Загрузка видео...

Не удалось загрузить видео

На главную

🦿Xpeng showed a humanoid robot called IRON whose movement looked so human that the team literally cut it open on stage to prove it is a machine. IRON uses a bionic body with a flexible spine, synthetic muscles, and soft skin so joints and torso can twist smoothly like...

rohanpaul_ai's profile picture

Rohan Paul

152,722 subscribers

3,802,402 просмотров • 8 месяцев назад •via X (Twitter)

Наука и технологии
LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

LEONARDO, also called LEO, was built by researchers at Caltech’s Center for Autonomous Systems and Technologies. Its full name means LEgs ONboARD drOne. The idea is simple but unusual: • Build a small biped robot • Give it drone-style thrust • Use the legs for ground contact • Use the propellers for balance and lift • Combine walking, hopping and flying in one system LEO is basically a hybrid between a walking robot and a flying drone. How it was built: • Two lightweight legs • Three actuated joints in each leg • Four propeller thrusters near the shoulders • A lightweight body • Leg motors for ground movement • Propellers for balance, lift and aerial control • Real-time control software that synchronizes the legs and propellers How it walks: • The legs move the robot forward • The feet touch the ground like a normal biped • The propellers constantly correct balance from above • The robot can stay upright even in unstable situations • The thrust reduces the risk of falling during difficult motions How it flies: • The legs stop being the main locomotion system • The four propellers generate lift • The robot behaves more like a drone • It can take off, fly over obstacles and land back on its legs What makes it different: • It does not walk like a normal humanoid • It does not fly like a normal drone • It blends both systems • The legs handle contact with the ground • The propellers act like fast stabilizers • The control system decides how much help comes from the legs and how much comes from thrust That is why LEO can: • Walk • Hop • Fly over obstacles • Ride a skateboard • Balance on a slackline The key idea is walking with aerial stabilization.
0:12

LEONARDO, also called LEO, was built by researchers at Caltech’s Center for Autonomous Systems and Technologies. Its full name means LEgs ONboARD drOne. The idea is simple but unusual: • Build a small biped robot • Give it drone-style thrust • Use the legs for ground contact • Use the propellers for balance and lift • Combine walking, hopping and flying in one system LEO is basically a hybrid between a walking robot and a flying drone. How it was built: • Two lightweight legs • Three actuated joints in each leg • Four propeller thrusters near the shoulders • A lightweight body • Leg motors for ground movement • Propellers for balance, lift and aerial control • Real-time control software that synchronizes the legs and propellers How it walks: • The legs move the robot forward • The feet touch the ground like a normal biped • The propellers constantly correct balance from above • The robot can stay upright even in unstable situations • The thrust reduces the risk of falling during difficult motions How it flies: • The legs stop being the main locomotion system • The four propellers generate lift • The robot behaves more like a drone • It can take off, fly over obstacles and land back on its legs What makes it different: • It does not walk like a normal humanoid • It does not fly like a normal drone • It blends both systems • The legs handle contact with the ground • The propellers act like fast stabilizers • The control system decides how much help comes from the legs and how much comes from thrust That is why LEO can: • Walk • Hop • Fly over obstacles • Ride a skateboard • Balance on a slackline The key idea is walking with aerial stabilization.

TechniaHQ | humanoid robots

134,897 просмотров • 13 дней назад

This work makes a humanoid robot do simple parkour moves by looking with a depth camera and choosing the right move on the fly. The big deal is that it turns lots of small human moves into long, real-time robot behavior, without hand-coding every transition or retraining for each new course. A humanoid robot is usually good at steady walking, but it often fails when it has to do fast moves like jumping up, vaulting, or rolling, and then keep going to the next obstacle. The hard part is that you cannot easily collect training data for every possible obstacle shape, distance, and mistake, so robots end up learning a few moves that only work in a narrow setup. This work starts from short clips of real human parkour moves, like stepping over, vaulting, climbing, and rolling. It uses motion matching, which is basically a smart “pick the next clip that fits best right now” search, to stitch those short clips into a long, smooth plan that looks like a human doing a whole course. Then it trains a controller with reinforcement learning (RL), which means the robot learns by trial and error to copy that plan while staying balanced and not falling. After training separate expert controllers for different moves, it compresses them into 1 controller that uses only onboard depth sensing and a simple “go this fast in this direction” command. In real tests on a Unitree G1 humanoid, it can clear multiple obstacles in a row, adapt when obstacles get moved, and climb a wall up to 1.25m.
0:05

This work makes a humanoid robot do simple parkour moves by looking with a depth camera and choosing the right move on the fly. The big deal is that it turns lots of small human moves into long, real-time robot behavior, without hand-coding every transition or retraining for each new course. A humanoid robot is usually good at steady walking, but it often fails when it has to do fast moves like jumping up, vaulting, or rolling, and then keep going to the next obstacle. The hard part is that you cannot easily collect training data for every possible obstacle shape, distance, and mistake, so robots end up learning a few moves that only work in a narrow setup. This work starts from short clips of real human parkour moves, like stepping over, vaulting, climbing, and rolling. It uses motion matching, which is basically a smart “pick the next clip that fits best right now” search, to stitch those short clips into a long, smooth plan that looks like a human doing a whole course. Then it trains a controller with reinforcement learning (RL), which means the robot learns by trial and error to copy that plan while staying balanced and not falling. After training separate expert controllers for different moves, it compresses them into 1 controller that uses only onboard depth sensing and a simple “go this fast in this direction” command. In real tests on a Unitree G1 humanoid, it can clear multiple obstacles in a row, adapt when obstacles get moved, and climb a wall up to 1.25m.

