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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. 🚀

X Square Robot

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21,516 views • 9 days ago •via X (Twitter)

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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 sustained by 5 unique Figure 03 robots taking turns. The same WALL-B model also runs household tasks and fine manipulation across dexterous hands, arms, and mobile manipulators. The highlight is fascinating to watch. The task is to flip the packages label-side up and slide them down the conveyor. Some unusual packages, like soft toys, get routed to a separate lane.

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 sustained by 5 unique Figure 03 robots taking turns. The same WALL-B model also runs household tasks and fine manipulation across dexterous hands, arms, and mobile manipulators. The highlight is fascinating to watch. The task is to flip the packages label-side up and slide them down the conveyor. Some unusual packages, like soft toys, get routed to a separate lane.

The Humanoid Hub

37,755 views • 9 days ago

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 views • 17 days ago

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

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 views • 17 days ago

Our livestream has wrapped—and the final result is in: 1,816 randomly selected parcels sorted per hour, with a success rate of over 98%. Since the beginning of this year, we’ve worked through the entire loop—from collecting real-world data and training the model to continuously testing and refining the system. We often ask ourselves why we remain committed to a purpose-built gripper. The answer is simple: not for hype, and not for a demo, but for real-world deployment. Our goal is to deliver high efficiency at a lower cost. Some jobs are dirty, dull, and dangerous. We want robots to take on more of that work, so people can focus on what is safer, more creative, and more meaningful. We’ll showcase the complete logistics sorting line live at WRC. Visit us in Hall C, Booth 107, and see it in action!

Our livestream has wrapped—and the final result is in: 1,816 randomly selected parcels sorted per hour, with a success rate of over 98%. Since the beginning of this year, we’ve worked through the entire loop—from collecting real-world data and training the model to continuously testing and refining the system. We often ask ourselves why we remain committed to a purpose-built gripper. The answer is simple: not for hype, and not for a demo, but for real-world deployment. Our goal is to deliver high efficiency at a lower cost. Some jobs are dirty, dull, and dangerous. We want robots to take on more of that work, so people can focus on what is safer, more creative, and more meaningful. We’ll showcase the complete logistics sorting line live at WRC. Visit us in Hall C, Booth 107, and see it in action!

X Square Robot

728,029 views • 17 days ago

Two years of vigorous exercise reversed the age-related structural changes in the hearts of sedentary 50-year-olds by about 20 years The protocol used by Dr. Ben Levine's team: • Norwegian 4x4 intervals (initially once per week, later increased to twice per week, eventually returning to once/week) • Strength training (twice weekly) • Recovery aerobics (20-30 minutes of light exercise) • Endurance sessions (weekly 1+ hour endurance training, plus a separate 30-min base-pace session) A few notes: • Exercise intensity and frequency gradually increased from the start • By 6 months, participants were exercising 5–6 hours/week

Two years of vigorous exercise reversed the age-related structural changes in the hearts of sedentary 50-year-olds by about 20 years The protocol used by Dr. Ben Levine's team: • Norwegian 4x4 intervals (initially once per week, later increased to twice per week, eventually returning to once/week) • Strength training (twice weekly) • Recovery aerobics (20-30 minutes of light exercise) • Endurance sessions (weekly 1+ hour endurance training, plus a separate 30-min base-pace session) A few notes: • Exercise intensity and frequency gradually increased from the start • By 6 months, participants were exercising 5–6 hours/week

FoundMyFitness Clips

198,918 views • 10 months ago

Generalist AI unveiled GEN-0, an embodied foundation model family that hits a 7B-parameter “intelligence threshold”; small models ossify, larger ones keep improving, and is trained on 270,000+ hours of real-world manipulation data growing by ~10,000 hours/week. It introduces Harmonic Reasoning (think+act in continuous time), shows predictable scaling laws, runs across 6–16+ DoF robots, and an infra stack that ingests the equivalent of 6.85 years of experience per training day, promising faster adaptation with less post-training for factories, logistics, and service workflows.

Generalist AI unveiled GEN-0, an embodied foundation model family that hits a 7B-parameter “intelligence threshold”; small models ossify, larger ones keep improving, and is trained on 270,000+ hours of real-world manipulation data growing by ~10,000 hours/week. It introduces Harmonic Reasoning (think+act in continuous time), shows predictable scaling laws, runs across 6–16+ DoF robots, and an infra stack that ingests the equivalent of 6.85 years of experience per training day, promising faster adaptation with less post-training for factories, logistics, and service workflows.

Chubby♨️

21,151 views • 9 months ago

An intense two-year exercise regimen, gradually increasing to 5–6 hours per week, reversed up to 20 years of age-related structural changes and stiffness in the hearts of sedentary 50-year-olds This is the protocol, totaling 5-6 hours per week, that Dr. Benjamin Levine and colleagues utilized: • Norwegian 4x4 interval training (once a week, later increasing to twice a week, and eventually going back to just once weekly) • Light aerobic activity on recovery days (on days following interval training sessions, participants engaged in light exercises lasting 20 to 30 minutes ) • Endurance base building (an hour-long, or longer, endurance session and a 30-minute base pace session each week) • Strength training sessions twice weekly Exercise is indeed medicine

An intense two-year exercise regimen, gradually increasing to 5–6 hours per week, reversed up to 20 years of age-related structural changes and stiffness in the hearts of sedentary 50-year-olds This is the protocol, totaling 5-6 hours per week, that Dr. Benjamin Levine and colleagues utilized: • Norwegian 4x4 interval training (once a week, later increasing to twice a week, and eventually going back to just once weekly) • Light aerobic activity on recovery days (on days following interval training sessions, participants engaged in light exercises lasting 20 to 30 minutes ) • Endurance base building (an hour-long, or longer, endurance session and a 30-minute base pace session each week) • Strength training sessions twice weekly Exercise is indeed medicine

FoundMyFitness Clips

259,620 views • 1 year ago

JUST IN: Dyna Robotics just published one of the most important research papers in robotics this year. It could fundamentally change how robot foundation models are trained. A scaling law that transfers from human video to robot performance. Dyna-2 is out and it's 🔥 Here's what that means in plain terms. Dyna-2 was pre-trained on ONE MILLION hours of egocentric human video, 170 years of continuous human experience, cooking, folding, assembling, cleaning. And as that human data scaled, robot performance improved. Predictably. Monotonically. Across 39 tasks on two different robot embodiments the model had never seen. → 1,000 hours pre-training → 20% normalised task performance → 10,000 hours → 28% → 100,000 hours → 45% → 1,000,000 hours → 53% Human video exists at effectively unlimited scale. Every cook, every factory worker, every craftsperson wearing a camera is generating training data for future robots. But the finding that stunned even the researchers, world modeling is what makes the transfer work. A model trained to predict future video AND actions massively outperforms one trained on actions alone. Video is the new scaling axis for robotics. One more jaw-dropping data point. 13 minutes of teleoperation data was enough to fine-tune Dyna-2 to open a bottle cap using two five-fingered robot hands. The robots are coming, and they're learning from us directly :D Read more here: Congrats Jason Ma and team! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

JUST IN: Dyna Robotics just published one of the most important research papers in robotics this year. It could fundamentally change how robot foundation models are trained. A scaling law that transfers from human video to robot performance. Dyna-2 is out and it's 🔥 Here's what that means in plain terms. Dyna-2 was pre-trained on ONE MILLION hours of egocentric human video, 170 years of continuous human experience, cooking, folding, assembling, cleaning. And as that human data scaled, robot performance improved. Predictably. Monotonically. Across 39 tasks on two different robot embodiments the model had never seen. → 1,000 hours pre-training → 20% normalised task performance → 10,000 hours → 28% → 100,000 hours → 45% → 1,000,000 hours → 53% Human video exists at effectively unlimited scale. Every cook, every factory worker, every craftsperson wearing a camera is generating training data for future robots. But the finding that stunned even the researchers, world modeling is what makes the transfer work. A model trained to predict future video AND actions massively outperforms one trained on actions alone. Video is the new scaling axis for robotics. One more jaw-dropping data point. 13 minutes of teleoperation data was enough to fine-tune Dyna-2 to open a bottle cap using two five-fingered robot hands. The robots are coming, and they're learning from us directly :D Read more here: Congrats Jason Ma and team! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

23,556 views • 19 days ago

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 views • 3 months ago

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 views • 4 months ago

Smash a Maserati into a wall at 100 miles per hour and it is gone. Tap the same wall 100 times at one mile per hour and it drives home. Nassim Taleb showed this for free at Google in 2012. He was an options trader for twenty years before anyone called him a philosopher. He told the room Harvard and Stanford teach students they can measure risk. "In reality, we can't measure risk. I can measure fragility." Fragility has one rule. The bigger the single dose, the worse the damage. Jump from 10 meters and you die. Jump 10 centimeters a hundred times and nothing happens. Same total distance. He pulled the same math on money. A 100 million pound project in the UK had 30% higher cost overruns than a 5 million pound one. Not worse management. Worse physics. Size itself is fragility. Then he flipped the famous line. "What doesn't kill me makes me stronger" is wrong. What kills me makes others stronger. The survivors learn. The dead don't get credit. The full lecture is in this post. Fifty five minutes. He opens by asking the audience to interrupt him if they're angry. They do.

Smash a Maserati into a wall at 100 miles per hour and it is gone. Tap the same wall 100 times at one mile per hour and it drives home. Nassim Taleb showed this for free at Google in 2012. He was an options trader for twenty years before anyone called him a philosopher. He told the room Harvard and Stanford teach students they can measure risk. "In reality, we can't measure risk. I can measure fragility." Fragility has one rule. The bigger the single dose, the worse the damage. Jump from 10 meters and you die. Jump 10 centimeters a hundred times and nothing happens. Same total distance. He pulled the same math on money. A 100 million pound project in the UK had 30% higher cost overruns than a 5 million pound one. Not worse management. Worse physics. Size itself is fragility. Then he flipped the famous line. "What doesn't kill me makes me stronger" is wrong. What kills me makes others stronger. The survivors learn. The dead don't get credit. The full lecture is in this post. Fifty five minutes. He opens by asking the audience to interrupt him if they're angry. They do.

Zyron

228,168 views • 5 days ago

This guy used Claude to build a Quant Bot and made +$589,139 on Polymarket. 25,388 predictions with a 63% win rate in 77 days. That comes out to roughly $7,651 in profit per day and almost 14 trades per hour. With $217K in deposits, he is now up 2.7x. About +271.5% ROI. The bot strategy is simple: It finds markets where the pricing is still off. Gets in before the odds fully catch up. Repeats the same setup again and again across many entries. The edge on each trade is small. But scale and repetition turn it into a massive result. Most profitable trades: $17,839 → $36,318 (+$18,478, +103.58%) $3,112 → $15,011 (+$11,898, +382.22%) $10,676 → $22,178 (+$11,502, +107.74%) What makes it work is not one huge trade. It is the same small advantage, applied fast, applied often, and repeated until the pricing gap is gone. I reverse-engineered it. Had Claude rebuild the same logic. One prompt. 90 minutes. Fully deployed. You only need: Claude + a device + 1 hour per day. Giving this free for 24 hours. To get it: 1. Comment the word PROFIT 2. Like and retweet this 3. Follow me Himanshu Kumar so I can DM you

This guy used Claude to build a Quant Bot and made +$589,139 on Polymarket. 25,388 predictions with a 63% win rate in 77 days. That comes out to roughly $7,651 in profit per day and almost 14 trades per hour. With $217K in deposits, he is now up 2.7x. About +271.5% ROI. The bot strategy is simple: It finds markets where the pricing is still off. Gets in before the odds fully catch up. Repeats the same setup again and again across many entries. The edge on each trade is small. But scale and repetition turn it into a massive result. Most profitable trades: $17,839 → $36,318 (+$18,478, +103.58%) $3,112 → $15,011 (+$11,898, +382.22%) $10,676 → $22,178 (+$11,502, +107.74%) What makes it work is not one huge trade. It is the same small advantage, applied fast, applied often, and repeated until the pricing gap is gone. I reverse-engineered it. Had Claude rebuild the same logic. One prompt. 90 minutes. Fully deployed. You only need: Claude + a device + 1 hour per day. Giving this free for 24 hours. To get it: 1. Comment the word PROFIT 2. Like and retweet this 3. Follow me Himanshu Kumar so I can DM you

Himanshu Kumar

85,687 views • 2 months ago

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 views • 3 months ago

A new father became so terrified of never learning anything again that he accidentally dismantled the biggest lie in education. His name is Josh Kaufman, and he wasn't a neuroscientist or a professor. He was an author working from home, running a business with his wife, with a newborn daughter who had just obliterated any concept of free time he thought he had. Around week 8 of sleep deprivation, he had the thought every parent has. I am never going to learn anything new ever again. And because he was the kind of person who responds to panic with research, he went to the library and started reading everything he could find about how humans acquire skills. He read book after book, study after study. Every single one said the same thing. 10,000 hours. He had a full-body reaction to that number. 10,000 hours is a full-time job for five years. He didn't have five years. He didn't have five hours. He had a newborn and a business and a wife who was also building a business in the same house. So he kept digging. And here is where it gets interesting. The 10,000 hour rule came from a researcher named K. Anders Ericsson at Florida State University. What Ericsson actually studied was professional athletes, world-class musicians, chess grandmasters people at the absolute tip of ultra-competitive, ultra-high-performing fields. His finding was that the people at the very top of those narrow fields had put in around 10,000 hours of deliberate practice. That is all the finding said. Then Malcolm Gladwell wrote Outliers in 2007, and the message went through a game of telephone that destroyed its meaning entirely. It takes 10,000 hours to reach the top of an ultra-competitive field became it takes 10,000 hours to become an expert, which became it takes 10,000 hours to become good at something, which became it takes 10,000 hours to learn something. That last statement is completely false. And the actual research had been showing something different the entire time. When cognitive psychologists study skill acquisition, they measure a graph that looks identical across every domain they have ever tested. At the start, performance is terrible. With a small amount of practice, it improves rapidly. Then it plateaus, and subsequent gains become much harder and slower to achieve. The steep part of that curve the jump from knowing nothing to being reasonably good happens much faster than anyone tells you. Not 10,000 hours. Not 1,000 hours. 20 hours. Kaufman tested this himself. He had always wanted to learn ukulele. He picked one up, put 20 hours of focused deliberate practice into it, and stood on a TEDx stage playing a medley of recognizable pop songs in front of a live audience. The crowd went wild. He then told them that performance was his 20th hour. But 20 hours is not just a number. There is a method inside it. The first step is to deconstruct the skill. Most things we think of as single skills are actually bundles of dozens of smaller skills. You do not need all of them. You need the ones that get you to your specific goal the fastest. In music, this means most songs use four or five chords. Learn those first. Ignore the rest until they matter. The second step is to learn just enough to self-correct. Get three to five resources books, courses, videos but do not use them as a reason to delay practice. The point of learning is not to master theory first. It is to get good enough at noticing your own mistakes that you can adjust as you go. The third step is to remove barriers to practice. Not through willpower. Through structure. If the instrument is in the case in the closet, you will not play it. If your phone is in the room, you will not focus. Kaufman was brutal about this. The environment does the work that discipline cannot sustain. The fourth step is the one that actually makes the system work. Pre-commit to 20 hours before you start. Here is why this matters. Every skill has what he called a frustration barrier. The early part of learning anything is genuinely terrible. You are incompetent and you know it. That feeling is so uncomfortable that most people quit before they ever cross to the other side of the curve. By pre-committing to 20 hours, you are making a contract with yourself to push through the frustration long enough to arrive at the part where things start clicking. The barrier to learning something new is never intellectual. It is emotional. We are afraid of feeling stupid. That fear costs most people everything they could have learned. Kaufman figured this out while holding a baby and running out of time, which is the most human possible condition for having a breakthrough. Most people are waiting for the perfect season to start. He just started. 20 hours is 45 minutes a day for a month. That is it. That is the price of going from knowing nothing to being genuinely capable at almost anything you can name. The 10,000 hour rule was never about learning. It was about becoming the best in the world. You probably do not need to be the best in the world. You just need to start.

A new father became so terrified of never learning anything again that he accidentally dismantled the biggest lie in education. His name is Josh Kaufman, and he wasn't a neuroscientist or a professor. He was an author working from home, running a business with his wife, with a newborn daughter who had just obliterated any concept of free time he thought he had. Around week 8 of sleep deprivation, he had the thought every parent has. I am never going to learn anything new ever again. And because he was the kind of person who responds to panic with research, he went to the library and started reading everything he could find about how humans acquire skills. He read book after book, study after study. Every single one said the same thing. 10,000 hours. He had a full-body reaction to that number. 10,000 hours is a full-time job for five years. He didn't have five years. He didn't have five hours. He had a newborn and a business and a wife who was also building a business in the same house. So he kept digging. And here is where it gets interesting. The 10,000 hour rule came from a researcher named K. Anders Ericsson at Florida State University. What Ericsson actually studied was professional athletes, world-class musicians, chess grandmasters people at the absolute tip of ultra-competitive, ultra-high-performing fields. His finding was that the people at the very top of those narrow fields had put in around 10,000 hours of deliberate practice. That is all the finding said. Then Malcolm Gladwell wrote Outliers in 2007, and the message went through a game of telephone that destroyed its meaning entirely. It takes 10,000 hours to reach the top of an ultra-competitive field became it takes 10,000 hours to become an expert, which became it takes 10,000 hours to become good at something, which became it takes 10,000 hours to learn something. That last statement is completely false. And the actual research had been showing something different the entire time. When cognitive psychologists study skill acquisition, they measure a graph that looks identical across every domain they have ever tested. At the start, performance is terrible. With a small amount of practice, it improves rapidly. Then it plateaus, and subsequent gains become much harder and slower to achieve. The steep part of that curve the jump from knowing nothing to being reasonably good happens much faster than anyone tells you. Not 10,000 hours. Not 1,000 hours. 20 hours. Kaufman tested this himself. He had always wanted to learn ukulele. He picked one up, put 20 hours of focused deliberate practice into it, and stood on a TEDx stage playing a medley of recognizable pop songs in front of a live audience. The crowd went wild. He then told them that performance was his 20th hour. But 20 hours is not just a number. There is a method inside it. The first step is to deconstruct the skill. Most things we think of as single skills are actually bundles of dozens of smaller skills. You do not need all of them. You need the ones that get you to your specific goal the fastest. In music, this means most songs use four or five chords. Learn those first. Ignore the rest until they matter. The second step is to learn just enough to self-correct. Get three to five resources books, courses, videos but do not use them as a reason to delay practice. The point of learning is not to master theory first. It is to get good enough at noticing your own mistakes that you can adjust as you go. The third step is to remove barriers to practice. Not through willpower. Through structure. If the instrument is in the case in the closet, you will not play it. If your phone is in the room, you will not focus. Kaufman was brutal about this. The environment does the work that discipline cannot sustain. The fourth step is the one that actually makes the system work. Pre-commit to 20 hours before you start. Here is why this matters. Every skill has what he called a frustration barrier. The early part of learning anything is genuinely terrible. You are incompetent and you know it. That feeling is so uncomfortable that most people quit before they ever cross to the other side of the curve. By pre-committing to 20 hours, you are making a contract with yourself to push through the frustration long enough to arrive at the part where things start clicking. The barrier to learning something new is never intellectual. It is emotional. We are afraid of feeling stupid. That fear costs most people everything they could have learned. Kaufman figured this out while holding a baby and running out of time, which is the most human possible condition for having a breakthrough. Most people are waiting for the perfect season to start. He just started. 20 hours is 45 minutes a day for a month. That is it. That is the price of going from knowing nothing to being genuinely capable at almost anything you can name. The 10,000 hour rule was never about learning. It was about becoming the best in the world. You probably do not need to be the best in the world. You just need to start.

