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Breaking: Jeff Bezos is in talks to raise $100 billion for a new fund that would buy manufacturing companies and use AI to automate them wsj.com/tech/jeff-bezo…

Let me explain what just happened, because I don’t think people realize how INSANE this is. > Cortical Labs put 200,000 real human brain cells onto a silicon chip and trained them to play Doom in just one week. > Each CL1 system costs $35,000. > A rack of 30 units consumes only 850–1,000 watts combined. > The human brain operates on 20 watts. > Large AI training clusters burn through megawatts. >Backed by In-Q-Tel. 115 units began shipping in 2025. > Cortical Labs is selling “Wetware as a Service” through Cortical Cloud, letting developers deploy code remotely to living human neurons with no lab required, > priced like a software subscription but powered by real brain cells grown from adult skin and blood samples. > it isn’t about gaming, it’s about biological computing that could eventually outperform traditional silicon in energy efficiency and adaptability. This is getting really scary and we’re still at the very beginning.

GPT-5.4 Thinking and GPT-5.4 Pro are rolling out now in ChatGPT. GPT-5.4 is also now available in the API and Codex. GPT-5.4 brings our advances in reasoning, coding, and agentic workflows into one frontier model.

Ok, I need to level up my AI skills. This is just amazing.

BREAKING: 🇮🇱 Israel used its laser air defense system against rockets launched from Lebanon. The “Laser Dome” (Or Eitan) laser system successfully intercepted rockets from Lebanon. A new era of defense.

🚨🇺🇸🇮🇷 Someone just built a live Iran situation room. Real-time Tehran webcams. Isfahan feeds. AI-generated intel briefings. Verified geolocations. Plus $3 million in live bets on what happens next. This is what modern warfare intelligence looks like.

This is monumental.

🚀⚠️Rockets were a choice made in chaos and curiosity is what fractured the world. Signs spread. Systems broke. Humans kept trying. Failing. Pushing forward. Pointing rockets at Mars wasn’t escape. It was proof we still chose to move on. 🤫 #ai #aifilm #aicg #shortfilm #aivideo #Curiosity

Introducing Gemini 3.1 Pro, our new SOTA model across most reasoning, coding, and stem use cases!

the first official AI movie is here and.. its wild China’s top director Jia Zhangke was so impressed by Seedance 2.0 that he made a film himself.. in just 3 days when asked if AI will replace filmmakers, he said cinema has always moved with tech. Digital cameras didn’t kill film. AI will just make it faster, simpler and better meanwhile Hollywood is busy hunting down AI creators and filing lawsuits check out this masterpiece

Paul Schrader on AI films But what does this Paul Schrader guy know about films anyway so

End-to-end neural networks racing drones in Abu Dhabi! 🚁 Check out the drone racing team from Delft University of Technology! A completely end-to-end neural network solution, from pixels to direct motor commands. No Kalman filters. No computer vision feature detectors. Just neurons flying the drone. The challenge is extreme. These drones fly at high speeds and need split-second decisions with minimal onboard resources: a single rolling-shutter camera and an IMU. Their approach is called SkyDreamer, based on the Dreamer-v3 reinforcement learning algorithm. First, a world model is trained in simulation. Then, the neural network learns how to fly in its dreams through reinforcement learning. The network's internal state can be read out to see where it thinks it is on the track or how fast it's going. Even better, the drone estimates some of its own body characteristics during flight, like the camera angle relative to the body, eliminating time-consuming manual calibration. The system uses only a single camera and the gyros from the IMU, ignoring the accelerometers, just like human FPV pilots do. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news → ziegler.substack.com

🚨WATCH: Jewish philanthropist and the owner of the New England Patriots, Robert Kraft, has unveiled an ad targeting Jew-hatred that will be aired during the Super Bowl this Sunday.

