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40 hours of human work. That’s what this humanoid could save every month! A construction company is already testing a Unitree G1 on a real job site, using the robot for site inspections, 360° imaging, data collection and progress monitoring. The robot starts at around $13,500, while the company...

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Future Memo

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18,925 görüntüleme • 12 gün önce •via X (Twitter)

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A HUMANOID ROBOT JUST FOUND ONE OF THE MOST EXPENSIVE AISLES IN THE WORLD. An airplane cabin. Think about why this environment is so interesting. Narrow aisle. Human-sized equipment. Repetitive service. Hundreds of passengers. Thousands of flights every day. And almost everything inside the cabin was already designed around the dimensions of a human worker. That's exactly why the humanoid form matters. You don't redesign the aircraft. You build a machine capable of operating inside what already exists. But the bigger opportunity isn't necessarily replacing the crew. Imagine robots eventually handling repetitive cabin tasks while humans remain responsible for safety, judgment and passenger care. Drinks. Trash collection. Moving supplies. Routine cabin checks. Every small task removed is another minute of human labor redirected somewhere more valuable. That's how humanoid automation may actually arrive. Not: “This robot replaced five people.” But: “This robot removed 30% of the repetitive work from their shift.” Multiply that across an entire industry... and suddenly the boring little drink cart becomes a very interesting business case.
0:15

A HUMANOID ROBOT JUST FOUND ONE OF THE MOST EXPENSIVE AISLES IN THE WORLD. An airplane cabin. Think about why this environment is so interesting. Narrow aisle. Human-sized equipment. Repetitive service. Hundreds of passengers. Thousands of flights every day. And almost everything inside the cabin was already designed around the dimensions of a human worker. That's exactly why the humanoid form matters. You don't redesign the aircraft. You build a machine capable of operating inside what already exists. But the bigger opportunity isn't necessarily replacing the crew. Imagine robots eventually handling repetitive cabin tasks while humans remain responsible for safety, judgment and passenger care. Drinks. Trash collection. Moving supplies. Routine cabin checks. Every small task removed is another minute of human labor redirected somewhere more valuable. That's how humanoid automation may actually arrive. Not: “This robot replaced five people.” But: “This robot removed 30% of the repetitive work from their shift.” Multiply that across an entire industry... and suddenly the boring little drink cart becomes a very interesting business case.

Mag1strY0da

16,282 görüntüleme • 6 gün önce

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

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

RoboHub🤖

16,818 görüntüleme • 4 ay önce

⚠️ THE WEIRDEST PART OF THIS ROBOT DEMO? NOBODY SEEMS SURPRISED ANYMORE! Humanoid robots are moving through a crowded public space while people walk around them like they're just another part of the environment. There’s no empty laboratory, no isolated testing area and no perfect path marked on the floor — just machines and humans sharing the same space. That makes this feel less like a robot demonstration and more like a preview of everyday life. Getting one humanoid to walk is one problem. Getting several machines to move safely around unpredictable people is another, because humans constantly stop, turn, cross paths and change direction without warning. Every movement requires the robots to perceive what is happening around them, maintain balance and continuously adjust their path. That's the shift worth paying attention to. The milestone isn't simply that robots can walk among humans — it's that we're starting to see environments where they can actually belong. Once that becomes reliable enough for shopping centers, airports, factories and offices, the question stops being whether humanoids can enter our world. It becomes what we're going to ask them to do once they're here.
0:12

⚠️ THE WEIRDEST PART OF THIS ROBOT DEMO? NOBODY SEEMS SURPRISED ANYMORE! Humanoid robots are moving through a crowded public space while people walk around them like they're just another part of the environment. There’s no empty laboratory, no isolated testing area and no perfect path marked on the floor — just machines and humans sharing the same space. That makes this feel less like a robot demonstration and more like a preview of everyday life. Getting one humanoid to walk is one problem. Getting several machines to move safely around unpredictable people is another, because humans constantly stop, turn, cross paths and change direction without warning. Every movement requires the robots to perceive what is happening around them, maintain balance and continuously adjust their path. That's the shift worth paying attention to. The milestone isn't simply that robots can walk among humans — it's that we're starting to see environments where they can actually belong. Once that becomes reliable enough for shopping centers, airports, factories and offices, the question stops being whether humanoids can enter our world. It becomes what we're going to ask them to do once they're here.

Future Memo

35,742 görüntüleme • 1 ay önce

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

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

Rohan Paul

37,121 görüntüleme • 7 ay önce

JUST IN: Reimagine Robotics has just emerged from stealth! 🥷🏻 Its approach to robot training is one of the most human-centric I've seen. The founder is Jonathan Scholz, the person who built and led Google DeepMind's Applied Robotics team in London for seven years. This is not a first-time founder taking a swing at robotics. He has spent a decade at the frontier of the field. The philosophy is powerful. He calls it "monkey-see, monkey-do." 🐒 A worker shows the robot what to do. Watches it attempt the task. Corrects it on the spot. The robot learns. No specialist programmers. No months of integration. And it's already working in the real world: → A made-to-order plastics business trained robots to tend 3D printers overnight, removing print beds, operating latches, pressing controls → A hard drive disassembly facility built a three-robot cell combining robots and people to recover critical materials → Time to prototype and test a new robot behaviour reduced from one day to 10 MINUTES That last number is the one that changes everything. When testing a new behaviour takes 10 minutes instead of a day, the entire pace of deployment transforms. Scholz's framing of the human-robot relationship is worth reading carefully: "A robot that learns on the job depends on people. The worker identifies the bottleneck, shows the robot how to help, and corrects it until it is useful." It's August, and we keep getting robotics bangers week in week. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:14

JUST IN: Reimagine Robotics has just emerged from stealth! 🥷🏻 Its approach to robot training is one of the most human-centric I've seen. The founder is Jonathan Scholz, the person who built and led Google DeepMind's Applied Robotics team in London for seven years. This is not a first-time founder taking a swing at robotics. He has spent a decade at the frontier of the field. The philosophy is powerful. He calls it "monkey-see, monkey-do." 🐒 A worker shows the robot what to do. Watches it attempt the task. Corrects it on the spot. The robot learns. No specialist programmers. No months of integration. And it's already working in the real world: → A made-to-order plastics business trained robots to tend 3D printers overnight, removing print beds, operating latches, pressing controls → A hard drive disassembly facility built a three-robot cell combining robots and people to recover critical materials → Time to prototype and test a new robot behaviour reduced from one day to 10 MINUTES That last number is the one that changes everything. When testing a new behaviour takes 10 minutes instead of a day, the entire pace of deployment transforms. Scholz's framing of the human-robot relationship is worth reading carefully: "A robot that learns on the job depends on people. The worker identifies the bottleneck, shows the robot how to help, and corrects it until it is useful." It's August, and we keep getting robotics bangers week in week. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

23,389 görüntüleme • 1 ay önce

TESLA HALTED MODEL S AND MODEL X PRODUCTION TO BUILD AN ARMY OF OPTIMUS ROBOTS The Fremont assembly line was torn down in 46 days. In its place, Tesla is building a line for humanoid production, aiming for a million units a year A humanoid robot is a body shaped like a human. Physical AI is the intelligence that controls that body Walking and making coffee is often just imitation learning from a scripted routine. But once the environment shifts, the learned trick stops working Language models had the entire internet to train on. Robotics has nothing close to that scale of data, which is why one giant brain hasn't worked for anyone yet The industry is moving toward modularity instead - separate models for vision, movement, and planning, each improved on its own The real question is no longer whether a robot can move impressively. It's whether it can pull its sensors into one picture of the world and adapt to whatever wasn't scripted for it
0:12

TESLA HALTED MODEL S AND MODEL X PRODUCTION TO BUILD AN ARMY OF OPTIMUS ROBOTS The Fremont assembly line was torn down in 46 days. In its place, Tesla is building a line for humanoid production, aiming for a million units a year A humanoid robot is a body shaped like a human. Physical AI is the intelligence that controls that body Walking and making coffee is often just imitation learning from a scripted routine. But once the environment shifts, the learned trick stops working Language models had the entire internet to train on. Robotics has nothing close to that scale of data, which is why one giant brain hasn't worked for anyone yet The industry is moving toward modularity instead - separate models for vision, movement, and planning, each improved on its own The real question is no longer whether a robot can move impressively. It's whether it can pull its sensors into one picture of the world and adapt to whatever wasn't scripted for it