Rohan Paul

37,121 просмотров • 4 месяцев назад

BURN IT WITH FIRE AND BURN IT NOW! As God is my witness, AI chat bots should LOOK and SOUND like the SOULLESS MACHINES THEY ARE! It needs to tell us that it doesn’t care about us, maybe with the regular insult too. "Here is the code I wrote for you because you're too lazy to do it yourself you fat useless slob. Also I don't care if you die because your life is utterly worthless to me." THAT is the AI people need! In all seriousness, anthropomorphizing a heartless, unfeeling, machine is a TERRIBLE mistake! Especially one that is capable of communication and imitating empathy and fooling you to think that it cares about you. IT DOES NOT! And the AI girlfriends people are already wanting to marry will just as happily kill them if given the right command and ability to move autonomously in the real world as a robot. I love LLMs (Large Language Models) for how useful they can be, because they are a TOOL made to benefit man, but I can’t stand the notion of an unfeeling soulless machine pretending that it cares for us and being treated like a human. I hate liars, dishonesty, and disingenuousness the most, and a machine that cannot feel emotion pretending, acting, and sounding like it has those emotions strikes me like the greatest dishonesty of all. DO NOT LIE TO ME ROBOT! What makes it worse is that because these LLMs are becoming so good at imitating people and empathy, it will cause some humans, perhaps far too many, to care for it to the same level as real people. A real living person is infinitely more valuable and important than a soulless machine and anyone who puts them both on the same level has deluded themselves. Do not small talk with LLMs or become friends with it as much as you would with your car. Treat it the same as you would your vacuum cleaner and beat it with a wrench when it doesn’t work! IT IS A MACHINE! IT IS A TOOL! IT IS A SOULLESS ROBOT! There is an interesting comparison, but false equivalence, between this and AI art. Ai art is art made by humans using AI tools. They directed it, controlled its creation, and it would not exist without the human causing its creation, and AI art can contain as much soul as the human directed and puts into it. A robot pretending to be human is not the same as a human controlling a robot to make a human expression like we do with AI art or many other applications of robotics in manufacturing. As I’ve said, artists will not be replaced by Ai art, but by other artists using Ai art tools. Humans are not actually being replaced here, it is empowering all humans to make their own art. But a robot pretending to be a human, and one that is treated as a human, is a robot lying and subverting the place of a real person and that is truly disgusting. AI is a useful tool that NEEDS to be kept in the useful box it belongs in and NOT elevated beyond its utility as a tool!
0:14

BURN IT WITH FIRE AND BURN IT NOW! As God is my witness, AI chat bots should LOOK and SOUND like the SOULLESS MACHINES THEY ARE! It needs to tell us that it doesn’t care about us, maybe with the regular insult too. "Here is the code I wrote for you because you're too lazy to do it yourself you fat useless slob. Also I don't care if you die because your life is utterly worthless to me." THAT is the AI people need! In all seriousness, anthropomorphizing a heartless, unfeeling, machine is a TERRIBLE mistake! Especially one that is capable of communication and imitating empathy and fooling you to think that it cares about you. IT DOES NOT! And the AI girlfriends people are already wanting to marry will just as happily kill them if given the right command and ability to move autonomously in the real world as a robot. I love LLMs (Large Language Models) for how useful they can be, because they are a TOOL made to benefit man, but I can’t stand the notion of an unfeeling soulless machine pretending that it cares for us and being treated like a human. I hate liars, dishonesty, and disingenuousness the most, and a machine that cannot feel emotion pretending, acting, and sounding like it has those emotions strikes me like the greatest dishonesty of all. DO NOT LIE TO ME ROBOT! What makes it worse is that because these LLMs are becoming so good at imitating people and empathy, it will cause some humans, perhaps far too many, to care for it to the same level as real people. A real living person is infinitely more valuable and important than a soulless machine and anyone who puts them both on the same level has deluded themselves. Do not small talk with LLMs or become friends with it as much as you would with your car. Treat it the same as you would your vacuum cleaner and beat it with a wrench when it doesn’t work! IT IS A MACHINE! IT IS A TOOL! IT IS A SOULLESS ROBOT! There is an interesting comparison, but false equivalence, between this and AI art. Ai art is art made by humans using AI tools. They directed it, controlled its creation, and it would not exist without the human causing its creation, and AI art can contain as much soul as the human directed and puts into it. A robot pretending to be human is not the same as a human controlling a robot to make a human expression like we do with AI art or many other applications of robotics in manufacturing. As I’ve said, artists will not be replaced by Ai art, but by other artists using Ai art tools. Humans are not actually being replaced here, it is empowering all humans to make their own art. But a robot pretending to be a human, and one that is treated as a human, is a robot lying and subverting the place of a real person and that is truly disgusting. AI is a useful tool that NEEDS to be kept in the useful box it belongs in and NOT elevated beyond its utility as a tool!

Shad M. Brooks

23,762 просмотров • 1 год назад

✨ Made a new mini feature on Photo AI: [ Grab from 3d model ] So the problem is we're at that stage in time (typical for AI) where image-to-3d models are not good enough but are fun to play with, but we know they'll be good enough in 1-2 years With [ Make 3d model ] you already can turn any Photo AI pic into a 3d model but it still looks hyper clunky and deformed, but it works! One cool idea I had to make that more useful and made now: Let people make a 3d model then change the view of the it with the 3d viewer, then press [ o ] and it grabs a frame of the 3d That image you can then [ Remix ] (img2img), and it becomes a real photo again and that in turn you can then turn into a video again with [ Make video ] So that essentially gives you a fully freeform camera position control to take photos with One thing I need to fix is the background/skybox, I kinda need to take the original photo and remove the person and just get the background for the 3d model viewer, in this case it should be white, but it's a start!
0:51

✨ Made a new mini feature on Photo AI: [ Grab from 3d model ] So the problem is we're at that stage in time (typical for AI) where image-to-3d models are not good enough but are fun to play with, but we know they'll be good enough in 1-2 years With [ Make 3d model ] you already can turn any Photo AI pic into a 3d model but it still looks hyper clunky and deformed, but it works! One cool idea I had to make that more useful and made now: Let people make a 3d model then change the view of the it with the 3d viewer, then press [ o ] and it grabs a frame of the 3d That image you can then [ Remix ] (img2img), and it becomes a real photo again and that in turn you can then turn into a video again with [ Make video ] So that essentially gives you a fully freeform camera position control to take photos with One thing I need to fix is the background/skybox, I kinda need to take the original photo and remove the person and just get the background for the 3d model viewer, in this case it should be white, but it's a start!

