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This robot just beat Figure's package sorting speed. Robotic arms powered by X Square Robot's WALL-B embodied AI sorted 10K parcels in 5 hours 14 minutes. That's 1.88 sec per parcel, ~35% faster than Figure's reported 2.88 seconds per parcel. Figure's 9-day continuous demo in May this year was... show more

The Humanoid Hub

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37,755 просмотров • 9 дней назад •via X (Twitter)

Наука и технологии Образование Новости и политика

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Final result: 10,000 parcels sorted in 5 hours, 14 minutes, and 1 second 📦⚡ Powered by the WALL-B model, the robots maintained stable, continuous performance throughout the endurance challenge—averaging 1,911 parcels per hour and just 1.88 seconds per parcel. Five hours. 10,000 parcels. WALL-B delivered. 🚀

Final result: 10,000 parcels sorted in 5 hours, 14 minutes, and 1 second 📦⚡ Powered by the WALL-B model, the robots maintained stable, continuous performance throughout the endurance challenge—averaging 1,911 parcels per hour and just 1.88 seconds per parcel. Five hours. 10,000 parcels. WALL-B delivered. 🚀

X Square Robot

21,516 просмотров • 9 дней назад

X Square ran a fully autonomous logistics livestream: sorting parcels and flipping the barcode side up for the scanner. - 1,816 parcels/hour (~2 seconds per parcel) - 98% accuracy - ~45% higher throughput than Figure's sub-3 seconds per parcel pace from its multiday livestream in May. It's driven by X Square's WALL-B embodied AI model, which continuously reacts to an unstructured pile of parcels, sorts and flips them, and recovers from failed grasps and parcel jams to keep the line moving.

X Square ran a fully autonomous logistics livestream: sorting parcels and flipping the barcode side up for the scanner. - 1,816 parcels/hour (~2 seconds per parcel) - 98% accuracy - ~45% higher throughput than Figure's sub-3 seconds per parcel pace from its multiday livestream in May. It's driven by X Square's WALL-B embodied AI model, which continuously reacts to an unstructured pile of parcels, sorts and flips them, and recovers from failed grasps and parcel jams to keep the line moving.

The Humanoid Hub

69,894 просмотров • 17 дней назад

$X Square just beat Figure’s package-sorting pace by ~45%—without a humanoid. In a 1-hour autonomous livestream, two WALL-B-powered 6-axis arms handled 1,816 mixed parcels at 98%+ accuracy. Figure’s 200-hour humanoid run averaged 1,248/hour. That’s 1.98 seconds per parcel vs. 2.88 seconds for Figure. Nearly a full second faster on every package. China processed nearly 199B express parcels in 2025. At that scale, every fraction of a second adds up. Figure is betting on the general-purpose humanoid: one body, eventually many jobs. X Square builds humanoids too, but went pragmatic here—pairing WALL-B with purpose-built arms and grippers for a faster, simpler, more economical logistics system. RobotEra made a similar choice. Its M7 at China Post skips the legs too, using an upper-body humanoid at the sorting line. This is one big difference I keep seeing between U.S. and Chinese robotics: U.S. teams like Figure are making a massive bet on the universal humanoid. Chinese teams are often more willing to change the body if it makes the economics work today. Back in June, I called logistics sorting the right first job for humanoids. X Square is making me rethink the “humanoid” part. If the job doesn’t need legs—or five fingers—why pay for them?$

X Square just beat Figure’s package-sorting pace by ~45%—without a humanoid. In a 1-hour autonomous livestream, two WALL-B-powered 6-axis arms handled 1,816 mixed parcels at 98%+ accuracy. Figure’s 200-hour humanoid run averaged 1,248/hour. That’s 1.98 seconds per parcel vs. 2.88 seconds for Figure. Nearly a full second faster on every package. China processed nearly 199B express parcels in 2025. At that scale, every fraction of a second adds up. Figure is betting on the general-purpose humanoid: one body, eventually many jobs. X Square builds humanoids too, but went pragmatic here—pairing WALL-B with purpose-built arms and grippers for a faster, simpler, more economical logistics system. RobotEra made a similar choice. Its M7 at China Post skips the legs too, using an upper-body humanoid at the sorting line. This is one big difference I keep seeing between U.S. and Chinese robotics: U.S. teams like Figure are making a massive bet on the universal humanoid. Chinese teams are often more willing to change the body if it makes the economics work today. Back in June, I called logistics sorting the right first job for humanoids. X Square is making me rethink the “humanoid” part. If the job doesn’t need legs—or five fingers—why pay for them?

RoboHub🤖

85,776 просмотров • 17 дней назад

A humanoid robot just unloaded a dishwasher for 67 hours straight. By end of 2026, the same robot will perform surgery. That's not a prediction from a sci-fi writer. That's Brett Adcock. CEO of Figure AI. Said it on camera this month. Here's what makes this insane. Two years ago Figure's robot put a Keurig cup in a coffee maker. That was the breakthrough moment. The whole team celebrated. A coffee cup. Today that same robot runs 67 consecutive hours in a BMW factory. Grabbing packages. Finding barcodes. Placing them. One operation every second. Just a neural network that learned by watching. And Brett just said the hands that do that the same fingers, the same sensors, the same neural net are already capable of surgical precision from a hardware standpoint. "From a hardware perspective in 2026 we'll be able to do what surgeons can do." The AI just has to catch up to the hands. Two years ago: a coffee cup. This year: a factory. Next: the operating table. The robot didn't change, the learning did and it's not stopping.

A humanoid robot just unloaded a dishwasher for 67 hours straight. By end of 2026, the same robot will perform surgery. That's not a prediction from a sci-fi writer. That's Brett Adcock. CEO of Figure AI. Said it on camera this month. Here's what makes this insane. Two years ago Figure's robot put a Keurig cup in a coffee maker. That was the breakthrough moment. The whole team celebrated. A coffee cup. Today that same robot runs 67 consecutive hours in a BMW factory. Grabbing packages. Finding barcodes. Placing them. One operation every second. Just a neural network that learned by watching. And Brett just said the hands that do that the same fingers, the same sensors, the same neural net are already capable of surgical precision from a hardware standpoint. "From a hardware perspective in 2026 we'll be able to do what surgeons can do." The AI just has to catch up to the hands. Two years ago: a coffee cup. This year: a factory. Next: the operating table. The robot didn't change, the learning did and it's not stopping.