iamigorekk

22,205 görüntüleme • 1 ay önce

this is more useful than my entire degree Elon Musk's rocket company signed a $60,000,000,000 deal for Cursor in June, and eight days ago the two of them put a worker on sale for $200 a month: it gets its own computer in the cloud, signs into your accounts, clicks through your real apps, and hands back finished work instead of a draft for you to paste i ran one against my receipts folder on sunday and got back 14 filed, 2 it held because they needed a card number, and a saved method i never wrote myself Grok Bot is the one you train by doing your own job in front of it, and the whole handover fits in four messages tonight: 1. write out one job you did today the way you would brief a new hire: what has to be finished, which sites and files to work from, what to hand back, and where it stops and asks you 2. let it run once on something safe to get wrong, then correct the result until it is worth your name 3. say "save what we just did as a skill", and add the one rule about what always needs your approval 4. say "run that skill every weekday at 8 and post the result here. if the source is missing, tell me instead of using yesterday's numbers" xAI wrote that order into its own manual: one real job, then the saved method, then the clock. a schedule sitting on top of a method nobody checked replaces two hours of your clicking with two hours of your mistake turns out you never get to pick the brain, and that is the part i would argue about: the manual says there is no model picker for members or admins, no plan to add one, and the bill follows whichever model answered bookmark this, then open the piece below: which jobs deserve a worker of their own, and which ones quietly burn the seat ↓
0:15

this is more useful than my entire degree Elon Musk's rocket company signed a $60,000,000,000 deal for Cursor in June, and eight days ago the two of them put a worker on sale for $200 a month: it gets its own computer in the cloud, signs into your accounts, clicks through your real apps, and hands back finished work instead of a draft for you to paste i ran one against my receipts folder on sunday and got back 14 filed, 2 it held because they needed a card number, and a saved method i never wrote myself Grok Bot is the one you train by doing your own job in front of it, and the whole handover fits in four messages tonight: 1. write out one job you did today the way you would brief a new hire: what has to be finished, which sites and files to work from, what to hand back, and where it stops and asks you 2. let it run once on something safe to get wrong, then correct the result until it is worth your name 3. say "save what we just did as a skill", and add the one rule about what always needs your approval 4. say "run that skill every weekday at 8 and post the result here. if the source is missing, tell me instead of using yesterday's numbers" xAI wrote that order into its own manual: one real job, then the saved method, then the clock. a schedule sitting on top of a method nobody checked replaces two hours of your clicking with two hours of your mistake turns out you never get to pick the brain, and that is the part i would argue about: the manual says there is no model picker for members or admins, no plan to add one, and the bill follows whichever model answered bookmark this, then open the piece below: which jobs deserve a worker of their own, and which ones quietly burn the seat ↓

Argona

21,946 görüntüleme • 1 ay önce

If you’ve been ignoring Axis Robotics because it looks like another random points farm, read this. In the last 48 hours, Unitree said robotics is approaching its "ChatGPT moment", while the chairman of ACE Robotics believes it could happen by the end of 2027. But for robots to reach that level, they need a crazy amount of training data. That’s exactly what Axis is building. You control simulated robots directly from your browser, complete simple tasks and earn points. Those movements also help create data that can be used to train real robots. And this isn’t some tiny experiment anymore: - $12M raised - 123K+ contributors - 3M+ robot trajectories - Community data already used to train a real robot So yeah, we’re basically farming a potential airdrop while teaching our future robot servants how to work 😂 If you haven’t started yet, it’s completely free. You only need a tiny amount of gas on Base to sign your completed tasks. ✅ Start farming Axis points: Important: Sign every completed task from the History page, otherwise you won’t receive the points.
0:12

If you’ve been ignoring Axis Robotics because it looks like another random points farm, read this. In the last 48 hours, Unitree said robotics is approaching its "ChatGPT moment", while the chairman of ACE Robotics believes it could happen by the end of 2027. But for robots to reach that level, they need a crazy amount of training data. That’s exactly what Axis is building. You control simulated robots directly from your browser, complete simple tasks and earn points. Those movements also help create data that can be used to train real robots. And this isn’t some tiny experiment anymore: - $12M raised - 123K+ contributors - 3M+ robot trajectories - Community data already used to train a real robot So yeah, we’re basically farming a potential airdrop while teaching our future robot servants how to work 😂 If you haven’t started yet, it’s completely free. You only need a tiny amount of gas on Base to sign your completed tasks. ✅ Start farming Axis points: Important: Sign every completed task from the History page, otherwise you won’t receive the points.

Pranjal Bora 🧭

29,321 görüntüleme • 1 ay önce

Elon Musk gave the entire entertainment industry its expiration date, and he is the one building the thing that kills it. Musk: “My guess is that we see the first compelling half hour, pure AI show next year.” Next year. A complete show generated entirely by AI. No writers. No actors. No cameras. No sets. No crew. No studio. Just a prompt and enough compute to render a reality that never physically existed. And shows are the easy part. Musk: “I say probably we’re maybe three years away from AI does the whole video game.” A show plays the same way every time. A game has to generate a living world that reacts to every decision in real time across every single frame. That is a fundamentally harder class of problem. And Musk put three years on it. Right now a single AAA title takes seven years and half a billion dollars across thousands of engineers and artists just to ship it. Musk is describing a world where one person types a paragraph and gets something comparable. The entire value proposition of a multi-billion dollar industry lives inside that gap. And it closes in thirty-six months. But the prediction is not the story. The person making it is. This is not an analyst speculating from the sidelines. This is the man building the largest AI compute clusters on the planet. The man who built xAI from zero in under two years. The man stacking hundreds of thousands of GPUs into facilities designed to do exactly what he is describing. When Musk says three years, he is not guessing about what someone else might eventually ship. He is reading you a delivery date off his own roadmap. Every media company on Earth is valued on a single assumption. That quality content is expensive and difficult to produce at scale. That one assumption is the structural foundation underneath every studio, every network, and every publisher in existence. Musk is dismantling it with raw compute. The studios still parading thousand-person production teams are not demonstrating strength. They are advertising the exact cost structure that one person with a prompt and a GPU allocation is about to make irrelevant. And it does not stop at entertainment. If AI can generate an interactive world that responds to human input in real time, it can generate anything. Advertising. Architecture. Training simulations. Product design. Every industry built on humans manually constructing visual experiences frame by frame is sitting on the same countdown Musk just read out loud. Now zoom out. Because this is not just an industry story. For the entire history of human civilization, the distance between imagining a world and actually creating one required thousands of people, millions of hours, and billions of dollars. That distance built Hollywood. That distance built the gaming industry. That distance made content scarce and studios powerful. Musk is collapsing that distance to zero. When the gap between imagining something and it existing disappears, every business model built on the difficulty of creation disappears with it. That is not disruption. That is a full inversion of how human beings create. Musk did not make a casual prediction on that podcast. He told you what he is building. He told you the timeline. And he told you which industries do not survive it. The entertainment industry is still debating whether this future is real. Musk is not part of that debate. He is building. And he just told you the delivery date.
0:17

Elon Musk gave the entire entertainment industry its expiration date, and he is the one building the thing that kills it. Musk: “My guess is that we see the first compelling half hour, pure AI show next year.” Next year. A complete show generated entirely by AI. No writers. No actors. No cameras. No sets. No crew. No studio. Just a prompt and enough compute to render a reality that never physically existed. And shows are the easy part. Musk: “I say probably we’re maybe three years away from AI does the whole video game.” A show plays the same way every time. A game has to generate a living world that reacts to every decision in real time across every single frame. That is a fundamentally harder class of problem. And Musk put three years on it. Right now a single AAA title takes seven years and half a billion dollars across thousands of engineers and artists just to ship it. Musk is describing a world where one person types a paragraph and gets something comparable. The entire value proposition of a multi-billion dollar industry lives inside that gap. And it closes in thirty-six months. But the prediction is not the story. The person making it is. This is not an analyst speculating from the sidelines. This is the man building the largest AI compute clusters on the planet. The man who built xAI from zero in under two years. The man stacking hundreds of thousands of GPUs into facilities designed to do exactly what he is describing. When Musk says three years, he is not guessing about what someone else might eventually ship. He is reading you a delivery date off his own roadmap. Every media company on Earth is valued on a single assumption. That quality content is expensive and difficult to produce at scale. That one assumption is the structural foundation underneath every studio, every network, and every publisher in existence. Musk is dismantling it with raw compute. The studios still parading thousand-person production teams are not demonstrating strength. They are advertising the exact cost structure that one person with a prompt and a GPU allocation is about to make irrelevant. And it does not stop at entertainment. If AI can generate an interactive world that responds to human input in real time, it can generate anything. Advertising. Architecture. Training simulations. Product design. Every industry built on humans manually constructing visual experiences frame by frame is sitting on the same countdown Musk just read out loud. Now zoom out. Because this is not just an industry story. For the entire history of human civilization, the distance between imagining a world and actually creating one required thousands of people, millions of hours, and billions of dollars. That distance built Hollywood. That distance built the gaming industry. That distance made content scarce and studios powerful. Musk is collapsing that distance to zero. When the gap between imagining something and it existing disappears, every business model built on the difficulty of creation disappears with it. That is not disruption. That is a full inversion of how human beings create. Musk did not make a casual prediction on that podcast. He told you what he is building. He told you the timeline. And he told you which industries do not survive it. The entertainment industry is still debating whether this future is real. Musk is not part of that debate. He is building. And he just told you the delivery date.

Dustin

22,458 görüntüleme • 2 ay önce

$CCXI The humanoid robot Digit from Agility is poised to dominate the logistics operations of giants like $AMZN and $MELI because it is built to fit seamlessly into existing human environments. Standing at 5'9", Digit can operate immediately within current warehouse layouts, completely eliminating the need for companies to spend millions on expensive structural retrofits. Furthermore, major corporations are heavily invested in its success; $AMZN actively supports $AGLT through its Industrial Innovation Fund, while $MELI has already signed commercial agreements to deploy Digit directly into its fulfillment centers. When compared to human labor, Digit offers a highly efficient solution to chronic workforce shortages by taking over the dullest, most repetitive physical tasks. The robot can work continuous 24/7 shifts without fatigue, illness, or breaks.
0:18

$CCXI The humanoid robot Digit from Agility is poised to dominate the logistics operations of giants like $AMZN and $MELI because it is built to fit seamlessly into existing human environments. Standing at 5'9", Digit can operate immediately within current warehouse layouts, completely eliminating the need for companies to spend millions on expensive structural retrofits. Furthermore, major corporations are heavily invested in its success; $AMZN actively supports $AGLT through its Industrial Innovation Fund, while $MELI has already signed commercial agreements to deploy Digit directly into its fulfillment centers. When compared to human labor, Digit offers a highly efficient solution to chronic workforce shortages by taking over the dullest, most repetitive physical tasks. The robot can work continuous 24/7 shifts without fatigue, illness, or breaks.

Pep Invest

24,891 görüntüleme • 2 ay önce

⚠️ FRAMERS, THAT HOUSE YOU'RE BUILDING BY HAND IS BEING BUILT BY A ROBOT CREW SOMEWHERE ELSE RIGHT NOW A dozen Tesla Optimus units just framed an entire house from the foundation up, no scaffolding crew, no framing contractor, nobody standing around waiting on materials. Framing is one of the hardest jobs on a site. You're balancing on trusses, reading blueprints on the fly, and timing every cut so the whole structure lines up by the time the roof goes on. It's the kind of work people spend years getting good at. Now watch what's happening in the footage. The robots are climbing the frame, setting studs, running the roof trusses, and doing it as a full crew, the way an actual framing team would, except none of them stop for lunch and none of them call in hurt. Nobody expected robots to learn a full trade before they learned to walk normally. They're already building the house. If you still think construction is years away from automation, the frame is already up.
0:15

⚠️ FRAMERS, THAT HOUSE YOU'RE BUILDING BY HAND IS BEING BUILT BY A ROBOT CREW SOMEWHERE ELSE RIGHT NOW A dozen Tesla Optimus units just framed an entire house from the foundation up, no scaffolding crew, no framing contractor, nobody standing around waiting on materials. Framing is one of the hardest jobs on a site. You're balancing on trusses, reading blueprints on the fly, and timing every cut so the whole structure lines up by the time the roof goes on. It's the kind of work people spend years getting good at. Now watch what's happening in the footage. The robots are climbing the frame, setting studs, running the roof trusses, and doing it as a full crew, the way an actual framing team would, except none of them stop for lunch and none of them call in hurt. Nobody expected robots to learn a full trade before they learned to walk normally. They're already building the house. If you still think construction is years away from automation, the frame is already up.

V0LYX

365,298 görüntüleme • 1 ay önce

$150,000. That's the price tag on a humanoid robot built to carry the same rifle a soldier carries — walk at almost 4 mph, haul 44+ pounds of gear, clear a building before any person steps through the door. A US startup called Foundation already holds $24 million in contracts with the Army, Navy, and Air Force. Their stated goal: 50,000 units manufactured by the end of 2027 — scaling from roughly 50 units this year to 10,000 next year alone. Here's the part people get wrong: nobody's building a robot that decides who to shoot. Current protocol requires a human to confirm before any automated system engages. What they're actually replacing is the soldier who has to be the first one through a door, the first one over a hilltop, the first one into a room nobody's checked yet. An Army general once floated the idea that robots could replace a quarter of combat soldiers by 2030. That prediction is now over a decade old and still hasn't happened — which tells you the timeline, not the direction, is what keeps being wrong. The honest version of this story isn't "robots are replacing soldiers." It's "the military just found a $150,000 way to stop sending a person first."
0:14

