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๐—ฌ๐—ผ๐˜‚ ๐—ฑ๐—ผ๐—ป'๐˜ ๐—ป๐—ฒ๐—ฒ๐—ฑ ๐—ฎ $๐Ÿฑ๐Ÿฌ,๐Ÿฌ๐Ÿฌ๐Ÿฌ ๐—ฟ๐—ฒ๐˜€๐—ฒ๐—ฎ๐—ฟ๐—ฐ๐—ต ๐—ฟ๐—ผ๐—ฏ๐—ผ๐˜ ๐˜๐—ผ ๐˜๐—ฟ๐—ฎ๐—ถ๐—ป ๐—”๐—œ ๐—ฝ๐—ผ๐—น๐—ถ๐—ฐ๐—ถ๐—ฒ๐˜€. Our ML team is integrating the Elephant Robotics - Robotic Arms mechArm 270 Pi with the Neuracore platform. The biggest barrier to learning robot learning isn't talent or ideas, it's access to expensive hardware. We've already open sourced...

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21,658 views โ€ข 6 months ago โ€ขvia X (Twitter)

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The robotics industry is about to go through the same shift software did 10 years ago. Here's why the next billion-dollar robotics company will be built by a creator โ€” not a corporation ๐Ÿ‘‡ 1/ Corporate robotics requires millions in capital, years of R&D, and a massive team just to ship a product. Creators with the right platform can prototype an idea in weeks, validate with real customers, and iterate fast. 2/ This isn't theory. It's the same pattern we saw with: GitHub โ†’ software developers App Store โ†’ mobile creators YouTube โ†’ video creators Every major platform shift minted a new class of independent builders. Robotics is next. 3/ The barrier isn't talent. There are brilliant engineers everywhere โ€” Ohio, Lagos, Mumbai, Berlin. The barrier is access. Access to tools, infrastructure, and a platform that lets them build and monetize. 4/ That's exactly what $ROBA is building โ€” an open platform where creators build, train, share, and monetize robotic behaviors. No corporate gatekeepers. No vendor lock-in. Just creators owning their innovations. Join the revolution today.
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The robotics industry is about to go through the same shift software did 10 years ago. Here's why the next billion-dollar robotics company will be built by a creator โ€” not a corporation ๐Ÿ‘‡ 1/ Corporate robotics requires millions in capital, years of R&D, and a massive team just to ship a product. Creators with the right platform can prototype an idea in weeks, validate with real customers, and iterate fast. 2/ This isn't theory. It's the same pattern we saw with: GitHub โ†’ software developers App Store โ†’ mobile creators YouTube โ†’ video creators Every major platform shift minted a new class of independent builders. Robotics is next. 3/ The barrier isn't talent. There are brilliant engineers everywhere โ€” Ohio, Lagos, Mumbai, Berlin. The barrier is access. Access to tools, infrastructure, and a platform that lets them build and monetize. 4/ That's exactly what $ROBA is building โ€” an open platform where creators build, train, share, and monetize robotic behaviors. No corporate gatekeepers. No vendor lock-in. Just creators owning their innovations. Join the revolution today.

