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Just saw something that actually feels like a real leap in robotics hardware.👋 Allonic built a robot hand using 3D Tissue Braiding,basically weaving high-strength fibers around a minimal rigid skeleton the way human connective tissue wraps around bone. No hundreds of screws, bearings, cables or fiddly joints. Instead,a continuous...

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CyberRobo

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262,236 次观看 • 6 个月前 •via X (Twitter)

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This robotic hand can weave itself together in minutes. Allonic developed a process that "braids" robot bodies around a 3D-printed skeleton in a single automated step. The tech draws from the textile industry, using braided fibers instead of traditional mechanical joints and bearings. Their braiding system grows tendon-driven structures directly onto skeletal cores, creating flexible yet durable parts without screws or assembly. One finger only needs 4 skeletal elements. Then thousands of braided fibers wrap around them to handle force and articulation. The software converts designs directly into machine code that runs the braiding process. But what matters is that robotic hands like this can cost up to $30,000, ~20-30% of a humanoid robot's total price. Allonic's braiding process could crush those costs while enabling custom designs at scale.
1:23

This robotic hand can weave itself together in minutes. Allonic developed a process that "braids" robot bodies around a 3D-printed skeleton in a single automated step. The tech draws from the textile industry, using braided fibers instead of traditional mechanical joints and bearings. Their braiding system grows tendon-driven structures directly onto skeletal cores, creating flexible yet durable parts without screws or assembly. One finger only needs 4 skeletal elements. Then thousands of braided fibers wrap around them to handle force and articulation. The software converts designs directly into machine code that runs the braiding process. But what matters is that robotic hands like this can cost up to $30,000, ~20-30% of a humanoid robot's total price. Allonic's braiding process could crush those costs while enabling custom designs at scale.

Rowan Cheung

74,776 次观看 • 5 个月前

THE WOVEN ROBOT! Wow! The woven robot hand is here and it is absolutely game-changing. Allonic just braided thousands of tendon-like fibers around a 3D-printed skeleton in minutes. No joints. No screws. No human assembly. Pure biomimetic tissue that flexes, grips, and lasts like biology itself. This isn’t incremental. This is the breakthrough that collapses robot hand costs from $30,000 to pennies. The old world of expensive, fragile robotic limbs is over. Now humanoid robots can build more of themselves scalable, replicable, anywhere. Robots building Robots building Robots, yeah you get it. In your freaking garage! Massive opportunities are about to explode as work as we know it fades. Rent one, have it build your fleet, send it back, repeat. Thousands if you want. The age of woven machines begins.
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THE WOVEN ROBOT! Wow! The woven robot hand is here and it is absolutely game-changing. Allonic just braided thousands of tendon-like fibers around a 3D-printed skeleton in minutes. No joints. No screws. No human assembly. Pure biomimetic tissue that flexes, grips, and lasts like biology itself. This isn’t incremental. This is the breakthrough that collapses robot hand costs from $30,000 to pennies. The old world of expensive, fragile robotic limbs is over. Now humanoid robots can build more of themselves scalable, replicable, anywhere. Robots building Robots building Robots, yeah you get it. In your freaking garage! Massive opportunities are about to explode as work as we know it fades. Rent one, have it build your fleet, send it back, repeat. Thousands if you want. The age of woven machines begins.

Brian Roemmele

21,376 次观看 • 5 个月前

This is WILD! MIT just solved one of the hardest unsolved problems in robotics (Save this). For decades, the fundamental problem with soft robots and wearable exoskeletons has not been compute or AI, it has been actuation. The moment you try to give a soft robot meaningful strength, you run into the same wall every engineer has hit since the field began, fluid-driven systems require external pumps, hydraulic reservoirs, and heavy infrastructure that makes the entire thing impractical to wear or embed into fabric. MIT's new Electrofluidic Fiber Muscles solve that problem by eliminating external infrastructure entirely. The key insight is electrohydrodynamic pumping using electric fields to generate pressure directly from electricity, with no moving parts, no motors, and no external fluid reservoir. The fibers are less than 2 millimeters thick, can be woven into fabric like ordinary textile, and operate in complete silence because nothing physically moves inside them, it is just ions propelling fluid through a closed circuit. The performance numbers published in Science Robotics are not conceptual, they are empirical results from actual hardware. These fibers achieve a power density of 50 watts per kilogram, matching skeletal muscle, with a contraction strain of 20% and a response time of 0.3 seconds. A single bundled configuration lifted 4 kilograms, 200 times its own weight while a separate configuration drove a robotic arm through a 40-degree bend compliant enough to safely complete a human handshake. Another configuration launched objects in under 100 milliseconds, which is faster than a human flinch reflex. The design mirrors biological muscle architecture in a way that prior artificial muscle approaches never achieved. The fibers are organized into antagonistic pairs, one contracts while the other extends, exactly like biceps and triceps and because the system runs in a closed loop, the relaxing fiber serves as the fluid reservoir for the contracting one, which is what allows the whole system to operate untethered with no external tank. The applications are not hypothetical but rather are the exact use cases the industry has been waiting years for the hardware to catch up to. Exoskeletons for physical labor, prosthetic limbs that move with the natural compliance of biological tissue, assistive garments for patients with motor disorders, and soft robots capable of safe physical contact with humans are all immediately unlocked by a muscle technology that is silent, lightweight, and weavable into clothing. The deeper significance is what this technology does when it meets the AI robotics wave that is already underway. Every major humanoid robot program, Figure, 1X, Boston Dynamics, Tesla Optimus is currently bottlenecked by the same hardware limitations these fibers address, actuators that are too rigid, too loud, too heavy, or too dependent on infrastructure to operate naturally alongside humans. Electrofluidic fiber muscles do not just solve a materials science problem but rather they remove one of the last physical barriers between robots that live in labs and robots that live in the world.
0:28

