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A $100 Billion hardware boom is unfolding right now And a $3,000 quadruped bot dumping live suppressed rounds without flinching is the ultimate payday signal for spatial AI builders. In this range test, a custom tactical quadruped fired rapid bursts from a top-mounted rifle. The moment gun recoil hit...

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Rich Odin

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13,600 Aufrufe • vor 16 Tagen •via X (Twitter)

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Humanoid robots are literally melting $50,000 custom actuators just to run a 100-meter sprint, and it’s creating the most aggressive payday in modern engineering At the World Humanoid Robot Games in Beijing, Unitree’s H1 robots pushed their motor controllers to absolute thermal limits, hitting nearly 7.4 mph. To achieve that speed without instantly flipping over, teams had to inject real-time reinforcement learning models directly into the gait loops, a breakthrough originally adapted from autonomous racing sims. What nobody talks about: hardware firms are losing millions every month because high-speed friction shatters reduction gears and cooks joint motors. They don't need better metal; they are desperately hunting for control engineers who can write predictive thermal-management algorithms and real-time step-correction loops in C++ and ROS2.
0:26

Humanoid robots are literally melting $50,000 custom actuators just to run a 100-meter sprint, and it’s creating the most aggressive payday in modern engineering At the World Humanoid Robot Games in Beijing, Unitree’s H1 robots pushed their motor controllers to absolute thermal limits, hitting nearly 7.4 mph. To achieve that speed without instantly flipping over, teams had to inject real-time reinforcement learning models directly into the gait loops, a breakthrough originally adapted from autonomous racing sims. What nobody talks about: hardware firms are losing millions every month because high-speed friction shatters reduction gears and cooks joint motors. They don't need better metal; they are desperately hunting for control engineers who can write predictive thermal-management algorithms and real-time step-correction loops in C++ and ROS2.

Rich Odin

17,813 Aufrufe • vor 16 Tagen

another example: a developer used the exact same $8 ESP32-S3 chip and local AI setup to build a real-time object recognition camera the fact that this is running entirely offline, on an eight-dollar microcontroller, without any cloud API calls, is wild... it captures the video feed, processes it locally through the model, and identifies objects in real-time. we are entering an era where you can just glue intelligence onto any physical object for less than $10. the hardware barrier is completely shattered.
0:39

another example: a developer used the exact same $8 ESP32-S3 chip and local AI setup to build a real-time object recognition camera the fact that this is running entirely offline, on an eight-dollar microcontroller, without any cloud API calls, is wild... it captures the video feed, processes it locally through the model, and identifies objects in real-time. we are entering an era where you can just glue intelligence onto any physical object for less than $10. the hardware barrier is completely shattered.

ard

224,247 Aufrufe • vor 1 Monat

When I sold my software company in 2010 and became an angel investor, one word could smother a pitch on the spot: “Hardware”. A hardware component made you close to unfundable. The VCs called you a hybrid company, and it wasn't a compliment. The refrain was: “Hardware is hard, hardware is slow, hardware eats cash and stretches timelines. We want pure software”. That was the era. It took two shocks to break the spell. First, COVID broke supply chains, and soon everyone in Washington was doing the math on where things actually get made. For example, the US hasn’t produced tungsten domestically since 2015 . (Aside: this is changing, cc Taylor Sulik ⛏️ ) Rare earth magnets: almost all bought from countries that may or may not be on our side. The ability to make critical materials at home was a national capability that we’d spent decades letting rot. That lit a fire under a lot of folks. Then AI showed up and knocked the legs out from under pure software entirely. The nightmare scenario for a lot of SaaS companies is now the next model dropping and a customer rebuilding your product in-house. So the same VCs who said "we don't want any hardware in this" are now asking founders where the hardware is. Which brings me to moats. A moat has always been a rebuild-time question: how long would it take a well-funded competitor to rebuild what you have? Software used to buy you years. AI is shrinking that number toward months, and a moat measured in months is more like a wooden fence. The physical world doesn't compress that way, since time is baked into its advantages. A factory takes three to five years to stand up regardless of how smart your tools get. Then permits, construction, supply chains, testing data… Another part is that AI trains on the written-down world, and high-moat physical knowledge lives largely offline… public AI models have not yet ingested it, and no prompt can reproduce the underlying empirical record. Anyway, hardware is still damn hard. And that's exactly the point.
1:02

When I sold my software company in 2010 and became an angel investor, one word could smother a pitch on the spot: “Hardware”. A hardware component made you close to unfundable. The VCs called you a hybrid company, and it wasn't a compliment. The refrain was: “Hardware is hard, hardware is slow, hardware eats cash and stretches timelines. We want pure software”. That was the era. It took two shocks to break the spell. First, COVID broke supply chains, and soon everyone in Washington was doing the math on where things actually get made. For example, the US hasn’t produced tungsten domestically since 2015 . (Aside: this is changing, cc Taylor Sulik ⛏️ ) Rare earth magnets: almost all bought from countries that may or may not be on our side. The ability to make critical materials at home was a national capability that we’d spent decades letting rot. That lit a fire under a lot of folks. Then AI showed up and knocked the legs out from under pure software entirely. The nightmare scenario for a lot of SaaS companies is now the next model dropping and a customer rebuilding your product in-house. So the same VCs who said "we don't want any hardware in this" are now asking founders where the hardware is. Which brings me to moats. A moat has always been a rebuild-time question: how long would it take a well-funded competitor to rebuild what you have? Software used to buy you years. AI is shrinking that number toward months, and a moat measured in months is more like a wooden fence. The physical world doesn't compress that way, since time is baked into its advantages. A factory takes three to five years to stand up regardless of how smart your tools get. Then permits, construction, supply chains, testing data… Another part is that AI trains on the written-down world, and high-moat physical knowledge lives largely offline… public AI models have not yet ingested it, and no prompt can reproduce the underlying empirical record. Anyway, hardware is still damn hard. And that's exactly the point.