Shruti

47,597 просмотров • 3 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.

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 • 3 месяцев назад

Figure AI, a 🇺🇸US humanoid robot startup, has ignited debate over robot commercialisation on industrial sites by livestreaming on YouTube a humanoid robot deployed at a logistics warehouse sorting packages non-stop for more than 81 hours, while it processed more than 100,000 packages. “Jim” visually recognizes boxes, identifies the orientation of labels, and moves them to designated locations—making all judgments using only its internal computer. It works autonomously without remote control. Expert opinions are divided. One expert described the demonstration as "closer to a science project than a fully commercial service." She cited accuracy as the biggest challenge, noting that scenes throughout the video showed the robot failing to place parcels in precise locations. Some point out that in actual logistics environments, precision matters more than speed, and areas such as handling exceptions or communication between workers remain difficult for robots at this stage. While the robot's sustainability in simple, repetitive tasks has been confirmed, additional verification remains before broad commercialization, observers said. When commercialised, Figure AI robots are expected to cost from $30,000 per unit.

Figure AI, a 🇺🇸US humanoid robot startup, has ignited debate over robot commercialisation on industrial sites by livestreaming on YouTube a humanoid robot deployed at a logistics warehouse sorting packages non-stop for more than 81 hours, while it processed more than 100,000 packages. “Jim” visually recognizes boxes, identifies the orientation of labels, and moves them to designated locations—making all judgments using only its internal computer. It works autonomously without remote control. Expert opinions are divided. One expert described the demonstration as "closer to a science project than a fully commercial service." She cited accuracy as the biggest challenge, noting that scenes throughout the video showed the robot failing to place parcels in precise locations. Some point out that in actual logistics environments, precision matters more than speed, and areas such as handling exceptions or communication between workers remain difficult for robots at this stage. While the robot's sustainability in simple, repetitive tasks has been confirmed, additional verification remains before broad commercialization, observers said. When commercialised, Figure AI robots are expected to cost from $30,000 per unit.

Dovydas Vitkauskas

21,911 просмотров • 3 месяцев назад

Humanoid logistics sorters are starting real shifts in both the U.S. and China 🤖 Figure is entering Catalyst Brands’ Reno logistics center, inside the retail network behind JCPenney, Aéropostale, and Brooks Brothers. RobotEra’s M7 is working at China Post’s Guangzhou logistics hub, feeding and sorting parcels on live lines. This is the right first job for humanoids: repetitive, physical, structured, high-volume, and easy to measure. Figure’s F.03 ran a 200-hour public sorting test and handled nearly 250,000 parcels. RobotEra’s system is being used for parcel feeding, package orientation, and exception handling, with peak throughput reported around 1,200 parcels per hour. The demand side is obvious. China handled nearly 199 billion express parcels in 2025. Guangzhou alone processed about 21.9 billion parcels, more than enough to turn sorting into a brutal labor and throughput problem. The U.S. has a different pressure point. Parcel volume is smaller, but warehouse labor is much more expensive, so a robot that can work long repetitive shifts has a cleaner ROI story if uptime, maintenance, and deployment cost hold up. The hard part is not the demo. It is barcode orientation, soft bags, crushed boxes, recovery after errors, battery swaps, fleet coordination, and keeping the line moving when nobody is filming. Sorting is still only one link in logistics. The bigger test is whether these robots can move from conveyor work into loading, unloading, picking, exception handling, and eventually the last mile — where the environment stops being friendly. That is the real question now. Not whether humanoids can sort packages, but whether they can earn the next job after sorting.

Humanoid logistics sorters are starting real shifts in both the U.S. and China 🤖 Figure is entering Catalyst Brands’ Reno logistics center, inside the retail network behind JCPenney, Aéropostale, and Brooks Brothers. RobotEra’s M7 is working at China Post’s Guangzhou logistics hub, feeding and sorting parcels on live lines. This is the right first job for humanoids: repetitive, physical, structured, high-volume, and easy to measure. Figure’s F.03 ran a 200-hour public sorting test and handled nearly 250,000 parcels. RobotEra’s system is being used for parcel feeding, package orientation, and exception handling, with peak throughput reported around 1,200 parcels per hour. The demand side is obvious. China handled nearly 199 billion express parcels in 2025. Guangzhou alone processed about 21.9 billion parcels, more than enough to turn sorting into a brutal labor and throughput problem. The U.S. has a different pressure point. Parcel volume is smaller, but warehouse labor is much more expensive, so a robot that can work long repetitive shifts has a cleaner ROI story if uptime, maintenance, and deployment cost hold up. The hard part is not the demo. It is barcode orientation, soft bags, crushed boxes, recovery after errors, battery swaps, fleet coordination, and keeping the line moving when nobody is filming. Sorting is still only one link in logistics. The bigger test is whether these robots can move from conveyor work into loading, unloading, picking, exception handling, and eventually the last mile — where the environment stops being friendly. That is the real question now. Not whether humanoids can sort packages, but whether they can earn the next job after sorting.

RoboHub🤖

37,493 просмотров • 3 месяцев назад

The All-In Podcast discussed Figure's AI breakthrough this week I believe this AI breakthrough was essential to enabling a billion robots on the planet - allowing humans to communicate with them and have them carry out actions Helix, our internal AI model, powers the Figure 02 robot. The robot can now generalize to objects it has never seen before and can semantically plan responses to spoken commands, such as "put the groceries away where you think they should go" It’s important to note: the robot’s actuators are not the limiting factor. Right now, both dexterity and actuator speed are constrained by the software. In fact, the actuators are capable of operating at more than 5x their current speed, but our software is holding them back. Over time, as Helix improves, the robot will ultimately surpass human speeds I like these guys. Looking forward to having Chamath Palihapitiya at Figure HQ to see these robots in action!

The All-In Podcast discussed Figure's AI breakthrough this week I believe this AI breakthrough was essential to enabling a billion robots on the planet - allowing humans to communicate with them and have them carry out actions Helix, our internal AI model, powers the Figure 02 robot. The robot can now generalize to objects it has never seen before and can semantically plan responses to spoken commands, such as "put the groceries away where you think they should go" It’s important to note: the robot’s actuators are not the limiting factor. Right now, both dexterity and actuator speed are constrained by the software. In fact, the actuators are capable of operating at more than 5x their current speed, but our software is holding them back. Over time, as Helix improves, the robot will ultimately surpass human speeds I like these guys. Looking forward to having Chamath Palihapitiya at Figure HQ to see these robots in action!