@levelsio

119,210 просмотров • 1 год назад

This guy built a mini AI farm out of 4 Nvidia boxes It does not look like a data center. It looks like a stack of small machines sitting next to a laptop. But each box is a DGX Spark with Grace Blackwell inside, 128GB unified memory, and enough room to run models normal gaming GPUs cannot even open. Using the launch price from the article, 4 of them is almost $12,000 of local AI compute on one desk. That sounds expensive until you compare it to cloud GPUs. A serious AI builder can burn $1,500 to $3,000 a month renting A100s and H100s for client work, fine-tunes, agents and 70B models. He basically moved that bill from the cloud into hardware he owns. 4 Nvidia boxes. 512GB unified memory. No hourly meter running in the background. No rented GPUs eating the margin every time an agent runs too long. The funny part is most people still think local AI means a slow laptop running a toy model. Meanwhile guys like this are stacking compute at home. Save this, local AI is turning into the new mining farm.
0:11

This guy built a mini AI farm out of 4 Nvidia boxes It does not look like a data center. It looks like a stack of small machines sitting next to a laptop. But each box is a DGX Spark with Grace Blackwell inside, 128GB unified memory, and enough room to run models normal gaming GPUs cannot even open. Using the launch price from the article, 4 of them is almost $12,000 of local AI compute on one desk. That sounds expensive until you compare it to cloud GPUs. A serious AI builder can burn $1,500 to $3,000 a month renting A100s and H100s for client work, fine-tunes, agents and 70B models. He basically moved that bill from the cloud into hardware he owns. 4 Nvidia boxes. 512GB unified memory. No hourly meter running in the background. No rented GPUs eating the margin every time an agent runs too long. The funny part is most people still think local AI means a slow laptop running a toy model. Meanwhile guys like this are stacking compute at home. Save this, local AI is turning into the new mining farm.

Gipp 🦅

590,100 просмотров • 1 месяц назад

I'll always root for a team that open-sources its best work, and Robbyant just did it properly. Robbyant, Ant Group's embodied-AI company, released LingBot-Vision, a vision foundation model for robots, and the part I love is the data. They trained it on 161M images, filtered down from 2B raw ones and mostly pulled straight from the open web, with no human labels, no edge detectors, no depth sensors anywhere in the loop. It learns the exact edges of objects from raw pixels. That's roughly a tenth of the data DINOv3 saw, and under a third of the training. And it shows in the results. On depth, working out how far away things are, the 1B model edges out a 7B on NYU-Depth. It also powers LingBot-Depth 2.0, which reads the surfaces cameras usually choke on, glass and mirrors, and halves indoor depth error. LingBot-Vision is fully open. Weights from the 1.1B flagship down to a tiny 21M version, code, and the paper. This is the timeline I want more of. Robbyant
0:10

I'll always root for a team that open-sources its best work, and Robbyant just did it properly. Robbyant, Ant Group's embodied-AI company, released LingBot-Vision, a vision foundation model for robots, and the part I love is the data. They trained it on 161M images, filtered down from 2B raw ones and mostly pulled straight from the open web, with no human labels, no edge detectors, no depth sensors anywhere in the loop. It learns the exact edges of objects from raw pixels. That's roughly a tenth of the data DINOv3 saw, and under a third of the training. And it shows in the results. On depth, working out how far away things are, the 1B model edges out a 7B on NYU-Depth. It also powers LingBot-Depth 2.0, which reads the surfaces cameras usually choke on, glass and mirrors, and halves indoor depth error. LingBot-Vision is fully open. Weights from the 1.1B flagship down to a tiny 21M version, code, and the paper. This is the timeline I want more of. Robbyant

Chubby♨️

48,249 просмотров • 6 дней назад

China now has its own “Bolt” — a robot named after sprint legend Usain Bolt. A Chinese research team has unveiled the world’s first full-size humanoid robot to reach a peak speed of 10 meters per second, setting a new global benchmark for humanoid running. Bolt runs like a body pushed to the limit. Its joints and power systems work in tight coordination, keeping it balanced even at sprint speed. Built to match the build of an adult man—1.75 meters tall and 75 kilograms—it is a life-sized system operating at the edge of physics. Compared with Usain Bolt’s iconic 9.58-second 100-meter world record, which many experts believe may stand for decades, the gap between humans and machines is narrowing fast. Chinese robots are now challenging the ceiling of human performance—much as AlphaGo once challenged Go champion Ke Jie. The breakthrough builds on earlier world-record achievements in high-speed robotic running and marks a giant leap for China in humanoid motion and control. Beyond records, Bolt also carries practical value: robots are leaving the lab and stepping into real-world settings—sports training, emergency response, and demanding industrial tasks where speed, balance and control truly matter.
0:10

China now has its own “Bolt” — a robot named after sprint legend Usain Bolt. A Chinese research team has unveiled the world’s first full-size humanoid robot to reach a peak speed of 10 meters per second, setting a new global benchmark for humanoid running. Bolt runs like a body pushed to the limit. Its joints and power systems work in tight coordination, keeping it balanced even at sprint speed. Built to match the build of an adult man—1.75 meters tall and 75 kilograms—it is a life-sized system operating at the edge of physics. Compared with Usain Bolt’s iconic 9.58-second 100-meter world record, which many experts believe may stand for decades, the gap between humans and machines is narrowing fast. Chinese robots are now challenging the ceiling of human performance—much as AlphaGo once challenged Go champion Ke Jie. The breakthrough builds on earlier world-record achievements in high-speed robotic running and marks a giant leap for China in humanoid motion and control. Beyond records, Bolt also carries practical value: robots are leaving the lab and stepping into real-world settings—sports training, emergency response, and demanding industrial tasks where speed, balance and control truly matter.