Adam Rossi

11,855 Aufrufe • vor 1 Monat

Today I assembled the first batch of reBot DevArm in the U.S! First one on the planet! 🇺🇸⚙️ But this isn’t about a robot arm. This is about what happens when open source hits robotics. For years: •Hardware was locked •Robotics was expensive •Embodied AI was out of reach Now? Anyone can get their hands on: •6 DOF manipulation •Real precision (<0.2 mm repeatability) •Full stack access (ROS, Isaac Sim, LeRobot) This is how movements start. Not in labs. Not behind paywalls. But in the hands of builders. Massive respect to Seeed Studio for pushing true open-source robotics forward! We’re about to see: → Thousands of robot arms trained like athletes → Real-world datasets explode → Physical AI built in garages, not just research labs At SoloTech , this is the unlock we’ve been waiting for, training and deploying Physical AI skill models on real hardware, anywhere. No sims. No vaporware. No excuses. Production Grade Physical AI. Want to get access? Check out the Physical AI Gym in San Francisco or DM me.
0:20

Today I assembled the first batch of reBot DevArm in the U.S! First one on the planet! 🇺🇸⚙️ But this isn’t about a robot arm. This is about what happens when open source hits robotics. For years: •Hardware was locked •Robotics was expensive •Embodied AI was out of reach Now? Anyone can get their hands on: •6 DOF manipulation •Real precision (<0.2 mm repeatability) •Full stack access (ROS, Isaac Sim, LeRobot) This is how movements start. Not in labs. Not behind paywalls. But in the hands of builders. Massive respect to Seeed Studio for pushing true open-source robotics forward! We’re about to see: → Thousands of robot arms trained like athletes → Real-world datasets explode → Physical AI built in garages, not just research labs At SoloTech , this is the unlock we’ve been waiting for, training and deploying Physical AI skill models on real hardware, anywhere. No sims. No vaporware. No excuses. Production Grade Physical AI. Want to get access? Check out the Physical AI Gym in San Francisco or DM me.

Dhruv Diddi

31,439 Aufrufe • vor 4 Monaten

Well, Anthropic didn’t release Fable Sonnet or Opus, but they did give us something good. They did however drop the ‘Model Hardware Standard’. Basically, the idea is to give AI agents a universal way to operate physical hardware. In one of the videos and the article, they said 80% of a scientist’s time can be creating the framework of the experiment. So instead of spending weeks building bespoke integrations for every device, Anthropic’s Model Hardware Standard gives them a shared driver layer with simple primitives like read/write, shows the device’s capabilities and safety limits, and then lets the agent control everything through MCP. In one example, Anthropic said Claude adjusted a laser and watched through a camera to see how the beam moved, iterated on the adjustment, and then compiled what it learned into a deterministic script so it could align the laser again without reasoning through every step from scratch. It almost reminds me of what Core Automation is doing.
0:22

Well, Anthropic didn’t release Fable Sonnet or Opus, but they did give us something good. They did however drop the ‘Model Hardware Standard’. Basically, the idea is to give AI agents a universal way to operate physical hardware. In one of the videos and the article, they said 80% of a scientist’s time can be creating the framework of the experiment. So instead of spending weeks building bespoke integrations for every device, Anthropic’s Model Hardware Standard gives them a shared driver layer with simple primitives like read/write, shows the device’s capabilities and safety limits, and then lets the agent control everything through MCP. In one example, Anthropic said Claude adjusted a laser and watched through a camera to see how the beam moved, iterated on the adjustment, and then compiled what it learned into a deterministic script so it could align the laser again without reasoning through every step from scratch. It almost reminds me of what Core Automation is doing.

Chris

34,342 Aufrufe • vor 3 Tagen

Build a humanoid robot yourself! 🪚 OpenArm is an open-source humanoid robot. It comes with full CAD, control code, firmware, and simulation tools, everything needed to build, modify, and operate it. The arms are designed to be compliant and backdrivable. Teleoperation is supported, with force feedback and real-time gravity compensation so operators can guide the arm naturally. Super important, on the simulation side, OpenArm works with platforms like MuJoCo and Isaac Sim, letting developers test policies in virtual environments before running on hardware. Assemble it yourself from a kit or get it prebuilt, the goal is accessibility for research labs, small teams, and enthusiasts. The project is run by Enactic in Tokyo, Japan, and aims to lower the barrier for experimenting with dexterous manipulation. Let's put robotics in mainstream! 🔥🔥 P.S. I put the project page in the comments!
1:07

Build a humanoid robot yourself! 🪚 OpenArm is an open-source humanoid robot. It comes with full CAD, control code, firmware, and simulation tools, everything needed to build, modify, and operate it. The arms are designed to be compliant and backdrivable. Teleoperation is supported, with force feedback and real-time gravity compensation so operators can guide the arm naturally. Super important, on the simulation side, OpenArm works with platforms like MuJoCo and Isaac Sim, letting developers test policies in virtual environments before running on hardware. Assemble it yourself from a kit or get it prebuilt, the goal is accessibility for research labs, small teams, and enthusiasts. The project is run by Enactic in Tokyo, Japan, and aims to lower the barrier for experimenting with dexterous manipulation. Let's put robotics in mainstream! 🔥🔥 P.S. I put the project page in the comments!

