot, right, pose for a group photo after viewing the Orion spacecraft in the well deck of USS John P. Murtha, Saturday, April 11, 2026, in the Pacific Ocean off the coast of California. Credit: NASA/Bill Ingalls




































