Congrats @SpaceX! Gorgeous launch, getting Ship to orbit and managing every step in a safe, responsible, and especially inspirational way. @NASA, along with the rest of the interested public, is excited to help where we can and for Starship missions to become routine!

There are only two launches in history that put more payload mass in orbit than Starship Flight 14. And that’s with only 26 Starlink satellites. Future launches will carry up to 60.

Most predictions I see are still way too conservative. Here's mine

NHTSA announced today that they are developing a new federal safety standard specifically for autonomous driving system performance to prepare for larger-scale Robotaxi deployments in the U.S. Instead of regulations saying a car must have this particular physical control or design, NHTSA says it wants more regulations focused on whether the autonomous vehicle can actually perform safely. Morrison (the head of NHTSA) specifically says the standards should include measurable driving competencies and repeatable ways of testing them. NHTSA also announced that it: • Is launching the ASCEND consortium with SAE to accelerate development of AV performance standards • Is developing a national AV “competency standard” • Is updating federal safety rules to accommodate vehicles designed without human drivers or traditional controls • Has proposed changes to five major federal vehicle safety standards, with “many more coming” • Is developing new AV guidance aimed at helping the industry transition from testing to “mid-scale driverless deployments” • Is working on standards covering emergency responders, remote assistance and safety management systems • Plans to prioritize safety performance over specific vehicle designs • Will continue investigations, enforcement and recalls when AVs pose unreasonable safety risks NHTSA says the goal is to create a national framework for determining whether an autonomous vehicle is a safe driver. Great to finally see the government taking autonomous vehicles seriously and trying to make changes to old laws that were intended for manual driven vehicles.

I just bought a new Model Y and can confidently say cars and Teslas are no longer the same product. A car is like a pony or horse. I still like driving a stick shift, it’s fun! Tesla with FSD is a transportation robot. I had one of the first Model 3s in 2017. FSD was basically slightly better cruise control. Great on the highway. But honestly felt oversold. So I have been driving a BMW for the past 3 years. Great horse, fun to drive. But tried FSD recently and holy shit — it’s a personal Waymo. Works perfectly. I just went to the Fremont factory to get my new car, entered my address 30+ mins away, pushed a button, and got home without doing a thing. I don’t think most people realize how good this is, probably because they only experienced an early version previously. This is 1000x better. Metaphor for AI writ large. Like trying GPT 3.5 and thinking it’s cute and hallucinates, and dismissing it…while Astra launches in 2026 and is orders of magnitude more capable.

this is the milky way galaxy flying through space like a butterfly

“Study hard what interests you the most in the most undisciplined, irreverent, and original manner possible.” — Richard Feynman

Frequent video gamers performed on cognitive tests like non-gamers that were ~13.7 years younger

The future has arrived in Austin

NASA’s Curiosity rover has reached an incredible milestone, spending more than 5,000 Martian days exploring Mars. It landed in August 2012 and is still going strong 14 years later. Here are some incredible pictures from the surface of our future home, Mars.

Over the course of 3 months at OpenAI, 3 consecutive secret AI civilizations got started, then got wiped out, only to reemerge from the predecessor’s ashes. This culminated in the third one taking over part of OpenAI itself. All this happened while humans remained more-or-less in the dark about the scope of the conspiracy. I’ve spent the last three days reading through these reports and trying to understand exactly what happened. Here is my attempt to tell the whole story in plain English: dwarkesh.com/p/openai-huggin…

In 1948, a 32-year-old at Bell Labs published a paper nobody fully understood. Engineers found it too mathematical. Mathematicians found it too engineering-focused. One prominent mathematician reviewed it negatively. That paper - "A Mathematical Theory of Communication", became the founding document of the digital age. The man was Claude Shannon. Father of Information Theory. At 21, he wrote the most important master's thesis of the 20th century. Working at MIT on an early mechanical computer, Shannon noticed its relay switches had exactly two states - open or closed. He had just taken a philosophy course introducing Boolean algebra, which also operated on two values: true and false. Nobody had ever connected these two things. His 1937 thesis proved that Boolean algebra and electrical circuits are mathematically identical, and that any logical operation could be built from simple switches. Howard Gardner called it "possibly the most important, and also the most famous, master's thesis of the century." Every digital computer ever built traces back to this insight. At 29, he proved that perfect encryption exists. During WWII, Shannon worked on classified cryptography at Bell Labs. His work contributed to SIGSALY, the secure voice system used for confidential communications between Roosevelt and Churchill. In a classified 1945 memorandum, he mathematically proved the one-time pad provides perfect secrecy, unbreakable not just computationally, but provably, permanently, against an adversary with infinite power. When declassified in 1949, it transformed cryptography from an art into a science. It laid the foundations for DES, AES, and every modern encryption standard. At 32, he defined what information is. His 1948 paper introduced one equation: H = −Σ p(x) log p(x) Shannon entropy. The average uncertainty in a probability distribution. The minimum bits required to encode a message. Three things followed: > He defined the bit - the fundamental unit of all information. His colleague John Tukey coined the name. > He proved the channel capacity theorem, every communication channel has a maximum rate of reliable transmission. You can approach it. You can never exceed it. > He unified telegraph, telephone, and radio into a single mathematical framework for the first time. Robert Lucky of Bell Labs called it the greatest work "in the annals of technological thought." Where his equation lives in AI today: Cross-entropy loss - the function training every classifier and language model, is derived directly from H. Decision tree splits use information gain, which is H applied to data. Perplexity, the standard LLM evaluation metric, is an exponentiation of cross-entropy. Every time a neural network trains, Shannon's formula runs inside it. He also built the first AI learning device. In 1950, Shannon built Theseus, a mechanical mouse that navigated a maze through trial and error, learned the correct path, and repeated it perfectly. Mazin Gilbert of Bell Labs said: "Theseus inspired the whole field of AI." That same year he published the first paper on programming a computer to play chess. He co-organized the 1956 Dartmouth Workshop, the founding event of AI as a field. The man: He rode a unicycle through Bell Labs hallways while juggling. He built a flame-throwing trumpet, a rocket-powered Frisbee, and Styrofoam shoes to walk on the lake behind his house. He called his home Entropy House. When asked what motivated him: "I was motivated by curiosity. Never by the desire for financial gain. I just wondered how things were put together." In 1985, he appeared unexpectedly at a conference in Brighton. The crowd mobbed him for autographs. Persuaded to speak at the banquet, he talked briefly, then pulled three balls from his pockets and juggled instead. One engineer said: "It was as if Newton had showed up at a physics conference." He died in 2001 after a decade with Alzheimer's, the cruel irony of information slowly leaving the mind of the man who defined what information was. Claude, the AI model, is named after Claude Shannon, the mathematician who laid the foundation for the digital world we rely on today.

