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This is not as good as it sounds, because Starlink required many satellites in low Earth orbit to achieve this bandwidth improvement globally. That said, there will ultimately be over 100k SpaceX communications satellites, most of which will be V4 or V5. Good chance that this system carries about half of global traffic.

I mean how cool is this! Let's prove memory safety for all C. Sounds crazy to say out loud, and tbh this is just a start... but we have to start somewhere. Explore it @

Everyone believes CUDA is NVIDIA's moat, the reason we've maintained our dominance in the AI era. I've never believed that. NVIDIA has only one true moat: its people. And the most important of them is Jensen Huang. I've worked on CUDA compilers, libraries, and DSLs for eight years, from Volta to Rubin. I think I've earned the right to share my perspective on what NVIDIA's moat really is. I hope you'll bear with me. 1. What is CUDA? Many people think CUDA is just a programming language and its compiler, and that NVIDIA's moat comes from the massive amount of software written in CUDA, making it impossible for developers to leave NVIDIA GPUs. This is completely wrong. It's an incredibly naive understanding of CUDA. To me, CUDA is everything NVIDIA does. CUDA isn't just a language, software, or hardware architecture. It's a product defined by a unique customer experience. When you find GPU programming too difficult, CUDA gives you an easier way. When your GPU code isn't fast enough, NVIDIA engineers respond and help you solve it. When a handful of GPUs can no longer push your technology forward, NVIDIA has already scaled its computing platform from desktops to massive data centers. When you need more data centers than anyone else can supply, NVIDIA has already secured wafer and HBM capacity years in advance to meet your demand. That experience is CUDA. It's made possible by the relentless, day-to-day work of everyone at NVIDIA. And the person who brings all these people together, who gives them a shared vision and purpose, is Jensen Huang. 2. What made CUDA possible? Patience. Perseverance. An unwavering commitment to a vision. Around 2006, NVIDIA bet that the future would demand far more computing power, and began transforming GPUs from graphics processors into a platform for general-purpose computing. Initially, it was a commercial disaster. The added costs hurt margins, the market wasn't ready, and the strategy faced enormous skepticism and pressure, including questions from the board. But Jensen persisted. For roughly a decade, NVIDIA kept investing in CUDA, building its hardware, software, and developer ecosystem long before the commercial payoff became clear. Eventually, deep learning arrived. And NVIDIA was ready. Today, this sounds like an inspiring story. But for those who lived through it, it meant a decade of perseverance, doubt, and being misunderstood. Looking back at the history of technology, how many people could have done the same? This is what I've always loved most about NVIDIA. And now, Jensen has been pushed out. This won't kill NVIDIA. The company can still create tremendous commercial value. But it has killed NVIDIA's soul. And how far can a body go without its soul? If this is how Jensen Huang is treated today, how will those of us who built CUDA be treated tomorrow? I love NVIDIA deeply, but I have to say it: NVIDIA is dead.

I have a major result to share. I’ve found conclusive forensic evidence that the top 4 Chinese AI labs—DeepSeek, Moonshot, MiniMax and Ant Group—have built their newest models on NVIDIA’s most advanced Blackwell chips, which are barred from export to China. This sounds insane, but I discovered that almost every AI model carries a hidden signature of the GPU that trained it. When a model is first set up, the starting values of its weights are laid out in a pattern that depends on the exact GPU. Each combination of GPU and seed number produces a unique pattern—a sort of fingerprint of the hardware used to create the model. Traces of that pattern survive training into the published model. Again, this evidence isn’t based on any insider information or leaking. It’s based on a forensic analysis of the model weights released by the companies themselves. I searched more than six trillion combinations of chip layout and seed number and matched them to open-weights models. In doing so, I found conclusive evidence that six Chinese AI models—including DeepSeek-V4-Pro, Kimi K3, and MiniMax-M3—were initialized on NVIDIA Blackwell systems. Four of the models match Grace Blackwell, the GB200 and GB300 parts in NVIDIA’s flagship liquid-cooled rack-scale systems. I then verified these results on real hardware. Running unmodified PyTorch with the seeds I’d recovered, a Blackwell B300 reproduced DeepSeek-V4-Flash’s untouched starting weights bit for bit, and a rented GB200 reproduced Kimi K3’s to a correlation of 0.999995. A Hopper H100 reproduced none of DeepSeek-V4-Flash’s, and changing the seed by one broke the match. (DeepSeek’s seed is 953049583 by the way.) The method also was verified to match 24 known open-weights models to the hardware that developers said they used. It is exceedingly unlikely that these results are a coincidence. Across six trillion combinations, the best accidental match should reach only about 8 sigma (i.e., eight standard deviations above what random chance would produce). The real matches range from 49 to 1,385 sigma, and even the weakest single test hits 20. These were data-center-scale training runs. With some analysis of release schedules and model details, I estimate that the largest, Kimi K3 and DeepSeek-V4-Pro, used 2,000 to 3,000 Blackwell GPUs each. Clusters of this size cost on the order of $150 to $200 million each to buy, and across these labs alone would add up to more than half a billion dollars of export-controlled GPUs, not counting experimental compute or otherwise. It’s quite likely that we see multi-billion dollar expenditure on export-controlled GPUs. I am highly confident in this result, and it has been independently verified by senior researchers at OpenAI and Anthropic. You can read the full report at There is much more I’ve identified about these models through other forms of checkpoint forensics like this, and will have more to share soon.