Brett Adcock

135,552 просмотров • 1 год назад

X Square Robot Unveils New Embodied AI Model, Says Robots Will Arrive in Homes in 35 Days Backed by Alibaba, ByteDance, Xiaomi and Meituan, X Square Robot unveiled a next-generation embodied AI foundation model for home robots and said its first deployments in everyday households will begin within 35 days. X Square Robot on Tuesday unveiled WALL-B, a new embodied AI foundation model designed for deployment in real-world homes, marking what the company described as a major step toward bringing general-purpose robots into daily family life. At a launch event themed "Born to Bot, Bot to Family," the company also introduced its World Unified Model (WUM) architecture, a training framework that combines vision, language, action and physical prediction within a single system from the outset. X Square said the model is intended to help robots operate in the far more unpredictable setting of a home, where tasks, layouts and interactions vary from moment to moment. "Robots in factories and in homes are completely different. In factories, they repeat the same action 10,000 times without variation. In a home, however, they need to perform 10,000 different actions, each unique and non-repetitive. Therefore, the challenge of a truly intelligent robot lies not in repeating a single action, but in the ability to execute new, untrained movements within unstructured environments. Deploying robots in the home is one of the most significant technical hurdles of our time," said Qian Wang, founder and CEO of X Square Robot. WALL-B is the first real-world implementation of the World Unified Model architecture. Unlike modular systems that train perception, language and control separately, X Square Robot said World Unified Model optimizes those capabilities jointly from the very beginning. The company said that allows physical prediction — including force, friction and collision dynamics — to emerge as part of the model itself, rather than being layered on afterward. "We train all capabilities—vision, language, action, and prediction—within the same network from day one. Much like infants, who do not learn to see, move and speak in isolated, sequential stages, but instead see, move listen and act simultaneously while receiving feedback, we have integrated all these capabilities into a unified whole," said Wang Hao, CTO of X Square. X Square Robot said the development of WALL-B rests on two pillars. The first is a data strategy that prioritizes training on authentic, non-staged home environments to cover the “long-tail” distribution of real-world scenarios, such as misplaced objects and temporary occlusions. Unlike models primarily trained on synthetic data or laboratory datasets, this strategy exposes WALL-B to the natural clutter of lived-in spaces—misplaced items, unexpected obstacles, and spontaneous human activity—ensuring that the training data reflects real-world conditions rather than a simplified version. The second is a physics-aware predictive mechanism that anticipates physical outcomes before an action is taken, enabling the model to respond to contact dynamics instead of just reacting. The development of the self-developed WUM architecture on physical robotic platforms highlights the company’s accumlated experience in bridging sim-to-real gaps across varied operational contexts. Wang commented that the current AI model is still in an "intern" stage, subject to errors requiring remote assistance. For instance, it may mistakenly place slippers in the kitchen or pause while wiping a table to "think". However, the model operates nonstop 24 hours a day, becoming increasingly "intelligent" as each day of operation generates new data. In 35 days, on May 25, X Square Robot will officially bring its robots into everyday homes, underscoring the company’s long-term commitment to the home robotics sector.

X Square Robot Unveils New Embodied AI Model, Says Robots Will Arrive in Homes in 35 Days Backed by Alibaba, ByteDance, Xiaomi and Meituan, X Square Robot unveiled a next-generation embodied AI foundation model for home robots and said its first deployments in everyday households will begin within 35 days. X Square Robot on Tuesday unveiled WALL-B, a new embodied AI foundation model designed for deployment in real-world homes, marking what the company described as a major step toward bringing general-purpose robots into daily family life. At a launch event themed "Born to Bot, Bot to Family," the company also introduced its World Unified Model (WUM) architecture, a training framework that combines vision, language, action and physical prediction within a single system from the outset. X Square said the model is intended to help robots operate in the far more unpredictable setting of a home, where tasks, layouts and interactions vary from moment to moment. "Robots in factories and in homes are completely different. In factories, they repeat the same action 10,000 times without variation. In a home, however, they need to perform 10,000 different actions, each unique and non-repetitive. Therefore, the challenge of a truly intelligent robot lies not in repeating a single action, but in the ability to execute new, untrained movements within unstructured environments. Deploying robots in the home is one of the most significant technical hurdles of our time," said Qian Wang, founder and CEO of X Square Robot. WALL-B is the first real-world implementation of the World Unified Model architecture. Unlike modular systems that train perception, language and control separately, X Square Robot said World Unified Model optimizes those capabilities jointly from the very beginning. The company said that allows physical prediction — including force, friction and collision dynamics — to emerge as part of the model itself, rather than being layered on afterward. "We train all capabilities—vision, language, action, and prediction—within the same network from day one. Much like infants, who do not learn to see, move and speak in isolated, sequential stages, but instead see, move listen and act simultaneously while receiving feedback, we have integrated all these capabilities into a unified whole," said Wang Hao, CTO of X Square. X Square Robot said the development of WALL-B rests on two pillars. The first is a data strategy that prioritizes training on authentic, non-staged home environments to cover the “long-tail” distribution of real-world scenarios, such as misplaced objects and temporary occlusions. Unlike models primarily trained on synthetic data or laboratory datasets, this strategy exposes WALL-B to the natural clutter of lived-in spaces—misplaced items, unexpected obstacles, and spontaneous human activity—ensuring that the training data reflects real-world conditions rather than a simplified version. The second is a physics-aware predictive mechanism that anticipates physical outcomes before an action is taken, enabling the model to respond to contact dynamics instead of just reacting. The development of the self-developed WUM architecture on physical robotic platforms highlights the company’s accumlated experience in bridging sim-to-real gaps across varied operational contexts. Wang commented that the current AI model is still in an "intern" stage, subject to errors requiring remote assistance. For instance, it may mistakenly place slippers in the kitchen or pause while wiping a table to "think". However, the model operates nonstop 24 hours a day, becoming increasingly "intelligent" as each day of operation generates new data. In 35 days, on May 25, X Square Robot will officially bring its robots into everyday homes, underscoring the company’s long-term commitment to the home robotics sector.

