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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

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Robotics doesn't have a model problem. It has a data problem. And underneath that, a deployment problem. Physical AI progresses through real-world interaction. Robots act, fail, recover, and adapt. Without shared standards, every team relearns the same lessons in isolation. Deployment standards determine whether learning compounds or resets. PrismaX is the service layer for physical AI. We run the systems that define how robots get deployed, standardize how interaction data is generated, and integrate human judgment where models fall short, turning fragmented robotics capability into deployable infrastructure. Our Mission: Enable people and robots to work together by setting the standards that allow physical AI systems to learn and improve through real-world operation. Our Vision: A world where intelligent robots are deployed responsibly and at scale, supported by systems that embed human judgment into how intelligence advances. The next chapter for physical AI is about turning real-world operation into scalable intelligence. More soon.

Robotics doesn't have a model problem. It has a data problem. And underneath that, a deployment problem. Physical AI progresses through real-world interaction. Robots act, fail, recover, and adapt. Without shared standards, every team relearns the same lessons in isolation. Deployment standards determine whether learning compounds or resets. PrismaX is the service layer for physical AI. We run the systems that define how robots get deployed, standardize how interaction data is generated, and integrate human judgment where models fall short, turning fragmented robotics capability into deployable infrastructure. Our Mission: Enable people and robots to work together by setting the standards that allow physical AI systems to learn and improve through real-world operation. Our Vision: A world where intelligent robots are deployed responsibly and at scale, supported by systems that embed human judgment into how intelligence advances. The next chapter for physical AI is about turning real-world operation into scalable intelligence. More soon.

PrismaX

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/web Creates a web. md file for any website. Every page, action, flow, and interactive element your coding agent needs to navigate it. AI agents fail on websites because they don't know the structure. Web. md gives them the map before they start ↓

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Hyperbrowser

71,707 görüntüleme • 4 ay önce

I don't know if you really understand what these people just did... They built an AI that looks at a few casual real-world videos or photos and instantly turns them into a full 3D playground. You can MOVE THE CAMERA HOWEVER YOU WANT, change the lighting, move objects around, and even let physics play out like in the real world. View events from impossible angles because Atlas knows what was there. This is massive! Let's take robotics. Until now training robots meant collecting tons of expensive real-world data. Now a handful of recordings can become thousands of different simulated situations. Robots can practice the same task in new rooms, with different objects and lighting without anyone having to film it all again. For the industry it means we can train robots way faster and cheaper. Home robots, warehouse robots, whatever... they can learn new environments at scale instead of staying stuck in the lab. This is one of the first real bridges between “cool AI video models” and actual physical machines that have to work in the real world. Blog: The team behind World Labs: Fei-Fei Li, Justin Johnson, Ben Mildenhall and Yunzhu Li. The video is from David Pantera. Follow for more insights into robotics and AI. —— Weekly robotics and AI insights. Subscribe free:

I don't know if you really understand what these people just did... They built an AI that looks at a few casual real-world videos or photos and instantly turns them into a full 3D playground. You can MOVE THE CAMERA HOWEVER YOU WANT, change the lighting, move objects around, and even let physics play out like in the real world. View events from impossible angles because Atlas knows what was there. This is massive! Let's take robotics. Until now training robots meant collecting tons of expensive real-world data. Now a handful of recordings can become thousands of different simulated situations. Robots can practice the same task in new rooms, with different objects and lighting without anyone having to film it all again. For the industry it means we can train robots way faster and cheaper. Home robots, warehouse robots, whatever... they can learn new environments at scale instead of staying stuck in the lab. This is one of the first real bridges between “cool AI video models” and actual physical machines that have to work in the real world. Blog: The team behind World Labs: Fei-Fei Li, Justin Johnson, Ben Mildenhall and Yunzhu Li. The video is from David Pantera. Follow for more insights into robotics and AI. —— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

273,520 görüntüleme • 1 ay önce

AI video companies are no longer claiming they can simply generate better clips. They are claiming their models understand the world. So Peter and I put that claim to the test. We sat down with Yaron Inger, Co-Founder and CTO of LTX.io , for a direct conversation about LTX-2.5, its new open-weights video and world model. We asked what the model actually understands beyond producing plausible frames. Whether its physics are good enough for robotics. What native multi-shot generation really remembers across cuts. Whether its speed benchmarks are fair. What creators can expect on hardware they can realistically afford. And how LTX plans to build a business when the weights are available to download. This is not a product demo. It is a conversation about whether AI video is becoming genuine infrastructure for filmmaking, simulation, and physical AI. Watch the full interview:

AI video companies are no longer claiming they can simply generate better clips. They are claiming their models understand the world. So Peter and I put that claim to the test. We sat down with Yaron Inger, Co-Founder and CTO of LTX.io , for a direct conversation about LTX-2.5, its new open-weights video and world model. We asked what the model actually understands beyond producing plausible frames. Whether its physics are good enough for robotics. What native multi-shot generation really remembers across cuts. Whether its speed benchmarks are fair. What creators can expect on hardware they can realistically afford. And how LTX plans to build a business when the weights are available to download. This is not a product demo. It is a conversation about whether AI video is becoming genuine infrastructure for filmmaking, simulation, and physical AI. Watch the full interview:

Chubby♨️

22,892 görüntüleme • 1 ay önce

🚨 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

332,089 görüntüleme • 4 ay önce

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 görüntüleme • 2 ay önce

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

38,329 görüntüleme • 6 ay önce

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 görüntüleme • 4 ay önce

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.