One Team 🚀 x.ai/news/xai-joins…

DeepSeek just dropped an OCR monster… again 🤯 @deepseek_ai just released DeepSeek-OCR 2, a 3B-parameter model that sets a new SOTA for visual + document understanding. At the core: DeepEncoder V2. Instead of scanning images in a rigid grid (top-left → bottom-right), it reads documents like a human. First, it builds a global understanding. Then it learns what to read first, next, and why. The result? Massively improved OCR on complex layouts. → follows multi-column documents correctly → links labels to values → reads tables coherently → handles mixed text + structure with high reliability Compared to OCR 1: ✓ +4% accuracy improvement ✓ Outperforms Gemini 3 Pro on OCR benchmarks ✓ Much stronger on real-world documents Still insanely efficient: Most OCR systems need thousands of tokens per page. DeepSeek-OCR keeps extreme compression without losing fidelity. Same philosophy. Better brain. Under the hood: A dual-stage design combining → a vision encoder for intelligent optical compression → a decoder that reconstructs structure, semantics, and layout In practice: ➤ Higher accuracy on messy documents ➤ Better structure understanding ➤ Cleaner outputs (HTML / JSON / structured data) Traditional OCR reads pixels. DeepSeek-OCR 2 understands documents 🔥 Open source. Free to use. Repo in 🧵 ↓

Our collaboration with @GE_Aerospace has demonstrated that a liquid-fuel rotating-detonation ramjet can power missile systems efficiently and affordably showcasing the power of next‑generation air‑breathing propulsion. Click for more:

Ever wonder what happens in the aftermath of a supernova? In this video, you’re watching the glowing remnants of a stellar explosion disperse over a period of 25 years, making this @chandraxray’s longest-spanning video ever released. go.nasa.gov/3NdVizV

I just read a paper that completely broke my brain. It describes a system that solved an AI task with over 1,000,000 sequential steps... with ZERO errors. Using AI models that are known to be flaky and make mistakes. How is that even possible? 🤯 We all know LLMs have an error rate. Even 99.9% accuracy is a death sentence for long tasks. Imagine you need 1,000 correct steps in a row. With a 99.9% success rate per step, your chance of finishing the whole thing is only ~36%. At a million steps? Forget it. It's statistically impossible. So for years, the race has been to build bigger, "smarter" models to get that per-step error rate closer to zero. We're trying to build a perfect genius. But this paper ("Solving a Million-Step LLM Task with Zero Errors") does the complete opposite. It's a total paradigm shift. Here's the "holy shit" moment: Stop trying to make the AI perfect. Instead, build a system that's immune to its imperfections. How? Smash the problem into the tiniest possible pieces. (They call it Maximal Agentic Decomposition). Have a team of simple, cheap AIs vote on the answer for each tiny piece. It's less like hiring one world-class chef and praying they don't have an off day, and more like designing the McDonald's kitchen. The system guarantees the burger is the same every time, even if any individual worker could mess up. The reliability comes from the process, not the person. They tested this on the Towers of Hanoi puzzle—a classic benchmark where AIs fail spectacularly as the task gets longer. They set it up for 20 disks. That requires 1,048,575 perfect moves in a row. (seriously, over a million steps) A single AI trying this would be a comedy of errors. But their system of "micro-agents" voting on every single move... nailed it. Flawlessly. And the plot twist? The most expensive, "state-of-the-art" models weren't even the best for the job. A smaller, cheaper model (gpt-4.1-mini) was more cost-effective because the tasks were so simple. This is a huge deal for AI safety, too. A single, god-like AI is a black box. It's unpredictable. But a system of a million simple agents? You can inspect it. You can audit each step. The agents have no grand "worldview"—their entire existence is to solve one tiny puzzle and then disappear. It's controllable. So next time you're building something with an LLM, maybe stop asking "how can I prompt the model to be smarter?" And start asking: "How can I design a system where it's okay for the model to be dumb?" The real power isn't just in the model. It's in the architecture you build around it. This isn't just about AI. It's a fundamental lesson in engineering and problem-solving. You don't always need perfect components to build a perfect machine. You just need a damn good design. ...which makes you wonder what else we're trying to solve by chasing individual perfection instead of building better systems.

How small is a transistor on a modern processors?