Roba Labs

15,225 views โ€ข 2 months ago

As a newly appointed ๐—”๐˜€๐˜€๐—ถ๐˜€๐˜๐—ฎ๐—ป๐˜ ๐—ฃ๐—ฟ๐—ผ๐—ณ๐—ฒ๐˜€๐˜€๐—ผ๐—ฟ at Imperial College London, I'm thrilled to announce the ๐—ฆ๐—ฎ๐—ณ๐—ฒ ๐—ช๐—ต๐—ผ๐—น๐—ฒ-๐—ฏ๐—ผ๐—ฑ๐˜† ๐—œ๐—ป๐˜๐—ฒ๐—น๐—น๐—ถ๐—ด๐—ฒ๐—ป๐˜ ๐—ฅ๐—ผ๐—ฏ๐—ผ๐˜๐—ถ๐—ฐ๐˜€ ๐—Ÿ๐—ฎ๐—ฏ (๐—ฆ๐—ช๐—œ๐—ฅ๐—Ÿ) at ๐—œ๐—บ๐—ฝ๐—ฒ๐—ฟ๐—ถ๐—ฎ๐—น ๐—–๐—ผ๐—น๐—น๐—ฒ๐—ด๐—ฒ ๐—Ÿ๐—ผ๐—ป๐—ฑ๐—ผ๐—ป. ๐—ฆ๐—ฎ๐—ณ๐—ฒ ๐—ช๐—ต๐—ผ๐—น๐—ฒ-๐—ฏ๐—ผ๐—ฑ๐˜† ๐—œ๐—ป๐˜๐—ฒ๐—น๐—น๐—ถ๐—ด๐—ฒ๐—ป๐˜ ๐—ฅ๐—ผ๐—ฏ๐—ผ๐˜๐—ถ๐—ฐ๐˜€ ๐—Ÿ๐—ฎ๐—ฏ (๐—ฆ๐—ช๐—œ๐—ฅ๐—Ÿ) ( is a new research lab focused on the intersection of safety and intelligence in next-generation robotics. We're hiring exceptional PhD students who are passionate about pushing the boundaries of robot learning. ๐—ช๐—ต๐—ฎ๐˜ ๐—บ๐—ฎ๐—ธ๐—ฒ๐˜€ ๐—ฆ๐—ช๐—œ๐—ฅ๐—Ÿ ๐˜‚๐—ป๐—ถ๐—พ๐˜‚๐—ฒ? We operate at the exciting convergence of: โ€ข Online & offline reinforcement learning โ€ข Imitation learning & human demonstrations โ€ข Sample-efficient learning methods โ€ข Whole-body and soft robotics systems We're ๐—น๐—ผ๐—ผ๐—ธ๐—ถ๐—ป๐—ด ๐—ณ๐—ผ๐—ฟ ๐—ฝ๐—ฟ๐—ผ๐˜€๐—ฝ๐—ฒ๐—ฐ๐˜๐—ถ๐˜ƒ๐—ฒ ๐—ฃ๐—ต๐—— ๐˜€๐˜๐˜‚๐—ฑ๐—ฒ๐—ป๐˜๐˜€ interested in: โ€ข Developing safe exploration algorithms for robotic systems โ€ข Creating sample-efficient learning methods that minimize real-world trials โ€ข Building foundation models for robotics with safety guarantees โ€ข Advancing soft robotics and compliant human-robot interaction โ€ข Bridging theory and practice in embodied AI Why now? As robots become more capable and work closer with humans, we need systems that are both intelligent enough to handle complex tasks ๐—”๐—ก๐—— safe enough for real-world deployment. Traditional approaches treat safety and intelligence as competing priorities, we believe they're synergistic. If you're a motivated researcher who wants to develop the theoretical foundations and practical algorithms for tomorrow's safe, intelligent robots, I'd love to hear from you. Want to join? Apply via
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As a newly appointed ๐—”๐˜€๐˜€๐—ถ๐˜€๐˜๐—ฎ๐—ป๐˜ ๐—ฃ๐—ฟ๐—ผ๐—ณ๐—ฒ๐˜€๐˜€๐—ผ๐—ฟ at Imperial College London, I'm thrilled to announce the ๐—ฆ๐—ฎ๐—ณ๐—ฒ ๐—ช๐—ต๐—ผ๐—น๐—ฒ-๐—ฏ๐—ผ๐—ฑ๐˜† ๐—œ๐—ป๐˜๐—ฒ๐—น๐—น๐—ถ๐—ด๐—ฒ๐—ป๐˜ ๐—ฅ๐—ผ๐—ฏ๐—ผ๐˜๐—ถ๐—ฐ๐˜€ ๐—Ÿ๐—ฎ๐—ฏ (๐—ฆ๐—ช๐—œ๐—ฅ๐—Ÿ) at ๐—œ๐—บ๐—ฝ๐—ฒ๐—ฟ๐—ถ๐—ฎ๐—น ๐—–๐—ผ๐—น๐—น๐—ฒ๐—ด๐—ฒ ๐—Ÿ๐—ผ๐—ป๐—ฑ๐—ผ๐—ป. ๐—ฆ๐—ฎ๐—ณ๐—ฒ ๐—ช๐—ต๐—ผ๐—น๐—ฒ-๐—ฏ๐—ผ๐—ฑ๐˜† ๐—œ๐—ป๐˜๐—ฒ๐—น๐—น๐—ถ๐—ด๐—ฒ๐—ป๐˜ ๐—ฅ๐—ผ๐—ฏ๐—ผ๐˜๐—ถ๐—ฐ๐˜€ ๐—Ÿ๐—ฎ๐—ฏ (๐—ฆ๐—ช๐—œ๐—ฅ๐—Ÿ) ( is a new research lab focused on the intersection of safety and intelligence in next-generation robotics. We're hiring exceptional PhD students who are passionate about pushing the boundaries of robot learning. ๐—ช๐—ต๐—ฎ๐˜ ๐—บ๐—ฎ๐—ธ๐—ฒ๐˜€ ๐—ฆ๐—ช๐—œ๐—ฅ๐—Ÿ ๐˜‚๐—ป๐—ถ๐—พ๐˜‚๐—ฒ? We operate at the exciting convergence of: โ€ข Online & offline reinforcement learning โ€ข Imitation learning & human demonstrations โ€ข Sample-efficient learning methods โ€ข Whole-body and soft robotics systems We're ๐—น๐—ผ๐—ผ๐—ธ๐—ถ๐—ป๐—ด ๐—ณ๐—ผ๐—ฟ ๐—ฝ๐—ฟ๐—ผ๐˜€๐—ฝ๐—ฒ๐—ฐ๐˜๐—ถ๐˜ƒ๐—ฒ ๐—ฃ๐—ต๐—— ๐˜€๐˜๐˜‚๐—ฑ๐—ฒ๐—ป๐˜๐˜€ interested in: โ€ข Developing safe exploration algorithms for robotic systems โ€ข Creating sample-efficient learning methods that minimize real-world trials โ€ข Building foundation models for robotics with safety guarantees โ€ข Advancing soft robotics and compliant human-robot interaction โ€ข Bridging theory and practice in embodied AI Why now? As robots become more capable and work closer with humans, we need systems that are both intelligent enough to handle complex tasks ๐—”๐—ก๐—— safe enough for real-world deployment. Traditional approaches treat safety and intelligence as competing priorities, we believe they're synergistic. If you're a motivated researcher who wants to develop the theoretical foundations and practical algorithms for tomorrow's safe, intelligent robots, I'd love to hear from you. Want to join? Apply via

Stephen James

16,605 views โ€ข 10 months ago

Robora Sim: A PyBullet-Powered Environment for Learning Robotic Physical Intelligence We are currently building our Robora simulation environment setup for our sim based learning, leveraging PyBullet, an industry-standard physics engine widely used in AI-driven robotics research and development. The environment is optimized with GPU-accelerated learning algorithms, enabling high-speed imitation learning and reinforcement learning within a safe and controlled virtual setup before shipping out to real world. This simulation platform allows our models to learn, adapt, and generalize across different robot morphologies, terrain types and task objectives - all before deployment to the real world. At it's core, the system combines a VLA-powered high-level planner with low-level motion control algorithms, working cohesively to produce emergent, physically intelligent behaviors. This synergy between simulation, learning, and real-world transfer marks a major step forward in our pursuit of adaptive and intelligent robotic systems. Through advanced domain randomization and synthetic data generation, the Robora Simulation Environment ensures that policies trained in simulation transfer effectively to real-world robots, minimizing the sim-to-real gap. Moreover, users will be able to test and integrate their own hardware kits within selected simulation environments in the Robora Dapp, ensuring seamless compatibility and safer real-world implementation.
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Robora Sim: A PyBullet-Powered Environment for Learning Robotic Physical Intelligence We are currently building our Robora simulation environment setup for our sim based learning, leveraging PyBullet, an industry-standard physics engine widely used in AI-driven robotics research and development. The environment is optimized with GPU-accelerated learning algorithms, enabling high-speed imitation learning and reinforcement learning within a safe and controlled virtual setup before shipping out to real world. This simulation platform allows our models to learn, adapt, and generalize across different robot morphologies, terrain types and task objectives - all before deployment to the real world. At it's core, the system combines a VLA-powered high-level planner with low-level motion control algorithms, working cohesively to produce emergent, physically intelligent behaviors. This synergy between simulation, learning, and real-world transfer marks a major step forward in our pursuit of adaptive and intelligent robotic systems. Through advanced domain randomization and synthetic data generation, the Robora Simulation Environment ensures that policies trained in simulation transfer effectively to real-world robots, minimizing the sim-to-real gap. Moreover, users will be able to test and integrate their own hardware kits within selected simulation environments in the Robora Dapp, ensuring seamless compatibility and safer real-world implementation.