$150,000. That's the price tag on a humanoid robot built to carry the same rifle a soldier carries — walk at almost 4 mph, haul 44+ pounds of gear, clear a building before any person steps through the door. A US startup called Foundation already holds $24 million in contracts with the Army, Navy, and Air Force. Their stated goal: 50,000 units manufactured by the end of 2027 — scaling from roughly 50 units this year to 10,000 next year alone. Here's the part people get wrong: nobody's building a robot that decides who to shoot. Current protocol requires a human to confirm before any automated system engages. What they're actually replacing is the soldier who has to be the first one through a door, the first one over a hilltop, the first one into a room nobody's checked yet. An Army general once floated the idea that robots could replace a quarter of combat soldiers by 2030. That prediction is now over a decade old and still hasn't happened — which tells you the timeline, not the direction, is what keeps being wrong. The honest version of this story isn't "robots are replacing soldiers." It's "the military just found a $150,000 way to stop sending a person first."

Future Memo

2,103,371 görüntüleme • 1 ay önce

It's 2030 and you are reviewing humanoid robots. A Tesla. A Google. An Apple. An OpenAI. A Meta. A Figure. And a bunch of Chinese-made ones. Which one is best, and why? I think the Tesla understands the world much better. Why? There were eight Teslas around me on the freeway today. Start there. No other robot company has that data. But my robot is parked at the local high school twice a day. Its cameras see humans in all of our weirdness. How we move. Where we go. Where we walk. Who we talk with. What you are wearing. Whether your hair was combed this morning. That data will lead to robotics breakthroughs. Apple might keep up with its Vision Pro data, but it is too freaked out by the privacy implications of using said data. (On the front are six cameras and a couple of TOF -- Time Of Flight -- sensors that can see everything in your home in great detail). Google has a lot of data, for sure. All my: 1. Email. 2. Calendars. 3. Photos. 4. TV watching behavior. 5. Contacts. 6. Documents and spreadsheets. 7. Files. 8. Location data. So I expect Google's robot will be attractive to many. But how do you see the others shake out over the next five years? Make some guesses. But remember what an AI pioneer told me years ago about AI: it's all about the data. The Chinese ones have huge advantages: the Chinese have more data on their citizens, and many more citizens to boot AND they can make robots cheaper than we can. But now that you know OpenAI is building its own robot you have caught wind of what I've heard from many in San Francisco and Silicon Valley: that humanoid robots are the real prize of AI and will be highly profitable for those that can make them and find customers willing to buy them. Here, too, I learned long ago never to bet against Elon Musk. Will you?
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It's 2030 and you are reviewing humanoid robots. A Tesla. A Google. An Apple. An OpenAI. A Meta. A Figure. And a bunch of Chinese-made ones. Which one is best, and why? I think the Tesla understands the world much better. Why? There were eight Teslas around me on the freeway today. Start there. No other robot company has that data. But my robot is parked at the local high school twice a day. Its cameras see humans in all of our weirdness. How we move. Where we go. Where we walk. Who we talk with. What you are wearing. Whether your hair was combed this morning. That data will lead to robotics breakthroughs. Apple might keep up with its Vision Pro data, but it is too freaked out by the privacy implications of using said data. (On the front are six cameras and a couple of TOF -- Time Of Flight -- sensors that can see everything in your home in great detail). Google has a lot of data, for sure. All my: 1. Email. 2. Calendars. 3. Photos. 4. TV watching behavior. 5. Contacts. 6. Documents and spreadsheets. 7. Files. 8. Location data. So I expect Google's robot will be attractive to many. But how do you see the others shake out over the next five years? Make some guesses. But remember what an AI pioneer told me years ago about AI: it's all about the data. The Chinese ones have huge advantages: the Chinese have more data on their citizens, and many more citizens to boot AND they can make robots cheaper than we can. But now that you know OpenAI is building its own robot you have caught wind of what I've heard from many in San Francisco and Silicon Valley: that humanoid robots are the real prize of AI and will be highly profitable for those that can make them and find customers willing to buy them. Here, too, I learned long ago never to bet against Elon Musk. Will you?

