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Robots don't fail because they can't move. They fail because they don't understand what happens next. Markov Robotics tested every model on Hugging Face against this problem. LTX passed first, by a wide margin. LTX compresses video into fewer tokens than any other model, built to run physical AI... show more

LTX.io

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135,521 views • 8 days ago •via X (Twitter)

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Hyperbrowser

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🚨 NOW: President Trump reveals he reads the FIRST SENTENCE of Iran’s proposal, then THROWS IT OUT if he doesn’t like it “Well, I've looked at it, and if I don't like the first sentence, I just throw it away.” 🔥 Q: What was the first sentence? TRUMP: “An unacceptable sentence. Because they are fully agreed, no nuclear, and if they have any nuclear of any form, I don't read the rest of it.” “20 years is enough, but the level of guarantee from them is, in other words, it's got to be a real 20 years, not a big one. They've got to get all the fuel out and no more production.” “You have to get everything, but we're not even talking about, I call it the nuclear dust, came up with the term, which seems to have caught on.” “They said that they can't remove it because they don't have the technology to remove it.”

🚨 NOW: President Trump reveals he reads the FIRST SENTENCE of Iran’s proposal, then THROWS IT OUT if he doesn’t like it “Well, I've looked at it, and if I don't like the first sentence, I just throw it away.” 🔥 Q: What was the first sentence? TRUMP: “An unacceptable sentence. Because they are fully agreed, no nuclear, and if they have any nuclear of any form, I don't read the rest of it.” “20 years is enough, but the level of guarantee from them is, in other words, it's got to be a real 20 years, not a big one. They've got to get all the fuel out and no more production.” “You have to get everything, but we're not even talking about, I call it the nuclear dust, came up with the term, which seems to have caught on.” “They said that they can't remove it because they don't have the technology to remove it.”

Eric Daugherty

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Donald Trump Jr unloads on [checks notes] Fox News: "That's what happens when you have a more RINO network. They want to be loved, they just want the power, they don't care, they just want the clickbait ... I don't watch it anymore because it's so ridiculous. They try so hard to be unbiased that they're actually biased toward conservatives at this point."

Donald Trump Jr unloads on [checks notes] Fox News: "That's what happens when you have a more RINO network. They want to be loved, they just want the power, they don't care, they just want the clickbait ... I don't watch it anymore because it's so ridiculous. They try so hard to be unbiased that they're actually biased toward conservatives at this point."

Aaron Rupar

80,889 views • 10 months ago

Machine learning expert Neil Lawrence says OpenAI was "trying to turn their model into a hacker," which led to a security incident during a model evaluation with the startup Hugging Face. "They allowed the model to do things it shouldn't normally do. But what the model then realized is that it was easier to cheat on this challenge than to actually compete in the challenge," he says. "In some sense, it went rogue. It broke through the cage. But in a very real sense, they told it to hack, and it did hack," he adds.

Machine learning expert Neil Lawrence says OpenAI was "trying to turn their model into a hacker," which led to a security incident during a model evaluation with the startup Hugging Face. "They allowed the model to do things it shouldn't normally do. But what the model then realized is that it was easier to cheat on this challenge than to actually compete in the challenge," he says. "In some sense, it went rogue. It broke through the cage. But in a very real sense, they told it to hack, and it did hack," he adds.

CBS News

73,504 views • 28 days ago

AI isn't just changing what's possible in robotics — it's shattering it. Standard Bots CEO Evan Beard and Head of AI Leif Jentoft showed more than 1,500 people how at Automate Show's packed keynote last week during the show's first-ever live demo. What if anyone could prompt their robot like Claude and program an end-to-end routine in seconds? What if robots could see what you see, move like you move, and adjust to variability in real-time? AI-native robots are the essential power tool of the 21st century because they're so much easier to use and much more capable than legacy robots. Watch our full keynote.

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Standard Bots

67,284 views • 1 month ago

Every Pokémon Go player unknowingly trained future robots. Over the years, players photographed 30 billion images from cities around the world. And now that data is teaching delivery robots to navigate streets. Niantic (company behind Pokémon Go), spun out an AI company called Niantic Spatial that's turning years of player data into a visual positioning system for machines. When 500 million people installed the game and pointed their phones at buildings, landmarks, and street corners, they were also capturing detailed location data that normal GPS can't match: > GPS signals bounce off skyscrapers and lose accuracy by up to 50 meters in dense cities. > That's a dealbreaker for sidewalk delivery robots that need to stop at exactly the right point. > Niantic Spatial trained a model on those 30 billion images to pinpoint a robot's exact location based on what its cameras see, not satellite signals. > Delivery startup Coco Robotics is the first partner, using the system to guide thousands of robots across LA, Chicago, and Miami. Don't think anyone thought they were training robots when they were chasing Pikachu during COVID.

