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THIS CHANGES EVERYTHING it's not another robot model. it's not another polished demo. it's the infrastructure nobody had built. LLMs scaled because they had the entire internet to train on. massive evaluation. RL at scale. robotics never had either. midcentury just came out of stealth with $15M to solve...

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36,516 görüntüleme • 19 gün önce •via X (Twitter)

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Robots don’t just need better brains. They need WAY more real-world data. 🤖 And collecting high-quality dexterous robot data at scale is one of the hardest problems in physical AI. A fascinating approach is emerging: Wearable human demonstrations + structurally matched dexterous robots. Instead of humans directly teleoperating a robot, Chinese embodied AI startup X Square Robot's TwinDEX system captures the motion, contact, and visual information needed to train the robot — while keeping the data closer to the hardware that will actually execute the task. Early results show promising performance on tool use, fine manipulation, and complex contact-rich tasks. If this approach scales, it could change how we build real-world robotics datasets. The next frontier of physical AI might not be bigger models. It might be better data.
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Robots don’t just need better brains. They need WAY more real-world data. 🤖 And collecting high-quality dexterous robot data at scale is one of the hardest problems in physical AI. A fascinating approach is emerging: Wearable human demonstrations + structurally matched dexterous robots. Instead of humans directly teleoperating a robot, Chinese embodied AI startup X Square Robot's TwinDEX system captures the motion, contact, and visual information needed to train the robot — while keeping the data closer to the hardware that will actually execute the task. Early results show promising performance on tool use, fine manipulation, and complex contact-rich tasks. If this approach scales, it could change how we build real-world robotics datasets. The next frontier of physical AI might not be bigger models. It might be better data.

The Daily Ai

33,341 görüntüleme • 1 ay önce

A Gaussian Splat can become a world where Robots and AI agents can act. In our latest OVER Research experiment, we placed a robot inside a real-world 3D capture, with a VLM making decisions based on what it sees. At every step, the robot holds a pose in the reconstruction, gets a newly rendered view of the environment, takes an action, moves, and sees the world again from its new position. Why does this matter? Because 3D captures can become more than reconstructions to explore. They can become environments where embodied AI and robots can navigate, act, be evaluated and eventually train across real-world spaces at scale. Capture a place once. Then turn it into a world where AI and Robots can act. The full experiment, including what we discovered once we actually put the loop to the test:
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A Gaussian Splat can become a world where Robots and AI agents can act. In our latest OVER Research experiment, we placed a robot inside a real-world 3D capture, with a VLM making decisions based on what it sees. At every step, the robot holds a pose in the reconstruction, gets a newly rendered view of the environment, takes an action, moves, and sees the world again from its new position. Why does this matter? Because 3D captures can become more than reconstructions to explore. They can become environments where embodied AI and robots can navigate, act, be evaluated and eventually train across real-world spaces at scale. Capture a place once. Then turn it into a world where AI and Robots can act. The full experiment, including what we discovered once we actually put the loop to the test:

Over the Reality 🌐

15,316 görüntüleme • 1 ay önce

🚨 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: → Unitree H2 Plus chassis, 6 feet tall, 150 pounds, 31 degrees of freedom → Sharpa Wave tactile five-finger hands, 22 degrees of freedom, bringing total to 75 across the full body → NVIDIA Jetson AGX Thor onboard compute, 2,070 FP4 teraflops of AI performance, 128GB unified memory → 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 Zürich, 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. 👀 Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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🚨 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: → Unitree H2 Plus chassis, 6 feet tall, 150 pounds, 31 degrees of freedom → Sharpa Wave tactile five-finger hands, 22 degrees of freedom, bringing total to 75 across the full body → NVIDIA Jetson AGX Thor onboard compute, 2,070 FP4 teraflops of AI performance, 128GB unified memory → 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 Zürich, 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. 👀 Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

16,062 görüntüleme • 4 ay önce

JUST IN: Reimagine Robotics has just emerged from stealth! 🥷🏻 Its approach to robot training is one of the most human-centric I've seen. The founder is Jonathan Scholz, the person who built and led Google DeepMind's Applied Robotics team in London for seven years. This is not a first-time founder taking a swing at robotics. He has spent a decade at the frontier of the field. The philosophy is powerful. He calls it "monkey-see, monkey-do." 🐒 A worker shows the robot what to do. Watches it attempt the task. Corrects it on the spot. The robot learns. No specialist programmers. No months of integration. And it's already working in the real world: → A made-to-order plastics business trained robots to tend 3D printers overnight, removing print beds, operating latches, pressing controls → A hard drive disassembly facility built a three-robot cell combining robots and people to recover critical materials → Time to prototype and test a new robot behaviour reduced from one day to 10 MINUTES That last number is the one that changes everything. When testing a new behaviour takes 10 minutes instead of a day, the entire pace of deployment transforms. Scholz's framing of the human-robot relationship is worth reading carefully: "A robot that learns on the job depends on people. The worker identifies the bottleneck, shows the robot how to help, and corrects it until it is useful." It's August, and we keep getting robotics bangers week in week. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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JUST IN: Reimagine Robotics has just emerged from stealth! 🥷🏻 Its approach to robot training is one of the most human-centric I've seen. The founder is Jonathan Scholz, the person who built and led Google DeepMind's Applied Robotics team in London for seven years. This is not a first-time founder taking a swing at robotics. He has spent a decade at the frontier of the field. The philosophy is powerful. He calls it "monkey-see, monkey-do." 🐒 A worker shows the robot what to do. Watches it attempt the task. Corrects it on the spot. The robot learns. No specialist programmers. No months of integration. And it's already working in the real world: → A made-to-order plastics business trained robots to tend 3D printers overnight, removing print beds, operating latches, pressing controls → A hard drive disassembly facility built a three-robot cell combining robots and people to recover critical materials → Time to prototype and test a new robot behaviour reduced from one day to 10 MINUTES That last number is the one that changes everything. When testing a new behaviour takes 10 minutes instead of a day, the entire pace of deployment transforms. Scholz's framing of the human-robot relationship is worth reading carefully: "A robot that learns on the job depends on people. The worker identifies the bottleneck, shows the robot how to help, and corrects it until it is useful." It's August, and we keep getting robotics bangers week in week. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