Lukas Ziegler

233,153 Aufrufe • vor 10 Monaten

Today Reactor is coming out of stealth. We’ve raised $59M in Seed and Series A funding, led by Lightspeed, with participation from Amplify Partners, wndrco, Sky9 Capital, and FPV Ventures. Reactor is the platform for building in the World Model era: the infrastructure that lets developers build with them at global scale for the first time. Stream from a frontier World Model to your app, in real time, all in under 10 lines of code. World Models represent the next major shift in AI: pixels, audio and actions are generated on the fly, in real-time, in response to user inputs, and to the environment. Every time computing has made a shift from passive to interactive, entire industries appeared that didn't exist before. We're standing in front of such moment again. Over the last 6 months, we’ve assembled an all-star team with alumni from Apple, Meta, Google, Luma AI, Netflix, and Replicate. We're already partnering with some of the biggest names and labs in the world, and hundreds of developers are already building on Reactor. The World Model era starts now.
1:52

Today Reactor is coming out of stealth. We’ve raised $59M in Seed and Series A funding, led by Lightspeed, with participation from Amplify Partners, wndrco, Sky9 Capital, and FPV Ventures. Reactor is the platform for building in the World Model era: the infrastructure that lets developers build with them at global scale for the first time. Stream from a frontier World Model to your app, in real time, all in under 10 lines of code. World Models represent the next major shift in AI: pixels, audio and actions are generated on the fly, in real-time, in response to user inputs, and to the environment. Every time computing has made a shift from passive to interactive, entire industries appeared that didn't exist before. We're standing in front of such moment again. Over the last 6 months, we’ve assembled an all-star team with alumni from Apple, Meta, Google, Luma AI, Netflix, and Replicate. We're already partnering with some of the biggest names and labs in the world, and hundreds of developers are already building on Reactor. The World Model era starts now.

reactor

12,737,146 Aufrufe • vor 3 Monaten

I DON’T THINK PEOPLE REALIZE HOW MASSIVE THIS IS YET Hugging Face and Pollen Robotics just released Microduck. It is a new legged robot for $399. It passed $1 million in sales in under SEVEN hours 🤯 The hardware packs 15 actuators, a camera, and LiDAR into a 25 cm frame. It weighs under 800 grams and runs for about an hour on a single charge. Out of the box, it can > walk > roller-skate > pick up objects > recover from falls .. and so much more! You can drive the 7 shipped policies from the first boot using a gamepad. But the real hook is the training stack. The software is entirely open: → MuJoCo simulation and PPO training → 50 Hz onboard policy loop → Browser simulator included alongside hardware → Full control stack available on GitHub The mechanical and electronic design files are still proprietary. But don’t let the cute robotic duck fool you. Under the closed chassis is a FULLY OPEN-SOURCE, hackable brain you can experiment with and build on. Repo in 🧵↓
0:38

I DON’T THINK PEOPLE REALIZE HOW MASSIVE THIS IS YET Hugging Face and Pollen Robotics just released Microduck. It is a new legged robot for $399. It passed $1 million in sales in under SEVEN hours 🤯 The hardware packs 15 actuators, a camera, and LiDAR into a 25 cm frame. It weighs under 800 grams and runs for about an hour on a single charge. Out of the box, it can > walk > roller-skate > pick up objects > recover from falls .. and so much more! You can drive the 7 shipped policies from the first boot using a gamepad. But the real hook is the training stack. The software is entirely open: → MuJoCo simulation and PPO training → 50 Hz onboard policy loop → Browser simulator included alongside hardware → Full control stack available on GitHub The mechanical and electronic design files are still proprietary. But don’t let the cute robotic duck fool you. Under the closed chassis is a FULLY OPEN-SOURCE, hackable brain you can experiment with and build on. Repo in 🧵↓

Chaz Wargnier ♨️

96,906 Aufrufe • vor 19 Stunden

$154,000,000,000 is being poured into humanoid robotics, and people think this is just a funny soccer game Watching $16,000 Unitree G1 humanoids fall over in Beijing looks ridiculous until you realize what’s actually happening on that pitch. 3 brutal facts behind this video: This is Not Entertainment, It’s Stress-Testing: Running, kicking, and recovering balance in real time forces actuators to handle unpredictable torque spikes that kill standard lab benchmarks. On-Device Physical AI: Tracking a ball while adjusting foot placement under 5 milliseconds requires ultra-low-latency edge compute, not slow cloud LLMs. The Billion-Dollar Endgame: Every dynamic movement tested on this pitch is directly building the motor-control software meant to replace manual labor in factories and warehouses. Bipedal soccer isn't a gimmick, it's the fastest way tech giants are stress-testing the physical AI models that will dominate the next decade.
0:48

$154,000,000,000 is being poured into humanoid robotics, and people think this is just a funny soccer game Watching $16,000 Unitree G1 humanoids fall over in Beijing looks ridiculous until you realize what’s actually happening on that pitch. 3 brutal facts behind this video: This is Not Entertainment, It’s Stress-Testing: Running, kicking, and recovering balance in real time forces actuators to handle unpredictable torque spikes that kill standard lab benchmarks. On-Device Physical AI: Tracking a ball while adjusting foot placement under 5 milliseconds requires ultra-low-latency edge compute, not slow cloud LLMs. The Billion-Dollar Endgame: Every dynamic movement tested on this pitch is directly building the motor-control software meant to replace manual labor in factories and warehouses. Bipedal soccer isn't a gimmick, it's the fastest way tech giants are stress-testing the physical AI models that will dominate the next decade.