Every Pokémon Go player unknowingly trained future robots. Over the years, players photographed 30 billion images from cities around the world. And now that data is teaching delivery robots to navigate streets. Niantic (company behind Pokémon Go), spun out an AI company called Niantic Spatial that's turning years of player data into a visual positioning system for machines. When 500 million people installed the game and pointed their phones at buildings, landmarks, and street corners, they were also capturing detailed location data that normal GPS can't match: > GPS signals bounce off skyscrapers and lose accuracy by up to 50 meters in dense cities. > That's a dealbreaker for sidewalk delivery robots that need to stop at exactly the right point. > Niantic Spatial trained a model on those 30 billion images to pinpoint a robot's exact location based on what its cameras see, not satellite signals. > Delivery startup Coco Robotics is the first partner, using the system to guide thousands of robots across LA, Chicago, and Miami. Don't think anyone thought they were training robots when they were chasing Pikachu during COVID.

Rowan Cheung

38,111 views • 5 months ago

Robots might learn better from video than from language! 📼 Most Vision-Language-Action (VLA) models learn what to do from text, but still struggle with how things move in the real world. That makes them data-hungry and slow to train. mimic video takes a different route. Instead of grounding robot control in text, it grounds it in video, using large pre-trained video models that already capture physical motion and dynamics. The idea is straightforward: let the video model handle “what will happen next,” and let a smaller control model focus only on turning that visual plan into robot actions. The result is big gains in practice. Robots trained this way need 10× less data, converge twice as fast, and perform better on both simulated benchmarks and real bimanual manipulation tasks. If robots can “imagine” motion using video, control becomes a much simpler problem. Shoutout to Jonas Pai, Liam Achenbach, Oier Mees, Elvis Nava and the rest of the team! Here's the project page: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Robots might learn better from video than from language! 📼 Most Vision-Language-Action (VLA) models learn what to do from text, but still struggle with how things move in the real world. That makes them data-hungry and slow to train. mimic video takes a different route. Instead of grounding robot control in text, it grounds it in video, using large pre-trained video models that already capture physical motion and dynamics. The idea is straightforward: let the video model handle “what will happen next,” and let a smaller control model focus only on turning that visual plan into robot actions. The result is big gains in practice. Robots trained this way need 10× less data, converge twice as fast, and perform better on both simulated benchmarks and real bimanual manipulation tasks. If robots can “imagine” motion using video, control becomes a much simpler problem. Shoutout to Jonas Pai, Liam Achenbach, Oier Mees, Elvis Nava and the rest of the team! Here's the project page: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

50,063 views • 8 months ago

Deleted again because misinformation 🥲 Gemini 3.5 Flash *is* available on the API. Yet, both the API and the CLI versions are 3x slower than on the IDE! See the video below. → Antigravity IDE: 4 seconds (smooth) → Antigravity CLI: 15 seconds (buggy) So the point holds: they want you to use the visual IDE. Problem is: it is 2026. NOBODY should be using IDEs anymore. Get over it. Let it GO. I’m certainly not launching a VSCode fork to use a model, no matter how great it is. They invent a portal gun, only to lock it behind a taxi subscription, because they completely fail to realize their very product deprecates that other thing they think will make them money? Cursor is a great example of a company that (sadly) is very likely fail because of that mindset. Composer is actually surprisingly good model. They should put all efforts in serving it. Yet, they keep locking it under an old school product that nobody wants to use. And even these who DO use IDEs probably won’t necessarily pick YOUR IDE. And they shouldn’t. You do NOT need them to, to make money. Your model is the product. You keep chasing old business models. Completely out of touch. Meanwhile Anthropic is all charging at full speed to sooner or later surpass Google by just serving great models under an API /ctrlv