Robora

23,489 views โ€ข 9 months ago

๐Ÿ† The Meba Awards 2025 Winner โ€“ โ€˜Most Promising RWA Projectโ€™ ๐Ÿ† ๐Ÿ”นOur mission has always been to innovate and push the boundaries of crypto mining, making it accessible to everyone. ๐Ÿ”นWeโ€™ve already made significant strides with our community share pool, having already distributed nearly $1.4 million to our holders - entirely sourced from our mining operations. ๐Ÿ”ธBut weโ€™re not stopping thereโ€ฆ ๐Ÿ”นVery soon, you will have the opportunity to own a share of high-performance mining hardware, leveraging our low operational costs for an unmatched yield. ๐Ÿ”นStay tuned - the future of crypto mining is being built at $HASHAI.
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๐Ÿ† The Meba Awards 2025 Winner โ€“ โ€˜Most Promising RWA Projectโ€™ ๐Ÿ† ๐Ÿ”นOur mission has always been to innovate and push the boundaries of crypto mining, making it accessible to everyone. ๐Ÿ”นWeโ€™ve already made significant strides with our community share pool, having already distributed nearly $1.4 million to our holders - entirely sourced from our mining operations. ๐Ÿ”ธBut weโ€™re not stopping thereโ€ฆ ๐Ÿ”นVery soon, you will have the opportunity to own a share of high-performance mining hardware, leveraging our low operational costs for an unmatched yield. ๐Ÿ”นStay tuned - the future of crypto mining is being built at $HASHAI.

Hash AI

22,657 views โ€ข 1 year ago

๐Ÿšจ BREAKING: NVIDIA just announced the Isaac GR00T Reference Humanoid Robot. The first fully open humanoid robot reference design built on Jetson Thor, and it's going straight to the world's top research institutions. This is Jensen Huang's bet on open physical AI infrastructure. The hardware stack is serious: โ†’ Unitree H2 Plus chassis, 6 feet tall, 150 pounds, 31 degrees of freedom โ†’ Sharpa Wave tactile five-finger hands, 22 degrees of freedom, bringing total to 75 across the full body โ†’ NVIDIA Jetson AGX Thor onboard compute, 2,070 FP4 teraflops of AI performance, 128GB unified memory โ†’ Multi-view sensing, stereo head camera, wrist cameras, IMU Alongside this announcement, Unitree also introduced the H2 Plus as a standalone product, a frontier humanoid combining Unitree's own body, Sharpa's five-finger hands and NVIDIA Robotics Jetson Thor compute into one fully integrated research platform. The full Isaac GR00T software stack ships with it, teleoperation for data capture, open foundation models, Isaac Sim for training, Isaac Lab for evaluation, and accelerated ROS middleware for deployment. The complete loop from data to real-world robot in one unified platform. ETH Zรผrich, Stanford Robotics Center, UC San Diego and Ai2 are already on board as launch research partners. NVIDIA Robotics did to AI what it's now doing to robotics, build the platform, open the ecosystem, let the world build on top of it. Whoever owns the infrastructure layer wins. NVIDIA knows this better than anyone. ๐Ÿ‘€ Read more here: ~~ โ™ป๏ธ Join the weekly robotics newsletter, and never miss any news โ†’
0:14

๐Ÿšจ BREAKING: NVIDIA just announced the Isaac GR00T Reference Humanoid Robot. The first fully open humanoid robot reference design built on Jetson Thor, and it's going straight to the world's top research institutions. This is Jensen Huang's bet on open physical AI infrastructure. The hardware stack is serious: โ†’ Unitree H2 Plus chassis, 6 feet tall, 150 pounds, 31 degrees of freedom โ†’ Sharpa Wave tactile five-finger hands, 22 degrees of freedom, bringing total to 75 across the full body โ†’ NVIDIA Jetson AGX Thor onboard compute, 2,070 FP4 teraflops of AI performance, 128GB unified memory โ†’ Multi-view sensing, stereo head camera, wrist cameras, IMU Alongside this announcement, Unitree also introduced the H2 Plus as a standalone product, a frontier humanoid combining Unitree's own body, Sharpa's five-finger hands and NVIDIA Robotics Jetson Thor compute into one fully integrated research platform. The full Isaac GR00T software stack ships with it, teleoperation for data capture, open foundation models, Isaac Sim for training, Isaac Lab for evaluation, and accelerated ROS middleware for deployment. The complete loop from data to real-world robot in one unified platform. ETH Zรผrich, Stanford Robotics Center, UC San Diego and Ai2 are already on board as launch research partners. NVIDIA Robotics did to AI what it's now doing to robotics, build the platform, open the ecosystem, let the world build on top of it. Whoever owns the infrastructure layer wins. NVIDIA knows this better than anyone. ๐Ÿ‘€ Read more here: ~~ โ™ป๏ธ Join the weekly robotics newsletter, and never miss any news โ†’

Lukas Ziegler

16,062 views โ€ข 2 months ago

NEW: AI Papers of the Week Collection I just released my AI Papers of the Week collection under our Resources hub. Now you can easily find some of the most important AI papers in one place. You can also use our new AI tutor to recommend top AI papers on any topic of interest or topics you are learning about on the platform. Learning about AI is not enough. It's important to keep up to date. So this is why we are building all these tools and resources to help with that. Go try it out here: We will update the collection every week and add new paper collections in the coming weeks. We have another killer feature dropping soon to read and annotate papers, including a completely new way to study and digest AI papers. Stay tuned!
0:15

NEW: AI Papers of the Week Collection I just released my AI Papers of the Week collection under our Resources hub. Now you can easily find some of the most important AI papers in one place. You can also use our new AI tutor to recommend top AI papers on any topic of interest or topics you are learning about on the platform. Learning about AI is not enough. It's important to keep up to date. So this is why we are building all these tools and resources to help with that. Go try it out here: We will update the collection every week and add new paper collections in the coming weeks. We have another killer feature dropping soon to read and annotate papers, including a completely new way to study and digest AI papers. Stay tuned!