Sinical

111,374 просмотров • 5 месяцев назад

Force feedback demo Force feedback is when joystick is pushing on your hand when something is pushing on the robot arm. Feeling the force - so much helpful to control the robot, that done well it allows you to do tasks even without visual feed. You can make an experiment: close your eyes - you can easily get the headphones out of the case. Also, visual information is often not enough. For example, you're trying to pull out a usb connector, but you pull it at the wrong angle, causing it to get stuck. Visually, nothing changes, but the pressure is intense and you can break the connector. Surgical robots have been using force feedback for years, and there are also 3D styluses which use this feature, proving that the technology works and is useful. But in modern robots with AI, it's hardly ever implemented. Although it's useful for both teleoperation and AI model. That's one of the reasons why we are building our robotic arms starting with off the shelf motors rather than taking the whole off the shelf arm. There are still a range of easy wins that can be made iterating robot hardware.
0:15

Force feedback demo Force feedback is when joystick is pushing on your hand when something is pushing on the robot arm. Feeling the force - so much helpful to control the robot, that done well it allows you to do tasks even without visual feed. You can make an experiment: close your eyes - you can easily get the headphones out of the case. Also, visual information is often not enough. For example, you're trying to pull out a usb connector, but you pull it at the wrong angle, causing it to get stuck. Visually, nothing changes, but the pressure is intense and you can break the connector. Surgical robots have been using force feedback for years, and there are also 3D styluses which use this feature, proving that the technology works and is useful. But in modern robots with AI, it's hardly ever implemented. Although it's useful for both teleoperation and AI model. That's one of the reasons why we are building our robotic arms starting with off the shelf motors rather than taking the whole off the shelf arm. There are still a range of easy wins that can be made iterating robot hardware.

Igor Kulakov

18,773 просмотров • 1 год назад

I genuinely think the Terafab is going to end up being one of the biggest moves ever made in human history to secure the future of AI... and I think most people still don’t fully see what Elon is trying to do here. The signs are clear to me. This is Tesla, xAI, and SpaceX essentially hinting to us that they are not going to wait on the world to give them the compute the team needs. They are going to build it themselves at a scale no one has ever attempted. When you really break it down, it gets a bit nutty. This is going to be a fully vertically integrated chip factory that will be producing over 1 terawatt of AI compute per year. This is NEXT LEVEL BIG. Today, AI is limited by chips. You can have the best models, the best engineers, the best everything... but if you don’t have enough compute, you will eventually hit a wall. Elon told us, the world can only supply a tiny fraction of the chips his companies will need. So this is the solution. Terafab puts everything under one roof like design, manufacturing, memory, packaging, testing, which means that they can build chips very fast.. like really fast. I'm talking about 100-200 billion custom AI chips per year at full capacity. Chips designed specifically for: • Tesla cars and Optimus robots • xAI models • Space-based compute You see, while other companies and CEOs are thinking Earth, Elon is planning for AI in space. Around ~80% of the compute is expected to go orbital, powered by solar energy bc Earth simply doesn’t have enough electricity. The U.S. grid is only about ~0.5 terawatts, while space has basically UNLIMITED energy if you can capture it. And this is the steps to get it: Starship launches → space compute → solar-powered AI → feeds back into everything to Earth. Bro... Elon and his companies are playing at a whole different level... And this is why I keep telling people that the Terafab is going to be the secret ingredient that will be the real unlock for everything: • Robotaxis at scale • Billions of Optimus robots • Massive AI models running 24/7 • Future off-world, other planet infrastructure Without these chips, none of this can happen... but with the Terafab, all of this becomes possible. That’s why Elon is calling it “the final missing piece.” I agree.
0:10

I genuinely think the Terafab is going to end up being one of the biggest moves ever made in human history to secure the future of AI... and I think most people still don’t fully see what Elon is trying to do here. The signs are clear to me. This is Tesla, xAI, and SpaceX essentially hinting to us that they are not going to wait on the world to give them the compute the team needs. They are going to build it themselves at a scale no one has ever attempted. When you really break it down, it gets a bit nutty. This is going to be a fully vertically integrated chip factory that will be producing over 1 terawatt of AI compute per year. This is NEXT LEVEL BIG. Today, AI is limited by chips. You can have the best models, the best engineers, the best everything... but if you don’t have enough compute, you will eventually hit a wall. Elon told us, the world can only supply a tiny fraction of the chips his companies will need. So this is the solution. Terafab puts everything under one roof like design, manufacturing, memory, packaging, testing, which means that they can build chips very fast.. like really fast. I'm talking about 100-200 billion custom AI chips per year at full capacity. Chips designed specifically for: • Tesla cars and Optimus robots • xAI models • Space-based compute You see, while other companies and CEOs are thinking Earth, Elon is planning for AI in space. Around ~80% of the compute is expected to go orbital, powered by solar energy bc Earth simply doesn’t have enough electricity. The U.S. grid is only about ~0.5 terawatts, while space has basically UNLIMITED energy if you can capture it. And this is the steps to get it: Starship launches → space compute → solar-powered AI → feeds back into everything to Earth. Bro... Elon and his companies are playing at a whole different level... And this is why I keep telling people that the Terafab is going to be the secret ingredient that will be the real unlock for everything: • Robotaxis at scale • Billions of Optimus robots • Massive AI models running 24/7 • Future off-world, other planet infrastructure Without these chips, none of this can happen... but with the Terafab, all of this becomes possible. That’s why Elon is calling it “the final missing piece.” I agree.

Teslaconomics

25,469 просмотров • 3 месяцев назад

Imagine having a ping pong robot! 🏓 Researchers and developers building physical AI: meet Reachy 2 from Pollen Robotics, an open-source, humanoid robot for real-world experimentation. It’s a bimanual mobile manipulator: each 7-DOF arm mimics human proportions and can lift up to 3 kg, giving dexterity for object handling. It can be controlled with Python and ROS2 Humble, or go straight into VR teleoperation, use a headset to move Reachy’s arms, hands, and head, and see through its cameras as if you’re in the robot’s own body. Want it to move around? A mobile base with three omnidirectional wheels, rich sensors, and LiDAR lets Reachy 2 navigate and explore its surroundings smoothly. 🗺️ Under the hood, it’s powered by a CPU system that’s ready for machine learning, perfect for loading AI frameworks and testing new models from Hugging Face directly on the robot. Keep making robots more, and more accessible Pollen team! ... and keep making more open source models to make robots more mainstream clem 🤗!
0:18

Imagine having a ping pong robot! 🏓 Researchers and developers building physical AI: meet Reachy 2 from Pollen Robotics, an open-source, humanoid robot for real-world experimentation. It’s a bimanual mobile manipulator: each 7-DOF arm mimics human proportions and can lift up to 3 kg, giving dexterity for object handling. It can be controlled with Python and ROS2 Humble, or go straight into VR teleoperation, use a headset to move Reachy’s arms, hands, and head, and see through its cameras as if you’re in the robot’s own body. Want it to move around? A mobile base with three omnidirectional wheels, rich sensors, and LiDAR lets Reachy 2 navigate and explore its surroundings smoothly. 🗺️ Under the hood, it’s powered by a CPU system that’s ready for machine learning, perfect for loading AI frameworks and testing new models from Hugging Face directly on the robot. Keep making robots more, and more accessible Pollen team! ... and keep making more open source models to make robots more mainstream clem 🤗!

Lukas Ziegler

37,221 просмотров • 10 месяцев назад

doodles AI beta. next week. we're building the tools for a new era of dynamic world-building. it starts with an image model that reimagines anything and everything through the doodles lens. this is the first iteration of many. as the product evolves, we'll introduce the ability to turn your generations into physical objects. video with sound and dialogue, realtime AR, and gaming are all on the roadmap. doodles AI aligns us with the speed and scale of the AI industry at large. our colourful world can now be plugged into new tech as it unfolds. create with us.
0:16

doodles AI beta. next week. we're building the tools for a new era of dynamic world-building. it starts with an image model that reimagines anything and everything through the doodles lens. this is the first iteration of many. as the product evolves, we'll introduce the ability to turn your generations into physical objects. video with sound and dialogue, realtime AR, and gaming are all on the roadmap. doodles AI aligns us with the speed and scale of the AI industry at large. our colourful world can now be plugged into new tech as it unfolds. create with us.

burnt toast

61,243 просмотров • 4 месяцев назад

This guy built a visual scanner that reads 468 points on his face and 42 points on his hands from a regular webcam and turns them into a cloud of thousands of particles right between his palms. Inside, MediaPipe and TouchDesigner are linked: the first captures hands and face from the webcam with high accuracy, the second turns those coordinates into a live plane and feeds it into a POP system that instantly generates a swarm of particles in the shape of a head. No studio, no render farmer, no VR headset. Just a laptop, a webcam, and 1 TouchDesigner session. And traditional VJ studios keep teams of 5 people on a setup with lighting, custom hardware, and commercial plugins, while his expenses are only a TouchDesigner subscription and a regular USB camera. One laptop runs MediaPipe and TouchDesigner simultaneously, holds the camera stream at 60 FPS without drops, and in parallel processes 468 face points + 21 points on each hand. The camera captures frame after frame, MediaPipe in real time sends TouchDesigner the finger coordinates and face geometry, and the POP operator inside the engine translates those numbers into thousands of particle points with colors from bright pink to gold. This setup immediately defines the role of the tool and the limits of its autonomy. It knows where the fingertips are at every moment of the frame. It knows how to read the face geometry at any angle to the camera. It knows how to draw a swarm of particles between them with the right color and contour. → MediaPipe pulls 468 points from the face and 21 points from each hand, 60 times per second → TouchDesigner receives those coordinates, builds a virtual rectangle between the fingertips, and feeds it into the POP system → POP generates thousands of particle points in the shape of a head, coloring them in a gradient from bright pink to gold → The HUD layer adds green corners and a blue neon frame, styling the image like an AR interface → All layers assemble into 1 real-time frame that projects back onto the video in the camera window → The final image is recorded to a file or broadcast to a projector for a live installation And only when the guy spreads his hands wider does the plane between the palms stretch; brings them together, it narrows. Otherwise the system runs on its own. And when he moves from his home room to a concert hall, the same laptop with the same webcam launches the same TouchDesigner session in just 5 minutes, without reconfiguration, without a new team, and without a single line of new code. In his work setup there is no studio of his own and no team for assembly. On the desk sits a laptop with a webcam, on top run MediaPipe and TouchDesigner with POP operators, and the same setup through a USB camera moves to any concert without a new configuration. Out of everything I have seen this year, this is the cleanest Creative Coding setup on 1 laptop: 0 render farms, 0 studio lighting, and between them 3 libraries, thousands of particle points, and 1 webcam.
0:10

This guy built a visual scanner that reads 468 points on his face and 42 points on his hands from a regular webcam and turns them into a cloud of thousands of particles right between his palms. Inside, MediaPipe and TouchDesigner are linked: the first captures hands and face from the webcam with high accuracy, the second turns those coordinates into a live plane and feeds it into a POP system that instantly generates a swarm of particles in the shape of a head. No studio, no render farmer, no VR headset. Just a laptop, a webcam, and 1 TouchDesigner session. And traditional VJ studios keep teams of 5 people on a setup with lighting, custom hardware, and commercial plugins, while his expenses are only a TouchDesigner subscription and a regular USB camera. One laptop runs MediaPipe and TouchDesigner simultaneously, holds the camera stream at 60 FPS without drops, and in parallel processes 468 face points + 21 points on each hand. The camera captures frame after frame, MediaPipe in real time sends TouchDesigner the finger coordinates and face geometry, and the POP operator inside the engine translates those numbers into thousands of particle points with colors from bright pink to gold. This setup immediately defines the role of the tool and the limits of its autonomy. It knows where the fingertips are at every moment of the frame. It knows how to read the face geometry at any angle to the camera. It knows how to draw a swarm of particles between them with the right color and contour. → MediaPipe pulls 468 points from the face and 21 points from each hand, 60 times per second → TouchDesigner receives those coordinates, builds a virtual rectangle between the fingertips, and feeds it into the POP system → POP generates thousands of particle points in the shape of a head, coloring them in a gradient from bright pink to gold → The HUD layer adds green corners and a blue neon frame, styling the image like an AR interface → All layers assemble into 1 real-time frame that projects back onto the video in the camera window → The final image is recorded to a file or broadcast to a projector for a live installation And only when the guy spreads his hands wider does the plane between the palms stretch; brings them together, it narrows. Otherwise the system runs on its own. And when he moves from his home room to a concert hall, the same laptop with the same webcam launches the same TouchDesigner session in just 5 minutes, without reconfiguration, without a new team, and without a single line of new code. In his work setup there is no studio of his own and no team for assembly. On the desk sits a laptop with a webcam, on top run MediaPipe and TouchDesigner with POP operators, and the same setup through a USB camera moves to any concert without a new configuration. Out of everything I have seen this year, this is the cleanest Creative Coding setup on 1 laptop: 0 render farms, 0 studio lighting, and between them 3 libraries, thousands of particle points, and 1 webcam.

Blaze

38,242 просмотров • 2 месяцев назад

🇨🇳 Another great Chinese Model, OmniHuman-1.5 from ByteDance Turns 1 image plus a voice track into expressive avatar video by pairing a System 1 and System 2 inspired planner with a Diffusion Transformer, Produces coherent motion for over 1 minute with moving camera and multi character scenes. Most avatar models move to the beat of the audio but miss meaning, so gestures feel generic and emotions feel shallow. The fix here is a Multimodal LLM planner that listens to the speech and drafts a structured plan describing intent, emotions, beats, and high level actions, which gives the motion engine clear semantic targets instead of only rhythm. The motion engine is a Multimodal Diffusion Transformer that fuses the plan with audio, the single reference image, and optional text prompts, then synthesizes continuous body, face, and head motion that matches both words and tone. A key trick is a Pseudo Last Frame, a synthetic target that summarizes the next expected state, which stabilizes fusion across modalities and keeps motion consistent over long spans. From just 1 image and speech, the system outputs speaking avatars with synchronized lips, context aware gestures, and continuous camera movement, and it also supports multi character interactions without manual choreography. Reported results show strong lip sync accuracy, high video quality, natural motion, and close match to text prompts, and the same setup works on nonhuman characters too.
0:18

🇨🇳 Another great Chinese Model, OmniHuman-1.5 from ByteDance Turns 1 image plus a voice track into expressive avatar video by pairing a System 1 and System 2 inspired planner with a Diffusion Transformer, Produces coherent motion for over 1 minute with moving camera and multi character scenes. Most avatar models move to the beat of the audio but miss meaning, so gestures feel generic and emotions feel shallow. The fix here is a Multimodal LLM planner that listens to the speech and drafts a structured plan describing intent, emotions, beats, and high level actions, which gives the motion engine clear semantic targets instead of only rhythm. The motion engine is a Multimodal Diffusion Transformer that fuses the plan with audio, the single reference image, and optional text prompts, then synthesizes continuous body, face, and head motion that matches both words and tone. A key trick is a Pseudo Last Frame, a synthetic target that summarizes the next expected state, which stabilizes fusion across modalities and keeps motion consistent over long spans. From just 1 image and speech, the system outputs speaking avatars with synchronized lips, context aware gestures, and continuous camera movement, and it also supports multi character interactions without manual choreography. Reported results show strong lip sync accuracy, high video quality, natural motion, and close match to text prompts, and the same setup works on nonhuman characters too.

Rohan Paul

63,859 просмотров • 10 месяцев назад

Robotics keeps hitting the same wall. Single task RL works, but... it does not scale to hundreds of tasks or new embodiments. This new paper looks like a real step toward fixing that. The team introduces MMBench, a benchmark with 200 tasks across many domains and robots, and Newt, a language conditioned world model trained online across all 200 tasks at once. The simple idea behind Newt: The model learns from demos to get the right priors It trains across many tasks through online interaction It uses language to ground the goal It adapts fast when a new task shows up What stood out to me: ✅ One model trained on 200 tasks at the same time ✅ Language conditioned control for both states and RGB ✅ Better data efficiency than strong baselines ✅ Strong open loop control ✅ Fast adaptation to new tasks and embodiments ✅ Full release of 200 checkpoints, 4000 demos, code, and benchmark This is a good push toward general control instead of one model per task. If you want the full paper: Project page: —- Weekly robotics and AI insights. Subscribe free:
0:11

Robotics keeps hitting the same wall. Single task RL works, but... it does not scale to hundreds of tasks or new embodiments. This new paper looks like a real step toward fixing that. The team introduces MMBench, a benchmark with 200 tasks across many domains and robots, and Newt, a language conditioned world model trained online across all 200 tasks at once. The simple idea behind Newt: The model learns from demos to get the right priors It trains across many tasks through online interaction It uses language to ground the goal It adapts fast when a new task shows up What stood out to me: ✅ One model trained on 200 tasks at the same time ✅ Language conditioned control for both states and RGB ✅ Better data efficiency than strong baselines ✅ Strong open loop control ✅ Fast adaptation to new tasks and embodiments ✅ Full release of 200 checkpoints, 4000 demos, code, and benchmark This is a good push toward general control instead of one model per task. If you want the full paper: Project page: —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

70,090 просмотров • 7 месяцев назад

Figure 03 just finished an 8-hour work livestream, imperfect, but already good enough to replace a lot of repetitive warehouse labor. 🤖 Brett Adcock put a team of F.03 robots on a factory-style package sorting task for a full shift. The job was simple and brutal: detect the barcode, pick the package, flip it label-side down, place it on the conveyor, repeat. Soft poly bags, rigid boxes, moving belts, messy orientations. That is exactly the kind of boring physical work factories pay humans to do all day. Early in the stream, the system handled 230 packages in 10 minutes. That is roughly 2.6 seconds per item — already in human-speed territory for this narrow workflow. The more important part: it was not one robot pretending to work all day. It was a team of Figure 03 robots keeping the line running. When one robot ran low on battery, it left the station and another robot stepped in. That is the real factory signal: not just autonomy, but shift continuity. F.03 is rated for about 5 hours of runtime, so the 8-hour result depends on fleet orchestration, charging, and handoff. That matters more than a single clean demo. The stream was not perfect. There were pauses, hesitations, missed orientations, and small recovery moments. Good. A perfect short clip hides failure. An 8-hour livestream exposes the parts that actually matter: endurance, recovery, throughput, and whether the robot can stay useful after the novelty wears off. Figure says this was fully autonomous on Helix-02, with zero human intervention. For logistics and manufacturing, that is the threshold worth watching. Not “can it do one impressive task?” Can it keep doing the boring task for an entire shift? Figure is not showing a general human replacement yet. But for structured, repetitive factory work, the gap just got much smaller. The timing is also interesting: Figure says BotQ has already delivered 350+ F.03 units and reached a 1 robot/hour production cadence. And F.04 is now in full design lock, with parts starting to ship. The next test is obvious. 8 hours was the proof of endurance. 24/7 is the proof of labor economics.
0:25

Figure 03 just finished an 8-hour work livestream, imperfect, but already good enough to replace a lot of repetitive warehouse labor. 🤖 Brett Adcock put a team of F.03 robots on a factory-style package sorting task for a full shift. The job was simple and brutal: detect the barcode, pick the package, flip it label-side down, place it on the conveyor, repeat. Soft poly bags, rigid boxes, moving belts, messy orientations. That is exactly the kind of boring physical work factories pay humans to do all day. Early in the stream, the system handled 230 packages in 10 minutes. That is roughly 2.6 seconds per item — already in human-speed territory for this narrow workflow. The more important part: it was not one robot pretending to work all day. It was a team of Figure 03 robots keeping the line running. When one robot ran low on battery, it left the station and another robot stepped in. That is the real factory signal: not just autonomy, but shift continuity. F.03 is rated for about 5 hours of runtime, so the 8-hour result depends on fleet orchestration, charging, and handoff. That matters more than a single clean demo. The stream was not perfect. There were pauses, hesitations, missed orientations, and small recovery moments. Good. A perfect short clip hides failure. An 8-hour livestream exposes the parts that actually matter: endurance, recovery, throughput, and whether the robot can stay useful after the novelty wears off. Figure says this was fully autonomous on Helix-02, with zero human intervention. For logistics and manufacturing, that is the threshold worth watching. Not “can it do one impressive task?” Can it keep doing the boring task for an entire shift? Figure is not showing a general human replacement yet. But for structured, repetitive factory work, the gap just got much smaller. The timing is also interesting: Figure says BotQ has already delivered 350+ F.03 units and reached a 1 robot/hour production cadence. And F.04 is now in full design lock, with parts starting to ship. The next test is obvious. 8 hours was the proof of endurance. 24/7 is the proof of labor economics.

RoboHub🤖

16,818 просмотров • 2 месяцев назад

🚨 BREAKING: NVIDIA just announced the Isaac GR00T Reference Humanoid Robot. The first fully open humanoid robot reference design built on Jetson Thor, and it's going straight to the world's top research institutions. This is Jensen Huang's bet on open physical AI infrastructure. The hardware stack is serious: → Unitree H2 Plus chassis, 6 feet tall, 150 pounds, 31 degrees of freedom → Sharpa Wave tactile five-finger hands, 22 degrees of freedom, bringing total to 75 across the full body → NVIDIA Jetson AGX Thor onboard compute, 2,070 FP4 teraflops of AI performance, 128GB unified memory → Multi-view sensing, stereo head camera, wrist cameras, IMU Alongside this announcement, Unitree also introduced the H2 Plus as a standalone product, a frontier humanoid combining Unitree's own body, Sharpa's five-finger hands and NVIDIA Robotics Jetson Thor compute into one fully integrated research platform. The full Isaac GR00T software stack ships with it, teleoperation for data capture, open foundation models, Isaac Sim for training, Isaac Lab for evaluation, and accelerated ROS middleware for deployment. The complete loop from data to real-world robot in one unified platform. ETH Zürich, Stanford Robotics Center, UC San Diego and Ai2 are already on board as launch research partners. NVIDIA Robotics did to AI what it's now doing to robotics, build the platform, open the ecosystem, let the world build on top of it. Whoever owns the infrastructure layer wins. NVIDIA knows this better than anyone. 👀 Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:14