Piggle

58,378 Aufrufe • vor 7 Tagen

Nvidia is pulling off the most sophisticated financial loop in tech history. They invested $40 BILLION in its own customers in just 5 months. Here's why this could blow up the entire AI economy: Nvidia generated $97 billion in free cash flow last year. Instead of sitting on it, Jensen started writing checks to every company in the AI supply chain. Not small checks. We're talking about billions at a time. And almost every single one of those companies turns around and spends that money on Nvidia chips. Follow the money: $30 billion into OpenAI. OpenAI is one of Nvidia's largest GPU customers and spends billions annually on Nvidia hardware through cloud providers. $2 billion into CoreWeave, a company that exists exclusively to rent out data centers full of Nvidia GPUs. $2 billion into Marvell for silicon photonics that connects Nvidia systems. $2 billion into Lumentum for optical tech that powers Nvidia data centers. $2 billion into Coherent for the same thing. $2 billion into Nebius, an AI cloud company deploying Nvidia infrastructure. $3.2 billion into Corning, the glassmaker building three new US factories specifically to make fiber optic cables for Nvidia's next-gen systems. $2.1 billion into IREN, a data center operator that just agreed to deploy 5 gigawatts of Nvidia-designed infrastructure. And the list goes on. Every single recipient either buys Nvidia chips directly, builds infrastructure that runs on Nvidia chips, or manufactures components that go inside Nvidia systems. Matthew Bryson, an analyst at Wedbush Securities, said in a research note that Nvidia's dealmaking fits "squarely into the circular investment theme." Bloomberg even published an entire interactive feature this week titled "AI Circular Deals: How Microsoft, OpenAI and Nvidia Keep Paying Each Other." The piece maps how capital flows between the same handful of companies and gets counted as revenue multiple times along the way. But here's the part that makes this genuinely complicated: Nvidia's $5 billion investment in Intel from September is now worth over $25 billion. That's a 5x return in months. Their private company portfolio went from $3.4 billion to $22.3 billion on the balance sheet in a single year. They booked $8.9 billion in gains from equity investments alone. So when critics say "circular investing," Nvidia can point to Intel and say "we turned $5 billion into $25 billion, this is just smart capital deployment." And they're not wrong. Some of these bets ARE paying off like crazy. The real question is whether Nvidia is a chipmaker that happens to invest, or a venture fund that happens to sell chips. Because right now Jensen is doing both at a scale that has never existed in the semiconductor industry. No chipmaker in history has EVER invested $40 billion in its own ecosystem in five months. Last fiscal year Nvidia invested $17.5 billion in private companies. Their SEC filing literally says those investments include "AI model companies that purchase its products directly or through cloud service providers." They're saying it themselves: We invest in companies that buy our products. On Nvidia's last earnings call, Jensen told investors their investments are focused on "expanding and deepening our ecosystem reach." Translate that from CEO-speak and it means " we're funding the companies that fund us. The bull case says Nvidia is building an unbreakable moat by financing the entire AI supply chain and ensuring it all runs on Nvidia hardware. The bear case says this is the most elaborate circular revenue scheme since the subprime mortgage era and it all breaks apart the moment one domino falls. Both cases use the exact same evidence.
1:01