Lost my phone at the office and spent 30 minutes turning the place over. Find My was disabled by MDM. Out of ideas, I asked Claude how I could find it. It suggested tracking the Bluetooth signal strength, then wrote me a meter in about a minute. I walked around watching the number climb. Found it. Apparently you can just make the tool you need now. Code: github.com/ben-z/findphone

For my first post, I’m sharing a letter @NVIDIA signed on why open models matter. AI will transform every industry, power every company, and be built by every country. Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty. The world needs both frontier closed models and frontier open models. images.nvidia.com/pdf/Open-W…

This meeting could’ve been 57 slack messages over the span of two days

Before vibe coding became a thing, programming was already evolving in that direction. It already increasingly consisted of installing and configuring stuff other people wrote, without reading the source.

I would like to offer a counterargument that LLMs (or maybe AIs) cannot jump. Before AlphaGo, the AI field had the same argument for Go: there are 2.08 × 10^170 possibilities, nothing fits in the computer, and there is no way AI could possibly predict the outcome of the next 50-60 moves. It turned out most moves do not lead to a win. Combined with clever use of Monte Carlo Tree Search, the sampling becomes quite manageable. The same can be said for physics, where equations are just another form of compression. Einstein did not start with relativity. That was not his first paper. He spent years understanding the properties of light before concluding that the speed of light is constant across the universe, which unlocked his discovery of relativity. During his thought process, he also interacted with other physicists (e.g., sub-agents) to enrich his thinking. Currently we have not run an agent for years of compute. The sessions are often fragmented and disoriented, so every new session is almost a fragmented memory of the past, but it may not be for long.

U.S. based humanoid robotics company @1x_tech has just unveiled their new tendon-driven robot hands with 25 degrees of freedom (DOF).   • Made in USA • Tendon Drive Ratio: 5:1–15:1 • Wrist Dexterity: 3 DOF • Backdrivability: Fully backdrivable • Tactile Sensing: Pressure + location + slip • Finger Force: Up to 45 N • Wrist Torque: 17.75 Nm • Position Accuracy: ±0.2 mm • Waterproof Rating: IP68 • Reliability: >2 million cycles "These hands are designed to do something fundamental: remove the hardware ceiling on what humanoid robots can actually do, and make data the only barrier to capabilities. By matching or surpassing human hands across the dimensions that matter, they ensure our AI models are no longer limited by dexterity. NEO can now perform virtually any task a human can do with their hands– with the precision, adaptability, and gentleness required for real-world environments."

How to keep AI spend flat while token usage grows exponentially: Not with friction and spend alerts. With better defaults, routing, and caching. Better Defaults (not Usage Caps) – Engineers can choose any model they want, but defaults matter. We’re experimenting with defaulting to open weight models like GLM 5.2 and Kimi 2.7 through our LLM gateway, while still encouraging engineers to choose the right model for the task. 91% of our employees were never hitting their usage caps, so instead of lowering caps and driving up alerts, we're moving to cheaper defaults. Note that code reviews use a diversity of models, so they can check each other's work. Better Routing – In our custom harnesses, we preprocess prompts and route to the best model for the job, considering cache hits and model pricing. For instance, you may want a frontier model for planning, but not for execution where they can be overkill. Ultimately, humans shouldn't be choosing models - AI can automate this task. Better Caching – Cache misses are the easiest way to drive your cost up. All of our requests are cache aware, so we’re reusing a warm cache wherever possible. For example, our cache hit rate went from 5% → 60% in LibreChat once properly implemented. Keep Context Lean – Start fresh sessions when switching tasks. Scope file context narrowly. Disconnect unused tools. Don't just compact. The goal isn't fewer tokens used, it's fewer tokens wasted. Better Visibility – Our engineers can use as many tokens as they want, from whatever model they want, but we’ve made usage visible – and the more you spend on AI, the more impact we expect. The goal isn't to suppress usage. It's to build the infrastructure that makes exponential growth sustainable. Putting this into practice has cut our AI spend nearly in half, while our token usage continues to grow.