elvis

16,882 views โ€ข 15 days ago

I spent a month in Shenzhen visiting factories and robotics companies, and the contrast with the U.S. was striking. While Figure and Boston Dynamics hide their humanoids behind closed doors, Chinese companies have massive showrooms open to the public. But what really stood out wasn't just the transparency, it was how good they are at selling. Take UBTech: they've already sold 1,200 humanoid units at $200k each to factories. And here's the kicker, these robots aren't even that useful yet. They can only pick up and drop boxes at 1/10th the speed of a human, and factories still need to hire system integrators to train them for specific tasks. My theory is that these factories are terrified of getting left behind in the robotics/AI wave. They're investing in new tech not because it's ready, but because they can't afford to wait. The second surprise was the breadth of their robotics portfolio. These companies aren't just building humanoids, they're deploying service robots everywhere: restaurants, hotels, apartments. Consumer robots are cleaning houses, pools, pet waste, dishes. They're covering the entire spectrum. But the education piece shocked me most. I picked up what I thought was a high school or college robotics textbook, it was for primary school. The government mandated AI and robotics education starting in elementary school. Almost every single school in China now has AI and robotics curriculum, complete with education robots so kids can learn by building. They're creating a generation that grows up fluent in robotics and AI. China owns the supply chain and the hardware stack. But here's what I think people are missing: the race isn't just about who can build robots faster or cheaper. The U.S. advantage has always been in the layer between hardware and human, the interaction design, the software intelligence, the intuitive interfaces that make complex technology feel natural. China is building the physical infrastructure, but they're also learning fast. Every deployed service robot, every classroom full of kids building with education kits, every factory running humanoids, that's all data collection at scale. The window for the U.S. to establish its wedge is narrowing. It's not enough to be better at AI or software anymore. We need to be building the integration layer, the intelligence that makes physical AI actually useful, not just impressive in a showroom. Because right now, China isn't just manufacturing robots. They're manufacturing a robotics-native culture, and that might be the most defensible moat of all.
0:10

I spent a month in Shenzhen visiting factories and robotics companies, and the contrast with the U.S. was striking. While Figure and Boston Dynamics hide their humanoids behind closed doors, Chinese companies have massive showrooms open to the public. But what really stood out wasn't just the transparency, it was how good they are at selling. Take UBTech: they've already sold 1,200 humanoid units at $200k each to factories. And here's the kicker, these robots aren't even that useful yet. They can only pick up and drop boxes at 1/10th the speed of a human, and factories still need to hire system integrators to train them for specific tasks. My theory is that these factories are terrified of getting left behind in the robotics/AI wave. They're investing in new tech not because it's ready, but because they can't afford to wait. The second surprise was the breadth of their robotics portfolio. These companies aren't just building humanoids, they're deploying service robots everywhere: restaurants, hotels, apartments. Consumer robots are cleaning houses, pools, pet waste, dishes. They're covering the entire spectrum. But the education piece shocked me most. I picked up what I thought was a high school or college robotics textbook, it was for primary school. The government mandated AI and robotics education starting in elementary school. Almost every single school in China now has AI and robotics curriculum, complete with education robots so kids can learn by building. They're creating a generation that grows up fluent in robotics and AI. China owns the supply chain and the hardware stack. But here's what I think people are missing: the race isn't just about who can build robots faster or cheaper. The U.S. advantage has always been in the layer between hardware and human, the interaction design, the software intelligence, the intuitive interfaces that make complex technology feel natural. China is building the physical infrastructure, but they're also learning fast. Every deployed service robot, every classroom full of kids building with education kits, every factory running humanoids, that's all data collection at scale. The window for the U.S. to establish its wedge is narrowing. It's not enough to be better at AI or software anymore. We need to be building the integration layer, the intelligence that makes physical AI actually useful, not just impressive in a showroom. Because right now, China isn't just manufacturing robots. They're manufacturing a robotics-native culture, and that might be the most defensible moat of all.

Miyu Horiuchi

90,718 views โ€ข 6 months ago

Hey #NeuraxonMini is literally out! , we manage to "transplant" a Neuraxon 2 bioinspired #AI brain to a physical robot the #SpheroMini moving from our last Scientific Paper (link bellow) by David Vivancos - e/acc & Jose Sรกnchez for Qubic #OpenScience hybridized with #Aigarth to the real World. First you need a Sphero Education Mini robot about 50$ Then you can try the first cool demos at Hugging Face: 1.- Neuraxon2MiniControl to drive the sphero robot 2.- Neuraxon2MiniWrite to write letters or words with physical moves of the sphero robot using Neuraxon Video Tutorials on youtube later today. Why this matters? Remember we are not building "dead" LLMs we are building #AliveAIs and for that we need to explore how it behaves in reality, from how it learns to how it fails, and what better way that in the emerging field of #robotics , time will tell if your next #HumanoidRobot have a #Neuraxon brain... Read the Paper: Explore the Neuraxon code here: Are you ready for #TrueAI ?
0:15

Hey #NeuraxonMini is literally out! , we manage to "transplant" a Neuraxon 2 bioinspired #AI brain to a physical robot the #SpheroMini moving from our last Scientific Paper (link bellow) by David Vivancos - e/acc & Jose Sรกnchez for Qubic #OpenScience hybridized with #Aigarth to the real World. First you need a Sphero Education Mini robot about 50$ Then you can try the first cool demos at Hugging Face: 1.- Neuraxon2MiniControl to drive the sphero robot 2.- Neuraxon2MiniWrite to write letters or words with physical moves of the sphero robot using Neuraxon Video Tutorials on youtube later today. Why this matters? Remember we are not building "dead" LLMs we are building #AliveAIs and for that we need to explore how it behaves in reality, from how it learns to how it fails, and what better way that in the emerging field of #robotics , time will tell if your next #HumanoidRobot have a #Neuraxon brain... Read the Paper: Explore the Neuraxon code here: Are you ready for #TrueAI ?

David Vivancos - e/acc

29,293 views โ€ข 5 months ago

Force feedback demo Force feedback is when joystick is pushing on your hand when something is pushing on the robot arm. Feeling the force - so much helpful to control the robot, that done well it allows you to do tasks even without visual feed. You can make an experiment: close your eyes - you can easily get the headphones out of the case. Also, visual information is often not enough. For example, you're trying to pull out a usb connector, but you pull it at the wrong angle, causing it to get stuck. Visually, nothing changes, but the pressure is intense and you can break the connector. Surgical robots have been using force feedback for years, and there are also 3D styluses which use this feature, proving that the technology works and is useful. But in modern robots with AI, it's hardly ever implemented. Although it's useful for both teleoperation and AI model. That's one of the reasons why we are building our robotic arms starting with off the shelf motors rather than taking the whole off the shelf arm. There are still a range of easy wins that can be made iterating robot hardware.
0:15

Force feedback demo Force feedback is when joystick is pushing on your hand when something is pushing on the robot arm. Feeling the force - so much helpful to control the robot, that done well it allows you to do tasks even without visual feed. You can make an experiment: close your eyes - you can easily get the headphones out of the case. Also, visual information is often not enough. For example, you're trying to pull out a usb connector, but you pull it at the wrong angle, causing it to get stuck. Visually, nothing changes, but the pressure is intense and you can break the connector. Surgical robots have been using force feedback for years, and there are also 3D styluses which use this feature, proving that the technology works and is useful. But in modern robots with AI, it's hardly ever implemented. Although it's useful for both teleoperation and AI model. That's one of the reasons why we are building our robotic arms starting with off the shelf motors rather than taking the whole off the shelf arm. There are still a range of easy wins that can be made iterating robot hardware.