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.

Standard Bots

69,452 görüntüleme • 3 ay önce

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

50,130 görüntüleme • 9 ay önce

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 görüntüleme • 3 ay önce

1/7 Physical AI has a data problem. More compute alone won’t teach a robot how to move, grasp, react, or recover from mistakes. LLMs had the entire internet to learn from. Robots need something different: huge amounts of real action data. That’s where Axis Robotics gets interesting. $AXIS is building a data engine for Physical AI, turning robot training into something people can contribute to directly through a browser. The loop is straightforward: Generate → Collect → Process → Deploy → Correct → Repeat. What looks simple on the surface could become something much bigger underneath: a scalable way to produce the training data robots need to become genuinely useful in the physical world. The real unlock for Physical AI might not be another bigger model. It might be better data.

1/7 Physical AI has a data problem. More compute alone won’t teach a robot how to move, grasp, react, or recover from mistakes. LLMs had the entire internet to learn from. Robots need something different: huge amounts of real action data. That’s where Axis Robotics gets interesting. $AXIS is building a data engine for Physical AI, turning robot training into something people can contribute to directly through a browser. The loop is straightforward: Generate → Collect → Process → Deploy → Correct → Repeat. What looks simple on the surface could become something much bigger underneath: a scalable way to produce the training data robots need to become genuinely useful in the physical world. The real unlock for Physical AI might not be another bigger model. It might be better data.

_Victorad

19,657 görüntüleme • 1 ay önce

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

395,548 görüntüleme • 2 ay önce

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 görüntüleme • 8 ay önce

.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

190,244 görüntüleme • 1 yıl önce

You need to know 4 things to run local AI on your own machine: 1) The model is the brain file. Gemma, Llama, Mistral, Qwen, Phi. 2) The warehouse is where you find the model. Hugging Face. 3) The software is what runs the model. LM Studio for the first try, Ollama when you build apps. 4) The workflow is the product you make from the other three. The size of the model decides the hardware: - 2B to 4B for phones and small tasks - 12B for the middle ground - 26B to 31B for a workstation Quantization compresses the model until it fits on a normal machine: - Q4 is easier to run - Q8 keeps more quality, but it needs more memory Q4 costs you a little quality but it saves you a $5,000 workstation. Start on the spare laptop from 2021. Open Hugging Face and read one model card slowly.

You need to know 4 things to run local AI on your own machine: 1) The model is the brain file. Gemma, Llama, Mistral, Qwen, Phi. 2) The warehouse is where you find the model. Hugging Face. 3) The software is what runs the model. LM Studio for the first try, Ollama when you build apps. 4) The workflow is the product you make from the other three. The size of the model decides the hardware: - 2B to 4B for phones and small tasks - 12B for the middle ground - 26B to 31B for a workstation Quantization compresses the model until it fits on a normal machine: - Q4 is easier to run - Q8 keeps more quality, but it needs more memory Q4 costs you a little quality but it saves you a $5,000 workstation. Start on the spare laptop from 2021. Open Hugging Face and read one model card slowly.

The Startup Ideas Podcast (SIP) 🧃

10,547 görüntüleme • 1 ay önce

⚠️ THE ROBOT DIDN’T ENTER A ROBOT TESTING LAB. IT ENTERED A CITY. A humanoid robot is moving through a real urban environment with pedestrians, traffic and constantly changing space around it. There’s no empty runway and no perfectly controlled path - the machine has to keep moving while the world around it does whatever it wants. That makes a few seconds of walking far more interesting than another polished stage demo. A city is one of the hardest places to test a humanoid because nothing stays predictable for long. People stop, cars move, distances change and obstacles appear without warning, while the robot still has to maintain balance and choose where to step next. The challenge isn't simply walking anymore - it's understanding how to move through a world that wasn't designed for robots. And that's the real milestone here. Humanoids don't need to look perfectly human to change the world; they need to become capable of functioning in spaces already built for us. The closer they get to doing that reliably, the less important the laboratory demos become. The future won't feel futuristic because robots look human. It will feel futuristic when robots can move through our world like they belong there.

⚠️ THE ROBOT DIDN’T ENTER A ROBOT TESTING LAB. IT ENTERED A CITY. A humanoid robot is moving through a real urban environment with pedestrians, traffic and constantly changing space around it. There’s no empty runway and no perfectly controlled path - the machine has to keep moving while the world around it does whatever it wants. That makes a few seconds of walking far more interesting than another polished stage demo. A city is one of the hardest places to test a humanoid because nothing stays predictable for long. People stop, cars move, distances change and obstacles appear without warning, while the robot still has to maintain balance and choose where to step next. The challenge isn't simply walking anymore - it's understanding how to move through a world that wasn't designed for robots. And that's the real milestone here. Humanoids don't need to look perfectly human to change the world; they need to become capable of functioning in spaces already built for us. The closer they get to doing that reliably, the less important the laboratory demos become. The future won't feel futuristic because robots look human. It will feel futuristic when robots can move through our world like they belong there.

Future Memo

10,138 görüntüleme • 1 ay önce

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 görüntüleme • 1 yıl önce