X Square Robot

52,968 просмотров • 4 месяцев назад

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

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

RoboHub🤖

12,975 просмотров • 2 месяцев назад

This guy connected a computer vision model to dual robotic manipulators on his desk and the system now folds shirts in 47 seconds per garment without any human intervention after loading Automated laundry folding is one of those problems that sounds trivial until you realize fabric has no rigid structure and every wrinkle changes the optimal fold path You need the robot to detect garment boundaries through visual segmentation, identify sleeve edges and collar positions on randomly oriented fabric, generate dynamic reference coordinates that shift with garment size, synchronize two independent robotic arms to pull opposing fabric edges without tearing, and execute all of this without a conveyor belt or fixed staging area Most people assume you need a commercial folding machine or at least a rigid frame to hold clothes in place This guy just bolted two robot arms to a workbench, ran a Flask server with a Laundrobot vision library, and built a preset selection interface that handles nine garment types The setup was minimal: a Python backend processing camera frames, a segmentation model running inference locally, two manipulators with soft grippers, and a heads-up display showing red and blue anchor points overlaid on live fabric The system scans the garment, the vision pipeline outputs coordinates like 284.262 and 965.262, the dashboard waits for a RUN command, and the arms fold the item in two geometric steps The robot picks up shirts, pants, towels, and socks from any position on the desk with zero calibration and zero pre-staging It is the same principle robotic pick-and-place systems use in factories but instead of metal parts it is handling deformable textiles that compress and slide unpredictably The arms have no concept of what clean laundry means to a human They think they are executing waypoint trajectories but the output is getting transformed into neatly stacked garments that take zero cognitive load from the operator If a household generates 14 loads of laundry per month and folding takes eleven minutes per load this is how you reclaim 154 minutes without outsourcing or spending four figures on hardware This is the cleanest domestic automation I have seen: one desk, two arms, one camera, and between them a folding operation that runs while you do anything else

This guy connected a computer vision model to dual robotic manipulators on his desk and the system now folds shirts in 47 seconds per garment without any human intervention after loading Automated laundry folding is one of those problems that sounds trivial until you realize fabric has no rigid structure and every wrinkle changes the optimal fold path You need the robot to detect garment boundaries through visual segmentation, identify sleeve edges and collar positions on randomly oriented fabric, generate dynamic reference coordinates that shift with garment size, synchronize two independent robotic arms to pull opposing fabric edges without tearing, and execute all of this without a conveyor belt or fixed staging area Most people assume you need a commercial folding machine or at least a rigid frame to hold clothes in place This guy just bolted two robot arms to a workbench, ran a Flask server with a Laundrobot vision library, and built a preset selection interface that handles nine garment types The setup was minimal: a Python backend processing camera frames, a segmentation model running inference locally, two manipulators with soft grippers, and a heads-up display showing red and blue anchor points overlaid on live fabric The system scans the garment, the vision pipeline outputs coordinates like 284.262 and 965.262, the dashboard waits for a RUN command, and the arms fold the item in two geometric steps The robot picks up shirts, pants, towels, and socks from any position on the desk with zero calibration and zero pre-staging It is the same principle robotic pick-and-place systems use in factories but instead of metal parts it is handling deformable textiles that compress and slide unpredictably The arms have no concept of what clean laundry means to a human They think they are executing waypoint trajectories but the output is getting transformed into neatly stacked garments that take zero cognitive load from the operator If a household generates 14 loads of laundry per month and folding takes eleven minutes per load this is how you reclaim 154 minutes without outsourcing or spending four figures on hardware This is the cleanest domestic automation I have seen: one desk, two arms, one camera, and between them a folding operation that runs while you do anything else

Blaze

24,799 просмотров • 3 месяцев назад

Milestone! We (robotic arms for gadgets assembly) finished the first commercial order, which brought the first revenue. Here are some learnings from this: The customer was a smart toy manufacturer. The task was to add a heatsink to Raspberry Pi. We received parts from them and returned the assembled modules back. Currently, it's done by teleoperation. Later it will be done by a remote employee via the Internet. Then it will be automated action by action, reducing the operator's time on this and making the task profitable. ps. If you have an assembly task that we can do for you asynchronically - leave a comment below. Learning 1. It's possible! This task which is usually done by the human arm with 5 fingers can be done with a two-finger gripper with the addition of a couple of simple tooling. The task was not simplified. We peeled off thin films from stickers, unpacked paper boxes, moved PCB boards full of components, etc. And no unsolvable problems have been encountered yet. Challenges: 1) The paper box shifted during the opening Solved with the plastic walls that you can lean against 2) Heat pad, stuck to the gripper instead of heat sync. Can be solved by gripper with a pump, but this time solved with the patience of the operator 3) The film on the pad is very thin. Turned out that sub-millimeter arm precision is enough to peel it off with just a regular gripper. 4) The working area has not enough space. You'll only know this by doing real tasks in bulk. This could be solved by an extra pair of long arms, but in this case, solved with the patience of the operator. I think that in the end, we will have 5-10 types of universal tooling and 5-10 types of grippers to solve almost all the problems in such assembly tasks. Learning 2. It's slow. It took 5 times more time, than doing it with human hands. But the good news is there's a lot of room for improvement. We now have specific “time for task” metrics, which we will decrease with iterations. The main reasons for slowness: 1) To rotate the gripper to a steep angle you are forced to control one robot arm with two hands instead of using both arms. We can fix this by just making more room for rotations. 2) Grabbing PCB board with two arms is hard. A slight difference in rotation can break the board, and it's hard to control these angles visually. To solve this, the best way is to use force feedback so you can feel the pressure applied to the item. 3) Accuracy and steadiness is still can be improved We will try a metal version and double the motors to do this. 4) It is physically difficult for the human hands to move with such precision To solve this, we will add a pad for the hands like in surgical robots Learning 3. It's a good business model The "Factory in the cloud" is a good business model for this stage. You send us parts and we send back assembled modules. Currently, it's more convenient than sending a robot to your place, as we can iterate/fix the robot quickly and utilize it 100% of the time. When we polish the set-up over time - we can send robots to your place. So if we can assemble something for you in the USA with Chinese prices by using modern automation - leave a comment below.