Igor Kulakov

18,773 views โ€ข 1 year ago

Imagine having a ping pong robot! ๐Ÿ“ Researchers and developers building physical AI: meet Reachy 2 from Pollen Robotics, an open-source, humanoid robot for real-world experimentation. Itโ€™s a bimanual mobile manipulator: each 7-DOF arm mimics human proportions and can lift up to 3 kg, giving dexterity for object handling. It can be controlled with Python and ROS2 Humble, or go straight into VR teleoperation, use a headset to move Reachyโ€™s arms, hands, and head, and see through its cameras as if youโ€™re in the robotโ€™s own body. Want it to move around? A mobile base with three omnidirectional wheels, rich sensors, and LiDAR lets Reachy 2 navigate and explore its surroundings smoothly. ๐Ÿ—บ๏ธ Under the hood, itโ€™s powered by a CPU system thatโ€™s ready for machine learning, perfect for loading AI frameworks and testing new models from Hugging Face directly on the robot. Keep making robots more, and more accessible Pollen team! ... and keep making more open source models to make robots more mainstream clem ๐Ÿค—!
0:18

Imagine having a ping pong robot! ๐Ÿ“ Researchers and developers building physical AI: meet Reachy 2 from Pollen Robotics, an open-source, humanoid robot for real-world experimentation. Itโ€™s a bimanual mobile manipulator: each 7-DOF arm mimics human proportions and can lift up to 3 kg, giving dexterity for object handling. It can be controlled with Python and ROS2 Humble, or go straight into VR teleoperation, use a headset to move Reachyโ€™s arms, hands, and head, and see through its cameras as if youโ€™re in the robotโ€™s own body. Want it to move around? A mobile base with three omnidirectional wheels, rich sensors, and LiDAR lets Reachy 2 navigate and explore its surroundings smoothly. ๐Ÿ—บ๏ธ Under the hood, itโ€™s powered by a CPU system thatโ€™s ready for machine learning, perfect for loading AI frameworks and testing new models from Hugging Face directly on the robot. Keep making robots more, and more accessible Pollen team! ... and keep making more open source models to make robots more mainstream clem ๐Ÿค—!

Lukas Ziegler

37,221 views โ€ข 11 months ago

The story behind OptimAI Network is deeper than you think. Have you ever wondered why we chose "OptimAI," or what our ฯ€-inspired logo truly represents? It's not just branding, it's a commitment: + OP is for OPTIMIZED โ€“ because we believe intelligence can always be smarter, faster, better. + I represents INTELLIGENCE โ€“ the Agentic AI we're collectively building. + And ฯ€ (Pi) symbolizes our endless pursuit of knowledge and innovation, seamlessly transforming into AI, highlighting an infinite cycle of learning, optimization, and evolution. ๐Ÿ’กWe've prepared a short clip to unveil the meaning behind our identity: + Watch it. Feel it. Share your insights. + How does OptimAI resonate with your vision of the future? Drop your thoughts below, we canโ€™t wait to hear your perspective! Together, we're redefining what's possible. OptimAI. Optimized Intelligence, Infinite Possibilities. #BUIDL with us:
0:16

The story behind OptimAI Network is deeper than you think. Have you ever wondered why we chose "OptimAI," or what our ฯ€-inspired logo truly represents? It's not just branding, it's a commitment: + OP is for OPTIMIZED โ€“ because we believe intelligence can always be smarter, faster, better. + I represents INTELLIGENCE โ€“ the Agentic AI we're collectively building. + And ฯ€ (Pi) symbolizes our endless pursuit of knowledge and innovation, seamlessly transforming into AI, highlighting an infinite cycle of learning, optimization, and evolution. ๐Ÿ’กWe've prepared a short clip to unveil the meaning behind our identity: + Watch it. Feel it. Share your insights. + How does OptimAI resonate with your vision of the future? Drop your thoughts below, we canโ€™t wait to hear your perspective! Together, we're redefining what's possible. OptimAI. Optimized Intelligence, Infinite Possibilities. #BUIDL with us:

OptimAI Network

47,239 views โ€ข 1 year ago

Most robots still need markers, checkerboards, or long calibration rituals just to know where their arms are. Now it works from raw images in seconds. roboreg is a markerless multi arm localization toolkit that plugs into ROS 2 and RViz. No special hardware. No custom setup. You toggle between robot descriptions and the system figures out the rest. The idea is simple: โœ… Hand eye calibration from plain RGB or RGB D images โœ… Only three robot poses needed for millimeter accuracy โœ… Works with any ROS 2 compatible robot and camera โœ… Fully open source under Apache 2.0 It is powered by Hydra, a new marker free ICP variant that converges far more reliably than classical baselines and runs in under a second. If you want to try it: roboreg: ROS 2 roboreg: Hydra paper: pip install roboreg More details and discussion on Open Robotics Discourse:
0:15

Most robots still need markers, checkerboards, or long calibration rituals just to know where their arms are. Now it works from raw images in seconds. roboreg is a markerless multi arm localization toolkit that plugs into ROS 2 and RViz. No special hardware. No custom setup. You toggle between robot descriptions and the system figures out the rest. The idea is simple: โœ… Hand eye calibration from plain RGB or RGB D images โœ… Only three robot poses needed for millimeter accuracy โœ… Works with any ROS 2 compatible robot and camera โœ… Fully open source under Apache 2.0 It is powered by Hydra, a new marker free ICP variant that converges far more reliably than classical baselines and runs in under a second. If you want to try it: roboreg: ROS 2 roboreg: Hydra paper: pip install roboreg More details and discussion on Open Robotics Discourse:

Ilir Aliu

18,406 views โ€ข 8 months ago

Today may be the ImageNet moment for robotics. RT-X: the largest open-source robot dataset ever compiled, across 33 institutes, 22 robot hardware, 527 skills, and 1M episodes. Why is robotics lagging so far behind NLP, vision, and other AI domains? Data scarcity is the main culprit to blame, among other difficulties. Unlike text, images, and videos, you cannot download mass amounts of onboard robot control data from the internet. They simply don't exist in the wild. 11 yrs ago, ImageNet kicked off the deep learning revolution. 3-4 yrs ago, internet-scale data fueled the first GPTs and Diffusions that define this era of foundation models. I think 2023 is finally the year for robotics to scale up. Robot foundation models like VIMA ( my team's work at NVIDIA) and RT-1/2 ( Google DeepMind's effort) are extremely data hungry. While massively parallel simulations like NVIDIA IsaacGym & Omniverse can alleviate the problem to some extent, it's still not quite enough to bridge the gap to the messy, physical world. This new dataset is not just a technical contribution. I also see it as a commendable effort to overcome institutional bureaucracies and unite researchers from around the world to tackle a grand challenge together. Robotics will be the final holy grail that we capture in AI. We are not there yet, but ascending in the right gradient direction. RT-X website: Launch blog:
0:11

Today may be the ImageNet moment for robotics. RT-X: the largest open-source robot dataset ever compiled, across 33 institutes, 22 robot hardware, 527 skills, and 1M episodes. Why is robotics lagging so far behind NLP, vision, and other AI domains? Data scarcity is the main culprit to blame, among other difficulties. Unlike text, images, and videos, you cannot download mass amounts of onboard robot control data from the internet. They simply don't exist in the wild. 11 yrs ago, ImageNet kicked off the deep learning revolution. 3-4 yrs ago, internet-scale data fueled the first GPTs and Diffusions that define this era of foundation models. I think 2023 is finally the year for robotics to scale up. Robot foundation models like VIMA ( my team's work at NVIDIA) and RT-1/2 ( Google DeepMind's effort) are extremely data hungry. While massively parallel simulations like NVIDIA IsaacGym & Omniverse can alleviate the problem to some extent, it's still not quite enough to bridge the gap to the messy, physical world. This new dataset is not just a technical contribution. I also see it as a commendable effort to overcome institutional bureaucracies and unite researchers from around the world to tackle a grand challenge together. Robotics will be the final holy grail that we capture in AI. We are not there yet, but ascending in the right gradient direction. RT-X website: Launch blog:

Jim Fan

265,038 views โ€ข 2 years ago

Something big is happening in robotics - and itโ€™s hiding in plain sight. This post is not about dancing robots but in the data that powers them. Open robotics datasets have exploded this year, turning the field into a more scalable and collaborative ecosystem. In just two years, Hugging Face datasets grew from 11k to over 600k - and robotics is by far the fastest-growing segment. We went from 1k robotics datasets in 2024 to 27k in 2025! For comparison, text generation, the second-largest category, has only around 5k datasets in 2025. That gap is massive. Open datasets are important because robotics lives and dies by real-world robot data - video, actions, sensors, failures. By making this data easy to upload, reuse, and benchmark, researchers, startups, and large players are now releasing real-robot datasets that would have stayed locked inside labs just a few years ago. Major contributors include NVIDIA, LeRobot initiative, and a rapidly growing maker community. This surge is also enabled by cheaper video storage, better tooling, and an open-source AI culture now spilling into the physical world. And it really matters: open robotics data dramatically lowers entry barriers, accelerates learning-by-doing, and speeds up progress toward generalist and humanoid robots. Robotics wonโ€™t scale through hardware alone - but to a large extent through shared data. Viz below from AI World - link to the story and more viz/filters in comment.
0:11

Something big is happening in robotics - and itโ€™s hiding in plain sight. This post is not about dancing robots but in the data that powers them. Open robotics datasets have exploded this year, turning the field into a more scalable and collaborative ecosystem. In just two years, Hugging Face datasets grew from 11k to over 600k - and robotics is by far the fastest-growing segment. We went from 1k robotics datasets in 2024 to 27k in 2025! For comparison, text generation, the second-largest category, has only around 5k datasets in 2025. That gap is massive. Open datasets are important because robotics lives and dies by real-world robot data - video, actions, sensors, failures. By making this data easy to upload, reuse, and benchmark, researchers, startups, and large players are now releasing real-robot datasets that would have stayed locked inside labs just a few years ago. Major contributors include NVIDIA, LeRobot initiative, and a rapidly growing maker community. This surge is also enabled by cheaper video storage, better tooling, and an open-source AI culture now spilling into the physical world. And it really matters: open robotics data dramatically lowers entry barriers, accelerates learning-by-doing, and speeds up progress toward generalist and humanoid robots. Robotics wonโ€™t scale through hardware alone - but to a large extent through shared data. Viz below from AI World - link to the story and more viz/filters in comment.

Pierre-Alexandre Balland

186,017 views โ€ข 7 months ago

LayerAI AI Agent Manifesto is Live: The Path Forward ๐Ÿงฌ We've made it easy for ecosystem veterans & newcomers to get excited about the market & product opportunity we're tackling next: AI Agent Infrastructure. We're building an AI-powered agent platform where people can deploy, market, and succeed with this new token subcategory. At the heart of this transformation lies a challenge: primitive & so far limited tech & AI capabilities of incumbent platforms. We believe that LayerAI is equipped to rise as the new leading infrastructure provider for this market. LayerAI has already demonstrated market validation for AI Agents and looks to build on what we believe is the very start of this category in web3. ๐Ÿ‘‰ Explore now:
0:19

LayerAI AI Agent Manifesto is Live: The Path Forward ๐Ÿงฌ We've made it easy for ecosystem veterans & newcomers to get excited about the market & product opportunity we're tackling next: AI Agent Infrastructure. We're building an AI-powered agent platform where people can deploy, market, and succeed with this new token subcategory. At the heart of this transformation lies a challenge: primitive & so far limited tech & AI capabilities of incumbent platforms. We believe that LayerAI is equipped to rise as the new leading infrastructure provider for this market. LayerAI has already demonstrated market validation for AI Agents and looks to build on what we believe is the very start of this category in web3. ๐Ÿ‘‰ Explore now:

LayerAI | AI2Earn

158,919 views โ€ข 1 year ago

A Letter to Our Community: The Road Ahead for Robotics To our Community and Partners, As we step into 2026, our mission at Axis is clearer than ever: Constructing the definitive End-to-End Scaling Layer for Robotics. Our goal is to accelerate the transfer of diverse human intelligence into Robotics General Intelligence (RGI). By owning the critical path of intelligence creation, we are turning the physical limitations of robotics into a scalable, software-driven future. Here is our strategic outlook and roadmap for the year ahead. The Core Thesis: Simulation is the Only Way Out The path to RGI is currently blocked by Data Scarcity, Generalization Fragility, and Hardware Fragmentation. At Axis, we believe Simulation is the only way out. Our Simulation Data Platform and Data Augmentation Engine transform raw data into "Synthetic Gold". Backed by academic milestones like Roboverse, Skill Blending, and GraspVLA, we have proven that pure simulation can achieve the generalization required for the real world. We donโ€™t just collect data; we architect it. The Engine: Why Crypto? We believe RGI should come from all, not a few. Crypto is not just a feature; it is the primitive that powers our entire ecosystem flywheel: - Incentive Mechanism: Democratizing contribution and rewarding the trainers and developers. - Assetization: Turning proprietary data and refined models into liquid, ownable assets. - Verifiable Workflow: We are opening the "Black Box" of AI. By bringing total transparency to the Task Generation โ†’ Data Collection โ†’ Model Training pipeline, we ensure every byte of intelligence is verifiable, traceable, and secure. 2026 Strategic Deliverables This year, we are committed to delivering three foundational pillars: - The World's Largest Training Dataset for Robots: A robot training setโ€”diverse, high-quality interaction data at an unprecedented scale. - A Robotics Foundation Model: A universal robotic brain trained on our pure simulation and synthetic data, capable of robust cross-embodiment transfer and open-world adaptability. - Evolvable Robot Hardware: Robots deployed with Axis models that autonomously evolve through continuous interaction, turning every deployment into a self-improving node within our RGI network. The Ultimate Vision We are building more than models; we are architecting the Distributed Machine Economy. A future where every dataset, model, and robotic embodiment is a verifiable asset in a global, autonomous network. Thank you for building the future of intelligence with usโœŒ๏ธ๐Ÿ“ท
0:13

A Letter to Our Community: The Road Ahead for Robotics To our Community and Partners, As we step into 2026, our mission at Axis is clearer than ever: Constructing the definitive End-to-End Scaling Layer for Robotics. Our goal is to accelerate the transfer of diverse human intelligence into Robotics General Intelligence (RGI). By owning the critical path of intelligence creation, we are turning the physical limitations of robotics into a scalable, software-driven future. Here is our strategic outlook and roadmap for the year ahead. The Core Thesis: Simulation is the Only Way Out The path to RGI is currently blocked by Data Scarcity, Generalization Fragility, and Hardware Fragmentation. At Axis, we believe Simulation is the only way out. Our Simulation Data Platform and Data Augmentation Engine transform raw data into "Synthetic Gold". Backed by academic milestones like Roboverse, Skill Blending, and GraspVLA, we have proven that pure simulation can achieve the generalization required for the real world. We donโ€™t just collect data; we architect it. The Engine: Why Crypto? We believe RGI should come from all, not a few. Crypto is not just a feature; it is the primitive that powers our entire ecosystem flywheel: - Incentive Mechanism: Democratizing contribution and rewarding the trainers and developers. - Assetization: Turning proprietary data and refined models into liquid, ownable assets. - Verifiable Workflow: We are opening the "Black Box" of AI. By bringing total transparency to the Task Generation โ†’ Data Collection โ†’ Model Training pipeline, we ensure every byte of intelligence is verifiable, traceable, and secure. 2026 Strategic Deliverables This year, we are committed to delivering three foundational pillars: - The World's Largest Training Dataset for Robots: A robot training setโ€”diverse, high-quality interaction data at an unprecedented scale. - A Robotics Foundation Model: A universal robotic brain trained on our pure simulation and synthetic data, capable of robust cross-embodiment transfer and open-world adaptability. - Evolvable Robot Hardware: Robots deployed with Axis models that autonomously evolve through continuous interaction, turning every deployment into a self-improving node within our RGI network. The Ultimate Vision We are building more than models; we are architecting the Distributed Machine Economy. A future where every dataset, model, and robotic embodiment is a verifiable asset in a global, autonomous network. Thank you for building the future of intelligence with usโœŒ๏ธ๐Ÿ“ท

Axis Robotics

27,858 views โ€ข 7 months ago

๐—ฅ๐—ผ๐—ฏ๐—ผ๐˜๐˜€ ๐—ฑ๐—ผ๐—ปโ€™๐˜ ๐—ป๐—ฒ๐—ฒ๐—ฑ ๐—บ๐—ผ๐—ฟ๐—ฒ ๐—ฑ๐—ฒ๐—บ๐—ผ๐—ป๐˜€๐˜๐—ฟ๐—ฎ๐˜๐—ถ๐—ผ๐—ป๐˜€. ๐—ง๐—ต๐—ฒ๐˜† ๐—ป๐—ฒ๐—ฒ๐—ฑ ๐˜๐—ผ ๐—น๐—ฒ๐—ฎ๐—ฟ๐—ป ๐—ณ๐—ฟ๐—ผ๐—บ ๐—ณ๐—ฎ๐—ถ๐—น๐˜‚๐—ฟ๐—ฒ โ€” ๐—ฎ๐—ณ๐˜๐—ฒ๐—ฟ ๐˜„๐—ฎ๐˜๐—ฐ๐—ต๐—ถ๐—ป๐—ด ๐—ต๐˜‚๐—บ๐—ฎ๐—ป๐˜€. Most robot learning systems assume failure is the end of learning. In our new work, we study whether robots can improve after deployment by learning from their own failures, without any human intervention, teleoperation, or corrective labels. The key idea is simple: human videos contain structure about how the world works. We use them to learn cross-embodiment representations of action, dynamics, and value, enabling a shared predictive space between human behavior and robot experience. This allows a new learning loop: ๐Ÿ‘‰ pretrain on human videos ๐Ÿ‘‰ deploy robot policy ๐Ÿ‘‰ observe failures ๐Ÿ‘‰ reinterpret failures using human priors ๐Ÿ‘‰ improve autonomously We evaluate this across 7 real-world manipulation tasks, showing: ๐Ÿ“ˆ 40% โ†’ 81% success rate ๐Ÿ† Strong improvements over ฯ€0.6 RECAP and RISE โœ”๏ธ Zero human intervention during post-deployment improvement ๐Ÿงฌ Generalizes across robot embodiments and policy backbones A key finding is that explicit failure repair significantly outperforms failure reweighting, yielding substantially larger gains under identical data conditions (+25 pts vs +5 pts on the same ฯ€0.5 base policy). Overall, the results suggest a shift in how we think about robot learning: Human videos are not only for pretraining policies. They can provide the structure needed for continual self-improvement after deployment. ๐Ÿ“„ Paper: ๐ŸŒ Project: I am grateful for working with the fantastic leads Hanzhi Chen and Anran Zhang, and our collaborators Simon Schaefer, Kejia Chen, Shi Chen, Daniel Cremers. Special thanks to Stefan Leutenegger for co-advising this project with me. ETH Zรผrich TU Mรผnchen Microsoft Check out Hanzhi's ๐Ÿงต for more details
0:12