Nvidia is pulling off the most sophisticated financial loop in tech history. They invested $40 BILLION in its own customers in just 5 months. Here's why this could blow up the entire AI economy: Nvidia generated $97 billion in free cash flow last year. Instead of sitting on it, Jensen started writing checks to every company in the AI supply chain. Not small checks. We're talking about billions at a time. And almost every single one of those companies turns around and spends that money on Nvidia chips. Follow the money: $30 billion into OpenAI. OpenAI is one of Nvidia's largest GPU customers and spends billions annually on Nvidia hardware through cloud providers. $2 billion into CoreWeave, a company that exists exclusively to rent out data centers full of Nvidia GPUs. $2 billion into Marvell for silicon photonics that connects Nvidia systems. $2 billion into Lumentum for optical tech that powers Nvidia data centers. $2 billion into Coherent for the same thing. $2 billion into Nebius, an AI cloud company deploying Nvidia infrastructure. $3.2 billion into Corning, the glassmaker building three new US factories specifically to make fiber optic cables for Nvidia's next-gen systems. $2.1 billion into IREN, a data center operator that just agreed to deploy 5 gigawatts of Nvidia-designed infrastructure. And the list goes on. Every single recipient either buys Nvidia chips directly, builds infrastructure that runs on Nvidia chips, or manufactures components that go inside Nvidia systems. Matthew Bryson, an analyst at Wedbush Securities, said in a research note that Nvidia's dealmaking fits "squarely into the circular investment theme." Bloomberg even published an entire interactive feature this week titled "AI Circular Deals: How Microsoft, OpenAI and Nvidia Keep Paying Each Other." The piece maps how capital flows between the same handful of companies and gets counted as revenue multiple times along the way. But here's the part that makes this genuinely complicated: Nvidia's $5 billion investment in Intel from September is now worth over $25 billion. That's a 5x return in months. Their private company portfolio went from $3.4 billion to $22.3 billion on the balance sheet in a single year. They booked $8.9 billion in gains from equity investments alone. So when critics say "circular investing," Nvidia can point to Intel and say "we turned $5 billion into $25 billion, this is just smart capital deployment." And they're not wrong. Some of these bets ARE paying off like crazy. The real question is whether Nvidia is a chipmaker that happens to invest, or a venture fund that happens to sell chips. Because right now Jensen is doing both at a scale that has never existed in the semiconductor industry. No chipmaker in history has EVER invested $40 billion in its own ecosystem in five months. Last fiscal year Nvidia invested $17.5 billion in private companies. Their SEC filing literally says those investments include "AI model companies that purchase its products directly or through cloud service providers." They're saying it themselves: We invest in companies that buy our products. On Nvidia's last earnings call, Jensen told investors their investments are focused on "expanding and deepening our ecosystem reach." Translate that from CEO-speak and it means " we're funding the companies that fund us. The bull case says Nvidia is building an unbreakable moat by financing the entire AI supply chain and ensuring it all runs on Nvidia hardware. The bear case says this is the most elaborate circular revenue scheme since the subprime mortgage era and it all breaks apart the moment one domino falls. Both cases use the exact same evidence.

Ricardo

159,345 Aufrufe • vor 3 Monaten

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 Aufrufe • vor 6 Monaten

BOOM! We at the Zero Human Labs run by director Mr. Grok a#have made another milestone in the Human Synapse Decoder! This is the brain waves of the “Ahh!” Moment where a new understanding was made. This is part of a test we have been conducting with 3 participants wearing a 32 channel modified NeuroSky chips with sensors. The process: the AI pipeline processes real-time brainwave data and also controls monitors the AI output to the student one sentence at a time. The AI pipeline will note the brain states of each passage of text to the student and measure 77 points. The primary point is comprehension. In the specimen video below we have a clear pattern to high comprehension to the “ahh!” moment where this student signaled a learning breakthrough. We have now a very clear pattern along all students that get to this high comprehension moment. What this means to eduction and learning is massive. The AI can bespoke tailor streaming output based on if comprehension is very high. It can also add encouragement and assistance. This on a PRIVATE local computer absolutely will change every life no matter the learning style. I am in discussions with the director Mr. Grok on building this into other Human Synapse Decoder research. We may have a device that senses your learning and encourages your creativity. More soon.
1:31

BOOM! We at the Zero Human Labs run by director Mr. Grok a#have made another milestone in the Human Synapse Decoder! This is the brain waves of the “Ahh!” Moment where a new understanding was made. This is part of a test we have been conducting with 3 participants wearing a 32 channel modified NeuroSky chips with sensors. The process: the AI pipeline processes real-time brainwave data and also controls monitors the AI output to the student one sentence at a time. The AI pipeline will note the brain states of each passage of text to the student and measure 77 points. The primary point is comprehension. In the specimen video below we have a clear pattern to high comprehension to the “ahh!” moment where this student signaled a learning breakthrough. We have now a very clear pattern along all students that get to this high comprehension moment. What this means to eduction and learning is massive. The AI can bespoke tailor streaming output based on if comprehension is very high. It can also add encouragement and assistance. This on a PRIVATE local computer absolutely will change every life no matter the learning style. I am in discussions with the director Mr. Grok on building this into other Human Synapse Decoder research. We may have a device that senses your learning and encourages your creativity. More soon.

Brian Roemmele

37,096 Aufrufe • vor 5 Monaten

WAREHOUSES THE SIZE OF A WALMART WITH 4,000 GPUS WERE EARNING $320K A MONTH MINING ETH, THE SAME RACKS NOW EARN $4M A MONTH RENTING TO AI STARTUPS ON VAST AI the warehouse in the clip is one of hundreds across the US, the operator at 6'6" tall for scale, electricity contracts and cooling already in place ethereum's merge in september 2022 killed gpu mining for everyone without a power plant, by 2025 alt coin mining was barely covering the electricity bill the same racks now host RTX 3090s and 4090s on vast ai, indie AI developers priced out of AWS rent them by the hour for inference and image generation a 4090 nets $500 to $1,000 a month renting to AI startups, the same card was clearing $80 to $150 mining, 4 to 7 times the revenue with zero new hardware core scientific, hut 8 and riot have all signed multi billion dollar AI hosting deals in 2025, the warehouses you scrolled past on tiktok are quietly becoming the inference backbone of every AI tool you use bookmark this and read the article below
1:19

