Forrest Will

[Forrest Will @willforrest94]

@911NewsBreaks The core team’s Unit 8200 ties:
• CEO Michael Shaulov: Alum w/ intel awards. Firm works w/ Israel's Ministry of Finance
• CPO Idan Ofrat: Mobile security pioneer
• CTO Pavel Berengoltz: Check Point vet
• Board Member Gili Raanan: 10+ yrs in Unit 8200

[Forrest Will @willforrest94]

@911NewsBreaks 🚨 Trump’s DNI Pick Jay Clayton Has Ties to Israeli Intel-Linked Crypto Firm.
President Trump just tapped Jay Clayton (ex-SEC Chair, now SDNY U.S. Attorney) as Director of National Intelligence. But it’s his private sector work that stands out... 🧵👇

[Forrest Will @willforrest94]

@911NewsBreaks Fireblocks was founded in 2018 by Israeli cybersecurity veterans. Many of these founders and executives have deep, decorated ties to Unit 8200 — the IDF’s elite signals intelligence unit.

[Forrest Will @willforrest94]

@911NewsBreaks Around 2021, Clayton joined the advisory board of Fireblocks — a major digital asset custody giant valued at billions.
While massive in the crypto space, the company's roots trace straight back to elite military intelligence.

[Siddhartha Saxena @siddsax]

Anthropic onboarding day: Michael Scott introducing Karpathy like he just signed Wemby in free agency.

[Andrew McCarthy @AJamesMcCarthy]

This is probably the best look at the shockwaves I’ve seen from the latest Starship flight.

Captured from a GoPro I clamped onto a proper camera to record simultaneous video. (I’ll show you the photo the better camera took in the reply)

[Jim Fan @DrJimFan]

We trained a humanoid with 22-DoF dexterous hands to assemble model cars, operate syringes, sort poker cards, fold/roll shirts, all learned primarily from 20,000+ hours of egocentric human video with no robot in the loop.

Humans are the most scalable embodiment on the planet. We discovered a near-perfect log-linear scaling law (R² = 0.998) between human video volume and action prediction loss, and this loss directly predicts real-robot success rate.

Humanoid robots will be the end game, because they are the practical form factor with minimal embodiment gap from humans. Call it the Bitter Lesson of robot hardware: the kinematic similarity lets us simply retarget human finger motion onto dexterous robot hand joints. No learned embeddings, no fancy transfer algorithms needed. Relative wrist motion + retargeted 22-DoF finger actions serve as a unified action space that carries through from pre-training to robot execution.

Our recipe is called "EgoScale":

- Pre-train GR00T N1.5 on 20K hours of human video, mid-train with only 4 hours (!) of robot play data with Sharpa hands. 54% gains over training from scratch across 5 highly dexterous tasks.
- Most surprising result: a *single* teleop demo is sufficient to learn a never-before-seen task. Our recipe enables extreme data efficiency.
- Although we pre-train in 22-DoF hand joint space, the policy transfers to a Unitree G1 with 7-DoF tri-finger hands. 30%+ gains over training on G1 data alone.

The scalable path to robot dexterity was never more robots. It was always us.

Deep dives in thread:

[Brian Roemmele @BrianRoemmele]

Meet Raven:

[Bilawal Sidhu @bilawalsidhu]

God's eye view 24-hour replay of Operation Epic Fury.

The Iran strikes kicked off and I set an AI agent swarm loose to record every OSINT signal I could find before the caches cleared. Built a full 4D reconstruction in WorldView.

I can scrub through minute by minute and watch the whole thing unfold on a 3D globe:

> Airspace clearing over Tehran
> Ground strike coordinates locking in
> Severe GPS interference blinding the region
> EO and SAR satellites making passes over the strike zone
> No-fly zones locking down 9 countries
> Shipping fleets scrambling at the Strait of Hormuz

It's pretty amazing how complete of a picture you can build without "proprietary data fusion" -- one dev with public signals and a love for computer graphics and geospatial intelligence.

Thank you for all the love on my last post. Dropping WorldView in April. This my friends is just the beginning.

[Elvis @elvissun]

https://t.co/DotZ3V6XhJ

[Forrest Will @willforrest94]

@mcuban I am both

[Forrest Will @willforrest94]

@Plinz Been following your work forever, so the newly released emails raised some real questions for me. What was the context behind your comments on developmental timing and culture? Genuinely curious to hear your own explanation.

[Forrest Will @willforrest94]

@JacklouisP Would be interesting to see a fiber laser head on it for cutting steel

[Google Earth @googleearth]

🌍 Announcing the latest advancements to Google Earth AI - a platform designed to unlock a new level of planetary understanding. ➡️ https://t.co/GDCalxFB93

🔗 Geospatial Reasoning framework
🧠 Deeper Insights in Google Earth
🎉 Expanded access to Earth AI

[Google AI @GoogleAI]

Today, we’re announcing a major breakthrough that marks a significant step forward in the world of quantum computing. For the first time in history, our teams at @GoogleQuantumAI demonstrated that a quantum computer can successfully run a verifiable algorithm, 13,000x faster than leading classical supercomputers.

This continues to build momentum on past quantum computing discoveries. Back in 2019, we proved a quantum computer could solve a problem that would take a classical computer thousands of years. Then in 2024, our new Willow chip solved a major issue in quantum error correction that challenged the field for nearly 30 years. Today’s breakthrough moves us closer to quantum computers that can drive discoveries in areas like medicine and materials science.

[Sundar Pichai @sundarpichai]

An exciting milestone for AI in science: Our C2S-Scale 27B foundation model, built with @Yale and based on Gemma, generated a novel hypothesis about cancer cellular behavior, which scientists experimentally validated in living cells. 

With more preclinical and clinical tests, this discovery may reveal a promising new pathway for developing therapies to fight cancer.

[Stephen James @stepjamUK]

𝗗𝗟𝗥 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵𝗲𝗿𝘀 𝗴𝗮𝘃𝗲 𝗮 𝗿𝗼𝗯𝗼𝘁𝗶𝗰 𝗮𝗿𝗺 𝗳𝘂𝗹𝗹-𝗯𝗼𝗱𝘆 𝘁𝗼𝘂𝗰𝗵 𝘀𝗲𝗻𝘀𝗶𝘁𝗶𝘃𝗶𝘁𝘆 𝘄𝗶𝘁𝗵 𝗻𝗼 𝗮𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝘀𝗸𝗶𝗻 𝗻𝗲𝗲𝗱𝗲𝗱.

They used internal force-torque sensors at 8 kHz + deep learning. The robot can feel where you touch it, recognize letters drawn on its surface, and respond to virtual buttons placed anywhere on its body.

What's interesting is the infrastructure behind it. To train these models, you need high-frequency sensor streams, manifold learning to unfold trajectories, and the ability to iterate fast.

They collected 2,300 samples from 20 people and hit 95.5% accuracy on digit recognition.

This is what's possible when you have the right data infrastructure.

📄 https://t.co/yadvb1iKnW

Video credit: @DLR_en

[The Humanoid Hub @TheHumanoidHub]

Remarkably lifelike motion and fluidity.

BeyondMimic is a framework for training humanoid whole-body control from large mocap datasets.

First, an open-source motion-tracking pipeline to reproduce diverse, highly dynamic human skills on real hardware, then distilling them into a guided state-action diffusion model for zero-shot, task-specific control.

Project page: https://t.co/VR6JT7TMYK