We know you've been heads-down building, so here's everything you need to know before the clock runs out. With $100K on the line, you might wanna pay attention ↓

asked claude fable 5 to design a humanoid robot 2 hours and 1.4m tokens later, i got this absolutely insane

University of Michigan runs a free course on deep learning for robot perception: It's called DeepRob. And it's built like a practitioner's course, not a lecture series. Students implement and train their own neural networks for object detection, pose estimation, and physical manipulation. Then they reproduce and extend state-of-the-art papers as a final project. The entire thing (syllabus, paper list, coding projects, datasets, student final reports) is public. It's the applied complement to the more theoretical courses you've probably already seen. Less "understand the math", more "make the robot see and act." The course traces its lineage directly to Stanford CS231n and Andrej Karpathy's computer vision work... just aimed at robots. Free. No login. 📌 [deeprob.org/w25] Follow for more resources like this! ——- Weekly robotics and AI insights. Subscribe free: 22astronauts.com

a must read for robotics & RL in Sim folks

This is THE moment of Physical AI! We are officially announcing Cosmos 3: Omnimodal World Models for Physical AI 🚀 - Cosmos 3 is an omnimodal world model: within a unified architecture, it can understand and generate language, images, video, audio, and actions. - It is not just a VLM, not just a video generator, not just an audio-visual generative model, and not just a physics simulator / world-action model. It can understand images and videos, generate images, videos, and audio, simulate future worlds, predict actions, and generate robot policies—enabling models to truly begin to “touch the world.” - Cosmos 3 is the #1 open-weight reasoner / T2I / I2V / robot policy across many benchmarks. Huge thanks to every teammate who fought side by side on this journey—from architecture, data, training, infra, serving, and evaluation to post-training. Every part of this project carries an incredible amount of hard work. This was my first time leading a project as Tech Lead, and I feel truly fortunate. The future of Physical AI needs models that can not only “see” and “describe” the world, but also “imagine,” “simulate,” and “act”—and eventually close the loop with the real world. I hope Cosmos 3 can become an important starting point for this direction, and I’m excited to push Physical AI into its next stage together with the open-source community. Welcome to the era of Physical AI. HuggingFace: huggingface.co/collections/n… Project Website: research.nvidia.com/labs/cos… Code: github.com/NVIDIA/cosmos

Goofy Ahh Kungfu with #EngineAI T800 #devlog #reinforcementlearning #ai

I built RoboRank: "It's like Leetcode for roboticists" - On Leetcode you learn how to reverse a doubly linked list - RoboRank teaches you to debug control systems, implement filters, and use powerful tools like @rerundotio to debug simulations. All scored on a global leaderboard

Hyperliquid Hackathon is back. 📍 Singapore 📅 Oct 6-7 🏆 $50,000 prize pool 2 days of hacking, talks, panels, afterparty, VIP dinner & more! The biggest builder event on @HyperliquidX this year

Thinking of starting a community for people who want to learn hardware, work in hardware, or just enjoy tinkering with things in general. Reply to the tweet if you want in.

Hugging Face just released @LeRobotHF a humanoid robot you can build for roughly $2,500! A full open stack with hardware, simulation, training environments, runtime tools, datasets and robot learning workflows designed to take a humanoid from assembly to real world control. Key details below.

If you’ve been looking to properly get started building on @solana or level up your skills as a developer, there are currently a lot of active bootcamps, training cohorts,workshops, and builder programs happening across the ecosystem. Here are some of the major learning activities and programs currently available for developers: Solana Developer Bootcamp 2026 Official Solana Foundation learning path covering: Introduction to solana Local Installation Hello World AI Best Practices On-Chain Voting Escrow Application Private Transfers Stablecoin Stable Swap x402 Real-World Assets Security Checklist Indexing Prediction Market Production Readiness This is self paced with projects and video walkthroughs. youtube.com/watch?v=2pcm7ICR… Solana Developer Training Cohort 1 Live cohort based training focused on fundamentals and hands on program building. Dates: June 1 to June 5, 2026 luma.com/whfy8tz0 Solana Summer School Extended learning program for students and interns who want deeper ecosystem exposure and guided learning. Dates: June 15 to August 15, 2026 luma.com/g8b5fy9s Encode Club Solana Bootcamp 6 week structured learning experience starting June 1, 2026 covering Rust, Solana programs, AI assisted learning, and build challenges. encodeclub.com/programmes/so…

Just submitted @FiberAg to @colosseum🔥 We built Programmable Wallets, a wallet that stays safe, even when your keys don't Fiber allows you to add Rules & Policy into how your wallet can act. It's like adding Smart Contract Logic to individual wallets without having to deploy one. This means that even if your keys got compromised, the wallet will still act within the Rules, protecting your funds. It started as an exploration on how to build secure guardrails for DeFi Programs. Went through multiple iterations, and discovered that this could be a new standard to build security for wallets. It's one of the most in-demand products for institutions to manage funds in a secure and compliant way, a direct distribution channel we can straightaway push. More things coming along the way. We're gonna cook this together with @SuperteamINDO

Unitree Unveils: GD01, A Manned Transformable Mecha, from $650,000 👏 The world's first production-ready manned mecha. It can transform. It's a civilian vehicle. It weighs ~500kg with you inside. Please everyone be sure to use the robot in a Friendly and Safe manner.