Milestone! We (robotic arms for gadgets assembly) finished the first commercial order, which brought the first revenue. Here are some learnings from this: The customer was a smart toy manufacturer. The task was to add a heatsink to Raspberry Pi. We received parts from them and returned the assembled modules back. Currently, it's done by teleoperation. Later it will be done by a remote employee via the Internet. Then it will be automated action by action, reducing the operator's time on this and making the task profitable. ps. If you have an assembly task that we can do for you asynchronically - leave a comment below. Learning 1. It's possible! This task which is usually done by the human arm with 5 fingers can be done with a two-finger gripper with the addition of a couple of simple tooling. The task was not simplified. We peeled off thin films from stickers, unpacked paper boxes, moved PCB boards full of components, etc. And no unsolvable problems have been encountered yet. Challenges: 1) The paper box shifted during the opening Solved with the plastic walls that you can lean against 2) Heat pad, stuck to the gripper instead of heat sync. Can be solved by gripper with a pump, but this time solved with the patience of the operator 3) The film on the pad is very thin. Turned out that sub-millimeter arm precision is enough to peel it off with just a regular gripper. 4) The working area has not enough space. You'll only know this by doing real tasks in bulk. This could be solved by an extra pair of long arms, but in this case, solved with the patience of the operator. I think that in the end, we will have 5-10 types of universal tooling and 5-10 types of grippers to solve almost all the problems in such assembly tasks. Learning 2. It's slow. It took 5 times more time, than doing it with human hands. But the good news is there's a lot of room for improvement. We now have specific “time for task” metrics, which we will decrease with iterations. The main reasons for slowness: 1) To rotate the gripper to a steep angle you are forced to control one robot arm with two hands instead of using both arms. We can fix this by just making more room for rotations. 2) Grabbing PCB board with two arms is hard. A slight difference in rotation can break the board, and it's hard to control these angles visually. To solve this, the best way is to use force feedback so you can feel the pressure applied to the item. 3) Accuracy and steadiness is still can be improved We will try a metal version and double the motors to do this. 4) It is physically difficult for the human hands to move with such precision To solve this, we will add a pad for the hands like in surgical robots Learning 3. It's a good business model The "Factory in the cloud" is a good business model for this stage. You send us parts and we send back assembled modules. Currently, it's more convenient than sending a robot to your place, as we can iterate/fix the robot quickly and utilize it 100% of the time. When we polish the set-up over time - we can send robots to your place. So if we can assemble something for you in the USA with Chinese prices by using modern automation - leave a comment below.

Igor Kulakov

37,266 просмотров • 1 год назад

BREAKING: First-Ever Full Tour of Figure's Humanoid HQ CEO Brett Adcock Exclusive look through every department on their San Jose campus: BotQ Factory, Testing, Design, Demos & more. Brett walks us through how Figure is built: - System integration lab: where robots are stress-tested with software faults & physical pushes - Helix AI: team floor where the controls & neural network engineers train the vision-language-action model that runs onboard every Figure robot - Reinforcement learning & stability testing: where Figure demos the Vulcan project — surviving a lost knee mid-task - Home: environment where Figure 03 autonomously tidies a living room using their Helix neural network (no teleoperation) - BotQ: manufacturing facility where heads, batteries, and limbs come together on the assembly line, including the custom-built battery line & end-of-line burn-in bays - Industrial design studio: (opened publicly for the first time) housing every generation of Figure robot ever built, including: Figure 01 with its Frankenstein forearms, Figure 02, & the sleek Figure 03 that recently appeared at the White House, plus the evolution of Figure's hands & feet Brett shares why he believes humanoid robots may achieve AGI before any other form factor, why Figure pivoted entirely from hand-coded controls to neural networks, & teases that Figure 04 will be their "iPhone 1 moment." This was so much fun! Big thank you to Brett & the team at Figure for opening the doors for us! Brett Adcock Figure 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Inside Figure’s Humanoid Campus (00:48) The humanoid factory (03:18) First humanoid guest at the White House (05:29) Controlling a robot with infinite movements (10:46) The truth about robot failures (13:00) Attacking a humanoid robot (testing responses) (16:12) Building a general purpose robot (23:05) The "Never Fall" protocol (28:56) Is the home robot teleoperated? (33:36) Leasing a 24/7 robot (35:01) Can a humanoid build a real car? (43:32) From flying robots to humanoids (45:59) The hidden path to physical AGI (56:21) Figure's secret design studio (01:00:44) Figure 4: The biggest leap in robotics (01:06:25) Training robots in spandex (01:10:26) Westworld, TIME Magazine, & Deadmau5

BREAKING: First-Ever Full Tour of Figure's Humanoid HQ CEO Brett Adcock Exclusive look through every department on their San Jose campus: BotQ Factory, Testing, Design, Demos & more. Brett walks us through how Figure is built: - System integration lab: where robots are stress-tested with software faults & physical pushes - Helix AI: team floor where the controls & neural network engineers train the vision-language-action model that runs onboard every Figure robot - Reinforcement learning & stability testing: where Figure demos the Vulcan project — surviving a lost knee mid-task - Home: environment where Figure 03 autonomously tidies a living room using their Helix neural network (no teleoperation) - BotQ: manufacturing facility where heads, batteries, and limbs come together on the assembly line, including the custom-built battery line & end-of-line burn-in bays - Industrial design studio: (opened publicly for the first time) housing every generation of Figure robot ever built, including: Figure 01 with its Frankenstein forearms, Figure 02, & the sleek Figure 03 that recently appeared at the White House, plus the evolution of Figure's hands & feet Brett shares why he believes humanoid robots may achieve AGI before any other form factor, why Figure pivoted entirely from hand-coded controls to neural networks, & teases that Figure 04 will be their "iPhone 1 moment." This was so much fun! Big thank you to Brett & the team at Figure for opening the doors for us! Brett Adcock Figure 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Inside Figure’s Humanoid Campus (00:48) The humanoid factory (03:18) First humanoid guest at the White House (05:29) Controlling a robot with infinite movements (10:46) The truth about robot failures (13:00) Attacking a humanoid robot (testing responses) (16:12) Building a general purpose robot (23:05) The "Never Fall" protocol (28:56) Is the home robot teleoperated? (33:36) Leasing a 24/7 robot (35:01) Can a humanoid build a real car? (43:32) From flying robots to humanoids (45:59) The hidden path to physical AGI (56:21) Figure's secret design studio (01:00:44) Figure 4: The biggest leap in robotics (01:06:25) Training robots in spandex (01:10:26) Westworld, TIME Magazine, & Deadmau5