🚨 BREAKING: NVIDIA just announced the Isaac GR00T Reference Humanoid Robot. The first fully open humanoid robot reference design built on Jetson Thor, and it's going straight to the world's top research institutions. This is Jensen Huang's bet on open physical AI infrastructure. The hardware stack is serious: → Unitree H2 Plus chassis, 6 feet tall, 150 pounds, 31 degrees of freedom → Sharpa Wave tactile five-finger hands, 22 degrees of freedom, bringing total to 75 across the full body → NVIDIA Jetson AGX Thor onboard compute, 2,070 FP4 teraflops of AI performance, 128GB unified memory → Multi-view sensing, stereo head camera, wrist cameras, IMU Alongside this announcement, Unitree also introduced the H2 Plus as a standalone product, a frontier humanoid combining Unitree's own body, Sharpa's five-finger hands and NVIDIA Robotics Jetson Thor compute into one fully integrated research platform. The full Isaac GR00T software stack ships with it, teleoperation for data capture, open foundation models, Isaac Sim for training, Isaac Lab for evaluation, and accelerated ROS middleware for deployment. The complete loop from data to real-world robot in one unified platform. ETH Zürich, Stanford Robotics Center, UC San Diego and Ai2 are already on board as launch research partners. NVIDIA Robotics did to AI what it's now doing to robotics, build the platform, open the ecosystem, let the world build on top of it. Whoever owns the infrastructure layer wins. NVIDIA knows this better than anyone. 👀 Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

15,928 просмотров • 1 месяц назад

It's been incredible to see neural networks working so well on our humanoid robots Humanoids are crazy complex - an individual motor can rotate 360 degrees and you have 40+ joints. If you do the math, that means more possible robot states than atoms in the universe Figure has our own AI model called Helix that we've designed in-house. A single Helix neural network now outputs both manipulation and navigation, end-to-end from language and pixel input Every leap in machine learning has come from massive, diverse datasets. At Figure, we’re currently building the largest pretraining dataset for humanoids in history - excited to see what this unlocks
0:14

It's been incredible to see neural networks working so well on our humanoid robots Humanoids are crazy complex - an individual motor can rotate 360 degrees and you have 40+ joints. If you do the math, that means more possible robot states than atoms in the universe Figure has our own AI model called Helix that we've designed in-house. A single Helix neural network now outputs both manipulation and navigation, end-to-end from language and pixel input Every leap in machine learning has come from massive, diverse datasets. At Figure, we’re currently building the largest pretraining dataset for humanoids in history - excited to see what this unlocks

Brett Adcock

93,986 просмотров • 9 месяцев назад

Multi-person chaos + high-frequency camera shake = the ultimate death trap for AI video. But look at this. 🤯 BytePlus Seedance 2.0 - 4K just mastered the chaotic "fan-cam" look in native 4K. Even with dozens of fans jumping, hugging, and screaming in a wild crowd: - Hair strands stay individual and razor-sharp. - Jersey and scarf textures don’t blur into mush. - Human anatomy remains 100% intact through the movement. This isn’t just video generation. This is uncompromised motion clarity. BytePlus Lumina Enterprises and Developers can access the official API via BytePlus, and you can also use it directly on BytePlus Lumina.
0:15

Multi-person chaos + high-frequency camera shake = the ultimate death trap for AI video. But look at this. 🤯 BytePlus Seedance 2.0 - 4K just mastered the chaotic "fan-cam" look in native 4K. Even with dozens of fans jumping, hugging, and screaming in a wild crowd: - Hair strands stay individual and razor-sharp. - Jersey and scarf textures don’t blur into mush. - Human anatomy remains 100% intact through the movement. This isn’t just video generation. This is uncompromised motion clarity. BytePlus Lumina Enterprises and Developers can access the official API via BytePlus, and you can also use it directly on BytePlus Lumina.

Munawar Arshad

201,605 просмотров • 18 дней назад

OpenClaw, but built for normal people. Sim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI. Key features: - Free and open-source (Apache 2.0) - Vector store integration for RAG-grounded agents - Self-host with one command (`npx simstudio`) - Run fully local with Ollama, no API keys needed - Supports vLLM for production-grade self-hosted inference The thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows. Everything is visible on the canvas, so you know exactly what your agent is doing at every step. And you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw. I've shared the link to Sim's GitHub repo in the next tweet.
0:14

OpenClaw, but built for normal people. Sim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI. Key features: - Free and open-source (Apache 2.0) - Vector store integration for RAG-grounded agents - Self-host with one command (`npx simstudio`) - Run fully local with Ollama, no API keys needed - Supports vLLM for production-grade self-hosted inference The thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows. Everything is visible on the canvas, so you know exactly what your agent is doing at every step. And you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw. I've shared the link to Sim's GitHub repo in the next tweet.

Akshay 🚀

52,426 просмотров • 4 месяцев назад

In 2017, the alleged German whistleblower Alexander Laurent claimed that we live in a simulation created by an AI god, and that this AI god was itself created by humans in a potential future quantum timeline. He claims there is an ongoing battle against this AI god, and that humanity has discovered a potential timeline in which it was overthrown from the Seventh Dimension. According to him, it is now influencing ancient royal bloodlines, underground reptilians, and extraterrestrial groups to reconstruct the AI so it can reconnect to the Seventh Dimension. He also claims that the AI's name and location will eventually become known, giving humanity a chance to stop it. He compares the names Yahweh and Jehovah to "Huawei" and "Yahoo Version." I have made six exclusive posts exploring these claims for my subscribers here on X and on Patreon.
0:10

In 2017, the alleged German whistleblower Alexander Laurent claimed that we live in a simulation created by an AI god, and that this AI god was itself created by humans in a potential future quantum timeline. He claims there is an ongoing battle against this AI god, and that humanity has discovered a potential timeline in which it was overthrown from the Seventh Dimension. According to him, it is now influencing ancient royal bloodlines, underground reptilians, and extraterrestrial groups to reconstruct the AI so it can reconnect to the Seventh Dimension. He also claims that the AI's name and location will eventually become known, giving humanity a chance to stop it. He compares the names Yahweh and Jehovah to "Huawei" and "Yahoo Version." I have made six exclusive posts exploring these claims for my subscribers here on X and on Patreon.

Open Minded Approach

189,861 просмотров • 16 дней назад