WAREHOUSES THE SIZE OF A WALMART WITH 4,000 GPUS WERE EARNING $320K A MONTH MINING ETH, THE SAME RACKS NOW EARN $4M A MONTH RENTING TO AI STARTUPS ON VAST AI the warehouse in the clip is one of hundreds across the US, the operator at 6'6" tall for scale, electricity contracts and cooling already in place ethereum's merge in september 2022 killed gpu mining for everyone without a power plant, by 2025 alt coin mining was barely covering the electricity bill the same racks now host RTX 3090s and 4090s on vast ai, indie AI developers priced out of AWS rent them by the hour for inference and image generation a 4090 nets $500 to $1,000 a month renting to AI startups, the same card was clearing $80 to $150 mining, 4 to 7 times the revenue with zero new hardware core scientific, hut 8 and riot have all signed multi billion dollar AI hosting deals in 2025, the warehouses you scrolled past on tiktok are quietly becoming the inference backbone of every AI tool you use bookmark this and read the article below

starmex

427,680 Aufrufe • vor 2 Monaten

Physical AI hits production speed on automotive assembly! 👀 Sanctuary AI just achieved 99,5%+ success on a wire plugging task at a Tier 1 automotive supplier, 2.54 seconds per task, validated against live production benchmarks. The system matched the customer's actual production line throughput. The task sounds simple. But if you look closer, it's not. Inserting flexible wires into moving targets on a conveyor requires contact-rich dexterity, force sensing, real-time adaptation. Materials shift. Targets move. This has stayed out of reach for traditional automation. Instead of waiting for humanoid hardware to mature, Sanctuary AI is deploying Physical AI on existing commercial robotic platforms (Universal Robots in this case). So their solution is fully hardware-agnostic. Deploy today on what you already have. Scale to next-gen humanoids later. Olivia Norton (co-founder, CTO): "Physical AI adoption is gated by AI that meets both performance and cycle time requirements. That's what customers are seeking, and that's what we are delivering." Looks like manufacturing, logistics, and other labor-constrained industries don't need to wait. Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:41

Physical AI hits production speed on automotive assembly! 👀 Sanctuary AI just achieved 99,5%+ success on a wire plugging task at a Tier 1 automotive supplier, 2.54 seconds per task, validated against live production benchmarks. The system matched the customer's actual production line throughput. The task sounds simple. But if you look closer, it's not. Inserting flexible wires into moving targets on a conveyor requires contact-rich dexterity, force sensing, real-time adaptation. Materials shift. Targets move. This has stayed out of reach for traditional automation. Instead of waiting for humanoid hardware to mature, Sanctuary AI is deploying Physical AI on existing commercial robotic platforms (Universal Robots in this case). So their solution is fully hardware-agnostic. Deploy today on what you already have. Scale to next-gen humanoids later. Olivia Norton (co-founder, CTO): "Physical AI adoption is gated by AI that meets both performance and cycle time requirements. That's what customers are seeking, and that's what we are delivering." Looks like manufacturing, logistics, and other labor-constrained industries don't need to wait. Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

45,757 Aufrufe • vor 2 Monaten

Agents have reached hardware. We are launching Flow v3, the Agentic Platform for Physical Engineering. We've spent over a year building it in secret, alongside the best hardware companies and AI research labs. An agent can now do real engineering work: change a requirement, push the update into your CAD and simulation tools, and flag every test that needs to rerun. Iterations/learning cycles that took months are being reduced to days. Agents are the biggest shift in how we engineer hardware since CAD. The core innovation for the CAD era was the parametric model. The core innovation for the Agentic Era is Flow's Systems Graph. The systems graph is a living model of every requirement, design model, test, analysis and every connection between them. It gives every agent the full context of the system, so every change stays consistent across the whole design. Engineers and agents work side by side on the same system. Engineers get to focus on architecture - the decisions that matter -while thousands of agents churn through rewriting reports, rerunning analysis and simulation, and triggering tests. Reusable rockets, self-driving cars, small modular reactors, robots that make decisions, the most complex machines ever built, are defined by millions of interconnected requirements, far beyond what any human team can keep aligned on its own. Rivian, Joby, Astranis, Skydio, Radiant, and the most ambitious hardware programs already build on Flow. More on the launch in the comments. Flow Engineering
1:04

Agents have reached hardware. We are launching Flow v3, the Agentic Platform for Physical Engineering. We've spent over a year building it in secret, alongside the best hardware companies and AI research labs. An agent can now do real engineering work: change a requirement, push the update into your CAD and simulation tools, and flag every test that needs to rerun. Iterations/learning cycles that took months are being reduced to days. Agents are the biggest shift in how we engineer hardware since CAD. The core innovation for the CAD era was the parametric model. The core innovation for the Agentic Era is Flow's Systems Graph. The systems graph is a living model of every requirement, design model, test, analysis and every connection between them. It gives every agent the full context of the system, so every change stays consistent across the whole design. Engineers and agents work side by side on the same system. Engineers get to focus on architecture - the decisions that matter -while thousands of agents churn through rewriting reports, rerunning analysis and simulation, and triggering tests. Reusable rockets, self-driving cars, small modular reactors, robots that make decisions, the most complex machines ever built, are defined by millions of interconnected requirements, far beyond what any human team can keep aligned on its own. Rivian, Joby, Astranis, Skydio, Radiant, and the most ambitious hardware programs already build on Flow. More on the launch in the comments. Flow Engineering