Molly O’Shea

733,985 просмотров • 4 месяцев назад

🇨🇳 It has started. A new home service in China pairs human cleaners with autonomous AI robots to tackle household chores. Residents in Shenzhen can now book a service where a human professional and an autonomous robot arrive together to clean their home. Real houses present a chaotic mess of dropped toys and random furniture that confuse traditional machines. X Square Robot and a major service platform named 58[.]com decided to tackle this chaos by launching China's first robot cleaner service in March-26. Customers use an application to hire a cleaning crew that consists of 1 human worker and 1 robot. The human takes care of the tricky chores that require complex judgment. The robot handles the repetitive physical work like picking up trash and wiping down flat surfaces. This machine runs on a system called WALL-A, which acts as a single continuous AI brain rather than a list of pre-written rules. They built this AI foundation model to perceive its surroundings and make its own decisions without human guidance. It processes visual data and plans multi-step actions. And deploying these robots into actual homes now provides the massive amounts of extremely important training data to improve it continuously. Alibaba and ByteDance backed this project. IMO, if the foundational model behind it figures out how to navigate a messy living room without getting stuck, it can learn to operate in almost any other physical environment.

🇨🇳 It has started. A new home service in China pairs human cleaners with autonomous AI robots to tackle household chores. Residents in Shenzhen can now book a service where a human professional and an autonomous robot arrive together to clean their home. Real houses present a chaotic mess of dropped toys and random furniture that confuse traditional machines. X Square Robot and a major service platform named 58[.]com decided to tackle this chaos by launching China's first robot cleaner service in March-26. Customers use an application to hire a cleaning crew that consists of 1 human worker and 1 robot. The human takes care of the tricky chores that require complex judgment. The robot handles the repetitive physical work like picking up trash and wiping down flat surfaces. This machine runs on a system called WALL-A, which acts as a single continuous AI brain rather than a list of pre-written rules. They built this AI foundation model to perceive its surroundings and make its own decisions without human guidance. It processes visual data and plans multi-step actions. And deploying these robots into actual homes now provides the massive amounts of extremely important training data to improve it continuously. Alibaba and ByteDance backed this project. IMO, if the foundational model behind it figures out how to navigate a messy living room without getting stuck, it can learn to operate in almost any other physical environment.

Rohan Paul

53,039 просмотров • 5 месяцев назад

Ran 21 km (13.1 miles) — and the motor was still cold. That’s the detail that matters. 🤖 Honor was the clear dark horse in this year’s robot half marathon. They swept 1st, 2nd, and 3rd, and also posted a strong top-6 finish overall. What stands out to me is that this was not just about bigger motors, or a gait tuned for long-distance running. They seem to have solved something more important — cooling. In a post-race interview, Honor engineers said the robot used liquid-cooling tech adapted from Honor smartphones, with cooling lines running deep into the motor system to carry heat away. Some reports added more detail: the setup used two high-speed micro pumps, with flow rates reaching up to 6 liters per minute, giving the system enough cooling capacity to handle sustained lower-joint motor load. That matters because once a robot starts overheating, output drops, stability goes with it, and the whole run can fall apart fast. And that’s exactly why this detail is interesting. Of course, that does not mean Honor has already surpassed teams like TienKung or Unitree across humanoid robotics as a whole. What it does suggest is that for the marathon task, they built a very strong system solution. And honestly, that alone is already a useful case for the industry. The bigger trend is moving fast. Last year, TienKung won in around 2 hours 40 minutes. This year, the winning time dropped to 50 minutes 26 seconds. Last year, most robots were still fully remote-controlled or only semi-autonomous. This year, around 40% were running with a much higher level of autonomy. So to me, the real signal is not just that robots got faster. It’s that the field is now moving past raw speed, and into the harder problems: autonomy, stability, and system reliability under load. If the pace of progress stays anywhere close to this, then next year’s race should be even more worth watching.

Ran 21 km (13.1 miles) — and the motor was still cold. That’s the detail that matters. 🤖 Honor was the clear dark horse in this year’s robot half marathon. They swept 1st, 2nd, and 3rd, and also posted a strong top-6 finish overall. What stands out to me is that this was not just about bigger motors, or a gait tuned for long-distance running. They seem to have solved something more important — cooling. In a post-race interview, Honor engineers said the robot used liquid-cooling tech adapted from Honor smartphones, with cooling lines running deep into the motor system to carry heat away. Some reports added more detail: the setup used two high-speed micro pumps, with flow rates reaching up to 6 liters per minute, giving the system enough cooling capacity to handle sustained lower-joint motor load. That matters because once a robot starts overheating, output drops, stability goes with it, and the whole run can fall apart fast. And that’s exactly why this detail is interesting. Of course, that does not mean Honor has already surpassed teams like TienKung or Unitree across humanoid robotics as a whole. What it does suggest is that for the marathon task, they built a very strong system solution. And honestly, that alone is already a useful case for the industry. The bigger trend is moving fast. Last year, TienKung won in around 2 hours 40 minutes. This year, the winning time dropped to 50 minutes 26 seconds. Last year, most robots were still fully remote-controlled or only semi-autonomous. This year, around 40% were running with a much higher level of autonomy. So to me, the real signal is not just that robots got faster. It’s that the field is now moving past raw speed, and into the harder problems: autonomy, stability, and system reliability under load. If the pace of progress stays anywhere close to this, then next year’s race should be even more worth watching.