23,404 görüntüleme • 2 ay önce

Trained on zero real-world data. Learned to walk, pick up boxes, and follow multi-step instructions... in the REAL world. ( 📌 Paper below) Researchers from Amazon FAR, Berkeley, Stanford, and CMU scanned real rooms with an iPhone, rebuilt them as 3D Gaussian Splatting scenes, then generated 48,000 synthetic trajectories of a Unitree G1 walking, grasping, and placing objects inside those virtual replicas. They rendered the robot's first-person camera view from each run and paired it with the matching language instruction and motion data. That's the dataset every humanoid team needs and nobody has: synced egocentric video + language + kinematics, at scale. Instead of collecting it in the real world, they manufactured it. They trained a vision-language-kinematics policy on that synthetic data alone, then deployed it on the physical G1 across five task types: navigation to a named object, lifting boxes of three different sizes with no per-size tuning, chained multi-step tasks, robustness to mid-task layout changes and flickering lights, and multi-minute long-horizon runs. No real-world fine-tuning at any point. Real-world interaction data has been the hard limit on humanoid learning... slow, expensive, and small. If scanning a room once and synthesizing thousands of labeled interactions holds up as a general recipe, that limit moves. Data stops being the bottleneck robotics teams have to solve for. 📌 Paper: Project: ——- Weekly robotics and AI insights. Subscribe free:
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Trained on zero real-world data. Learned to walk, pick up boxes, and follow multi-step instructions... in the REAL world. ( 📌 Paper below) Researchers from Amazon FAR, Berkeley, Stanford, and CMU scanned real rooms with an iPhone, rebuilt them as 3D Gaussian Splatting scenes, then generated 48,000 synthetic trajectories of a Unitree G1 walking, grasping, and placing objects inside those virtual replicas. They rendered the robot's first-person camera view from each run and paired it with the matching language instruction and motion data. That's the dataset every humanoid team needs and nobody has: synced egocentric video + language + kinematics, at scale. Instead of collecting it in the real world, they manufactured it. They trained a vision-language-kinematics policy on that synthetic data alone, then deployed it on the physical G1 across five task types: navigation to a named object, lifting boxes of three different sizes with no per-size tuning, chained multi-step tasks, robustness to mid-task layout changes and flickering lights, and multi-minute long-horizon runs. No real-world fine-tuning at any point. Real-world interaction data has been the hard limit on humanoid learning... slow, expensive, and small. If scanning a room once and synthesizing thousands of labeled interactions holds up as a general recipe, that limit moves. Data stops being the bottleneck robotics teams have to solve for. 📌 Paper: Project: ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

12,950 görüntüleme • 2 ay önce

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:
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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

265,061 görüntüleme • 3 yıl önce

Furniture assembly is the task everyone name-drops and nobody actually attempts at real scale. Every demo I have seen is a scaled down IKEA leg or a single arm on a toy chair. This paper does it properly, real scale, bimanual, up to 7 subtasks and 1,550 control steps per episode, and it is validated on a real Kinova Gen3, not just in sim. That real-robot number is the one that matters: only a 16 percent drop on the hardest task going from simulation to hardware. That is a small enough gap to take seriously, and it did not happen by accident. They built a VR teleoperation rig specifically for coordinated dual-arm collection, because generic single-arm teleop setups do not capture the coordination real assembly needs, and the model predicts a continuous progress signal alongside the action chunk rather than a discrete subtask label, letting it auto-transition and catch drift before it compounds into total failure. The simulation ablation is what got them there, 48 to 80 percent over baselines, with another 21 points from their perception and control design study alone, but that is groundwork, not the headline. Watch the video, there is a clip of the robot misgrasping the seat panel, reopening the gripper, and regrasping on its own. That is not scripted recovery behaviour, it emerged from training, and it emerged on hardware. Excellent work from the team from Mitsubishi Electric Research Laboratories, with Oxford and UNC Chapel Hill Clinical Laboratory Science. Video and project page in comments. #Robotics #Manipulation #VLA
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Furniture assembly is the task everyone name-drops and nobody actually attempts at real scale. Every demo I have seen is a scaled down IKEA leg or a single arm on a toy chair. This paper does it properly, real scale, bimanual, up to 7 subtasks and 1,550 control steps per episode, and it is validated on a real Kinova Gen3, not just in sim. That real-robot number is the one that matters: only a 16 percent drop on the hardest task going from simulation to hardware. That is a small enough gap to take seriously, and it did not happen by accident. They built a VR teleoperation rig specifically for coordinated dual-arm collection, because generic single-arm teleop setups do not capture the coordination real assembly needs, and the model predicts a continuous progress signal alongside the action chunk rather than a discrete subtask label, letting it auto-transition and catch drift before it compounds into total failure. The simulation ablation is what got them there, 48 to 80 percent over baselines, with another 21 points from their perception and control design study alone, but that is groundwork, not the headline. Watch the video, there is a clip of the robot misgrasping the seat panel, reopening the gripper, and regrasping on its own. That is not scripted recovery behaviour, it emerged from training, and it emerged on hardware. Excellent work from the team from Mitsubishi Electric Research Laboratories, with Oxford and UNC Chapel Hill Clinical Laboratory Science. Video and project page in comments. #Robotics #Manipulation #VLA