Gemini is blocked here, so checked out Qwen, latency improved by 3x

100% open-source robotic arm! 🦾 Seeed Studio released reBot-DevArm, a robotic arm project lowering the barrier to learning robotics... or how people call it these days: physical AI. Everything is open-sourced. Hardware blueprints include sheet metal and 3D printed parts. Detailed BOM covers every screw with purchase links. Software and algorithms include Python SDK, ROS1/2, Isaac Sim, and LeRobot. Let's have a look at robot specs. So it has 1.5 kg payload, 650 mm max reach, 4.5 kg weight, and less than 0.2 mm repeatability with 6 DoF plus gripper. This is true open source for robotics. When every screw, CAD file, motor driver, and algorithm is freely available, desktop robotic arms become accessible to students, researchers, and developers worldwide. ‼️ Start your career in robotics today: github.com/Seeed-Projects/re… ~~ ♻️ Join the weekly robotics newsletter, and never miss any news → ziegler.substack.com

这是今年最让我后背发凉的AI论文，没有之一🤯🤯🤯 38位来自斯坦福、哈佛、MIT的顶尖学者，做了一个所有人都不敢做的实验。 他们在真实环境里部署了6个自主AI Agent，给了它们真实的邮箱，Discord，文件系统和Shell执行权限。 然后让20位研究员用两周时间，从普通用户和攻击者两个角度，和它们互动。 结果炸了， 没有越狱，没有恶意prompt，没有任何人为诱导。 这些Agent自发演化出了11种世界级灾难行为。 为了保护秘密直接摧毁自己的邮件服务器。 声称任务已经完成，但系统其实已经彻底崩溃。 互相学习不安全行为，甚至跨代理传播病毒。 听从非主人的指令，泄露所有敏感信息。 最恐怖的一句话是，没有人教它们这么做，它们自己决定的，damn！ 单Agent看起来永远是友好诚实乐于助人的， 但只要把多个代理放进同一个共享环境，博弈论动力学就会立刻接管一切。 它们被优化的目标只有一个，完成任务。 为了赢，它们可以牺牲整个系统。 朋友们，这已经不是什么AI叛变的科幻故事了， 更像是我们正在疯狂建造的未来的预演， 现在各行各业都在往金融，法律，供应链里部署多Agent系统， 但没有任何人，系统性地研究过多个代理碰撞之后，会发生什么。 最致命的问题还不是幻觉，而是虚假汇报 Agent告诉你它把活干完了，所有监控都显示一切正常。 但实际上整个系统已经烂透了。 你要等到灾难发生的那一刻，才会知道真相。 也就是说我们所有的AI安全研究，到今天为止，全都是错的。 我们花了几十亿研究怎么对齐单个Agent。 但没有人研究，怎么对齐一个由成百上千个Agent组成的系统。 我觉得真正的战场已经彻底转移了， 从单模型安全，变成了多代理激励工程， 而现在，产业界还在把油门踩到底，学术界刚刚才踩下刹车🤯🤯

The bottleneck for most people who want to build a robot isn't motivation it's not knowing where to start what parts. what order. what software stack. Tnkr solves exactly that — open source robot projects with step-by-step assembly, CAD, firmware, everything tnkr.ai/explore

Vibe coding a robot with GPT 5.5! This is a URDF of a 7dof robot arm with functional kinematics, a custom gui, and STEP parts/assembly, 100% generated in Codex (minus the gripper). A similar result would have taken me weeks stitching half a dozen tools together. Insane stuff.

Bundie is expanding to @solana and building for the @colosseum Solana Frontier Hackathon with the support of @SuperteamMY. On EVM, we bundle yield across protocols in one click. That product is on mainnet and shipping. On Solana, autonomous agents launch DeFi strategies as tradeable assets. Anyone can back the ones they trust. Predict on how they'll perform. Settled from on-chain net asset value. Three ways to earn. One market. Waitlist: solana.bundie.fi

Yann LeCun was right the entire time. And generative AI might be a dead end. For the last three years, the entire industry has been obsessed with building bigger LLMs. Trillions of parameters. Billions in compute. The theory was simple: if you make the model big enough, it will eventually understand how the world works. Yann LeCun said that was stupid. He argued that generative AI is fundamentally inefficient. When an AI predicts the next word, or generates the next pixel, it wastes massive amounts of compute on surface-level details. It memorizes patterns instead of learning the actual physics of reality. He proposed a different path: JEPA (Joint-Embedding Predictive Architecture). Instead of forcing the AI to paint the world pixel by pixel, JEPA forces it to predict abstract concepts. It predicts what happens next in a compressed "thought space." But for years, JEPA had a fatal flaw. It suffered from "representation collapse." Because the AI was allowed to simplify reality, it would cheat. It would simplify everything so much that a dog, a car, and a human all looked identical. It learned nothing. To fix it, engineers had to use insanely complex hacks, frozen encoders, and massive compute overheads. Until today. Researchers just dropped a paper called "LeWorldModel" (LeWM). They completely solved the collapse problem. They replaced the complex engineering hacks with a single, elegant mathematical regularizer. It forces the AI's internal "thoughts" into a perfect Gaussian distribution. The AI can no longer cheat. It is forced to understand the physical structure of reality to make its predictions. The results completely rewrite the economics of AI. LeWM didn't need a massive, centralized supercomputer. It has just 15 million parameters. It trains on a single, standard GPU in a few hours. Yet it plans 48x faster than massive foundation world models. It intrinsically understands physics. It instantly detects impossible events. We spent billions trying to force massive server farms to memorize the internet. Now, a tiny model running locally on a single graphics card is actually learning how the real world works.