Ihtesham Ali

44,762 views • 4 months ago

Formula 1 pistons are Forged from ultra light high strength Aluminum alloys like 2618 grade racing alloys, then coated with thermal barrier and anti friction surface treatments, each piston is designed to survive combustion temperatures and inertial loads that would destroy conventional engine components within seconds. At around 20,000 RPM, the piston is reversing direction roughly 333 times per second, meaning it is constantly accelerating and decelerating under combustion pressures while experiencing peak inertial loads in the range of 10,000-15,000 g equivalents in high performance racing conditions. Each piston in a Formula 1 spec engine can cost roughly $3,000 to $7,000 per unit, not because of material alone, but because of the machining precision, weight balancing, and microscopic tolerance control required to keep it stable at that frequency. What makes it remarkable is not that it moves fast, but that it survives repeated directional failure hundreds of times every second without breaking apart for 5000+kms or extreme racing. 📹The AutoLife

Formula 1 pistons are Forged from ultra light high strength Aluminum alloys like 2618 grade racing alloys, then coated with thermal barrier and anti friction surface treatments, each piston is designed to survive combustion temperatures and inertial loads that would destroy conventional engine components within seconds. At around 20,000 RPM, the piston is reversing direction roughly 333 times per second, meaning it is constantly accelerating and decelerating under combustion pressures while experiencing peak inertial loads in the range of 10,000-15,000 g equivalents in high performance racing conditions. Each piston in a Formula 1 spec engine can cost roughly $3,000 to $7,000 per unit, not because of material alone, but because of the machining precision, weight balancing, and microscopic tolerance control required to keep it stable at that frequency. What makes it remarkable is not that it moves fast, but that it survives repeated directional failure hundreds of times every second without breaking apart for 5000+kms or extreme racing. 📹The AutoLife

Ammanichanda

255,989 views • 1 month ago

Hyundai Glovis achieves 100% robot uptime with wireless charging! 🔌 Hyundai Glovis faced a problem common to warehouse automation: charging downtime was killing efficiency. Their fleet of AGVs operated with a 6.75:1 work-to-charge ratio. Every seventh robot was charging at any given moment. This meant 15% operational efficiency loss, or the need to purchase 15% extra robots just to compensate for charging downtime. CaPow solution is wireless power transfer while robots work! 🛜 The Genesis platform uses capacitive charging pads placed in the floor where robots naturally stop during operations, in this case, at picking stations. No docking required, no deviation from routes, no excavation needed. The test compared two identical setups. Section A used three robots with traditional charging (operate until 40% battery, charge to 95%). Section B used three robots with CaPow's system, charging at the picking station while operators picked items from bins. Traditional robots lost 8.3% battery per hour and suffered 33% operational inefficiency (150 minutes downtime out of 447 minutes). CaPowered robots gained 1% battery per hour on average and achieved 100% uptime for the entire 8-hour shift. The math on a 100-robot fleet is clear. Traditional charging means only 85 robots working at any time. To maintain full throughput, you need to buy 15 extra robots plus 15 extra chargers. Those chargers consume valuable warehouse space and add extra downtime as robots travel to and from charging zones. CaPow eliminates all of it. No extra robots, no chargers taking up floor space, no charging routes, no fleet management complexity. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Hyundai Glovis achieves 100% robot uptime with wireless charging! 🔌 Hyundai Glovis faced a problem common to warehouse automation: charging downtime was killing efficiency. Their fleet of AGVs operated with a 6.75:1 work-to-charge ratio. Every seventh robot was charging at any given moment. This meant 15% operational efficiency loss, or the need to purchase 15% extra robots just to compensate for charging downtime. CaPow solution is wireless power transfer while robots work! 🛜 The Genesis platform uses capacitive charging pads placed in the floor where robots naturally stop during operations, in this case, at picking stations. No docking required, no deviation from routes, no excavation needed. The test compared two identical setups. Section A used three robots with traditional charging (operate until 40% battery, charge to 95%). Section B used three robots with CaPow's system, charging at the picking station while operators picked items from bins. Traditional robots lost 8.3% battery per hour and suffered 33% operational inefficiency (150 minutes downtime out of 447 minutes). CaPowered robots gained 1% battery per hour on average and achieved 100% uptime for the entire 8-hour shift. The math on a 100-robot fleet is clear. Traditional charging means only 85 robots working at any time. To maintain full throughput, you need to buy 15 extra robots plus 15 extra chargers. Those chargers consume valuable warehouse space and add extra downtime as robots travel to and from charging zones. CaPow eliminates all of it. No extra robots, no chargers taking up floor space, no charging routes, no fleet management complexity. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