Stephen James

14,952 görüntüleme • 3 ay önce

You can't 3D reconstruct glass from images... ...WRONG! Thanks for video diffusion, now just about anything is possible! Introducing...Diffusion Knows Transparency (DKT) Transparent and reflective objects usually break robot vision and photogrammetry pipelines because they don't follow the "solid object" rules standard cameras expect. DKT is a new AI model that repurposes the "internal physics engine" found in video generation models to solve this problem. Researchers took a massive video diffusion model (WAN) and fine-tuned it using a custom-built synthetic dataset to turn it into a high-precision depth sensor. To train the AI, they built the first massive synthetic video library of transparent objects, 1.32 million frames of perfectly labeled glass and metal objects in motion. Without ever seeing a "real" labeled video of glass during training, the model (DKT) outperformed all previous specialized systems on real-world benchmarks (ClearPose, DREDS). They created a "lightweight" 1.3B parameter version that runs fast enough (0.17s per frame) to be used on actual robot hardware. Two reasons I find this project important: 1. It further proves that synthetic data will be essential for training the next generation vision models. 2. In real-world robotic tests, using DKT's depth maps nearly doubled the success rate of robot arms trying to pick up objects on tricky reflective or translucent surfaces. At home robots will need to interact with these types of objects on a daily basis. Check out the project page here: Code is LIVE! #Computervision #Robotics #AI
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You can't 3D reconstruct glass from images... ...WRONG! Thanks for video diffusion, now just about anything is possible! Introducing...Diffusion Knows Transparency (DKT) Transparent and reflective objects usually break robot vision and photogrammetry pipelines because they don't follow the "solid object" rules standard cameras expect. DKT is a new AI model that repurposes the "internal physics engine" found in video generation models to solve this problem. Researchers took a massive video diffusion model (WAN) and fine-tuned it using a custom-built synthetic dataset to turn it into a high-precision depth sensor. To train the AI, they built the first massive synthetic video library of transparent objects, 1.32 million frames of perfectly labeled glass and metal objects in motion. Without ever seeing a "real" labeled video of glass during training, the model (DKT) outperformed all previous specialized systems on real-world benchmarks (ClearPose, DREDS). They created a "lightweight" 1.3B parameter version that runs fast enough (0.17s per frame) to be used on actual robot hardware. Two reasons I find this project important: 1. It further proves that synthetic data will be essential for training the next generation vision models. 2. In real-world robotic tests, using DKT's depth maps nearly doubled the success rate of robot arms trying to pick up objects on tricky reflective or translucent surfaces. At home robots will need to interact with these types of objects on a daily basis. Check out the project page here: Code is LIVE! #Computervision #Robotics #AI

Jonathan Stephens

17,712 görüntüleme • 9 ay önce

TESLA HALTED MODEL S AND MODEL X PRODUCTION TO BUILD AN ARMY OF OPTIMUS ROBOTS The Fremont assembly line was torn down in 46 days. In its place, Tesla is building a line for humanoid production, aiming for a million units a year A humanoid robot is a body shaped like a human. Physical AI is the intelligence that controls that body Walking and making coffee is often just imitation learning from a scripted routine. But once the environment shifts, the learned trick stops working Language models had the entire internet to train on. Robotics has nothing close to that scale of data, which is why one giant brain hasn't worked for anyone yet The industry is moving toward modularity instead - separate models for vision, movement, and planning, each improved on its own The real question is no longer whether a robot can move impressively. It's whether it can pull its sensors into one picture of the world and adapt to whatever wasn't scripted for it
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TESLA HALTED MODEL S AND MODEL X PRODUCTION TO BUILD AN ARMY OF OPTIMUS ROBOTS The Fremont assembly line was torn down in 46 days. In its place, Tesla is building a line for humanoid production, aiming for a million units a year A humanoid robot is a body shaped like a human. Physical AI is the intelligence that controls that body Walking and making coffee is often just imitation learning from a scripted routine. But once the environment shifts, the learned trick stops working Language models had the entire internet to train on. Robotics has nothing close to that scale of data, which is why one giant brain hasn't worked for anyone yet The industry is moving toward modularity instead - separate models for vision, movement, and planning, each improved on its own The real question is no longer whether a robot can move impressively. It's whether it can pull its sensors into one picture of the world and adapt to whatever wasn't scripted for it

iamigorekk

22,205 görüntüleme • 1 ay önce

40 hours of human work. That’s what this humanoid could save every month! A construction company is already testing a Unitree G1 on a real job site, using the robot for site inspections, 360° imaging, data collection and progress monitoring. The robot starts at around $13,500, while the company says its deployment can save roughly 40 hours of work every month. That adds up to around 480 hours a year from a machine that costs a fraction of traditional industrial equipment. The interesting part is that this isn't about replacing an entire construction worker. It's about removing hundreds of repetitive hours from the workflow, including walking inspection routes, documenting progress and collecting information across the site. Humans can then spend their time on decisions and tasks that actually require them. This is where humanoid robots become economically interesting. Construction sites are already designed around human movement, so a robot with two arms, two legs and a human-sized body can potentially work in the same spaces without rebuilding the entire environment. Every additional task it learns turns those same hardware costs into more productive hours. And the economics get even more interesting as prices fall and production scales. A robot that saves 480 hours per year doesn't need to be perfect or replace a full-time employee to justify its existence. It just needs to reliably take over the boring, repetitive work that companies are already paying humans to do. 480 hours saved. Thousands of dollars in hardware. One construction site. This is how humanoids will enter the workforce, not by replacing everyone overnight, but by quietly taking over the hours nobody wants to spend.
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40 hours of human work. That’s what this humanoid could save every month! A construction company is already testing a Unitree G1 on a real job site, using the robot for site inspections, 360° imaging, data collection and progress monitoring. The robot starts at around $13,500, while the company says its deployment can save roughly 40 hours of work every month. That adds up to around 480 hours a year from a machine that costs a fraction of traditional industrial equipment. The interesting part is that this isn't about replacing an entire construction worker. It's about removing hundreds of repetitive hours from the workflow, including walking inspection routes, documenting progress and collecting information across the site. Humans can then spend their time on decisions and tasks that actually require them. This is where humanoid robots become economically interesting. Construction sites are already designed around human movement, so a robot with two arms, two legs and a human-sized body can potentially work in the same spaces without rebuilding the entire environment. Every additional task it learns turns those same hardware costs into more productive hours. And the economics get even more interesting as prices fall and production scales. A robot that saves 480 hours per year doesn't need to be perfect or replace a full-time employee to justify its existence. It just needs to reliably take over the boring, repetitive work that companies are already paying humans to do. 480 hours saved. Thousands of dollars in hardware. One construction site. This is how humanoids will enter the workforce, not by replacing everyone overnight, but by quietly taking over the hours nobody wants to spend.

Future Memo

18,925 görüntüleme • 28 gün önce

Elon Musk nearly went bankrupt building something nobody on Earth wanted to build. Then he gave it all away for free. Most people still don’t understand what actually happened. Before Tesla, not a single major automaker was serious about electric vehicles. EVs were a punchline. Glorified golf carts. The entire industry laughed them off. Elon looked at that vacuum and decided he would fill it himself. Not because it was profitable. Because if he didn’t, nobody would. That bet nearly killed Tesla. The company came within weeks of total collapse. Multiple times. Consider what they were up against. Combustion engines had 150 years of compounding innovation. 150 years of supply chains, manufacturing infrastructure, and institutional knowledge threaded into every single component. Tesla had a vision and 15 years. They built everything from nothing. The cars. The batteries. The software. The factories. Then came the part nobody talks about. Tesla had to build the entire charging infrastructure itself. A global Supercharger network. Thousands of stations spanning continents. Just so people could actually drive the cars they were selling. That is not a car company. That is a civilization-scale engineering project. Built by a company that was weeks from death. Then the numbers landed. The Tesla Model Y became the best-selling car in the world. Not the best-selling EV. The best-selling car on Earth. Three straight years. 2023. 2024. 2025. After 150 years of combustion dominance, an electric vehicle from a company that almost didn’t survive outsold every gas car on the planet. Then the story turned. Tesla open-sourced their patents. Opened the Supercharger network to competitors. The company nearly bled out building what nobody else would. Then they handed the blueprints to everyone. For free. Elon Musk: “It’s because it’s very important to accelerate the transition to sustainable transport. And this is really important for the future of the world.” He had the monopoly. He gave it away. He chose the mission over the moat. Without Elon, the auto industry would still be polishing combustion engines and pretending the future was decades away. He built what nobody would. Nearly lost everything in the process. Outsold the entire industry. Then handed the blueprints to his competitors for free. The mission was never about Tesla.
0:16

Elon Musk nearly went bankrupt building something nobody on Earth wanted to build. Then he gave it all away for free. Most people still don’t understand what actually happened. Before Tesla, not a single major automaker was serious about electric vehicles. EVs were a punchline. Glorified golf carts. The entire industry laughed them off. Elon looked at that vacuum and decided he would fill it himself. Not because it was profitable. Because if he didn’t, nobody would. That bet nearly killed Tesla. The company came within weeks of total collapse. Multiple times. Consider what they were up against. Combustion engines had 150 years of compounding innovation. 150 years of supply chains, manufacturing infrastructure, and institutional knowledge threaded into every single component. Tesla had a vision and 15 years. They built everything from nothing. The cars. The batteries. The software. The factories. Then came the part nobody talks about. Tesla had to build the entire charging infrastructure itself. A global Supercharger network. Thousands of stations spanning continents. Just so people could actually drive the cars they were selling. That is not a car company. That is a civilization-scale engineering project. Built by a company that was weeks from death. Then the numbers landed. The Tesla Model Y became the best-selling car in the world. Not the best-selling EV. The best-selling car on Earth. Three straight years. 2023. 2024. 2025. After 150 years of combustion dominance, an electric vehicle from a company that almost didn’t survive outsold every gas car on the planet. Then the story turned. Tesla open-sourced their patents. Opened the Supercharger network to competitors. The company nearly bled out building what nobody else would. Then they handed the blueprints to everyone. For free. Elon Musk: “It’s because it’s very important to accelerate the transition to sustainable transport. And this is really important for the future of the world.” He had the monopoly. He gave it away. He chose the mission over the moat. Without Elon, the auto industry would still be polishing combustion engines and pretending the future was decades away. He built what nobody would. Nearly lost everything in the process. Outsold the entire industry. Then handed the blueprints to his competitors for free. The mission was never about Tesla.

Dustin

15,500 görüntüleme • 5 ay önce

NVIDIA might have just declared war on the cloud GPU business For years, AI builders had one option Rent compute Pay every month Watch the bill grow every time usage increased Now NVIDIA is putting serious AI hardware directly on people's desks Small enough to fit next to a monitor Powerful enough to run workloads that used to require expensive cloud infrastructure That's why this launch is getting so much attention The real story isn't the hardware specs It's the business model shift Every month, developers send money to cloud providers for inference, testing, fine-tuning and AI applications The question nobody can answer yet is what happens if enough developers decide they'd rather buy infrastructure once than rent it forever Because if local AI hardware keeps getting more powerful, the economics start changing very quickly Cloud providers built empires on renting access to compute NVIDIA is betting more people will eventually want to own it And that's a much bigger story than a new piece of hardware sitting on a desk
0:17

NVIDIA might have just declared war on the cloud GPU business For years, AI builders had one option Rent compute Pay every month Watch the bill grow every time usage increased Now NVIDIA is putting serious AI hardware directly on people's desks Small enough to fit next to a monitor Powerful enough to run workloads that used to require expensive cloud infrastructure That's why this launch is getting so much attention The real story isn't the hardware specs It's the business model shift Every month, developers send money to cloud providers for inference, testing, fine-tuning and AI applications The question nobody can answer yet is what happens if enough developers decide they'd rather buy infrastructure once than rent it forever Because if local AI hardware keeps getting more powerful, the economics start changing very quickly Cloud providers built empires on renting access to compute NVIDIA is betting more people will eventually want to own it And that's a much bigger story than a new piece of hardware sitting on a desk

beamnxw ./

30,361 görüntüleme • 4 ay önce

I am posting after a Long Time on Twitter, but its to announce a big change. I have started and we are Collecting Egocentric Data at Scale from India, Covering 300 + Commercial Locations 1500 + Households This is the network and base we have built in just past 2 months, as the Robotics companies, VLMs and World Models increase their requirements on Real World Data collection, Human Loops will be keep scaling out capacity. We are on track to collect 1M Hours of Egocentric in the coming 6 months for our clients exclusively. We are maintaining 95% Quality standards across industrial data and running a End - End Operational Management complying all Indian Laws and compensating our partners/operators. (From Environment sourcing, to hardware, Legal contracts, deployment, training, collection, processing) Check our Samples: - Commercial Videos -- Household Videos --- Multimodel ---- Egocentric + Live Audio Narration Links : On the Journey to become #1 India Physical AI Data Partner. We are also building our capacity as annotation and labelling partner for data companies to become end-end partner for companies. A big change from the world of crypto and web3 but physical AI and data space is where i want to build my next venture #Egocentric #EgocentricIndia #PhysicalAI #Robotics #India #AIdata #data #Multimodeldata #worldmodels #VLMs #Humanloops #Egocentridata #Multimodeldata
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I am posting after a Long Time on Twitter, but its to announce a big change. I have started and we are Collecting Egocentric Data at Scale from India, Covering 300 + Commercial Locations 1500 + Households This is the network and base we have built in just past 2 months, as the Robotics companies, VLMs and World Models increase their requirements on Real World Data collection, Human Loops will be keep scaling out capacity. We are on track to collect 1M Hours of Egocentric in the coming 6 months for our clients exclusively. We are maintaining 95% Quality standards across industrial data and running a End - End Operational Management complying all Indian Laws and compensating our partners/operators. (From Environment sourcing, to hardware, Legal contracts, deployment, training, collection, processing) Check our Samples: - Commercial Videos -- Household Videos --- Multimodel ---- Egocentric + Live Audio Narration Links : On the Journey to become #1 India Physical AI Data Partner. We are also building our capacity as annotation and labelling partner for data companies to become end-end partner for companies. A big change from the world of crypto and web3 but physical AI and data space is where i want to build my next venture #Egocentric #EgocentricIndia #PhysicalAI #Robotics #India #AIdata #data #Multimodeldata #worldmodels #VLMs #Humanloops #Egocentridata #Multimodeldata

Shloak

28,996 görüntüleme • 4 ay önce

Your model crushed the benchmark. Then it couldn’t pick up a cup. That’s the reality nobody talks about. You train in simulation, it falls apart on real hardware. You collect real-world data instead (months of teleop, physical setups, safety protocols) and still can’t scale it. Meanwhile, your model outgrows every available benchmark, and you have no way to know if you’re actually getting better. That costs you iteration speed. Engineering hours. And confidence in every decision about when to ship. This year I’m collaborating with Lightwheel to cover robotics and embodied AI at NVIDIA GTC — Booth 1406, March 16–19, San Jose. Live demos of what happens when the physics ACTUALLY match. Worth a stop. I’ll be there. Saying hello to Steve Xie 👋
0:14

Your model crushed the benchmark. Then it couldn’t pick up a cup. That’s the reality nobody talks about. You train in simulation, it falls apart on real hardware. You collect real-world data instead (months of teleop, physical setups, safety protocols) and still can’t scale it. Meanwhile, your model outgrows every available benchmark, and you have no way to know if you’re actually getting better. That costs you iteration speed. Engineering hours. And confidence in every decision about when to ship. This year I’m collaborating with Lightwheel to cover robotics and embodied AI at NVIDIA GTC — Booth 1406, March 16–19, San Jose. Live demos of what happens when the physics ACTUALLY match. Worth a stop. I’ll be there. Saying hello to Steve Xie 👋

Ilir Aliu

10,575 görüntüleme • 7 ay önce

InterLink’s Journey to the World’s Top 10 Most Accurate AI Models Artificial Intelligence has rapidly become the defining force of this decade powering breakthroughs across every industry. But while most projects chase trends, InterLink Labs 👤 + 🌐 has been quietly building something deeper: an AI ecosystem grounded in verified human intelligence. Long before AI captured global headlines, InterLink Labs 👤 + 🌐 had already begun its research and engineering efforts back in 2019, assembling a world-class team of engineers and researchers from Big Tech companies and top QS-ranked universities. Their vision was clear - to build a model that truly understands humans, not just data. Unlike traditional AI systems trained purely on digital information, InterLink Labs 👤 + 🌐’s Human-AI Model learns from verified human behavior across millions of Human Nodes. This unique layer of authentic, real-world human input gives InterLink’s AI an unprecedented advantage in trustworthiness, bias reduction, and contextual understanding. Beyond algorithmic optimization, InterLink Labs 👤 + 🌐’s R&D efforts are being scaled to an unprecedented level. The team operates over 100 NVIDIA H100 servers, processing massive volumes of verified behavioral data contributed by real Human Nodes across the world. This data - diverse, decentralized, and human-validated forms the foundation of a next-generation intelligence system designed to mirror real human reasoning patterns. At the same time, InterLink Labs 👤 + 🌐’s AI-powered Human Credit Score applies advanced machine learning to analyze authenticity, contribution, and reliability. Creating an ethical model of digital reputation and fairness. Aligned with National Institute of Standards and Technology (National Institute of Standards and Technology) evaluation standards, InterLink Labs 👤 + 🌐 now aims to achieve Top 10 accuracy globally among AI models. Competing with research teams from Samsung Electronics, Kakao, キヤノン株式会社 / Canon Inc., and other global giants, InterLink Labs 👤 + 🌐’s engineers continue to train, benchmark, and refine their architecture daily to reach world-class precision and consistency. But this is more than a technical race. It’s a human mission. Every verified user contributes to the world’s first Human-Powered Intelligence Network, where real people fuel the evolution of trustworthy AI. As InterLink Labs 👤 + 🌐 advances toward global National Institute of Standards and Technology recognition, one truth becomes clear: The future of intelligence won’t be artificial. It will be human-powered. #InterLink #ITLG #ITL
0:12

InterLink’s Journey to the World’s Top 10 Most Accurate AI Models Artificial Intelligence has rapidly become the defining force of this decade powering breakthroughs across every industry. But while most projects chase trends, InterLink Labs 👤 + 🌐 has been quietly building something deeper: an AI ecosystem grounded in verified human intelligence. Long before AI captured global headlines, InterLink Labs 👤 + 🌐 had already begun its research and engineering efforts back in 2019, assembling a world-class team of engineers and researchers from Big Tech companies and top QS-ranked universities. Their vision was clear - to build a model that truly understands humans, not just data. Unlike traditional AI systems trained purely on digital information, InterLink Labs 👤 + 🌐’s Human-AI Model learns from verified human behavior across millions of Human Nodes. This unique layer of authentic, real-world human input gives InterLink’s AI an unprecedented advantage in trustworthiness, bias reduction, and contextual understanding. Beyond algorithmic optimization, InterLink Labs 👤 + 🌐’s R&D efforts are being scaled to an unprecedented level. The team operates over 100 NVIDIA H100 servers, processing massive volumes of verified behavioral data contributed by real Human Nodes across the world. This data - diverse, decentralized, and human-validated forms the foundation of a next-generation intelligence system designed to mirror real human reasoning patterns. At the same time, InterLink Labs 👤 + 🌐’s AI-powered Human Credit Score applies advanced machine learning to analyze authenticity, contribution, and reliability. Creating an ethical model of digital reputation and fairness. Aligned with National Institute of Standards and Technology (National Institute of Standards and Technology) evaluation standards, InterLink Labs 👤 + 🌐 now aims to achieve Top 10 accuracy globally among AI models. Competing with research teams from Samsung Electronics, Kakao, キヤノン株式会社 / Canon Inc., and other global giants, InterLink Labs 👤 + 🌐’s engineers continue to train, benchmark, and refine their architecture daily to reach world-class precision and consistency. But this is more than a technical race. It’s a human mission. Every verified user contributes to the world’s first Human-Powered Intelligence Network, where real people fuel the evolution of trustworthy AI. As InterLink Labs 👤 + 🌐 advances toward global National Institute of Standards and Technology recognition, one truth becomes clear: The future of intelligence won’t be artificial. It will be human-powered. #InterLink #ITLG #ITL

InterLink Labs 👤 + 🌐

51,650 görüntüleme • 11 ay önce

AI has had exactly two scaling axes that worked so far, and the second one is starting to look finite too the first one was pretraining: with scaling parameters and data, we got world knowledge (i.e. ChatGPT had read enough to know things), but it started saturating a while ago the second one was RL, and people had been doing RL the whole time before that: RLHF is RL but it never scaled far because it was trying to control the exact output, which tokens come out, how the text reads, but you can only push that so far before you’re just polishing RLVR dropped that constraint: giving the model a task, then checking whether the final answer is right, and ignoring everything in between -- so the model does whatever it wants in the middle and only the endpoint gets graded, and that’s much closer to actual RL and it’s what bought us planning and reasoning (arguably, tool use sits around 2.5 on this list -- while useful, it's not a different kind of thing) so one axis gave knowledge, the other gave reasoning, and both of them are one model working alone the next axis is how many models you can get working on the same problem, which is a different kind of axis than the previous two we know that multi-agent RL has always been the harder problem: I spent years in that literature and the gap between single-agent and multi-agent is definitely not incremental -- it’s a whole different class of difficulty! which is also why the derivatives are steep at the start, nobody has picked the easy wins yet... and the thing that gates this multi-agent coordination is communication: models can only coordinate as well as they can exchange information, and right now they do that by writing sentences to each other imagine what could we possibly achieve if we properly open that third axis development by letting models to exchange information in their native "language" without loosing any computational data that they produce during inference
0:14

AI has had exactly two scaling axes that worked so far, and the second one is starting to look finite too the first one was pretraining: with scaling parameters and data, we got world knowledge (i.e. ChatGPT had read enough to know things), but it started saturating a while ago the second one was RL, and people had been doing RL the whole time before that: RLHF is RL but it never scaled far because it was trying to control the exact output, which tokens come out, how the text reads, but you can only push that so far before you’re just polishing RLVR dropped that constraint: giving the model a task, then checking whether the final answer is right, and ignoring everything in between -- so the model does whatever it wants in the middle and only the endpoint gets graded, and that’s much closer to actual RL and it’s what bought us planning and reasoning (arguably, tool use sits around 2.5 on this list -- while useful, it's not a different kind of thing) so one axis gave knowledge, the other gave reasoning, and both of them are one model working alone the next axis is how many models you can get working on the same problem, which is a different kind of axis than the previous two we know that multi-agent RL has always been the harder problem: I spent years in that literature and the gap between single-agent and multi-agent is definitely not incremental -- it’s a whole different class of difficulty! which is also why the derivatives are steep at the start, nobody has picked the easy wins yet... and the thing that gates this multi-agent coordination is communication: models can only coordinate as well as they can exchange information, and right now they do that by writing sentences to each other imagine what could we possibly achieve if we properly open that third axis development by letting models to exchange information in their native "language" without loosing any computational data that they produce during inference

Sasha Malysheva

15,147 görüntüleme • 2 ay önce

We are building the home of the doge economy Projects across every major vertical are already gearing up to launch on DogeOS. Builders are cooking. Here's what the community should be excited to explore.... Much Thoughts ✍️ Very 🐕 So Soon... 💱 DeFi Swaps, lending, yield, memes all running on DogeOS with Dogecoin at the heart of it all. This means the most beloved and recognized asset in crypto finally gets a real financial system built around it. Doge stops being just a vibe and starts being productive. The community has been asking for this for years. 🎮 Gaming On-chain games built on DogeOS: play, compete, unlock with Dogecoin. Doge was born from meme and gaming culture. Bringing games on-chain is just Doge coming home. Play to unlock, play to grow, play because it's fun. Gaming is one of the strongest onramps in all of crypto. It's how you turn curious onlookers into active participants. This is a chance to bring the fun and the love of this community to a whole new wave of users, and remind the old ones why they fell in love with Doge in the first place. 🤖 AI Builders are already positioned to ship AI powered dApps, agents, and tools natively on DogeOS. Think automated trading agents, AI assistants that manage your wallet, dApp builders, and solutions that get smarter the more the ecosystem grows. Agents benefit from fast and cheap transaction environments, and that's exactly what DogeOS is built for. DogeOS becomes the rails for the next wave of on-chain AI, with Doge as the fuel. 📣 Community and Social Apps built for the culture. Social tools, creator platforms, community coordination, all on-chain and all for the Doge economy. Dogecoin has always had the strongest community in crypto. Now that community gets its own social layer instead of living as a guest in another chain's world. Dogecoin should be the focal point, not just another asset being served. 🐕 All your desires. One ecosystem. We are building toward the future this community has dreamt of, making Dogecoin the central piece of a real on-chain economy. This is just the start. Much building. Prepare your paws.
0:11