RoboHub🤖

60,151 просмотров • 4 месяцев назад

A mysterious embodied AI demo has recently sparked a lot of discussion. In the video, two robots from different manufacturers with significantly different hardware architectures, the Unitree G1 and AgiBot Yuanzheng A3, are reportedly running on the same “brain.” In a complex indoor environment, they work continuously for around 10 minutes in a single uncut take, performing a series of long-horizon tasks including cleaning windows, organizing objects, resuming interrupted tasks, and cooperating with each other. What makes it even more interesting is that when one robot cannot reach a high shelf, it attempts to use a box to solve the problem. The two robots also appear capable of cooperating based on each other’s physical capabilities. If the claimed level of autonomy and the use of the same model across different embodiments are eventually verified, I think there are three things that really deserve attention: 1. Cross-embodiment generalization. If the same foundation model can operate two substantially different robotic platforms, it could mean that robotic “intelligence” is gradually becoming decoupled from a specific physical body. 2. Long-horizon closed-loop execution. Continuously performing complex tasks for 10 minutes, while being able to pause, switch tasks, and later resume previous ones, is much more meaningful than completing a single 10-second demo. 3. Collaboration and dynamic planning. The two robots appear able to adjust their behavior according to environmental changes and each other’s physical capabilities. These are some of the characteristics that truly general-purpose embodied intelligence will eventually need. That said, I would remain cautious for now. The video demonstrates extremely impressive behavior, but stronger claims such as “self-evolution,” “true understanding of the physical world,” or overturning the Scaling Law with only dozens of hours of training data still require much stronger evidence. Failure recovery and replanning during a task also do not automatically demonstrate that the model is learning by itself. So I wouldn’t call this the “ChatGPT moment” of embodied AI yet. But if the team later discloses the model architecture, training data scale, level of human intervention, and can repeatedly reproduce these capabilities in completely unfamiliar environments, this seemingly rough 10-minute video could become one of the most memorable embodied AI demos of 2026. For now, my biggest question is simple: Who is the mysterious team behind it?

A mysterious embodied AI demo has recently sparked a lot of discussion. In the video, two robots from different manufacturers with significantly different hardware architectures, the Unitree G1 and AgiBot Yuanzheng A3, are reportedly running on the same “brain.” In a complex indoor environment, they work continuously for around 10 minutes in a single uncut take, performing a series of long-horizon tasks including cleaning windows, organizing objects, resuming interrupted tasks, and cooperating with each other. What makes it even more interesting is that when one robot cannot reach a high shelf, it attempts to use a box to solve the problem. The two robots also appear capable of cooperating based on each other’s physical capabilities. If the claimed level of autonomy and the use of the same model across different embodiments are eventually verified, I think there are three things that really deserve attention: 1. Cross-embodiment generalization. If the same foundation model can operate two substantially different robotic platforms, it could mean that robotic “intelligence” is gradually becoming decoupled from a specific physical body. 2. Long-horizon closed-loop execution. Continuously performing complex tasks for 10 minutes, while being able to pause, switch tasks, and later resume previous ones, is much more meaningful than completing a single 10-second demo. 3. Collaboration and dynamic planning. The two robots appear able to adjust their behavior according to environmental changes and each other’s physical capabilities. These are some of the characteristics that truly general-purpose embodied intelligence will eventually need. That said, I would remain cautious for now. The video demonstrates extremely impressive behavior, but stronger claims such as “self-evolution,” “true understanding of the physical world,” or overturning the Scaling Law with only dozens of hours of training data still require much stronger evidence. Failure recovery and replanning during a task also do not automatically demonstrate that the model is learning by itself. So I wouldn’t call this the “ChatGPT moment” of embodied AI yet. But if the team later discloses the model architecture, training data scale, level of human intervention, and can repeatedly reproduce these capabilities in completely unfamiliar environments, this seemingly rough 10-minute video could become one of the most memorable embodied AI demos of 2026. For now, my biggest question is simple: Who is the mysterious team behind it?

Ice Universe

25,724 просмотров • 4 дней назад

The CEO of Anthropic just said we are 1 to 3 years away from AGI. On camera. With 90% confidence. Almost nobody watched it. INSTEAD OF ARGUING ABOUT AI TAKING JOBS. Spend 2 hours watching the CEO of Anthropic explain exactly when and how it happens. The AI compute arms race just got a reality check nobody expected. Dario Amodei, CEO of Anthropic just did the math out loud: → Revenue growing 10x per year means $100B by end of 2026 and $1T by end of 2027 → At that trajectory you could theoretically buy $5T in compute over 5 years → But if revenue comes in at even $800B instead of $1T, bankruptcy. No exceptions → And if growth slows from 10x to just 5x per year, the entire model collapses by one year This is the most honest thing anyone in AI has said publicly in years. The companies building AI infrastructure are making trillion-dollar bets on growth curves that have never existed before in history. One year off. One multiplier wrong. The whole thing unravels. The AI compute boom is real. The risk underneath it is equally real. The question is not whether AI is the future. It is whether anyone can survive long enough to get there

The CEO of Anthropic just said we are 1 to 3 years away from AGI. On camera. With 90% confidence. Almost nobody watched it. INSTEAD OF ARGUING ABOUT AI TAKING JOBS. Spend 2 hours watching the CEO of Anthropic explain exactly when and how it happens. The AI compute arms race just got a reality check nobody expected. Dario Amodei, CEO of Anthropic just did the math out loud: → Revenue growing 10x per year means $100B by end of 2026 and $1T by end of 2027 → At that trajectory you could theoretically buy $5T in compute over 5 years → But if revenue comes in at even $800B instead of $1T, bankruptcy. No exceptions → And if growth slows from 10x to just 5x per year, the entire model collapses by one year This is the most honest thing anyone in AI has said publicly in years. The companies building AI infrastructure are making trillion-dollar bets on growth curves that have never existed before in history. One year off. One multiplier wrong. The whole thing unravels. The AI compute boom is real. The risk underneath it is equally real. The question is not whether AI is the future. It is whether anyone can survive long enough to get there

Dami-Defi

16,080 просмотров • 3 месяцев назад

Turns out every viral robot demo you've seen was... staged. The backflips, the coffee, the laundry-folding — the robot got handed the answers before the test. Jagdeep Singh says the whole industry's obsession with VLA models is lighting money on fire. You may know my guest as a CEO took QuantumScape public and built Infinera before that, betting against the "impossible" physics twice and proven right each time. Now he's taking another huge swing in Physical AI. The bet everyone else is making: pay humans to tele-operate robots through a task thousands of times, then train on the footage. Jagdeep calls this a dead end. "A drop in the bucket" next to what a robot actually needs to understand the physics of our world. Rhoda AI's approach is revolutionary, training on internet video instead. Turns out 80% of the internet is video, more than enough to teach a model how the physical world really moves. The payoff is that while the the teleoperation crowd burns 70,000 hours of data, some claim 500,000, Rhoda gets a robot doing real factory work on 10 to 20 hours. Proven fact. We're staring down labor shortages in every dirty, dangerous corner of the economy. Turns out the problem was never the robot. It was the number of hours it took to make one useful. Thrilled to be an investor in Jagdeep and Rhoda alongside great investors like Vinod Khosla and Navin Chaddha. New CLIMB is live below and linked in comments 👇

