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🚨 BREAKING: Gravis Robotics has raised $200M in the largest Series A in construction robotics history, led by SoftBank! 🔥 Spun out of ETH Zürich in 2022. Three years later, $200M and SoftBank backing. The company is bringing autonomous AI to the most physically demanding machines on earth. Excavators....

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Lukas Ziegler

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71,024 次观看 • 5 天前 •via X (Twitter)

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🚨 Breaking News: Solo Tech won at CES 2026. 🦾🏆 In a 10×10 booth, we showcased the highest density of working robots on the floor proving that the future of AI is physical, performant, and deployable today. Up close with real systems. No slides. No simulations. Just Physical AI in action. Alongside leaders shaping what’s next NVIDIA Robotics , Sharpa , and Boston Dynamics the message was clear: This is the ChatGPT moment for robotics. And it won’t happen behind screens. It will happen in Robotics Gyms spaces around the world where machines train, models adapt, and builders learn by doing. The era of AI that acts has begun. #BreakingNews #CES2026 #SoloTech #PhysicalAI #RoboticsGym #EdgeAI
2:09

🚨 Breaking News: Solo Tech won at CES 2026. 🦾🏆 In a 10×10 booth, we showcased the highest density of working robots on the floor proving that the future of AI is physical, performant, and deployable today. Up close with real systems. No slides. No simulations. Just Physical AI in action. Alongside leaders shaping what’s next NVIDIA Robotics , Sharpa , and Boston Dynamics the message was clear: This is the ChatGPT moment for robotics. And it won’t happen behind screens. It will happen in Robotics Gyms spaces around the world where machines train, models adapt, and builders learn by doing. The era of AI that acts has begun. #BreakingNews #CES2026 #SoloTech #PhysicalAI #RoboticsGym #EdgeAI

Dhruv Diddi

14,821 次观看 • 7 个月前

Another awesome robotics company raised funds in Europe. 🇪🇺 This time a company that is shipping robots that build! Monumental has raised $32M Series B led by Khosla Ventures, and its fleet of autonomous bricklaying robots has already built more than 100 real structures across Europe. Houses. A school. A community centre. A hotel. Canal walls. Founded by Salar al Khafaji al Khafaji and Sebastiaan Visser, the co-founders of data visualisation firm Silk, acquired by Palantir in 2016, this is a team that knows how to build and sell a company. The technology is serious: → Fleet of 100+ electric autonomous robots operating on live job sites today → Advanced sensors, computer vision and small cranes laying brick to millimetre precision → All driven by Atrium, their proprietary AI software platform → Nearly half of all homes built in the last three months alone, pace is compounding fast The business model is also worth taking a look at. Contractors don't buy the robots. They hire Monumental as an autonomous subcontractor and pay for finished wall. So in the end you only pay for the output. This is the same forward-deployed engineering model Palantir pioneered in software. Monumental brought it to physical robotics years before anyone else in the industry caught on. The backdrop makes this urgent. US construction is short 200,000-400,000 workers every month. Since 1945, US manufacturing productivity rose eightfold. Construction gained just 10%, and has actually declined since the 1960s. LET'S MAKE CONSTRUCTION BUSINESSES SEXY AGAIN! 🧱 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:28

Another awesome robotics company raised funds in Europe. 🇪🇺 This time a company that is shipping robots that build! Monumental has raised $32M Series B led by Khosla Ventures, and its fleet of autonomous bricklaying robots has already built more than 100 real structures across Europe. Houses. A school. A community centre. A hotel. Canal walls. Founded by Salar al Khafaji al Khafaji and Sebastiaan Visser, the co-founders of data visualisation firm Silk, acquired by Palantir in 2016, this is a team that knows how to build and sell a company. The technology is serious: → Fleet of 100+ electric autonomous robots operating on live job sites today → Advanced sensors, computer vision and small cranes laying brick to millimetre precision → All driven by Atrium, their proprietary AI software platform → Nearly half of all homes built in the last three months alone, pace is compounding fast The business model is also worth taking a look at. Contractors don't buy the robots. They hire Monumental as an autonomous subcontractor and pay for finished wall. So in the end you only pay for the output. This is the same forward-deployed engineering model Palantir pioneered in software. Monumental brought it to physical robotics years before anyone else in the industry caught on. The backdrop makes this urgent. US construction is short 200,000-400,000 workers every month. Since 1945, US manufacturing productivity rose eightfold. Construction gained just 10%, and has actually declined since the 1960s. LET'S MAKE CONSTRUCTION BUSINESSES SEXY AGAIN! 🧱 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

74,774 次观看 • 1 个月前

JUST IN: microagi is opening its Global Robotics Research HQ on Bahnhofstrasse in Zürich, and choosing it over San Francisco. Eight months ago microagi was five people in a Munich hacker house. Today they operate in 15+ countries. The reasoning behind Zürich is compelling: → Highest density of robotics talent in the world, ETH Zürich, EPFL, University of Zürich, IBM Research, Google, NVIDIA, Meta, Apple and Microsoft all run serious ML and robotics teams here → ABB, one of the most important industrial automation companies on earth, is headquartered in the city itself → Within a 6-hour radius: German automotive, Italian manufacturing, French aerospace, Benelux logistics and Swiss machine tools But the line that stuck with me most: "Europe was late to consumer internet. Europe was late to cloud. Europe was late to the foundation-model wave. But Europe is not late to robotics." That is exactly right. The industrial base that physical AI sits on top of has been in Europe for 150 years. Precision mechanics. Machine-tool culture. Safety-critical engineering. Automation-grade manufacturing. The next decade of AI value will be created where bits meet atoms. And Europe is finally in the right position at the right moment. microagi gets it. And they're planting their flag right in the heart of it. 🇨🇭🇪🇺 Bercan, Yoan Iliev, Zeno, Gianni Hodel LFG! 🔥 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:35

JUST IN: microagi is opening its Global Robotics Research HQ on Bahnhofstrasse in Zürich, and choosing it over San Francisco. Eight months ago microagi was five people in a Munich hacker house. Today they operate in 15+ countries. The reasoning behind Zürich is compelling: → Highest density of robotics talent in the world, ETH Zürich, EPFL, University of Zürich, IBM Research, Google, NVIDIA, Meta, Apple and Microsoft all run serious ML and robotics teams here → ABB, one of the most important industrial automation companies on earth, is headquartered in the city itself → Within a 6-hour radius: German automotive, Italian manufacturing, French aerospace, Benelux logistics and Swiss machine tools But the line that stuck with me most: "Europe was late to consumer internet. Europe was late to cloud. Europe was late to the foundation-model wave. But Europe is not late to robotics." That is exactly right. The industrial base that physical AI sits on top of has been in Europe for 150 years. Precision mechanics. Machine-tool culture. Safety-critical engineering. Automation-grade manufacturing. The next decade of AI value will be created where bits meet atoms. And Europe is finally in the right position at the right moment. microagi gets it. And they're planting their flag right in the heart of it. 🇨🇭🇪🇺 Bercan, Yoan Iliev, Zeno, Gianni Hodel LFG! 🔥 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

80,482 次观看 • 3 个月前

Before an autonomous machine can act, it has to understand. A construction site is one of the most unpredictable environments imaginable. The terrain changes with every pass. Material piles shift. Other machines move nearby. No two digs are the same. For a human operator, reading that environment is second nature, built from years of experience and constant sensory feedback. Teaching a machine to do the same is one of the hardest problems in autonomy. Scene understanding is how we're working to solve it. Our system fuses LiDAR point clouds, camera feeds, and real-time terrain data into a continuous, three-dimensional picture of everything happening around the machine. It's a living model of the environment, updated moment to moment so the machine can make decisions grounded in what's actually in front of it.
0:31

Before an autonomous machine can act, it has to understand. A construction site is one of the most unpredictable environments imaginable. The terrain changes with every pass. Material piles shift. Other machines move nearby. No two digs are the same. For a human operator, reading that environment is second nature, built from years of experience and constant sensory feedback. Teaching a machine to do the same is one of the hardest problems in autonomy. Scene understanding is how we're working to solve it. Our system fuses LiDAR point clouds, camera feeds, and real-time terrain data into a continuous, three-dimensional picture of everything happening around the machine. It's a living model of the environment, updated moment to moment so the machine can make decisions grounded in what's actually in front of it.

Bedrock Robotics

33,148 次观看 • 3 个月前

Japan Just Built a HouseBot You Control Without Speaking and It Changes Everything! Donut Robotics has officially unveiled its first bipedal humanoid, Cinnamon 1, and instead of focusing on louder voices or bigger motors, the company went in the opposite direction. Silence. Cinnamon 1 introduces what Donut Robotics calls Silent Gesture Control, a system that allows the humanoid to be guided using simple hand and finger movements rather than spoken commands. This approach feels especially well suited for real world environments where traditional voice control falls apart. Busy factory floors. Construction sites filled with constant noise. Even quiet indoor settings where voice commands feel awkward or intrusive. It also opens the door for far more accessible human robot interaction, particularly for users with impairments. While the current Cinnamon 1 hardware is built on an OEM platform, the intelligence driving it is where Donut Robotics is placing its long term bet. The team is actively developing custom Vision Language Action AI that allows the robot to interpret what it sees, understand intent, and respond with physical action. The goal is not just smarter robots, but robots that feel more natural. Even more ambitious is the company’s plan for full domestic production. Donut Robotics has stated its intention to localize both manufacturing and AI development in Japan, reinforcing the country’s reputation for precision engineering and thoughtful robotics design. If timelines hold, Cinnamon 1 units are expected to begin deployment in factories and construction environments by the end of 2026. That puts this humanoid squarely in the category of near term reality rather than distant concept. The takeaway is simple but important. As humanoid robots move out of labs and into daily work environments, the winners may not be the loudest or flashiest machines. They may be the ones that understand us without a word being spoken.
0:31

Japan Just Built a HouseBot You Control Without Speaking and It Changes Everything! Donut Robotics has officially unveiled its first bipedal humanoid, Cinnamon 1, and instead of focusing on louder voices or bigger motors, the company went in the opposite direction. Silence. Cinnamon 1 introduces what Donut Robotics calls Silent Gesture Control, a system that allows the humanoid to be guided using simple hand and finger movements rather than spoken commands. This approach feels especially well suited for real world environments where traditional voice control falls apart. Busy factory floors. Construction sites filled with constant noise. Even quiet indoor settings where voice commands feel awkward or intrusive. It also opens the door for far more accessible human robot interaction, particularly for users with impairments. While the current Cinnamon 1 hardware is built on an OEM platform, the intelligence driving it is where Donut Robotics is placing its long term bet. The team is actively developing custom Vision Language Action AI that allows the robot to interpret what it sees, understand intent, and respond with physical action. The goal is not just smarter robots, but robots that feel more natural. Even more ambitious is the company’s plan for full domestic production. Donut Robotics has stated its intention to localize both manufacturing and AI development in Japan, reinforcing the country’s reputation for precision engineering and thoughtful robotics design. If timelines hold, Cinnamon 1 units are expected to begin deployment in factories and construction environments by the end of 2026. That puts this humanoid squarely in the category of near term reality rather than distant concept. The takeaway is simple but important. As humanoid robots move out of labs and into daily work environments, the winners may not be the loudest or flashiest machines. They may be the ones that understand us without a word being spoken.

The AI Robot Guy on X

257,928 次观看 • 7 个月前

Godfather of AI Yann LeCun reveals the humanoid robot industry's secret: the companies building the robots have no idea how to make them smart enough to be useful. "There is a lot of expectations that robotics is going to be a revolution, essentially, in a big market, eventually." "But the secret of the industry is that none of the companies at the moment that are building robots, like humanoid robots, none of those companies has any idea how to make those robots smart enough to be useful." "There's a big push for AI to make progress towards... understanding the physical world. That's what is prompting this investment." "There's nothing wrong with investing in LLMs. LLMs are useful. They're just addressing a completely different set of applications, really." LeCun is not describing someone else's problem. He has spent 15 years on world models, raised more than $1 billion for AMI Labs to build that missing intelligence layer, and in the same interview he calls 2026 the year of the world model and puts consumer robots 3 to 5 years out. Every humanoid robot demo you have seen is a body waiting for a brain. The winner in robotics will not be the company that shipped the body; it will be the one that ships the intelligence. - Yann LeCun (Yann LeCun), Turing Award winner and founder of AMI Labs, on Bloomberg Television.
1:01

Godfather of AI Yann LeCun reveals the humanoid robot industry's secret: the companies building the robots have no idea how to make them smart enough to be useful. "There is a lot of expectations that robotics is going to be a revolution, essentially, in a big market, eventually." "But the secret of the industry is that none of the companies at the moment that are building robots, like humanoid robots, none of those companies has any idea how to make those robots smart enough to be useful." "There's a big push for AI to make progress towards... understanding the physical world. That's what is prompting this investment." "There's nothing wrong with investing in LLMs. LLMs are useful. They're just addressing a completely different set of applications, really." LeCun is not describing someone else's problem. He has spent 15 years on world models, raised more than $1 billion for AMI Labs to build that missing intelligence layer, and in the same interview he calls 2026 the year of the world model and puts consumer robots 3 to 5 years out. Every humanoid robot demo you have seen is a body waiting for a brain. The winner in robotics will not be the company that shipped the body; it will be the one that ships the intelligence. - Yann LeCun (Yann LeCun), Turing Award winner and founder of AMI Labs, on Bloomberg Television.

Karl Mehta

44,425 次观看 • 1 个月前

Dr. Fei-Fei Li just called out the biggest blind spot in the entire AI industry. We have been building half of human intelligence. And calling it the finish line. Li: “If you look at human intelligence, it pretty much boils down to two buckets.” The first bucket is language. Symbolic reasoning. Communication. The ability to think in words and abstractions. That’s what every major AI lab has spent the last decade building. The second bucket is the one the industry has almost entirely ignored. Li: “We call that in AI spatial intelligence.” How humans and animals perceive, navigate, and interact with the three-dimensional physical world. How we reach for objects. How we move through space. How we build and manipulate physical reality. From painting masterpieces to constructing the pyramids, non-verbal spatial intelligence is what actually shapes the world. Language describes reality. Spatial intelligence acts on it. And the gap between those two things is the gap between a chatbot and a robot. Li: “When this technology is ready, the robotic revolution is gonna start. We’re already seeing that trend.” Every robot is a moving agent. Every moving agent requires spatial intelligence to function in the real world. The humanoid robots being deployed in factories right now are hitting the ceiling of what language models alone can power. Spatial intelligence is the unlock. But Li didn’t stop at robotics. Li: “From a geopolitics point of view, this is part of the technology that goes straight into weapons.” Autonomous drone swarms. Battlefield navigation. Physical target acquisition without human oversight. Every military application of AI that operates in the real world runs on spatial intelligence. The nation that masters the transition from static text to dynamic three-dimensional perception doesn’t just win the software race. It commands the physical battlefield. The AI arms race just broke out of the data center. It’s operating in three dimensions now.
3:08

Dr. Fei-Fei Li just called out the biggest blind spot in the entire AI industry. We have been building half of human intelligence. And calling it the finish line. Li: “If you look at human intelligence, it pretty much boils down to two buckets.” The first bucket is language. Symbolic reasoning. Communication. The ability to think in words and abstractions. That’s what every major AI lab has spent the last decade building. The second bucket is the one the industry has almost entirely ignored. Li: “We call that in AI spatial intelligence.” How humans and animals perceive, navigate, and interact with the three-dimensional physical world. How we reach for objects. How we move through space. How we build and manipulate physical reality. From painting masterpieces to constructing the pyramids, non-verbal spatial intelligence is what actually shapes the world. Language describes reality. Spatial intelligence acts on it. And the gap between those two things is the gap between a chatbot and a robot. Li: “When this technology is ready, the robotic revolution is gonna start. We’re already seeing that trend.” Every robot is a moving agent. Every moving agent requires spatial intelligence to function in the real world. The humanoid robots being deployed in factories right now are hitting the ceiling of what language models alone can power. Spatial intelligence is the unlock. But Li didn’t stop at robotics. Li: “From a geopolitics point of view, this is part of the technology that goes straight into weapons.” Autonomous drone swarms. Battlefield navigation. Physical target acquisition without human oversight. Every military application of AI that operates in the real world runs on spatial intelligence. The nation that masters the transition from static text to dynamic three-dimensional perception doesn’t just win the software race. It commands the physical battlefield. The AI arms race just broke out of the data center. It’s operating in three dimensions now.

Dustin

122,781 次观看 • 5 个月前

On Bloomberg last night, I was asked why Amazon drives more than a third of U.S. e-commerce. It is not because of their storefront. It is not because of their payments. It is not even because of product selection. It is because consumers trust their deliveries. That trust is built on physical infrastructure at scale with intelligence layered on top. And that is what Stord is building for every independent merchant. The most defensible companies in the world combine software intelligence with physical execution. Today, 9 of the 10 largest U.S. companies by revenue operate physical networks at scale. Software alone cannot replicate the outcomes created when you control the movement of physical atoms. That is the physical intelligence layer for commerce. And that is what Stord Labs is built to advance. Deploying AI and robotics against real operational complexity, in the real world, across a vertically integrated network operating at scale. Bloomberg TV
5:51

On Bloomberg last night, I was asked why Amazon drives more than a third of U.S. e-commerce. It is not because of their storefront. It is not because of their payments. It is not even because of product selection. It is because consumers trust their deliveries. That trust is built on physical infrastructure at scale with intelligence layered on top. And that is what Stord is building for every independent merchant. The most defensible companies in the world combine software intelligence with physical execution. Today, 9 of the 10 largest U.S. companies by revenue operate physical networks at scale. Software alone cannot replicate the outcomes created when you control the movement of physical atoms. That is the physical intelligence layer for commerce. And that is what Stord Labs is built to advance. Deploying AI and robotics against real operational complexity, in the real world, across a vertically integrated network operating at scale. Bloomberg TV

Sean Henry

1,275,331 次观看 • 2 个月前

That's sick! 🤯 Genesis AI simulates robots playing yo-yo! 🪀 Genesis AI just open-sourced Genesis World 1.0, and it might be one of the most important infrastructure releases in robotics this year. Robotics is still bottlenecked by the 1× speed of the physical world. Every model needs to be tested on real hardware, slowly, expensively, with limited coverage. Genesis World 1.0 from Genesis AI flips that equation: One hour in reality becomes 100 days in simulation. That turns a wall-clock bottleneck into a compute problem. And compute problems are solvable. The technical stack they rebuilt from scratch is serious: → GPU-accelerated cross-platform compiler via Quadrants, 10x faster launch time and up to 4.6x runtime vs the initial Genesis release → Penetration-free multi-physics contact solvers, the thing that makes simulation actually trustworthy → Unified rigid AND deformable physics in a single engine → Nyx, a high-performance path-traced rendering engine purpose-built for physical AI The sim-to-real gap has historically been the graveyard of robotics research. Policies that work beautifully in simulation fall apart on real hardware. Genesis World 1.0 is a direct attack on that problem. And it's fully open-source. The companies that master simulation infrastructure will train better robots faster than anyone else. Find it here: Genesis World 1.0: Quadrants: Nyx: Theophile Gervet, Zhou Xian congrats! 👏🏼 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:46

That's sick! 🤯 Genesis AI simulates robots playing yo-yo! 🪀 Genesis AI just open-sourced Genesis World 1.0, and it might be one of the most important infrastructure releases in robotics this year. Robotics is still bottlenecked by the 1× speed of the physical world. Every model needs to be tested on real hardware, slowly, expensively, with limited coverage. Genesis World 1.0 from Genesis AI flips that equation: One hour in reality becomes 100 days in simulation. That turns a wall-clock bottleneck into a compute problem. And compute problems are solvable. The technical stack they rebuilt from scratch is serious: → GPU-accelerated cross-platform compiler via Quadrants, 10x faster launch time and up to 4.6x runtime vs the initial Genesis release → Penetration-free multi-physics contact solvers, the thing that makes simulation actually trustworthy → Unified rigid AND deformable physics in a single engine → Nyx, a high-performance path-traced rendering engine purpose-built for physical AI The sim-to-real gap has historically been the graveyard of robotics research. Policies that work beautifully in simulation fall apart on real hardware. Genesis World 1.0 is a direct attack on that problem. And it's fully open-source. The companies that master simulation infrastructure will train better robots faster than anyone else. Find it here: Genesis World 1.0: Quadrants: Nyx: Theophile Gervet, Zhou Xian congrats! 👏🏼 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

57,061 次观看 • 2 个月前

Karol Hausman is the co-founder and CEO of Physical Intelligence, a robotics company building a general-purpose “AI brain for the physical world.” The company has raised more than $1 billion in funding to develop foundation models that allow robots to operate across many machines, environments, and tasks rather than being programmed for a single purpose. In our conversation, we explore: • The moment a lecture from Sergey Levine convinced him to abandon his PhD research direction and pivot fully to deep learning • The case for building a general “AI brain” for the physical world rather than a single specialized robot • The role of real-world data in training robots, the limits of simulation, and how deployment could create a powerful data flywheel • The unique challenges of physical intelligence and why robots must operate with far higher reliability than language models Thank you to the partners who make this possible - Brex: The intelligent finance platform: - Granola: The app that might actually make you love meetings: Timestamps (00:00) Intro (04:05) Karol’s early fascination with robots (18:21) Karol’s entry point to robotics and PhD program (25:49) Combining robotics with LLMs: The Taylor Swift demo (30:48) The 1970s SHRDLU AI experiment (39:40) How research shapes what Physical Intelligence builds (49:07) The return of reinforcement learning in robotics (1:00:00) NVIDIA’s simulation engines (1:07:31) Compensating for missing senses
1:14:20

Karol Hausman is the co-founder and CEO of Physical Intelligence, a robotics company building a general-purpose “AI brain for the physical world.” The company has raised more than $1 billion in funding to develop foundation models that allow robots to operate across many machines, environments, and tasks rather than being programmed for a single purpose. In our conversation, we explore: • The moment a lecture from Sergey Levine convinced him to abandon his PhD research direction and pivot fully to deep learning • The case for building a general “AI brain” for the physical world rather than a single specialized robot • The role of real-world data in training robots, the limits of simulation, and how deployment could create a powerful data flywheel • The unique challenges of physical intelligence and why robots must operate with far higher reliability than language models Thank you to the partners who make this possible - Brex: The intelligent finance platform: - Granola: The app that might actually make you love meetings: Timestamps (00:00) Intro (04:05) Karol’s early fascination with robots (18:21) Karol’s entry point to robotics and PhD program (25:49) Combining robotics with LLMs: The Taylor Swift demo (30:48) The 1970s SHRDLU AI experiment (39:40) How research shapes what Physical Intelligence builds (49:07) The return of reinforcement learning in robotics (1:00:00) NVIDIA’s simulation engines (1:07:31) Compensating for missing senses

Mario Gabriele 🦊

27,871 次观看 • 5 个月前

🚨 BREAKING: ABB Robotics + NVIDIA close the sim-to-real gap with 99% accuracy! 👾 ABB Robotics is integrating NVIDIA Omniverse libraries into RobotStudio to deliver physical AI for industry, closing the gap from virtual training to real-world deployment with up to 99% accuracy. RobotStudio HyperReality, available second half of 2026, will fundamentally change how quickly manufacturers can scale production: reducing costs by up to 40%, accelerating time-to-market by 50%, and cutting setup and commissioning times by up to 80%. For decades, the deficit between simulation accuracy and real-world lighting, materials, and environments has limited manufacturers' ability to design advanced manufacturing processes in the virtual world. The only robot manufacturer with a virtual controller running the same firmware as the hardware, ensuring near-perfect correlation between simulation and real-world performance. The system uses physically accurate simulations and foundation models endlessly optimized with real-world data feedback. These models can train any number of ABB robots anywhere in the world with industrial-grade reliability. Foxconn is using RobotStudio HyperReality for consumer electronics assembly. Assembly robots are trained virtually using synthetic data to perfect multiple production processes across various scenarios, then moved to production lines with 99% accuracy. This eliminates physical training and tests, reducing setup times and costs. Workr is demonstrating AI-powered robotic systems at NVIDIA GTC 2026. Built on ABB technology, trained with synthetic data using NVIDIA Omniverse, deployed without operators needing programming knowledge . 🚨 I’ll be onsite in San Jose during GTC 2026, and will be showing all the cool stuff that ABB Robotics prepared this year! Can’t wait! 🫡 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:48

🚨 BREAKING: ABB Robotics + NVIDIA close the sim-to-real gap with 99% accuracy! 👾 ABB Robotics is integrating NVIDIA Omniverse libraries into RobotStudio to deliver physical AI for industry, closing the gap from virtual training to real-world deployment with up to 99% accuracy. RobotStudio HyperReality, available second half of 2026, will fundamentally change how quickly manufacturers can scale production: reducing costs by up to 40%, accelerating time-to-market by 50%, and cutting setup and commissioning times by up to 80%. For decades, the deficit between simulation accuracy and real-world lighting, materials, and environments has limited manufacturers' ability to design advanced manufacturing processes in the virtual world. The only robot manufacturer with a virtual controller running the same firmware as the hardware, ensuring near-perfect correlation between simulation and real-world performance. The system uses physically accurate simulations and foundation models endlessly optimized with real-world data feedback. These models can train any number of ABB robots anywhere in the world with industrial-grade reliability. Foxconn is using RobotStudio HyperReality for consumer electronics assembly. Assembly robots are trained virtually using synthetic data to perfect multiple production processes across various scenarios, then moved to production lines with 99% accuracy. This eliminates physical training and tests, reducing setup times and costs. Workr is demonstrating AI-powered robotic systems at NVIDIA GTC 2026. Built on ABB technology, trained with synthetic data using NVIDIA Omniverse, deployed without operators needing programming knowledge . 🚨 I’ll be onsite in San Jose during GTC 2026, and will be showing all the cool stuff that ABB Robotics prepared this year! Can’t wait! 🫡 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

22,482 次观看 • 5 个月前

A 10-minute walk. That's all it takes to transform a real-world neighborhood into a machine-readable digital twin. Captured with an Insta360 X5 and reconstructed by Over the Reality, this environment is no longer just a place on a map. It's spatial data that can power Visual AI, Robotics localization, VPS, XR experiences, and next-generation world models. Today, the OVER community has already created more than 270,000 machine-readable locations like this, forming one of the world's largest 3D datasets for Physical AI, an increasingly valuable asset as demand for high quality real-world data continues to grow.
1:30

A 10-minute walk. That's all it takes to transform a real-world neighborhood into a machine-readable digital twin. Captured with an Insta360 X5 and reconstructed by Over the Reality, this environment is no longer just a place on a map. It's spatial data that can power Visual AI, Robotics localization, VPS, XR experiences, and next-generation world models. Today, the OVER community has already created more than 270,000 machine-readable locations like this, forming one of the world's largest 3D datasets for Physical AI, an increasingly valuable asset as demand for high quality real-world data continues to grow.

Over the Reality 🌐

16,724 次观看 • 1 个月前

I am stocked to announce that I won the OpenAI Developers Codex x Mollie Hacka Worldwide Hackathon in Paris. 60+ builders, every one of us working solo, one day to ship. I built mine around a single question: who gets to own intelligence? The default answer is scary. You hand your data to a handful of labs, they train the model, they own it, and you rent back a thin slice of what your own data made possible. That is the bargain on the table today. I do not accept it. So I built Lensemble: a Tapestry like distributed training platform for JEPA based World Models. What does it enable: World Models that a community improves together, keeps sovereign, and co-owns. Two bets sit underneath it. First, the paradigm. Language models predict the next token. Powerful for text, a dead end for the physical world. A robot does not need to autocomplete sentences, it needs to predict what happens next in the world. That is what JEPA does: it learns by predicting representations instead of pixels or tokens. I am convinced world models are the most underrated paradigm in AI right now, and the closest thing we have to a ChatGPT moment for robotics. Second, the politics. Your raw trajectories never leave your machine. Each participant trains locally against a shared protocol and ships only an update, never the data. A federated round folds those updates into one shared world model, a LeWorldModel based model, and the gain is measured, not claimed: a 12k-parameter adapter on a frozen backbone, held-out prediction error down about 12 percent, the model measurably less surprised by the world. Then the upside is split by contribution weight, so the people who improved the model own a share of what it earns. This is the thesis behind Project Tapestry, the AI Alliance and Yann LeCun's push for federated, sovereign frontier AI, carried into world models and robotics. Call it Tapestry for the physical world. All of it built solo, in a single day, with Codex as my pair the whole way. Thank you to OpenAI Codex and Mollie for backing builders who ship real things, and to Boris and the organizing crew for the room and the standard you set. Intelligence the world improves, and the world owns. That is the future I want for my kids, and the one I will keep building.
1:13

I am stocked to announce that I won the OpenAI Developers Codex x Mollie Hacka Worldwide Hackathon in Paris. 60+ builders, every one of us working solo, one day to ship. I built mine around a single question: who gets to own intelligence? The default answer is scary. You hand your data to a handful of labs, they train the model, they own it, and you rent back a thin slice of what your own data made possible. That is the bargain on the table today. I do not accept it. So I built Lensemble: a Tapestry like distributed training platform for JEPA based World Models. What does it enable: World Models that a community improves together, keeps sovereign, and co-owns. Two bets sit underneath it. First, the paradigm. Language models predict the next token. Powerful for text, a dead end for the physical world. A robot does not need to autocomplete sentences, it needs to predict what happens next in the world. That is what JEPA does: it learns by predicting representations instead of pixels or tokens. I am convinced world models are the most underrated paradigm in AI right now, and the closest thing we have to a ChatGPT moment for robotics. Second, the politics. Your raw trajectories never leave your machine. Each participant trains locally against a shared protocol and ships only an update, never the data. A federated round folds those updates into one shared world model, a LeWorldModel based model, and the gain is measured, not claimed: a 12k-parameter adapter on a frozen backbone, held-out prediction error down about 12 percent, the model measurably less surprised by the world. Then the upside is split by contribution weight, so the people who improved the model own a share of what it earns. This is the thesis behind Project Tapestry, the AI Alliance and Yann LeCun's push for federated, sovereign frontier AI, carried into world models and robotics. Call it Tapestry for the physical world. All of it built solo, in a single day, with Codex as my pair the whole way. Thank you to OpenAI Codex and Mollie for backing builders who ship real things, and to Boris and the organizing crew for the room and the standard you set. Intelligence the world improves, and the world owns. That is the future I want for my kids, and the one I will keep building.

abdel

20,037 次观看 • 2 个月前

BlackRock CEO Larry Fink says the biggest job boom coming to America has nothing to do with AI software or Wall Street, it's in infrastructure. Specifically, the construction, power grids, and data centers. That is where he believes the massive job growth is coming from. And his warning is that we are not ready for it. Companies are committing hundreds of billions to build out AI infrastructure across the US, Microsoft, Amazon, Meta, and Alphabet are projected to spend $650 billion on it this year alone. But that buildout requires physical workers. Electricians, pipefitters, construction crews, grid engineers. Fink told the Trump administration directly, the US doesn't have enough skilled workers to meet the demand these projects will create. Microsoft's Brad Smith called the skilled labor shortage the "single biggest challenge for data center expansion in the U.S." In some regions, Microsoft had to relocate workers or ask them to commute 75 miles just to fill critical roles. The electrical work alone accounts for 45–70% of total data center construction costs. Over 200,000 electricians are expected to retire in the next decade. The pipeline to replace them doesn't exist at the scale needed. Energy resilience is the other piece Fink flagged. Data centers don't just need to be built, they need consistent, reliable power. The US grid hasn't been meaningfully expanded in decades. China, by contrast, generates roughly twice the electricity the US does and is actively building more. Fink's point is direct because if the US doesn't win the buildout of its physical infrastructure, China will. That is why BlackRock just committed $100 million to fund training programs for 50,000 skilled trade workers over the next five years. The largest asset manager in the world is putting money into electricians and plumbers because it sees the bottleneck clearly.
0:29

BlackRock CEO Larry Fink says the biggest job boom coming to America has nothing to do with AI software or Wall Street, it's in infrastructure. Specifically, the construction, power grids, and data centers. That is where he believes the massive job growth is coming from. And his warning is that we are not ready for it. Companies are committing hundreds of billions to build out AI infrastructure across the US, Microsoft, Amazon, Meta, and Alphabet are projected to spend $650 billion on it this year alone. But that buildout requires physical workers. Electricians, pipefitters, construction crews, grid engineers. Fink told the Trump administration directly, the US doesn't have enough skilled workers to meet the demand these projects will create. Microsoft's Brad Smith called the skilled labor shortage the "single biggest challenge for data center expansion in the U.S." In some regions, Microsoft had to relocate workers or ask them to commute 75 miles just to fill critical roles. The electrical work alone accounts for 45–70% of total data center construction costs. Over 200,000 electricians are expected to retire in the next decade. The pipeline to replace them doesn't exist at the scale needed. Energy resilience is the other piece Fink flagged. Data centers don't just need to be built, they need consistent, reliable power. The US grid hasn't been meaningfully expanded in decades. China, by contrast, generates roughly twice the electricity the US does and is actively building more. Fink's point is direct because if the US doesn't win the buildout of its physical infrastructure, China will. That is why BlackRock just committed $100 million to fund training programs for 50,000 skilled trade workers over the next five years. The largest asset manager in the world is putting money into electricians and plumbers because it sees the bottleneck clearly.

StockMarket.News

46,903 次观看 • 5 个月前

Jensen Huang just validated Elon Musk’s entire ecosystem in a single breath. Not one product. All of it. Huang: “The work he’s doing in Grok, self-driving cars, and Optimus. These are all world-class. Every single one of them is revolutionary. Every single one of them is going to be a gigantic opportunity.” To the public, a chatbot, a car, and a robot look like three separate bets. They are one project. The total automation of human cognition and physical labor. A digital brain. A spatial nervous system. A physical body. Musk is building all three simultaneously. Huang is supplying the compute to fuse them. Huang: “We do a lot of business with Tesla and xAI. Elon is an extraordinary engineer, and I love working with him. We’ve built some amazing computers together, and we’re going to build many more.” This is not a vendor relationship. It is the most consequential technological alliance in history and most people think it is a business partnership. Then Huang said what should end every debate about Optimus. Huang: “This is the first robot that really has a chance to achieve the high volume and technology scale necessary to advance technology.” Huang: “Right around the corner. Likely to be the next multi-trillion dollar industry.” The humanoid robot race will not be won in a research lab. It will be won on the manufacturing floor. Every other robotics company on earth can build a robot. Tesla can flood the planet with them. Because Tesla already knows how to stamp metal, build batteries, and deploy autonomous inference at global scale. The rest of the industry has prototypes. Tesla has the most sophisticated manufacturing operation on earth. When the world’s leading chipmaker calls your robot the next multi-trillion dollar industry, the debate is over. One supplies the chips. One builds everything the chips make possible. When that infrastructure scales across Grok, FSD, and Optimus simultaneously, the question stops being whether this changes everything. It becomes how fast.
1:08

Jensen Huang just validated Elon Musk’s entire ecosystem in a single breath. Not one product. All of it. Huang: “The work he’s doing in Grok, self-driving cars, and Optimus. These are all world-class. Every single one of them is revolutionary. Every single one of them is going to be a gigantic opportunity.” To the public, a chatbot, a car, and a robot look like three separate bets. They are one project. The total automation of human cognition and physical labor. A digital brain. A spatial nervous system. A physical body. Musk is building all three simultaneously. Huang is supplying the compute to fuse them. Huang: “We do a lot of business with Tesla and xAI. Elon is an extraordinary engineer, and I love working with him. We’ve built some amazing computers together, and we’re going to build many more.” This is not a vendor relationship. It is the most consequential technological alliance in history and most people think it is a business partnership. Then Huang said what should end every debate about Optimus. Huang: “This is the first robot that really has a chance to achieve the high volume and technology scale necessary to advance technology.” Huang: “Right around the corner. Likely to be the next multi-trillion dollar industry.” The humanoid robot race will not be won in a research lab. It will be won on the manufacturing floor. Every other robotics company on earth can build a robot. Tesla can flood the planet with them. Because Tesla already knows how to stamp metal, build batteries, and deploy autonomous inference at global scale. The rest of the industry has prototypes. Tesla has the most sophisticated manufacturing operation on earth. When the world’s leading chipmaker calls your robot the next multi-trillion dollar industry, the debate is over. One supplies the chips. One builds everything the chips make possible. When that infrastructure scales across Grok, FSD, and Optimus simultaneously, the question stops being whether this changes everything. It becomes how fast.

Dustin

66,374 次观看 • 5 个月前

The biggest marketplace on Earth won't be the assets humans own. It'll be the data AI needs. Everything that can be digitised is getting pulled in to train the next models. Every scientific dataset, every piece of research sitting in a university, every sensor reading off every John Deere tractor in a field. All of it feeds the machine, and almost none of it is visible to us. What comes out is an agentic economy running at machine speed. You plug your data into a vault, and agents monetise it on your behalf, trading with other agents in real time, in a marketplace we never see. The company sitting on proprietary data becomes part of it without even knowing. This is what the Economic Singularity actually looks like from the inside. The real economy stops being the one we can see and price, and moves somewhere we can't follow. You own a piece of it, or you get priced out of it. Jordi Visser.
1:27

The biggest marketplace on Earth won't be the assets humans own. It'll be the data AI needs. Everything that can be digitised is getting pulled in to train the next models. Every scientific dataset, every piece of research sitting in a university, every sensor reading off every John Deere tractor in a field. All of it feeds the machine, and almost none of it is visible to us. What comes out is an agentic economy running at machine speed. You plug your data into a vault, and agents monetise it on your behalf, trading with other agents in real time, in a marketplace we never see. The company sitting on proprietary data becomes part of it without even knowing. This is what the Economic Singularity actually looks like from the inside. The real economy stops being the one we can see and price, and moves somewhere we can't follow. You own a piece of it, or you get priced out of it. Jordi Visser.

Raoul Pal

79,047 次观看 • 1 个月前

Today, we’re announcing that we’ve raised $115 million in funding, including a $100M Series A led by Kleiner Perkins. America has lost the ability to build, and we’re here to restore it. My co-founder, Noah McGuinness, and I left our jobs at SpaceX , where we worked on programs including Starship, Starshield, and Starlink, to build a company that will solve construction’s greatest challenges. Infrastructure is the foundation of civilization, and construction is the precursor to innovation. If America wants to build a brighter future for the next generation, we have to make it faster, cheaper, and safer to build. That’s where TerraFirma comes in. We’re a new type of company, a robotic construction company that builds the full technology stack needed to deliver an order-of-magnitude improvement in one of the world’s oldest, largest, most important, but least efficient industries. We are building technology that expands what’s possible in construction on Earth, and then we'll use that same technology to build megastructures and colonies on the Moon and Mars. We’ve made tremendous progress over the past year, growing the company more than 10x in the last 12 months. We are performing projects across the world. By the end of October 2026, we are on track to operate 3 of the top 3 largest robotic construction fleets in the world, each on a different continent, bringing unprecedented speed, scale, and efficiency to some of the world’s most complex critical infrastructure projects. This funding will allow us to step on the gas and scale our manufacturing, software, operations, and construction deployments, including work on massive commercial and government contracts. We’re building the future of construction right here in Austin, Texas, and scaling it globally. If you want to be part of the team changing the world, now and on Mars, join us. Our Series A was led by Kleiner Perkins, with participation from Bain Capital Ventures, Glade Brook Capital Partners, BANNER VC, Saga Ventures, Trust Ventures, Definition, PEAK6, Magnetar Capital, and Ravelin Capital. Huge thanks to all of our angel investors, friends, and family who have helped and supported us throughout this journey. Apply here:
1:31

Today, we’re announcing that we’ve raised $115 million in funding, including a $100M Series A led by Kleiner Perkins. America has lost the ability to build, and we’re here to restore it. My co-founder, Noah McGuinness, and I left our jobs at SpaceX , where we worked on programs including Starship, Starshield, and Starlink, to build a company that will solve construction’s greatest challenges. Infrastructure is the foundation of civilization, and construction is the precursor to innovation. If America wants to build a brighter future for the next generation, we have to make it faster, cheaper, and safer to build. That’s where TerraFirma comes in. We’re a new type of company, a robotic construction company that builds the full technology stack needed to deliver an order-of-magnitude improvement in one of the world’s oldest, largest, most important, but least efficient industries. We are building technology that expands what’s possible in construction on Earth, and then we'll use that same technology to build megastructures and colonies on the Moon and Mars. We’ve made tremendous progress over the past year, growing the company more than 10x in the last 12 months. We are performing projects across the world. By the end of October 2026, we are on track to operate 3 of the top 3 largest robotic construction fleets in the world, each on a different continent, bringing unprecedented speed, scale, and efficiency to some of the world’s most complex critical infrastructure projects. This funding will allow us to step on the gas and scale our manufacturing, software, operations, and construction deployments, including work on massive commercial and government contracts. We’re building the future of construction right here in Austin, Texas, and scaling it globally. If you want to be part of the team changing the world, now and on Mars, join us. Our Series A was led by Kleiner Perkins, with participation from Bain Capital Ventures, Glade Brook Capital Partners, BANNER VC, Saga Ventures, Trust Ventures, Definition, PEAK6, Magnetar Capital, and Ravelin Capital. Huge thanks to all of our angel investors, friends, and family who have helped and supported us throughout this journey. Apply here:

Noah Schochet

2,284,669 次观看 • 1 个月前

Robot trained entirely in simulation! 🤯 Sudo AI just released #sudo R1. This could be the moment the data bottleneck in robotics finally breaks, a major step forward for getting simulation-trained robots to actually work in the real world. #sudo R1 is a manipulation foundation model that reliably picks objects it has never seen: glass, fabric, metal, irregular shapes. All with zero real-world training data. No tele-op. No demos. No manual labeling. Pure simulation. And picking isn't just any skill, it's the foundation. Almost everything we want robots to do starts with being able to reliably pick something up. Data collection is the bottleneck holding the whole field back. Every new task means hours of tele-op and armies of labelers. If simulation alone can actually deliver production-grade picking, that bottleneck starts to disappear. Really exciting to see! If the results hold, the path to general-purpose robots just got a whole lot shorter. 👇 Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:59

Robot trained entirely in simulation! 🤯 Sudo AI just released #sudo R1. This could be the moment the data bottleneck in robotics finally breaks, a major step forward for getting simulation-trained robots to actually work in the real world. #sudo R1 is a manipulation foundation model that reliably picks objects it has never seen: glass, fabric, metal, irregular shapes. All with zero real-world training data. No tele-op. No demos. No manual labeling. Pure simulation. And picking isn't just any skill, it's the foundation. Almost everything we want robots to do starts with being able to reliably pick something up. Data collection is the bottleneck holding the whole field back. Every new task means hours of tele-op and armies of labelers. If simulation alone can actually deliver production-grade picking, that bottleneck starts to disappear. Really exciting to see! If the results hold, the path to general-purpose robots just got a whole lot shorter. 👇 Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

25,901 次观看 • 4 个月前

In the AI era, the traditional biopharma industry is the underdog. Big tech and AI labs are building wet labs. China has overtaken Europe in molecules produced. But the tools available to the industry discuss science, not do it. The hard problem in AI for science is at the interface between the physical and digital worlds. We built an AI Scientist at that seam. It wires together the digital and physical worlds of R&D. Predictive models, data infrastructure, wet lab execution feed into a single loop that reasons, acts, and improves with every experiment. Our ambition: get molecules to the clinic twice as fast. Last fall I wrote about why biotech needs to be rebuilt for the AI era. Today I'm sharing the next chapter: what the AI Scientist is, a blueprint for how it works, and why even Richard Feynman couldn't hack it in a wet lab.
1:44

In the AI era, the traditional biopharma industry is the underdog. Big tech and AI labs are building wet labs. China has overtaken Europe in molecules produced. But the tools available to the industry discuss science, not do it. The hard problem in AI for science is at the interface between the physical and digital worlds. We built an AI Scientist at that seam. It wires together the digital and physical worlds of R&D. Predictive models, data infrastructure, wet lab execution feed into a single loop that reasons, acts, and improves with every experiment. Our ambition: get molecules to the clinic twice as fast. Last fall I wrote about why biotech needs to be rebuilt for the AI era. Today I'm sharing the next chapter: what the AI Scientist is, a blueprint for how it works, and why even Richard Feynman couldn't hack it in a wet lab.

Sajith Wickramasekara

30,736 次观看 • 4 个月前

Mining will create more millionaires than AI over the next decade. Yet everyone is chasing chips and software. Here's why the real money is in what powers all of it: The world is obsessed with AI. Every portfolio, every headline, every allocation decision points in the same direction. But here's what most investors are missing. AI runs on infrastructure. Infrastructure runs on metals. Copper, nickel, palladium, platinum. The physical materials that make electrification, data centres, and energy grids possible. Supply is constrained. From discovery to production, the average mine takes around 18 years. And demand is growing faster than at any point in the last 30 years. This is the setup for a commodity supercycle. And historically, supercycles are where generational wealth gets built in the resource sector. Power Metallic sits at the centre of this thesis. A zoned polymetallic system with copper, nickel, and PGEs. Only a handful of comparable systems exist on the planet. Well funded with C$50 million raised and 100,000 metres of drilling through 2026. While everyone fights over the next AI stock, the real asymmetric opportunity might be sitting underground in Quebec. Full conversation: $PNPN $PNPNF
0:41

Mining will create more millionaires than AI over the next decade. Yet everyone is chasing chips and software. Here's why the real money is in what powers all of it: The world is obsessed with AI. Every portfolio, every headline, every allocation decision points in the same direction. But here's what most investors are missing. AI runs on infrastructure. Infrastructure runs on metals. Copper, nickel, palladium, platinum. The physical materials that make electrification, data centres, and energy grids possible. Supply is constrained. From discovery to production, the average mine takes around 18 years. And demand is growing faster than at any point in the last 30 years. This is the setup for a commodity supercycle. And historically, supercycles are where generational wealth gets built in the resource sector. Power Metallic sits at the centre of this thesis. A zoned polymetallic system with copper, nickel, and PGEs. Only a handful of comparable systems exist on the planet. Well funded with C$50 million raised and 100,000 metres of drilling through 2026. While everyone fights over the next AI stock, the real asymmetric opportunity might be sitting underground in Quebec. Full conversation: $PNPN $PNPNF

Terry Lynch

35,775 次观看 • 3 个月前