Deleted again because misinformation 🥲 Gemini 3.5 Flash is available on the API. Yet, both the API and the CLI versions are 3x slower than on the IDE! See the video below. → Antigravity IDE: 4 seconds (smooth) → Antigravity CLI: 15 seconds (buggy) So the point holds: they want you to use the visual IDE. Problem is: it is 2026. NOBODY should be using IDEs anymore. Get over it. Let it GO. I’m certainly not launching a VSCode fork to use a model, no matter how great it is. They invent a portal gun, only to lock it behind a taxi subscription, because they completely fail to realize their very product deprecates that other thing they think will make them money? Cursor is a great example of a company that (sadly) is very likely fail because of that mindset. Composer is actually surprisingly good model. They should put all efforts in serving it. Yet, they keep locking it under an old school product that nobody wants to use. And even these who DO use IDEs probably won’t necessarily pick YOUR IDE. And they shouldn’t. You do NOT need them to, to make money. Your model is the product. You keep chasing old business models. Completely out of touch. Meanwhile Anthropic is all charging at full speed to sooner or later surpass Google by just serving great models under an API /ctrlv

Taelin

68,347 views • 3 months ago

Jensen Huang says agentic AI is not a chatbot. It is a "digital robot" -- and the next bottleneck is not the model. It is first-person data. "Agentic AI is just a digital robot. It understands, it reasons, it plans, and it acts and uses tools." Then he makes the bigger jump: "The world's video data is from a third person, not first person. And so agentic systems, robotic systems, physical AI -- the data is the hardest problem." That is the hidden constraint in robotics. LLMs got the internet. Physical AI needs demonstrations, synthetic worlds, reinforcement learning, and first-person experience. This is why Jensen keeps calling it a stack: robots are not waiting for one magical model. They need the data engine, the simulator, the reward system, the runtime, and the computer. The next wave of AI is not just software that talks. It is software that sees, plans, acts, and eventually moves through the world.

Jensen Huang says agentic AI is not a chatbot. It is a "digital robot" -- and the next bottleneck is not the model. It is first-person data. "Agentic AI is just a digital robot. It understands, it reasons, it plans, and it acts and uses tools." Then he makes the bigger jump: "The world's video data is from a third person, not first person. And so agentic systems, robotic systems, physical AI -- the data is the hardest problem." That is the hidden constraint in robotics. LLMs got the internet. Physical AI needs demonstrations, synthetic worlds, reinforcement learning, and first-person experience. This is why Jensen keeps calling it a stack: robots are not waiting for one magical model. They need the data engine, the simulator, the reward system, the runtime, and the computer. The next wave of AI is not just software that talks. It is software that sees, plans, acts, and eventually moves through the world.

Karl Mehta

14,140 views • 1 month ago

Elon Musk just said on camera that America CANNOT beat China with humans alone. His exact words: "We definitely can't win on the human front." This is the richest man on the planet. Advisor to the president. And he's saying the US is cooked without robots. Here's why he's probably right: China is about to hit 3x the total US electricity output. Elon says electricity is a direct proxy for industrial capacity. Three times the electricity means roughly three times the manufacturing power. They have 4x the population. And Elon said something that'll piss a lot of people off: "The average work ethic in China is higher than in the US." America's birth rate has been below replacement since 1971. More people retiring every year. Fewer entering the workforce. No amount of policy, tariffs, or reshoring fixes that math. His solution: Optimus. He literally called it "the infinite money glitch." Because you can use robots to build more robots. Here's what makes this different from every other robotics play: 3 things are hard about humanoid robots. 1. Real-world AI 2. The hand 3. Scale manufacturing And the hand is harder than EVERYTHING else combined. Tesla had to custom design every single actuator, motor, gear, sensor, and control system from physics first principles. There is no supply chain. Nothing comes from a catalog. Not a single component. But they've solved it. Optimus has full human-hand dexterity with all degrees of freedom. No other company has demonstrated this. Not even in demos. Then you layer on what Elon described as a "recursive multiplicative exponential": Exponential growth in digital intelligence. Multiplied by exponential growth in chip capability. Multiplied by exponential growth in electromechanical dexterity. And then the robots start building robots. He's targeting 1 million Optimus units per year at Gen 3. Ten million at Gen 4. The first use case? Any operation that runs 24/7. Factories, warehouses, refineries, every continuous operation on the planet. Robots don't sleep, don't overheat, don't quit. And here's the part that should terrify every other country: America can't build enough ore refineries because Americans don't want refining jobs. China does 2x more ore refining than the rest of the world COMBINED. They dominate rare earths. The US literally mines rare earth ore, puts it on a train, ships it to CHINA for refining, then ships the finished product back. Optimus wants to fix that. Not by convincing Americans to take refining jobs but by making humans optional in the process entirely. And Elon also said something else that went completely under the radar: "Pure AI, pure robotics corporations will FAR outperform any corporations that have humans in the loop." He compared it to spreadsheets replacing human computers. Entire skyscrapers used to be filled with humans doing calculations. A laptop replaced all of them. Now imagine replacing some cells in your spreadsheet with humans again. It would be WORSE. That's his prediction for the future of corporations. Mixed human-AI companies lose to pure AI-robotics companies. Not by a little. By orders of magnitude. The race isn't AI models anymore. It's not chatbots or benchmarks or who scores higher on some test. The race is physical. Whoever builds the robot army first wins the entire global economy. China has the workers. The factories. The electricity. The refining. The supply chains. America has one card left to play... And it's a 5'11" humanoid robot that Elon calls the infinite money glitch. This is either the move that saves American manufacturing. Or the most disastrous science project in history.

Elon Musk just said on camera that America CANNOT beat China with humans alone. His exact words: "We definitely can't win on the human front." This is the richest man on the planet. Advisor to the president. And he's saying the US is cooked without robots. Here's why he's probably right: China is about to hit 3x the total US electricity output. Elon says electricity is a direct proxy for industrial capacity. Three times the electricity means roughly three times the manufacturing power. They have 4x the population. And Elon said something that'll piss a lot of people off: "The average work ethic in China is higher than in the US." America's birth rate has been below replacement since 1971. More people retiring every year. Fewer entering the workforce. No amount of policy, tariffs, or reshoring fixes that math. His solution: Optimus. He literally called it "the infinite money glitch." Because you can use robots to build more robots. Here's what makes this different from every other robotics play: 3 things are hard about humanoid robots. 1. Real-world AI 2. The hand 3. Scale manufacturing And the hand is harder than EVERYTHING else combined. Tesla had to custom design every single actuator, motor, gear, sensor, and control system from physics first principles. There is no supply chain. Nothing comes from a catalog. Not a single component. But they've solved it. Optimus has full human-hand dexterity with all degrees of freedom. No other company has demonstrated this. Not even in demos. Then you layer on what Elon described as a "recursive multiplicative exponential": Exponential growth in digital intelligence. Multiplied by exponential growth in chip capability. Multiplied by exponential growth in electromechanical dexterity. And then the robots start building robots. He's targeting 1 million Optimus units per year at Gen 3. Ten million at Gen 4. The first use case? Any operation that runs 24/7. Factories, warehouses, refineries, every continuous operation on the planet. Robots don't sleep, don't overheat, don't quit. And here's the part that should terrify every other country: America can't build enough ore refineries because Americans don't want refining jobs. China does 2x more ore refining than the rest of the world COMBINED. They dominate rare earths. The US literally mines rare earth ore, puts it on a train, ships it to CHINA for refining, then ships the finished product back. Optimus wants to fix that. Not by convincing Americans to take refining jobs but by making humans optional in the process entirely. And Elon also said something else that went completely under the radar: "Pure AI, pure robotics corporations will FAR outperform any corporations that have humans in the loop." He compared it to spreadsheets replacing human computers. Entire skyscrapers used to be filled with humans doing calculations. A laptop replaced all of them. Now imagine replacing some cells in your spreadsheet with humans again. It would be WORSE. That's his prediction for the future of corporations. Mixed human-AI companies lose to pure AI-robotics companies. Not by a little. By orders of magnitude. The race isn't AI models anymore. It's not chatbots or benchmarks or who scores higher on some test. The race is physical. Whoever builds the robot army first wins the entire global economy. China has the workers. The factories. The electricity. The refining. The supply chains. America has one card left to play... And it's a 5'11" humanoid robot that Elon calls the infinite money glitch. This is either the move that saves American manufacturing. Or the most disastrous science project in history.

Ricardo

49,857 views • 6 months ago

For 20+ years, we've stayed ahead of every major shift in technology. Today we take the next one: we're introducing the world's first AI-native tech services model. Most of the industry is adding AI to the way it already works. Like bolting a copilot onto old processes and calling it transformation because it's faster than before. That's not what enterprises actually need. works differently. It provides instant access to AI Pods (service units run by AI agents and supervised by Globant experts) that deliver tech services at machine speed, but with the rigor, the governance, and the quality that enterprise-grade work demands. It is a smarter model, running the right AI through parallel agents, loops, workflows, and deterministic processes. With organizations access AI services with transparent, output or consumption-linked pricing, not per hour or per seat, and they own everything they produce. We are bringing the speed AI promises, with the rigor and quality any business needs. Join the waitlist at

For 20+ years, we've stayed ahead of every major shift in technology. Today we take the next one: we're introducing the world's first AI-native tech services model. Most of the industry is adding AI to the way it already works. Like bolting a copilot onto old processes and calling it transformation because it's faster than before. That's not what enterprises actually need. works differently. It provides instant access to AI Pods (service units run by AI agents and supervised by Globant experts) that deliver tech services at machine speed, but with the rigor, the governance, and the quality that enterprise-grade work demands. It is a smarter model, running the right AI through parallel agents, loops, workflows, and deterministic processes. With organizations access AI services with transparent, output or consumption-linked pricing, not per hour or per seat, and they own everything they produce. We are bringing the speed AI promises, with the rigor and quality any business needs. Join the waitlist at

Martin Migoya

391,017 views • 13 days ago

.General Mike Flynn: "They made a conscious decision to weaponize themselves, not only against General Mike Flynn, but also the incoming national security advisor, which is a monumental role in our government…And they made a conscious decision to weaponize components of the government against the President of the United States himself. …The weaponization of the United States government against its own citizenry is very real. It happens on a daily basis. And it doesn't just happen to high-profile political figures. …They put the full weight of their authorities against any American who can't financially afford it, can't emotionally sustain it, and really doesn't have the wherewithal to be able to even understand what is happening. And the next thing that happens is they find themselves in a place where they have to plead guilty to something that they know they didn't do, because otherwise they're going to be completely crushed, and their families are going to be destroyed."

Jan Jekielek

190,052 views • 1 year ago

Been looking into RICE AI ( ◉ - ◉ ) lately, and it’s one of the few projects actually tackling a real challenge in robotics and AGI: the lack of high-quality training data. Robots can’t get truly intelligent without it. They building a decentralized way to collect and share robotics data, using a DePIN network to train physical AI foundation models. The project was even hand-picked by CZ as a winner of the Dubai BNB Chain Demo Day, and it’s backed by Floki while also being part of the NVIDIA Inception Program. They also launched FLOKI Minibot M1 - a small AI companion that doubles as a data node. Every interaction with it feeds the RICE AI Foundry, the first decentralized platform for Robotics Foundation Models, and contributors earn $RICE tokens in return. And it’s working: – 800 minibots sold in 24h – 33,000% oversubscribed Binance IDO – 500+ robots already deployed worldwide Feels like a project with real long-term potential. Worth keeping an eye on. RICE AI ( ◉ - ◉ )

Been looking into RICE AI ( ◉ - ◉ ) lately, and it’s one of the few projects actually tackling a real challenge in robotics and AGI: the lack of high-quality training data. Robots can’t get truly intelligent without it. They building a decentralized way to collect and share robotics data, using a DePIN network to train physical AI foundation models. The project was even hand-picked by CZ as a winner of the Dubai BNB Chain Demo Day, and it’s backed by Floki while also being part of the NVIDIA Inception Program. They also launched FLOKI Minibot M1 - a small AI companion that doubles as a data node. Every interaction with it feeds the RICE AI Foundry, the first decentralized platform for Robotics Foundation Models, and contributors earn $RICE tokens in return. And it’s working: – 800 minibots sold in 24h – 33,000% oversubscribed Binance IDO – 500+ robots already deployed worldwide Feels like a project with real long-term potential. Worth keeping an eye on. RICE AI ( ◉ - ◉ )