Turns out every viral robot demo you've seen was... staged. The backflips, the coffee, the laundry-folding — the robot got handed the answers before the test. Jagdeep Singh says the whole industry's obsession with VLA models is lighting money on fire. You may know my guest as a CEO took QuantumScape public and built Infinera before that, betting against the "impossible" physics twice and proven right each time. Now he's taking another huge swing in Physical AI. The bet everyone else is making: pay humans to tele-operate robots through a task thousands of times, then train on the footage. Jagdeep calls this a dead end. "A drop in the bucket" next to what a robot actually needs to understand the physics of our world. Rhoda AI's approach is revolutionary, training on internet video instead. Turns out 80% of the internet is video, more than enough to teach a model how the physical world really moves. The payoff is that while the the teleoperation crowd burns 70,000 hours of data, some claim 500,000, Rhoda gets a robot doing real factory work on 10 to 20 hours. Proven fact. We're staring down labor shortages in every dirty, dangerous corner of the economy. Turns out the problem was never the robot. It was the number of hours it took to make one useful. Thrilled to be an investor in Jagdeep and Rhoda alongside great investors like Vinod Khosla and Navin Chaddha. New CLIMB is live below and linked in comments 👇

Jay Kapoor

40,462 просмотров • 3 месяцев назад

How AI Can Supercharge GDP and Bring Down Costs in the Medical Field On E232, Thomas Laffont explained how at a recent event he hosted, there was a lot of talk about AI having a more positive impact on GDP than previously thought. "There was a lot of talk on the GDP side, what if AI can increase productivity and regrow GDP faster than expectations?" He gave an example of doctors using an AI tool to 10x productivity: "Even taking doctors as an example, this new company comes in and develops kind of a diagnosis engine." "And already a third of US physicians are on the platform using it 10x a day to help diagnoses." "You multiply that by the legal profession, coding I think we're already seeing." "What if we just see an explosion of productivity gains across both the physical and the digital economy?" Friedberg broke down how this could also increase patient access and reduce costs while growing GDP: "The doctor one is a good example. If someone had the opportunity to go get more regular preventative checkups, they would." "The problem is, it's very expensive, it's hard to get an appointment, or insurance won't cover it." "But if the cost to a doctor goes down because they can leverage AI, the throughput goes up by 10x." "They can see 10x as many patients per day. Then suddenly diagnostic care becomes more available." "They can charge for that. They don't need to charge the same amount." "The price will come down per checkup, more people will be able to get a checkup per day." "So that grows GDP in diagnostic care."

How AI Can Supercharge GDP and Bring Down Costs in the Medical Field On E232, Thomas Laffont explained how at a recent event he hosted, there was a lot of talk about AI having a more positive impact on GDP than previously thought. "There was a lot of talk on the GDP side, what if AI can increase productivity and regrow GDP faster than expectations?" He gave an example of doctors using an AI tool to 10x productivity: "Even taking doctors as an example, this new company comes in and develops kind of a diagnosis engine." "And already a third of US physicians are on the platform using it 10x a day to help diagnoses." "You multiply that by the legal profession, coding I think we're already seeing." "What if we just see an explosion of productivity gains across both the physical and the digital economy?" Friedberg broke down how this could also increase patient access and reduce costs while growing GDP: "The doctor one is a good example. If someone had the opportunity to go get more regular preventative checkups, they would." "The problem is, it's very expensive, it's hard to get an appointment, or insurance won't cover it." "But if the cost to a doctor goes down because they can leverage AI, the throughput goes up by 10x." "They can see 10x as many patients per day. Then suddenly diagnostic care becomes more available." "They can charge for that. They don't need to charge the same amount." "The price will come down per checkup, more people will be able to get a checkup per day." "So that grows GDP in diagnostic care."

The All-In Podcast

31,353 просмотров • 1 год назад

ANTHROPIC LEAKED A $3.4M FILE THAT BUILDS AN AI BUSINESS AIMED AT $120K A MONTH IN 60 MINUTES AND COSTS $1.55 A DAY team of 5 costs $12,000 a month - this file costs $1.55 a day and never asks for a standup. the whole thing runs on one rule: the cheap model triages, the middle one builds, the expensive one decides. Opus 5 runs exactly once a day and is 13% of the bill - the priciest model is the smallest line on it. Sonnet 5 carries 3 parallel branches, and together with the hunt that's 81% of the entire bill. Haiku 4.5 reads 60 signals for pennies and hands back 12 letters - the block that used to eat your evenings now runs while you sleep. swap the models around and you get 5x overpaid, or a month of selling to nobody. the landing page, payment link, auto-replies and analytics get assembled at once in 12 minutes. 5 dashboards collapse into 3 numbers and one decision - and those numbers get written back into the root. $120K a month is either 400 customers at $300 or 1000 at $120 - and the price decides which of the two machines you built. the hour produces the machine, not the customers - it hunts just as hard even when nobody wants the product. save this and paste it into Claude Code - route by the cost of being wrong, not the price per token ↓

ANTHROPIC LEAKED A $3.4M FILE THAT BUILDS AN AI BUSINESS AIMED AT $120K A MONTH IN 60 MINUTES AND COSTS $1.55 A DAY team of 5 costs $12,000 a month - this file costs $1.55 a day and never asks for a standup. the whole thing runs on one rule: the cheap model triages, the middle one builds, the expensive one decides. Opus 5 runs exactly once a day and is 13% of the bill - the priciest model is the smallest line on it. Sonnet 5 carries 3 parallel branches, and together with the hunt that's 81% of the entire bill. Haiku 4.5 reads 60 signals for pennies and hands back 12 letters - the block that used to eat your evenings now runs while you sleep. swap the models around and you get 5x overpaid, or a month of selling to nobody. the landing page, payment link, auto-replies and analytics get assembled at once in 12 minutes. 5 dashboards collapse into 3 numbers and one decision - and those numbers get written back into the root. $120K a month is either 400 customers at $300 or 1000 at $120 - and the price decides which of the two machines you built. the hour produces the machine, not the customers - it hunts just as hard even when nobody wants the product. save this and paste it into Claude Code - route by the cost of being wrong, not the price per token ↓

Sprytix

29,960 просмотров • 6 дней назад