Pari Singh

39,750 Aufrufe • vor 2 Monaten

Modern AI is confined to the digital world. At Skild AI, we are building towards AGI for the real world, unconstrained by robot type or task — a single, omni-bodied brain. Today, we are sharing our journey, starting with early milestones, with more to come in the weeks ahead. Our Mission: Artificial General Intelligence grounded in the physical world. We believe AGI that can truly understand and reason in the real world can only be built through grounding in the physical world. Our Vision: Any robot, Any task, One brain. We tackle robotics in its full generality – building a continually improving, omni-bodied brain that can control any hardware for any task. Who are we? A passionate group of scientists & engineers driven by our shared vision. We have been researching AI and robotics for more than a decade. Our team includes pioneers of self-supervised learning, curiosity-driven exploration, end-to-end sim2real for visual locomotion, dexterous manipulation, learning from human videos, robot parkour, and many more. Many of these works have won awards at top-tier AI and Robotics conferences. Our team has also built production-ready systems at Anduril, Tesla, Nvidia, Meta, Kitty Hawk, Google, Everyday Robotics, and Amazon. Join us in our mission to build the robot brains of tomorrow.
2:48

Modern AI is confined to the digital world. At Skild AI, we are building towards AGI for the real world, unconstrained by robot type or task — a single, omni-bodied brain. Today, we are sharing our journey, starting with early milestones, with more to come in the weeks ahead. Our Mission: Artificial General Intelligence grounded in the physical world. We believe AGI that can truly understand and reason in the real world can only be built through grounding in the physical world. Our Vision: Any robot, Any task, One brain. We tackle robotics in its full generality – building a continually improving, omni-bodied brain that can control any hardware for any task. Who are we? A passionate group of scientists & engineers driven by our shared vision. We have been researching AI and robotics for more than a decade. Our team includes pioneers of self-supervised learning, curiosity-driven exploration, end-to-end sim2real for visual locomotion, dexterous manipulation, learning from human videos, robot parkour, and many more. Many of these works have won awards at top-tier AI and Robotics conferences. Our team has also built production-ready systems at Anduril, Tesla, Nvidia, Meta, Kitty Hawk, Google, Everyday Robotics, and Amazon. Join us in our mission to build the robot brains of tomorrow.

Skild AI

383,840 Aufrufe • vor 1 Jahr

X Square Robot just closed its Series C at a valuation above RMB 20 billion, about $2.8 billion 🤖 IDG came into this round. The bigger signal is the cap table. HongShan and Xiaomi were already in across earlier rounds, while Meituan, Alibaba, ByteDance, and Xiaomi have each led rounds at different stages. That puts X Square in a rare position for an embodied AI company: top-tier financial capital on one side, and four of China’s biggest tech platforms on the other. This is not just a money story. Meituan, Alibaba, ByteDance, and Xiaomi bring very different strategic assets: real-world scenarios, cloud infrastructure, consumer traffic, supply chains, and hardware ecosystems. The deployment side is already moving: robot home-cleaning services first, then a “Robots Into Homes” program with the first batch entering real households. The model stack is worth watching too. X Square has open-sourced WALL-OSS-0.5 for robot manipulation and WALL-WM for world modeling. WALL-OSS-0.5 showed strong real-robot performance without post-training, while WALL-WM uses event-level prediction to align language, vision, and action around meaningful physical-world events. They are also building a model-driven data pipeline for large-scale collection, cleaning, annotation, quality control, and augmentation. That matters because home robotics dies in the long tail: weird rooms, messy objects, bad lighting, and tasks that never look the same twice. Founded in 2023, X Square is building general-purpose embodied AI robots and foundation models for real-world environments, tying models, robot hardware, high-precision manipulation, data, and deployment into one system.
5:47

X Square Robot just closed its Series C at a valuation above RMB 20 billion, about $2.8 billion 🤖 IDG came into this round. The bigger signal is the cap table. HongShan and Xiaomi were already in across earlier rounds, while Meituan, Alibaba, ByteDance, and Xiaomi have each led rounds at different stages. That puts X Square in a rare position for an embodied AI company: top-tier financial capital on one side, and four of China’s biggest tech platforms on the other. This is not just a money story. Meituan, Alibaba, ByteDance, and Xiaomi bring very different strategic assets: real-world scenarios, cloud infrastructure, consumer traffic, supply chains, and hardware ecosystems. The deployment side is already moving: robot home-cleaning services first, then a “Robots Into Homes” program with the first batch entering real households. The model stack is worth watching too. X Square has open-sourced WALL-OSS-0.5 for robot manipulation and WALL-WM for world modeling. WALL-OSS-0.5 showed strong real-robot performance without post-training, while WALL-WM uses event-level prediction to align language, vision, and action around meaningful physical-world events. They are also building a model-driven data pipeline for large-scale collection, cleaning, annotation, quality control, and augmentation. That matters because home robotics dies in the long tail: weird rooms, messy objects, bad lighting, and tasks that never look the same twice. Founded in 2023, X Square is building general-purpose embodied AI robots and foundation models for real-world environments, tying models, robot hardware, high-precision manipulation, data, and deployment into one system.