This is WILD! MIT just solved one of the hardest unsolved problems in robotics (Save this). For decades, the fundamental problem with soft robots and wearable exoskeletons has not been compute or AI, it has been actuation. The moment you try to give a soft robot meaningful strength, you run into the same wall every engineer has hit since the field began, fluid-driven systems require external pumps, hydraulic reservoirs, and heavy infrastructure that makes the entire thing impractical to wear or embed into fabric. MIT's new Electrofluidic Fiber Muscles solve that problem by eliminating external infrastructure entirely. The key insight is electrohydrodynamic pumping using electric fields to generate pressure directly from electricity, with no moving parts, no motors, and no external fluid reservoir. The fibers are less than 2 millimeters thick, can be woven into fabric like ordinary textile, and operate in complete silence because nothing physically moves inside them, it is just ions propelling fluid through a closed circuit. The performance numbers published in Science Robotics are not conceptual, they are empirical results from actual hardware. These fibers achieve a power density of 50 watts per kilogram, matching skeletal muscle, with a contraction strain of 20% and a response time of 0.3 seconds. A single bundled configuration lifted 4 kilograms, 200 times its own weight while a separate configuration drove a robotic arm through a 40-degree bend compliant enough to safely complete a human handshake. Another configuration launched objects in under 100 milliseconds, which is faster than a human flinch reflex. The design mirrors biological muscle architecture in a way that prior artificial muscle approaches never achieved. The fibers are organized into antagonistic pairs, one contracts while the other extends, exactly like biceps and triceps and because the system runs in a closed loop, the relaxing fiber serves as the fluid reservoir for the contracting one, which is what allows the whole system to operate untethered with no external tank. The applications are not hypothetical but rather are the exact use cases the industry has been waiting years for the hardware to catch up to. Exoskeletons for physical labor, prosthetic limbs that move with the natural compliance of biological tissue, assistive garments for patients with motor disorders, and soft robots capable of safe physical contact with humans are all immediately unlocked by a muscle technology that is silent, lightweight, and weavable into clothing. The deeper significance is what this technology does when it meets the AI robotics wave that is already underway. Every major humanoid robot program, Figure, 1X, Boston Dynamics, Tesla Optimus is currently bottlenecked by the same hardware limitations these fibers address, actuators that are too rigid, too loud, too heavy, or too dependent on infrastructure to operate naturally alongside humans. Electrofluidic fiber muscles do not just solve a materials science problem but rather they remove one of the last physical barriers between robots that live in labs and robots that live in the world.

Milk Road AI

1,205,848 次观看 • 3 个月前

🚨 BREAKING: Swiss startup mimic just raised €13.8M to teach robots the one thing they still struggle with human-like dexterity. 🇨🇭 The Zurich-based company is developing physical AI systems that allow robots to learn directly from human demonstrations. Skilled operators wear custom motion-tracking gear on real factory floors, generating data that trains robots to grasp, move, and adapt with human precision. Founded in 2024 as a spin-off from ETH Zurich, mimic pairs AI-driven dexterous robotic hands with standard industrial robot arms, skipping the humanoid form factor in favor of practical, deployable automation. The oversubscribed seed round was led by Elaia with participation from Speedinvest, Founderful, 1st Kind, 10x Founders, 2100 Ventures, and the Sequoia Scout Fund, bringing total funding to over $20M. Their technology is already being piloted with Fortune 500 manufacturers and logistics operators, where robots are learning complex manipulation tasks once reserved for humans. Why it matters: Europe is stepping up in the AI robotics race. By making dexterity scalable, mimic bridges the gap between lab breakthroughs and real factory deployment, where automation still meets its hardest challenges. As CEO Stefan Weirich put it: “We make dexterity deployable at scale, closing the gap between what AI can do in the lab and what factories actually need.” Let's go robotics in Europe! 🔥
0:26

🚨 BREAKING: Swiss startup mimic just raised €13.8M to teach robots the one thing they still struggle with human-like dexterity. 🇨🇭 The Zurich-based company is developing physical AI systems that allow robots to learn directly from human demonstrations. Skilled operators wear custom motion-tracking gear on real factory floors, generating data that trains robots to grasp, move, and adapt with human precision. Founded in 2024 as a spin-off from ETH Zurich, mimic pairs AI-driven dexterous robotic hands with standard industrial robot arms, skipping the humanoid form factor in favor of practical, deployable automation. The oversubscribed seed round was led by Elaia with participation from Speedinvest, Founderful, 1st Kind, 10x Founders, 2100 Ventures, and the Sequoia Scout Fund, bringing total funding to over $20M. Their technology is already being piloted with Fortune 500 manufacturers and logistics operators, where robots are learning complex manipulation tasks once reserved for humans. Why it matters: Europe is stepping up in the AI robotics race. By making dexterity scalable, mimic bridges the gap between lab breakthroughs and real factory deployment, where automation still meets its hardest challenges. As CEO Stefan Weirich put it: “We make dexterity deployable at scale, closing the gap between what AI can do in the lab and what factories actually need.” Let's go robotics in Europe! 🔥

Lukas Ziegler

77,152 次观看 • 9 个月前

MIT engineers built an AI wristband that controls robots by reading your hand muscles. It works by using an ultrasound to capture images of the muscles and tendons in your wrist. An AI algorithm then translates those images into the exact position of all 5 fingers, tracking 22 different ways your hand can bend, twist, and extend. In demos, wearers directed a robotic hand to play piano just by moving their own fingers. To put the complexity in perspective, it takes: > 34 muscles > 27 joints > 100+ tendons ...Just to scroll your phone This device captures it all from a band the size of a smartwatch. They tested across 8 volunteers with different hand sizes, tracking everything from sign language letters to holding scissors. The team is now collecting data from hundreds more users to train robots in surgical procedures. Something like this could one day become the universal remote for robots and virtual worlds, and we're way closer than most people expect.
2:40

MIT engineers built an AI wristband that controls robots by reading your hand muscles. It works by using an ultrasound to capture images of the muscles and tendons in your wrist. An AI algorithm then translates those images into the exact position of all 5 fingers, tracking 22 different ways your hand can bend, twist, and extend. In demos, wearers directed a robotic hand to play piano just by moving their own fingers. To put the complexity in perspective, it takes: > 34 muscles > 27 joints > 100+ tendons ...Just to scroll your phone This device captures it all from a band the size of a smartwatch. They tested across 8 volunteers with different hand sizes, tracking everything from sign language letters to holding scissors. The team is now collecting data from hundreds more users to train robots in surgical procedures. Something like this could one day become the universal remote for robots and virtual worlds, and we're way closer than most people expect.

Rowan Cheung

27,999 次观看 • 3 个月前

We might be solving the wrong problem in robotics. That’s what this makes clear. UMI → Universal Manipulation Interface A simple $400 gripper that lets you teach robots by demonstration. You hold it like a tool. Show the task. The robot learns. No teleoperation. No expensive hardware. No robot-specific data. Stanford open-sourced everything → hardware, code, datasets. What stands out to me is the bottleneck. Not algorithms. Data. Teleoperation → ~35 demos/hour UMI → ~111 demos/hour And the data transfers across robots → UR5, Franka, others. The design is surprisingly practical: → GoPro fisheye lens (155° FOV) + mirrors for depth → SLAM + IMU for precise 6DoF tracking → latency matching for dynamic tasks → diffusion policies for multimodal actions Then it scales. Cheng Chi takes this further with Sunday Robotics (with Tony Zhao). A $200 glove → deployed in 500+ homes → ~10 million real-world interactions. Not lab data. Real human behavior. Their robot learns dishes, laundry, espresso → with zero robot-specific data. This is where the shift becomes obvious. From training robots in controlled environments → to learning directly from humans at scale So here’s the real question: Will robotics be unlocked by better models… or by unlocking data? #ArtificialIntelligence #Robotics #AI #Innovation #FutureOfWork
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We might be solving the wrong problem in robotics. That’s what this makes clear. UMI → Universal Manipulation Interface A simple $400 gripper that lets you teach robots by demonstration. You hold it like a tool. Show the task. The robot learns. No teleoperation. No expensive hardware. No robot-specific data. Stanford open-sourced everything → hardware, code, datasets. What stands out to me is the bottleneck. Not algorithms. Data. Teleoperation → ~35 demos/hour UMI → ~111 demos/hour And the data transfers across robots → UR5, Franka, others. The design is surprisingly practical: → GoPro fisheye lens (155° FOV) + mirrors for depth → SLAM + IMU for precise 6DoF tracking → latency matching for dynamic tasks → diffusion policies for multimodal actions Then it scales. Cheng Chi takes this further with Sunday Robotics (with Tony Zhao). A $200 glove → deployed in 500+ homes → ~10 million real-world interactions. Not lab data. Real human behavior. Their robot learns dishes, laundry, espresso → with zero robot-specific data. This is where the shift becomes obvious. From training robots in controlled environments → to learning directly from humans at scale So here’s the real question: Will robotics be unlocked by better models… or by unlocking data? #ArtificialIntelligence #Robotics #AI #Innovation #FutureOfWork

Pascal Bornet

186,551 次观看 • 4 个月前

Twitter boi, do you want to see a humanoid AI lab from the inside? Well I do ;) Join me to visit Flexion A world-leading lab building the AI brains for humanoids – in Zurich, one of the best hubs for Physical AI. Their goal? Build the Android of robotics. One operating system that works on any humanoid hardware. It is crazy what their robots can do. We pushed a robot on the stairs. It didn't care. They left one in a rainy forest it was never trained on. Chilled and hiked there. And because they let robots train on their own, from zero through reinforcement learning, the robots do odd inhuman things. Eg they stand up from the ground in ways no human ever would. If their humanoid brain works, this will be industry defining. This is why Flexion is one of Europe's Most Ambitious Startups. 🇪🇺🔥 0:00 Intro 0:38 Inside a Lab with 14 Humanoids 2:17 What’s Actually Inside a Humanoid? 4:43 The Robot Navigates on Its Own 6:31 Why Grasping Is So Hard 7:01 Stairs, Pushes, and Balance Tests 7:55 Training 4,000 Robots in Parallel 10:50 Teleoperation vs Reinforcement Learning 14:35 The Case for Humanoids 15:55 Why Europe Could Win Robotics Please like & RT 🙏
18:51

Twitter boi, do you want to see a humanoid AI lab from the inside? Well I do ;) Join me to visit Flexion A world-leading lab building the AI brains for humanoids – in Zurich, one of the best hubs for Physical AI. Their goal? Build the Android of robotics. One operating system that works on any humanoid hardware. It is crazy what their robots can do. We pushed a robot on the stairs. It didn't care. They left one in a rainy forest it was never trained on. Chilled and hiked there. And because they let robots train on their own, from zero through reinforcement learning, the robots do odd inhuman things. Eg they stand up from the ground in ways no human ever would. If their humanoid brain works, this will be industry defining. This is why Flexion is one of Europe's Most Ambitious Startups. 🇪🇺🔥 0:00 Intro 0:38 Inside a Lab with 14 Humanoids 2:17 What’s Actually Inside a Humanoid? 4:43 The Robot Navigates on Its Own 6:31 Why Grasping Is So Hard 7:01 Stairs, Pushes, and Balance Tests 7:55 Training 4,000 Robots in Parallel 10:50 Teleoperation vs Reinforcement Learning 14:35 The Case for Humanoids 15:55 Why Europe Could Win Robotics Please like & RT 🙏

Andreas Klinger 🦾

14,761 次观看 • 4 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 • 6 个月前

AheadForm just raised a new A1 round worth hundreds of millions of RMB (~tens of millions USD) 🤖 That matters because this is not another humanoid company chasing locomotion first. AheadForm is building around the part most robotics startups still underestimate: face, emotion, and real-time human connection. The new funding will go into multimodal embodied interaction, emotion foundation models, facial hardware and materials, standardized delivery, and global expansion. Founded in June 2024, the company is still young, but the founder’s research trail is not. Yuhang Hu, a Columbia PhD and AheadForm’s CEO/CTO, has published work spanning facial coexpression, realistic lip motion for humanoid face robots, and self-supervised robot self-modeling. That is the deeper signal here. In a market crowded with hands, arms, and walking demos, investors are now putting serious money behind embodied AI that can express, respond, and hold attention face to face. And the company is moving fast. According to public reports, AheadForm has completed five funding rounds since the second half of 2025, while its robots have already broken out of lab-only visibility through public activations like the NetEase Justice mobile game collaboration and large robot-stage appearances. If humanoid robotics is about physical labor, AheadForm is making the case that the next layer is emotional presence. That may end up being one of the more important categories in embodied AI.
0:31

AheadForm just raised a new A1 round worth hundreds of millions of RMB (~tens of millions USD) 🤖 That matters because this is not another humanoid company chasing locomotion first. AheadForm is building around the part most robotics startups still underestimate: face, emotion, and real-time human connection. The new funding will go into multimodal embodied interaction, emotion foundation models, facial hardware and materials, standardized delivery, and global expansion. Founded in June 2024, the company is still young, but the founder’s research trail is not. Yuhang Hu, a Columbia PhD and AheadForm’s CEO/CTO, has published work spanning facial coexpression, realistic lip motion for humanoid face robots, and self-supervised robot self-modeling. That is the deeper signal here. In a market crowded with hands, arms, and walking demos, investors are now putting serious money behind embodied AI that can express, respond, and hold attention face to face. And the company is moving fast. According to public reports, AheadForm has completed five funding rounds since the second half of 2025, while its robots have already broken out of lab-only visibility through public activations like the NetEase Justice mobile game collaboration and large robot-stage appearances. If humanoid robotics is about physical labor, AheadForm is making the case that the next layer is emotional presence. That may end up being one of the more important categories in embodied AI.

RoboHub🤖

155,548 次观看 • 4 个月前

Seeing XPENG’s new IRON again,it remains truly stunning. It would be absolutely fantastic if it could actually walk around the venue lol -- During today’s interview session, founder Mr. He Xiaopeng told me that this anthropomorphic humanoid robot will initially be deployed in XPENG’s flagship stores to serve as a sales consultant and guide... (To be honest, this particular application scenario demonstrates strong backward compatibility.) Furthermore, some updates are scheduled for Q3 of this year. (I haven't finished transcribing his interview yet, but I will share more details later.) Essentially, auto companies worldwide are venturing into the field of humanoid robots to varying degrees,whether through application, R&D, or investment. However, few are like XPENG:a "Physical AI" company that covers the entire vertical stack,from software to hardware, from foundational infrastructure (AI chips, foundation models, etc.) to manufacturing, and finally to the closed-loop application of real-world scenarios. So, the branches are diverging,the ultimate forms that these various humanoid robot ecosystems take will be distinct from one another. The future is wild.
0:48

Seeing XPENG’s new IRON again,it remains truly stunning. It would be absolutely fantastic if it could actually walk around the venue lol -- During today’s interview session, founder Mr. He Xiaopeng told me that this anthropomorphic humanoid robot will initially be deployed in XPENG’s flagship stores to serve as a sales consultant and guide... (To be honest, this particular application scenario demonstrates strong backward compatibility.) Furthermore, some updates are scheduled for Q3 of this year. (I haven't finished transcribing his interview yet, but I will share more details later.) Essentially, auto companies worldwide are venturing into the field of humanoid robots to varying degrees,whether through application, R&D, or investment. However, few are like XPENG:a "Physical AI" company that covers the entire vertical stack,from software to hardware, from foundational infrastructure (AI chips, foundation models, etc.) to manufacturing, and finally to the closed-loop application of real-world scenarios. So, the branches are diverging,the ultimate forms that these various humanoid robot ecosystems take will be distinct from one another. The future is wild.

CyberRobo

1,419,528 次观看 • 3 个月前

Curtis Yarvin on how "the Left" has executed population replacement in the White Dominions as a "stalk and charge." "This is a predator... [and] they're starting to speed run...mass immigration is not 2 million people entering the US a year...[it's] 10... to 100 million a year" This clip of Yarvin (Curtis Yarvin), a political blogger and software developer (according to Wikipedia), is taken from a conversation with Peter McCormack (Peter McCormack 🏴‍☠️🇬🇧🇮🇪) posted to McCormack's eponymous YouTube channel on February 23, 2026. ---------------Partial transcription of clip---------------- "You might think that people getting red-pilled in various ways is something that would really put a stop to this craziness of population replacement. Oh, no, actually it's quite very much the reverse. "Because there's something anyone who's totally lost faith in the Left, in a way, they have many complaints with the Left. But it all boils down to, you basically recognize that this is not a vegetarian thing. This is a predator, right? "And so this is a predator. And so when a predator such as a lion is hunting, there are basically two phases of the pursuit. There's the stalk and the charge. And the stalk is like, well, nobody really knows what's going on here. Like, let's take the demographic replacement in the U.S. It's from the, you know, the 1965 Immigration Act. And politicians swore up and down that this was not what it, what it was. Basically, since that act, basically it's brought like 100 million people from the third world into the US, right? "Something like that. Solid, solid numbers like that. And, but in order to sort of do that, you know, you couldn't have this giant boat lift where, you know, some gigantic version of the Empire Windrush brought 100 million people at the same time. It's more a case of like, you know, slowly, slowly, catchy, catchy fish, right? You know, where you're kind of tickling it and you're like, oh yeah, oh yeah, our friends from, you know, we're open, our hearts are welcoming, right? "You know, like all of this, like, you know, stuff that's just of course, unbelievably sinister because it's sort of trading on people's goodwill and people's good wishes. It's like this con. But the thing is, when people, when enough people see the con, and all of these systems are incentive-based, they're not based on planning, the Left is not run by some cabal. It's incentives that drive things. "What you're seeing, especially in what used to be called the white dominions, Canada, Australia, New Zealand, especially is you're seeing they're starting to speed run, they're starting to come out of their crouch and basically just be like, yeah, actually we're, I mean in the 21st century, I think they've increased the population of Canada by like 35%. Right? "And so even that is small. Even that is small. The Spanish government was just like, we're going to legalize 500,000. Oh, but that's just an estimate. That's just an estimate of how many people that plan will suck in it probably will be more like over a million. Right. "And so you know and those people are Schengen. They can go anywhere in Europe and you will see them do that and the like and and so what you could see in the US if basically the next time Democrats win the presidency people go on about like mass immigration. Like no, we've not seen mass immigration. You have no idea what mass immigration is. "Mass Immigration is not 2 million people entering the US a year. Mass immigration starts I I would call it mass until it was like 10 a year. And like mass immigration is really the 10 to 50 to 100 million a year. But that's what could spark civil war. No, no— I don't know I'm seeing it here. No because people have no balls. People have no balls—"
3:27

Curtis Yarvin on how "the Left" has executed population replacement in the White Dominions as a "stalk and charge." "This is a predator... [and] they're starting to speed run...mass immigration is not 2 million people entering the US a year...[it's] 10... to 100 million a year" This clip of Yarvin (Curtis Yarvin), a political blogger and software developer (according to Wikipedia), is taken from a conversation with Peter McCormack (Peter McCormack 🏴‍☠️🇬🇧🇮🇪) posted to McCormack's eponymous YouTube channel on February 23, 2026. ---------------Partial transcription of clip---------------- "You might think that people getting red-pilled in various ways is something that would really put a stop to this craziness of population replacement. Oh, no, actually it's quite very much the reverse. "Because there's something anyone who's totally lost faith in the Left, in a way, they have many complaints with the Left. But it all boils down to, you basically recognize that this is not a vegetarian thing. This is a predator, right? "And so this is a predator. And so when a predator such as a lion is hunting, there are basically two phases of the pursuit. There's the stalk and the charge. And the stalk is like, well, nobody really knows what's going on here. Like, let's take the demographic replacement in the U.S. It's from the, you know, the 1965 Immigration Act. And politicians swore up and down that this was not what it, what it was. Basically, since that act, basically it's brought like 100 million people from the third world into the US, right? "Something like that. Solid, solid numbers like that. And, but in order to sort of do that, you know, you couldn't have this giant boat lift where, you know, some gigantic version of the Empire Windrush brought 100 million people at the same time. It's more a case of like, you know, slowly, slowly, catchy, catchy fish, right? You know, where you're kind of tickling it and you're like, oh yeah, oh yeah, our friends from, you know, we're open, our hearts are welcoming, right? "You know, like all of this, like, you know, stuff that's just of course, unbelievably sinister because it's sort of trading on people's goodwill and people's good wishes. It's like this con. But the thing is, when people, when enough people see the con, and all of these systems are incentive-based, they're not based on planning, the Left is not run by some cabal. It's incentives that drive things. "What you're seeing, especially in what used to be called the white dominions, Canada, Australia, New Zealand, especially is you're seeing they're starting to speed run, they're starting to come out of their crouch and basically just be like, yeah, actually we're, I mean in the 21st century, I think they've increased the population of Canada by like 35%. Right? "And so even that is small. Even that is small. The Spanish government was just like, we're going to legalize 500,000. Oh, but that's just an estimate. That's just an estimate of how many people that plan will suck in it probably will be more like over a million. Right. "And so you know and those people are Schengen. They can go anywhere in Europe and you will see them do that and the like and and so what you could see in the US if basically the next time Democrats win the presidency people go on about like mass immigration. Like no, we've not seen mass immigration. You have no idea what mass immigration is. "Mass Immigration is not 2 million people entering the US a year. Mass immigration starts I I would call it mass until it was like 10 a year. And like mass immigration is really the 10 to 50 to 100 million a year. But that's what could spark civil war. No, no— I don't know I'm seeing it here. No because people have no balls. People have no balls—"

Sense Receptor

245,834 次观看 • 5 个月前

A Breakthrough in Robotics: Spherical Gears Enter Mass Production Spherical gears, the kind of joint that would allow a robot to move like a human shoulder, have been notoriously difficult to manufacture with high precision. That's changing, thanks to a new design and a path to mass production that could have a huge impact on robotics. The breakthrough comes from a new design called the ABENICS spherical gear, developed by researchers at Yamagata University. This innovative mechanism enables a joint to move in three degrees of freedom without the slippage issues of earlier designs. It achieves this by using a "cross-spherical gear" that meshes with one or more "monopole gears." Mass production is now on the horizon. Although the initial manufacturing of the gears was inefficient, Nissei Corporation improved the process and established the necessary technology. The companies Kanematsu and Nissei have now entered the marketing phase, with production expected to begin in 2027. The impact of this technology is significant. Mass-produced spherical gears are expected to enable highly versatile and efficient robotic limbs. ► Humanoid and Mobile Robots: The design allows for compact, high-torque ball joints ideal for creating versatile and efficient robotic limbs. ► Aerospace: Potential applications include deployment mechanisms for satellite solar panels. ► Other Industries: The technology is also being explored for its potential to enhance productivity in healthcare, nursing care, and restaurants.
2:01

A Breakthrough in Robotics: Spherical Gears Enter Mass Production Spherical gears, the kind of joint that would allow a robot to move like a human shoulder, have been notoriously difficult to manufacture with high precision. That's changing, thanks to a new design and a path to mass production that could have a huge impact on robotics. The breakthrough comes from a new design called the ABENICS spherical gear, developed by researchers at Yamagata University. This innovative mechanism enables a joint to move in three degrees of freedom without the slippage issues of earlier designs. It achieves this by using a "cross-spherical gear" that meshes with one or more "monopole gears." Mass production is now on the horizon. Although the initial manufacturing of the gears was inefficient, Nissei Corporation improved the process and established the necessary technology. The companies Kanematsu and Nissei have now entered the marketing phase, with production expected to begin in 2027. The impact of this technology is significant. Mass-produced spherical gears are expected to enable highly versatile and efficient robotic limbs. ► Humanoid and Mobile Robots: The design allows for compact, high-torque ball joints ideal for creating versatile and efficient robotic limbs. ► Aerospace: Potential applications include deployment mechanisms for satellite solar panels. ► Other Industries: The technology is also being explored for its potential to enhance productivity in healthcare, nursing care, and restaurants.

RoboHub🤖

384,888 次观看 • 11 个月前

Elon Musk says the robot inflection point is not a better demo. It is the moment Optimus can learn from YouTube like a human. "Humanoid robots will be the biggest product ever. The demand will be insatiable." "Who wouldn't want their own personal C-3PO, R2-D2?" Then he gives the real threshold: "If Optimus can watch videos, YouTube videos or how-to videos... and based on that video, just like a human can learn how to do that thing, then you really have task extensibility that is dramatic." "Because then it can learn anything very quickly." Right now Tesla still bootstraps with humans in mocap suits teaching primitive tasks. But Elon's endgame is self-play: "How does a child learn? A child has toys... at some point figures out how to put the triangle in the triangle hole and the circle in the circle hole." "Once you have a lot of robots, you can do this self-play... literally have the robot play with toys." That is the shift: Robots stop being programmed task-by-task. They start absorbing the internet's how-to layer, then practicing in the physical world.
2:09

Elon Musk says the robot inflection point is not a better demo. It is the moment Optimus can learn from YouTube like a human. "Humanoid robots will be the biggest product ever. The demand will be insatiable." "Who wouldn't want their own personal C-3PO, R2-D2?" Then he gives the real threshold: "If Optimus can watch videos, YouTube videos or how-to videos... and based on that video, just like a human can learn how to do that thing, then you really have task extensibility that is dramatic." "Because then it can learn anything very quickly." Right now Tesla still bootstraps with humans in mocap suits teaching primitive tasks. But Elon's endgame is self-play: "How does a child learn? A child has toys... at some point figures out how to put the triangle in the triangle hole and the circle in the circle hole." "Once you have a lot of robots, you can do this self-play... literally have the robot play with toys." That is the shift: Robots stop being programmed task-by-task. They start absorbing the internet's how-to layer, then practicing in the physical world.

Karl Mehta

27,488 次观看 • 1 个月前

Sub-1 hour? Chasing the human half-marathon record? That’s suddenly the conversation around Beijing’s humanoid robot race this Sunday. 🏃‍♂️🤖 A year ago, this event was mostly about finishing. Now the bar is speed, stability, and full-course autonomy. More than 100 teams and 300+ humanoid robots are expected on the line, and nearly 40% of them are entering the autonomous navigation group instead of relying on remote control. The course is not getting easier either. Same 21.0975 km, but with more urban slopes, rolling sections, and park terrain built to expose weak points in control, perception, and endurance. That is what makes this race worth watching. If some of these machines really get close to one hour, the story is no longer “robots can run.” It becomes “humanoids are starting to hold speed, balance, and navigation together over real distance.” And that matters far beyond a race course. This is one of the clearest public tests yet for where humanoid robotics actually stands in hardware, motion control, power systems, and autonomous mobility. Sunday’s headline will be the time. The bigger takeaway will be how much of that pace is still there at kilometer 15, 18, and 21. I want to see who is actually fast, who is truly autonomous, and who can finish without falling apart. That is where the real signal will be.
2:50

Sub-1 hour? Chasing the human half-marathon record? That’s suddenly the conversation around Beijing’s humanoid robot race this Sunday. 🏃‍♂️🤖 A year ago, this event was mostly about finishing. Now the bar is speed, stability, and full-course autonomy. More than 100 teams and 300+ humanoid robots are expected on the line, and nearly 40% of them are entering the autonomous navigation group instead of relying on remote control. The course is not getting easier either. Same 21.0975 km, but with more urban slopes, rolling sections, and park terrain built to expose weak points in control, perception, and endurance. That is what makes this race worth watching. If some of these machines really get close to one hour, the story is no longer “robots can run.” It becomes “humanoids are starting to hold speed, balance, and navigation together over real distance.” And that matters far beyond a race course. This is one of the clearest public tests yet for where humanoid robotics actually stands in hardware, motion control, power systems, and autonomous mobility. Sunday’s headline will be the time. The bigger takeaway will be how much of that pace is still there at kilometer 15, 18, and 21. I want to see who is actually fast, who is truly autonomous, and who can finish without falling apart. That is where the real signal will be.

RoboHub🤖

209,946 次观看 • 4 个月前

Everything Elon said about Optimus at the All-In Summit today: • We’re finalizing the design of Optimus v3. That release is going to be a very remarkable robot. It will have manual dexterity comparable to a human, meaning a very complex hand, an AI mind that can navigate and comprehend reality, and will be made in very high volume. • Other robotics companies are missing those three very hard things. • I spend more mental cycles on Optimus than any other single thing. Solving real-world AI, all of the electrical-mechanical issues, the supply chain, and production challenges. • There is no supply chain for humanoid robots, so it has to be created from scratch, which requires a lot of vertical integration. None of the actuators in Optimus are available from an existing supply chain. • I think if successful, Optimus would be the biggest product ever. • The marginal cost of production, once we hit a million units per year, will probably be around $20,000. It depends on how much we spend on the AI chip in the robot, and we’ll need to achieve a lot of efficiencies in the actuators—26 actuators per arm (26 motors, gearboxes, and power electronics). The AI chip might cost $5,000 or $6,000, maybe more. At 1 million units a year, production cost will be $20,000, maybe $25,000. Price will be a function of demand. • Human hands have evolved to be incredibly sophisticated machines. Hands are a very first instrument. You can swing a baseball bat, thread a needle, play a piano or violin, and assemble a car. Hands are incredibly versatile instruments. Most of the muscles of the hands are actually in the forearm, and the hand is almost like a puppet. Human tendon evolution is incredibly good. The human hand has 27 or 28 degrees of freedom, depending on how you count it; it’s amazing. • In order to create a robot that can be a generalized humanoid, you must solve the “hands problem.” • Even though there are 10,000 to 20,000 electric motors out there, we couldn’t buy the actuators for any amount of money. We had to design every electric motor, gearbox, and controlling electronics from scratch, from first principles of physics. • Optimus is harder than developing any previous Tesla product, but not harder than Starship. • Right now, we’re struggling with the final design of the hardware, primarily the hand. The hands and forearm are the majority of the engineering difficulty of the entire robot. • If you want to do all the things that a human can do, it turns out you need a humanoid robot. If you want to do a subset, that’s much easier. Humans evolved to the shape and capability that we have for a good reason. There is value to having four fingers and a thumb; even the pinky is quite useful. Toes are much more of a question mark. • The AI5 inference chip will be 40 times better than AI4 by some measures. We know the limiting factors of the chip because the AI software and hardware teams work so closely. Effectively, the Tesla AI hardware and software teams are co-designing the chip. • The Softmax function on AI4 takes 40 steps in emulation mode, which will take only a few steps in AI5 natively. AI5 will easily handle mixed precision. • In terms of nominal raw compute, the AI5 inference chip has 8 times more compute, 9 times more memory, and 5 times more memory bandwidth compared to AI4. Because we’re addressing some core limitations and optimizations at the silicon level, we’re able to realize 40x improvements.
12:26

Everything Elon said about Optimus at the All-In Summit today: • We’re finalizing the design of Optimus v3. That release is going to be a very remarkable robot. It will have manual dexterity comparable to a human, meaning a very complex hand, an AI mind that can navigate and comprehend reality, and will be made in very high volume. • Other robotics companies are missing those three very hard things. • I spend more mental cycles on Optimus than any other single thing. Solving real-world AI, all of the electrical-mechanical issues, the supply chain, and production challenges. • There is no supply chain for humanoid robots, so it has to be created from scratch, which requires a lot of vertical integration. None of the actuators in Optimus are available from an existing supply chain. • I think if successful, Optimus would be the biggest product ever. • The marginal cost of production, once we hit a million units per year, will probably be around $20,000. It depends on how much we spend on the AI chip in the robot, and we’ll need to achieve a lot of efficiencies in the actuators—26 actuators per arm (26 motors, gearboxes, and power electronics). The AI chip might cost $5,000 or $6,000, maybe more. At 1 million units a year, production cost will be $20,000, maybe $25,000. Price will be a function of demand. • Human hands have evolved to be incredibly sophisticated machines. Hands are a very first instrument. You can swing a baseball bat, thread a needle, play a piano or violin, and assemble a car. Hands are incredibly versatile instruments. Most of the muscles of the hands are actually in the forearm, and the hand is almost like a puppet. Human tendon evolution is incredibly good. The human hand has 27 or 28 degrees of freedom, depending on how you count it; it’s amazing. • In order to create a robot that can be a generalized humanoid, you must solve the “hands problem.” • Even though there are 10,000 to 20,000 electric motors out there, we couldn’t buy the actuators for any amount of money. We had to design every electric motor, gearbox, and controlling electronics from scratch, from first principles of physics. • Optimus is harder than developing any previous Tesla product, but not harder than Starship. • Right now, we’re struggling with the final design of the hardware, primarily the hand. The hands and forearm are the majority of the engineering difficulty of the entire robot. • If you want to do all the things that a human can do, it turns out you need a humanoid robot. If you want to do a subset, that’s much easier. Humans evolved to the shape and capability that we have for a good reason. There is value to having four fingers and a thumb; even the pinky is quite useful. Toes are much more of a question mark. • The AI5 inference chip will be 40 times better than AI4 by some measures. We know the limiting factors of the chip because the AI software and hardware teams work so closely. Effectively, the Tesla AI hardware and software teams are co-designing the chip. • The Softmax function on AI4 takes 40 steps in emulation mode, which will take only a few steps in AI5 natively. AI5 will easily handle mixed precision. • In terms of nominal raw compute, the AI5 inference chip has 8 times more compute, 9 times more memory, and 5 times more memory bandwidth compared to AI4. Because we’re addressing some core limitations and optimizations at the silicon level, we’re able to realize 40x improvements.

The Humanoid Hub

238,877 次观看 • 11 个月前

Here are the graphics we made at Intel. I work at Nike innovation now so don’t work on them anymore so not sure all the details for this version but basically we used cameras and computer vision to track each athlete in each lane. Because the cameras were calibrated to specific locations and each frame of video timestamped, we could get distance and time data which you can use to get speed, naturally. Feed the data to a 3D rendered stadium and overlay graphics, and voila. We chose to show speed at the end of each “phase” of the race which I based the location on the data we got from testing and common knowledge of the 100m. So stopping at 30m (drive) and 60 (maintenance) and the end was intentional. Looks like a white label indicates a leader at the end of the phase. The green thing at the front of their label looks to indicate if their are accelerating. This is a bit of a deception because you’re always either accelerating or decelerating. So what it’s actually an indication of is if you’re within a certain percentage of your top speed. The green at the end is graded based on that which is why it’s a gradient. Basically shows that Noah not only has the highest tops speed but holds it best. The final view is everyone’s top speed and where they hit it in the race. It’s freaking wild that Tobago hit his top speed so late.
0:35

Here are the graphics we made at Intel. I work at Nike innovation now so don’t work on them anymore so not sure all the details for this version but basically we used cameras and computer vision to track each athlete in each lane. Because the cameras were calibrated to specific locations and each frame of video timestamped, we could get distance and time data which you can use to get speed, naturally. Feed the data to a 3D rendered stadium and overlay graphics, and voila. We chose to show speed at the end of each “phase” of the race which I based the location on the data we got from testing and common knowledge of the 100m. So stopping at 30m (drive) and 60 (maintenance) and the end was intentional. Looks like a white label indicates a leader at the end of the phase. The green thing at the front of their label looks to indicate if their are accelerating. This is a bit of a deception because you’re always either accelerating or decelerating. So what it’s actually an indication of is if you’re within a certain percentage of your top speed. The green at the end is graded based on that which is why it’s a gradient. Basically shows that Noah not only has the highest tops speed but holds it best. The final view is everyone’s top speed and where they hit it in the race. It’s freaking wild that Tobago hit his top speed so late.

Ashton Eaton

1,164,536 次观看 • 2 年前

🚨 This viral bionic humanoid robot company just raised hundreds of millions RMB in funding ,and it may have finally crossed the Uncanny Valley AheadForm, the startup behind those ultra-realistic face robots that have racked up hundreds of millions of views across social media, just closed a massive A1 round. This new funding will accelerate bringing these wildly popular humanoid robots into everyday life. Worth noting: Founder Yuhang Hu's latest paper on realistic lip motions for humanoid face robots has landed on the cover of Science Robotics (Jan 2026 issue). Using self-supervised AI, their robots now generate incredibly natural, continuous lip sync,supporting multiple languages, different speaking speeds, emotions, and even singing. Powered by soft silicone skin and 10+ DoF actuators, gone are the stiff puppet faces. This marks a major leap toward truly empathetic, “alive” human-robot interaction. While factories and warehouses will be filled with heavy duty, rugged humanoids for labor, Companies like AheadForm is building the warm, emotionally intelligent ones for people,the kind that can smile, make eye contact, and create real connection. The future isn’t just efficient. It’s getting warmer.
1:18

🚨 This viral bionic humanoid robot company just raised hundreds of millions RMB in funding ,and it may have finally crossed the Uncanny Valley AheadForm, the startup behind those ultra-realistic face robots that have racked up hundreds of millions of views across social media, just closed a massive A1 round. This new funding will accelerate bringing these wildly popular humanoid robots into everyday life. Worth noting: Founder Yuhang Hu's latest paper on realistic lip motions for humanoid face robots has landed on the cover of Science Robotics (Jan 2026 issue). Using self-supervised AI, their robots now generate incredibly natural, continuous lip sync,supporting multiple languages, different speaking speeds, emotions, and even singing. Powered by soft silicone skin and 10+ DoF actuators, gone are the stiff puppet faces. This marks a major leap toward truly empathetic, “alive” human-robot interaction. While factories and warehouses will be filled with heavy duty, rugged humanoids for labor, Companies like AheadForm is building the warm, emotionally intelligent ones for people,the kind that can smile, make eye contact, and create real connection. The future isn’t just efficient. It’s getting warmer.

CyberRobo

112,752 次观看 • 4 个月前

Marc Andreessen just told every trillion-dollar AI lab who they’re actually building for. Not eventually. By design. Andreessen: “99.9999% of the value of AI is gonna accrue to the users, not to the companies that make the AI.” This is not a prediction. It is a pattern. Andreessen: “If you look at the total amount of economic value creation downstream of the internet, something like 99% of that accrued to the users of the internet, not the companies that built the internet.” The internet created trillions in value. The companies that built it captured a rounding error. Andreessen: “Who gets the economic value of the smartphone? Everybody in the world who uses a smartphone to become more productive in their life or in their business gets 99% of the value. Apple and Google get 1%.” Apple and Google built the device that reorganized human life. Their cut is one percent. Electricity. The internet. The smartphone. The builders carry the cost. The users keep the value. The ratio has never once moved. The labs spending hundreds of billions right now are building wealth they will never keep. Andreessen: “It’s almost like dark matter. Everybody’s gonna experience that in their own life and in their own business.” The largest wealth transfer in history will carry no headline. No metric. No announcement. It will simply complete. Not because it is hidden. Because everyone is watching the builders.
1:52

Marc Andreessen just told every trillion-dollar AI lab who they’re actually building for. Not eventually. By design. Andreessen: “99.9999% of the value of AI is gonna accrue to the users, not to the companies that make the AI.” This is not a prediction. It is a pattern. Andreessen: “If you look at the total amount of economic value creation downstream of the internet, something like 99% of that accrued to the users of the internet, not the companies that built the internet.” The internet created trillions in value. The companies that built it captured a rounding error. Andreessen: “Who gets the economic value of the smartphone? Everybody in the world who uses a smartphone to become more productive in their life or in their business gets 99% of the value. Apple and Google get 1%.” Apple and Google built the device that reorganized human life. Their cut is one percent. Electricity. The internet. The smartphone. The builders carry the cost. The users keep the value. The ratio has never once moved. The labs spending hundreds of billions right now are building wealth they will never keep. Andreessen: “It’s almost like dark matter. Everybody’s gonna experience that in their own life and in their own business.” The largest wealth transfer in history will carry no headline. No metric. No announcement. It will simply complete. Not because it is hidden. Because everyone is watching the builders.

Dustin

42,965 次观看 • 1 个月前

Can United States manufacture robots? Matic Robots says "yes." It makes the best floor cleaning robot, that has won many perfect scores from Wired to many others. We love ours. But my trip there to get a tour from AI pioneer Navneet Dalal Navneet Dalal provided some real insights into how hard it is for a hardware company to make hardware in the United States. And how deeply AI is changing consumer electronics products that are going to be in many more homes soon. In this first part (Part II coming tomorrow) we get a look at how long it took for this company to go through prototypes to a shipping product. In the second part, you'll see the scaling hell that it takes to even ship a few thousand robots and the kinds of problems that scaling up a factory brings. Matic is one of my favorite small Silicon Valley companies. It has found what we call "product market fit." I just came back from CES where I saw many of its competitors, and the Matic wins because of not just the product thinking of Mehul and Navneet Dalal but because of their AI leadership. In a way their robot took many lessons from Tesla, from where to put the batteries to its bet on computer vision, which Navneet has been a pioneer in for years, working quietly behind the scenes. It is about to move into a new location that will allow it to grow to meet the demand that now is showing up (the boxes in its lobby show that it's outgrowing its current facilities). In terms of AI, it has aspirations of making a humanoid too, but it is taking a far more measured approach to getting there. By starting on the floor it can not just build world models based on real world data (customers are given a choice whether to allow its data to be used that way. Most customers choose to keep their data on the robot only, for privacy reasons, but if you opt in you can help them improve their models). They are using that data to understand homes. Navneet told me they hit very unusual situations in people's homes already that they couldn't really predict in simulators, like full-wall mirrors that confuse computer vision systems, or pools and water features in people's homes. Having real customers brings a ton of customer feedback about how to further improve the robot, and, as Navneet demonstrates in the second video, forces them to build a manufacturing muscle memory. Getting teams to work together, figuring out how to solve supply chain problems, from Trump's tarriffs, to a new one that showed up over the past couple of weeks. A supplier for its bags (one of the cheaper parts that goes into the robot) changed the glue it used, which caused robots to fail quality tests and the manufacturing line to stop. Reminds me a lot of the hell Elon Musk faced in its Fremont factory when Tesla was first starting to manufacture its Model 3, which almost bankrupted the company. Off the record Mehul and Navneet 🇮🇳 showed me some of the prototypes and plans for its next products that will show up over the next few years. Certainly not as sexy as Tesla, Figure, 1x_tech, and all the Chinese manufacturers are showing off already, but far better thought out for the typical Western home and AI plays a huge role in its future. It is the product that speaks for itself. It's amazing, and is about to get better this year due to AI. It's the first real vision-only robot to be in my home and I bet it won't be the last from this company. Real honor that they invited me over with my Insta360 camera (another company launched in my home, just like Matic was last year). In Part II we go into the factory.
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Can United States manufacture robots? Matic Robots says "yes." It makes the best floor cleaning robot, that has won many perfect scores from Wired to many others. We love ours. But my trip there to get a tour from AI pioneer Navneet Dalal Navneet Dalal provided some real insights into how hard it is for a hardware company to make hardware in the United States. And how deeply AI is changing consumer electronics products that are going to be in many more homes soon. In this first part (Part II coming tomorrow) we get a look at how long it took for this company to go through prototypes to a shipping product. In the second part, you'll see the scaling hell that it takes to even ship a few thousand robots and the kinds of problems that scaling up a factory brings. Matic is one of my favorite small Silicon Valley companies. It has found what we call "product market fit." I just came back from CES where I saw many of its competitors, and the Matic wins because of not just the product thinking of Mehul and Navneet Dalal but because of their AI leadership. In a way their robot took many lessons from Tesla, from where to put the batteries to its bet on computer vision, which Navneet has been a pioneer in for years, working quietly behind the scenes. It is about to move into a new location that will allow it to grow to meet the demand that now is showing up (the boxes in its lobby show that it's outgrowing its current facilities). In terms of AI, it has aspirations of making a humanoid too, but it is taking a far more measured approach to getting there. By starting on the floor it can not just build world models based on real world data (customers are given a choice whether to allow its data to be used that way. Most customers choose to keep their data on the robot only, for privacy reasons, but if you opt in you can help them improve their models). They are using that data to understand homes. Navneet told me they hit very unusual situations in people's homes already that they couldn't really predict in simulators, like full-wall mirrors that confuse computer vision systems, or pools and water features in people's homes. Having real customers brings a ton of customer feedback about how to further improve the robot, and, as Navneet demonstrates in the second video, forces them to build a manufacturing muscle memory. Getting teams to work together, figuring out how to solve supply chain problems, from Trump's tarriffs, to a new one that showed up over the past couple of weeks. A supplier for its bags (one of the cheaper parts that goes into the robot) changed the glue it used, which caused robots to fail quality tests and the manufacturing line to stop. Reminds me a lot of the hell Elon Musk faced in its Fremont factory when Tesla was first starting to manufacture its Model 3, which almost bankrupted the company. Off the record Mehul and Navneet 🇮🇳 showed me some of the prototypes and plans for its next products that will show up over the next few years. Certainly not as sexy as Tesla, Figure, 1x_tech, and all the Chinese manufacturers are showing off already, but far better thought out for the typical Western home and AI plays a huge role in its future. It is the product that speaks for itself. It's amazing, and is about to get better this year due to AI. It's the first real vision-only robot to be in my home and I bet it won't be the last from this company. Real honor that they invited me over with my Insta360 camera (another company launched in my home, just like Matic was last year). In Part II we go into the factory.

Robert Scoble

69,229 次观看 • 6 个月前