Robert Scoble

33,804 görüntüleme • 1 yıl önce

Robots don’t just need better brains. They need WAY more real-world data. 🤖 And collecting high-quality dexterous robot data at scale is one of the hardest problems in physical AI. A fascinating approach is emerging: Wearable human demonstrations + structurally matched dexterous robots. Instead of humans directly teleoperating a robot, Chinese embodied AI startup X Square Robot's TwinDEX system captures the motion, contact, and visual information needed to train the robot — while keeping the data closer to the hardware that will actually execute the task. Early results show promising performance on tool use, fine manipulation, and complex contact-rich tasks. If this approach scales, it could change how we build real-world robotics datasets. The next frontier of physical AI might not be bigger models. It might be better data.
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Robots don’t just need better brains. They need WAY more real-world data. 🤖 And collecting high-quality dexterous robot data at scale is one of the hardest problems in physical AI. A fascinating approach is emerging: Wearable human demonstrations + structurally matched dexterous robots. Instead of humans directly teleoperating a robot, Chinese embodied AI startup X Square Robot's TwinDEX system captures the motion, contact, and visual information needed to train the robot — while keeping the data closer to the hardware that will actually execute the task. Early results show promising performance on tool use, fine manipulation, and complex contact-rich tasks. If this approach scales, it could change how we build real-world robotics datasets. The next frontier of physical AI might not be bigger models. It might be better data.

The Daily Ai

33,311 görüntüleme • 23 gün önce

whoever leaked this has bigger balls than sense someone at Anthropic hired 80 AI helpers onto one project, gave them twelve hours, and counted what came back usable: the two older models handed in 980 and 876 finished pieces of work, and almost none of it could be kept turns out the newest helpers did better for a reason nobody wants to hear: they went off into their own corners and stopped opening each other's work i ran two helpers at one document last week and got two confident versions of it, and i kept the one i wrote myself Grok Bot is the version of this you can actually hire: a helper with a name, one job it owns, and nobody else allowed inside that job you already pay about $20 a month for one chat window, and the setup that won in that report is one helper with one job it owns run it tonight in a normal chat, 3 moves: 1. write the one job each helper owns in a single sentence before you open a second chat 2. keep every helper in its own chat with one document, so two of them can never rewrite the same thing 3. add a third only when you can say what it owns without repeating a job that is already taken save this, then open the piece below: what one hired helper is really worth, and the point where the next one starts taking it back ↓
0:15

whoever leaked this has bigger balls than sense someone at Anthropic hired 80 AI helpers onto one project, gave them twelve hours, and counted what came back usable: the two older models handed in 980 and 876 finished pieces of work, and almost none of it could be kept turns out the newest helpers did better for a reason nobody wants to hear: they went off into their own corners and stopped opening each other's work i ran two helpers at one document last week and got two confident versions of it, and i kept the one i wrote myself Grok Bot is the version of this you can actually hire: a helper with a name, one job it owns, and nobody else allowed inside that job you already pay about $20 a month for one chat window, and the setup that won in that report is one helper with one job it owns run it tonight in a normal chat, 3 moves: 1. write the one job each helper owns in a single sentence before you open a second chat 2. keep every helper in its own chat with one document, so two of them can never rewrite the same thing 3. add a third only when you can say what it owns without repeating a job that is already taken save this, then open the piece below: what one hired helper is really worth, and the point where the next one starts taking it back ↓

Argona

449,029 görüntüleme • 1 ay önce

What If One Motor Could Move an Entire Robot Leg? Quaddle takes a very different approach to quadruped mechanics. Instead of using separate motors for the hip and knee, each leg uses a single servo with a mechanical linkage that drives both joints. That cuts the actuator count to just four servos for the whole robot. The interesting part is that the mechanical design does more of the work normally handled by extra actuators. With the right linkage and gait timing, Quaddle can walk, move sideways and spin in place. Less motors. Less wiring. Less weight. More mechanical engineering. Also, the hardware and motion system can be hacked and experimented with. Would you try designing your own leg mechanism around this idea?
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What If One Motor Could Move an Entire Robot Leg? Quaddle takes a very different approach to quadruped mechanics. Instead of using separate motors for the hip and knee, each leg uses a single servo with a mechanical linkage that drives both joints. That cuts the actuator count to just four servos for the whole robot. The interesting part is that the mechanical design does more of the work normally handled by extra actuators. With the right linkage and gait timing, Quaddle can walk, move sideways and spin in place. Less motors. Less wiring. Less weight. More mechanical engineering. Also, the hardware and motion system can be hacked and experimented with. Would you try designing your own leg mechanism around this idea?