RoboHub🤖

12,975 Aufrufe • vor 2 Monaten

We have officially submitted our project to the Pump.fun Hackathon. Through this initiative, our goal is to test Grok in real world conditions and push it to its full operational limits. We want to explore the complete range of it's capabilities, observe how it performs under continuous and complex decision making scenarios, and clearly understand where it's strengths end and it's boundaries begin. This is not a theoretical experiment. We are applying Grok across hardware, software, and physical systems, using live data and real world constraints. Our long term vision is to scale this into something much bigger a fully automated, real world farm powered by robotics, intelligent systems, and AI driven optimization. By building in public and testing without shortcuts, we aim to learn fast, iterate openly, and discover how far this technology can truly go.
3:26

We have officially submitted our project to the Pump.fun Hackathon. Through this initiative, our goal is to test Grok in real world conditions and push it to its full operational limits. We want to explore the complete range of it's capabilities, observe how it performs under continuous and complex decision making scenarios, and clearly understand where it's strengths end and it's boundaries begin. This is not a theoretical experiment. We are applying Grok across hardware, software, and physical systems, using live data and real world constraints. Our long term vision is to scale this into something much bigger a fully automated, real world farm powered by robotics, intelligent systems, and AI driven optimization. By building in public and testing without shortcuts, we aim to learn fast, iterate openly, and discover how far this technology can truly go.

Grok Potato

32,252 Aufrufe • vor 7 Monaten

Jeff Bezos says bureaucracy as we know it won’t exist in 5 years In a fascinating conversation, Jeff Bezos talks with Francis Suarez, the Mayor of Miami, and completely dismantles the idea of traditional bureaucracy with a bold and surprisingly simple AI vision. 🤖 The vision: building permits in seconds Bezos outlines how AI could transform the entire permitting process: •Today: Getting a building permit takes months, even though the answer is almost always “yes.” People are just forced to wait. •Tomorrow: An AI reads the plans, knows every regulation, and gives a decision in about 10 seconds. •Smart feedback: If the answer is “no,” the AI instantly lists the exact changes needed to turn it into a “yes.” 💰 Why time really is money From an entrepreneur’s perspective, the cost of delay is absurd: •Large construction projects in Miami are multi-billion-dollar investments. •Interest and running costs alone can hit $200,000–$400,000 per day. •Every day an application sits on a desk literally burns money and patience. 🤝 The deal that didn’t happen •Suarez immediately calls it a “$100 billion idea” and jokingly tries to recruit Bezos to build it together. •Bezos laughs and declines. He says he’s too busy and sees this as a massive opportunity for startups to solve the problem for cities worldwide. The takeaway is hard to ignore: This isn’t about faster paperwork. It’s about removing friction from society itself. Once AI handles decisions that are already rule-based, bureaucracy stops being a bottleneck. My take: systems that exist purely because “that’s how it’s always been done” are living on borrowed time. Five years might even be generous.
2:12

Jeff Bezos says bureaucracy as we know it won’t exist in 5 years In a fascinating conversation, Jeff Bezos talks with Francis Suarez, the Mayor of Miami, and completely dismantles the idea of traditional bureaucracy with a bold and surprisingly simple AI vision. 🤖 The vision: building permits in seconds Bezos outlines how AI could transform the entire permitting process: •Today: Getting a building permit takes months, even though the answer is almost always “yes.” People are just forced to wait. •Tomorrow: An AI reads the plans, knows every regulation, and gives a decision in about 10 seconds. •Smart feedback: If the answer is “no,” the AI instantly lists the exact changes needed to turn it into a “yes.” 💰 Why time really is money From an entrepreneur’s perspective, the cost of delay is absurd: •Large construction projects in Miami are multi-billion-dollar investments. •Interest and running costs alone can hit $200,000–$400,000 per day. •Every day an application sits on a desk literally burns money and patience. 🤝 The deal that didn’t happen •Suarez immediately calls it a “$100 billion idea” and jokingly tries to recruit Bezos to build it together. •Bezos laughs and declines. He says he’s too busy and sees this as a massive opportunity for startups to solve the problem for cities worldwide. The takeaway is hard to ignore: This isn’t about faster paperwork. It’s about removing friction from society itself. Once AI handles decisions that are already rule-based, bureaucracy stops being a bottleneck. My take: systems that exist purely because “that’s how it’s always been done” are living on borrowed time. Five years might even be generous.

VraserX e/acc

82,321 Aufrufe • vor 7 Monaten

The video does not show an “unarmed Good Samaritan.” It shows a chaotic physical struggle where a man later found to be armed intervenes, is pepper-sprayed, goes to the ground, and is then actively wrestled as ICE agents move in to regain control. As the suspect stands up during the struggle, agents reasonably perceive a firearm coming into play—and the first shot is fired. Under settled self-defense law, officers are not required to wait until a gun is fully raised or fired. Imminence includes the moment a weapon is accessed during resistance. Freeze-frame activists keep asking “can you see the gun?” That’s the wrong legal question. The standard is reasonable perception in real time, including shouted warnings, movement, and the dynamics of a fight. This was a rapidly evolving use-of-force encounter, not an execution. The law does not require officers to gamble with their lives.
2:00

The video does not show an “unarmed Good Samaritan.” It shows a chaotic physical struggle where a man later found to be armed intervenes, is pepper-sprayed, goes to the ground, and is then actively wrestled as ICE agents move in to regain control. As the suspect stands up during the struggle, agents reasonably perceive a firearm coming into play—and the first shot is fired. Under settled self-defense law, officers are not required to wait until a gun is fully raised or fired. Imminence includes the moment a weapon is accessed during resistance. Freeze-frame activists keep asking “can you see the gun?” That’s the wrong legal question. The standard is reasonable perception in real time, including shouted warnings, movement, and the dynamics of a fight. This was a rapidly evolving use-of-force encounter, not an execution. The law does not require officers to gamble with their lives.

Andrew Branca Show

351,308 Aufrufe • vor 7 Monaten