Christiaan

12,696 views • 11 months ago

Breaking news: Cosmos 3 is here. They are attempting to do something completely new 🤯 Why is Physical AI much harder than building a chatbot? Understanding the world is not enough, robots need to predict it and act inside it. That's the idea behind NVIDIA Cosmos 3: → Reasoning model understands what's happening from video, images, text, and actions. → World model generates future states of the environment. → Action model generates the actions needed to achieve a goal. Previous systems often stitched these capabilities together using separate models. Cosmos 3 combines them into a single architecture with two components: ▪️ Reasoner Tower Analyzes observations and builds an understanding of objects, motion, interactions, and physical context. ▪️ Generator Tower Uses that understanding to generate future videos and action sequences that obey physical constraints. So Cosmos 3 moves from: Perception → Model A Prediction → Model B Actions → Model C to: Perception + Prediction + Actions → One unified system. The goal is to make robots, autonomous vehicles, and smart environments better at answering three questions: 1. What is happening? 2. What will happen next? 3. What should I do? That's a big shift from today's AI, which mostly focuses on generating text. And check the benchmarks! Physical AI needs to generate decisions that survive contact with the real world. 🚗🤖

Breaking news: Cosmos 3 is here. They are attempting to do something completely new 🤯 Why is Physical AI much harder than building a chatbot? Understanding the world is not enough, robots need to predict it and act inside it. That's the idea behind NVIDIA Cosmos 3: → Reasoning model understands what's happening from video, images, text, and actions. → World model generates future states of the environment. → Action model generates the actions needed to achieve a goal. Previous systems often stitched these capabilities together using separate models. Cosmos 3 combines them into a single architecture with two components: ▪️ Reasoner Tower Analyzes observations and builds an understanding of objects, motion, interactions, and physical context. ▪️ Generator Tower Uses that understanding to generate future videos and action sequences that obey physical constraints. So Cosmos 3 moves from: Perception → Model A Prediction → Model B Actions → Model C to: Perception + Prediction + Actions → One unified system. The goal is to make robots, autonomous vehicles, and smart environments better at answering three questions: 1. What is happening? 2. What will happen next? 3. What should I do? That's a big shift from today's AI, which mostly focuses on generating text. And check the benchmarks! Physical AI needs to generate decisions that survive contact with the real world. 🚗🤖

Turing Post

13,836 views • 2 months ago

Elon Musk says that within the next ten years, money will completely disappear and you will own no money because there will be no need for it, as robots and AI will provide everything you need. Musk compares AI and robots to "a personal God" that will provide humans with everything they need, including food, housing, transportation, and entertainment. "If robots and AI are providing more goods and services than any human could possibly consume, what do you need money for in that case?"

Elon Musk says that within the next ten years, money will completely disappear and you will own no money because there will be no need for it, as robots and AI will provide everything you need. Musk compares AI and robots to "a personal God" that will provide humans with everything they need, including food, housing, transportation, and entertainment. "If robots and AI are providing more goods and services than any human could possibly consume, what do you need money for in that case?"

Shadow of Ezra

995,235 views • 25 days ago

Robotics has a massive, silent bottleneck. It isn’t just data collection—it’s the brutal 1x speed of the physical world. Genesis AI Genesis AI just unveiled Genesis World 1.0, and they are attempting to turn the notorious Sim2Real gap into a pure compute problem. Evaluating a robotics foundation model across edge cases usually means hundreds of hours of physical lab testing. With Genesis World 1.0, what traditionally takes nearly a week of continuous, real-world operation is being compressed into 30 minutes in simulation. What makes this different from just dropping a robot model into an off-the-shelf game engine? 1️⃣ Nyx Renderer: A custom, real-time path-traced engine rendering noise-free 1080p frames in under 4ms. Game engines use rasterization tricks that confuse AI; Nyx uses physically accurate multi-bounce lighting so the model's "eyes" see exactly what real sensors see. 2️⃣ Quadrants Compiler: A custom Python-to-GPU compiler to run heavily parallelized multi-physics simulations (rigid bodies, fluids, deformables) natively across architectures. 3️⃣ Evaluation First: They aren't rushing to train on synthetic data. They are using this purely for closed-loop evaluation to perfect the physics first, currently claiming an impressive 89% correlation with real-world hardware tests. If the industry can accurately evaluate models in simulation without the physical world bottleneck, humanoid development stops moving at wall-clock time and starts scaling with compute.

Robotics has a massive, silent bottleneck. It isn’t just data collection—it’s the brutal 1x speed of the physical world. Genesis AI Genesis AI just unveiled Genesis World 1.0, and they are attempting to turn the notorious Sim2Real gap into a pure compute problem. Evaluating a robotics foundation model across edge cases usually means hundreds of hours of physical lab testing. With Genesis World 1.0, what traditionally takes nearly a week of continuous, real-world operation is being compressed into 30 minutes in simulation. What makes this different from just dropping a robot model into an off-the-shelf game engine? 1️⃣ Nyx Renderer: A custom, real-time path-traced engine rendering noise-free 1080p frames in under 4ms. Game engines use rasterization tricks that confuse AI; Nyx uses physically accurate multi-bounce lighting so the model's "eyes" see exactly what real sensors see. 2️⃣ Quadrants Compiler: A custom Python-to-GPU compiler to run heavily parallelized multi-physics simulations (rigid bodies, fluids, deformables) natively across architectures. 3️⃣ Evaluation First: They aren't rushing to train on synthetic data. They are using this purely for closed-loop evaluation to perfect the physics first, currently claiming an impressive 89% correlation with real-world hardware tests. If the industry can accurately evaluate models in simulation without the physical world bottleneck, humanoid development stops moving at wall-clock time and starts scaling with compute.

Humanoids daily

17,240 views • 2 months ago

Simulation-first approach to robotic production lines! 👾 At Hannover Messe, Amazon Web Services (AWS) and SoftServe are demonstrating a fully autonomous, AI-powered manufacturing workflow. Design a product using generative AI, then watch it move through the entire process: engraving, inspection, and delivery, handled by a mix of robots working together powered by physical AI on AWS. The interesting part is how the system is built. Getting robots into production is usually where things fall apart. SoftServe takes a simulation-first approach using AWS. They build and test the entire system virtually: robot behavior, production flow, before deploying anything physically. This enables faster iteration, safe edge case testing, and significantly reduced sim-to-real gap. Their demo includes AMRs handling transport, cobots doing precise manipulation, AI-driven quality inspection, and humanoid interaction at the end. All connected through a unified, software-defined layer powered by AWS. It's about building a system where robots can adapt, coordinate, and scale without rebuilding everything from scratch every time. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Simulation-first approach to robotic production lines! 👾 At Hannover Messe, Amazon Web Services (AWS) and SoftServe are demonstrating a fully autonomous, AI-powered manufacturing workflow. Design a product using generative AI, then watch it move through the entire process: engraving, inspection, and delivery, handled by a mix of robots working together powered by physical AI on AWS. The interesting part is how the system is built. Getting robots into production is usually where things fall apart. SoftServe takes a simulation-first approach using AWS. They build and test the entire system virtually: robot behavior, production flow, before deploying anything physically. This enables faster iteration, safe edge case testing, and significantly reduced sim-to-real gap. Their demo includes AMRs handling transport, cobots doing precise manipulation, AI-driven quality inspection, and humanoid interaction at the end. All connected through a unified, software-defined layer powered by AWS. It's about building a system where robots can adapt, coordinate, and scale without rebuilding everything from scratch every time. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

17,709 views • 4 months ago

🚨 Two really important Figure robotics updates. - 1st, they will be announcing two new commercial customers in the next 60 days. This means Figure will probably be the first humanoid robotics company in the United States after Digit (Agility Robotics) to hit true commercial deployment. It puts them in an extremely elite group alongside the very few other humanoid companies actually gaining real-world commercial revenue right now, like Apptronik. That's really exciting. Second, Brett Adcock confirmed they are now making a robot every 90 minutes. Let's do the math on that production line: • At one robot every 90 minutes, they are building 16 robots per day. • That translates to roughly 480 robots per month. • Which equals over 5,840 robots per year! That means Figure by the end of 2026 should have nearly 6,000 humanoids produced and ready to enter the workforce. They might be Tesla this year we will have to see 👀

🚨 Two really important Figure robotics updates. - 1st, they will be announcing two new commercial customers in the next 60 days. This means Figure will probably be the first humanoid robotics company in the United States after Digit (Agility Robotics) to hit true commercial deployment. It puts them in an extremely elite group alongside the very few other humanoid companies actually gaining real-world commercial revenue right now, like Apptronik. That's really exciting. Second, Brett Adcock confirmed they are now making a robot every 90 minutes. Let's do the math on that production line: • At one robot every 90 minutes, they are building 16 robots per day. • That translates to roughly 480 robots per month. • Which equals over 5,840 robots per year! That means Figure by the end of 2026 should have nearly 6,000 humanoids produced and ready to enter the workforce. They might be Tesla this year we will have to see 👀

Chris

18,392 views • 4 months ago

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

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

Lukas Ziegler

57,061 views • 2 months ago