Praveen Kumar Verma

12,229 görüntüleme • 9 gün önce

Interestingly, Xynova’s technological approach shares the same origins as the dexterous hand technology used in Optimus v3 (though Elon has noted that this design still needs further refinement). The Flex2 is an upgraded version built on the Flex1: v1 featured 25 DOF and used a cable-driven system; the v2 introduces direct drive, which reduces the DOF to 23 but also sheds 400g in weight. It seems a hybrid drive mechanism may be the more practical solution. In March this year, following the successful completion of its Series A funding round (with investors including Xiaomi and others), this robotics company--founded in 2024--began construction of a large-scale production facility. Spanning over 5,000 square meters, the base is designed to achieve an annual output of 200,000 miniature electric cylinders and 10,000 dexterous hands. However, hardware alone is far from enough. A truly capable dexterous hand must be the result of the co-evolution of data, models, and the physical hardware. In other words, in addition to mass production, Xynova is simultaneously developing a complete integrated system that combines perception capabilities, robotic manipulation intelligence, and hand-specific coordination. This is essentially a foundational robotic module. Yet its applications go far beyond that. It can be directly adapted to industrial robotic arms on production lines, as well as integrated into the bodies of humanoid robots. That said, what I’m most eager to see is its use in advanced bionic prosthetics for humans. If it can successfully demonstrate this expanded capability, its impact will reach well beyond the realm of humanoid robots. (Cyborg)
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Interestingly, Xynova’s technological approach shares the same origins as the dexterous hand technology used in Optimus v3 (though Elon has noted that this design still needs further refinement). The Flex2 is an upgraded version built on the Flex1: v1 featured 25 DOF and used a cable-driven system; the v2 introduces direct drive, which reduces the DOF to 23 but also sheds 400g in weight. It seems a hybrid drive mechanism may be the more practical solution. In March this year, following the successful completion of its Series A funding round (with investors including Xiaomi and others), this robotics company--founded in 2024--began construction of a large-scale production facility. Spanning over 5,000 square meters, the base is designed to achieve an annual output of 200,000 miniature electric cylinders and 10,000 dexterous hands. However, hardware alone is far from enough. A truly capable dexterous hand must be the result of the co-evolution of data, models, and the physical hardware. In other words, in addition to mass production, Xynova is simultaneously developing a complete integrated system that combines perception capabilities, robotic manipulation intelligence, and hand-specific coordination. This is essentially a foundational robotic module. Yet its applications go far beyond that. It can be directly adapted to industrial robotic arms on production lines, as well as integrated into the bodies of humanoid robots. That said, what I’m most eager to see is its use in advanced bionic prosthetics for humans. If it can successfully demonstrate this expanded capability, its impact will reach well beyond the realm of humanoid robots. (Cyborg)

CyberRobo

45,071 görüntüleme • 4 ay önce

while all of AI twitter is arguing about Fable 5.1 vs GPT-6 Astra, 95% of people out in public don't know either one exists. The gap is wild. Explaining doesn't work. Showing them AI built games doesn't work. Software developers are blown away because AI can do our job better and faster. - when robots build an entire house in 24 hours, the construction worker will get it. - when robots cook the food perfect every time, the chefs will understand. - when robots run into a burning building and save a family, the firefighters will understand too. people don't get it yet because they can't see what is coming as clearly as we can. we have been impacted, and what we see has far exceeded our expectations.
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while all of AI twitter is arguing about Fable 5.1 vs GPT-6 Astra, 95% of people out in public don't know either one exists. The gap is wild. Explaining doesn't work. Showing them AI built games doesn't work. Software developers are blown away because AI can do our job better and faster. - when robots build an entire house in 24 hours, the construction worker will get it. - when robots cook the food perfect every time, the chefs will understand. - when robots run into a burning building and save a family, the firefighters will understand too. people don't get it yet because they can't see what is coming as clearly as we can. we have been impacted, and what we see has far exceeded our expectations.

Veee

102,263 görüntüleme • 19 gün önce

Qualia has been selected for the Google DeepMind Robotics Program. We train embodied models that put a robot on a real manual task and make it work, on the floor, not in a demo. Foundation models and reasoning are where robotics is heading, and doing that work alongside DeepMind, who are pushing this frontier, is exactly where we want to be. If you are a company looking to see how a new generation of robots can help your manual tasks, contact us at hello@qualiastudios.dev More soon
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Qualia has been selected for the Google DeepMind Robotics Program. We train embodied models that put a robot on a real manual task and make it work, on the floor, not in a demo. Foundation models and reasoning are where robotics is heading, and doing that work alongside DeepMind, who are pushing this frontier, is exactly where we want to be. If you are a company looking to see how a new generation of robots can help your manual tasks, contact us at [email protected] More soon

Qualia

87,991 görüntüleme • 3 ay önce