๐—ฅ๐—ผ๐—ฏ๐—ผ๐˜๐˜€ ๐—ฑ๐—ผ๐—ปโ€™๐˜ ๐—ป๐—ฒ๐—ฒ๐—ฑ ๐—บ๐—ผ๐—ฟ๐—ฒ ๐—ฑ๐—ฒ๐—บ๐—ผ๐—ป๐˜€๐˜๐—ฟ๐—ฎ๐˜๐—ถ๐—ผ๐—ป๐˜€. ๐—ง๐—ต๐—ฒ๐˜† ๐—ป๐—ฒ๐—ฒ๐—ฑ ๐˜๐—ผ ๐—น๐—ฒ๐—ฎ๐—ฟ๐—ป ๐—ณ๐—ฟ๐—ผ๐—บ ๐—ณ๐—ฎ๐—ถ๐—น๐˜‚๐—ฟ๐—ฒ โ€” ๐—ฎ๐—ณ๐˜๐—ฒ๐—ฟ ๐˜„๐—ฎ๐˜๐—ฐ๐—ต๐—ถ๐—ป๐—ด ๐—ต๐˜‚๐—บ๐—ฎ๐—ป๐˜€. Most robot learning systems assume failure is the end of learning. In our new work, we study whether robots can improve after deployment by learning from their own failures, without any human intervention, teleoperation, or corrective labels. The key idea is simple: human videos contain structure about how the world works. We use them to learn cross-embodiment representations of action, dynamics, and value, enabling a shared predictive space between human behavior and robot experience. This allows a new learning loop: ๐Ÿ‘‰ pretrain on human videos ๐Ÿ‘‰ deploy robot policy ๐Ÿ‘‰ observe failures ๐Ÿ‘‰ reinterpret failures using human priors ๐Ÿ‘‰ improve autonomously We evaluate this across 7 real-world manipulation tasks, showing: ๐Ÿ“ˆ 40% โ†’ 81% success rate ๐Ÿ† Strong improvements over ฯ€0.6 RECAP and RISE โœ”๏ธ Zero human intervention during post-deployment improvement ๐Ÿงฌ Generalizes across robot embodiments and policy backbones A key finding is that explicit failure repair significantly outperforms failure reweighting, yielding substantially larger gains under identical data conditions (+25 pts vs +5 pts on the same ฯ€0.5 base policy). Overall, the results suggest a shift in how we think about robot learning: Human videos are not only for pretraining policies. They can provide the structure needed for continual self-improvement after deployment. ๐Ÿ“„ Paper: ๐ŸŒ Project: I am grateful for working with the fantastic leads Hanzhi Chen and Anran Zhang, and our collaborators Simon Schaefer, Kejia Chen, Shi Chen, Daniel Cremers. Special thanks to Stefan Leutenegger for co-advising this project with me. ETH Zรผrich TU Mรผnchen Microsoft Check out Hanzhi's ๐Ÿงต for more details

Oier Mees

12,277 views โ€ข 1 month ago

China is smart. China learned from the US's successes, using only the best parts. It's our turn. We should be learning from China. China's economy is growing three times faster than that of the USA. China leads in almost all areas of science, and has the best infrastructure the world has ever seen. The number one priority of China is tell help their people become more prosperous, especially the least well off. Is there still work to be done. Hell yeah. But there difference is that they are trying and we are not. I've been to hundreds of Chinese villages, for more than a decade, and people are becoming better off each year. They call it 'Socialism with Chinese Characteristics.' We don't need to replicate their system. But we should be learning from it. For those ignoring it, you are hurting America. Pretending China's model isn't successful is preventing the US from improving and learning. The China-Haters should be ashamed of the damage that they are doing to the West. Instead of slander and Sinophobia, we need to be learning from the successes of other nations and their peoples.
0:19

China is smart. China learned from the US's successes, using only the best parts. It's our turn. We should be learning from China. China's economy is growing three times faster than that of the USA. China leads in almost all areas of science, and has the best infrastructure the world has ever seen. The number one priority of China is tell help their people become more prosperous, especially the least well off. Is there still work to be done. Hell yeah. But there difference is that they are trying and we are not. I've been to hundreds of Chinese villages, for more than a decade, and people are becoming better off each year. They call it 'Socialism with Chinese Characteristics.' We don't need to replicate their system. But we should be learning from it. For those ignoring it, you are hurting America. Pretending China's model isn't successful is preventing the US from improving and learning. The China-Haters should be ashamed of the damage that they are doing to the West. Instead of slander and Sinophobia, we need to be learning from the successes of other nations and their peoples.

Jason Smith - ไธŠๅฎ˜ๆฐๆ–‡

82,781 views โ€ข 1 year ago

๐Ÿฅณ WE'RE THRILLED TO ANNOUNCE THE LAUNCH OF THE GENIUS YIELD's DEX! ๐Ÿฅณ ๐Ÿš€๐Ÿš€๐Ÿš€Try it out now! โค๏ธ ๐Ÿซ‚ A massive thank you to everyone who helped us reach this milestone. This achievement is a testament to our community's incredible effort and dedication. We strive to be decentralized, open-sourced, and permissionless. Our Smart Order Routers (SORs) matching orders? They're in the hands of our community, not just us. ๐Ÿค โ˜๏ธ Let's not forget about fee sharing! We're growing together with you. Stay tuned for the myriad of exciting features we'll be adding throughout 2024! #theCardanoway #DEX #mainnet $GENS
0:10

๐Ÿฅณ WE'RE THRILLED TO ANNOUNCE THE LAUNCH OF THE GENIUS YIELD's DEX! ๐Ÿฅณ ๐Ÿš€๐Ÿš€๐Ÿš€Try it out now! โค๏ธ ๐Ÿซ‚ A massive thank you to everyone who helped us reach this milestone. This achievement is a testament to our community's incredible effort and dedication. We strive to be decentralized, open-sourced, and permissionless. Our Smart Order Routers (SORs) matching orders? They're in the hands of our community, not just us. ๐Ÿค โ˜๏ธ Let's not forget about fee sharing! We're growing together with you. Stay tuned for the myriad of exciting features we'll be adding throughout 2024! #theCardanoway #DEX #mainnet $GENS

Genius Yield official

116,372 views โ€ข 2 years ago