17,778 views • 6 months ago

🚨 Open-source AI just got serious. Alibaba just released Qwen 3.5-Plus 🤯 397B parameters. Only 17B active per inference. Performance comparable to Gemini 3 Pro — at just $0.8 per million tokens (1/18 the cost). But here’s what makes it different 👇 This isn’t just another benchmark drop. In the demo, Qwen runs directly inside the terminal: ➙ Reviews GitHub PRs ➙ Fetches diffs automatically ➙ Runs CLI & smoke tests ➙ Generates integration tests ➙ Executes /context ➙ Shows real-time token usage breakdown That’s not a chatbot. That’s a real developer workflow. Under the hood: ⚡ 60% lower deployment memory ⚡ Inference throughput increased by 8.6× (32K context) ⚡ Inference throughput increased by up to 19× (256K ultra-long context) ⚡ Native multimodal model ⚡ Supports direct video input (up to 2 hours) And the ecosystem is massive: ✔ 201 languages ✔ 250K vocabulary ✔ 400+ open-sourced models ✔ 1B+ downloads ✔ 200K+ derivative models Available now via Qwen App, MoDa Community, Hugging Face, and Alibaba Cloud Bailian. ✅ The AI race isn’t about who’s biggest anymore. → It’s about who’s usable. 🔥 Watch the demo 👇 #Qwen #OpenSourceAI #AI #DevTools

🚨 Open-source AI just got serious. Alibaba just released Qwen 3.5-Plus 🤯 397B parameters. Only 17B active per inference. Performance comparable to Gemini 3 Pro — at just $0.8 per million tokens (1/18 the cost). But here’s what makes it different 👇 This isn’t just another benchmark drop. In the demo, Qwen runs directly inside the terminal: ➙ Reviews GitHub PRs ➙ Fetches diffs automatically ➙ Runs CLI & smoke tests ➙ Generates integration tests ➙ Executes /context ➙ Shows real-time token usage breakdown That’s not a chatbot. That’s a real developer workflow. Under the hood: ⚡ 60% lower deployment memory ⚡ Inference throughput increased by 8.6× (32K context) ⚡ Inference throughput increased by up to 19× (256K ultra-long context) ⚡ Native multimodal model ⚡ Supports direct video input (up to 2 hours) And the ecosystem is massive: ✔ 201 languages ✔ 250K vocabulary ✔ 400+ open-sourced models ✔ 1B+ downloads ✔ 200K+ derivative models Available now via Qwen App, MoDa Community, Hugging Face, and Alibaba Cloud Bailian. ✅ The AI race isn’t about who’s biggest anymore. → It’s about who’s usable. 🔥 Watch the demo 👇 #Qwen #OpenSourceAI #AI #DevTools

Toha Khan

123,375 views • 6 months ago

This trader built a bot with Claude Fable 5 that makes 876 trades per hour. Result: $226,000 profit on Polymarket. Starting capital: $2,366. Time frame: 1 month. The bot does high-frequency scalping and arbitrages 5-min and 15-min Bitcoin markets. Execution speed: 14.6 trades per minute. The strategy is simple: 1. Buys only with limit orders to control entry price Average market cost: $10 per position. No market orders. No slippage. Just precise entries. 2. Uses 5-min markets for arbitrage Average edge per trade: 7.27% The bot identifies mispriced outcomes and captures the spread before it closes. 3. Builds directional positions on 15-min markets Trigger: order book imbalance appears. The edge is reading order flow faster than everyone else and leaning into the side with momentum. The entire edge is execution speed + order book reading. While manual traders place 1-2 trades per hour, this bot completes 876. No hesitation. No emotion. Just high-frequency scalping repeating itself at scale. Some context: Most people try to predict where Bitcoin goes next. This system just identifies arbitrage windows on 5-min markets, builds directional positions when order book imbalance appears on 15-min markets, and captures micro-edges before they disappear. Limit order arbitrage + scalping turned $2,366 into $226,000 in 30 days. The system runs autonomous: → Claude Fable 5 handles decision logic → Monitors 5-min and 15-min BTC markets continuously → Executes limit orders when arbitrage edge appears → Builds directional positions on order book imbalance → Scalps micro-edges at 14.6 trades per minute No manual trading. No chart analysis. Just finding mispriced markets and exploiting the edge before anyone else. 💡 I'm sharing the complete Claude Fable 5 prompt and high-frequency scalping workflow. Free for 24 hours. To get it: 1️⃣ Comment the "Fable" 2️⃣ Like and Repost 3️⃣ Follow Himanshu Kumar I'll DM you the setup.

This trader built a bot with Claude Fable 5 that makes 876 trades per hour. Result: $226,000 profit on Polymarket. Starting capital: $2,366. Time frame: 1 month. The bot does high-frequency scalping and arbitrages 5-min and 15-min Bitcoin markets. Execution speed: 14.6 trades per minute. The strategy is simple: 1. Buys only with limit orders to control entry price Average market cost: $10 per position. No market orders. No slippage. Just precise entries. 2. Uses 5-min markets for arbitrage Average edge per trade: 7.27% The bot identifies mispriced outcomes and captures the spread before it closes. 3. Builds directional positions on 15-min markets Trigger: order book imbalance appears. The edge is reading order flow faster than everyone else and leaning into the side with momentum. The entire edge is execution speed + order book reading. While manual traders place 1-2 trades per hour, this bot completes 876. No hesitation. No emotion. Just high-frequency scalping repeating itself at scale. Some context: Most people try to predict where Bitcoin goes next. This system just identifies arbitrage windows on 5-min markets, builds directional positions when order book imbalance appears on 15-min markets, and captures micro-edges before they disappear. Limit order arbitrage + scalping turned $2,366 into $226,000 in 30 days. The system runs autonomous: → Claude Fable 5 handles decision logic → Monitors 5-min and 15-min BTC markets continuously → Executes limit orders when arbitrage edge appears → Builds directional positions on order book imbalance → Scalps micro-edges at 14.6 trades per minute No manual trading. No chart analysis. Just finding mispriced markets and exploiting the edge before anyone else. 💡 I'm sharing the complete Claude Fable 5 prompt and high-frequency scalping workflow. Free for 24 hours. To get it: 1️⃣ Comment the "Fable" 2️⃣ Like and Repost 3️⃣ Follow Himanshu Kumar I'll DM you the setup.

Himanshu Kumar

55,447 views • 21 days ago

AI UGC talking head is fire.. 🔥 preview of how we create content for Arcade this costs less than $1 to make this system has: realistic voice with no robotic AI tone natural human-like movement premium-quality visuals and these videos are insanely fast to create. no hours of editing just to get a decent result. we made this Arcade video in under 7 minutes from first prompt to final export. AI content creation is changing the game. 🚀 Comment 'ARCADE' and I'll send you the step by step guide

AI UGC talking head is fire.. 🔥 preview of how we create content for Arcade this costs less than $1 to make this system has: realistic voice with no robotic AI tone natural human-like movement premium-quality visuals and these videos are insanely fast to create. no hours of editing just to get a decent result. we made this Arcade video in under 7 minutes from first prompt to final export. AI content creation is changing the game. 🚀 Comment 'ARCADE' and I'll send you the step by step guide

Oliver

11,279 views • 2 months ago