We are building the home of the doge economy Projects across every major vertical are already gearing up to launch on DogeOS. Builders are cooking. Here's what the community should be excited to explore.... Much Thoughts ✍️ Very 🐕 So Soon... 💱 DeFi Swaps, lending, yield, memes all running on DogeOS with Dogecoin at the heart of it all. This means the most beloved and recognized asset in crypto finally gets a real financial system built around it. Doge stops being just a vibe and starts being productive. The community has been asking for this for years. 🎮 Gaming On-chain games built on DogeOS: play, compete, unlock with Dogecoin. Doge was born from meme and gaming culture. Bringing games on-chain is just Doge coming home. Play to unlock, play to grow, play because it's fun. Gaming is one of the strongest onramps in all of crypto. It's how you turn curious onlookers into active participants. This is a chance to bring the fun and the love of this community to a whole new wave of users, and remind the old ones why they fell in love with Doge in the first place. 🤖 AI Builders are already positioned to ship AI powered dApps, agents, and tools natively on DogeOS. Think automated trading agents, AI assistants that manage your wallet, dApp builders, and solutions that get smarter the more the ecosystem grows. Agents benefit from fast and cheap transaction environments, and that's exactly what DogeOS is built for. DogeOS becomes the rails for the next wave of on-chain AI, with Doge as the fuel. 📣 Community and Social Apps built for the culture. Social tools, creator platforms, community coordination, all on-chain and all for the Doge economy. Dogecoin has always had the strongest community in crypto. Now that community gets its own social layer instead of living as a guest in another chain's world. Dogecoin should be the focal point, not just another asset being served. 🐕 All your desires. One ecosystem. We are building toward the future this community has dreamt of, making Dogecoin the central piece of a real on-chain economy. This is just the start. Much building. Prepare your paws.

DogeOS

43,197 görüntüleme • 1 ay önce

Robotics keeps hitting the same wall. Single task RL works, but... it does not scale to hundreds of tasks or new embodiments. This new paper looks like a real step toward fixing that. The team introduces MMBench, a benchmark with 200 tasks across many domains and robots, and Newt, a language conditioned world model trained online across all 200 tasks at once. The simple idea behind Newt: The model learns from demos to get the right priors It trains across many tasks through online interaction It uses language to ground the goal It adapts fast when a new task shows up What stood out to me: ✅ One model trained on 200 tasks at the same time ✅ Language conditioned control for both states and RGB ✅ Better data efficiency than strong baselines ✅ Strong open loop control ✅ Fast adaptation to new tasks and embodiments ✅ Full release of 200 checkpoints, 4000 demos, code, and benchmark This is a good push toward general control instead of one model per task. If you want the full paper: Project page: —- Weekly robotics and AI insights. Subscribe free:
0:11

Robotics keeps hitting the same wall. Single task RL works, but... it does not scale to hundreds of tasks or new embodiments. This new paper looks like a real step toward fixing that. The team introduces MMBench, a benchmark with 200 tasks across many domains and robots, and Newt, a language conditioned world model trained online across all 200 tasks at once. The simple idea behind Newt: The model learns from demos to get the right priors It trains across many tasks through online interaction It uses language to ground the goal It adapts fast when a new task shows up What stood out to me: ✅ One model trained on 200 tasks at the same time ✅ Language conditioned control for both states and RGB ✅ Better data efficiency than strong baselines ✅ Strong open loop control ✅ Fast adaptation to new tasks and embodiments ✅ Full release of 200 checkpoints, 4000 demos, code, and benchmark This is a good push toward general control instead of one model per task. If you want the full paper: Project page: —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

70,090 görüntüleme • 10 ay önce

Every year, the best young engineers in the world compete at the FIRST Robotics Competition. This year, a team coached by one of our own came out on top. Jonathan Mi, an electrical design engineer at Shield AI, mentored Team 4414, HighTide, who took first place across 93,000 students, 3,700 teams, and 35 countries. They designed, programmed, and built their robot from scratch. Watch what it does. The strongest engineers build up everyone around them. That's who we hire. Congrats to HighTide, and when graduation comes, you know where to find us! Autonomy for the world. The greatest victory requires no war.
0:12

Every year, the best young engineers in the world compete at the FIRST Robotics Competition. This year, a team coached by one of our own came out on top. Jonathan Mi, an electrical design engineer at Shield AI, mentored Team 4414, HighTide, who took first place across 93,000 students, 3,700 teams, and 35 countries. They designed, programmed, and built their robot from scratch. Watch what it does. The strongest engineers build up everyone around them. That's who we hire. Congrats to HighTide, and when graduation comes, you know where to find us! Autonomy for the world. The greatest victory requires no war.

Shield AI

27,466 görüntüleme • 5 ay önce