I'm proud I worked at @NVIDIA. @JensenHuang is the biggest reason why. I'm not there anymore, so I can speak freely. His departure is a huge loss. Especially now. I spent almost five years in the culture he built and knew people who'd worked with him for 20+ years. They respected him technically and loved him as a person. They said it without a hint of doubt. The focus stayed on the mission and on people. Good ideas got support regardless of title. That sounds like standard company values. It wasn't. Behind it was his vision and a way of leading that is out of fashion in today's founder advice: - Be kind, not arrogant. - Innovation, not marketing. - The wins belong to the people, not the CEO. - Don't fire by default. Find the person a new role. It's counterintuitive, and I doubt there is a harder way to lead. What I saw was leadership through kindness and care, not by squeezing people. A real visionary. He understood the technology and the people building it. His internal talks had vision, conviction, technical depth and jokes good enough for stand-up. You left wanting to build, and you couldn't wait for the next one. An affair, on its own, shouldn't outweigh decades of leadership. The serious questions deserve real answers. But the soap opera shouldn't decide his future. We're at a critical moment for AI. His technical judgment, vision, and commitment to the work are incredibly hard to replace. I want Jensen to keep building and leading. There's too much important work ahead.

i can't talk to friends or family about ASI and what might happen next year without sounding like a chronically online person lost in sci-fi fantasies. it's increasingly frustrating that trying to be rational about what's happening sounds delusional to normies. like i'm the one detached from reality when, from where i'm standing, they're the ones missing what's unfolding right in front of them. the gap in perception is getting so absurd it's making me feel like I am the schizo who needs to touch grass but im just trying to inform them because the media doesnt

Five suicide attempts couldn’t kill her. An HIV diagnosis was supposed to finish the job. Instead, she walked into a body modification studio and started cutting away everything that made her human. Both ears. 32 teeth. Her nose. She split her tongue, implanted 18 horns into her skull, and covered every inch of skin in scales. Today she goes by “it” - and says she’s never been happier. Her full name is Eva Tiamat Baphhomet Medusa. The world calls her the Dragon Lady. She calls herself “the world’s most modified transsexual.” Before any of this, she was Richard Hernandez - a vice president at a bank in Texas. She grew up in Bruni, a town of 300 people near the Mexican border. As a freshman in high school, she walked up and down the streets screaming “I’m gay” at the top of her lungs in one of the most machismo regions in the country. “Closets are for clothes and tennis shoes,” she says. At 18, she left for Houston. Fresh from a town with no skyscrapers, she didn’t know you couldn’t trust just anyone. Two men raped her. She doesn’t hate them. “I’m not them,” she says. She became a banker. Built a career. Then came the HIV diagnosis, which later progressed to AIDS. She left the bank to take care of her health and nearly died. That’s when something shifted. “I decided I’m not going to die looking like a human,” she told Soft White Underbelly. “Being human has been such a negative part of life.” She spent over $60,000 on body modifications. Not with doctors - with body modification artists. She was the fifth person in the world to get horns implanted into her cranium. She was the first person ever to have both ears surgically removed for cosmetic reasons. She had her testicles removed and plans a full penectomy. “I will be a genderless dragon,” she says. “That’s what dragons are.” Her son hasn’t spoken to her since the transformation. She did some of the modifications specifically so he could see what his father was becoming. “His weird-looking, strange, used to be a man, is now a woman, is now a reptilian, dad,” she says. She was abandoned in the woods as a child by her birth parents. She denounced them in favor of a snake. She tried to take her own life five times before the transformation gave her a reason to stay. “My transformation is by and large what saved my life after five failed suicide attempts,” she says. “I am truly happy for the first time in my life.” She prefers the pronoun “it.” Just like a snake. She swallows food whole because she only has six teeth left - representing the mouth of a rattlesnake. Sirens drive her crazy because without ears, everything sounds louder. She makes cotton candy for the neighborhood kids, and her neighbors say her energy is “the most gentle and pleasant” they’ve ever been around. In the video below, you can see Tiamat sitting across from the interviewer with her full-face scale tattoos, horn implants, and no ears, calmly describing how she eats, why she removed her teeth, and the moment she decided to stop being human. Some will say she’s mentally ill and needs help, not a body modification studio. Others will say she found the only thing that kept her alive when nothing else could. If removing every trace of your humanity is what finally made you happy - were you ever really human to begin with?

Our strongest frontier model, Gemini 4 Carbon, is now available. Built for deeply complex, long-horizon workflows that push a frontier model’s intelligence, Carbon delivers frontier performance in agentic workflows across complex knowledge work, software engineering and web development, cybersecurity, and scientific reasoning. While Gemini 4 Argon remains the ideal model for the majority of software engineering and knowledge work, Carbon steps in for your hardest tasks that push a model to its limits — from complex, multi-step debugging to open-ended scientific research. Available starting today: — Consumers: use it in the @GeminiApp and AI Mode in Search for Google AI Ultra subscribers — Developers and Enterprises: build via the API in @GoogleAIStudio and @AndroidStudio, Google @Antigravity for Google AI Ultra subscribers, and Gemini for business — Scientists and Researchers: apply for a chance to get 6 months free via @Antigravity — Cybersecurity Defenders: apply to join the Fairwind Program for access to Carbon for cyber Watch how Carbon brings reasoning and coding together, turning avian research into an interactive 3D simulation of a songbird’s vocal organ that synthesizes the sounds of six bird species live.

I wanted to test Fable 5.2's quality with one-shot prompts without human feedback. Everything in this game is from scratch: models, sounds, textures. ~$550 at API pricing, ~2.7M output and 445M input tokens, heavily cached. Took 8 hours! Play it here: