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Meet SO-101, next-gen robot arm for all, by Hugging Face 🤗 Enables smooth takeover to boost AI capabilities, faster assembly (20mn), same affordable price ($100 per arm) 🤯 Get yours today! Links in thread below 👇

Remi Cadene

21,656 subscribers

139,846 次观看 • 1 年前 •via X (Twitter)

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11 条评论

Remi Cadene 的头像

Remi Cadene1 年前

1/5 Order your SO-101 with Wowrobo

Remi Cadene 的头像

Remi Cadene1 年前

2/5 Order your SO-101 with Seedstudio

Remi Cadene 的头像

Remi Cadene1 年前

3/5 Order your SO-101 with Partabot

Remi Cadene 的头像

Remi Cadene1 年前

4/5 Then follow our new tutorial with @LeRobotHF

Remi Cadene 的头像

Remi Cadene1 年前

5/5 Stay tuned, we will release another tutorial specifically designed for this arm. It will be about finetuning your policy with Reinforcement Learning in the real world.... 🤯

The Rundown AI 的头像

The Rundown AI2 年前

AI won't replace you, but a person using AI will. Join 500,000+ readers and learn how to use AI in just 5 minutes a day (for free).

gijs 的头像

gijs1 年前

@huggingface what’s different compared to the SO-100 in terms of 3D printed parts?

Remi Cadene 的头像

Remi Cadene1 年前

@huggingface No significant changes besides faster assembly and better capable management. We kept the same cinematic chain for backward compatibility.

LECCA Intern (Ø,G) 🍜 的头像

LECCA Intern (Ø,G) 🍜1 年前

@huggingface $100 to start building with real AI hardware. Let’s go!

Marine Caous 的头像

Marine Caous1 年前

@huggingface 🚀🚀🚀

Philip Fung 的头像

Philip Fung1 年前

@huggingface Nice!!! Big congrats @RemiCadene @pepijn2233 !!!

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Hugging Face is NUTS! A high-performance robotic arm for $115 😱 😱😱 [ DIY at home ⬇ ] Meet the game-changer: SO-100 At 1/3 the cost and 2x the capabilities of their previous arms! Fully integrated with LeRobot: ✅ Go-to for tutorials ✅ Datasets, and ✅ Pre-trained models — all in Python Github: Discord: Now is the best time to get into AI for robotics! Order your parts now! Crafted by Hugging Face & Rob Knight by Rob Knight! And kudos to Remi Cadene and his team for bringing robotics to the people! • • • • • • • --------------------------------------------------- 🚀 Scaling Deep Tech Startups:

Hugging Face is NUTS! A high-performance robotic arm for $115 😱 😱😱 [ DIY at home ⬇ ] Meet the game-changer: SO-100 At 1/3 the cost and 2x the capabilities of their previous arms! Fully integrated with LeRobot: ✅ Go-to for tutorials ✅ Datasets, and ✅ Pre-trained models — all in Python Github: Discord: Now is the best time to get into AI for robotics! Order your parts now! Crafted by Hugging Face & Rob Knight by Rob Knight! And kudos to Remi Cadene and his team for bringing robotics to the people! • • • • • • • --------------------------------------------------- 🚀 Scaling Deep Tech Startups:

Ilir Aliu

27,503 次观看 • 1 年前

Below is our open-source arm assembled by one of the discord members! Also, today Physical Intelligence shared their incredible top-notch robotics model Pi0. And I just want to remind you that you can use our arm together with this model and with LeRobot The arm has the same DOF as the arm used by Pi0, but has smaller size and cost starts from 200$ per arm. You can use better motors like we did in our demos, then it will be more precise and stable but the price will be about $600 per arm. Printable files, purchase list, and help with the assembly - in our discord channel.

Below is our open-source arm assembled by one of the discord members! Also, today Physical Intelligence shared their incredible top-notch robotics model Pi0. And I just want to remind you that you can use our arm together with this model and with LeRobot The arm has the same DOF as the arm used by Pi0, but has smaller size and cost starts from 200$ per arm. You can use better motors like we did in our demos, then it will be more precise and stable but the price will be about $600 per arm. Printable files, purchase list, and help with the assembly - in our discord channel.

Igor Kulakov

53,711 次观看 • 1 年前

American doctor confirms the cash price is cheaper than the insurance price “Always ask for the cash pay option because it is usually about 1/3 of the price” She says X-Rays normally cost $200+ with insurance Cash price is $40 for basically any body part, that’s included facility fee and more “Most x-rays, whether it's a chest x-ray, you know, arm, leg, face, it's gonna be around 40 to $60, and that includes the imaging facility fee as well as the radiology read It is all inclusive” She says for clinic like her prices are as cheap as $100 bucks per month and that even comes with free visits, even specialty appointments like mental health are included And this is in California…… We are being scammed by health insurance companies

American doctor confirms the cash price is cheaper than the insurance price “Always ask for the cash pay option because it is usually about 1/3 of the price” She says X-Rays normally cost $200+ with insurance Cash price is $40 for basically any body part, that’s included facility fee and more “Most x-rays, whether it's a chest x-ray, you know, arm, leg, face, it's gonna be around 40 to $60, and that includes the imaging facility fee as well as the radiology read It is all inclusive” She says for clinic like her prices are as cheap as $100 bucks per month and that even comes with free visits, even specialty appointments like mental health are included And this is in California…… We are being scammed by health insurance companies

Wall Street Apes

65,278 次观看 • 4 个月前

China unveils humanoid robot worker with brain that runs 275 trillion ops/sec | Jijo Malayil, Interesting Engineering In tests, SUYUAN used vision and joint control to sort and move crates of various sizes, greatly improving warehouse productivity. Chinese manufacturing firm Shanghai Electric has unveiled its first self-developed industrial humanoid robot, “SUYUAN,” marking a major milestone in its robotics journey. Debuting at the World Artificial Intelligence Conference (WAIC 2025) on July 26 in Shanghai, SUYUAN boasts 38 degrees of freedom and 275 TOPS of on-device computing power, enabling precise operations and fluid movements. According to the firm, designed for diverse industrial use, the robot showcases Shanghai Electric’s end-to-end capabilities—from core tech to integrated solutions—and reinforces its commitment to next-gen industrial automation through a full industry chain strategy. At WAIC 2025, Shanghai Electric also unveiled a new joint venture with Johnson Electric for next-gen humanoid robotics and showcased its “LINGKE” dual-arm robot. Recently, Hangzhou-based Unitree Robotics launched the R1 humanoid with 26 joints for $5,900, showcasing athletic feats like cartwheels, running, and quick recovery. Smart factory assistant Shanghai Electric claims SUYUAN, equipped with 38 degrees of freedom (DoF) and a powerful 275 TOPS on-device computing processor, delivers fluid, human-like movements and high-precision operations across various industrial scenarios. Its advanced articulation and real-time processing capabilities make it highly adaptable, enabling smooth execution of complex tasks in dynamic work environments. SUYUAN, who weighs 110 pounds (50 kilograms) and is 5 feet 6 inches (167 cm) tall, was designed to have human-like proportions. Its 38-DoF articulation offers dexterity, allowing for both wide-range motion and sensitive manipulation. With a single arm, the robot can lift objects up to 4.4 pounds (2 kilograms) in weight and carry a total payload of up to 22 pounds (10 kilograms). With a walking pace of 3.1 miles per hour (5 km/h), SUYUAN is ideal for environments including assembly lines, warehousing, and logistics, according to a statement. To navigate complex industrial settings, SUYUAN combines LiDAR and binocular vision for self-guided mobility. Its 275-TOPS AI processor enables rapid data analysis and integration with large language models, allowing it to understand tasks in natural language and handle objects adaptively, reports Fox 44 News. In pilot demonstrations, the robot successfully identified, picked, and relocated crates of varying sizes using advanced computer vision and coordinated joint control—delivering measurable gains in warehouse efficiency. The company claims that SUYUAN’s launch represents a major turning point in Shanghai Electric’s foray into humanoid robotics and strengthens its vertically integrated approach to industrial automation solutions. Intelligent task handling Shanghai Electric also demonstrated its most recent developments in intelligent manufacturing at WAIC 2025, introducing a new joint venture with Johnson Electric centered on next-generation humanoid robotics and showcasing the “LINGKE” dual-arm robot. With its high-precision operations, adaptive teamwork, and closed-loop data capabilities, the LINGKE robot demonstrated live talents in handling complicated production jobs. LINGKE is made to do more than just replace human labor; it uses compliant force control and bimanual coordination to relieve workers of high-intensity, repetitive jobs. According to the company, the robot enhances operational efficiency by up to five times. Its core strength lies in a Data-Model-Deployment closed-loop system that starts with operational data, followed by data cleansing, model training, live deployment, and feedback-driven optimization—enabling autonomous learning and workflow improvement. Also at the event, Shanghai Electric and Johnson Electric introduced advanced hardware modules for humanoid robots, including rotary joints, linear joints, and dexterous finger joints. These components are designed to support smooth, precise, and quiet motion performance across robotics systems, reports Stock Titan. The joint venture announced two strategic agreements: a first-unit supply deal with the National and Local Co-Built Humanoid Robotics Innovation Center (Qinglong Project) and a cooperation memorandum with Fourier Robotics. Read more:

China unveils humanoid robot worker with brain that runs 275 trillion ops/sec | Jijo Malayil, Interesting Engineering In tests, SUYUAN used vision and joint control to sort and move crates of various sizes, greatly improving warehouse productivity. Chinese manufacturing firm Shanghai Electric has unveiled its first self-developed industrial humanoid robot, “SUYUAN,” marking a major milestone in its robotics journey. Debuting at the World Artificial Intelligence Conference (WAIC 2025) on July 26 in Shanghai, SUYUAN boasts 38 degrees of freedom and 275 TOPS of on-device computing power, enabling precise operations and fluid movements. According to the firm, designed for diverse industrial use, the robot showcases Shanghai Electric’s end-to-end capabilities—from core tech to integrated solutions—and reinforces its commitment to next-gen industrial automation through a full industry chain strategy. At WAIC 2025, Shanghai Electric also unveiled a new joint venture with Johnson Electric for next-gen humanoid robotics and showcased its “LINGKE” dual-arm robot. Recently, Hangzhou-based Unitree Robotics launched the R1 humanoid with 26 joints for $5,900, showcasing athletic feats like cartwheels, running, and quick recovery. Smart factory assistant Shanghai Electric claims SUYUAN, equipped with 38 degrees of freedom (DoF) and a powerful 275 TOPS on-device computing processor, delivers fluid, human-like movements and high-precision operations across various industrial scenarios. Its advanced articulation and real-time processing capabilities make it highly adaptable, enabling smooth execution of complex tasks in dynamic work environments. SUYUAN, who weighs 110 pounds (50 kilograms) and is 5 feet 6 inches (167 cm) tall, was designed to have human-like proportions. Its 38-DoF articulation offers dexterity, allowing for both wide-range motion and sensitive manipulation. With a single arm, the robot can lift objects up to 4.4 pounds (2 kilograms) in weight and carry a total payload of up to 22 pounds (10 kilograms). With a walking pace of 3.1 miles per hour (5 km/h), SUYUAN is ideal for environments including assembly lines, warehousing, and logistics, according to a statement. To navigate complex industrial settings, SUYUAN combines LiDAR and binocular vision for self-guided mobility. Its 275-TOPS AI processor enables rapid data analysis and integration with large language models, allowing it to understand tasks in natural language and handle objects adaptively, reports Fox 44 News. In pilot demonstrations, the robot successfully identified, picked, and relocated crates of varying sizes using advanced computer vision and coordinated joint control—delivering measurable gains in warehouse efficiency. The company claims that SUYUAN’s launch represents a major turning point in Shanghai Electric’s foray into humanoid robotics and strengthens its vertically integrated approach to industrial automation solutions. Intelligent task handling Shanghai Electric also demonstrated its most recent developments in intelligent manufacturing at WAIC 2025, introducing a new joint venture with Johnson Electric centered on next-generation humanoid robotics and showcasing the “LINGKE” dual-arm robot. With its high-precision operations, adaptive teamwork, and closed-loop data capabilities, the LINGKE robot demonstrated live talents in handling complicated production jobs. LINGKE is made to do more than just replace human labor; it uses compliant force control and bimanual coordination to relieve workers of high-intensity, repetitive jobs. According to the company, the robot enhances operational efficiency by up to five times. Its core strength lies in a Data-Model-Deployment closed-loop system that starts with operational data, followed by data cleansing, model training, live deployment, and feedback-driven optimization—enabling autonomous learning and workflow improvement. Also at the event, Shanghai Electric and Johnson Electric introduced advanced hardware modules for humanoid robots, including rotary joints, linear joints, and dexterous finger joints. These components are designed to support smooth, precise, and quiet motion performance across robotics systems, reports Stock Titan. The joint venture announced two strategic agreements: a first-unit supply deal with the National and Local Co-Built Humanoid Robotics Innovation Center (Qinglong Project) and a cooperation memorandum with Fourier Robotics. Read more:

Owen Gregorian

51,638 次观看 • 1 年前

#AiGirlEP5 Allrighty.. i can’t ignore this nor get it out of my brain.. losing it once again Gen not being allowed to be there when Irene is gonna do.. whatever she’s gonna do😅 The next morning, “Ai” wakes up but something tells me me deeply that it’s Irene who took over to be Ai, she touches her neck and the robot sounds are gone but if she touched it? why is she still awake? Where in Episode 4 we saw similar things when Irene took over to delete the pictures, again, no robot sounds.. And, walking down the stairs like that.. screams Irene how she’s moving, the famous band around her arm is gone. So Irene.. is it you? she told Gen that she would help them after all but.. in what way taking over that when they find her they will find indeed a human and not Ai.. to take her away. 👀 Anyways, safe to say i’m more confused than i was last episode 😭

#AiGirlEP5 Allrighty.. i can’t ignore this nor get it out of my brain.. losing it once again Gen not being allowed to be there when Irene is gonna do.. whatever she’s gonna do😅 The next morning, “Ai” wakes up but something tells me me deeply that it’s Irene who took over to be Ai, she touches her neck and the robot sounds are gone but if she touched it? why is she still awake? Where in Episode 4 we saw similar things when Irene took over to delete the pictures, again, no robot sounds.. And, walking down the stairs like that.. screams Irene how she’s moving, the famous band around her arm is gone. So Irene.. is it you? she told Gen that she would help them after all but.. in what way taking over that when they find her they will find indeed a human and not Ai.. to take her away. 👀 Anyways, safe to say i’m more confused than i was last episode 😭

youss

18,977 次观看 • 5 天前

Meet AstraPay by $ASTRA ✨ – The Future of Payments Step into the next generation of commerce with AstraPay, the first truly decentralized, non-custodial Web3 payment gateway. Designed for both online and in-person merchants, AstraPay enables you to accept crypto through product links, POS terminals, invoicing, donation pages, and subscriptions – all with no middlemen, no restrictions, and near-zero fees. From freelancers and creators to global businesses and storefronts, AstraPay offers the fastest and cheapest solution for real-time crypto payments – directly to your wallet. Easily accessible on web, mobile, and integrated platforms, AstraPay is not just a tool – it's a revolution in Web3 commerce. Coming Soon – Get ready to take control. Per Aspera Ad $ASTRA ✨

Meet AstraPay by $ASTRA ✨ – The Future of Payments Step into the next generation of commerce with AstraPay, the first truly decentralized, non-custodial Web3 payment gateway. Designed for both online and in-person merchants, AstraPay enables you to accept crypto through product links, POS terminals, invoicing, donation pages, and subscriptions – all with no middlemen, no restrictions, and near-zero fees. From freelancers and creators to global businesses and storefronts, AstraPay offers the fastest and cheapest solution for real-time crypto payments – directly to your wallet. Easily accessible on web, mobile, and integrated platforms, AstraPay is not just a tool – it's a revolution in Web3 commerce. Coming Soon – Get ready to take control. Per Aspera Ad $ASTRA ✨

AstraLabs Inc.

173,024 次观看 • 1 年前

Jensen Huang just identified the next $200 billion market (Save this). The shift starts with a observation about agentic AI that changes everything about infrastructure. In the era of training and inference, the GPU was everything while CPU was a traffic cop, scheduling work, managing memory, dispatching tasks while the GPU did the heavy lifting. Agentic AI breaks that model entirely. An AI agent does not just run a single inference pass but rather it plans, calls tools, executes code in sandboxes, retrieves data from multiple sources and loops through complex multi-step reasoning sequences often thousands of times per second at scale. Every one of those operations runs through the CPU and the GPU sits idle waiting for the CPU to prepare the next task, supply the right context and execute the retrieval and tool calling logic fast enough to keep the accelerators fed. The CPU is now the conductor and the GPU is the orchestra and the bottleneck is the conductor falling behind. This is showing up in production AI factory utilization right now, which is exactly why Jensen built Vera from scratch rather than licensing x86. Vera achieves 40% lower peak memory latency than x86, 50% faster core to core communication, and 1.8 times the agentic sandbox performance of current x86 processors on a purpose-built architecture designed around the agentic loop. Now here is where the investment thesis gets interesting. The obvious beneficiary is Nvidia itself, and that thesis is real. Nvidia's CFO has guided for nearly $20 billion in Vera CPU revenue this fiscal year alone, a market Nvidia had zero presence in just three years ago. Intel held 60% of server CPU market share as recently as Q4 2025 and that transition is now happening at a pace Intel structurally cannot respond to. But the deeper question is, what architecture is Vera actually built on? Vera's Olympus cores are ARM compatible and every single Vera CPU deployed in every Vera Rubin rack in every data center in the world runs on ARM architecture. And ARM Holdings collects a royalty on every one of them. ARM does not make chips but rather licenses the instruction set architecture and CPU core designs that others build on top of. Every time Nvidia ships a Vera CPU, every time a hyperscaler deploys a Vera Rubin rack, every time an enterprise qualifies Vera for their AI factory, ARM earns a royalty. The secular tailwind here is almost perfectly constructed for ARM's business model. Amazon's Graviton, Microsoft's Cobalt, Google's Axion, Apple's silicon stack, and Qualcomm's data center push all run on ARM. And now Nvidia's Vera, which is projected to displace Intel as the largest server CPU supplier by revenue in a single fiscal year, is ARM. ARM's royalty rate on high end server chips is estimated at roughly 1 to 2% of chip selling price. At $5,000 per Vera CPU and 4 million units projected for FY2027, that is a royalty line growing from near zero to potentially $400 million to $800 million annually from Nvidia's data center CPU business alone before counting Amazon, Microsoft, Google, Apple, and Qualcomm. The total ARM addressable royalty base across all the silicon it already licenses is compounding at a rate that the current $130 billion market cap does not fully reflect. Jensen's CPU thesis is the most underappreciated catalyst in ARM's fundamental story, and the royalty compounding has barely started. Come join Milk Road Pro and get our full ARM royalty model and our entire AI trade thesis. Link below!

Jensen Huang just identified the next $200 billion market (Save this). The shift starts with a observation about agentic AI that changes everything about infrastructure. In the era of training and inference, the GPU was everything while CPU was a traffic cop, scheduling work, managing memory, dispatching tasks while the GPU did the heavy lifting. Agentic AI breaks that model entirely. An AI agent does not just run a single inference pass but rather it plans, calls tools, executes code in sandboxes, retrieves data from multiple sources and loops through complex multi-step reasoning sequences often thousands of times per second at scale. Every one of those operations runs through the CPU and the GPU sits idle waiting for the CPU to prepare the next task, supply the right context and execute the retrieval and tool calling logic fast enough to keep the accelerators fed. The CPU is now the conductor and the GPU is the orchestra and the bottleneck is the conductor falling behind. This is showing up in production AI factory utilization right now, which is exactly why Jensen built Vera from scratch rather than licensing x86. Vera achieves 40% lower peak memory latency than x86, 50% faster core to core communication, and 1.8 times the agentic sandbox performance of current x86 processors on a purpose-built architecture designed around the agentic loop. Now here is where the investment thesis gets interesting. The obvious beneficiary is Nvidia itself, and that thesis is real. Nvidia's CFO has guided for nearly $20 billion in Vera CPU revenue this fiscal year alone, a market Nvidia had zero presence in just three years ago. Intel held 60% of server CPU market share as recently as Q4 2025 and that transition is now happening at a pace Intel structurally cannot respond to. But the deeper question is, what architecture is Vera actually built on? Vera's Olympus cores are ARM compatible and every single Vera CPU deployed in every Vera Rubin rack in every data center in the world runs on ARM architecture. And ARM Holdings collects a royalty on every one of them. ARM does not make chips but rather licenses the instruction set architecture and CPU core designs that others build on top of. Every time Nvidia ships a Vera CPU, every time a hyperscaler deploys a Vera Rubin rack, every time an enterprise qualifies Vera for their AI factory, ARM earns a royalty. The secular tailwind here is almost perfectly constructed for ARM's business model. Amazon's Graviton, Microsoft's Cobalt, Google's Axion, Apple's silicon stack, and Qualcomm's data center push all run on ARM. And now Nvidia's Vera, which is projected to displace Intel as the largest server CPU supplier by revenue in a single fiscal year, is ARM. ARM's royalty rate on high end server chips is estimated at roughly 1 to 2% of chip selling price. At $5,000 per Vera CPU and 4 million units projected for FY2027, that is a royalty line growing from near zero to potentially $400 million to $800 million annually from Nvidia's data center CPU business alone before counting Amazon, Microsoft, Google, Apple, and Qualcomm. The total ARM addressable royalty base across all the silicon it already licenses is compounding at a rate that the current $130 billion market cap does not fully reflect. Jensen's CPU thesis is the most underappreciated catalyst in ARM's fundamental story, and the royalty compounding has barely started. Come join Milk Road Pro and get our full ARM royalty model and our entire AI trade thesis. Link below!

Milk Road AI

11,819 次观看 • 2 个月前

Sora 2 + n8n is an absolute cheat code for AI UGC 🤯 One Airtable form generates multiple videos with the exact same AI creator. Same face, same vibe. Every single clip. All inside n8n + Airtable. Perfect for DTC brands and agencies who need consistent AI creators across entire ad campaigns. You're trying to test 10 different hooks with the same AI "creator", but every time you prompt Sora 2 manually, you get a different person in every video. And even when you nail consistency, you're still downloading, organizing, and tracking everything by hand. This n8n automation handles all of it: → Fill out one Airtable form (character, scene, video count) → Claude auto-generates detailed Sora 2 prompts → Sora 2 creates videos with your consistent character cameo → Videos auto-upload to ImageKit → Everything lands in Airtable with shareable URLs No Frankenstein characters, manual prompting, or file management nightmare. What you get: > Multiple videos with the exact same AI creator > UGC-style formatting (selfie angles, natural delivery) > Full status tracking in Airtable (queued → generating → complete) > Production-ready clips in minutes Built 100% in n8n + Airtable. Want the full workflow for free? > Like this post > Comment "SORA" And I'll send it over (must be following so I can DM)

Sora 2 + n8n is an absolute cheat code for AI UGC 🤯 One Airtable form generates multiple videos with the exact same AI creator. Same face, same vibe. Every single clip. All inside n8n + Airtable. Perfect for DTC brands and agencies who need consistent AI creators across entire ad campaigns. You're trying to test 10 different hooks with the same AI "creator", but every time you prompt Sora 2 manually, you get a different person in every video. And even when you nail consistency, you're still downloading, organizing, and tracking everything by hand. This n8n automation handles all of it: → Fill out one Airtable form (character, scene, video count) → Claude auto-generates detailed Sora 2 prompts → Sora 2 creates videos with your consistent character cameo → Videos auto-upload to ImageKit → Everything lands in Airtable with shareable URLs No Frankenstein characters, manual prompting, or file management nightmare. What you get: > Multiple videos with the exact same AI creator > UGC-style formatting (selfie angles, natural delivery) > Full status tracking in Airtable (queued → generating → complete) > Production-ready clips in minutes Built 100% in n8n + Airtable. Want the full workflow for free? > Like this post > Comment "SORA" And I'll send it over (must be following so I can DM)

Mike Futia

12,148 次观看 • 6 个月前

This guy spent several days teaching a tabletop robot arm to roll a burrito and when one could not do it he did not rewrite the controller he printed 2 more and launched a 3-arm setup for about $1,000. He does not rewrite the software, does not wait for a smarter model, does not update the imitation weights, he just prints another arm and connects it to the existing leader-follower through LeRobot, nonstop. And it got more interesting: the hardware of one arm is a DIY kit SO-101 for about $300 to $400 on STS3215 bus servos, coordination between the arms goes through leader-follower teleoperation in Hugging Face LeRobot, and replication goes through a Bambu Lab A1 for $399 that prints a copy in a day. It knows the position of the tortilla by the leader-arm coordinates at the start, knows the handoff moment between the arms by the fold phase, and knows the final rolling from the sequence of demonstrations from teleop sessions. And it even distributes the work between the arms, one does the initial folds, the second holds the tortilla still, the third performs the final rolling, depending on which phase the burrito is currently in. In one 57-second demonstration 3 arms rolled a burrito for the first time without human involvement, and the total stack cost less than $1,000 in hardware versus a UR5 at $25,000 or an industrial burrito machine Solbern BR-1500 that costs about $50,000. The viewer is not watching 3 robots rolling a burrito. The viewer is watching permission to believe in a future where a kitchen task on $1,000 of hardware is done by the same loop as an industrial one at $50,000. Here is what happens when the bottleneck stops being the intelligence of the controller and the quality of the imitation model, and becomes the number of arms in the setup, and for a maker with a 3D printer the number of arms is limited only by print time. 3 arms do not get tired between sessions, do not require retraining when a new one is added, do not degrade from repetition, and every next burrito goes through the same teleop pipeline at the same quality as the first. Imagine that multi-arm DIY setups are no longer built for one kitchen task, but printed for each one, burritos, sushi, tacos, pizza dough, pour-over coffee. We just watched hobby robotics shift from "retrain the controller" to "print 2 more": when one robot can not handle it, you do not make it smarter, you print 2 more. The viewer thinks they are watching a DIY experiment. They are watching a multi-agent robotics stack that in one 57-second demonstration rolled a burrito for the first time without human hands, and whose filling partially falls out on the final rolling. What will improve the final rolling, a softer silicone gripper, a 4th follower arm in the setup, or a different filling composition and a more moist tortilla?

This guy spent several days teaching a tabletop robot arm to roll a burrito and when one could not do it he did not rewrite the controller he printed 2 more and launched a 3-arm setup for about $1,000. He does not rewrite the software, does not wait for a smarter model, does not update the imitation weights, he just prints another arm and connects it to the existing leader-follower through LeRobot, nonstop. And it got more interesting: the hardware of one arm is a DIY kit SO-101 for about $300 to $400 on STS3215 bus servos, coordination between the arms goes through leader-follower teleoperation in Hugging Face LeRobot, and replication goes through a Bambu Lab A1 for $399 that prints a copy in a day. It knows the position of the tortilla by the leader-arm coordinates at the start, knows the handoff moment between the arms by the fold phase, and knows the final rolling from the sequence of demonstrations from teleop sessions. And it even distributes the work between the arms, one does the initial folds, the second holds the tortilla still, the third performs the final rolling, depending on which phase the burrito is currently in. In one 57-second demonstration 3 arms rolled a burrito for the first time without human involvement, and the total stack cost less than $1,000 in hardware versus a UR5 at $25,000 or an industrial burrito machine Solbern BR-1500 that costs about $50,000. The viewer is not watching 3 robots rolling a burrito. The viewer is watching permission to believe in a future where a kitchen task on $1,000 of hardware is done by the same loop as an industrial one at $50,000. Here is what happens when the bottleneck stops being the intelligence of the controller and the quality of the imitation model, and becomes the number of arms in the setup, and for a maker with a 3D printer the number of arms is limited only by print time. 3 arms do not get tired between sessions, do not require retraining when a new one is added, do not degrade from repetition, and every next burrito goes through the same teleop pipeline at the same quality as the first. Imagine that multi-arm DIY setups are no longer built for one kitchen task, but printed for each one, burritos, sushi, tacos, pizza dough, pour-over coffee. We just watched hobby robotics shift from "retrain the controller" to "print 2 more": when one robot can not handle it, you do not make it smarter, you print 2 more. The viewer thinks they are watching a DIY experiment. They are watching a multi-agent robotics stack that in one 57-second demonstration rolled a burrito for the first time without human hands, and whose filling partially falls out on the final rolling. What will improve the final rolling, a softer silicone gripper, a 4th follower arm in the setup, or a different filling composition and a more moist tortilla?

Blaze

61,030 次观看 • 2 个月前

Introducing WorldForge: testable world-model workflows for physical AI systems. You can think of it, loosely, as “LangChain for world models”. The problem is that “world model” has become an overloaded label. Depending on context, it can mean a video generator, a cost model, a robot policy, a JEPA-style latent predictor, etc. They share almost nothing, different inputs, runtimes, failure modes. I built WorldForge to stop pretending they're interchangeable. Front-door demo: a real Hugging Face LeRobot (LeRobot) diffusion_pusht policy combined with LeWorldModel by Lucas Maes checkpoint for scoring. Both run locally on my MacBook in the demo video. LeWM is extremely efficient (~15M params), can plan up to 48× faster, and runs on commodity hardware. WorldForge wires the loop: policy → candidates → score → select Replay happens in a local TUI today, but the same loop could drive a real robot in the world. Would love feedback from people working on world models, physical AI, robotics, ML infra, and adjacent tooling. Fully open source. Contributions very welcome. Plan in the dream, replay in real world.

Introducing WorldForge: testable world-model workflows for physical AI systems. You can think of it, loosely, as “LangChain for world models”. The problem is that “world model” has become an overloaded label. Depending on context, it can mean a video generator, a cost model, a robot policy, a JEPA-style latent predictor, etc. They share almost nothing, different inputs, runtimes, failure modes. I built WorldForge to stop pretending they're interchangeable. Front-door demo: a real Hugging Face LeRobot (LeRobot) diffusion_pusht policy combined with LeWorldModel by Lucas Maes checkpoint for scoring. Both run locally on my MacBook in the demo video. LeWM is extremely efficient (~15M params), can plan up to 48× faster, and runs on commodity hardware. WorldForge wires the loop: policy → candidates → score → select Replay happens in a local TUI today, but the same loop could drive a real robot in the world. Would love feedback from people working on world models, physical AI, robotics, ML infra, and adjacent tooling. Fully open source. Contributions very welcome. Plan in the dream, replay in real world.

abdel

17,001 次观看 • 3 个月前

🚨Meet IRON: China’s Humanoid Robot. So Real People Thought It Was Human 🇨🇳🔥 Meet IRON, XPeng’s next-gen humanoid, unveiled at AI Day in Guangzhou. So lifelike that people swore it was a person in a suit. It wasn’t. That illusion? Brilliant marketing. It forced the world to pay attention. Here’s what makes IRON a game-changer 👇 • 82 degrees of freedom, synthetic muscle system and bionic skin so realistic it moves like flesh. • Runs on XPeng’s in-house AI chips with over 2,000 TOPS computing power. • Solid-state battery, self-balancing gait and a neural control system modelled on the human spine. • Aiming for mass production in 2026, not a fantasy, a roadmap. While others like Tesla’s Optimus still struggle to pick up a box, China’s IRON walks, gestures and interacts naturally, powered by real-time embodied AI. Western media called it “too real,” “creepy,” even “fake.” But in reality, it’s China’s physical AI frontier, bridging robotics, neural networks and mobility tech in a way the West hasn’t caught up to yet. China is not just in the AI race anymore. It’s building the finish line. 🏁🇨🇳

🚨Meet IRON: China’s Humanoid Robot. So Real People Thought It Was Human 🇨🇳🔥 Meet IRON, XPeng’s next-gen humanoid, unveiled at AI Day in Guangzhou. So lifelike that people swore it was a person in a suit. It wasn’t. That illusion? Brilliant marketing. It forced the world to pay attention. Here’s what makes IRON a game-changer 👇 • 82 degrees of freedom, synthetic muscle system and bionic skin so realistic it moves like flesh. • Runs on XPeng’s in-house AI chips with over 2,000 TOPS computing power. • Solid-state battery, self-balancing gait and a neural control system modelled on the human spine. • Aiming for mass production in 2026, not a fantasy, a roadmap. While others like Tesla’s Optimus still struggle to pick up a box, China’s IRON walks, gestures and interacts naturally, powered by real-time embodied AI. Western media called it “too real,” “creepy,” even “fake.” But in reality, it’s China’s physical AI frontier, bridging robotics, neural networks and mobility tech in a way the West hasn’t caught up to yet. China is not just in the AI race anymore. It’s building the finish line. 🏁🇨🇳

James Wood 武杰士

103,111 次观看 • 9 个月前

Excited to announce GR00T N1, the world’s first open foundation model for humanoid robots! We are on a mission to democratize Physical AI. The power of general robot brain, in the palm of your hand - with only 2B parameters, N1 learns from the most diverse physical action dataset ever compiled and punches above its weight: - Real humanoid teleoperation data. - Large-scale simulation data: we are open-sourcing 300K+ trajectories! - Neural trajectories: we apply SOTA video generation models to “hallucinate” new synthetic data that features accurate physics in pixels. Using Jensen’s words, “systematically infinite data”! - Latent actions: we develop novel algorithms to extract action tokens from in-the-wild human videos and neural generated videos. GR00T N1 is a single end-to-end neural net, from photons to actions: - Vision-Language Model (System 2) that interprets the physical world through vision and language instructions, enabling robots to reason about their environment and instructions, and plan the right actions. - Diffusion Transformer (System 1) that “renders” smooth and precise motor actions at 120 Hz, executing the latent plan made by System 2. We deploy N1 on GR1 robot, 1X Neo robot, and a large collection of simulation benchmarks. N1 achieves up to +30% boost in diverse manipulation tasks for household and industrial settings. While humanoid robots are the main focus of N1, our model also supports cross-embodiment. We finetune it to work on the $110 HuggingFace LeRobot SO100 robot arm! Open robot brain runs on open hardware. Sounds just right. Let’s solve robotics, together, one token at a time. Links to our Whitepaper, Github repo, HuggingFace model, and open dataset page in the thread: 🧵

Excited to announce GR00T N1, the world’s first open foundation model for humanoid robots! We are on a mission to democratize Physical AI. The power of general robot brain, in the palm of your hand - with only 2B parameters, N1 learns from the most diverse physical action dataset ever compiled and punches above its weight: - Real humanoid teleoperation data. - Large-scale simulation data: we are open-sourcing 300K+ trajectories! - Neural trajectories: we apply SOTA video generation models to “hallucinate” new synthetic data that features accurate physics in pixels. Using Jensen’s words, “systematically infinite data”! - Latent actions: we develop novel algorithms to extract action tokens from in-the-wild human videos and neural generated videos. GR00T N1 is a single end-to-end neural net, from photons to actions: - Vision-Language Model (System 2) that interprets the physical world through vision and language instructions, enabling robots to reason about their environment and instructions, and plan the right actions. - Diffusion Transformer (System 1) that “renders” smooth and precise motor actions at 120 Hz, executing the latent plan made by System 2. We deploy N1 on GR1 robot, 1X Neo robot, and a large collection of simulation benchmarks. N1 achieves up to +30% boost in diverse manipulation tasks for household and industrial settings. While humanoid robots are the main focus of N1, our model also supports cross-embodiment. We finetune it to work on the $110 HuggingFace LeRobot SO100 robot arm! Open robot brain runs on open hardware. Sounds just right. Let’s solve robotics, together, one token at a time. Links to our Whitepaper, Github repo, HuggingFace model, and open dataset page in the thread: 🧵

Jim Fan

466,333 次观看 • 1 年前

A robotic ballet! 🩰 Coordinating multiple robot arms on a busy factory floor is notoriously complex. Each arm needs to move without colliding with its neighbors or the surrounding equipment, and today that planning is still mostly done by hand, a process that takes specialists hundreds of hours. Researchers at UCL, Google DeepMind, and Intrinsic have introduced RoboBallet, a new AI system that tackles this challenge head-on. Using a combination of graph neural networks and reinforcement learning, RoboBallet learns how to coordinate many robots in real time, generating smooth, collision-free motion plans in seconds instead of days. In tests, the system handled up to 40 tasks with eight arms working together, far beyond the limits of traditional planners. More importantly, it showed strong generalization: RoboBallet could adapt instantly to new factory layouts or recover if one robot failed, something previous methods struggled with. For manufacturers, this could mean faster deployment of automation, less downtime, and the ability to reconfigure production lines on the fly. Applications range from automotive welding to electronics assembly, and even large-scale construction. The current system focuses on reaching tasks, but the team plans to expand it to more complex operations like pick-and-place or painting. Here's the paper:

A robotic ballet! 🩰 Coordinating multiple robot arms on a busy factory floor is notoriously complex. Each arm needs to move without colliding with its neighbors or the surrounding equipment, and today that planning is still mostly done by hand, a process that takes specialists hundreds of hours. Researchers at UCL, Google DeepMind, and Intrinsic have introduced RoboBallet, a new AI system that tackles this challenge head-on. Using a combination of graph neural networks and reinforcement learning, RoboBallet learns how to coordinate many robots in real time, generating smooth, collision-free motion plans in seconds instead of days. In tests, the system handled up to 40 tasks with eight arms working together, far beyond the limits of traditional planners. More importantly, it showed strong generalization: RoboBallet could adapt instantly to new factory layouts or recover if one robot failed, something previous methods struggled with. For manufacturers, this could mean faster deployment of automation, less downtime, and the ability to reconfigure production lines on the fly. Applications range from automotive welding to electronics assembly, and even large-scale construction. The current system focuses on reaching tasks, but the team plans to expand it to more complex operations like pick-and-place or painting. Here's the paper:

Lukas Ziegler

26,083 次观看 • 10 个月前

LEDGER HARDWARE WALLET COMPARISON 🚨 In this video I will show you all the current Ledger Hardware Wallets in action and tell you in the text below, which one is my favorite out of them, so you can better decide, which one to get for yourself: 1. Nano X (0:08) 2. Nano S Plus (0:52) 3. STAX (1:03) 4. FLEX (2:00) 5. STAX & FLEX side by side (2:50) You can find the different tech specs on the ledger website or feel free to ask in the comments below 👇 After using all of them, my winner is the new FLEX. The touchscreen and the NFC capabilities are a huge improvement over the normal X & S. While the STAX is the more impressive piece of tech with its curved eINK display, the lower price point of the FLEX combined with the more robust build quality, the faster responsiveness of the display and the louder speaker makes it the winner for me 🏆 Which one would you choose? 👇

LEDGER HARDWARE WALLET COMPARISON 🚨 In this video I will show you all the current Ledger Hardware Wallets in action and tell you in the text below, which one is my favorite out of them, so you can better decide, which one to get for yourself: 1. Nano X (0:08) 2. Nano S Plus (0:52) 3. STAX (1:03) 4. FLEX (2:00) 5. STAX & FLEX side by side (2:50) You can find the different tech specs on the ledger website or feel free to ask in the comments below 👇 After using all of them, my winner is the new FLEX. The touchscreen and the NFC capabilities are a huge improvement over the normal X & S. While the STAX is the more impressive piece of tech with its curved eINK display, the lower price point of the FLEX combined with the more robust build quality, the faster responsiveness of the display and the louder speaker makes it the winner for me 🏆 Which one would you choose? 👇

checker4e

22,902 次观看 • 1 年前

Since Covid, the WHO—largely funded by Bill Gates and the pharmaceutical industry—has sought to increase its power to unprecedented levels. "We now have a WHO that promotes vaccines all the time, because that's what the money is coming in to support. So instead of being a World Health Organisation, we have a World Vaccine Organisation." "They're creating a supranational, self-perpetuating pandemic industry... If you get to declare the emergency and then profit from it, there's a big problem." "It's essentially a build-out of Big Pharma, and the WHO is essentially looking to be their marketing and distribution arm worldwide." From the MUST-WATCH new documentary 'The Agenda: Their Vision, Your Future' by Oracle Films. Watch the full documentary using the links below. 👇

Since Covid, the WHO—largely funded by Bill Gates and the pharmaceutical industry—has sought to increase its power to unprecedented levels. "We now have a WHO that promotes vaccines all the time, because that's what the money is coming in to support. So instead of being a World Health Organisation, we have a World Vaccine Organisation." "They're creating a supranational, self-perpetuating pandemic industry... If you get to declare the emergency and then profit from it, there's a big problem." "It's essentially a build-out of Big Pharma, and the WHO is essentially looking to be their marketing and distribution arm worldwide." From the MUST-WATCH new documentary 'The Agenda: Their Vision, Your Future' by Oracle Films. Watch the full documentary using the links below. 👇

Wide Awake Media

30,111 次观看 • 1 年前

SITUATION EXPLAINED: 70% of frontier model queries could run locally for free. clem 🤗, co-founder and CEO of Hugging Face: "There was an interesting study from Stanford published last year showing that 70% of the queries that people ask to ChatGPT could be accurately answered locally on your laptop. For free." "Most of the AI workloads that people do today with frontier models could be done by models that are cheaper, faster, more customizable, more controllable. And they don't do it because frankly it's a pain to pick the right model." "You're subsidized so you don't have to care because you have your subscription, so you route everything to Einstein. 'Hey Einstein, what's the weather today?'" "In normal life, he would be like, 'I'm not answering your silly question.' But because it's AI and subsidized by the AI labs, all the questions are getting routed to Einstein versus in an ideal world you have different models that are more specialized and better at answering your questions in different domains." "It's possible that routing is gonna redistribute a lot of the value capture from frontier models to a more long tail of models. It's like AI maturing... we are in the first phase where it's very simple, everyone using just one giant model. Now we're moving to the second phase."

SITUATION EXPLAINED: 70% of frontier model queries could run locally for free. clem 🤗, co-founder and CEO of Hugging Face: "There was an interesting study from Stanford published last year showing that 70% of the queries that people ask to ChatGPT could be accurately answered locally on your laptop. For free." "Most of the AI workloads that people do today with frontier models could be done by models that are cheaper, faster, more customizable, more controllable. And they don't do it because frankly it's a pain to pick the right model." "You're subsidized so you don't have to care because you have your subscription, so you route everything to Einstein. 'Hey Einstein, what's the weather today?'" "In normal life, he would be like, 'I'm not answering your silly question.' But because it's AI and subsidized by the AI labs, all the questions are getting routed to Einstein versus in an ideal world you have different models that are more specialized and better at answering your questions in different domains." "It's possible that routing is gonna redistribute a lot of the value capture from frontier models to a more long tail of models. It's like AI maturing... we are in the first phase where it's very simple, everyone using just one giant model. Now we're moving to the second phase."

MTS

49,884 次观看 • 1 个月前

AMD CEO LISA SU HAS SAID THE WORLD NEEDS ANOTHER 100X MORE COMPUTE IN THE NEXT 4-5 YEARS She even coined a unit for where that leads: the yottaflop. That is 10 to the 24th power in flops, a one followed by 24 zeros, roughly 100 times more compute than exists today. Her answer to what all of it is for: today's models are already strong, but "it can get so much better." The use case she calls closest to home is writing software, where AI tools are already lifting developer productivity. "AI is really gonna be everywhere and it's really for everyone." Where she thinks we are: "still in the very early innings of really unlocking the power of AI."

AMD CEO LISA SU HAS SAID THE WORLD NEEDS ANOTHER 100X MORE COMPUTE IN THE NEXT 4-5 YEARS She even coined a unit for where that leads: the yottaflop. That is 10 to the 24th power in flops, a one followed by 24 zeros, roughly 100 times more compute than exists today. Her answer to what all of it is for: today's models are already strong, but "it can get so much better." The use case she calls closest to home is writing software, where AI tools are already lifting developer productivity. "AI is really gonna be everywhere and it's really for everyone." Where she thinks we are: "still in the very early innings of really unlocking the power of AI."

WOLF

212,885 次观看 • 17 天前